pax_global_header00006660000000000000000000000064151635366330014525gustar00rootroot0000000000000052 comment=5244ec37be5e51136ad5804b7ece4e8fa2a1170f TPCAV-0.8.4/000077500000000000000000000000001516353663300124135ustar00rootroot00000000000000TPCAV-0.8.4/.github/000077500000000000000000000000001516353663300137535ustar00rootroot00000000000000TPCAV-0.8.4/.github/workflows/000077500000000000000000000000001516353663300160105ustar00rootroot00000000000000TPCAV-0.8.4/.github/workflows/publish-pypi.yml000066400000000000000000000050431516353663300211620ustar00rootroot00000000000000name: Build and publish to PyPI on: release: types: [published] workflow_dispatch: jobs: build: name: Build distributions runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v5 with: python-version: "3.11" cache: 'pip' - name: Install build tools run: python -m pip install --upgrade pip build twine - name: Install project dependencies run: python -m pip install . - name: Read package version id: version run: | python - <<'PY' import os import pathlib import tomllib version = tomllib.loads(pathlib.Path("pyproject.toml").read_text())["project"][ "version" ] print(version) with open(os.environ["GITHUB_OUTPUT"], "a", encoding="utf-8") as f: f.write(f"version={version}\n") PY - name: Validate release tag matches version if: github.event_name == 'release' env: TAG_NAME: ${{ github.event.release.tag_name }} run: | python - <<'PY' import os tag = os.environ["TAG_NAME"] if tag.startswith("v"): tag = tag[1:] # `steps.version.outputs.version` is available via GHA expressions only, so re-read here. import pathlib, tomllib version = tomllib.loads(pathlib.Path("pyproject.toml").read_text())["project"]["version"] if tag != version: raise SystemExit(f"Release tag ({tag}) does not match pyproject.toml version ({version})") print("Tag matches version:", tag) PY - name: Build sdist and wheel run: python -m build --sdist --wheel --outdir dist/ - name: Python compile check run: python -m py_compile tpcav/*.py scripts/*.py test/*.py - name: Twine check run: python -m twine check --strict dist/* - uses: actions/upload-artifact@v4 with: name: dist path: dist/ publish: name: Publish to PyPI needs: build runs-on: ubuntu-latest if: github.event_name == 'release' environment: name: pypi url: https://pypi.org/project/tpcav/ permissions: id-token: write contents: read steps: - uses: actions/download-artifact@v4 with: name: dist path: dist/ - name: Publish uses: pypa/gh-action-pypi-publish@release/v1 with: packages-dir: dist/ TPCAV-0.8.4/.gitignore000066400000000000000000000005101516353663300143770ustar00rootroot00000000000000* !data/ !data/report_tpcav_score.html.zip !data/motif-clustering-v2.1beta_consensus_pwms.meme !data/hg38_rmsk.sample.bed !data/mcf7_ctcf_best.pt !tpcav/ !tpcav/*.py !scripts/ !scripts/*.py !examples/ !examples/*.ipynb !test/ !test/*.py !pyproject.toml !LICENSE !README.md !.github/ !.github/workflows/ !.github/workflows/*.yml TPCAV-0.8.4/LICENSE000066400000000000000000000020501516353663300134150ustar00rootroot00000000000000MIT License Copyright (c) 2025 seqcode Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. TPCAV-0.8.4/README.md000066400000000000000000000260131516353663300136740ustar00rootroot00000000000000# TPCAV > Testing with PCA projected Concept Activation Vectors This repository contains code to compute TPCAV (Testing with PCA projected Concept Activation Vectors) on deep learning models. TPCAV is an extension of the original TCAV method, which uses PCA to reduce the dimensionality of the activations at a selected intermediate layer before computing Concept Activation Vectors (CAVs) to improve the consistency of the results. For more technical details, please check our manuscript on Biorxiv [TPCAV: Interpreting deep learning genomics models via concept attribution](https://doi.org/10.64898/2026.01.20.700723)! ## When should I use TPCAV? TPCAV is a global feature attribution method that can be applied to any model, provided that a set of examples is available to represent the concept of interest. It is input-agnostic, meaning it can operate on raw inputs, engineered features, or **tokenized representations**, including **foundation models**. Typical concepts in Genomics include: - Transcription factor motifs - Cis-regulatory regions - DNA repeats The same framework naturally extends to other domains, such as protein structure prediction, transcriptomics, or any field with a well established knowledge base, by defining appropriate concept sets. ## Installation `pip install tpcav` ## Inputs - Required inputs: - Genome fasta file - Model in Pytorch - Concept source file: - If you want to test motif concepts, please provide motifs in either of the two formats: - Candidate motif PWMs in MEME MINIMAL format, motif database used in our manuscript is from [Jeff Viestra Lab](https://resources.altius.org/~jvierstra/projects/motif-clustering-v2.1beta/) - A tab delimited file with consensus motif information, you can provide multiple consensus sequences for a single motif: ```text motif1 ATATAAAA motif2 AACGGGCA motif2 ATTCCCAA ... ``` - If you want to test concepts provided in genomic coordinates, please provide them as bed file in the following way, repeats coordinates in the manuscript are downloaded from [RepeatMasker database](https://www.repeatmasker.org/): ```text chr1 16363 16459 - DNA/hAT-Charlie_Charlie15a chr1 16713 16744 + Simple_repeat_(TGG)n chr1 18907 19048 + LINE/L2_L2a ... ``` > `tpcav` only works with Pytorch model, if your model is built using other libraries, you should port the model into Pytorch first. For Tensorflow models, you can use [tf2onnx](https://github.com/onnx/tensorflow-onnx) and [onnx2pytorch](https://github.com/Talmaj/onnx2pytorch) for the conversion. ## Detailed Usage For detailed usage for more flexibility on defining concepts, please refer to this [jupyter notebook](https://github.com/seqcode/TPCAV/tree/main/examples/tpcav_detailed_usage.ipynb) ## Quick start Example usage on a simple model trained for predicting CTCF binding in MCF-7 cell line, you would need to download [hg38 genome](https://hgdownload.gi.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz) to run, other used files can be found in `data/` directory Here we test a couple of motif concepts (including CTCF cognate motif) and some sampled repeat concepts. ```python import torch from tpcav import run_tpcav, helper device = 'cuda:0' if torch.cuda.is_available() else 'cpu' #==================== Prepare Model and Data transform function ================================ class DummyModelSeq(torch.nn.Module): def __init__(self): super().__init__() self.conv_layer_1 = torch.nn.Sequential( torch.nn.Conv1d(4, 64, 25, padding=12, bias=True), torch.nn.BatchNorm1d(64), torch.nn.LeakyReLU() ) self.conv_layer_2 = torch.nn.Sequential( torch.nn.Conv1d(64, 128, 3, padding=1, stride=2, bias=True), torch.nn.BatchNorm1d(128), torch.nn.LeakyReLU(), ) self.linear_layer_1 = torch.nn.Sequential( torch.nn.Linear(512, 1), torch.nn.LeakyReLU() ) self.linear_layer_2 = torch.nn.Linear(128, 1) def forward(self, seq): y_hat = self.conv_layer_1(seq) y_hat = self.conv_layer_2(y_hat) y_hat = self.linear_layer_1(y_hat) y_hat = y_hat.squeeze(-1) y_hat = self.linear_layer_2(y_hat) return y_hat # By default, every concept extracts fasta sequences and bigwig signals from the given region # Use your own custom transformation function to get your desired inputs # Here we transform them into one-hot coded DNA sequences def transform_fasta_to_one_hot_seq(seq, chrom): # `seq` is a list of fasta sequences # `chrom` is a numpy array of bigwig signals of shape [-1, # bigwigs, len] return (helper.fasta_to_one_hot_sequences(seq),) # it has to return a tuple of inputs, even if there is only one input #==================== Construct concepts ================================ motif_path = "data/motif-clustering-v2.1beta_consensus_pwms.test.meme" # motif file in meme format for constructing motif concepts bed_seq_concept = "data/hg38_rmsk.sample.bed" # a bed file to supply concepts described by a set of regions, format [chrom, start, end, label, concept_name] genome_fasta = "data/hg38.analysisSet.fa" model = torch.load("data/mcf7_ctcf_best.pt", map_location=device, weights_only=False) # load the model layer_name = "linear_layer_1" # name of the layer to be interpreted, you should be able to retrieve the layer object by getattr(model, layer_name) # concept_fscores_dataframe: fscores of each concept # motif_cav_trainers: each trainer contains the cav weights of motifs inserted different number of times # bed_cav_trainer: trainer contains the cav weights of the sequence concepts provided in bed file concept_fscores_dataframe, motif_cav_trainers, bed_cav_trainer = run_tpcav( model=model, layer_name=layer_name, motif_file=motif_path, motif_file_fmt='meme', # specify your motif file format, either meme or consensus (tab delimited file in form [motif_name, consensus_sequence]) genome_fasta=genome_fasta, num_motif_insertions=[12, 24, 36], bed_seq_file=bed_seq_concept, # bed file is provided as source for sequence concepts, if you are providing them as chromatin concepts, use bed_chrom_file option instead and provide your bigwig files using bws option output_dir="test_run_tpcav_output/", input_transform_func=transform_fasta_to_one_hot_seq, p=4) # number of concurrent SGDClassifier can be run at the same time, increase it if you have available CPU power, it speeds up training significantly ``` There will be a `report.html` file generated in the output folder for quick inspection of the F-scores of concepts and the ranking. Then we can provide regions we are interested in to further evaluate how concepts influence model predictions. ```python from tpcav import report #==================== Compuate layer attributions of target testing regions ================================ # retrieve the tpcav model tpcav_model = bed_cav_trainer.tpcav # create input regions and baseline regions for attribution ctcf_peaks = helper.load_bed_and_center("data/MCF-7_CTCF_ENCFF942TCG.bed", window=1024).sample(n=100) random_regions = helper.random_regions_dataframe(genome_fasta + ".fai", 1024, 100, seed=2) # create iterators to yield one-hot encoded sequences from the region dataframes # adjust this funtion to fit your model input format requirements def pack_data_iters(df): seq_fasta_iter = helper.dataframe_to_fasta_iter(df, genome_fasta, batch_size=8) seq_one_hot_iter = (helper.fasta_to_one_hot_sequences(seq_fasta) for seq_fasta in seq_fasta_iter) return zip(seq_one_hot_iter, ) # compute layer attributions given the iterators of testing regions and control regions attributions = tpcav_model.layer_attributions(pack_data_iters(ctcf_peaks), pack_data_iters(random_regions)) # generate a new html report with TPCAV score computed report.generate_tpcav_html_report("report_tpcav_score.html", motif_cav_trainers, non_motif_cav_trainers = {'repeats': bed_cav_trainer}, attributions = [attributions, ], # if you have multiple sets of attributions you can provide a list motif_file=motif_path, motif_file_fmt='meme', fscore_thresh=0.8) # save the trainers for future use torch.save(motif_cav_trainers, "motif_cav_trainers.pt") torch.save(bed_cav_trainer, "bed_cav_trainer.pt") ``` Check `report_tpcav_score.html` for Log TPCAV score of each concept. ## HTML output An example report can be found [here](https://github.com/seqcode/TPCAV/blob/main/data/report_tpcav_score.html.zip?raw=true). The HTML output contains three sections in general: 1. Tables listing F-scores of all concepts, for motif concepts there are F-scores at different number of insertions. 2. Motif concept ranking by AUC F-scores or corrected AUC F-scores (motif concept sensitivity) 3. Concept activaton vectors (CAVs) similarity matrix heatmap and TPCAV score ## Restore trained concepts The results of TPCAV are stored in `CavTrainer` object, it contains the F-score of each concept, the corresponding concept activation vector (CAV), and the model object decorated by TPCAV parameters & functions, given the example in Quick Usage: ```python # reload trainers back motif_cav_trainers = torch.load("motif_cav_trainers.pt") bed_cav_trainer = torch.load("bed_cav_trainer.pt") # inspect trainer properties cav_trainer = motif_cav_trainers[4] # here we take the first motif cav trainer that correponds to the first number of motif insertions (4 insertions) # retrieve F-scores motif_cav_trainers[4].cav_fscores # retrieve CAVs motif_cav_trainers[4].cav_weights ``` You can also retrieve the model decorated by TPCAV parameters by ```python tpcav_mode = cav_trainer.tpcav ``` So that you can compute attributions for new inputs ```python # compute layer attributions, and compute new tpcav score attrs = tpcav_model.layer_attributions(target_batches, baseline_batches) cav_trainer.tpcav_score_all_concepts_log_ratio(attrs) ``` You can also generate new reports using the computed attributions ```python report.generate_tpcav_html_report("report.html", motif_cav_trainers, non_motif_cav_trainers = {'repeats': bed_cav_trainer}, motif_file=motif_path, motif_file_fmt='meme', fscore_thresh=0.8) ``` You can also extract concept specific attribution score by providing a list of cavs ```python # input attributions input_attrs = tpcav_model.input_attributions(target_batches, baseline_batches, multiply_by_inputs=True,) # or concept specific input attributions (parts explained by the provided concepts CAVs) input_attrs = tpcav_model.input_attributions(target_batches, baseline_batches, multiply_by_inputs=True, cavs_list=[cav_trainer.cav_weights[concept_name]) ``` If you find any issue, feel free to open an issue (strongly suggested) or contact [Jianyu Yang](mailto:yztxwd@gmail.com). 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-1.00000 4.90427 178 chr16 14164944 14165047 . 1000 . 52.15728 -1.00000 4.90427 36 chr9 93922453 93922809 . 1000 . 48.55469 -1.00000 4.90427 178 chr1 145396625 145396981 . 583 . 23.95967 -1.00000 4.26927 178 chr3 6902806 6902897 . 1000 . 37.87122 -1.00000 4.90427 50 chr18 34983330 34983686 . 578 . 26.43763 -1.00000 4.21636 178 chr19 11998905 11999261 . 904 . 36.68934 -1.00000 4.90427 178 chr20 48429062 48429418 . 803 . 31.21933 -1.00000 4.90427 178 chr3 158679256 158679612 . 655 . 11.28409 -1.00000 1.60035 178 chr14 97832687 97833043 . 1000 . 51.08368 -1.00000 4.90427 178 chr11 131056711 131057067 . 759 . 34.27784 -1.00000 4.90427 178 chr8 103422846 103423202 . 628 . 25.22719 -1.00000 4.24185 178 chr19 58694600 58694774 . 1000 . 69.90831 -1.00000 4.90427 104 TPCAV-0.8.4/data/hg19.fa.fai000066400000000000000000000067111516353663300151470ustar00rootroot00000000000000chr10 135534747 7 50 51 chr11 135006516 138245456 50 51 chr11_gl000202_random 40103 275952126 50 51 chr12 133851895 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chr4_gl000193_random 189789 2127588169 50 51 chr4_gl000194_random 191469 2127781776 50 51 chr5 180915260 2127977081 50 51 chr6_apd_hap1 4622290 2312510662 50 51 chr6_cox_hap2 4795371 2317225413 50 51 chr6_dbb_hap3 4610396 2322116707 50 51 chr6 171115067 2326819317 50 51 chr6_mann_hap4 4683263 2501356702 50 51 chr6_mcf_hap5 4833398 2506133646 50 51 chr6_qbl_hap6 4611984 2511063727 50 51 chr6_ssto_hap7 4928567 2515767967 50 51 chr7 159138663 2520795112 50 51 chr7_gl000195_random 182896 2683116571 50 51 chr8 146364022 2683303131 50 51 chr8_gl000196_random 38914 2832594456 50 51 chr8_gl000197_random 37175 2832634171 50 51 chr9 141213431 2832672096 50 51 chr9_gl000198_random 90085 2976709818 50 51 chr9_gl000199_random 169874 2976801727 50 51 chr9_gl000200_random 187035 2976975021 50 51 chr9_gl000201_random 36148 2977165819 50 51 chrM 16571 2977202696 50 51 chrUn_gl000211 166566 2977219615 50 51 chrUn_gl000212 186858 2977389529 50 51 chrUn_gl000213 164239 2977580141 50 51 chrUn_gl000214 137718 2977747681 50 51 chrUn_gl000215 172545 2977888170 50 51 chrUn_gl000216 172294 2978064182 50 51 chrUn_gl000217 172149 2978239938 50 51 chrUn_gl000218 161147 2978415546 50 51 chrUn_gl000219 179198 2978579932 50 51 chrUn_gl000220 161802 2978762730 50 51 chrUn_gl000221 155397 2978927785 50 51 chrUn_gl000222 186861 2979086306 50 51 chrUn_gl000223 180455 2979276921 50 51 chrUn_gl000224 179693 2979461002 50 51 chrUn_gl000225 211173 2979644305 50 51 chrUn_gl000226 15008 2979859718 50 51 chrUn_gl000227 128374 2979875043 50 51 chrUn_gl000228 129120 2980006001 50 51 chrUn_gl000229 19913 2980137720 50 51 chrUn_gl000230 43691 2980158048 50 51 chrUn_gl000231 27386 2980202629 50 51 chrUn_gl000232 40652 2980230579 50 51 chrUn_gl000233 45941 2980272061 50 51 chrUn_gl000234 40531 2980318937 50 51 chrUn_gl000235 34474 2980360295 50 51 chrUn_gl000236 41934 2980395475 50 51 chrUn_gl000237 45867 2980438264 50 51 chrUn_gl000238 39939 2980485065 50 51 chrUn_gl000239 33824 2980525819 50 51 chrUn_gl000240 41933 2980560336 50 51 chrUn_gl000241 42152 2980603124 50 51 chrUn_gl000242 43523 2980646136 50 51 chrUn_gl000243 43341 2980690546 50 51 chrUn_gl000244 39929 2980734770 50 51 chrUn_gl000245 36651 2980775514 50 51 chrUn_gl000246 38154 2980812915 50 51 chrUn_gl000247 36422 2980851849 50 51 chrUn_gl000248 39786 2980889016 50 51 chrUn_gl000249 38502 2980929614 50 51 chrX 155270560 2980968893 50 51 chrY 59373566 3139344871 50 51 TPCAV-0.8.4/data/hg38_rmsk.sample.bed000066400000000000000000077364711516353663300171160ustar00rootroot00000000000000chr1 10001 10468 + Simple_repeat_(TAACCC)n chr1 10469 11447 - Satellite/telo_TAR1 chr1 11485 11676 - LINE/L1_L1MC5a chr1 11678 11780 - DNA/hAT-Charlie_MER5B chr1 15265 15355 - SINE/MIR_MIR3 chr1 15798 15849 + Simple_repeat_(TGCTCC)n chr1 16363 16459 - DNA/hAT-Charlie_Charlie15a chr1 16713 16744 + Simple_repeat_(TGG)n chr1 18907 19048 + LINE/L2_L2a chr1 19972 20405 + LINE/CR1_L3 chr1 20531 20679 + LINE/CR1_Plat_L3 chr1 21949 22075 + LTR/ERVL-MaLR_MLT1K chr1 23120 23371 - SINE/MIR_MIR chr1 23804 23824 + LINE/L2_L2a chr1 23825 24011 + LINE/L2_L2b chr1 24012 24038 + LINE/L2_L2a chr1 24088 24250 + SINE/MIR_MIR chr1 24255 24448 + LINE/L2_L2a chr1 26356 26412 + SINE/MIR_MIR1_Amn chr1 26583 26790 - LINE/L2_L2c chr1 26791 27053 + SINE/Alu_AluSp chr1 27054 27137 - LINE/L2_L2c chr1 27269 27518 + DNA/hAT-Charlie_MER33 chr1 27833 28014 - SINE/MIR_MIRb chr1 28151 28302 - SINE/MIR_MIR chr1 29745 29792 + Simple_repeat_(ACGCCC)n chr1 29902 30198 + LINE/L1_L1MB3 chr1 30343 30532 - DNA/hAT_MER53 chr1 30694 30848 + LTR/ERVL-MaLR_MLT1A chr1 30855 30952 + Simple_repeat_(TC)n chr1 30953 31131 + LTR/ERVL-MaLR_MLT1A chr1 31293 31435 + SINE/MIR_MIRc chr1 31436 31733 + SINE/Alu_AluJo chr1 31734 31754 + SINE/MIR_MIRc chr1 32841 33037 + SINE/MIR_MIR chr1 33048 33456 + LINE/L1_L1MB5 chr1 33466 33509 + SINE/Alu_Alu chr1 33529 34041 - LINE/L1_L1PA6 chr1 34048 34108 + LINE/L1_L1P1 chr1 34204 34322 - LTR/ERVL-MaLR_MLT1L chr1 34451 34560 - LINE/L2_L2c chr1 34565 34921 - LTR/ERVL-MaLR_MLT1J2 chr1 35217 35366 - SINE/MIR_MIRb chr1 35367 35493 + SINE/Alu_AluJr chr1 37045 37431 + DNA/hAT-Charlie_Charlie5 chr1 37733 37861 + LINE/L2_L2c chr1 38059 38191 + LINE/L2_L2a chr1 38256 39464 + LTR/ERVL-MaLR_MLT1F2-int chr1 39465 39623 + LTR/ERVL-MaLR_MLT1F2 chr1 39624 39924 + SINE/Alu_AluSx chr1 39925 40294 + LTR/ERVL-MaLR_MLT1F2 chr1 40308 40626 + LINE/L2_L2a chr1 40629 40729 - SINE/Alu_AluSx3 chr1 40736 40878 - LTR/ERVL_LTR16C chr1 41380 42285 - LTR/ERVL_ERVL-E-int chr1 42370 42504 + LTR_MamRep1527 chr1 43243 44835 + LINE/L1_L1MA8 chr1 44836 44876 + Simple_repeat_(AAAT)n chr1 44877 45753 + LINE/L1_L1MA8 chr1 45884 46064 + LINE/L1_L1ME3G chr1 46064 46198 + LINE/L1_L1MA9 chr1 46416 46493 - LTR/ERV1_LTR12F chr1 46553 46722 + DNA/hAT-Tip100_MER45A chr1 46893 47092 - DNA/hAT-Charlie_MER58A chr1 48417 49518 + LINE/L1_L1PA14 chr1 49826 49861 + Simple_repeat_(AACAA)n chr1 50482 50513 + Simple_repeat_(GT)n chr1 50561 50626 + Simple_repeat_(TTCCT)n chr1 51585 51880 + SINE/Alu_AluYj4 chr1 52257 52303 + Simple_repeat_(TTCT)n chr1 53745 53792 + Low_complexity_A-rich chr1 54465 54703 + LINE/L2_L2 chr1 54713 54817 + Simple_repeat_(TTTC)n chr1 54819 54938 - SINE/Alu_FLAM_A chr1 54945 55608 + LINE/L2_L2 chr1 59056 59208 - LINE/L2_L2a chr1 60379 60686 - DNA/TcMar-Tigger_MER47A chr1 61604 61862 - LINE/L1_L1ME4c chr1 61863 62160 - SINE/Alu_AluSc chr1 62161 62229 - LINE/L1_L1ME4c chr1 62232 62289 + Simple_repeat_(AC)n chr1 64426 64666 + LINE/L1_L1PA4 chr1 65977 66134 - SINE/MIR_MIR3 chr1 66158 66632 + Simple_repeat_(AT)n chr1 67999 68246 - SINE/MIR_MIRb chr1 70504 70712 - LTR/ERVL?_LTR89 chr1 70723 70955 + SINE/MIR_MIRb chr1 71741 71829 + LINE/L1_L1MC chr1 72090 72128 + Simple_repeat_(TA)n chr1 72129 72163 + Simple_repeat_(ATAC)n chr1 72185 72525 - LINE/L1_L1PA7 chr1 72526 72897 + LINE/L1_L1PA7 chr1 73009 73678 - LINE/L1_L1PA16 chr1 73688 73843 + LINE/L1_L1MC chr1 73845 74895 - LINE/L1_L1PA2 chr1 74898 76697 + LINE/L1_L1PA2 chr1 76693 76892 + LINE/L1_L1MC5a chr1 76893 77201 + SINE/Alu_AluSz chr1 77202 77638 + LINE/L1_L1MC5a chr1 77717 78235 + LINE/L1_L1MC5a chr1 78232 78283 + LINE/L1_L1M2 chr1 78286 78421 + SINE/Alu_AluJr chr1 78459 78890 + LINE/L1_L1M2 chr1 78891 79850 + LINE/L1_L1PREC2 chr1 79830 80634 + LINE/L1_L1PB chr1 80712 80804 + DNA/TcMar-Tigger_Tigger5b chr1 80805 81096 + SINE/Alu_AluSc5 chr1 81097 81231 + DNA/TcMar-Tigger_Tigger5b chr1 81232 81691 + DNA/TcMar-Tigger_Tigger5 chr1 81694 82270 + LINE/L1_L1M5 chr1 82285 83584 + LINE/L1_L1M4 chr1 83592 83802 + LINE/L1_L1M4 chr1 83803 84051 + Simple_repeat_(GAAA)n chr1 84076 85700 + LINE/L1_L1M4 chr1 85700 85986 + LINE/L1_L1M4 chr1 86030 86619 + LINE/L1_L1MC5a chr1 86646 86717 + Simple_repeat_(AT)n chr1 86818 87087 + LINE/L1_HAL1M8 chr1 87126 87413 - SINE/Alu_AluJr chr1 87423 87493 + LTR/ERV1?_LTR103b_Mam chr1 88144 88823 + LINE/L2_L2 chr1 88925 89135 + LINE/L2_L2a chr1 89107 89174 + LINE/L2_L2a chr1 89286 89381 - LINE/L1_L1M5 chr1 89859 90056 + LTR/ERVL-MaLR_MLT1H2 chr1 90443 90576 + LTR/ERVL-MaLR_MLT1H2 chr1 90608 90636 + Simple_repeat_(CTGT)n chr1 90921 91213 + SINE/Alu_AluYk2 chr1 91609 91677 - LTR/ERV1_MER4D1 chr1 91678 92110 + LTR/ERV1_MER4CL34 chr1 92114 92392 - LTR/ERV1_MER4D1 chr1 92553 92783 + SINE/MIR_MIR chr1 92860 92903 + SINE/MIR_MIR3 chr1 93159 93421 + LINE/RTE-X_L5 chr1 93729 94031 - SINE/Alu_AluSc chr1 94078 94355 + LINE/L2_L2 chr1 94520 94816 - LTR/ERVL-MaLR_MLT1H chr1 94820 95080 - SINE/Alu_AluY chr1 95539 95569 + Simple_repeat_(AAC)n chr1 97018 97068 + Simple_repeat_(TGTGTG)n chr1 97802 98085 + LTR/ERVL-MaLR_MLT1I chr1 99000 99042 + Simple_repeat_(TTTA)n chr1 99047 99116 + Simple_repeat_(T)n chr1 99118 99397 - SINE/Alu_AluSx3 chr1 99724 99796 + LINE/CR1_L3 chr1 99885 100292 - LTR?_MamRep605 chr1 100344 100421 - LTR?_MamRep605 chr1 101056 101352 + SINE/Alu_AluSz chr1 101404 101690 + SINE/Alu_AluJr chr1 101822 102122 + SINE/Alu_AluSx chr1 102548 102780 - SINE/MIR_MIRb chr1 102783 102975 + LINE/CR1_L3 chr1 102976 103282 + SINE/Alu_AluSx3 chr1 103283 103357 + LINE/CR1_L3 chr1 103375 103467 - LTR/ERVL_LTR33 chr1 103468 103616 + LTR/ERVL-MaLR_MSTB1 chr1 103617 103909 - SINE/Alu_AluSq chr1 103910 104160 + LTR/ERVL-MaLR_MSTB1 chr1 104161 104196 + Simple_repeat_(AC)n chr1 104197 104215 + LTR/ERVL-MaLR_MSTB1 chr1 104216 104254 - LTR/ERVL_LTR33 chr1 104338 104506 - LTR/ERVL_LTR33 chr1 104518 105076 + LINE/CR1_L3 chr1 105609 105627 + Simple_repeat_(AT)n chr1 106430 106531 - DNA/hAT-Charlie_MER5B chr1 106929 106951 + Simple_repeat_(CT)n chr1 107481 107773 + LTR/ERVL-MaLR_MLT1C chr1 107778 107869 - SINE/MIR_MIR chr1 107868 108329 + LINE/L2_L2a chr1 108330 108564 + SINE/Alu_AluJb chr1 108565 108680 + LINE/L2_L2a chr1 109565 109614 + Simple_repeat_(TG)n chr1 109773 109831 + Simple_repeat_(AT)n chr1 109836 110009 - SINE/Alu_FAM chr1 110086 110944 + LINE/L1_L1PA14 chr1 111003 111049 - SINE/MIR_MIR chr1 111081 111386 + SINE/Alu_AluSq2 chr1 111884 111953 + LINE/L2_L2a chr1 112045 112582 + LINE/L2_L2b chr1 112715 112847 + SINE/MIR_MIRc chr1 113384 113447 - LINE/L2_L2a chr1 113692 114101 + LTR/ERVL-MaLR_MLT1F2 chr1 114189 114677 + LINE/L1_L1ME3E chr1 115058 115194 + LTR/ERVL_LTR33B chr1 115343 115445 - LINE/L2_L2 chr1 115577 115714 + LTR/ERV1_MER65D chr1 115789 116069 + LTR/ERV1_MER65D chr1 116092 116208 - LINE/L1_L1M5 chr1 116373 117031 + LINE/L2_L2a chr1 117032 117228 + LINE/L1_L1ME3E chr1 117221 117608 + LINE/L1_L1ME3E chr1 117611 118100 + LTR/ERVL-MaLR_MLT1D chr1 118101 119102 + LINE/L1_L1ME3E chr1 119440 119474 + Simple_repeat_(TA)n chr1 119564 119944 - LTR/ERVL-MaLR_THE1C chr1 120968 121138 + Simple_repeat_(TTATATA)n chr1 121270 121565 - SINE/Alu_AluY chr1 121640 121718 + Simple_repeat_(TAA)n chr1 123086 123134 + Simple_repeat_(AT)n chr1 123171 123267 + LINE/L1_L1MA5 chr1 123302 123334 + Simple_repeat_(AACTGT)n chr1 123632 123702 + LINE/L2_L2a chr1 123709 123826 + LINE/L2_L2a chr1 123833 124404 + LINE/L2_L2c chr1 124568 124870 + SINE/Alu_AluJb chr1 125832 125891 + Simple_repeat_(ATAA)n chr1 126035 126235 + LINE/L1_L1MA5 chr1 126237 126991 + LINE/L1_L1M4 chr1 127548 127657 - LINE/L2_L2a chr1 127782 128051 - SINE/Alu_AluSz chr1 128484 128595 - LINE/L2_L2a chr1 128596 128893 - SINE/Alu_AluSq2 chr1 128894 129019 - LINE/L2_L2a chr1 129383 129675 - SINE/Alu_AluSx chr1 129678 129788 - SINE/Alu_FLAM_C chr1 129789 129891 + SINE/Alu_AluJo chr1 129789 129846 + Retroposon/SVA_SVA_E chr1 129907 129998 + LINE/L1_L1M2 chr1 129999 130313 + SINE/Alu_AluSg chr1 130314 130748 + LINE/L1_L1M2 chr1 130756 130976 + SINE/Alu_AluSq2 chr1 134066 134374 - SINE/Alu_AluSg chr1 139708 140127 - DNA/hAT-Charlie_MER1A chr1 140413 140492 + SINE/Alu_FLAM_A chr1 140495 140784 + SINE/Alu_AluJo chr1 140785 141290 + LTR/ERVL_MER21C chr1 141291 141597 - SINE/Alu_AluJb chr1 141598 141667 + LTR/ERVL_MER21C chr1 141668 141970 + SINE/Alu_AluJr chr1 141971 142271 + LTR/ERVL_MER21C chr1 142308 142506 + SINE/MIR_MIRb chr1 142766 142869 + LINE/L2_L2b chr1 143165 143272 - SINE/Alu_AluJb chr1 143306 143430 + SINE/Alu_AluJr chr1 143444 143875 - DNA/hAT-Charlie_MER1A chr1 144178 144240 - LTR/ERVL_LTR41 chr1 144241 144525 + SINE/Alu_AluJb chr1 144528 144576 + Simple_repeat_(TA)n chr1 144577 144605 - LTR/ERVL_LTR41 chr1 144606 144899 + SINE/Alu_AluSp chr1 144900 144986 - LTR/ERVL_LTR41 chr1 144987 145294 + SINE/Alu_AluJr chr1 145626 145884 + LINE/L2_L2b chr1 145912 146004 + LINE/L2_L2b chr1 146052 146227 + LTR/ERVL-MaLR_MLT1D chr1 146229 146522 + SINE/Alu_AluJr chr1 146776 147049 + SINE/Alu_AluJr chr1 147060 147161 - SINE/Alu_AluJb chr1 147162 147431 + SINE/Alu_AluSx chr1 147434 147475 + Simple_repeat_(CAA)n chr1 147476 147535 - SINE/Alu_AluJb chr1 147595 147625 + LINE/L1_L1MB8 chr1 147626 147917 + SINE/Alu_AluJb chr1 147918 148005 + LINE/L1_L1MB8 chr1 148040 148150 - SINE/Alu_FLAM_A chr1 148151 148770 + LINE/L1_L1MB8 chr1 148792 149093 + SINE/Alu_AluJr chr1 149097 149211 - DNA/TcMar-Tigger_Tigger12 chr1 149396 149703 + SINE/Alu_AluSq2 chr1 149865 150008 - LTR/ERV1_LTR37A chr1 150154 150449 - SINE/Alu_AluJo chr1 150450 150475 + Simple_repeat_(ATTTT)n chr1 150478 150612 - SINE/Alu_AluJr chr1 150712 151315 + LINE/L2_L2a chr1 151317 151479 - SINE/Alu_AluJb chr1 151480 151766 - SINE/Alu_AluSg4 chr1 151767 151894 - SINE/Alu_AluJb chr1 151915 152272 - LTR/ERVL-MaLR_MLT1D chr1 152276 152566 + SINE/Alu_AluJr chr1 152567 152825 + LTR/ERVL_MER21C chr1 152826 153119 - SINE/Alu_AluSp chr1 153120 153429 + LTR/ERVL_MER21C chr1 153430 153735 + SINE/Alu_AluJo chr1 153736 154035 + LTR/ERVL_MER21C chr1 154037 155099 + LTR/ERVK_MER11B chr1 155145 155339 + SINE/MIR_MIRb chr1 155563 155614 + Simple_repeat_(CCATCC)n chr1 155995 156114 - SINE/Alu_AluJb chr1 156117 156417 - SINE/Alu_AluJo chr1 156457 156597 + LINE/L1_L1ME4b chr1 156828 157135 + SINE/Alu_AluJr chr1 157183 157350 + SINE/Alu_AluSg chr1 157359 157495 + LINE/L1_L1ME4c chr1 157499 157618 - SINE/Alu_AluJr4 chr1 157619 157742 + LINE/L1_L1ME4c chr1 157784 157886 - snRNA_U6 chr1 157962 158002 + SINE/MIR_MIRb chr1 158107 158214 - DNA/TcMar-Tigger_MER8 chr1 158226 158370 + SINE/Alu_AluJb chr1 158389 158495 - DNA/hAT-Charlie_Charlie29b chr1 158510 158629 - DNA/hAT-Charlie_Charlie29b chr1 158713 158996 + SINE/Alu_AluJr chr1 158998 159031 + Simple_repeat_(AC)n chr1 159356 159579 + SINE/Alu_AluSx1 chr1 161300 161705 + LINE/L1_L1MC5a chr1 161716 161807 + LINE/L1_L1MC5a chr1 162116 162181 - LTR/ERVL-MaLR_MSTD chr1 162182 162393 + Low_complexity_GA-rich chr1 162394 162711 - LTR/ERVL-MaLR_MSTD chr1 163848 164153 + SINE/Alu_AluSp chr1 164359 164695 + SINE/Alu_AluSq2 chr1 165007 165310 + SINE/Alu_AluSx1 chr1 166056 166334 + SINE/Alu_AluSg chr1 166368 166501 - SINE/MIR_MIRb chr1 167134 167433 - SINE/Alu_AluSp chr1 167495 167789 + SINE/Alu_AluSg7 chr1 168485 168786 + SINE/Alu_AluSx1 chr1 168915 169342 + LTR/ERVL-MaLR_MLT1D chr1 169374 169679 + SINE/Alu_AluSz chr1 169857 170149 - SINE/Alu_AluSz6 chr1 170894 170935 + DNA/hAT-Charlie_Charlie4z chr1 170936 171236 - SINE/Alu_AluSq2 chr1 171237 171337 + DNA/hAT-Charlie_Charlie4z chr1 172096 172146 - SINE/Alu_AluSq2 chr1 172407 172489 - SINE/MIR_MIR1_Amn chr1 172661 172696 + LINE/L2_L2c chr1 172833 172913 - SINE/MIR_MIRb chr1 174368 174381 + Low_complexity_A-rich chr1 174382 174517 + SINE/Alu_AluSz chr1 174518 174820 + SINE/Alu_AluSp chr1 174821 174957 + SINE/Alu_AluSz chr1 175166 175478 + SINE/Alu_AluSx chr1 175597 175891 - SINE/Alu_AluSp chr1 175915 176202 + SINE/Alu_AluSx1 chr1 176203 176231 + Simple_repeat_(AAAAC)n chr1 176232 176248 + SINE/Alu_AluSx1 chr1 176257 176291 + Low_complexity_A-rich chr1 176379 176445 - SINE/MIR_MIRb chr1 178283 178336 + Simple_repeat_(AAC)n chr1 178508 178568 + Simple_repeat_(CTAT)n chr1 178685 178904 + DNA/hAT-Charlie_MER58A chr1 178987 179306 + LINE/L2_L2c chr1 179625 179815 + SINE/MIR_MIRb chr1 180089 180998 + Simple_repeat_(ACCCTA)n chr1 180999 181980 - Satellite/telo_TAR1 chr1 182024 182194 - LINE/L1_L1MC5a chr1 182197 182299 - DNA/hAT-Charlie_MER5B chr1 185786 185876 - SINE/MIR_MIR3 chr1 186317 186346 + Simple_repeat_(CTT)n chr1 186885 186981 - DNA/hAT-Charlie_Charlie15a chr1 187235 187266 + Simple_repeat_(TGG)n chr1 189430 189571 + LINE/L2_L2a chr1 190495 190928 + LINE/CR1_L3 chr1 191054 191252 + LINE/CR1_Plat_L3 chr1 192474 192599 + LTR/ERVL-MaLR_MLT1K chr1 193650 193865 - SINE/MIR_MIR chr1 194331 194349 + LINE/L2_L2a chr1 194350 194536 + LINE/L2_L2b chr1 194537 194563 + LINE/L2_L2a chr1 194590 194726 + SINE/MIR_MIR chr1 194783 194973 + LINE/L2_L2a chr1 196883 196934 + SINE/MIR_MIR1_Amn chr1 197096 197166 - LINE/L2_L2c chr1 197313 197576 + SINE/Alu_AluSp chr1 197578 197644 - LINE/L2_L2c chr1 197791 198040 + DNA/hAT-Charlie_MER33 chr1 198355 198532 - SINE/MIR_MIRc chr1 198669 198820 - SINE/MIR_MIR chr1 200248 200294 + Simple_repeat_(GCCCCG)n chr1 200409 200714 + LINE/L1_L1MB3 chr1 200857 201046 - DNA/hAT_MER53 chr1 201208 201376 + LTR/ERVL-MaLR_MLT1A chr1 201377 201479 + Simple_repeat_(TC)n chr1 201480 201651 + LTR/ERVL-MaLR_MLT1A chr1 201813 201955 + SINE/MIR_MIRc chr1 201956 202252 + SINE/Alu_AluJr chr1 202253 202273 + SINE/MIR_MIRc chr1 203361 203557 + SINE/MIR_MIR chr1 203568 203976 + LINE/L1_L1MB5 chr1 203984 204027 + SINE/Alu_Alu chr1 204045 204557 - LINE/L1_L1PA6 chr1 204564 204624 + LINE/L1_L1P1 chr1 204720 204862 - LTR/ERVL-MaLR_MLT1L chr1 204967 205076 - LINE/L2_L2c chr1 205081 205453 - LTR/ERVL-MaLR_MLT1J2 chr1 205733 205881 - SINE/MIR_MIRb chr1 205883 206015 + SINE/Alu_AluJr chr1 207563 207666 + DNA/hAT-Charlie_Charlie5 chr1 257678 257745 - LTR/ERVL_MLT2B3 chr1 257771 257838 - LTR/ERVL_MLT2B3 chr1 257864 257931 - LTR/ERVL_MLT2B3 chr1 257957 258024 - LTR/ERVL_MLT2B3 chr1 258028 258466 + LTR/ERV1_MER31-int chr1 258955 258989 + Simple_repeat_(TTGGCT)n chr1 259038 259537 + LTR/ERV1_MER92-int chr1 259604 259773 + LTR/ERV1_MER31-int chr1 259774 260073 + SINE/Alu_AluY chr1 260074 260100 + LTR/ERV1_MER31-int chr1 260105 260296 + SINE/Alu_AluSx chr1 260308 260388 + LTR/ERV1_MER31-int chr1 260452 260534 + LTR/ERV1_MER34A chr1 260537 260945 + LINE/L1_L1MC3 chr1 260946 261385 + LTR/ERVL-MaLR_MSTA1 chr1 261386 262742 + LINE/L1_L1MC3 chr1 263620 264060 + LTR/ERV1_MER4A1 chr1 264755 264940 + Simple_repeat_(CTAACC)n chr1 265187 265582 + LTR/ERVL-MaLR_MLT1I chr1 265586 265657 - LINE/L2_L2b chr1 266095 266197 - LINE/L1_L1M5 chr1 266672 266841 + LTR/ERVL-MaLR_MLT1H2 chr1 267020 267153 + LTR/ERVL-MaLR_MLT1H2 chr1 267185 267213 + Simple_repeat_(CTGT)n chr1 267498 267793 + SINE/Alu_AluYk2 chr1 268184 268253 - LTR/ERV1_MER4E chr1 268254 268672 + LTR/ERV1_MER4CL34 chr1 268690 268968 - LTR/ERV1_MER4D1 chr1 269129 269360 + SINE/MIR_MIR chr1 269736 269992 + LINE/RTE-X_L5 chr1 270306 270608 - SINE/Alu_AluSc chr1 270655 270932 + LINE/L2_L2 chr1 271124 271380 - LTR/ERVL-MaLR_MLT1H chr1 271386 271410 + Simple_repeat_(CTTTT)n chr1 271411 271664 - SINE/Alu_AluY chr1 272123 272153 + Simple_repeat_(AAC)n chr1 273602 273652 + Simple_repeat_(TGTGTG)n chr1 274386 274515 + LTR/ERVL-MaLR_MLT1I chr1 274610 274669 + LTR/ERVL-MaLR_MLT1I chr1 275584 275614 + Simple_repeat_(TTTA)n chr1 275619 275649 + Simple_repeat_(T)n chr1 275650 275960 - SINE/Alu_AluSx3 chr1 276287 276359 + LINE/CR1_L3 chr1 276450 276855 - LTR?_MamRep605 chr1 277622 277918 + SINE/Alu_AluSz chr1 277970 278255 + SINE/Alu_AluJr chr1 278387 278687 + SINE/Alu_AluSx chr1 279113 279345 - SINE/MIR_MIRb chr1 279348 279541 + LINE/CR1_L3 chr1 279542 279848 + SINE/Alu_AluSx3 chr1 279849 279923 + LINE/CR1_L3 chr1 279941 280033 - LTR/ERVL_LTR33 chr1 280034 280182 + LTR/ERVL-MaLR_MSTB1 chr1 280183 280475 - SINE/Alu_AluSq chr1 280476 280726 + LTR/ERVL-MaLR_MSTB1 chr1 280727 280760 + Simple_repeat_(AC)n chr1 280761 280779 + LTR/ERVL-MaLR_MSTB1 chr1 280780 280818 - LTR/ERVL_LTR33 chr1 280902 281070 - LTR/ERVL_LTR33 chr1 281082 281640 + LINE/CR1_L3 chr1 282173 282191 + Simple_repeat_(AT)n chr1 282984 283095 - DNA/hAT-Charlie_MER5B chr1 283493 283515 + Simple_repeat_(CT)n chr1 284045 284337 + LTR/ERVL-MaLR_MLT1C chr1 284347 284433 - SINE/MIR_MIRb chr1 284614 284893 + LINE/L2_L2a chr1 284894 285131 + SINE/Alu_AluJb chr1 285132 285250 + LINE/L2_L2a chr1 286135 286178 + Simple_repeat_(TG)n chr1 286337 286395 + Simple_repeat_(AT)n chr1 286400 286573 - SINE/Alu_FAM chr1 286650 287508 + LINE/L1_L1PA14 chr1 287567 287613 - SINE/MIR_MIR chr1 287645 287950 + SINE/Alu_AluSq2 chr1 288450 288519 + LINE/L2_L2a chr1 288611 289148 + LINE/L2_L2b chr1 290258 290667 + LTR/ERVL-MaLR_MLT1F2 chr1 290755 291083 + LINE/L1_L1ME3E chr1 291111 291136 + Simple_repeat_(AC)n chr1 291622 291758 + LTR/ERVL_LTR33B chr1 291907 292009 - LINE/L2_L2 chr1 292141 292278 + LTR/ERV1_MER65D chr1 292355 292635 + LTR/ERV1_MER65D chr1 292658 292774 - LINE/L1_L1M5 chr1 292939 293597 + LINE/L2_L2a chr1 293598 293794 + LINE/L1_L1ME3E chr1 293788 293847 + LINE/L1_L1ME3E chr1 293848 293979 + LINE/L1_L1MC chr1 293980 294174 + LINE/L1_L1ME3E chr1 294177 294666 + LTR/ERVL-MaLR_MLT1D chr1 294667 295706 + LINE/L1_L1ME3E chr1 296010 296044 + Simple_repeat_(TA)n chr1 296134 296514 - LTR/ERVL-MaLR_THE1C chr1 297539 297674 + Simple_repeat_(TA)n chr1 297809 297956 - SINE/Alu_AluYc chr1 348443 348994 + LTR/ERV1_MER92-int chr1 349009 349178 + LTR/ERV1_MER92-int chr1 349179 349478 + SINE/Alu_AluY chr1 349479 349505 + LTR/ERV1_MER92-int chr1 349510 349701 + SINE/Alu_AluSx chr1 349705 349814 + LTR/ERV1_MER92-int chr1 349855 349939 + LTR/ERV1_MER34A chr1 349942 350350 + LINE/L1_L1MC3 chr1 350351 350790 + LTR/ERVL-MaLR_MSTA1 chr1 350791 352148 + LINE/L1_L1MC3 chr1 353014 353455 + LTR/ERV1_MER4A1 chr1 354154 354321 + Simple_repeat_(CCTGAC)n chr1 354569 354964 + LTR/ERVL-MaLR_MLT1I chr1 355477 355579 - LINE/L1_L1M5 chr1 356054 356223 + LTR/ERVL-MaLR_MLT1H2 chr1 356402 356535 + LTR/ERVL-MaLR_MLT1H2 chr1 356567 356595 + Simple_repeat_(CTGT)n chr1 356880 357175 + SINE/Alu_AluYk2 chr1 357566 357635 - LTR/ERV1_MER4D1 chr1 357636 358067 + LTR/ERV1_MER4A chr1 358068 358859 - LTR/ERV1_MER4D1 chr1 358946 359031 + SINE/MIR_MIRb chr1 359296 359338 + Simple_repeat_(CCT)n chr1 359691 359844 - DNA/hAT-Charlie_Charlie30a chr1 360031 360064 + Simple_repeat_(CACA)n chr1 360231 360495 + LTR/ERV1_MER31B chr1 360496 360784 - SINE/Alu_AluJr chr1 360800 361618 + LINE/L1_L1MA4 chr1 361619 361922 + SINE/Alu_AluSx3 chr1 361926 362238 + SINE/Alu_AluSc chr1 362239 362920 + LINE/L1_L1MA4 chr1 362986 363108 + SINE/Alu_FRAM chr1 364336 364420 - DNA/hAT-Charlie_MER112 chr1 364859 365032 - SINE/MIR_MIR3 chr1 365405 365585 - LINE/L1_L1M5 chr1 365676 366144 + DNA/MULE-MuDR_Ricksha_0 chr1 366256 366350 + SINE/MIR_MIRb chr1 366599 366662 + DNA/TcMar-Tigger_Tigger4b chr1 366947 367109 + DNA/hAT-Charlie_Charlie20a chr1 368212 368313 + DNA/hAT-Charlie_Charlie20a chr1 368314 368609 - SINE/Alu_AluSg chr1 368651 368780 - LINE/L1_L1MD3 chr1 370064 370626 - LINE/L1_L1MD3 chr1 370627 370933 - SINE/Alu_AluSq chr1 370934 371004 - LINE/L1_L1MD3 chr1 371009 371186 - SINE/Alu_AluYf1 chr1 371198 371274 - LINE/L1_L1MD3 chr1 371275 371314 + Simple_repeat_(TG)n chr1 371315 371751 - LINE/L1_L1MC4a chr1 371827 371903 - LINE/L1_L1MD3 chr1 371985 372067 - LINE/L1_L1MD3 chr1 372064 372146 - LINE/L1_L1MD3 chr1 372385 372463 + Simple_repeat_(TA)n chr1 372568 372594 + Simple_repeat_(TA)n chr1 372699 372777 + Simple_repeat_(TA)n chr1 372882 372908 + Simple_repeat_(TA)n chr1 372920 373494 - LINE/L1_L1MD3 chr1 374632 375244 - LTR/ERV1_MER39B chr1 375560 375587 + Simple_repeat_(CT)n chr1 375923 376219 + SINE/Alu_AluY chr1 376484 377225 + LINE/L1_L1ME1 chr1 377415 377536 + LINE/L1_L1ME1 chr1 379082 379126 + Low_complexity_GA-rich chr1 379445 379731 + Simple_repeat_(TCTA)n chr1 382721 382986 + SINE/Alu_AluJr chr1 382990 383050 + Simple_repeat_(AATTTAA)n chr1 383231 383530 + SINE/Alu_AluSq2 chr1 384836 385135 + SINE/Alu_AluSx1 chr1 386049 386137 + LTR/ERVL-MaLR_MLT1K chr1 386143 386800 + LTR/ERV1_LTR9B chr1 386902 387072 + LTR/ERVL-MaLR_MLT1K chr1 387218 387399 - DNA/hAT-Charlie_MER5A chr1 387652 387842 - SINE/MIR_MIRb chr1 388085 388433 + LTR/ERV1_MER4E1 chr1 388434 388732 - SINE/Alu_AluSg4 chr1 388733 389154 + LTR/ERV1_MER4E1 chr1 389326 389564 - LINE/L2_L2c chr1 390051 390203 - LINE/L1_L1M5 chr1 390330 390538 - SINE/MIR_MIRc chr1 390636 390706 - DNA/hAT-Charlie_Charlie4z chr1 390708 391170 + LTR/ERVL-MaLR_MLT1E3 chr1 391319 391477 + SINE/Alu_FRAM chr1 392722 392820 + SINE/MIR_MIRc chr1 393038 393205 - DNA/hAT-Charlie_MER5B chr1 393782 393807 + Simple_repeat_(GT)n chr1 394383 394537 + LINE/L2_L2a chr1 394542 394617 + Low_complexity_GA-rich chr1 394618 394820 + SINE/MIR_MIRb chr1 395273 395717 - LTR/ERV1_LTR37B chr1 395719 396012 - SINE/Alu_AluSq chr1 396115 396199 - LTR/ERVL-MaLR_MLT1O chr1 396629 396685 - SINE/Alu_Alu chr1 396699 396748 + LTR/ERVL-MaLR_MSTA chr1 396749 396901 - SINE/Alu_AluSg chr1 396902 396941 + Simple_repeat_(GT)n chr1 396942 397059 - SINE/Alu_AluSg chr1 397060 397123 + LTR/ERVL-MaLR_MSTA chr1 397124 397271 + SINE/MIR_MIRb chr1 397521 397647 + LINE/L2_L2c chr1 398306 398476 - SINE/MIR_MIRb chr1 399228 399346 - DNA/hAT-Charlie_MER103C chr1 399609 399782 - SINE/MIR_MIRb chr1 399791 400104 - LTR/ERV1_LTR39 chr1 400105 400403 + SINE/Alu_AluSz6 chr1 400404 400705 - LTR/ERV1_LTR39 chr1 400706 400825 - LINE/L1_L1PA4 chr1 400826 400969 - LTR/ERV1_LTR39 chr1 401461 401662 - LINE/L2_L2a chr1 401982 402287 + SINE/Alu_AluSg chr1 402481 402608 + LTR/ERVL_LTR33 chr1 402902 403136 + SINE/MIR_MIR chr1 403509 403765 + LINE/RTE-X_L5 chr1 403887 404070 + LINE/L2_L2 chr1 404071 404370 - SINE/Alu_AluSc chr1 404371 404722 + LINE/L2_L2 chr1 404864 405149 - LTR/ERVL-MaLR_MLT1H chr1 405150 405469 - SINE/Alu_AluY chr1 405470 405515 - LTR/ERVL-MaLR_MLT1H chr1 405980 406010 + Simple_repeat_(AAC)n chr1 407457 407507 + Simple_repeat_(TGTGTG)n chr1 408239 408396 + LTR/ERVL-MaLR_MLT1I chr1 408460 408522 + LTR/ERVL-MaLR_MLT1I chr1 409437 409471 + Simple_repeat_(TTTA)n chr1 409487 409796 - SINE/Alu_AluSx3 chr1 410123 410195 + LINE/CR1_L3 chr1 410284 410691 - LTR?_MamRep605 chr1 410743 410821 - LTR?_MamRep605 chr1 411459 411758 + SINE/Alu_AluSz chr1 411810 412098 + SINE/Alu_AluJr chr1 412230 412530 + SINE/Alu_AluSx chr1 412956 413083 - SINE/MIR_MIRb chr1 413113 413249 - SINE/MIR_MIRb chr1 413252 413445 + LINE/CR1_L3 chr1 413446 413752 + SINE/Alu_AluSx3 chr1 413753 413827 + LINE/CR1_L3 chr1 413845 413937 - LTR/ERVL_LTR33 chr1 413938 414086 + LTR/ERVL-MaLR_MSTB1 chr1 414087 414378 - SINE/Alu_AluSq chr1 414379 414629 + LTR/ERVL-MaLR_MSTB1 chr1 414630 414675 + Simple_repeat_(AC)n chr1 414676 414694 + LTR/ERVL-MaLR_MSTB1 chr1 414695 414733 - LTR/ERVL_LTR33 chr1 414817 414985 - LTR/ERVL_LTR33 chr1 414997 415262 + LINE/CR1_L3 chr1 415315 415772 + DNA/TcMar-Tigger_Tigger5 chr1 415775 416346 + LINE/L1_L1M5 chr1 416361 419710 + LINE/L1_L1M4 chr1 419710 419997 + LINE/L1_L1M4 chr1 420195 420612 + LINE/L1_L1MC5a chr1 420656 420729 + Simple_repeat_(AT)n chr1 420830 421093 + LINE/L1_HAL1M8 chr1 421216 421392 + LINE/L1_L1ME3B chr1 421995 422041 - LINE/L1_L1PA6 chr1 422983 423332 - LINE/L2_L2c chr1 423485 423750 - LINE/L1_L1PA5 chr1 423754 423986 - LINE/L2_L2c chr1 424153 424229 - LINE/L1_L1ME3D chr1 424236 424397 + SINE/Alu_AluSp chr1 424424 425008 - LINE/L1_L1ME3D chr1 425057 426080 - LINE/L1_L1P4 chr1 426131 426871 - LINE/L1_L1ME3D chr1 426906 427029 - LINE/L1_L1MA8 chr1 427030 427765 + LINE/L1_L1MA2 chr1 427766 429747 - LINE/L1_L1MA8 chr1 429746 429884 - LINE/L1_L1ME3D chr1 429902 430382 + LTR/ERVK_LTR22C2 chr1 430418 431458 + LINE/L1_L1PA15-16 chr1 431452 431864 + LINE/L1_L1P4 chr1 431867 432156 + SINE/Alu_AluSp chr1 432157 432271 + Simple_repeat_(TA)n chr1 432272 432460 + LINE/L1_L1ME3A chr1 432310 432835 + LINE/L1_L1PA8A chr1 432815 432912 + LINE/L1_L1ME3A chr1 432892 432989 + LINE/L1_L1PA8 chr1 432969 433066 + LINE/L1_L1PA8 chr1 433046 433143 + LINE/L1_L1PA8 chr1 433123 433220 + LINE/L1_L1PA8 chr1 433200 433297 + LINE/L1_L1PA8 chr1 433277 433374 + LINE/L1_L1PA8 chr1 433354 433451 + LINE/L1_L1PA8 chr1 433431 433528 + LINE/L1_L1PA8 chr1 433508 433605 + LINE/L1_L1PA8 chr1 433585 433682 + LINE/L1_L1PA8 chr1 433662 433759 + LINE/L1_L1PA8 chr1 433739 433836 + LINE/L1_L1PA8 chr1 433816 433913 + LINE/L1_L1PA8 chr1 433893 433990 + LINE/L1_L1PA8 chr1 433970 434067 + LINE/L1_L1PA8 chr1 434047 434144 + LINE/L1_L1PA8 chr1 434124 434221 + LINE/L1_L1PA8 chr1 434201 434298 + LINE/L1_L1PA8 chr1 434278 434452 + LINE/L1_L1PA8 chr1 434432 434529 + LINE/L1_L1PA8 chr1 434509 434606 + LINE/L1_L1PA8 chr1 434586 434683 + LINE/L1_L1PA8 chr1 434663 434760 + LINE/L1_L1PA8 chr1 434740 434837 + LINE/L1_L1PA8 chr1 434817 434893 + LINE/L1_L1PA8 chr1 434894 435108 + LINE/L1_L1PA10 chr1 435128 435565 - LINE/L1_L1MEd chr1 435573 435666 + Low_complexity_GA-rich chr1 435753 435850 + LINE/L1_L1PA5 chr1 436298 437125 + LINE/L1_L1M2 chr1 437133 437267 + LTR/ERVL_ERVL-int chr1 437272 437746 + LINE/L1_L1MA7 chr1 437763 438484 - LINE/L1_L1PA4 chr1 438485 438662 + LINE/L1_L1PA4 chr1 438682 439766 + LINE/L1_L1MA7 chr1 440936 447357 + LINE/L1_L1PA7 chr1 447744 447767 + Simple_repeat_(AATA)n chr1 448109 449613 + LINE/L1_L1MA7 chr1 449889 450014 + LINE/L1_L1M2 chr1 449998 450553 + LINE/L1_L1M4 chr1 453499 453630 - SINE/MIR_MIR3 chr1 455112 455212 + SINE/MIR_MIRc chr1 455618 455759 + SINE/MIR_MIRb chr1 455768 455816 + LINE/L1_L1MA4 chr1 455833 456079 + LINE/L1_L1MA4 chr1 456094 456126 + Low_complexity_A-rich chr1 456129 456265 + SINE/MIR_MIRb chr1 457114 457197 + SINE/Alu_AluSz chr1 458256 458289 + Low_complexity_GA-rich chr1 458398 458451 + LINE/L2_L2c chr1 459679 459838 - SINE/MIR_MIRb chr1 460873 461140 + LINE/L1_L1M7 chr1 461141 461282 + LINE/L1_HAL1b chr1 461283 461293 + LINE/L1_L1M7 chr1 461319 461359 + LINE/L1_L1M7 chr1 461360 461504 + SINE/Alu_FLAM_C chr1 461505 461721 + LINE/L1_L1M7 chr1 462041 462622 - LINE/L2_L2c chr1 462744 463010 + LINE/L2_L2b chr1 463226 463465 + LTR/ERVL-MaLR_MLT1H1 chr1 463466 463823 - LTR/ERVL-MaLR_THE1B chr1 463824 465387 - LTR/ERVL-MaLR_THE1B-int chr1 465388 465747 - LTR/ERVL-MaLR_THE1B chr1 465798 466024 + LTR/ERVL-MaLR_MLT1H1 chr1 466025 466186 + LTR/ERVL-MaLR_MLT1H1-int chr1 466172 466423 + LTR/ERVL-MaLR_MLT1H1-int chr1 466428 466588 - LTR/ERV1_LTR64 chr1 466591 466952 + LINE/L1_L1PA14 chr1 467089 467392 + SINE/Alu_AluSq2 chr1 468092 468590 + LINE/L2_L2b chr1 469700 470109 + LTR/ERVL-MaLR_MLT1F2 chr1 470210 470525 + LINE/L1_L1ME3E chr1 470553 470578 + Simple_repeat_(AC)n chr1 471064 471200 + LTR/ERVL_LTR33B chr1 471349 471451 - LINE/L2_L2 chr1 471583 471720 + LTR/ERV1_MER65D chr1 471796 472076 + LTR/ERV1_MER65D chr1 472099 472215 - LINE/L1_L1M5 chr1 472380 473038 + LINE/L2_L2a chr1 473039 473235 + LINE/L1_L1ME3E chr1 473229 473615 + LINE/L1_L1ME3E chr1 473618 474107 + LTR/ERVL-MaLR_MLT1D chr1 474108 475113 + LINE/L1_L1ME3E chr1 475451 475485 + Simple_repeat_(TA)n chr1 475575 475955 - LTR/ERVL-MaLR_THE1D chr1 476981 477153 + Simple_repeat_(TTATATA)n chr1 477285 477578 - SINE/Alu_AluY chr1 477653 477731 + Simple_repeat_(TAA)n chr1 479099 479145 + Simple_repeat_(AT)n chr1 479182 479278 + LINE/L1_L1MA5 chr1 479313 479345 + Simple_repeat_(AACTGT)n chr1 479643 479713 + LINE/L2_L2a chr1 479720 479832 + LINE/L2_L2a chr1 479844 480394 + LINE/L2_L2c chr1 480558 480859 + SINE/Alu_AluJb chr1 481821 481880 + Simple_repeat_(ATAA)n chr1 481948 481975 + Simple_repeat_(ATAA)n chr1 482024 482224 + LINE/L1_L1MA5 chr1 482226 482976 + LINE/L1_L1M4 chr1 483532 483641 - LINE/L2_L2a chr1 483766 484035 - SINE/Alu_AluSz chr1 484472 484579 - LINE/L2_L2a chr1 484580 484878 - SINE/Alu_AluSq2 chr1 484879 485004 - LINE/L2_L2a chr1 485368 485660 - SINE/Alu_AluSx chr1 485665 485775 - SINE/Alu_FLAM_C chr1 485776 485878 + SINE/Alu_AluJo chr1 485892 485985 + LINE/L1_L1M2 chr1 485986 486300 + SINE/Alu_AluSg chr1 486301 486735 + LINE/L1_L1M2 chr1 486743 486963 + SINE/Alu_AluSq2 chr1 490053 490357 - SINE/Alu_AluSg chr1 494910 495329 - DNA/hAT-Charlie_MER1A chr1 495596 495748 - LTR/ERVL_LTR41 chr1 495752 496027 + SINE/Alu_AluJr chr1 496031 496251 + LINE/L1_L1ME4c chr1 496252 496392 - SINE/Alu_AluJo chr1 496629 496928 - SINE/Alu_AluJr chr1 497038 497139 - DNA/TcMar-Tigger_MER8 chr1 497140 497327 + SINE/Alu_AluJb chr1 497906 498062 + SINE/Alu_AluJr chr1 498317 498736 - DNA/hAT-Charlie_MER1A chr1 499022 499101 + SINE/Alu_FLAM_A chr1 499104 499393 + SINE/Alu_AluJo chr1 499394 499899 + LTR/ERVL_MER21C chr1 499900 500206 - SINE/Alu_AluJb chr1 500207 500276 + LTR/ERVL_MER21C chr1 500277 500579 + SINE/Alu_AluJr chr1 500580 500880 + LTR/ERVL_MER21C chr1 500917 501115 + SINE/MIR_MIRb chr1 501375 501478 + LINE/L2_L2b chr1 501774 501881 - SINE/Alu_AluJb chr1 501915 502039 + SINE/Alu_AluJr chr1 502053 502484 - DNA/hAT-Charlie_MER1A chr1 502787 502843 - LTR/ERVL_LTR41 chr1 502844 503134 + SINE/Alu_AluJb chr1 503139 503172 + Simple_repeat_(TA)n chr1 503173 503198 - LTR/ERVL_LTR41 chr1 503199 503494 + SINE/Alu_AluSp chr1 503495 503580 - LTR/ERVL_LTR41 chr1 503581 503888 + SINE/Alu_AluJr chr1 504249 504478 + LINE/L2_L2b chr1 504506 504593 + LINE/L2_L2b chr1 504663 504821 + LTR/ERVL-MaLR_MLT1D chr1 504823 505116 + SINE/Alu_AluJr chr1 505370 505644 + SINE/Alu_AluJr chr1 505655 505756 - SINE/Alu_AluJb chr1 505757 506026 + SINE/Alu_AluSx chr1 506033 506073 + Simple_repeat_(AAC)n chr1 506074 506133 - SINE/Alu_AluJb chr1 506193 506223 + LINE/L1_L1MB8 chr1 506224 506515 + SINE/Alu_AluJb chr1 506516 506603 + LINE/L1_L1MB8 chr1 506638 506748 - SINE/Alu_FLAM_A chr1 506749 507367 + LINE/L1_L1MB8 chr1 507389 507690 + SINE/Alu_AluJr chr1 507694 507808 - DNA/TcMar-Tigger_Tigger12 chr1 507993 508300 + SINE/Alu_AluSq2 chr1 508512 508605 - LTR/ERV1_LTR37A chr1 508751 509046 - SINE/Alu_AluJo chr1 509047 509072 + Simple_repeat_(ATTTT)n chr1 509075 509206 - SINE/Alu_AluJr chr1 509306 509913 + LINE/L2_L2a chr1 509915 510073 - SINE/Alu_AluJb chr1 510074 510358 - SINE/Alu_AluSg4 chr1 510359 510485 - SINE/Alu_AluJb chr1 510513 510863 - LTR/ERVL-MaLR_MLT1D chr1 510867 511157 + SINE/Alu_AluJr chr1 511158 511416 + LTR/ERVL_MER21C chr1 511417 511711 - SINE/Alu_AluSp chr1 511712 512021 + LTR/ERVL_MER21C chr1 512022 512327 + SINE/Alu_AluJo chr1 512328 512627 + LTR/ERVL_MER21C chr1 512629 513691 + LTR/ERVK_MER11B chr1 513737 513931 + SINE/MIR_MIRb chr1 514155 514206 + Simple_repeat_(CCATCC)n chr1 514587 514706 - SINE/Alu_AluJb chr1 514709 515009 - SINE/Alu_AluJo chr1 515049 515189 + LINE/L1_L1ME4b chr1 515420 515727 + SINE/Alu_AluJr chr1 515775 515950 + SINE/Alu_AluSg chr1 515951 516087 + LINE/L1_L1ME4c chr1 516091 516210 - SINE/Alu_AluJr4 chr1 516211 516334 + LINE/L1_L1ME4c chr1 516376 516478 - snRNA_U6 chr1 516699 516806 - DNA/TcMar-Tigger_MER8 chr1 516818 516962 + SINE/Alu_AluJb chr1 516981 517087 - DNA/hAT-Charlie_Charlie29b chr1 517305 517588 + SINE/Alu_AluJr chr1 517590 517629 + Simple_repeat_(AC)n chr1 517952 518175 + SINE/Alu_AluSx1 chr1 519896 520301 + LINE/L1_L1MC5a chr1 520312 520403 + LINE/L1_L1MC5a chr1 520712 520777 - LTR/ERVL-MaLR_MSTD chr1 520778 520989 + Low_complexity_GA-rich chr1 520990 521307 - LTR/ERVL-MaLR_MSTD chr1 522444 522749 + SINE/Alu_AluSp chr1 522955 523291 + SINE/Alu_AluSq2 chr1 523603 523906 + SINE/Alu_AluSx1 chr1 524652 524930 + SINE/Alu_AluSg chr1 524964 525099 - SINE/MIR_MIRb chr1 525730 526029 - SINE/Alu_AluSp chr1 526091 526385 + SINE/Alu_AluSg7 chr1 527081 527382 + SINE/Alu_AluSx1 chr1 527511 527938 + LTR/ERVL-MaLR_MLT1D chr1 527970 528275 + SINE/Alu_AluSz chr1 528453 528744 - SINE/Alu_AluSz6 chr1 529489 529530 + DNA/hAT-Charlie_Charlie4z chr1 529531 529831 - SINE/Alu_AluSq2 chr1 529832 529932 + DNA/hAT-Charlie_Charlie4z chr1 530691 530741 - SINE/Alu_AluSq2 chr1 531002 531084 - SINE/MIR_MIR1_Amn chr1 531256 531291 + LINE/L2_L2c chr1 531428 531508 - SINE/MIR_MIRb chr1 532963 532976 + Low_complexity_A-rich chr1 532977 533112 + SINE/Alu_AluSz chr1 533113 533415 + SINE/Alu_AluSp chr1 533416 533512 + SINE/Alu_AluSz chr1 533513 533545 + Simple_repeat_(AG)n chr1 533753 534065 + SINE/Alu_AluSx chr1 534186 534478 - SINE/Alu_AluSp chr1 534502 534789 + SINE/Alu_AluSx1 chr1 534790 534818 + Simple_repeat_(AAAAC)n chr1 534844 534878 + Low_complexity_A-rich chr1 534948 535032 - SINE/MIR_MIRb chr1 586338 586407 - LTR/ERV1_MER4D1 chr1 586408 586839 + LTR/ERV1_MER4A chr1 586840 587631 - LTR/ERV1_MER4D1 chr1 587718 587805 + SINE/MIR_MIRb chr1 588068 588110 + Simple_repeat_(CCT)n chr1 588463 588616 - DNA/hAT-Charlie_Charlie30a chr1 588803 588863 + Simple_repeat_(CACACA)n chr1 589004 589268 + LTR/ERV1_MER31B chr1 589269 589555 - SINE/Alu_AluJr chr1 589562 590374 + LINE/L1_L1MA4 chr1 590375 590674 + SINE/Alu_AluSx3 chr1 590678 590990 + SINE/Alu_AluSc chr1 590991 591452 + LINE/L1_L1MA4 chr1 591453 591761 + SINE/Alu_AluY chr1 591762 591984 + LINE/L1_L1MA4 chr1 592050 592172 + SINE/Alu_FRAM chr1 593923 594096 - SINE/MIR_MIR3 chr1 594469 594649 - LINE/L1_L1M5 chr1 594740 595208 + DNA/MULE-MuDR_Ricksha_0 chr1 595320 595414 + SINE/MIR_MIRb chr1 595663 595726 + DNA/TcMar-Tigger_Tigger4b chr1 596011 596173 + DNA/hAT-Charlie_Charlie20a chr1 597052 597101 + Simple_repeat_(TACACACTCA)n chr1 597260 597361 + DNA/hAT-Charlie_Charlie20a chr1 597362 597657 - SINE/Alu_AluSg chr1 597699 597827 - LINE/L1_L1MD3 chr1 598735 599301 - LINE/L1_L1MD3 chr1 599302 599607 - SINE/Alu_AluSx chr1 599608 599678 - LINE/L1_L1MD3 chr1 599683 599860 - SINE/Alu_AluYf1 chr1 599872 599948 - LINE/L1_L1MD3 chr1 599949 599988 + Simple_repeat_(TG)n chr1 599989 600425 - LINE/L1_L1MC4a chr1 600501 600577 - LINE/L1_L1MD3 chr1 600659 600741 - LINE/L1_L1MD3 chr1 600738 600820 - LINE/L1_L1MD3 chr1 600822 600900 + Simple_repeat_(TA)n chr1 601005 601031 + Simple_repeat_(TA)n chr1 601136 601162 + Simple_repeat_(TA)n chr1 601174 601748 - LINE/L1_L1MD3 chr1 602832 603444 - LTR/ERV1_MER39B chr1 603760 603787 + Simple_repeat_(CT)n chr1 604123 604419 + SINE/Alu_AluY chr1 604545 604706 - LINE/L2_L2 chr1 604775 604833 + Low_complexity_A-rich chr1 604883 605417 + LINE/L1_L1ME1 chr1 605579 605728 + LINE/L1_L1ME1 chr1 607268 607312 + Low_complexity_GA-rich chr1 607631 607917 + Simple_repeat_(TCTA)n chr1 612611 612876 + SINE/Alu_AluJr chr1 612880 612940 + Simple_repeat_(AATTTAA)n chr1 613121 613420 + SINE/Alu_AluSq2 chr1 614726 615025 + SINE/Alu_AluSx1 chr1 615939 616027 + LTR/ERVL-MaLR_MLT1K chr1 616033 616689 + LTR/ERV1_LTR9B chr1 616791 616961 + LTR/ERVL-MaLR_MLT1K chr1 617107 617288 - DNA/hAT-Charlie_MER5A chr1 617541 617731 - SINE/MIR_MIRb chr1 617974 618322 + LTR/ERV1_MER4E1 chr1 618323 618622 - SINE/Alu_AluSg4 chr1 618623 619044 + LTR/ERV1_MER4E1 chr1 619216 619454 - LINE/L2_L2c chr1 619941 620093 - LINE/L1_L1M5 chr1 620220 620428 - SINE/MIR_MIRc chr1 620526 620596 - DNA/hAT-Charlie_Charlie4z chr1 620598 621060 + LTR/ERVL-MaLR_MLT1E3 chr1 621209 621367 + SINE/Alu_FRAM chr1 622612 622710 + SINE/MIR_MIRc chr1 622928 623095 - DNA/hAT-Charlie_MER5B chr1 623672 623699 + Simple_repeat_(GT)n chr1 624275 624429 + LINE/L2_L2a chr1 624434 624526 + Low_complexity_G-rich chr1 624527 624708 + SINE/MIR_MIRb chr1 625161 625605 - LTR/ERV1_LTR37B chr1 625607 625900 - SINE/Alu_AluSq chr1 626003 626087 - LTR/ERVL-MaLR_MLT1O chr1 626517 626573 - SINE/Alu_Alu chr1 626587 626632 + LTR/ERVL-MaLR_MSTA chr1 626633 626781 - SINE/Alu_AluSg chr1 626782 626821 + Simple_repeat_(GT)n chr1 626822 626941 - SINE/Alu_AluSg chr1 626942 627005 + LTR/ERVL-MaLR_MSTA chr1 627006 627153 + SINE/MIR_MIRb chr1 627403 627529 + LINE/L2_L2c chr1 628184 628354 - SINE/MIR_MIRb chr1 629499 629570 - tRNA_tRNA-Gln-CAA_ chr1 632619 632685 - tRNA_tRNA-Ser-TCA(m) chr1 634980 635098 - DNA/hAT-Charlie_MER103C chr1 635361 635534 - SINE/MIR_MIRb chr1 635543 635856 - LTR/ERV1_LTR39 chr1 635857 636155 + SINE/Alu_AluSz6 chr1 636156 636458 - LTR/ERV1_LTR39 chr1 636459 636578 - LINE/L1_L1PA4 chr1 636579 636722 - LTR/ERV1_LTR39 chr1 637214 637415 - LINE/L2_L2a chr1 637735 638040 + SINE/Alu_AluSg chr1 638234 638361 + LTR/ERVL_LTR33 chr1 638655 638885 + SINE/MIR_MIR chr1 639258 639520 + LINE/RTE-X_L5 chr1 639633 639829 + LINE/L2_L2 chr1 639830 640120 - SINE/Alu_AluSc chr1 640121 640472 + LINE/L2_L2 chr1 640609 640898 - LTR/ERVL-MaLR_MLT1H chr1 640899 641219 - SINE/Alu_AluY chr1 641220 641265 - LTR/ERVL-MaLR_MLT1H chr1 641730 641760 + Simple_repeat_(AAC)n chr1 643207 643257 + Simple_repeat_(TGTGTG)n chr1 643990 644273 + LTR/ERVL-MaLR_MLT1I chr1 645188 645222 + Simple_repeat_(TTTA)n chr1 645238 645548 - SINE/Alu_AluSx3 chr1 645671 645865 + DNA?_MER124 chr1 645875 645947 + LINE/CR1_L3 chr1 646036 646479 - LTR?_MamRep605 chr1 646495 646573 - LTR?_MamRep605 chr1 647211 647512 + SINE/Alu_AluSz chr1 647564 647853 + SINE/Alu_AluJr chr1 647985 648285 + SINE/Alu_AluSx chr1 648711 648838 - SINE/MIR_MIRb chr1 648868 649004 - SINE/MIR_MIRb chr1 649007 649199 + LINE/CR1_L3 chr1 649200 649506 + SINE/Alu_AluSx3 chr1 649507 649581 + LINE/CR1_L3 chr1 649599 649691 - LTR/ERVL_LTR33 chr1 649692 649840 + LTR/ERVL-MaLR_MSTB1 chr1 649841 650132 - SINE/Alu_AluSq chr1 650133 650383 + LTR/ERVL-MaLR_MSTB1 chr1 650384 650423 + Simple_repeat_(AC)n chr1 650424 650442 + LTR/ERVL-MaLR_MSTB1 chr1 650443 650481 - LTR/ERVL_LTR33 chr1 650565 650733 - LTR/ERVL_LTR33 chr1 650745 651010 + LINE/CR1_L3 chr1 651063 651520 + DNA/TcMar-Tigger_Tigger5 chr1 651523 652097 + LINE/L1_L1M4 chr1 652108 655449 + LINE/L1_L1MC5a chr1 655449 655736 + LINE/L1_L1MC5a chr1 655779 656369 + LINE/L1_L1MC5a chr1 656395 656468 + Simple_repeat_(AT)n chr1 656569 656832 + LINE/L1_HAL1M8 chr1 656955 657133 + LINE/L1_L1ME3B chr1 657736 657782 - LINE/L1_L1PA6 chr1 658724 659073 - LINE/L2_L2c chr1 659226 659491 - LINE/L1_L1PA5 chr1 659495 659727 - LINE/L2_L2c chr1 659894 659970 - LINE/L1_L1ME3D chr1 659977 660138 + SINE/Alu_AluSp chr1 660165 660749 - LINE/L1_L1ME3D chr1 660798 661821 - LINE/L1_L1P4 chr1 661872 662612 - LINE/L1_L1ME3D chr1 662647 662770 - LINE/L1_L1MA8 chr1 662771 663506 + LINE/L1_L1MA2 chr1 663507 665488 - LINE/L1_L1MA8 chr1 665487 665625 - LINE/L1_L1ME3D chr1 665643 666123 + LTR/ERVK_LTR22C2 chr1 666159 667201 + LINE/L1_L1PA15-16 chr1 667195 667606 + LINE/L1_L1P4 chr1 667609 667898 + SINE/Alu_AluSp chr1 667899 668023 + Simple_repeat_(TA)n chr1 668024 668212 + LINE/L1_L1ME3A chr1 668062 668509 + LINE/L1_L1PA8A chr1 668489 668585 + LINE/L1_L1ME3A chr1 668565 668662 + LINE/L1_L1PA8 chr1 668642 668739 + LINE/L1_L1PA8 chr1 668719 668816 + LINE/L1_L1PA8 chr1 668796 668892 + LINE/L1_L1PA8 chr1 668872 668969 + LINE/L1_L1PA8 chr1 668949 669046 + LINE/L1_L1PA8 chr1 669026 669123 + LINE/L1_L1PA8 chr1 669103 669200 + LINE/L1_L1PA8 chr1 669180 669277 + LINE/L1_L1PA8 chr1 669257 669354 + LINE/L1_L1PA8 chr1 669334 669431 + LINE/L1_L1PA8 chr1 669411 669508 + LINE/L1_L1PA8 chr1 669488 669585 + LINE/L1_L1PA8 chr1 669565 669662 + LINE/L1_L1PA8 chr1 669642 669739 + LINE/L1_L1PA8 chr1 669719 669816 + LINE/L1_L1PA8 chr1 669796 669870 + LINE/L1_L1PA11 chr1 669873 670087 + LINE/L1_L1PA10 chr1 670107 670544 - LINE/L1_L1MEd chr1 670552 670645 + Low_complexity_GA-rich chr1 670732 670829 + LINE/L1_L1PA5 chr1 671277 672104 + LINE/L1_L1M2 chr1 672112 672246 + LTR/ERVL_ERVL-int chr1 672251 672725 + LINE/L1_L1MA7 chr1 672742 673463 - LINE/L1_L1PA4 chr1 673464 673641 + LINE/L1_L1PA4 chr1 673661 674745 + LINE/L1_L1MA7 chr1 675912 682333 + LINE/L1_L1PA7 chr1 682720 682743 + Simple_repeat_(AATA)n chr1 683085 684589 + LINE/L1_L1MA7 chr1 684865 684990 + LINE/L1_L1M2 chr1 684974 685529 + LINE/L1_L1M4 chr1 688475 688606 - SINE/MIR_MIR3 chr1 690093 690185 + SINE/MIR_MIRc chr1 690591 690732 + SINE/MIR_MIRb chr1 690741 690789 + LINE/L1_L1MA4 chr1 690806 691052 + LINE/L1_L1MA4 chr1 691067 691099 + Low_complexity_A-rich chr1 691102 691238 + SINE/MIR_MIRb chr1 692085 692168 + SINE/Alu_AluSz chr1 693227 693260 + Low_complexity_GA-rich chr1 693369 693422 + LINE/L2_L2c chr1 694650 694809 - SINE/MIR_MIRb chr1 695844 696111 + LINE/L1_L1M7 chr1 696112 696253 + LINE/L1_HAL1b chr1 696254 696264 + LINE/L1_L1M7 chr1 696290 696330 + LINE/L1_L1M7 chr1 696331 696475 + SINE/Alu_FLAM_C chr1 696476 696692 + LINE/L1_L1M7 chr1 697012 697318 - LINE/L2_L2c chr1 697319 697355 - LINE/L2_L2b chr1 697461 697705 - LINE/L2_L2b chr1 697720 697986 + LINE/L2_L2b chr1 698203 698441 + LTR/ERVL-MaLR_MLT1H1 chr1 698442 698799 - LTR/ERVL-MaLR_THE1B chr1 698800 700363 - LTR/ERVL-MaLR_THE1B-int chr1 700364 700723 - LTR/ERVL-MaLR_THE1B chr1 700724 701000 + LTR/ERVL-MaLR_MLT1H1 chr1 701001 701162 + LTR/ERVL-MaLR_MLT1H1-int chr1 701148 701399 + LTR/ERVL-MaLR_MLT1H1-int chr1 701404 701564 - LTR/ERV1_LTR64 chr1 701567 701928 + LINE/L1_L1PA14 chr1 702064 702369 + SINE/Alu_AluSq2 chr1 703069 703567 + LINE/L2_L2b chr1 704677 705086 + LTR/ERVL-MaLR_MLT1F2 chr1 705187 705502 + LINE/L1_L1ME3E chr1 705530 705555 + Simple_repeat_(AC)n chr1 706041 706177 + LTR/ERVL_LTR33B chr1 706326 706428 - LINE/L2_L2 chr1 706560 706697 + LTR/ERV1_MER65D chr1 706774 707054 + LTR/ERV1_MER65D chr1 707077 707193 - LINE/L1_L1M5 chr1 707358 708016 + LINE/L2_L2a chr1 708017 708213 + LINE/L1_L1ME3E chr1 708207 708593 + LINE/L1_L1ME3E chr1 708596 709085 + LTR/ERVL-MaLR_MLT1D chr1 709086 710091 + LINE/L1_L1ME3E chr1 710429 710463 + Simple_repeat_(TA)n chr1 710553 710933 - LTR/ERVL-MaLR_THE1C chr1 711959 712094 + Simple_repeat_(TA)n chr1 712229 712524 - SINE/Alu_AluY chr1 712599 712677 + Simple_repeat_(TAA)n chr1 713991 714042 + Simple_repeat_(TATATA)n chr1 714043 714091 + Simple_repeat_(AT)n chr1 714128 714224 + LINE/L1_L1MA5 chr1 714259 714291 + Simple_repeat_(AACTGT)n chr1 714639 714709 + LINE/L2_L2a chr1 714716 714828 + LINE/L2_L2a chr1 714840 715390 + LINE/L2_L2c chr1 715554 715848 + SINE/Alu_AluJb chr1 716810 716869 + Simple_repeat_(ATAA)n chr1 716937 716964 + Simple_repeat_(ATAA)n chr1 717013 717213 + LINE/L1_L1MA5 chr1 717216 717970 + LINE/L1_L1M4 chr1 718525 718634 - LINE/L2_L2a chr1 718759 719028 - SINE/Alu_AluSz chr1 719465 719572 - LINE/L2_L2a chr1 719573 719870 - SINE/Alu_AluSq2 chr1 719871 719996 - LINE/L2_L2a chr1 720360 720652 - SINE/Alu_AluSx chr1 720656 720766 - SINE/Alu_FLAM_C chr1 720767 720869 + SINE/Alu_AluJo chr1 720885 720976 + LINE/L1_L1M2 chr1 720977 721291 + SINE/Alu_AluSg chr1 721292 721726 + LINE/L1_L1M2 chr1 721734 721954 + SINE/Alu_AluSq2 chr1 725052 725360 - SINE/Alu_AluSg chr1 729816 730235 - DNA/hAT-Charlie_MER1A chr1 730502 730654 - LTR/ERVL_LTR41 chr1 730658 730941 + SINE/Alu_AluJr chr1 730946 731166 + LINE/L1_L1ME4c chr1 731169 731315 - SINE/Alu_FLAM_C chr1 731535 731834 - SINE/Alu_AluJr chr1 731946 732047 - DNA/TcMar-Tigger_MER8 chr1 732048 732235 + SINE/Alu_AluJb chr1 732836 732969 + SINE/Alu_AluJr chr1 733225 733644 - DNA/hAT-Charlie_MER1A chr1 733910 734062 - LTR/ERVL_LTR41 chr1 734066 734349 + SINE/Alu_AluJr chr1 734354 734565 + LINE/L1_L1ME4c chr1 734566 734706 - SINE/Alu_AluJb chr1 734943 735242 - SINE/Alu_AluJr chr1 735352 735453 - DNA/TcMar-Tigger_MER8 chr1 735454 735641 + SINE/Alu_AluJb chr1 736220 736376 + SINE/Alu_AluJr chr1 736631 737050 - DNA/hAT-Charlie_MER1A chr1 737317 737469 - LTR/ERVL_LTR41 chr1 737473 737748 + SINE/Alu_AluJr chr1 737752 737981 + LINE/L1_L1ME4c chr1 737984 738130 - SINE/Alu_FLAM_C chr1 738352 738651 - SINE/Alu_AluJr chr1 738763 738864 - DNA/TcMar-Tigger_MER8 chr1 738865 739057 + SINE/Alu_AluJb chr1 739636 739792 + SINE/Alu_AluJr chr1 740047 740466 - DNA/hAT-Charlie_MER1A chr1 740752 740838 + SINE/Alu_AluJo chr1 740839 741123 + SINE/Alu_AluJo chr1 741124 741629 + LTR/ERVL_MER21C chr1 741630 741936 - SINE/Alu_AluJb chr1 741937 742006 + LTR/ERVL_MER21C chr1 742007 742309 + SINE/Alu_AluJr chr1 742310 742610 + LTR/ERVL_MER21C chr1 742647 742845 + SINE/MIR_MIRb chr1 743105 743208 + LINE/L2_L2b chr1 743504 743611 - SINE/Alu_AluJb chr1 743645 743769 + SINE/Alu_AluJr chr1 743783 744214 - DNA/hAT-Charlie_MER1A chr1 744517 744573 - LTR/ERVL_LTR41 chr1 744574 744864 + SINE/Alu_AluJb chr1 744865 744912 - LTR/ERVL_LTR41 chr1 744913 745209 + SINE/Alu_AluSp chr1 745210 745295 - LTR/ERVL_LTR41 chr1 745296 745603 + SINE/Alu_AluJr chr1 745964 746193 + LINE/L2_L2b chr1 746221 746308 + LINE/L2_L2b chr1 746378 746536 + LTR/ERVL-MaLR_MLT1D chr1 746538 746831 + SINE/Alu_AluJr chr1 747086 747357 + SINE/Alu_AluJr chr1 747368 747467 - SINE/Alu_AluJr4 chr1 747468 747737 + SINE/Alu_AluSx chr1 747744 747784 + Simple_repeat_(AAC)n chr1 747785 747844 - SINE/Alu_AluJr4 chr1 747904 747934 + LINE/L1_L1MB8 chr1 747935 748226 + SINE/Alu_AluJb chr1 748227 748314 + LINE/L1_L1MB8 chr1 748349 748459 - SINE/Alu_FLAM_A chr1 748460 749079 + LINE/L1_L1MB8 chr1 749101 749402 + SINE/Alu_AluJr chr1 749406 749520 - DNA/TcMar-Tigger_Tigger12 chr1 749705 750012 + SINE/Alu_AluSq2 chr1 750224 750317 - LTR/ERV1_LTR37A chr1 750463 750758 - SINE/Alu_AluJo chr1 750759 750784 + Simple_repeat_(ATTTT)n chr1 750787 750918 - SINE/Alu_AluJr chr1 751018 751625 + LINE/L2_L2a chr1 751627 751789 - SINE/Alu_AluJb chr1 751790 752075 - SINE/Alu_AluSg4 chr1 752076 752202 - SINE/Alu_AluJb chr1 752230 752580 - LTR/ERVL-MaLR_MLT1D chr1 752584 752875 + SINE/Alu_AluJr chr1 752876 753134 + LTR/ERVL_MER21C chr1 753135 753428 - SINE/Alu_AluSp chr1 753429 753738 + LTR/ERVL_MER21C chr1 753739 754045 + SINE/Alu_AluJo chr1 754046 754345 + LTR/ERVL_MER21C chr1 754347 755409 + LTR/ERVK_MER11B chr1 755455 755649 + SINE/MIR_MIRb chr1 755873 755924 + Simple_repeat_(CCATCC)n chr1 756303 756422 - SINE/Alu_AluJb chr1 756425 756724 - SINE/Alu_AluJo chr1 756764 756904 + LINE/L1_L1ME4b chr1 757135 757441 + SINE/Alu_AluJr chr1 757489 757782 + SINE/Alu_AluSg chr1 757791 757944 + LINE/L1_L1ME4c chr1 757948 758067 - SINE/Alu_AluJr4 chr1 758068 758191 + LINE/L1_L1ME4c chr1 758233 758335 - snRNA_U6 chr1 758556 758663 - DNA/TcMar-Tigger_MER8 chr1 758675 758819 + SINE/Alu_AluJb chr1 758838 758944 - DNA/hAT-Charlie_Charlie29b chr1 759162 759445 + SINE/Alu_AluJr chr1 759447 759498 + Simple_repeat_(AC)n chr1 759821 760044 + SINE/Alu_AluSx1 chr1 761764 762169 + LINE/L1_L1MC5a chr1 762180 762271 + LINE/L1_L1MC5a chr1 762580 762645 - LTR/ERVL-MaLR_MSTD chr1 762646 762853 + Low_complexity_GA-rich chr1 762854 763171 - LTR/ERVL-MaLR_MSTD chr1 764308 764613 + SINE/Alu_AluSp chr1 764819 765151 + SINE/Alu_AluSq2 chr1 765463 765756 + SINE/Alu_AluSx1 chr1 766500 766778 + SINE/Alu_AluSg chr1 766812 766945 - SINE/MIR_MIRb chr1 767578 767881 - SINE/Alu_AluSp chr1 767943 768237 + SINE/Alu_AluSg7 chr1 768933 769234 + SINE/Alu_AluSx1 chr1 769363 769790 + LTR/ERVL-MaLR_MLT1D chr1 769822 770127 + SINE/Alu_AluSz chr1 770305 770568 - SINE/Alu_AluSg chr1 771313 771354 + DNA/hAT-Charlie_Charlie4z chr1 771355 771655 - SINE/Alu_AluSq2 chr1 771656 771756 + DNA/hAT-Charlie_Charlie4z chr1 772515 772565 - SINE/Alu_AluSq2 chr1 772845 772908 - SINE/MIR_MIR1_Amn chr1 773252 773332 - SINE/MIR_MIRb chr1 774777 774790 + Low_complexity_A-rich chr1 774791 774926 + SINE/Alu_AluSz chr1 774927 775230 + SINE/Alu_AluSp chr1 775231 775351 + SINE/Alu_AluSz chr1 775560 775872 + SINE/Alu_AluSx chr1 776001 776293 - SINE/Alu_AluSp chr1 776317 776604 + SINE/Alu_AluSx1 chr1 776605 776633 + Simple_repeat_(AAAAC)n chr1 776659 776693 + Low_complexity_A-rich chr1 776781 776847 - SINE/MIR_MIRb chr1 777361 777389 + Simple_repeat_(TATC)n chr1 777390 777597 - SINE/Alu_AluJo chr1 777600 777872 - SINE/Alu_AluSx chr1 777910 778062 + SINE/Alu_AluJo chr1 778063 778194 - SINE/Alu_AluJo chr1 778255 778442 - SINE/Alu_FRAM chr1 779336 779630 + SINE/Alu_AluSg7 chr1 779683 779791 + SINE/Alu_AluSg4 chr1 779851 780056 + SINE/Alu_AluJb chr1 780067 780139 - SINE/Alu_AluSp chr1 780227 780257 + Simple_repeat_(TGTTT)n chr1 780261 780575 - SINE/Alu_AluJr chr1 780617 780773 - SINE/Alu_AluSc chr1 780774 780860 + SINE/Alu_AluJb chr1 781532 781570 + Simple_repeat_(ATTATTT)n chr1 782312 782501 - DNA/hAT-Charlie_MER3 chr1 782641 782795 - DNA/hAT-Charlie_MER5A1 chr1 782829 783115 + DNA/hAT-Charlie_MER33 chr1 783621 783884 - LINE/L1_L1ME3 chr1 784084 784112 + Simple_repeat_(TA)n chr1 784116 784356 + LINE/L1_L1MEg chr1 784344 785369 + LINE/L1_L1MC4 chr1 785424 785494 + LINE/L1_L1MEg chr1 785495 785733 + LINE/L1_L1PA4 chr1 785734 785761 + LINE/L1_L1MEg chr1 785994 786249 - LTR/Gypsy_LTR104_Mam chr1 786262 786313 + DNA/hAT-Blackjack_BLACKJACK chr1 786314 786376 - LINE/L1_L1M3 chr1 786377 786507 - SINE/Alu_AluJr chr1 786508 787339 - LINE/L1_L1M3 chr1 787352 787718 + LTR/ERV1_LTR7 chr1 787729 787833 - SINE/MIR_MIRc chr1 788085 788363 + SINE/Alu_AluSp chr1 788365 788438 + Low_complexity_GA-rich chr1 788434 788465 + Simple_repeat_(GAAC)n chr1 788635 788750 - LTR/ERV1_MER67C chr1 791053 791098 + Simple_repeat_(GGAATG)n chr1 791822 791993 - LTR/ERVL_LTR67B chr1 792384 793725 - LINE/L1_L1M7 chr1 793726 794040 + DNA/TcMar-Tigger_Tigger4b chr1 794041 794177 - LINE/L1_L1M7 chr1 794556 794727 - LINE/L1_L1M7 chr1 794994 795251 - LTR/ERVL_LTR79 chr1 795362 795592 + DNA/hAT-Charlie_Charlie1b chr1 795607 795932 - SINE/Alu_AluSq2 chr1 795939 796100 + DNA/hAT-Charlie_Charlie1a chr1 796539 796837 - DNA/TcMar-Tigger_Tigger3a chr1 796916 797131 + LINE/L1_L1M5 chr1 797344 797585 - SINE/MIR_MIR chr1 798734 799039 + SINE/Alu_AluSx1 chr1 799270 799325 - LINE/L2_L2c chr1 799502 800409 - LINE/L1_L1PA7 chr1 800410 800908 + LINE/L1_L1PA7 chr1 801204 801381 - SINE/MIR_MIR chr1 801589 801715 - SINE/Alu_AluJo chr1 802512 802604 - LINE/L2_L2a chr1 803150 803446 + SINE/Alu_AluSz chr1 804218 804482 + DNA/hAT-Charlie_MER113A chr1 807025 807086 - LINE/L2_L2a chr1 807402 807502 + LTR/ERVL-MaLR_MLT1O chr1 808290 808338 + Simple_repeat_(ACATACAC)n chr1 809061 809590 + DNA/TcMar-Tigger_MER44B chr1 812115 813014 + LINE/L1_L1MB3 chr1 813369 813399 + Low_complexity_GA-rich chr1 813743 813829 + DNA/hAT-Charlie_Chap1a_Mam chr1 813876 814012 + LTR/ERVL-MaLR_MLT1C2 chr1 815155 815512 + LINE/L1_L1M8 chr1 815700 815811 + LINE/L1_L1M8 chr1 815885 815941 + Low_complexity_A-rich chr1 816164 816234 + LINE/L1_HAL1M8 chr1 816228 816461 + LINE/L1_L1ME3B chr1 816629 817288 + LINE/L1_L1P4 chr1 818040 818201 - DNA/hAT-Charlie_MER58A chr1 818622 818794 + DNA/hAT-Charlie_MER102c chr1 819066 819303 + LTR/ERVL-MaLR_MLT1I chr1 819610 819698 - LINE/L2_L2d2 chr1 819731 819948 + DNA/TcMar-Tigger_Tigger7 chr1 827542 827629 + Low_complexity_G-rich chr1 828212 828273 + Simple_repeat_(TATT)n chr1 828713 828809 - LINE/L1_L1MC chr1 828812 828986 - LTR/ERV1_MER41D chr1 828987 829262 - SINE/Alu_AluSp chr1 829263 829390 - LTR/ERV1_MER41D chr1 829414 829478 + Simple_repeat_(GTGCT)n chr1 829596 830064 - LINE/L1_HAL1 chr1 830065 830433 + LTR/ERVL-MaLR_MLT1A1 chr1 830434 830847 - LINE/L1_HAL1 chr1 830887 831011 - LINE/L1_HAL1 chr1 831109 831353 - LTR/ERV1_LTR78 chr1 832657 832701 + Low_complexity_G-rich chr1 832737 832781 + Simple_repeat_(GTTTT)n chr1 832782 833054 - SINE/Alu_AluSx chr1 833607 833708 - SINE/MIR_MIR3 chr1 834761 834905 - LTR/ERVL-MaLR_MLT1K chr1 834912 834988 + Simple_repeat_(CACC)n chr1 835627 835728 - LINE/L2_L2c chr1 836042 836120 + SINE/MIR_MIR3 chr1 836348 836399 + LINE/L2_L2b chr1 836414 836851 + LTR/ERVL_LTR16C chr1 836853 836925 + LINE/L2_L2b chr1 837024 837170 - SINE/Alu_AluJb chr1 837099 837180 + Retroposon/SVA_SVA_D chr1 837340 837504 - DNA/hAT-Charlie_MER58A chr1 837512 840216 + LINE/L1_L1M4b chr1 840199 840402 + LINE/L1_L1MB8 chr1 840410 840440 + Simple_repeat_(A)n chr1 840501 840618 - SINE/MIR_MIRb chr1 841508 841658 + DNA/hAT-Charlie_MER5A1 chr1 841663 841828 + SINE/MIR_MIRb chr1 842137 842302 - Unknown_UCON76 chr1 843987 844346 + LTR/ERVL-MaLR_MLT1B chr1 844931 844983 + DNA/hAT-Charlie_Charlie5 chr1 845970 846625 + LINE/L1_L1MC1 chr1 846741 846893 - LINE/L2_L2d chr1 846972 847188 + DNA/hAT-Charlie_MER103C chr1 847809 847932 - SINE/MIR_MIR chr1 849557 849911 - LTR/ERVL-MaLR_MSTD chr1 850646 851208 - LINE/L1_L1PA3 chr1 852835 852959 - SINE/Alu_FLAM_A chr1 852971 853263 - SINE/Alu_AluSz chr1 853264 853435 - DNA/hAT-Charlie_Charlie19a chr1 855216 855298 + Simple_repeat_(TGTGCA)n chr1 855395 855476 + Simple_repeat_(TG)n chr1 858970 859039 - LINE/CR1_L3 chr1 859352 859744 - LINE/L1_L1ME4a chr1 860179 860211 + Simple_repeat_(T)n chr1 860215 860497 - SINE/Alu_AluJr chr1 860602 860628 + Simple_repeat_(CCCT)n chr1 860788 860972 - LTR/ERV1_LTR2C chr1 861648 861726 + Simple_repeat_(CATGTG)n chr1 861694 862184 + Simple_repeat_(GATAGTA)n chr1 862755 863199 - LTR/ERVL-MaLR_MLT1K chr1 863516 863653 - LINE/L2_L2b chr1 865563 865716 + SINE/MIR_MIRc chr1 866279 866448 - LTR/ERVL-MaLR_MLT1B chr1 869214 869280 + Low_complexity_A-rich chr1 869463 869520 + Simple_repeat_(TATC)n chr1 869625 869730 - SINE/MIR_MIRb chr1 871048 871260 - LINE/L1_L1M4 chr1 871261 871425 - SINE/Alu_FRAM chr1 871426 871792 - LINE/L1_L1M4 chr1 874305 874344 + Low_complexity_GA-rich chr1 877339 877508 + SINE/MIR_MIR chr1 879078 879247 - LINE/L1_L1MB4 chr1 879381 879414 + Simple_repeat_(AT)n chr1 879418 881416 - LINE/L1_L1M3 chr1 882685 882948 - LINE/L1_L1PA7 chr1 882949 883243 - SINE/Alu_AluSp chr1 883244 885344 - LINE/L1_L1PA7 chr1 885341 887183 - LINE/L1_L1PA7 chr1 887453 887910 + LINE/L1_L1ME4b chr1 888098 888264 - LINE/L1_L1M5 chr1 888310 888696 + Low_complexity_A-rich chr1 888957 889347 + LTR/ERVL-MaLR_MSTD chr1 889627 889725 + Satellite_BSR/Beta chr1 889767 889923 + SINE/MIR_MIRb chr1 889928 889998 - LTR/ERVL-MaLR_MLT1I chr1 889999 890324 - SINE/Alu_AluSc8 chr1 890325 890375 - LTR/ERVL-MaLR_MLT1I chr1 890387 890457 + SINE/Alu_Alu chr1 890614 890679 + LTR/ERVL_LTR41 chr1 890935 891105 - LTR/ERV1_MER101-int chr1 891269 891354 - LTR/ERV1_MER101-int chr1 891355 891499 - LTR/ERV1_LOR1-int chr1 891500 891748 - LTR/ERV1_MER101-int chr1 891511 891802 - LTR/ERV1_LOR1-int chr1 891803 892094 - SINE/Alu_AluSp chr1 892095 892183 - LTR/ERV1_LOR1-int chr1 892184 892456 + SINE/Alu_AluJr chr1 892457 892928 - LTR/ERV1_LOR1-int chr1 892939 893255 + SINE/Alu_AluJb chr1 893283 893302 - LTR/ERVL_LTR57-int chr1 893303 893495 - LTR/ERV1_LOR1-int chr1 893496 893801 + SINE/Alu_AluSz6 chr1 893802 893840 + Simple_repeat_(AT)n chr1 893848 894130 + SINE/Alu_AluSz chr1 894131 894203 - LTR/ERV1_LOR1-int chr1 894361 894466 - LTR/ERV1_MER101-int chr1 894862 895173 - SINE/Alu_AluSp chr1 895290 895508 - SINE/Alu_AluYf1 chr1 895510 895548 + LTR/ERVK_HERVK9-int chr1 895544 895868 - LTR/ERVK_HERVK9-int chr1 895870 896099 - LTR/ERVK_HERVK9-int chr1 896100 897617 - LTR/ERVK_HERVK9-int chr1 897610 898198 - LTR/ERVK_HERVK9-int chr1 898199 898699 - LTR/ERVK_MER9a2 chr1 899022 899326 - SINE/Alu_AluY chr1 899671 899729 + DNA/hAT-Charlie_MER102a chr1 899916 899946 + Simple_repeat_(GGATG)n chr1 900211 900937 + LINE/L1_L1MC3 chr1 900942 901241 + SINE/Alu_AluJb chr1 901363 901555 - LTR/ERV1_MER67A chr1 901672 901800 - LINE/L2_L2c chr1 902484 902501 + Simple_repeat_(TAA)n chr1 902502 902788 + SINE/Alu_AluSq2 chr1 902789 902804 + Simple_repeat_(TAA)n chr1 902970 903274 - LTR/ERVL-MaLR_MLT1B chr1 903382 903639 + LTR/ERVL-MaLR_MLT1A chr1 903640 903726 - DNA/TcMar-Tigger_MER2 chr1 905266 905461 + LINE/L1_L1MB5 chr1 905569 905799 + LINE/L1_L1MB5 chr1 906474 906556 - LTR/ERVL_HERV16-int chr1 907171 907250 + SINE/Alu_Alu chr1 907253 907562 + SINE/Alu_AluSz chr1 908056 908165 + LINE/L2_L2a chr1 908220 908259 + Simple_repeat_(GCAG)n chr1 910061 910090 + Simple_repeat_(CGCGC)n chr1 910107 910140 + Low_complexity_G-rich chr1 911852 911997 - SINE/MIR_MIRb chr1 912133 912268 - LINE/L2_X24_LINE chr1 912320 912400 + SINE/MIR_MIRb chr1 912463 912661 - LINE/L2_L2d chr1 912842 912915 + LINE/L2_L2b chr1 913581 913633 + Low_complexity_G-rich chr1 914456 914665 + LINE/L1_L1MEc chr1 914666 914977 + SINE/Alu_AluSp chr1 914978 915397 + LINE/L1_L1MEc chr1 915717 915837 - SINE/MIR_MIRc chr1 919107 919193 - LINE/L2_L2c chr1 922025 922065 + Simple_repeat_(CCCCAG)n chr1 923333 923423 + SINE/MIR_MIR chr1 923702 923740 + Simple_repeat_(AGCCC)n chr1 923911 923930 + Simple_repeat_(GGC)n chr1 924467 924509 + Simple_repeat_(GGCCCT)n chr1 928112 928412 + SINE/Alu_AluY chr1 930088 930142 + Simple_repeat_(CCTCT)n chr1 931162 931223 + Simple_repeat_(CTGCTGT)n chr1 933011 933048 + Simple_repeat_(CCTCCC)n chr1 935225 935278 + Simple_repeat_(CCTGGTG)n chr1 935305 935345 + SINE/MIR_MIR3 chr1 936187 936229 + Simple_repeat_(ACGC)n chr1 939999 940043 + Simple_repeat_(CCCCCTG)n chr1 940285 940318 + Simple_repeat_(GCC)n chr1 941911 941960 + Simple_repeat_(CCG)n chr1 942055 942105 + Low_complexity_G-rich chr1 943527 943548 + Simple_repeat_(T)n chr1 944499 944526 + Simple_repeat_(CAGCC)n chr1 945074 945115 + Simple_repeat_(TCC)n chr1 946777 947062 + SINE/Alu_AluSx1 chr1 954548 954838 + SINE/Alu_AluJo chr1 954842 954876 + Simple_repeat_(AAAAC)n chr1 956986 957016 + Simple_repeat_(CTC)n chr1 957535 958046 + LINE/L2_L2a chr1 958062 958243 - SINE/Alu_AluSx chr1 958244 958549 - SINE/Alu_AluSx chr1 958551 958795 + LINE/L2_L2a chr1 959305 959352 + Simple_repeat_(CCGCC)n chr1 960278 960297 + Simple_repeat_(GGA)n chr1 960713 960774 + Simple_repeat_(GCCCGGA)n chr1 960770 960801 + Simple_repeat_(CGC)n chr1 961038 961080 + Low_complexity_G-rich chr1 962063 962095 + Simple_repeat_(CCCAC)n chr1 965296 965346 + Simple_repeat_(TTTA)n chr1 967026 967053 + Simple_repeat_(GCGGG)n chr1 967362 967392 + Simple_repeat_(CCACCC)n chr1 968520 968877 - LINE/L2_L2a chr1 968997 969136 + DNA/hAT-Blackjack_MER63B chr1 971208 971263 + Simple_repeat_(GGTG)n chr1 971670 971720 + Simple_repeat_(TGCGTG)n chr1 972484 972777 + SINE/Alu_AluSz chr1 975093 975214 + Low_complexity_GA-rich chr1 975795 975872 + DNA/hAT_MER53 chr1 975917 975958 + Simple_repeat_(CCAC)n chr1 983096 983187 - LINE/L1_L1M5 chr1 983336 983452 - LINE/L1_L1M5 chr1 983456 983719 - SINE/Alu_AluSp chr1 983736 983863 - LINE/L1_L1M5 chr1 984581 984611 - SINE/Alu_AluJb chr1 984612 984657 + Simple_repeat_(TTAT)n chr1 984658 984822 - SINE/Alu_AluJb chr1 984861 984892 + Simple_repeat_(TATC)n chr1 984894 985172 - SINE/Alu_AluSz chr1 985176 985599 - LINE/L1_L1ME1 chr1 985600 986089 + LTR/ERV1_MER4A1_ chr1 986090 986389 - SINE/Alu_AluY chr1 986390 986445 + LTR/ERV1_MER4A1_ chr1 986446 986775 - LINE/L1_L1ME1 chr1 986776 987071 - SINE/Alu_AluSz chr1 987072 987084 - LINE/L1_L1ME1 chr1 987108 987241 + SINE/Alu_AluSz chr1 987249 987283 - LINE/L1_L1ME1 chr1 987284 987580 - SINE/Alu_AluSq chr1 987581 987654 - LINE/L1_L1M4 chr1 987655 987959 - SINE/Alu_AluSx1 chr1 987960 988305 - LINE/L1_L1M4 chr1 988306 988614 + SINE/Alu_AluSg chr1 988615 988694 - LINE/L1_L1M4 chr1 989357 989508 - LINE/L1_L1ME1 chr1 989509 989864 - LTR/ERVL-MaLR_MSTD chr1 989865 989985 - LINE/L1_L1ME1 chr1 989986 990297 - SINE/Alu_AluSx1 chr1 990299 990536 - SINE/Alu_AluSq2 chr1 990537 990718 - LINE/L1_L1ME1 chr1 990719 990994 + SINE/Alu_AluSx1 chr1 990996 991175 - LINE/L1_L1ME1 chr1 991176 991489 - SINE/Alu_AluJb chr1 991490 991509 - LINE/L1_L1ME1 chr1 991510 991543 + Simple_repeat_(TTTCT)n chr1 991544 991825 - SINE/Alu_AluSp chr1 991826 991915 - LINE/L1_L1ME1 chr1 991966 992151 - LINE/L1_L1ME1 chr1 992152 992469 - SINE/Alu_AluSx1 chr1 992470 992655 - LINE/L1_L1ME1 chr1 992697 992759 - SINE/Alu_FLAM_A chr1 992744 992794 - SINE/Alu_FLAM_A chr1 993731 994041 + LTR/ERVL-MaLR_MSTA chr1 994042 994334 + SINE/Alu_AluSz chr1 994335 994401 + LTR/ERVL-MaLR_MSTA chr1 994402 994667 - SINE/Alu_AluSx chr1 994668 994730 + LINE/L1_L1M4 chr1 994735 994791 + SINE/Alu_Alu chr1 994810 994873 + LINE/L1_L1MEc chr1 995068 995364 + SINE/Alu_AluSx1 chr1 995381 995512 + LINE/L1_L1MEc chr1 997105 997178 - LINE/L2_L2b chr1 998787 998851 + Simple_repeat_(CGCCCC)n chr1 999480 999521 + Simple_repeat_(CCCCCG)n chr1 1000024 1000057 + Low_complexity_G-rich chr1 1000441 1000482 + Low_complexity_G-rich chr1 1000791 1000835 + Simple_repeat_(GGCCG)n chr1 1001404 1001460 + Low_complexity_G-rich chr1 1002192 1002312 - SINE/Alu_AluJr4 chr1 1002346 1002414 + SINE/MIR_MIR1_Amn chr1 1002416 1002624 - SINE/Alu_AluSx chr1 1002641 1002941 - SINE/Alu_AluSp chr1 1003379 1003425 + LTR/ERVL-MaLR_MLT1G1 chr1 1003500 1003623 + LTR/ERVL-MaLR_MLT1G1 chr1 1004702 1004886 + LINE/L2_L2 chr1 1004967 1005033 + Simple_repeat_(CTCCCCC)n chr1 1005447 1005607 - LTR/ERVL-MaLR_MLT1H chr1 1007308 1007617 + SINE/Alu_AluSq chr1 1007747 1008060 - SINE/Alu_AluYa5 chr1 1008371 1008480 - LINE/L1_L1MD chr1 1008513 1008823 - SINE/Alu_AluSq2 chr1 1008825 1009110 - SINE/Alu_AluJb chr1 1009113 1009245 + LTR/ERVL-MaLR_MLT1C2 chr1 1009400 1009701 + SINE/Alu_AluY chr1 1009768 1010058 - SINE/Alu_AluJb chr1 1010065 1010354 - SINE/Alu_AluSz chr1 1010467 1010534 + Simple_repeat_(TTAT)n chr1 1010733 1011045 - SINE/Alu_AluSx1 chr1 1011059 1011333 - SINE/Alu_AluSz chr1 1011415 1012064 - LINE/L1_L1MB5 chr1 1012065 1012375 - SINE/Alu_AluSx chr1 1012376 1012684 - LINE/L1_L1MB5 chr1 1012747 1013057 + SINE/Alu_AluSz6 chr1 1015757 1016057 - SINE/Alu_AluY chr1 1016061 1016360 - SINE/Alu_AluSx3 chr1 1016361 1016495 - SINE/Alu_AluSz chr1 1016552 1016677 - SINE/Alu_AluSz chr1 1016678 1016971 - SINE/Alu_AluSg chr1 1016972 1017104 - SINE/Alu_AluSz chr1 1017115 1017169 - SINE/Alu_FRAM chr1 1017170 1018876 - LTR/ERV1_LTR12C chr1 1018877 1019116 - SINE/Alu_AluSx1 chr1 1019128 1019259 - SINE/Alu_AluJr4 chr1 1019467 1019507 + Low_complexity_G-rich chr1 1019613 1019662 + Low_complexity_G-rich chr1 1019816 1019882 + Simple_repeat_(CCCGC)n chr1 1019934 1020044 + Low_complexity_G-rich chr1 1020044 1020055 + Simple_repeat_(GCCCC)n chr1 1020056 1020097 + Simple_repeat_(CCCC)n chr1 1020098 1020200 + Simple_repeat_(GCCCC)n chr1 1020562 1020603 + Simple_repeat_(CGC)n chr1 1020686 1020724 + Low_complexity_G-rich chr1 1022709 1022752 + Simple_repeat_(CCTCCTC)n chr1 1025169 1025210 + Simple_repeat_(GCCAGG)n chr1 1026125 1026153 + Simple_repeat_(ACCCC)n chr1 1027214 1027253 + Simple_repeat_(TG)n chr1 1027504 1027542 + Simple_repeat_(CCTGCCC)n chr1 1028321 1028340 + Simple_repeat_(C)n chr1 1033359 1033395 + Simple_repeat_(GCACAC)n chr1 1033499 1033852 + Simple_repeat_(CCAGCC)n chr1 1035541 1035624 + SINE/MIR_MIR1_Amn chr1 1036570 1036635 + Low_complexity_G-rich chr1 1039000 1039158 - SINE/MIR_MIR chr1 1040871 1041142 + Low_complexity_G-rich chr1 1047925 1047979 + Simple_repeat_(CCTGCC)n chr1 1048489 1048789 + SINE/Alu_AluSp chr1 1049062 1049205 + Low_complexity_G-rich chr1 1050965 1051003 + Simple_repeat_(CTCTGCT)n chr1 1055957 1055999 + Simple_repeat_(CCCATC)n chr1 1057027 1057311 + SINE/Alu_AluJr chr1 1057474 1057785 + SINE/Alu_AluSx1 chr1 1057809 1058108 + SINE/Alu_AluSx1 chr1 1058490 1058531 + Simple_repeat_(AGCGGCC)n chr1 1058524 1058566 + Low_complexity_G-rich chr1 1058985 1059020 + Low_complexity_GA-rich chr1 1059381 1059430 + Simple_repeat_(GCCCC)n chr1 1060063 1060086 - LINE/L2_L2 chr1 1060087 1060375 - SINE/Alu_AluSg chr1 1060376 1060626 - LINE/L2_L2 chr1 1060973 1061273 - SINE/Alu_AluSq chr1 1061318 1061384 - LINE/L2_L2 chr1 1061549 1061665 - SINE/Alu_FLAM_A chr1 1062033 1062212 + Simple_repeat_(TCCC)n chr1 1062718 1062795 - LTR/ERVL-MaLR_MLT1B chr1 1063601 1063687 + Simple_repeat_(GT)n chr1 1064152 1064202 + Simple_repeat_(CCTGG)n chr1 1064784 1064831 + Low_complexity_G-rich chr1 1065545 1066494 - LTR/ERVK_LTR13A chr1 1067140 1067168 + Simple_repeat_(TTTGCA)n chr1 1068759 1068813 + Simple_repeat_(GTGCAG)n chr1 1071271 1071563 - SINE/Alu_AluSx1 chr1 1073278 1073312 + Simple_repeat_(GGCAGG)n chr1 1074415 1074446 + Low_complexity_GA-rich chr1 1080049 1080078 + Simple_repeat_(CCCCG)n chr1 1083321 1083655 + LTR/ERVL-MaLR_MLT1C2 chr1 1092726 1092778 - DNA/TcMar-Tigger_Tigger19b chr1 1092807 1093048 + SINE/Alu_AluJr chr1 1093138 1093329 - LTR/ERV1_LTR78 chr1 1093361 1093466 - LINE/L1_L1MD3 chr1 1093599 1094091 - LINE/L1_L1MD3 chr1 1094092 1094339 - SINE/Alu_AluJb chr1 1094340 1094397 - LINE/L1_L1MD3 chr1 1094398 1094699 - SINE/Alu_AluY chr1 1094700 1096200 - LINE/L1_L1MD3 chr1 1096201 1096464 - SINE/Alu_AluJr chr1 1096465 1096735 - LINE/L1_L1MD3 chr1 1096736 1097028 - SINE/Alu_AluJr chr1 1097029 1097116 - LINE/L1_L1MD3 chr1 1097117 1097404 - SINE/Alu_AluSx1 chr1 1097407 1097712 - SINE/Alu_AluJr chr1 1097713 1098076 - LINE/L1_L1MD3 chr1 1098077 1098368 - SINE/Alu_AluSx3 chr1 1098369 1098619 - LINE/L1_L1MD3 chr1 1098620 1098911 - SINE/Alu_AluSq chr1 1098912 1099077 - LINE/L1_L1MD3 chr1 1099217 1099284 - LINE/L1_L1MD chr1 1099285 1099304 - LINE/L1_L1MD3 chr1 1099286 1099381 - LINE/L1_L1MD3 chr1 1099440 1099532 - LINE/L1_L1MD3 chr1 1099525 1099613 - LINE/L1_L1MEf chr1 1099602 1099667 - LINE/L1_L1MD chr1 1099671 1099769 - LINE/L1_L1MEf chr1 1099752 1099832 - LINE/L1_L1MEf chr1 1099833 1099904 - LINE/L1_L1MD chr1 1099905 1099936 - LINE/L1_L1MEf chr1 1099947 1100108 + SINE/Alu_FLAM_C chr1 1100114 1100137 + Simple_repeat_(AGAC)n chr1 1100141 1100274 - LINE/L1_L1M4a2 chr1 1100275 1100295 - LINE/L1_L1MEf chr1 1100356 1100587 - LINE/L1_L1MC1 chr1 1100578 1101201 - LINE/L1_L1MEf chr1 1101202 1101488 + SINE/Alu_AluSz6 chr1 1101489 1101789 - LINE/L1_L1MEf chr1 1101790 1102100 + SINE/Alu_AluSp chr1 1102101 1102497 - LINE/L1_L1MEf chr1 1102498 1102818 + SINE/Alu_AluSx1 chr1 1102826 1103142 - SINE/Alu_AluSq2 chr1 1103143 1103719 - LINE/L1_L1MEf chr1 1103723 1103975 - SINE/Alu_AluJr chr1 1103987 1104286 - SINE/Alu_AluSx3 chr1 1104288 1104517 - LINE/L1_L1MEf chr1 1104650 1104829 + SINE/Alu_FRAM chr1 1104936 1105165 - SINE/MIR_MIR chr1 1105238 1105529 + SINE/Alu_AluJr chr1 1105606 1105883 + SINE/Alu_AluY chr1 1105885 1105919 + Simple_repeat_(AAAT)n chr1 1106022 1106311 + LINE/L1_L1MEc chr1 1106025 1106603 + LINE/L1_L1MEf chr1 1106614 1108045 + LTR/ERV1_LTR12C chr1 1109085 1109411 + LINE/L1_L1MB3 chr1 1109412 1109452 + Simple_repeat_(TTTCC)n chr1 1109453 1109719 - SINE/Alu_AluJo chr1 1109720 1110352 - LTR/ERV1_MER49 chr1 1110353 1110585 + LINE/L1_L1MEc chr1 1110988 1111082 + LINE/L1_L1ME2 chr1 1111246 1111551 + SINE/Alu_AluJb chr1 1112044 1112400 + LTR/ERV1_MER50 chr1 1112648 1112917 + LTR/ERV1_MER50 chr1 1112918 1113211 + SINE/Alu_AluY chr1 1113212 1113335 + LTR/ERV1_MER50 chr1 1113435 1113531 - SINE/Alu_AluJb chr1 1113585 1113913 - LTR/ERV1_MER50 chr1 1113914 1114221 - SINE/Alu_AluSx4 chr1 1114222 1114263 - LTR/ERV1_MER50 chr1 1114261 1114311 - LTR/ERV1_MER50 chr1 1114312 1114402 - SINE/Alu_AluJb chr1 1114805 1115033 + LINE/L1_L1PB1 chr1 1116008 1116051 + Simple_repeat_(CCCCAGC)n chr1 1116237 1116279 + Low_complexity_G-rich chr1 1116461 1116502 + Simple_repeat_(AGGCGG)n chr1 1117731 1118031 - SINE/Alu_AluJb chr1 1118975 1119127 - SINE/Alu_FRAM chr1 1119131 1119405 + LTR?_MamRep1151 chr1 1119457 1119673 + LINE/L1_L1MD chr1 1119751 1120058 + SINE/Alu_AluSz chr1 1120198 1120296 - SINE/Alu_AluJo chr1 1120623 1120917 + SINE/Alu_AluSx chr1 1126195 1126236 + Simple_repeat_(GCCT)n chr1 1126303 1126564 - SINE/Alu_AluSq2 chr1 1126947 1127254 - SINE/Alu_AluSx3 chr1 1129940 1130216 + SINE/Alu_AluSx1 chr1 1130548 1130882 - LINE/L1_HAL1 chr1 1130956 1131376 - LINE/L1_HAL1 chr1 1131428 1131715 - SINE/Alu_AluY chr1 1132633 1132986 - LTR/ERVL-MaLR_MLT1B chr1 1133446 1133475 + Simple_repeat_(CCTG)n chr1 1133803 1134109 + SINE/Alu_AluY chr1 1135295 1135888 - LTR/ERV1_MER41B chr1 1136915 1136968 + Simple_repeat_(GCTGG)n chr1 1137547 1137718 - DNA/hAT-Charlie_MER30 chr1 1137774 1137804 + Simple_repeat_(CCCTCC)n chr1 1137821 1137864 + Low_complexity_G-rich chr1 1138198 1138242 - SINE/Alu_AluSp chr1 1138288 1138598 + SINE/Alu_AluSx chr1 1142218 1142521 - SINE/Alu_AluSx1 chr1 1143872 1144037 + LINE/L1_L1MB5 chr1 1147052 1147173 - LINE/L2_L2 chr1 1147727 1147768 - DNA/hAT-Tip100_Arthur1B chr1 1147769 1148070 + SINE/Alu_AluJb chr1 1148071 1148199 - DNA/hAT-Tip100_Arthur1B chr1 1148309 1148481 + LINE/L1_L1MC4 chr1 1151807 1151885 + Simple_repeat_(GCCTCC)n chr1 1151956 1152342 - LINE/L1_L1MC3 chr1 1153542 1153637 - LINE/L1_L1MC3 chr1 1153660 1153896 - SINE/Alu_AluJb chr1 1154133 1154507 - LINE/L1_L1MEg chr1 1154508 1154804 + SINE/Alu_AluSp chr1 1154805 1154880 - LINE/L1_L1MEg chr1 1154881 1155176 - SINE/Alu_AluSx1 chr1 1155177 1155526 - LINE/L1_L1MEg chr1 1155965 1156013 + LINE/L2_L2 chr1 1157282 1157345 + Simple_repeat_(CAGCC)n chr1 1157346 1157428 + Simple_repeat_(CCATCCCCG)n chr1 1157429 1157493 + Simple_repeat_(CAGCC)n chr1 1159044 1159337 + SINE/Alu_AluSx chr1 1159824 1160111 - SINE/Alu_AluJb chr1 1160230 1160279 + Low_complexity_G-rich chr1 1163268 1163301 + Simple_repeat_(GGCGGA)n chr1 1166405 1166449 + Simple_repeat_(TGTCTG)n chr1 1166518 1166548 + Simple_repeat_(CACCCC)n chr1 1168000 1168144 + Low_complexity_G-rich chr1 1169354 1169393 + Simple_repeat_(ACCCCC)n chr1 1170255 1170275 + Simple_repeat_(CTC)n chr1 1170468 1170577 + Simple_repeat_(CTCC)n chr1 1172050 1172411 - LTR/ERVL-MaLR_MLT1C2 chr1 1172615 1172724 + SINE/Alu_AluSx chr1 1172838 1173025 + SINE/Alu_AluSx chr1 1173027 1173396 - LTR/ERV1_LTR46 chr1 1174839 1175134 + SINE/Alu_AluSg chr1 1175646 1175756 - SINE/MIR_MIR chr1 1177316 1177609 - SINE/Alu_AluYj4 chr1 1178279 1178576 + SINE/Alu_AluSp chr1 1179682 1179734 + Simple_repeat_(CCCTG)n chr1 1180380 1180428 + Simple_repeat_(CCCGCCG)n chr1 1180867 1180985 + Simple_repeat_(CTGCCC)n chr1 1181010 1181040 + Simple_repeat_(GCCCCT)n chr1 1181201 1181248 + Simple_repeat_(CACC)n chr1 1181272 1181296 + Simple_repeat_(CATC)n chr1 1181656 1181698 + Simple_repeat_(CCCATGC)n chr1 1182524 1182612 + Low_complexity_G-rich chr1 1185821 1186082 - LINE/L1_L1MB8 chr1 1186083 1186388 - SINE/Alu_AluSx1 chr1 1186389 1186833 - LINE/L1_L1MB8 chr1 1186834 1187145 - SINE/Alu_AluY chr1 1187146 1187348 - LINE/L1_L1MB8 chr1 1187349 1187649 + SINE/Alu_AluSx1 chr1 1187656 1187919 + SINE/Alu_AluSx chr1 1187920 1188405 - LINE/L1_L1MB8 chr1 1188406 1188704 - SINE/Alu_AluSx1 chr1 1188705 1189832 - LINE/L1_L1MB8 chr1 1189833 1190130 + SINE/Alu_AluY chr1 1190131 1190417 - LINE/L1_L1MB8 chr1 1190418 1190655 - SINE/Alu_AluSx3 chr1 1190656 1190815 - LINE/L1_L1MB8 chr1 1190816 1191118 - SINE/Alu_AluSq2 chr1 1191119 1191273 - LINE/L1_L1MB8 chr1 1191274 1191440 + SINE/Alu_FLAM_C chr1 1191441 1191594 - LINE/L1_L1MB8 chr1 1191595 1192108 - LTR/ERVK_MER9a1 chr1 1192109 1192383 - LINE/L1_L1MB8 chr1 1192842 1193044 - LINE/L1_L1MB8 chr1 1193045 1193345 + SINE/Alu_AluY chr1 1193346 1193469 - LINE/L1_L1MB8 chr1 1193470 1193763 - SINE/Alu_AluSq chr1 1193764 1193879 - LINE/L1_L1MB8 chr1 1193880 1194167 + SINE/Alu_AluJr chr1 1194168 1194982 - LINE/L1_L1MB8 chr1 1194983 1195291 - SINE/Alu_AluSz chr1 1195292 1195508 - LINE/L1_L1MB8 chr1 1195512 1195558 - SINE/Alu_AluSx3 chr1 1195560 1195649 - SINE/Alu_AluJo chr1 1195650 1195940 - SINE/Alu_AluJr chr1 1195941 1196074 - SINE/Alu_AluJo chr1 1196093 1196298 - LINE/L1_L1MB8 chr1 1196334 1196428 + LINE/L2_L2c chr1 1197857 1197887 + Simple_repeat_(CCCGCGC)n chr1 1200941 1200977 + Low_complexity_G-rich chr1 1203508 1203564 - LINE/L2_L2b chr1 1204501 1204521 + Simple_repeat_(GGAG)n chr1 1207835 1208126 + SINE/Alu_AluSx chr1 1208859 1208901 + Simple_repeat_(CCCTGCC)n chr1 1209555 1209789 - SINE/Alu_AluJr chr1 1209825 1210128 + SINE/Alu_AluSx1 chr1 1214190 1214235 + Low_complexity_G-rich chr1 1214236 1214247 + Low_complexity_GA-rich chr1 1214567 1214862 + SINE/Alu_AluSx1 chr1 1214911 1215005 + LTR/ERVL-MaLR_MLT1B chr1 1220973 1221082 + SINE/Alu_AluJo chr1 1221276 1221657 + SINE/Alu_AluJb chr1 1221679 1221965 + SINE/Alu_AluJb chr1 1222542 1222579 + Simple_repeat_(CAGGCC)n chr1 1223781 1223839 + Simple_repeat_(GCCCC)n chr1 1224282 1224578 - SINE/Alu_AluSz chr1 1224630 1224931 + SINE/Alu_AluJb chr1 1225065 1225099 + Simple_repeat_(CAGGCCA)n chr1 1225180 1225263 + LTR/ERVL-MaLR_MSTD chr1 1226034 1226230 + LINE/L1_L1MC3 chr1 1226357 1226578 + LINE/L1_L1MC3 chr1 1228780 1228853 + Low_complexity_G-rich chr1 1229191 1229511 - SINE/Alu_AluJr chr1 1229669 1229784 + SINE/MIR_MIRc chr1 1229868 1229941 + Simple_repeat_(CA)n chr1 1230041 1230125 + LINE/L2_L2 chr1 1230182 1230294 + LINE/L2_L2c chr1 1230463 1230757 - SINE/Alu_AluSx1 chr1 1230995 1231070 + SINE/Alu_AluJr chr1 1231418 1231594 - SINE/MIR_MIRb chr1 1232284 1232317 + Simple_repeat_(GCG)n chr1 1232410 1232448 + Simple_repeat_(CCGCGC)n chr1 1234293 1234336 + Low_complexity_G-rich chr1 1234761 1234867 - SINE/MIR_MIRb chr1 1235837 1236130 + LTR/ERVL-MaLR_MLT1C2 chr1 1238549 1238593 + Simple_repeat_(CCCAGCT)n chr1 1239944 1239988 + Low_complexity_G-rich chr1 1243817 1243860 + Simple_repeat_(CAGCCCC)n chr1 1243918 1243956 + Simple_repeat_(CCCAGCT)n chr1 1246873 1246905 + Simple_repeat_(TCCCGC)n chr1 1247573 1247632 + Simple_repeat_(AGCCAC)n chr1 1249688 1250658 - LTR/ERVK_LTR13 chr1 1251357 1251652 - SINE/Alu_AluSx chr1 1251750 1252032 + SINE/Alu_AluSg chr1 1252045 1252258 + SINE/Alu_AluJb chr1 1252268 1253209 + LTR/ERVK_LTR13_ chr1 1253210 1253295 + SINE/Alu_AluJb chr1 1253321 1253587 - SINE/Alu_AluJb chr1 1255836 1255889 + Simple_repeat_(CCCAGCC)n chr1 1256601 1256911 + SINE/Alu_AluY chr1 1257376 1257408 + Simple_repeat_(C)n chr1 1258029 1258340 - SINE/Alu_AluSc8 chr1 1258727 1258842 + LINE/L2_L2 chr1 1259547 1259908 + LTR/ERVL_MER54A chr1 1260722 1260913 - LINE/L1_L1MD chr1 1261061 1261181 - LINE/L2_L2 chr1 1261646 1261880 - SINE/Alu_AluYk2 chr1 1261881 1262156 - SINE/Alu_AluSp chr1 1263734 1264001 + SINE/Alu_AluJo chr1 1264490 1264625 + SINE/Alu_AluSx chr1 1264626 1264892 + SINE/Alu_AluSx chr1 1265307 1265513 + LTR/ERVL_MER68 chr1 1265565 1265649 + Simple_repeat_(GT)n chr1 1265652 1265930 - SINE/Alu_AluSg chr1 1266310 1266539 + SINE/Alu_AluSx chr1 1266540 1266839 + SINE/Alu_AluY chr1 1266881 1266905 + Simple_repeat_(ATTTT)n chr1 1266906 1267188 - SINE/Alu_AluSq chr1 1268094 1268131 + Simple_repeat_(CAGGTCA)n chr1 1268696 1268965 - SINE/Alu_AluJr chr1 1269458 1269763 - SINE/Alu_AluSx1 chr1 1269774 1269837 + LINE/CR1_L3b chr1 1269890 1270250 + LTR/ERVL-MaLR_THE1C chr1 1271678 1271692 + SINE/Alu_AluSg4 chr1 1271693 1272004 + SINE/Alu_AluSp chr1 1272005 1272161 + SINE/Alu_AluSg4 chr1 1272646 1272710 + Simple_repeat_(CCTG)n chr1 1273795 1273828 + Simple_repeat_(CCG)n chr1 1273983 1274021 + Low_complexity_G-rich chr1 1274489 1274525 + Low_complexity_G-rich chr1 1275713 1276255 - LTR/ERV1_MER4B chr1 1276256 1276481 - LTR/ERV1_MER4B-int chr1 1276485 1276774 - SINE/Alu_AluSz chr1 1276779 1277126 - LTR/ERV1_MER4B-int chr1 1277127 1277421 - SINE/Alu_AluSx chr1 1277422 1277548 - LTR/ERV1_MER4B-int chr1 1277549 1277843 + SINE/Alu_AluY chr1 1277844 1278126 - LTR/ERV1_MER4B-int chr1 1278127 1278421 + SINE/Alu_AluSx chr1 1278422 1278488 - LTR/ERV1_MER4B-int chr1 1278489 1278792 - SINE/Alu_AluSp chr1 1278793 1279090 - LTR/ERV1_MER4B-int chr1 1279194 1279400 - LTR/ERV1_MER4B-int chr1 1279401 1279700 - SINE/Alu_AluY chr1 1279701 1279992 - LTR/ERV1_MER4B-int chr1 1280045 1280182 + SINE/Alu_AluSx4 chr1 1280183 1280482 + SINE/Alu_AluYe5 chr1 1280483 1280788 - LTR/ERV1_MER4B chr1 1282153 1282284 - SINE/MIR_MIRb chr1 1282753 1283049 - SINE/Alu_AluY chr1 1283417 1283719 + SINE/Alu_AluSp chr1 1284293 1284344 + Low_complexity_G-rich chr1 1285956 1285981 + Simple_repeat_(CCA)n chr1 1288042 1288091 + Simple_repeat_(GGCTG)n chr1 1293748 1293803 + Simple_repeat_(GCCCC)n chr1 1293938 1294075 + Low_complexity_G-rich chr1 1296965 1297025 + Simple_repeat_(GCACAT)n chr1 1299703 1299755 + Low_complexity_G-rich chr1 1300765 1301066 - SINE/Alu_AluSp chr1 1301070 1301243 - SINE/Alu_AluJb chr1 1301257 1301558 - SINE/Alu_AluJr chr1 1301693 1301776 + LINE/L2_L2 chr1 1302363 1302431 + Simple_repeat_(CCTGGAG)n chr1 1302400 1302437 + Low_complexity_G-rich chr1 1303733 1303782 + Simple_repeat_(GCCCCA)n chr1 1304981 1305040 + Low_complexity_G-rich chr1 1307640 1307707 + Simple_repeat_(CCGGC)n chr1 1307816 1307879 + Simple_repeat_(GGC)n chr1 1308179 1308213 + Simple_repeat_(GGGCGC)n chr1 1308541 1308565 + Simple_repeat_(G)n chr1 1308741 1308798 + Simple_repeat_(GGCG)n chr1 1312551 1312597 + Simple_repeat_(CCTGC)n chr1 1315661 1315863 + Low_complexity_GA-rich chr1 1316054 1316301 + SINE/Alu_AluSz chr1 1316307 1316593 + SINE/Alu_AluSq2 chr1 1316644 1316945 + SINE/Alu_AluSx chr1 1316950 1317093 - SINE/Alu_AluJr chr1 1317108 1317420 + SINE/Alu_AluSp chr1 1317739 1317864 + LINE/L1_L1MEg chr1 1317865 1318169 - SINE/Alu_AluY chr1 1318170 1318365 + LINE/L1_L1MEg chr1 1318377 1318662 + SINE/Alu_AluJb chr1 1319608 1319873 - LINE/L2_L2a chr1 1320021 1320051 + Simple_repeat_(GCAGG)n chr1 1322014 1322126 + LINE/L2_L2b chr1 1322536 1322742 + Low_complexity_G-rich chr1 1323417 1323708 - SINE/Alu_AluJr chr1 1329103 1329165 - SINE/MIR_MIR1_Amn chr1 1329484 1329883 + Simple_repeat_(TGCGTG)n chr1 1329884 1329939 + Simple_repeat_(TG)n chr1 1329940 1330058 + Simple_repeat_(TGCGTG)n chr1 1330059 1330114 + Simple_repeat_(TGCA)n chr1 1330115 1330203 + Simple_repeat_(TGCGTG)n chr1 1333607 1333663 + Simple_repeat_(GCT)n chr1 1335054 1335101 + Simple_repeat_(CCAGCA)n chr1 1339460 1339520 + Simple_repeat_(GAGGACA)n chr1 1344055 1344109 + Low_complexity_G-rich chr1 1346067 1346140 - SINE/MIR_MIR chr1 1348086 1348127 + Simple_repeat_(ACTGGTG)n chr1 1349066 1349127 + Simple_repeat_(GGCGC)n chr1 1349507 1349548 + Simple_repeat_(CGCCC)n chr1 1349764 1349810 + Low_complexity_G-rich chr1 1352260 1352356 + LTR/ERVL_LTR16B1 chr1 1352622 1352656 + Simple_repeat_(CCCAGG)n chr1 1355136 1355232 + Low_complexity_G-rich chr1 1355385 1355454 + Simple_repeat_(GCCCCCG)n chr1 1357233 1357526 + LTR/ERVL_MER54A chr1 1357527 1357816 + SINE/Alu_AluSq2 chr1 1357817 1357890 + LTR/ERVL_MER54A chr1 1360851 1361237 + Simple_repeat_(CA)n chr1 1361290 1361391 - LINE/L2_L2b chr1 1362498 1362800 + SINE/Alu_AluSx1 chr1 1362802 1363098 + SINE/Alu_AluSc chr1 1363102 1363297 + SINE/Alu_AluSx4 chr1 1363299 1363430 - SINE/Alu_AluJb chr1 1363432 1363601 - SINE/Alu_AluSq4 chr1 1363602 1363901 - SINE/Alu_AluY chr1 1363902 1364197 - SINE/Alu_AluSg chr1 1364198 1364328 - SINE/Alu_AluSq4 chr1 1364624 1364634 + SINE/Alu_AluYc3 chr1 1364635 1364766 + SINE/Alu_AluSz6 chr1 1364767 1365048 + SINE/Alu_AluSx chr1 1365049 1365352 + SINE/Alu_AluSp chr1 1365353 1365662 + SINE/Alu_AluSp chr1 1365663 1365843 + SINE/Alu_AluSz6 chr1 1365844 1365919 + SINE/Alu_AluYc3 chr1 1366336 1366388 + Simple_repeat_(GTGTGAATG)n chr1 1367717 1368017 - SINE/Alu_AluYh3 chr1 1368018 1368178 - SINE/Alu_AluJo chr1 1368179 1368211 + Simple_repeat_(T)n chr1 1368212 1368309 - SINE/Alu_AluJo chr1 1368467 1368747 + SINE/Alu_AluJr chr1 1368755 1369052 + SINE/Alu_AluSx chr1 1369160 1369454 - SINE/Alu_AluYf1 chr1 1369475 1369758 - SINE/Alu_AluJr chr1 1369804 1370096 - SINE/Alu_AluY chr1 1370275 1370571 + SINE/Alu_AluSg chr1 1371630 1371735 - DNA/hAT-Charlie_MER20 chr1 1372716 1373005 + SINE/Alu_AluSx1 chr1 1375060 1375120 + Simple_repeat_(GCCCGGC)n chr1 1375701 1376008 + SINE/Alu_AluSx1 chr1 1377216 1377346 + DNA/hAT-Tag1_hAT-1_Mam chr1 1377324 1377347 + SINE/Alu_AluJo chr1 1377348 1377653 + SINE/Alu_AluSx chr1 1377654 1377696 + SINE/Alu_AluJo chr1 1377776 1377944 - DNA/hAT-Blackjack_MER63D chr1 1378172 1378483 - SINE/Alu_AluSx chr1 1378485 1378605 - SINE/Alu_AluSx3 chr1 1378779 1378856 + LINE/L2_L2c chr1 1379086 1379111 + Simple_repeat_(TTG)n chr1 1379123 1379356 + SINE/Alu_AluSz chr1 1379432 1379481 + SINE/Alu_AluJo chr1 1379482 1379610 + SINE/Alu_AluJr chr1 1379611 1379928 + SINE/Alu_AluY chr1 1379929 1380041 + SINE/Alu_AluJr chr1 1380063 1380339 + SINE/Alu_AluSx chr1 1382090 1382288 + LTR/ERV1_MER66C chr1 1382839 1383063 + LTR/ERV1_MER66C chr1 1383064 1383332 + SINE/Alu_AluSx1 chr1 1383333 1383347 + LTR/ERV1_MER66C chr1 1383348 1383521 + SINE/Alu_AluSg4 chr1 1383522 1383628 + LTR/ERV1_MER66C chr1 1383651 1383887 + SINE/Alu_AluYk3 chr1 1384033 1384156 - LTR/ERV1_MER66-int chr1 1384247 1384554 - SINE/Alu_AluSc8 chr1 1384568 1384715 - SINE/Alu_AluJb chr1 1384716 1385121 + LTR/ERV1_MER66-int chr1 1385122 1385413 - SINE/Alu_AluSc chr1 1385414 1385458 - SINE/Alu_Alu chr1 1385459 1385634 - SINE/Alu_AluY chr1 1385651 1386135 + LTR/ERV1_MER66-int chr1 1388241 1388543 + SINE/Alu_AluSc chr1 1388566 1388584 + Simple_repeat_(TC)n chr1 1388613 1388794 + SINE/Alu_AluSg4 chr1 1388795 1389086 + SINE/Alu_AluSp chr1 1389091 1389379 + SINE/Alu_AluY chr1 1389551 1389678 + SINE/Alu_AluJr chr1 1389679 1389968 + SINE/Alu_AluY chr1 1389969 1390133 + SINE/Alu_AluJr chr1 1393845 1394140 + SINE/Alu_AluSx chr1 1394468 1394776 + SINE/Alu_AluSx chr1 1394777 1395086 + SINE/Alu_AluSc chr1 1395897 1396209 + SINE/Alu_AluSx1 chr1 1396211 1396260 + SINE/Alu_Alu chr1 1396271 1396564 - SINE/Alu_AluSp chr1 1396572 1396746 - SINE/Alu_AluSq2 chr1 1396747 1397046 - SINE/Alu_AluY chr1 1397047 1397161 - SINE/Alu_AluSq2 chr1 1397299 1397456 - SINE/Alu_AluJb chr1 1397462 1397580 + DNA/hAT-Charlie_MER20 chr1 1397581 1397876 + SINE/Alu_AluJo chr1 1397877 1397920 + DNA/hAT-Charlie_MER20 chr1 1398839 1398943 + Low_complexity_G-rich chr1 1399243 1399304 + Simple_repeat_(CGC)n chr1 1400943 1400999 + LINE/CR1_L3 chr1 1402703 1402834 + SINE/Alu_AluSp chr1 1402847 1403155 + SINE/Alu_AluY chr1 1403193 1403241 + SINE/MIR_MIRc chr1 1403242 1403541 - SINE/Alu_AluY chr1 1403542 1403634 + SINE/MIR_MIRc chr1 1403846 1404144 - SINE/Alu_AluSx chr1 1404148 1404453 - SINE/Alu_AluY chr1 1404494 1404771 - SINE/Alu_AluSc8 chr1 1404994 1405274 - SINE/Alu_AluSx1 chr1 1405280 1405442 - SINE/Alu_FRAM chr1 1405446 1405752 - srpRNA_7SLRNA chr1 1405995 1406070 + SINE/Alu_AluYk3 chr1 1406075 1406366 + SINE/Alu_AluSz chr1 1406683 1406728 + Low_complexity_G-rich chr1 1407559 1407625 + Low_complexity_G-rich chr1 1407718 1407798 - SINE/Alu_AluYh9 chr1 1407800 1407953 - SINE/Alu_AluSx3 chr1 1408002 1408288 + SINE/Alu_AluJr chr1 1408481 1408622 - LINE/L1_L1MC4 chr1 1408763 1409016 - SINE/Alu_AluSz chr1 1409034 1409163 + SINE/Alu_AluSp chr1 1409167 1409448 + SINE/Alu_AluSx1 chr1 1409453 1409688 - SINE/Alu_AluSg chr1 1409764 1409806 - LTR/ERVL_MER74B chr1 1409807 1410773 + LTR/ERVK_LTR5_Hs chr1 1410774 1410933 - LTR/ERVL_MER74B chr1 1410934 1411231 + SINE/Alu_AluSq2 chr1 1411232 1411538 + SINE/Alu_AluYf1 chr1 1411539 1411668 - LTR/ERVL_MER74B chr1 1411848 1411887 + Simple_repeat_(CCAGGTC)n chr1 1411892 1412196 + Simple_repeat_(GTCCCAG)n chr1 1412252 1412721 - LTR/ERV1_LTR2B chr1 1412722 1416234 - LTR/ERV1_Harlequin-int chr1 1416234 1416770 - LTR/ERV1_Harlequin-int chr1 1416771 1417071 + SINE/Alu_AluY chr1 1417072 1417504 - LTR/ERV1_Harlequin-int chr1 1417491 1418377 - LTR/ERV1_Harlequin-int chr1 1418378 1418852 - LTR/ERV1_LTR2B chr1 1419039 1419098 + Simple_repeat_(AGCCTGG)n chr1 1420642 1420668 + Simple_repeat_(CCCCG)n chr1 1420956 1420978 + Simple_repeat_(TCC)n chr1 1421877 1421915 + Low_complexity_G-rich chr1 1421992 1422029 + Low_complexity_G-rich chr1 1422305 1422486 + LINE/L2_L2c chr1 1422381 1422495 + LINE/L2_L2a chr1 1422514 1422650 - LTR/ERVL_LTR75 chr1 1422773 1423374 + LTR/ERV1_MER41B chr1 1423378 1423421 + LINE/L2_L2c chr1 1423479 1423771 - SINE/Alu_AluSx1 chr1 1423776 1424087 - SINE/Alu_AluSx chr1 1424088 1424222 - SINE/Alu_AluSz chr1 1424440 1424751 + SINE/Alu_AluSq2 chr1 1424813 1425101 + SINE/Alu_AluJb chr1 1425181 1425480 + SINE/Alu_AluSc chr1 1425880 1425924 + Simple_repeat_(GGAGCT)n chr1 1426122 1426174 + Low_complexity_GA-rich chr1 1427636 1427719 + Simple_repeat_(CGCTGC)n chr1 1427958 1428217 + Simple_repeat_(GGAGG)n chr1 1428719 1429019 + SINE/Alu_AluYk2 chr1 1429020 1429198 + SINE/Alu_AluSq2 chr1 1429202 1429360 + SINE/Alu_AluSx4 chr1 1429367 1429668 - LINE/L1_L1MEd chr1 1429868 1430157 - SINE/Alu_AluJb chr1 1431696 1431719 + Simple_repeat_(GT)n chr1 1435466 1435555 + Simple_repeat_(GTGC)n chr1 1435597 1435630 + Low_complexity_G-rich chr1 1435695 1435731 + Simple_repeat_(CGAG)n chr1 1435912 1435968 + Low_complexity_G-rich chr1 1439368 1439403 + Low_complexity_G-rich chr1 1439711 1439771 + Simple_repeat_(CGCCCCG)n chr1 1442934 1443242 - SINE/Alu_AluY chr1 1443397 1443671 - SINE/Alu_AluSx chr1 1443674 1443998 - SINE/Alu_AluJb chr1 1444003 1444129 - SINE/Alu_FLAM_C chr1 1445343 1445642 - SINE/Alu_AluJr chr1 1445719 1446012 + SINE/Alu_AluSx3 chr1 1446023 1446160 + SINE/Alu_AluSx chr1 1446161 1446457 + SINE/Alu_AluSq2 chr1 1446458 1446625 + SINE/Alu_AluSx chr1 1446704 1446719 - SINE/Alu_AluY chr1 1446721 1446845 - SINE/Alu_AluJo chr1 1446846 1446921 - SINE/Alu_AluYa8 chr1 1446922 1447108 - LTR/ERV1_LTR10A chr1 1447109 1447402 - SINE/Alu_AluY chr1 1447403 1447706 - SINE/Alu_AluSx chr1 1447707 1447837 - LTR/ERV1_HERV9N-int chr1 1447838 1448138 + SINE/Alu_AluYk3 chr1 1448139 1448272 - LTR/ERV1_HERV9N-int chr1 1448273 1448592 - SINE/Alu_AluSx1 chr1 1448593 1448681 - LTR/ERV1_HERV9N-int chr1 1448773 1449070 - SINE/Alu_AluY chr1 1449072 1449411 - LTR/ERV1_HERVIP10F-int chr1 1449389 1449730 - LTR/ERV1_HERVIP10F-int chr1 1449743 1449825 - DNA?_MER131 chr1 1450048 1450341 + SINE/Alu_AluY chr1 1451045 1451334 - SINE/Alu_AluY chr1 1451344 1451645 - SINE/Alu_AluSx1 chr1 1452665 1452827 + SINE/Alu_AluSc chr1 1452828 1452945 + LTR/ERV1_HUERS-P1-int chr1 1453206 1453501 - SINE/Alu_AluSx chr1 1453502 1453635 - SINE/Alu_AluSz6 chr1 1453644 1453940 - SINE/Alu_AluSp chr1 1454934 1455243 + SINE/Alu_AluY chr1 1456579 1456617 + Low_complexity_G-rich chr1 1457313 1457623 + SINE/Alu_AluY chr1 1457671 1457975 - SINE/Alu_AluSc chr1 1457978 1458280 - SINE/Alu_AluSz chr1 1458287 1458579 - SINE/Alu_AluSp chr1 1458580 1458722 - SINE/Alu_AluJb chr1 1458727 1459018 - SINE/Alu_AluSx chr1 1460070 1460380 - SINE/Alu_AluSp chr1 1461194 1461487 - SINE/Alu_AluSq2 chr1 1463483 1463538 - LTR/ERVL-MaLR_MLT1I chr1 1463626 1463926 + SINE/Alu_AluJr chr1 1463935 1464224 + SINE/Alu_AluSz chr1 1464275 1464412 - LTR/ERVL-MaLR_MLT1I chr1 1464455 1464489 - LINE/L1_L1M3 chr1 1464490 1464749 + SINE/Alu_AluSx chr1 1464750 1464883 - LINE/L1_L1M3 chr1 1464884 1465188 - SINE/Alu_AluSx chr1 1465189 1465309 - LINE/L1_L1M3 chr1 1465310 1465616 + SINE/Alu_AluY chr1 1465617 1466008 - LINE/L1_L1M3 chr1 1466009 1466264 + SINE/Alu_AluSp chr1 1466266 1466557 + SINE/Alu_AluY chr1 1466558 1466991 - LINE/L1_L1M3 chr1 1466987 1467057 - LINE/L1_L1MC4a chr1 1467120 1467183 - LINE/L1_L1MC3 chr1 1468987 1469304 + SINE/Alu_AluSq2 chr1 1469305 1469464 + SINE/Alu_AluSx3 chr1 1469534 1469544 - SINE/Alu_AluSx chr1 1469548 1469675 - SINE/Alu_AluJo chr1 1469676 1469768 - SINE/Alu_AluSx chr1 1469769 1470201 + LTR/ERV1_LTR7B chr1 1470204 1470411 - SINE/Alu_AluSx chr1 1470418 1470499 - SINE/Alu_AluYe5 chr1 1470519 1470858 - LTR/ERV1_LTR10A chr1 1470859 1471236 + LTR/ERV1_LTR7 chr1 1471237 1471375 - LTR/ERV1_LTR10A chr1 1471604 1471650 + Simple_repeat_(CCCCGCC)n chr1 1471854 1471877 + Simple_repeat_(CGG)n chr1 1472065 1472139 + Simple_repeat_(GCGGC)n chr1 1472140 1472148 + Simple_repeat_(GGCG)n chr1 1472829 1473120 - SINE/Alu_AluSx chr1 1473131 1473368 - SINE/Alu_AluY chr1 1473369 1473495 - SINE/Alu_AluY chr1 1473504 1473793 - SINE/Alu_AluSc5 chr1 1474057 1474164 - SINE/Alu_FLAM_A chr1 1474183 1474488 - SINE/Alu_AluY chr1 1474501 1474800 - SINE/Alu_AluSx1 chr1 1476516 1476806 - SINE/Alu_AluYk2 chr1 1476815 1477022 - SINE/Alu_AluSx chr1 1477026 1477223 - SINE/Alu_AluSx chr1 1477678 1478003 - SINE/Alu_AluSp chr1 1478006 1478296 - SINE/Alu_AluSx1 chr1 1481177 1481473 - SINE/Alu_AluSx3 chr1 1482806 1483010 + SINE/Alu_AluJb chr1 1483012 1483317 + SINE/Alu_AluSc8 chr1 1483318 1483490 + SINE/Alu_AluSp chr1 1483491 1483809 + SINE/Alu_AluSz chr1 1484185 1484480 - SINE/Alu_AluYk2 chr1 1484485 1484613 - SINE/Alu_AluSp chr1 1486898 1486936 + Low_complexity_G-rich chr1 1487210 1487504 + SINE/Alu_AluY chr1 1487998 1488255 - SINE/Alu_AluSz chr1 1488262 1488478 - SINE/Alu_AluSz chr1 1488479 1488768 + SINE/Alu_AluSg chr1 1488769 1488823 - SINE/Alu_AluSz chr1 1488864 1489061 - SINE/Alu_AluSx chr1 1491254 1491542 + SINE/Alu_AluSc5 chr1 1491758 1491813 - LTR/ERVL-MaLR_MLT1I chr1 1491912 1492190 + SINE/Alu_AluJr chr1 1492211 1492509 + SINE/Alu_AluSx1 chr1 1492514 1492657 + SINE/Alu_AluSx1 chr1 1492658 1492961 + SINE/Alu_AluSx3 chr1 1492962 1493118 + SINE/Alu_AluSx1 chr1 1493162 1493436 - LINE/L1_L1M3 chr1 1493497 1493616 - SINE/Alu_AluSx chr1 1493632 1494048 - LINE/L1_L1M3 chr1 1494049 1494112 - LINE/L1_L1MC3 chr1 1494176 1494239 - LINE/L1_L1MC3 chr1 1497160 1497206 + Simple_repeat_(CT)n chr1 1497209 1497508 - SINE/Alu_AluJr chr1 1497585 1497873 + SINE/Alu_AluSx3 chr1 1497885 1498022 + SINE/Alu_AluSx chr1 1498023 1498319 + SINE/Alu_AluSq2 chr1 1498320 1498487 + SINE/Alu_AluSx chr1 1498566 1498581 - SINE/Alu_AluYa8 chr1 1498583 1498707 - SINE/Alu_AluJo chr1 1498708 1498783 - SINE/Alu_AluYa8 chr1 1498784 1498960 - LTR/ERV1_LTR10A chr1 1498963 1499270 - SINE/Alu_AluY chr1 1499280 1499299 + Simple_repeat_(T)n chr1 1499300 1499531 - SINE/Alu_AluSg chr1 1499587 1499893 - SINE/Alu_AluSp chr1 1499894 1500197 - SINE/Alu_AluSx chr1 1500198 1500270 - LTR/ERV1_HERVIP10F-int chr1 1500271 1500568 + SINE/Alu_AluY chr1 1500569 1500604 - LTR/ERV1_HERVIP10F-int chr1 1500605 1500660 + Simple_repeat_(AT)n chr1 1500672 1500959 + SINE/Alu_AluY chr1 1500960 1501092 - LTR/ERV1_HERVIP10F-int chr1 1501093 1501252 - SINE/Alu_AluSx1 chr1 1501253 1501554 - SINE/Alu_AluY chr1 1501555 1501690 - SINE/Alu_AluSx1 chr1 1501691 1501783 - LTR/ERV1_HERVIP10F-int chr1 1501868 1502170 - SINE/Alu_AluSx1 chr1 1502205 1502506 - SINE/Alu_AluY chr1 1502508 1502811 - SINE/Alu_AluSx3 chr1 1502812 1502909 - LTR/ERV1_HERV9N-int chr1 1502910 1502932 + Simple_repeat_(AT)n chr1 1502943 1503245 + SINE/Alu_AluYk3 chr1 1503955 1503981 + Simple_repeat_(TTGCA)n chr1 1503982 1504285 - SINE/Alu_AluSx1 chr1 1504368 1504674 + SINE/Alu_AluSp chr1 1504926 1504950 + Simple_repeat_(GT)n chr1 1504966 1505957 - LTR/ERVK_LTR13 chr1 1505958 1506249 + SINE/Alu_AluSx chr1 1506263 1506377 - LINE/L1_L1M1 chr1 1506373 1506402 - LINE/L1_L1M3 chr1 1506403 1506559 - SINE/Alu_FRAM chr1 1506560 1506775 - LINE/L1_L1M3 chr1 1506776 1507078 - SINE/Alu_AluY chr1 1507079 1507812 - LINE/L1_L1M3 chr1 1507808 1507878 - LINE/L1_L1MC4a chr1 1507944 1508004 - LINE/L1_L1MC3 chr1 1509802 1510105 + SINE/Alu_AluSq2 chr1 1510106 1510269 + SINE/Alu_AluSx3 chr1 1510355 1510479 - SINE/Alu_AluJo chr1 1510490 1510793 - SINE/Alu_AluSx chr1 1510798 1510883 - SINE/Alu_AluYa8 chr1 1510898 1511404 - LTR/ERV1_LTR10A chr1 1512238 1512261 + Simple_repeat_(CGG)n chr1 1512448 1512532 + Simple_repeat_(GGCG)n chr1 1513046 1513149 - SINE/Alu_AluSx chr1 1514869 1515090 - SINE/Alu_AluSx3 chr1 1515094 1515376 - SINE/Alu_AluSz chr1 1515570 1515736 - DNA/hAT-Charlie_Charlie22a chr1 1516404 1516709 - SINE/Alu_AluSx1 chr1 1516712 1517011 - SINE/Alu_AluSx1 chr1 1517917 1518742 + Simple_repeat_(AC)n chr1 1519231 1519527 - SINE/Alu_AluSx3 chr1 1520809 1521010 + SINE/Alu_AluJb chr1 1521023 1521328 + SINE/Alu_AluYk2 chr1 1521700 1522028 - SINE/Alu_AluSz chr1 1522036 1522340 - SINE/Alu_AluSp chr1 1525414 1525713 - SINE/Alu_AluY chr1 1525726 1526007 - SINE/Alu_AluJb chr1 1526012 1526318 - SINE/Alu_AluSx chr1 1527955 1528261 - SINE/Alu_AluSx1 chr1 1530110 1530165 - LTR/ERVL-MaLR_MLT1I chr1 1530253 1530546 + SINE/Alu_AluJr chr1 1530548 1530855 + SINE/Alu_AluYa5 chr1 1530873 1531164 + SINE/Alu_AluSx chr1 1531173 1531477 + SINE/Alu_AluSc chr1 1531492 1531805 + SINE/Alu_AluY chr1 1531813 1531867 + SINE/Alu_Alu chr1 1531870 1531949 - SINE/Alu_AluSp chr1 1531961 1532416 - LINE/L1_L1M3 chr1 1532412 1532482 - LINE/L1_L1MC4a chr1 1532545 1532608 - LINE/L1_L1MC3 chr1 1535144 1535184 + Simple_repeat_(CCCCAA)n chr1 1536826 1536969 + LINE/L2_L2 chr1 1538816 1538997 + SINE/MIR_MIRb chr1 1540347 1540355 + Simple_repeat_(CGGCC)n chr1 1540356 1540410 + Simple_repeat_(CCGGCC)n chr1 1540494 1540539 + Low_complexity_GA-rich chr1 1540556 1540605 + Simple_repeat_(CGCCC)n chr1 1540606 1540639 + Simple_repeat_(GCTCG)n chr1 1540640 1540702 + Simple_repeat_(CCT)n chr1 1541327 1541417 - SINE/Alu_AluJb chr1 1542380 1542421 + Simple_repeat_(GGAGCT)n chr1 1543051 1543075 + Simple_repeat_(CAGAGG)n chr1 1544336 1544635 + SINE/Alu_AluSz chr1 1545528 1545799 + SINE/Alu_AluSc8 chr1 1546511 1546554 + SINE/Alu_Alu chr1 1546555 1546884 + SINE/Alu_AluSx chr1 1546886 1547051 + SINE/Alu_AluSp chr1 1547093 1547158 + Low_complexity_G-rich chr1 1548278 1548576 + SINE/Alu_AluSx4 chr1 1548999 1549067 + SINE/Alu_FLAM_A chr1 1549240 1549548 + SINE/Alu_AluY chr1 1549558 1549692 + SINE/Alu_AluSz6 chr1 1549693 1549991 + SINE/Alu_AluYk2 chr1 1549992 1550191 + SINE/Alu_AluSz6 chr1 1550193 1550217 + Simple_repeat_(TA)n chr1 1550821 1550872 + Simple_repeat_(CACC)n chr1 1552739 1553039 + SINE/Alu_AluSz chr1 1553394 1553493 + SINE/Alu_AluY chr1 1553494 1553808 + SINE/Alu_AluY chr1 1555716 1556003 + SINE/Alu_AluJr chr1 1556014 1556150 + SINE/Alu_AluSq2 chr1 1556151 1556355 + SINE/Alu_AluSc8 chr1 1556356 1556555 + SINE/Alu_AluSq2 chr1 1557131 1557442 + SINE/Alu_AluSz chr1 1557512 1557806 + SINE/Alu_AluSx chr1 1557929 1558220 + SINE/Alu_AluSx chr1 1558221 1558388 + SINE/Alu_AluJb chr1 1559340 1559464 + LINE/L1_L1MB8 chr1 1559600 1559726 - SINE/Alu_AluSx chr1 1559727 1560023 - SINE/Alu_AluSp chr1 1560024 1560151 - SINE/Alu_AluSx chr1 1560198 1560426 - SINE/Alu_AluJo chr1 1560706 1560836 + SINE/Alu_AluJr chr1 1560869 1560964 - SINE/MIR_MIR chr1 1561085 1561381 - SINE/Alu_AluSg chr1 1563252 1563558 + SINE/Alu_AluSg7 chr1 1563570 1563858 + SINE/Alu_AluSx chr1 1564383 1564409 + Simple_repeat_(CACG)n chr1 1565058 1565356 + SINE/Alu_AluYk3 chr1 1565977 1566282 + SINE/Alu_AluSz6 chr1 1566504 1566552 + snRNA_U6 chr1 1566571 1566869 + SINE/Alu_AluYf1 chr1 1567689 1567929 + SINE/Alu_AluSz chr1 1568331 1568622 + SINE/Alu_AluSz chr1 1568643 1568886 + SINE/Alu_AluJr chr1 1568890 1569203 + SINE/Alu_AluSx chr1 1569209 1569242 + Low_complexity_A-rich chr1 1569499 1569802 - SINE/Alu_AluJo chr1 1570149 1570302 + SINE/Alu_FRAM chr1 1570830 1570964 + SINE/Alu_AluSc8 chr1 1570965 1571274 + SINE/Alu_AluY chr1 1571275 1571454 + SINE/Alu_AluSc8 chr1 1571795 1572094 - SINE/Alu_AluYj4 chr1 1572147 1572432 + SINE/Alu_AluYk3 chr1 1572451 1572756 - SINE/Alu_AluY chr1 1573007 1573139 + SINE/Alu_AluSx chr1 1573142 1573459 + SINE/Alu_AluSc8 chr1 1573528 1573614 - SINE/Alu_AluSp chr1 1574178 1574222 + Simple_repeat_(GCC)n chr1 1574632 1574708 + Simple_repeat_(GGC)n chr1 1575375 1575512 - LINE/L2_L2 chr1 1575525 1575713 - LTR/ERV1_MER65A chr1 1575714 1576011 + SINE/Alu_AluY chr1 1576012 1576231 - LTR/ERV1_MER65A chr1 1576237 1576556 - LTR/ERV1_MER65-int chr1 1576557 1576855 + SINE/Alu_AluSc chr1 1576856 1576990 - LTR/ERV1_MER65-int chr1 1577204 1577276 - LTR/ERV1_MER65-int chr1 1577277 1578972 + LTR/ERV1_MER52A chr1 1578973 1579189 - LTR/ERV1_MER65-int chr1 1579201 1579266 + SINE/Alu_AluJr chr1 1579267 1579552 - SINE/Alu_AluYf1 chr1 1579553 1579625 + SINE/Alu_AluJr chr1 1579628 1579686 + Simple_repeat_(TACA)n chr1 1579708 1579995 - SINE/Alu_AluSc chr1 1580021 1580302 - SINE/Alu_AluSx1 chr1 1580343 1581246 + LTR/ERVK_LTR5_Hs chr1 1581247 1581513 - SINE/Alu_AluJr chr1 1581514 1581637 - LTR/ERVL_LTR57-int chr1 1581640 1581914 + SINE/Alu_AluSx1 chr1 1581915 1581993 - LTR/ERV1_MER65-int chr1 1581924 1582017 - LTR/ERV1_LOR1-int chr1 1582026 1582320 + SINE/Alu_AluSc8 chr1 1582321 1582527 - LTR/ERV1_MER65-int chr1 1582794 1583236 + LTR/ERVL-MaLR_MLT1H chr1 1583943 1584227 - SINE/Alu_AluSp chr1 1584236 1584531 - SINE/Alu_AluY chr1 1584542 1584847 - SINE/Alu_AluSx3 chr1 1584939 1585114 - LTR/ERV1_MER65-int chr1 1585117 1585290 - SINE/Alu_AluSg chr1 1585291 1585321 + Simple_repeat_(TTTG)n chr1 1585322 1585468 - SINE/Alu_AluSg chr1 1585472 1585762 - SINE/Alu_AluY chr1 1585785 1585949 + SINE/Alu_AluJb chr1 1585950 1585978 + Simple_repeat_(AAT)n chr1 1586154 1586206 - LTR/ERV1_MER65A chr1 1586208 1586449 + LTR/ERV1_MER52C chr1 1586473 1586777 - SINE/Alu_AluY chr1 1586804 1586902 - LTR/ERV1_MER65A chr1 1586913 1587219 - SINE/Alu_AluSx chr1 1587220 1587397 - SINE/Alu_AluSx3 chr1 1587398 1587689 - SINE/Alu_AluY chr1 1587690 1587825 - SINE/Alu_AluSx3 chr1 1587826 1587894 - LTR/ERV1_MER65A chr1 1587895 1588161 + SINE/Alu_AluY chr1 1588249 1588479 + SINE/Alu_AluY chr1 1588480 1588697 - LTR/ERV1_MER65A chr1 1588712 1588740 + Simple_repeat_(CTTCC)n chr1 1588756 1588887 - LINE/L2_L2 chr1 1588890 1589157 + SINE/Alu_AluSx chr1 1589161 1589406 - SINE/Alu_AluY chr1 1589494 1589769 - SINE/Alu_AluY chr1 1589814 1589965 - LINE/L2_L2 chr1 1590166 1590460 + SINE/Alu_AluSx1 chr1 1590471 1590533 - DNA/hAT-Charlie_MER3 chr1 1590534 1590826 + SINE/Alu_AluY chr1 1590827 1590888 - DNA/hAT-Charlie_MER3 chr1 1590889 1591262 + LTR/ERV1_MER83 chr1 1591263 1591557 - SINE/Alu_AluSx chr1 1591558 1591617 + LTR/ERV1_MER83 chr1 1591618 1591648 - DNA/hAT-Charlie_MER3 chr1 1591642 1591762 + LTR/ERVL-MaLR_MLT1C chr1 1591780 1591845 + LTR/ERVL_MER21C chr1 1591846 1591999 + SINE/Alu_AluYc chr1 1592002 1592098 + LTR/ERVL_MER21C chr1 1592099 1592227 + LTR/ERVL-MaLR_MLT1C chr1 1592236 1592518 + SINE/Alu_AluSx chr1 1592519 1592543 + Simple_repeat_(AAAT)n chr1 1592544 1592560 + SINE/Alu_AluSx chr1 1592563 1592873 + SINE/Alu_AluSx chr1 1592881 1593177 + SINE/Alu_AluSc8 chr1 1593186 1593303 + SINE/Alu_AluSq2 chr1 1593316 1593600 + SINE/Alu_AluSz chr1 1593604 1593717 + SINE/Alu_FLAM_C chr1 1593718 1593742 + Simple_repeat_(AGAAA)n chr1 1594231 1594362 - LINE/L2_L2 chr1 1597452 1597491 + Simple_repeat_(CTCCCC)n chr1 1599236 1599275 + Low_complexity_G-rich chr1 1599531 1599584 + Simple_repeat_(GGC)n chr1 1599643 1599722 + Simple_repeat_(GC)n chr1 1600062 1600158 + Simple_repeat_(CCGCTCC)n chr1 1600232 1600338 + Low_complexity_G-rich chr1 1601018 1601042 + Simple_repeat_(TGGCC)n chr1 1601870 1602009 + LTR/ERVL-MaLR_MLT1K chr1 1602566 1602618 + Low_complexity_GA-rich chr1 1602732 1602851 - DNA/hAT-Charlie_MER113 chr1 1604278 1604427 - SINE/Alu_FLAM_C chr1 1607337 1607638 - SINE/Alu_AluSx1 chr1 1608030 1608126 + Simple_repeat_(AT)n chr1 1608130 1608245 - SINE/Alu_AluSx3 chr1 1608274 1608438 + SINE/Alu_FRAM chr1 1608441 1608756 + SINE/Alu_AluJr chr1 1608913 1609216 + SINE/Alu_AluY chr1 1609226 1609526 + SINE/Alu_AluSz chr1 1609533 1609849 + SINE/Alu_AluSz chr1 1609966 1610049 + LTR/ERV1_MER65D chr1 1610095 1610181 + LTR/ERV1_MER65D chr1 1610182 1610533 + LTR/ERVL-MaLR_THE1C chr1 1610534 1610720 + LTR/ERV1_MER65D chr1 1610721 1610829 - LTR/ERV1_LTR26B chr1 1610930 1611407 - LTR/ERVL_LTR42 chr1 1611436 1611522 + LINE/L1_HAL1 chr1 1611568 1611872 - SINE/Alu_AluSg chr1 1611875 1612167 - SINE/Alu_AluJb chr1 1612173 1612414 - LTR/ERV1_MER4D chr1 1612418 1612493 - LTR/ERV1_MER49 chr1 1612545 1612855 - SINE/Alu_AluSx chr1 1612859 1613160 - SINE/Alu_AluSc chr1 1613161 1613479 - SINE/Alu_AluSz chr1 1613480 1613754 + SINE/Alu_AluSx3 chr1 1613793 1614085 + LINE/L1_HAL1 chr1 1614166 1614629 + LTR/ERV1_MER65D chr1 1614735 1615044 + SINE/Alu_AluSg chr1 1615445 1615487 + Low_complexity_G-rich chr1 1616209 1616263 + Simple_repeat_(CCGTGC)n chr1 1619092 1619387 + SINE/Alu_AluY chr1 1622273 1622325 - SINE/Alu_AluJb chr1 1624918 1624946 + Low_complexity_G-rich chr1 1629847 1629904 + Simple_repeat_(CCCCCAG)n chr1 1631607 1631674 + Simple_repeat_(CCCCCT)n chr1 1631948 1631997 + Simple_repeat_(CCCAGGC)n chr1 1633125 1633175 + Simple_repeat_(GCCCCC)n chr1 1638820 1638930 + SINE/Alu_AluJr chr1 1638931 1639240 - SINE/Alu_AluSg chr1 1639241 1639426 + SINE/Alu_AluJr chr1 1641064 1641114 + Simple_repeat_(TTCATC)n chr1 1641227 1641528 + SINE/Alu_AluSx chr1 1641702 1641740 + Simple_repeat_(CTC)n chr1 1641757 1641800 + Simple_repeat_(CTC)n chr1 1642771 1644214 + LINE/L1_L1M4c chr1 1644215 1644464 + SINE/Alu_AluJr chr1 1644465 1644709 + LINE/L1_L1M4c chr1 1644710 1645008 + SINE/Alu_AluSc8 chr1 1645364 1645676 - SINE/Alu_AluSc8 chr1 1645682 1645829 - LINE/L1_L1M4 chr1 1645835 1646052 - SINE/Alu_AluSx chr1 1646057 1646391 - LINE/L1_L1M4 chr1 1646392 1647207 - LINE/L1_L1M4a1 chr1 1647204 1648030 - LINE/L1_L1M4 chr1 1648045 1648116 - SINE/Alu_AluJo chr1 1648777 1648932 - SINE/Alu_FRAM chr1 1649102 1649399 - SINE/Alu_AluY chr1 1649694 1649956 + SINE/Alu_AluSx1 chr1 1649992 1650288 + SINE/Alu_AluY chr1 1650290 1650330 + Low_complexity_A-rich chr1 1650356 1650664 - SINE/Alu_AluY chr1 1650673 1650793 - SINE/Alu_FLAM_C chr1 1652079 1652174 - LINE/CR1_L3 chr1 1652725 1653022 - SINE/Alu_AluY chr1 1653027 1653321 - SINE/Alu_AluSg chr1 1653322 1653620 - SINE/Alu_AluSx chr1 1653702 1653825 + SINE/Alu_AluSx chr1 1653826 1653863 + Simple_repeat_(AC)n chr1 1653864 1654035 + SINE/Alu_AluSx chr1 1654447 1654650 - SINE/Alu_AluSx chr1 1654651 1654957 - SINE/Alu_AluY chr1 1654958 1655086 - SINE/Alu_AluSx chr1 1655404 1655487 + LINE/L2_L2 chr1 1655642 1655909 + SINE/Alu_AluSc chr1 1656103 1656204 - DNA/TcMar-Tigger_Tigger1a_Art chr1 1656205 1656504 + SINE/Alu_AluSx1 chr1 1656505 1656716 + SINE/Alu_AluY chr1 1656718 1656760 + SINE/Alu_AluY chr1 1656765 1656888 - DNA/TcMar-Tigger_Tigger1a_Art chr1 1657618 1657926 + SINE/Alu_AluJb chr1 1657936 1658185 + SINE/Alu_AluSg chr1 1658198 1658328 + SINE/Alu_AluJr chr1 1658329 1658531 + SINE/MIR_MIRb chr1 1658919 1658950 + Simple_repeat_(CGC)n chr1 1659539 1659847 - SINE/Alu_AluSz chr1 1660228 1660552 - SINE/Alu_AluY chr1 1660610 1660913 - SINE/Alu_AluY chr1 1660915 1661207 - SINE/Alu_AluSz chr1 1661233 1661372 + LTR/ERV1_MER65B chr1 1663480 1663768 - SINE/Alu_AluSg chr1 1663912 1664197 + SINE/Alu_AluJr chr1 1665328 1665376 + Simple_repeat_(GGCCTCAG)n chr1 1666416 1666537 - SINE/Alu_FLAM_C chr1 1666538 1666604 - LINE/L1_L1MC5a chr1 1666609 1666649 + SINE/Alu_AluYj4 chr1 1666650 1666693 + SINE/Alu_AluSx chr1 1666694 1666737 + SINE/Alu_AluYi6 chr1 1666738 1666788 + SINE/Alu_Alu chr1 1666824 1667118 + SINE/Alu_AluSx chr1 1667125 1667437 + SINE/Alu_AluYk2 chr1 1667524 1667707 - DNA/hAT-Charlie_MER3 chr1 1667845 1668151 - SINE/Alu_AluSx1 chr1 1668639 1668677 + Simple_repeat_(T)n chr1 1668703 1668779 + LINE/L1_L1P5 chr1 1668791 1669008 + SINE/Alu_AluSx1 chr1 1669009 1669072 + LINE/L1_L1M3 chr1 1669239 1669396 + SINE/Alu_AluSx3 chr1 1669443 1669469 + Simple_repeat_(AAAC)n chr1 1670264 1670417 - SINE/Alu_AluSx1 chr1 1670424 1670716 - SINE/Alu_AluY chr1 1671219 1671271 - DNA/hAT-Ac_OldhAT1 chr1 1672096 1672364 + SINE/Alu_AluJb chr1 1672512 1672623 - LINE/L1_L1MEg chr1 1672706 1672974 - LINE/L1_L1MEg chr1 1672974 1673088 - LINE/L1_L1MC5 chr1 1673089 1673234 + SINE/Alu_AluJb chr1 1673388 1673679 + SINE/Alu_AluSg chr1 1673681 1673991 + SINE/Alu_AluYk2 chr1 1674452 1674646 + SINE/Alu_AluJb chr1 1676946 1677247 + SINE/Alu_AluSx1 chr1 1677468 1677750 - SINE/Alu_AluY chr1 1679545 1679850 + SINE/Alu_AluSx1 chr1 1679852 1680139 + SINE/Alu_AluSp chr1 1680153 1680327 + SINE/Alu_AluJb chr1 1680800 1680976 - SINE/Alu_AluSg4 chr1 1681188 1681506 - SINE/Alu_AluY chr1 1681507 1681703 - SINE/Alu_AluSx chr1 1681704 1682018 + SINE/Alu_AluY chr1 1682019 1682158 - SINE/Alu_AluSx chr1 1682802 1683106 + SINE/Alu_AluSx1 chr1 1683131 1683546 - LTR/ERVL_MER21C chr1 1683792 1683884 - LTR/ERVL_MER21C chr1 1683986 1684052 + SINE/Alu_AluJo chr1 1684229 1684434 - DNA/TcMar-Tigger_MER47A chr1 1684510 1684814 + SINE/Alu_AluY chr1 1684827 1685124 + SINE/Alu_AluSp chr1 1685142 1685442 + SINE/Alu_AluJb chr1 1685485 1685517 + Simple_repeat_(CAGTG)n chr1 1685533 1685692 + LINE/L1_L1MC4 chr1 1685829 1685998 - SINE/Alu_AluSg4 chr1 1685999 1686302 - SINE/Alu_AluSp chr1 1686303 1686318 - SINE/Alu_AluSg4 chr1 1686319 1686339 + Simple_repeat_(T)n chr1 1686363 1686449 + LINE/L1_L1MC4 chr1 1686473 1686499 + Low_complexity_A-rich chr1 1686502 1686774 + SINE/Alu_AluSx chr1 1686775 1687085 + SINE/Alu_AluY chr1 1687180 1687213 + Simple_repeat_(AC)n chr1 1687217 1687294 + LINE/L1_L1MC4 chr1 1687295 1687457 + SINE/Alu_AluSx1 chr1 1687458 1687752 + SINE/Alu_AluSx3 chr1 1687753 1687920 + SINE/Alu_AluSx1 chr1 1687921 1688324 + LINE/L1_L1MC4 chr1 1688325 1688612 + SINE/Alu_AluSc8 chr1 1688613 1688686 + LINE/L1_L1MC4 chr1 1688890 1689186 + SINE/Alu_AluSc8 chr1 1689331 1689511 - SINE/MIR_MIR chr1 1689517 1689704 + LINE/L1_L1M5 chr1 1689705 1690002 + SINE/Alu_AluSz chr1 1690012 1690308 + SINE/Alu_AluSp chr1 1690309 1690454 + LINE/L1_L1M5 chr1 1690457 1690762 + SINE/Alu_AluSx1 chr1 1690785 1690887 - SINE/Alu_AluSq2 chr1 1690940 1691361 - LTR/ERVL-MaLR_MLT1F2 chr1 1692132 1692171 + Simple_repeat_(TGT)n chr1 1692366 1692412 + Simple_repeat_(GCACCCA)n chr1 1693694 1693995 + SINE/Alu_AluSg chr1 1694004 1694314 + SINE/Alu_AluSx chr1 1694545 1694724 + SINE/Alu_FRAM chr1 1694725 1695048 + SINE/Alu_AluSc8 chr1 1695091 1695268 + DNA/hAT-Tip100_ORSL-2b chr1 1695269 1695561 - SINE/Alu_AluSg chr1 1695562 1695857 - SINE/Alu_AluY chr1 1695858 1695898 + DNA/hAT-Tip100_ORSL-2b chr1 1695906 1696160 - SINE/Alu_AluJr chr1 1696611 1696680 - SINE/MIR_MIR1_Amn chr1 1696783 1697047 + Simple_repeat_(TG)n chr1 1699599 1699638 + Simple_repeat_(GTGTGCGGG)n chr1 1700284 1700334 + Simple_repeat_(GCCCCC)n chr1 1705962 1706072 + SINE/Alu_AluJr chr1 1706073 1706372 - SINE/Alu_AluSg chr1 1706373 1706557 + SINE/Alu_AluJr chr1 1708196 1708246 + Simple_repeat_(TTCATC)n chr1 1708359 1708660 + SINE/Alu_AluSx chr1 1708834 1708872 + Simple_repeat_(CTC)n chr1 1708889 1708932 + Simple_repeat_(CTC)n chr1 1709903 1711351 + LINE/L1_L1M4c chr1 1711352 1711601 + SINE/Alu_AluJr chr1 1711602 1711846 + LINE/L1_L1M4c chr1 1711847 1712146 + SINE/Alu_AluSc8 chr1 1712502 1712814 - SINE/Alu_AluSc8 chr1 1712820 1712967 - LINE/L1_L1M4 chr1 1712973 1713206 - SINE/Alu_AluSx chr1 1713211 1713561 - LINE/L1_L1M4 chr1 1713562 1714361 - LINE/L1_L1M4a1 chr1 1714358 1714506 - LINE/L1_L1M4 chr1 1714540 1715181 - LINE/L1_L1M4 chr1 1715196 1715267 - SINE/Alu_AluJo chr1 1715934 1716088 - SINE/Alu_FRAM chr1 1716532 1716844 + SINE/Alu_AluSx1 chr1 1716921 1717055 - SINE/Alu_FLAM_C chr1 1718636 1718935 - SINE/Alu_AluYm1 chr1 1719614 1719914 - SINE/Alu_AluY chr1 1720086 1720396 - SINE/Alu_AluY chr1 1720401 1720694 - SINE/Alu_AluSg chr1 1720697 1720892 - SINE/Alu_AluSx chr1 1720959 1721210 + SINE/Alu_AluYk3 chr1 1721631 1721714 + LINE/L2_L2 chr1 1721869 1722077 + SINE/Alu_AluY chr1 1722079 1722121 + SINE/Alu_AluY chr1 1722126 1722241 - DNA/TcMar-Tigger_Tigger1 chr1 1722951 1723254 + SINE/Alu_AluJb chr1 1723265 1723518 + SINE/Alu_AluSg chr1 1723542 1723672 + SINE/Alu_AluJr chr1 1723673 1723875 + SINE/MIR_MIRb chr1 1724850 1725158 - SINE/Alu_AluSz chr1 1725539 1725863 - SINE/Alu_AluY chr1 1725921 1726231 - SINE/Alu_AluY chr1 1726234 1726531 - SINE/Alu_AluY chr1 1726533 1726818 - SINE/Alu_AluSc8 chr1 1726819 1727113 - SINE/Alu_AluSz chr1 1727139 1727279 + LTR/ERV1_MER65B chr1 1729390 1729678 - SINE/Alu_AluSg chr1 1729822 1730103 + SINE/Alu_AluJo chr1 1730935 1730994 + SINE/Alu_AluJo chr1 1731009 1731053 + SINE/Alu_Alu chr1 1731054 1731095 + SINE/Alu_Alu chr1 1731096 1731403 + SINE/Alu_AluY chr1 1731575 1731603 + Low_complexity_A-rich chr1 1731631 1731933 + SINE/Alu_AluSx1 chr1 1731937 1732256 + SINE/Alu_AluSz6 chr1 1732891 1733155 - SINE/Alu_AluSx3 chr1 1733196 1733491 + SINE/Alu_AluSx chr1 1733498 1733809 + SINE/Alu_AluY chr1 1733898 1734067 - DNA/hAT-Charlie_MER3 chr1 1734204 1734514 - SINE/Alu_AluSx1 chr1 1735002 1735034 + Simple_repeat_(T)n chr1 1735060 1735136 + LINE/L1_L1P5 chr1 1735145 1735342 + SINE/Alu_AluSx1 chr1 1735395 1735509 - LINE/L1_L1MEg chr1 1735509 1735624 - LINE/L1_L1MC5 chr1 1735625 1735770 + SINE/Alu_AluJb chr1 1735924 1736214 + SINE/Alu_AluSg chr1 1736216 1736518 + SINE/Alu_AluYk2 chr1 1736519 1736831 + SINE/Alu_AluSz chr1 1737308 1737502 + SINE/Alu_AluJb chr1 1739804 1740105 + SINE/Alu_AluSx1 chr1 1740326 1740620 - SINE/Alu_AluY chr1 1740621 1740777 - SINE/Alu_AluSx chr1 1740778 1741082 - SINE/Alu_AluSp chr1 1741083 1741098 - SINE/Alu_AluSx chr1 1741123 1741214 + LINE/L1_L1MC4 chr1 1741238 1741264 + Low_complexity_A-rich chr1 1741267 1741554 + SINE/Alu_AluSc8 chr1 1741832 1742148 + SINE/Alu_AluYm1 chr1 1742161 1742453 + SINE/Alu_AluSx1 chr1 1742618 1742778 - SINE/MIR_MIRb chr1 1742784 1742971 + LINE/L1_L1M5 chr1 1742972 1743265 + SINE/Alu_AluSz chr1 1743275 1743565 + SINE/Alu_AluSp chr1 1743566 1743726 + LINE/L1_L1M5 chr1 1743732 1744037 + SINE/Alu_AluSx1 chr1 1744060 1744162 - SINE/Alu_AluSq2 chr1 1744215 1744636 - LTR/ERVL-MaLR_MLT1F2 chr1 1745407 1745446 + Simple_repeat_(TGT)n chr1 1745641 1745687 + Simple_repeat_(GCACCCA)n chr1 1746176 1746195 + Simple_repeat_(T)n chr1 1746960 1747269 + SINE/Alu_AluSg chr1 1747320 1747497 + DNA/hAT-Tip100_ORSL-2b chr1 1747498 1747792 - SINE/Alu_AluSg chr1 1747793 1748088 - SINE/Alu_AluY chr1 1748089 1748129 + DNA/hAT-Tip100_ORSL-2b chr1 1748137 1748391 - SINE/Alu_AluJr chr1 1749313 1749382 - SINE/MIR_MIR1_Amn chr1 1749485 1749670 + Simple_repeat_(TG)n chr1 1752909 1752936 + Simple_repeat_(CCT)n chr1 1754824 1755118 - SINE/Alu_AluSc chr1 1756709 1756850 + LTR/ERVL-MaLR_MSTD chr1 1756888 1757027 + LTR/ERVL-MaLR_MSTD chr1 1760840 1760995 - SINE/Alu_AluJb chr1 1760996 1761290 - SINE/Alu_AluSg chr1 1761291 1761425 - SINE/Alu_AluJb chr1 1762474 1762759 + SINE/Alu_AluSq2 chr1 1762760 1762794 + Simple_repeat_(AATG)n chr1 1763203 1763337 + SINE/Alu_AluY chr1 1763338 1763646 + SINE/Alu_AluSx chr1 1764620 1764908 + SINE/Alu_AluSx chr1 1765630 1765914 + SINE/Alu_AluSq2 chr1 1765986 1766129 + SINE/Alu_AluJr chr1 1766178 1766440 + SINE/Alu_AluSp chr1 1766679 1766703 + Simple_repeat_(T)n chr1 1766713 1766909 - SINE/Alu_AluJr chr1 1766920 1767216 - SINE/Alu_AluSz chr1 1767681 1767812 - SINE/Alu_FLAM_C chr1 1767816 1767894 + LTR/ERVL-MaLR_MLT1B chr1 1767895 1768194 + SINE/Alu_AluSq chr1 1768195 1768255 + LTR/ERVL-MaLR_MLT1B chr1 1768256 1768566 + SINE/Alu_AluJr chr1 1768567 1768805 + LTR/ERVL-MaLR_MLT1B chr1 1768927 1769088 + DNA/hAT-Charlie_MER5B chr1 1769136 1769264 + SINE/Alu_AluSz chr1 1769265 1769578 + SINE/Alu_AluSx1 chr1 1769579 1769757 + SINE/Alu_AluSz chr1 1769795 1769969 + SINE/Alu_AluSx1 chr1 1769970 1770015 + Simple_repeat_(AAAACA)n chr1 1770016 1770193 + SINE/Alu_AluSx1 chr1 1770237 1770519 + LINE/L1_L1M4 chr1 1770520 1770756 + SINE/Alu_AluY chr1 1770757 1770767 + Simple_repeat_(TA)n chr1 1770768 1771056 + SINE/Alu_AluSx chr1 1771057 1771138 + Simple_repeat_(TA)n chr1 1771140 1771795 + LINE/L1_L1M4 chr1 1771803 1772107 - SINE/Alu_AluJb chr1 1772161 1772463 - SINE/Alu_AluJb chr1 1772475 1772578 + SINE/Alu_FLAM_C chr1 1772579 1772757 + LINE/L1_L1MC4a chr1 1772758 1773060 + SINE/Alu_AluSp chr1 1773061 1773140 + LINE/L1_L1MC4a chr1 1773141 1773440 - SINE/Alu_AluSq2 chr1 1773441 1773468 + LINE/L1_L1MC4a chr1 1773469 1773604 + SINE/Alu_AluJr chr1 1773677 1773730 + Simple_repeat_(TG)n chr1 1773731 1773761 + Low_complexity_GA-rich chr1 1773766 1774034 - SINE/Alu_AluJb chr1 1774146 1774203 + Simple_repeat_(TA)n chr1 1774234 1774553 - SINE/Alu_AluSp chr1 1774562 1774613 - SINE/Alu_Alu chr1 1774621 1774689 - SINE/Alu_FLAM_C chr1 1774826 1775124 + SINE/Alu_AluSg4 chr1 1776324 1776431 + DNA/TcMar-Tigger_Tigger16a chr1 1776502 1776796 + SINE/Alu_AluSg chr1 1776806 1777080 + SINE/Alu_AluJr chr1 1778558 1778602 + Low_complexity_G-rich chr1 1779483 1779667 - SINE/Alu_AluJb chr1 1779668 1779692 + Simple_repeat_(TG)n chr1 1779693 1779794 - SINE/Alu_AluJb chr1 1780949 1781040 + DNA/hAT-Tip100_Arthur1A chr1 1781470 1781565 - LINE/L2_L2a chr1 1781632 1781703 - LINE/L2_L2a chr1 1781740 1781769 - SINE/MIR_MIR chr1 1781770 1782050 - SINE/Alu_AluSx chr1 1782051 1782153 - SINE/MIR_MIR chr1 1783748 1784040 + SINE/Alu_AluYk2 chr1 1784056 1784359 + SINE/Alu_AluSc8 chr1 1784360 1784528 + SINE/Alu_AluSx chr1 1784566 1784739 - SINE/Alu_AluYk2 chr1 1785734 1785772 + Simple_repeat_(TCCC)n chr1 1787753 1788052 + SINE/Alu_AluSx4 chr1 1788085 1788158 + LINE/L2_L2b chr1 1789411 1789711 + SINE/Alu_AluSp chr1 1791173 1791474 - SINE/Alu_AluSp chr1 1792416 1792721 + SINE/Alu_AluSc chr1 1792784 1793077 + SINE/Alu_AluSq2 chr1 1794218 1794326 + SINE/Alu_AluSq chr1 1794704 1794987 - SINE/Alu_AluSc chr1 1795484 1795766 + SINE/Alu_AluSx1 chr1 1795986 1796204 + DNA/TcMar-Tigger_MER47A chr1 1796359 1796484 + SINE/MIR_MIR chr1 1797171 1797294 + LINE/L1_L1M5 chr1 1797442 1797736 - SINE/Alu_AluSx1 chr1 1798388 1798512 - LINE/CR1_L3 chr1 1798712 1798914 - SINE/Alu_AluSq chr1 1798926 1799219 - SINE/Alu_AluYe5 chr1 1799606 1800777 + LINE/L1_L1MEb chr1 1800784 1801034 + LINE/L1_L1MEb chr1 1801035 1801328 - SINE/Alu_AluSx chr1 1801329 1801666 + LINE/L1_L1MEb chr1 1801667 1801696 + SINE/Alu_Alu chr1 1801824 1802486 + LINE/L1_L1MEb chr1 1802487 1802779 + SINE/Alu_AluSz chr1 1802780 1802966 + LINE/L1_L1MEb chr1 1803317 1803580 - SINE/Alu_AluJr chr1 1803688 1803701 + Simple_repeat_(TTAA)n chr1 1803702 1804011 + SINE/Alu_AluSx4 chr1 1804012 1804306 + SINE/Alu_AluY chr1 1804307 1804325 + Simple_repeat_(TTAA)n chr1 1804878 1805174 + SINE/Alu_AluSx chr1 1805177 1805476 + SINE/Alu_AluSq2 chr1 1805548 1805841 - SINE/Alu_AluSq chr1 1806692 1806996 + SINE/Alu_AluSx chr1 1807193 1807493 + SINE/Alu_AluSx3 chr1 1807699 1808008 - SINE/Alu_AluY chr1 1808011 1808305 - SINE/Alu_AluSp chr1 1808625 1808662 + Simple_repeat_(GTTT)n chr1 1808663 1808938 - SINE/Alu_AluSz6 chr1 1809190 1809450 - SINE/Alu_AluSz6 chr1 1809798 1809955 + DNA/TcMar-Tigger_Tigger15a chr1 1809959 1810088 + SINE/Alu_AluJr chr1 1810089 1810364 - SINE/Alu_AluYm1 chr1 1810365 1810558 + SINE/Alu_AluJr chr1 1810588 1810632 + Simple_repeat_(ACAT)n chr1 1810663 1810967 - SINE/Alu_AluSx1 chr1 1810989 1811045 - SINE/Alu_Alu chr1 1811060 1811081 + Simple_repeat_(TA)n chr1 1811082 1811387 - SINE/Alu_AluSz chr1 1811413 1811712 + SINE/Alu_AluYk3 chr1 1811784 1812086 + SINE/Alu_AluY chr1 1812192 1812352 + LINE/L1_L1PA6 chr1 1812370 1812393 + Simple_repeat_(AT)n chr1 1812394 1812429 + Simple_repeat_(AC)n chr1 1813150 1813410 - SINE/Alu_AluSz6 chr1 1813477 1813492 + Simple_repeat_(CTTA)n chr1 1813493 1813788 - SINE/Alu_AluJo chr1 1813789 1813802 + Simple_repeat_(CTTA)n chr1 1813822 1814112 - LINE/L1_L1MA7 chr1 1814525 1814827 + SINE/Alu_AluJr chr1 1814831 1815141 + SINE/Alu_AluY chr1 1815397 1815472 - SINE/MIR_MIR chr1 1815480 1815515 + Low_complexity_A-rich chr1 1816645 1816920 - SINE/Alu_AluSz chr1 1816930 1817513 - LINE/L1_L1MB5 chr1 1818306 1818329 + Simple_repeat_(A)n chr1 1818436 1818563 + SINE/Alu_AluJo chr1 1818581 1818892 + SINE/Alu_AluSx chr1 1818983 1819094 - LINE/CR1_L3 chr1 1819226 1819520 - SINE/Alu_AluSx chr1 1819523 1819841 - SINE/Alu_AluJb chr1 1820075 1820380 + SINE/Alu_AluSx1 chr1 1821751 1821857 + DNA/hAT-Charlie_MER33 chr1 1821858 1822125 + SINE/Alu_AluJb chr1 1822126 1822153 + DNA/hAT-Charlie_MER33 chr1 1822154 1822286 - SINE/Alu_FLAM_C chr1 1822294 1822614 - SINE/Alu_AluY chr1 1822615 1822644 + DNA/hAT-Charlie_MER33 chr1 1822961 1823267 + SINE/Alu_AluY chr1 1823856 1824018 - SINE/Alu_AluJb chr1 1824351 1824450 + SINE/Alu_AluSx3 chr1 1824579 1824874 - SINE/Alu_AluSx chr1 1825587 1825883 + SINE/Alu_AluYm1 chr1 1826135 1826434 + SINE/Alu_AluSz chr1 1826821 1826876 - DNA/TcMar-Tigger_X26_DNA chr1 1828039 1828331 + SINE/Alu_AluSp chr1 1828766 1828874 + SINE/Alu_FLAM_C chr1 1828875 1828913 + Simple_repeat_(AC)n chr1 1828914 1828939 + SINE/Alu_AluY chr1 1829053 1829354 - SINE/Alu_AluSq2 chr1 1829739 1830039 - SINE/Alu_AluSq chr1 1830266 1830548 - SINE/Alu_AluYc3 chr1 1830582 1830851 - SINE/Alu_AluJr chr1 1830883 1831173 + SINE/Alu_AluSx chr1 1831191 1831340 + SINE/Alu_FRAM chr1 1831455 1831728 - SINE/Alu_AluSz chr1 1831779 1832091 + SINE/Alu_AluJb chr1 1832803 1832954 + SINE/MIR_MIRc chr1 1833858 1834091 + LINE/L1_L1MC5 chr1 1834668 1834967 - SINE/Alu_AluY chr1 1835228 1835476 + SINE/MIR_MIR chr1 1835678 1835790 - LINE/L1_L1MD chr1 1835791 1836122 + SINE/Alu_AluJo chr1 1836123 1836387 - LINE/L1_L1MD chr1 1836388 1836559 - SINE/Alu_AluJr chr1 1836560 1836849 - SINE/Alu_AluSx chr1 1836850 1836986 - SINE/Alu_AluJr chr1 1836987 1837027 - LINE/L1_L1M5 chr1 1837028 1837255 - LINE/L1_L1MD chr1 1837256 1837553 - SINE/Alu_AluY chr1 1837554 1837860 - SINE/Alu_AluSc chr1 1837861 1837917 - LINE/L1_L1MD chr1 1837923 1838218 + SINE/Alu_AluSx1 chr1 1838264 1838359 - LINE/L1_L1MC4 chr1 1838436 1838743 - SINE/Alu_AluY chr1 1838744 1838809 + DNA/hAT-Charlie_Charlie15b chr1 1838976 1839098 + SINE/Alu_FLAM_A chr1 1839565 1839863 + SINE/Alu_AluSx chr1 1839864 1839874 + Simple_repeat_(CA)n chr1 1839875 1840247 + SINE/Alu_AluSx1 chr1 1840248 1840531 + SINE/Alu_AluSx3 chr1 1840532 1840563 + Simple_repeat_(CA)n chr1 1840896 1841189 - SINE/Alu_AluSc5 chr1 1841201 1841479 - SINE/Alu_AluSx chr1 1841777 1842025 + LINE/L1_L1MB5 chr1 1842026 1842149 + SINE/Alu_AluSx chr1 1842150 1842453 + SINE/Alu_AluY chr1 1842470 1842800 + LINE/L1_L1MB5 chr1 1842949 1843094 - DNA/hAT-Charlie_MER20 chr1 1843231 1843525 - SINE/Alu_AluJo chr1 1843796 1843913 + SINE/Alu_AluSx chr1 1843914 1844224 + SINE/Alu_AluY chr1 1844225 1844411 + SINE/Alu_AluSx chr1 1845292 1845578 + SINE/Alu_AluYf1 chr1 1845579 1845615 + Simple_repeat_(AAT)n chr1 1845810 1845824 + LINE/L1_L1MC4 chr1 1845825 1845872 + Simple_repeat_(AC)n chr1 1845873 1846279 + LINE/L1_L1MC4 chr1 1846286 1846570 + SINE/Alu_AluSx1 chr1 1846626 1846850 - SINE/Alu_AluJr4 chr1 1846860 1848097 + DNA/TcMar-Tigger_Tigger2 chr1 1848098 1848387 + SINE/Alu_AluSx3 chr1 1848388 1848802 + DNA/TcMar-Tigger_Tigger2 chr1 1848882 1849292 + LTR/ERVL_MER21C chr1 1849340 1849418 - LINE/L1_L1MEf chr1 1849419 1849700 - SINE/Alu_AluJb chr1 1849701 1849712 - LINE/L1_L1MEf chr1 1849713 1849943 - LINE/L1_L1MEg chr1 1849944 1849962 - LINE/L1_L1MEf chr1 1849975 1850132 - SINE/Alu_AluSg4 chr1 1850133 1850432 - SINE/Alu_AluSp chr1 1850439 1850921 - LINE/L1_L1MEf chr1 1850988 1851128 + SINE/Alu_AluSg chr1 1851129 1851431 + SINE/Alu_AluSp chr1 1851432 1851558 + SINE/Alu_AluSg chr1 1851762 1852059 + SINE/Alu_AluSp chr1 1852138 1852238 + SINE/Alu_AluJr chr1 1852239 1852529 - SINE/Alu_AluYm1 chr1 1852530 1852700 + SINE/Alu_AluJr chr1 1853118 1853370 + LINE/L2_L2b chr1 1853986 1854288 - LINE/L2_L2a chr1 1854626 1854756 + SINE/Alu_FLAM_A chr1 1854757 1854880 + SINE/Alu_AluSx1 chr1 1854881 1855172 + SINE/Alu_AluSg4 chr1 1855173 1855349 + SINE/Alu_AluSx1 chr1 1855420 1855723 + SINE/Alu_AluYe5 chr1 1855923 1856020 + LINE/L2_L2b chr1 1856022 1856284 - SINE/Alu_AluJr chr1 1856291 1856425 - SINE/Alu_FLAM_C chr1 1856428 1856725 - SINE/Alu_AluSc chr1 1857670 1857966 - DNA/TcMar-Tigger_MER44A chr1 1858667 1859016 + DNA/hAT-Charlie_MER119 chr1 1859017 1859317 - SINE/Alu_AluSz chr1 1859318 1859386 + DNA/hAT-Charlie_MER119 chr1 1859387 1859499 + SINE/Alu_FLAM_C chr1 1859727 1859865 + SINE/Alu_AluY chr1 1859896 1860090 + LINE/L1_L1PA5 chr1 1860091 1860481 + LINE/L1_L1PA16 chr1 1860482 1860657 + SINE/Alu_AluY chr1 1860679 1860723 + SINE/Alu_Alu chr1 1860860 1861128 + SINE/Alu_AluSz chr1 1861176 1861474 + SINE/Alu_AluJr4 chr1 1861793 1861908 + LINE/L1_L1MC4 chr1 1861919 1862232 + SINE/Alu_AluSz chr1 1862412 1862461 - LINE/CR1_X7A_LINE chr1 1862462 1862758 - SINE/Alu_AluJb chr1 1862759 1862772 - LINE/CR1_X7A_LINE chr1 1863917 1864024 + SINE/Alu_FLAM_C chr1 1864042 1864344 + SINE/Alu_AluSg7 chr1 1864418 1864448 + LINE/L2_L2b chr1 1864981 1865288 + SINE/Alu_AluY chr1 1865316 1865593 + SINE/Alu_AluSc8 chr1 1865940 1866239 - SINE/Alu_AluSp chr1 1866413 1866534 - LINE/L1_L1ME4b chr1 1866539 1866672 - DNA/hAT-Charlie_MER30 chr1 1866673 1866973 + SINE/Alu_AluY chr1 1866974 1866984 - DNA/hAT-Charlie_MER30 chr1 1866985 1867270 + SINE/Alu_AluSq chr1 1867271 1867341 - DNA/hAT-Charlie_MER30 chr1 1867348 1867460 - LINE/L2_L2a chr1 1867823 1867955 - LINE/L1_L1ME4b chr1 1868171 1868469 - SINE/Alu_AluSq2 chr1 1868502 1868801 + SINE/Alu_AluSg4 chr1 1868904 1869214 + SINE/Alu_AluY chr1 1869862 1870160 - SINE/Alu_AluSx1 chr1 1870177 1870454 - SINE/Alu_AluJr4 chr1 1870680 1870956 + SINE/Alu_AluSx chr1 1870957 1871160 + LINE/L2_L2 chr1 1871170 1871452 + SINE/Alu_AluSz chr1 1871717 1871994 + LINE/L2_L2 chr1 1871995 1872277 - SINE/Alu_AluJo chr1 1872278 1872367 + LINE/L2_L2 chr1 1872811 1872881 + LINE/L2_L2d2 chr1 1872992 1873057 + LTR/ERVL-MaLR_MLT1H chr1 1873058 1873199 - SINE/Alu_FLAM_C chr1 1873200 1873587 + LTR/ERVL-MaLR_MLT1H chr1 1873629 1873689 + SINE/Alu_Alu chr1 1873690 1873824 + SINE/Alu_AluSx chr1 1873936 1874121 + DNA/hAT-Tag1_hAT-1_Mam chr1 1874189 1874322 + SINE/Alu_AluSx3 chr1 1874323 1874632 + SINE/Alu_AluSx chr1 1874633 1874813 + SINE/Alu_AluSx3 chr1 1874859 1875169 - DNA/hAT-Charlie_MER1B chr1 1875181 1875492 - SINE/Alu_AluSz6 chr1 1875509 1875672 - SINE/Alu_FRAM chr1 1875825 1876228 + LTR/ERVL-MaLR_MLT1C2 chr1 1876309 1876452 - SINE/Alu_AluJr chr1 1876458 1876519 + Low_complexity_GA-rich chr1 1876492 1876555 + Simple_repeat_(CGAG)n chr1 1876567 1876666 - SINE/Alu_AluJo chr1 1876761 1876884 - SINE/MIR_MIRb chr1 1877006 1877018 + Simple_repeat_(AT)n chr1 1877019 1877317 + SINE/Alu_AluSz chr1 1877318 1877331 + Simple_repeat_(AT)n chr1 1877500 1877637 - SINE/Alu_AluJr chr1 1878062 1878221 + SINE/MIR_MIR chr1 1878438 1878525 + LINE/L2_L2a chr1 1879217 1879424 - DNA/hAT-Charlie_Charlie1 chr1 1879425 1879730 + SINE/Alu_AluY chr1 1879731 1879914 - DNA/hAT-Charlie_Charlie1 chr1 1879923 1880232 - SINE/Alu_AluJr chr1 1880272 1880318 - DNA/hAT-Charlie_Charlie1a chr1 1880319 1880595 + SINE/Alu_AluYc3 chr1 1880596 1880695 - DNA/hAT-Charlie_Charlie1a chr1 1880822 1880906 - LINE/L2_L2a chr1 1880958 1881078 - LINE/L2_L2a chr1 1881421 1881699 - SINE/Alu_AluSz chr1 1881801 1881875 - LINE/L2_L2b chr1 1881935 1882166 + LINE/L2_L2b chr1 1882167 1882456 + SINE/Alu_AluSx3 chr1 1882457 1882486 + LINE/L2_L2b chr1 1882641 1882933 + SINE/Alu_AluSg chr1 1883020 1883308 + SINE/Alu_AluJb chr1 1883549 1883656 - SINE/MIR_MIR chr1 1883669 1883765 + rRNA_LSU-rRNA_Hsa chr1 1883968 1884132 - SINE/Alu_AluSp chr1 1884133 1884374 - SINE/Alu_AluSp chr1 1884375 1884511 - SINE/Alu_AluSp chr1 1884683 1884982 - SINE/Alu_AluY chr1 1885132 1885438 + SINE/Alu_AluSq2 chr1 1885550 1885857 - SINE/Alu_AluY chr1 1886030 1886326 + SINE/Alu_AluSx1 chr1 1886704 1887012 - SINE/Alu_AluY chr1 1887633 1887912 - SINE/Alu_AluSz6 chr1 1888502 1888553 - LINE/CR1_L3 chr1 1888556 1888853 + SINE/Alu_AluSq chr1 1889530 1889674 + SINE/Alu_FLAM_C chr1 1890729 1890779 + Simple_repeat_(GGCG)n chr1 1890885 1890935 + Simple_repeat_(CG)n chr1 1890946 1891030 + Simple_repeat_(CGCTCC)n chr1 1891109 1891186 + Simple_repeat_(CACGCG)n chr1 1891294 1891328 + Low_complexity_G-rich chr1 1891399 1891496 + Low_complexity_G-rich chr1 1892470 1892852 - LINE/L1_L1MC4a chr1 1893013 1893056 + Simple_repeat_(TTTT)n chr1 1893058 1893184 - SINE/Alu_FLAM_C chr1 1896397 1896690 - SINE/Alu_AluSg chr1 1896706 1896883 - SINE/Alu_AluSx3 chr1 1896884 1897190 - SINE/Alu_AluY chr1 1897191 1897334 - SINE/Alu_AluSx3 chr1 1897401 1897704 + SINE/Alu_AluSp chr1 1897713 1898004 + SINE/Alu_AluJr chr1 1898691 1898757 + Simple_repeat_(ACATGT)n chr1 1898994 1899122 - LTR/ERVL-MaLR_MLT1K chr1 1900197 1900329 + SINE/Alu_AluJb chr1 1900330 1900498 - SINE/Alu_AluSq chr1 1900499 1900804 - SINE/Alu_AluY chr1 1900805 1900939 - SINE/Alu_AluSq chr1 1900940 1901125 + SINE/Alu_AluJb chr1 1901128 1901253 + Simple_repeat_(GAAA)n chr1 1902344 1902637 + SINE/Alu_AluY chr1 1902638 1902682 - LINE/L1_L1MB5 chr1 1902683 1902995 - SINE/Alu_AluSc chr1 1902998 1903106 - SINE/Alu_FLAM_A chr1 1903107 1903513 - LINE/L1_L1MB5 chr1 1903514 1903817 - SINE/Alu_AluY chr1 1903821 1903852 - SINE/Alu_AluJb chr1 1903853 1903990 + SINE/Alu_AluSp chr1 1903991 1904258 + SINE/Alu_AluSx chr1 1904259 1904424 + SINE/Alu_AluSp chr1 1904425 1904468 + Simple_repeat_(AAAT)n chr1 1904469 1904674 - SINE/Alu_AluJb chr1 1904676 1904707 - LINE/L1_L1MB5 chr1 1904708 1905003 - SINE/Alu_AluSx chr1 1905004 1905086 - LINE/L1_L1MB5 chr1 1905087 1905382 - SINE/Alu_AluSx chr1 1905383 1905533 - SINE/Alu_AluJb chr1 1905534 1905697 - SINE/Alu_AluJb chr1 1905698 1905998 - SINE/Alu_AluY chr1 1905999 1906150 - SINE/Alu_AluJb chr1 1906151 1906254 - LINE/L1_L1MB5 chr1 1907716 1908009 - SINE/Alu_AluY chr1 1908010 1908310 - SINE/Alu_AluSz chr1 1909165 1909300 + Low_complexity_G-rich chr1 1909409 1909706 + SINE/Alu_AluSx1 chr1 1909996 1910042 - LINE/L1_HAL1 chr1 1910056 1910362 - SINE/Alu_AluJb chr1 1910364 1910530 - LINE/L1_L1MEd chr1 1910681 1910785 - LTR/ERVL_LTR16E1 chr1 1911113 1911253 - LTR/ERVL_ERV3-16A3_I-int chr1 1911585 1911711 - LTR/ERVL_ERV3-16A3_LTR chr1 1911945 1912151 - LTR/ERVL_MLT2D chr1 1912152 1912456 - SINE/Alu_AluY chr1 1912457 1912496 - LTR/ERVL_MLT2D chr1 1912497 1912630 + LINE/L1_L1MB2 chr1 1912642 1912941 + SINE/Alu_AluSc8 chr1 1912948 1913255 - SINE/Alu_AluSg7 chr1 1913261 1913478 + LINE/L1_L1MB2 chr1 1913490 1913801 - SINE/Alu_AluY chr1 1917689 1917730 + Simple_repeat_(AGGCTG)n chr1 1919184 1919235 + Simple_repeat_(GCAGCA)n chr1 1920053 1920094 + Low_complexity_G-rich chr1 1922001 1922050 + Low_complexity_G-rich chr1 1923175 1923210 + Simple_repeat_(AGGCTG)n chr1 1927229 1927263 + Low_complexity_G-rich chr1 1930574 1930716 - SINE/Alu_FRAM chr1 1930724 1931143 - DNA/hAT-Charlie_Charlie2b chr1 1931153 1931454 + SINE/Alu_AluY chr1 1931458 1931778 + SINE/Alu_AluY chr1 1931781 1932096 + SINE/Alu_AluSx1 chr1 1932115 1932422 + SINE/Alu_AluY chr1 1932423 1932526 - SINE/Alu_AluJo chr1 1932582 1932891 - SINE/Alu_AluSc8 chr1 1932892 1933185 - SINE/Alu_AluY chr1 1933186 1933479 - SINE/Alu_AluSx chr1 1933482 1933949 - LINE/L1_L1M5 chr1 1934087 1934184 + DNA/hAT-Blackjack_MER81 chr1 1935799 1935930 + LINE/L1_L1M8 chr1 1935936 1936247 + SINE/Alu_AluSp chr1 1936264 1936572 + SINE/Alu_AluSc chr1 1936623 1936928 + SINE/Alu_AluJr chr1 1936931 1937032 + LINE/L1_L1M8 chr1 1937312 1937442 + LINE/L1_L1MC5 chr1 1937461 1937619 + LINE/L1_L1MD chr1 1939057 1939158 + Simple_repeat_(GTGAGT)n chr1 1940831 1941138 + SINE/Alu_AluYk3 chr1 1941754 1941869 + LINE/L1_L1MD chr1 1942618 1942814 + LTR/ERV1_MER89 chr1 1943716 1944106 - LTR/ERVL-MaLR_MLT1D chr1 1944613 1944905 - SINE/Alu_AluSx1 chr1 1945026 1945255 + SINE/Alu_AluJb chr1 1945546 1945852 + SINE/Alu_AluSz chr1 1945858 1946172 + Simple_repeat_(TG)n chr1 1947874 1948170 - SINE/Alu_AluSg chr1 1948278 1948560 - SINE/Alu_AluJo chr1 1948580 1948790 - SINE/Alu_AluJb chr1 1948875 1949166 + Simple_repeat_(TTCC)n chr1 1949167 1949454 - SINE/Alu_AluSz6 chr1 1949494 1950336 - LINE/L1_L1ME1 chr1 1950337 1950634 - SINE/Alu_AluSq2 chr1 1950635 1951023 - LINE/L1_L1ME1 chr1 1951024 1951363 - LTR/ERVL-MaLR_THE1D chr1 1951364 1951754 - LINE/L1_L1ME1 chr1 1951789 1951880 + SINE/Alu_AluSz chr1 1952022 1952252 - SINE/Alu_AluSx1 chr1 1952300 1952517 + LINE/L1_L1MEd chr1 1952530 1952601 + Low_complexity_GA-rich chr1 1952602 1952663 + LINE/L1_L1MEd chr1 1952664 1952957 - SINE/Alu_AluJo chr1 1952958 1953296 + LINE/L1_L1MEd chr1 1953297 1953594 - SINE/Alu_AluSq2 chr1 1953595 1954234 + LINE/L1_L1MEd chr1 1954235 1954441 + DNA/hAT-Charlie_MER3 chr1 1954442 1954499 + LINE/L1_L1MEd chr1 1954502 1954791 + SINE/Alu_AluJb chr1 1955261 1955290 + Simple_repeat_(GCAGG)n chr1 1955698 1955749 + Simple_repeat_(CTGCTC)n chr1 1956868 1956958 + DNA/hAT-Tip100_MER91B chr1 1959252 1959561 - SINE/Alu_AluJb chr1 1960338 1961114 + LINE/L1_L1MEd chr1 1961183 1961531 + LINE/L1_L1MEd chr1 1961549 1961977 + LINE/L1_L1MC4 chr1 1962133 1962190 + LINE/L1_L1MC4 chr1 1962191 1962494 + SINE/Alu_AluSq chr1 1962495 1962602 + LINE/L1_L1MC4 chr1 1962603 1962910 + SINE/Alu_AluJb chr1 1962911 1963173 + LINE/L1_L1MC4 chr1 1963174 1963483 + SINE/Alu_AluSx1 chr1 1963484 1963629 + LINE/L1_L1MC4 chr1 1964508 1964803 + SINE/Alu_AluSx1 chr1 1966838 1967134 - SINE/Alu_AluSx3 chr1 1967470 1967545 - LINE/L2_L2b chr1 1967882 1968195 + Simple_repeat_(TGAA)n chr1 1970454 1970500 + Low_complexity_G-rich chr1 1972112 1972253 - SINE/Alu_AluJb chr1 1972564 1972860 + SINE/Alu_AluSx chr1 1975177 1975263 + LTR/ERV1_MER31A chr1 1975709 1975798 - LINE/L1_L1MEd chr1 1975906 1976203 - LTR/ERVL-MaLR_MLT1A0 chr1 1976204 1976534 + LTR/ERVL-MaLR_MSTB chr1 1976535 1976842 - SINE/Alu_AluSx chr1 1976849 1977147 - SINE/Alu_AluY chr1 1977148 1977212 + LTR/ERVL-MaLR_MSTB chr1 1977213 1977268 - LTR/ERVL-MaLR_MLT1A0 chr1 1977727 1977824 + LTR/ERVL_ERVL-E-int chr1 1977840 1978146 - SINE/Alu_AluJb chr1 1978428 1978499 + LTR/ERVL_ERVL-E-int chr1 1978533 1978799 + LTR/ERVL-MaLR_MLT1D chr1 1979946 1980061 + LINE/L2_L2a chr1 1980800 1980834 + Simple_repeat_(ACCCCA)n chr1 1981143 1981387 + LTR/ERVL_ERVL-E-int chr1 1982104 1982184 + Simple_repeat_(ACAG)n chr1 1983142 1983370 + LINE/L1_L1MC4 chr1 1983603 1983684 + LINE/L1_L1MC4 chr1 1983686 1983988 - SINE/Alu_AluY chr1 1983990 1984293 - SINE/Alu_AluSx chr1 1984301 1984319 + Simple_repeat_(T)n chr1 1986103 1986337 + SINE/Alu_AluSg7 chr1 1987548 1987839 - SINE/Alu_AluSx4 chr1 1987962 1988133 - SINE/Alu_AluJb chr1 1988802 1988873 + Simple_repeat_(CCACCC)n chr1 1989071 1989156 + LTR/ERVL_LTR18B chr1 1989143 1989459 + LTR/ERVL_LTR18B chr1 1989493 1989760 - SINE/Alu_AluSz chr1 1989848 1990059 + DNA/hAT-Charlie_Charlie1b chr1 1990076 1990247 + DNA/hAT-Charlie_Charlie1b chr1 1990480 1990705 + LINE/L1_HAL1 chr1 1991033 1991193 + LINE/L1_L1MD chr1 1991194 1991612 + SINE/Alu_AluSx chr1 1991613 1991757 + LINE/L1_L1MD chr1 1991758 1992052 + SINE/Alu_AluY chr1 1992053 1992166 + LINE/L1_L1MD chr1 1992168 1992466 - SINE/Alu_AluJr chr1 1992478 1992773 - SINE/Alu_AluY chr1 1992919 1993225 + SINE/Alu_AluSx1 chr1 1993287 1993585 - SINE/Alu_AluJo chr1 1993702 1994011 + SINE/Alu_AluY chr1 1994012 1994193 + SINE/Alu_AluJb chr1 1994766 1995105 + LINE/L1_HAL1 chr1 1995219 1995511 + SINE/Alu_AluSz chr1 1995614 1995627 + Simple_repeat_(AAC)n chr1 1995628 1995935 + SINE/Alu_AluY chr1 1995936 1995970 + Simple_repeat_(AAC)n chr1 1995977 1996284 - SINE/Alu_AluSx chr1 1996639 1996950 + SINE/Alu_AluSx1 chr1 1996956 1997140 + LINE/L1_L1ME3B chr1 1997141 1997446 + SINE/Alu_AluSp chr1 1997447 1997720 + LINE/L1_L1M5 chr1 1997721 1997774 + LINE/L1_L1ME3B chr1 1997773 1997995 + LINE/L1_L1MC5a chr1 1997996 1998292 + SINE/Alu_AluSx1 chr1 1998293 1998579 + LINE/L1_L1MC5a chr1 1998595 1998906 + SINE/Alu_AluY chr1 1998907 1999534 + LINE/L1_L1ME3B chr1 1999535 1999832 + SINE/Alu_AluSz chr1 1999833 1999883 + LINE/L1_L1ME3B chr1 1999884 2000183 + SINE/Alu_AluSc8 chr1 2000184 2000353 + LINE/L1_L1ME3B chr1 2000471 2000851 - LTR/ERVL-MaLR_MLT1C2 chr1 2001318 2001625 - SINE/Alu_AluY chr1 2001626 2002260 + LINE/L1_L1MC4 chr1 2002398 2002682 + SINE/Alu_AluSz chr1 2002831 2002907 + LINE/L1_L1ME3B chr1 2002946 2003172 + LINE/L1_L1ME3B chr1 2003926 2004016 + Low_complexity_G-rich chr1 2004136 2004218 + DNA/hAT-Charlie_Charlie8 chr1 2004220 2004526 + SINE/Alu_AluSx1 chr1 2004590 2004667 + SINE/Alu_AluYk3 chr1 2004671 2004980 + SINE/Alu_AluSz chr1 2005627 2005901 + SINE/Alu_AluSx chr1 2005904 2005947 + Simple_repeat_(CT)n chr1 2005985 2006151 + LINE/L1_L1MC5a chr1 2006152 2006311 + SINE/Alu_FRAM chr1 2006316 2006611 + SINE/Alu_AluJb chr1 2006612 2006695 + LINE/L1_L1MC5a chr1 2006712 2006903 + LINE/L1_L1MC5a chr1 2006904 2007167 - SINE/Alu_AluSz chr1 2007171 2007325 - SINE/Alu_AluJb chr1 2007334 2007557 - SINE/Alu_AluYf1 chr1 2007567 2007658 + SINE/Alu_Alu chr1 2007727 2008033 + SINE/Alu_AluSx1 chr1 2008037 2008132 - DNA/hAT-Charlie_MER3 chr1 2008159 2008468 + LINE/L1_L1MC5a chr1 2008469 2008545 + SINE/Alu_AluJb chr1 2008546 2008840 + SINE/Alu_AluSg chr1 2008842 2009132 + SINE/Alu_AluSc chr1 2009133 2009292 + SINE/Alu_AluJb chr1 2009293 2009344 + LINE/L1_L1MC5a chr1 2009348 2009835 + LTR/ERVL_ERV3-16A3_LTR chr1 2009838 2010126 + LTR/ERVL_HERV16-int chr1 2010127 2010440 + SINE/Alu_AluY chr1 2010441 2010935 + LTR/ERVL_HERV16-int chr1 2010964 2011147 + LINE/L1_L1ME1 chr1 2011259 2011568 - SINE/Alu_AluSz chr1 2011603 2011833 + LINE/L1_L1MEc chr1 2011879 2012155 + LINE/L1_L1ME1 chr1 2012156 2012273 + SINE/Alu_AluJo chr1 2012280 2012313 + Simple_repeat_(AAAAT)n chr1 2012314 2012324 + LINE/L1_L1ME1 chr1 2012325 2012632 + SINE/Alu_AluSz chr1 2012633 2012791 + LINE/L1_L1ME1 chr1 2012792 2013104 + SINE/Alu_AluSx chr1 2013105 2013422 + LINE/L1_L1ME1 chr1 2013448 2013800 - LTR/ERVL-MaLR_MLT1F2 chr1 2013801 2014098 - SINE/Alu_AluSz chr1 2014099 2014365 - SINE/Alu_AluJb chr1 2014366 2014482 - LTR/ERVL-MaLR_MLT1F2 chr1 2016027 2016191 - SINE/MIR_MIR3 chr1 2016273 2016314 + Simple_repeat_(CACAGCCA)n chr1 2016542 2016854 + SINE/Alu_AluSq chr1 2016855 2016903 + Low_complexity_A-rich chr1 2017088 2017387 + SINE/Alu_AluJr chr1 2018057 2018334 + SINE/Alu_AluSx3 chr1 2018335 2018397 + Simple_repeat_(ATAAC)n chr1 2019496 2019542 + Low_complexity_G-rich chr1 2020732 2021036 + DNA/TcMar-Tigger_MER46C chr1 2023799 2024199 - LTR/ERVL-MaLR_MLT1C2 chr1 2024449 2024494 + Simple_repeat_(TCCTCT)n chr1 2025985 2026495 - LINE/L1_L1MB5 chr1 2026554 2026862 + SINE/Alu_AluY chr1 2026873 2027177 + SINE/Alu_AluJb chr1 2027179 2027263 + DNA/hAT-Charlie_Charlie8 chr1 2029058 2029084 + Simple_repeat_(GGCTG)n chr1 2029389 2029414 + Simple_repeat_(GGCCA)n chr1 2029727 2029790 + Simple_repeat_(AAGGCC)n chr1 2033460 2033527 - LTR/ERVL_LTR16E1 chr1 2033541 2033904 - LTR/ERVL-MaLR_MLT1C2 chr1 2033948 2033994 + Low_complexity_GA-rich chr1 2034121 2034247 - SINE/Alu_AluJo chr1 2034249 2034302 - LTR/ERVL_LTR16E1 chr1 2034400 2034709 - SINE/Alu_AluSc chr1 2034719 2035024 - SINE/Alu_AluSc chr1 2035564 2035833 - LTR/ERVL_ERVL-E-int chr1 2036675 2036944 + SINE/Alu_AluJr chr1 2036986 2037326 - LTR/ERVL-MaLR_MLT1A0 chr1 2037619 2038028 + LTR/ERVL-MaLR_MLT1C2 chr1 2038281 2038368 - LTR/ERVL_ERVL-E-int chr1 2038526 2039015 - LTR/ERVL_LTR16E1 chr1 2039119 2039312 + SINE/MIR_MIR chr1 2039341 2039490 + LTR/Gypsy_LTR81B chr1 2039509 2039735 + SINE/Alu_AluSx1 chr1 2040180 2040382 + SINE/Alu_AluSx chr1 2040397 2040700 + SINE/Alu_AluY chr1 2040701 2040800 + LTR/ERVL_LTR40a chr1 2040879 2041010 + LTR/Gypsy_LTR81 chr1 2041998 2042226 + Simple_repeat_(GT)n chr1 2042360 2042473 - LINE/L1_L1M5 chr1 2042485 2042793 + SINE/Alu_AluY chr1 2043551 2043684 + SINE/MIR_MIRb chr1 2043982 2044297 + Simple_repeat_(CA)n chr1 2044368 2044679 - SINE/Alu_AluJr chr1 2045229 2045262 + Simple_repeat_(CA)n chr1 2045263 2045569 + SINE/Alu_AluJb chr1 2045570 2045750 + Simple_repeat_(CA)n chr1 2048415 2048562 - LINE/L2_L2a chr1 2048711 2048881 - LINE/L2_L2a chr1 2048884 2049195 + SINE/Alu_AluSp chr1 2050070 2050131 - DNA/hAT-Charlie_MER5A chr1 2052425 2052521 + Simple_repeat_(CCTT)n chr1 2052537 2052611 + Simple_repeat_(CTCCCTC)n chr1 2053116 2053398 - SINE/Alu_AluSg chr1 2054942 2055240 - SINE/Alu_AluY chr1 2056716 2057027 - SINE/Alu_AluY chr1 2057584 2057708 + SINE/Alu_AluJr chr1 2057709 2057871 - SINE/Alu_AluSx chr1 2057872 2058166 - SINE/Alu_AluSc8 chr1 2058167 2058302 - SINE/Alu_AluSx chr1 2058303 2058488 + SINE/Alu_AluJr chr1 2058614 2058919 + SINE/Alu_AluSz chr1 2058975 2059094 - SINE/Alu_AluSz6 chr1 2060397 2060490 - LINE/L2_L2 chr1 2060794 2060807 + Low_complexity_G-rich chr1 2060808 2060848 + Simple_repeat_(AGGAGGCT)n chr1 2060849 2060864 + Low_complexity_G-rich chr1 2062182 2062436 - LINE/L1_L1MB5 chr1 2062432 2062482 - LINE/L1_L1MB3 chr1 2062483 2062787 - SINE/Alu_AluSz6 chr1 2062788 2063306 - LINE/L1_L1MB3 chr1 2063307 2063612 - SINE/Alu_AluJb chr1 2063613 2063841 - LINE/L1_L1MB3 chr1 2063842 2064145 - SINE/Alu_AluSx1 chr1 2064146 2065396 - LINE/L1_L1MB3 chr1 2065397 2065702 + SINE/Alu_AluY chr1 2065703 2066391 - LINE/L1_L1MB3 chr1 2066392 2066641 - SINE/Alu_AluSx chr1 2066642 2066774 - LINE/L1_L1M4 chr1 2066775 2066903 + SINE/Alu_AluJr chr1 2066905 2067339 - LINE/L1_L1M4 chr1 2068317 2068663 - LTR/ERVL-MaLR_MLT1C2 chr1 2069347 2069528 + SINE/MIR_MIR3 chr1 2071107 2071213 + SINE/MIR_MIR chr1 2071230 2071271 + SINE/MIR_MIR chr1 2071881 2072035 - DNA/hAT-Charlie_MER58B chr1 2072224 2072266 - SINE/MIR_MIR chr1 2072309 2072780 - LTR/ERVL_MER74A chr1 2074863 2074895 + Low_complexity_A-rich chr1 2076461 2076763 + SINE/Alu_AluSx1 chr1 2077629 2078844 - LINE/L1_HAL1 chr1 2078845 2079141 - SINE/Alu_AluY chr1 2079142 2079215 - LINE/L1_HAL1 chr1 2079266 2079471 - SINE/MIR_MIR chr1 2079461 2079614 + SINE/MIR_MIR chr1 2080698 2081322 - LINE/L1_L1MC3 chr1 2082512 2082686 - DNA/hAT-Blackjack_MER63A chr1 2083319 2083949 - DNA/hAT-Charlie_Charlie1 chr1 2083950 2084258 + SINE/Alu_AluSz chr1 2084259 2084315 - DNA/hAT-Charlie_Charlie1 chr1 2084322 2084451 - DNA/hAT-Charlie_Charlie1 chr1 2084724 2085028 + SINE/Alu_AluJr chr1 2086056 2086355 - SINE/Alu_AluSx1 chr1 2086356 2086405 - SINE/Alu_Alu chr1 2086410 2086645 - LINE/L1_L1MC4 chr1 2087081 2087382 - SINE/Alu_AluSx1 chr1 2088096 2088157 + SINE/MIR_MIRc chr1 2088401 2088481 + LINE/L2_L2a chr1 2089435 2089731 - LTR/ERVL-MaLR_MLT1A0 chr1 2089732 2089800 - LTR/ERVL-MaLR_MLT1B chr1 2089807 2089915 + SINE/Alu_FLAM_A chr1 2089909 2090243 - LTR/ERVL-MaLR_MLT1B chr1 2091032 2091336 - SINE/Alu_AluSq chr1 2091343 2091473 - LINE/L1_L1MB2 chr1 2091476 2091723 - LINE/L1_L1M3 chr1 2097759 2098300 - LTR/ERV1_LTR10A chr1 2098649 2098980 - SINE/Alu_AluY chr1 2101020 2101038 + Simple_repeat_(A)n chr1 2101482 2101538 + Simple_repeat_(TTCT)n chr1 2101512 2101540 + Simple_repeat_(TTCA)n chr1 2101559 2101761 + SINE/MIR_MIRb chr1 2102303 2102631 - SINE/Alu_AluY chr1 2102898 2103136 - SINE/Alu_AluJr chr1 2103151 2103206 + LINE/L2_L2c chr1 2103601 2103722 + SINE/Alu_AluJb chr1 2103942 2104305 - LTR/ERVL_LTR52 chr1 2105403 2105699 - SINE/Alu_AluSx1 chr1 2106279 2106361 - LINE/L1_L1MC5 chr1 2107438 2107582 - LINE/L1_L1MC5a chr1 2108760 2109191 - LTR/ERVL-MaLR_MLT1D chr1 2109384 2109492 + SINE/MIR_MIRc chr1 2112662 2112690 + Simple_repeat_(GTTT)n chr1 2112692 2112991 - SINE/Alu_AluSq2 chr1 2113619 2113704 + DNA/TcMar-Tc2_Kanga1 chr1 2114028 2114065 + Simple_repeat_(GTGTGG)n chr1 2114494 2114800 + SINE/Alu_AluSc chr1 2115428 2116169 - LTR/ERVL_MER21C chr1 2116312 2116561 - LINE/L1_L1MEg chr1 2116603 2116970 - LINE/L1_L1MEg chr1 2116971 2117249 - SINE/Alu_AluJb chr1 2117250 2117991 - LINE/L1_L1MEg chr1 2117992 2118296 - SINE/Alu_AluSx chr1 2118297 2118326 - LINE/L1_L1M5 chr1 2118337 2118636 - SINE/Alu_AluYk3 chr1 2118637 2118713 - LINE/L1_L1MEg chr1 2118714 2118755 + Simple_repeat_(TG)n chr1 2118756 2119202 - LINE/L1_L1MEg chr1 2119203 2119514 - SINE/Alu_AluSq2 chr1 2119515 2119772 - LINE/L1_L1MEg chr1 2119776 2119960 - SINE/Alu_AluSp chr1 2119968 2120293 - LINE/L1_L1MEg chr1 2120294 2120322 + Simple_repeat_(T)n chr1 2120323 2120595 - SINE/Alu_AluSq2 chr1 2120596 2120836 - LINE/L1_L1MEg chr1 2120837 2121112 - SINE/Alu_AluSx chr1 2121152 2121339 - LTR/ERVL-MaLR_MLT-int chr1 2121340 2121444 + LTR/ERVL-MaLR_MLT1A chr1 2124513 2124680 + LTR/ERVL-MaLR_MLT1A chr1 2125144 2125177 + Simple_repeat_(TTG)n chr1 2127188 2127350 + DNA/hAT-Charlie_Charlie16 chr1 2129302 2129664 + LINE/L1_L1MC3 chr1 2129891 2130205 - SINE/Alu_AluJb chr1 2130492 2130860 - LTR/ERVL_MLT2C1 chr1 2131323 2131460 + SINE/Alu_AluSx chr1 2131818 2132105 - SINE/Alu_AluY chr1 2132115 2132150 + Simple_repeat_(GTTT)n chr1 2132844 2132930 - LTR/ERVL-MaLR_MLT1B chr1 2132924 2132996 - LTR/ERVL-MaLR_MLT1B chr1 2136404 2136633 - LINE/L2_L2c chr1 2136795 2136898 - LTR/ERVL-MaLR_MLT1C2 chr1 2136899 2137477 - LTR/ERVL_MLT2B3 chr1 2137478 2137605 - LTR/ERVL-MaLR_MLT1C2 chr1 2138654 2138941 + SINE/Alu_AluSz chr1 2139308 2139616 + SINE/Alu_AluSg7 chr1 2140371 2140674 + SINE/Alu_AluSx1 chr1 2140687 2140959 + SINE/Alu_AluSx1 chr1 2141311 2141645 - SINE/Alu_AluSz chr1 2142733 2142776 - LINE/L2_L2a chr1 2143017 2143328 - SINE/Alu_AluY chr1 2145632 2145927 + SINE/Alu_AluJb chr1 2146841 2148571 + Simple_repeat_(TCCA)n chr1 2148634 2148770 - DNA/hAT-Charlie_MER5A chr1 2149579 2149867 + SINE/Alu_AluJb chr1 2149877 2150182 + SINE/Alu_AluY chr1 2151110 2151307 - DNA/TcMar-Tigger_Tigger3c chr1 2151670 2151819 - SINE/Alu_FRAM chr1 2151827 2152150 - SINE/Alu_AluJb chr1 2152189 2152250 - LINE/L1_L1MB5 chr1 2152251 2152546 + SINE/Alu_AluSx chr1 2152547 2153263 - LINE/L1_L1MB5 chr1 2153433 2153519 - DNA/hAT-Tip100_MER91A chr1 2154515 2154672 + SINE/Alu_AluJr chr1 2154877 2155012 + SINE/MIR_MIRb chr1 2155082 2155347 + Simple_repeat_(TGGTGA)n chr1 2155348 2155409 + Simple_repeat_(GGTGATGAC)n chr1 2155410 2155848 + Simple_repeat_(TGGTGA)n chr1 2156291 2156398 - LINE/L2_L2a chr1 2156579 2156875 - LTR/ERVL_MER68 chr1 2156876 2157339 - LTR/ERVL_MLT2B1 chr1 2157340 2157404 - LTR/ERVL_MER68 chr1 2157405 2157705 - SINE/Alu_AluSx1 chr1 2157706 2157716 - LTR/ERVL_MER68 chr1 2157717 2157841 - SINE/Alu_FLAM_C chr1 2157842 2157977 - LTR/ERVL_MER68 chr1 2158922 2159120 - SINE/Alu_AluYc chr1 2160233 2160272 + Simple_repeat_(GT)n chr1 2162068 2162131 - LINE/L1_L1M5 chr1 2162132 2162431 - SINE/Alu_AluSx1 chr1 2162432 2162615 - LINE/L1_L1M5 chr1 2162616 2162747 - SINE/Alu_FLAM_C chr1 2162748 2162783 - LINE/L1_L1M5 chr1 2163551 2163608 - LINE/L1_L1M5 chr1 2163609 2163923 + SINE/Alu_AluYk2 chr1 2163924 2164177 - LINE/L1_L1M5 chr1 2164257 2164645 - LTR/ERVL_MER54A chr1 2165340 2165533 + SINE/MIR_MIR chr1 2165539 2165716 - DNA/hAT-Charlie_MER58A chr1 2166143 2166425 + SINE/Alu_AluJb chr1 2167596 2167905 - SINE/Alu_AluSx chr1 2168119 2168297 - DNA/hAT-Charlie_MER58A chr1 2169791 2169831 + Low_complexity_G-rich chr1 2170477 2171048 - LINE/L1_L1MB3 chr1 2171049 2171358 + SINE/Alu_AluY chr1 2171359 2171370 - LINE/L1_L1MB3 chr1 2171371 2171666 - SINE/Alu_AluSx chr1 2171667 2171762 - LINE/L1_L1MB3 chr1 2171821 2171895 - LINE/L1_L1MB3 chr1 2176072 2176390 + DNA/TcMar-Tigger_MER47B chr1 2176402 2176499 + DNA/TcMar-Tigger_Tigger5 chr1 2178094 2178516 - LINE/L1_L1MB7 chr1 2178520 2178612 - LINE/L1_L1M4 chr1 2180474 2180535 + Simple_repeat_(GACGCG)n chr1 2181084 2181122 + Simple_repeat_(CCCCA)n chr1 2181172 2181322 + LINE/L2_L2a chr1 2182202 2182225 + Simple_repeat_(TTG)n chr1 2182800 2182933 + Simple_repeat_(AGTG)n chr1 2182944 2183246 + SINE/Alu_AluSx1 chr1 2183247 2183402 + SINE/Alu_AluSx3 chr1 2183543 2183767 - LTR/ERVL-MaLR_MLT1A1 chr1 2183548 2183905 - LTR/ERVL-MaLR_MSTD chr1 2187293 2187603 - SINE/Alu_AluSx1 chr1 2187614 2188052 - LINE/L1_L1MC1 chr1 2188468 2188581 - LTR/ERVL-MaLR_MLT1A1 chr1 2188728 2189034 + SINE/Alu_AluY chr1 2189048 2189355 + SINE/Alu_AluJo chr1 2189990 2190082 + DNA/hAT-Charlie_MER5A chr1 2191466 2191755 + SINE/Alu_AluSx chr1 2192636 2192899 - SINE/Alu_AluJr chr1 2192907 2192924 + Simple_repeat_(TTG)n chr1 2194655 2194696 + Simple_repeat_(CCGC)n chr1 2195398 2195459 + Simple_repeat_(CTGGCCC)n chr1 2196280 2196555 + SINE/Alu_AluJr chr1 2196558 2196867 + SINE/Alu_AluSz6 chr1 2197584 2197638 + Low_complexity_G-rich chr1 2198994 2199025 + SINE/tRNA-Deu_AmnSINE2 chr1 2199392 2199421 + Simple_repeat_(CCCAG)n chr1 2199627 2199797 + LTR/ERVL-MaLR_MLT1C2 chr1 2199798 2200105 + SINE/Alu_AluSx1 chr1 2200114 2200423 + SINE/Alu_AluSq2 chr1 2200424 2200663 + LTR/ERVL-MaLR_MLT1C2 chr1 2201438 2201735 + SINE/Alu_AluSx chr1 2201738 2202046 + SINE/Alu_AluYk3 chr1 2202341 2202448 - SINE/Alu_AluSx3 chr1 2202455 2202775 - SINE/Alu_AluJb chr1 2202776 2202883 - SINE/Alu_AluJr chr1 2203742 2203907 - SINE/MIR_MIRb chr1 2206552 2206848 + SINE/Alu_AluY chr1 2207328 2207517 - SINE/MIR_MIR3 chr1 2207910 2207955 + Simple_repeat_(GT)n chr1 2207957 2208032 + Simple_repeat_(CGTG)n chr1 2208344 2208379 + Simple_repeat_(CCCT)n chr1 2211217 2211399 - SINE/Alu_AluSq chr1 2211400 2211436 + Simple_repeat_(TA)n chr1 2211437 2211748 - SINE/Alu_AluY chr1 2211749 2211884 - SINE/Alu_AluSq chr1 2211901 2212201 - SINE/Alu_AluY chr1 2214368 2214430 + Simple_repeat_(GGCAG)n chr1 2216123 2216151 + Simple_repeat_(TGGCG)n chr1 2217030 2217336 + SINE/Alu_AluY chr1 2217350 2217648 + SINE/Alu_AluSx chr1 2217649 2217958 + SINE/Alu_AluYk3 chr1 2217992 2218242 - LINE/L1_L1M5 chr1 2218243 2218543 - SINE/Alu_AluSx chr1 2218544 2218572 - LINE/L1_L1M5 chr1 2218867 2219353 - LINE/L1_HAL1 chr1 2219559 2219878 + SINE/Alu_AluYk2 chr1 2219887 2220192 + SINE/Alu_AluSx1 chr1 2220194 2220498 + SINE/Alu_AluSc8 chr1 2220718 2220790 - LINE/L1_L1MC4a chr1 2220791 2221327 - LINE/L1_L1MC4 chr1 2221333 2221365 + Simple_repeat_(CCTC)n chr1 2221368 2221529 - SINE/Alu_AluJr chr1 2221530 2221839 - SINE/Alu_AluJo chr1 2221882 2222100 - LINE/L1_L1MC4a chr1 2222101 2222408 - SINE/Alu_AluSp chr1 2222409 2222443 - LINE/L1_L1MC4a chr1 2222444 2222740 + SINE/Alu_AluSc8 chr1 2222741 2223031 - LINE/L1_L1MC4a chr1 2223082 2223263 + LTR/ERVL_LTR16C chr1 2223265 2223391 + SINE/Alu_FLAM_C chr1 2223402 2223711 + SINE/Alu_AluSx chr1 2223722 2223767 + LTR/ERVL_LTR16C chr1 2223791 2224098 + SINE/Alu_AluSq2 chr1 2224250 2224284 + Simple_repeat_(GCAGG)n chr1 2224345 2224619 + LINE/L2_L2b chr1 2224835 2224864 + Low_complexity_G-rich chr1 2225461 2225767 - SINE/Alu_AluSz chr1 2225779 2226069 - SINE/Alu_AluY chr1 2226071 2226387 - SINE/Alu_AluJr chr1 2227254 2227297 + Low_complexity_G-rich chr1 2228934 2229017 + Simple_repeat_(CGC)n chr1 2230964 2230993 + Simple_repeat_(TG)n chr1 2232471 2232515 + Simple_repeat_(CCTGTG)n chr1 2232710 2232850 + SINE/MIR_MIR chr1 2235031 2235343 - SINE/Alu_AluSz chr1 2236414 2236704 - SINE/Alu_AluSc8 chr1 2237407 2237449 - LINE/L2_L2 chr1 2241259 2241396 - LINE/L1_L1MC5a chr1 2241397 2241692 - SINE/Alu_AluYk2 chr1 2241693 2241882 - LINE/L1_L1MC5a chr1 2241946 2242017 + Simple_repeat_(TG)n chr1 2242517 2242807 - SINE/Alu_AluJb chr1 2243956 2244263 - SINE/Alu_AluY chr1 2244296 2244603 + SINE/Alu_AluSz chr1 2244800 2245026 + LINE/L1_L1M3 chr1 2245045 2245466 - LINE/L1_L1MB3 chr1 2245467 2245775 - SINE/Alu_AluSx chr1 2245789 2246095 - SINE/Alu_AluSz chr1 2246096 2246416 - LINE/L1_L1MB3 chr1 2246941 2247239 + SINE/Alu_AluSx4 chr1 2249914 2250205 - SINE/Alu_AluSx1 chr1 2251289 2251417 - SINE/Alu_AluJo chr1 2252109 2252125 + Simple_repeat_(GAG)n chr1 2252813 2253111 + SINE/Alu_AluSz6 chr1 2254727 2254750 + Simple_repeat_(TG)n chr1 2255259 2255340 + LINE/RTE-BovB_MamRTE1 chr1 2257719 2258037 - SINE/Alu_AluSp chr1 2258498 2258519 + Simple_repeat_(T)n chr1 2258552 2258845 - SINE/Alu_AluSx chr1 2259019 2259120 - DNA/hAT-Charlie_MER5A chr1 2259140 2259326 + DNA/hAT-Charlie_MER58A chr1 2259539 2260406 - LINE/L1_L1MC4 chr1 2260275 2260529 - LINE/L1_L1MD chr1 2260530 2260838 - SINE/Alu_AluJb chr1 2260839 2261814 - LINE/L1_L1MD chr1 2262558 2262893 - LINE/L1_L1MC4 chr1 2262894 2263200 - SINE/Alu_AluSz chr1 2263201 2263234 - LINE/L1_L1MC4 chr1 2263235 2263533 - SINE/Alu_AluSx chr1 2263534 2263688 - LINE/L1_L1MC4 chr1 2263717 2263884 + SINE/Alu_AluJb chr1 2263894 2264025 + SINE/Alu_FLAM_C chr1 2264152 2264234 - LINE/L1_L1MC4 chr1 2264277 2264568 - SINE/Alu_AluSc8 chr1 2264571 2264701 - SINE/Alu_AluSz chr1 2264707 2264799 - LINE/L1_L1MC4 chr1 2264800 2265110 - SINE/Alu_AluSx1 chr1 2265111 2265161 - LINE/L1_L1MC4 chr1 2265281 2265684 - LINE/L1_L1MC4 chr1 2265685 2265994 + SINE/Alu_AluSz chr1 2265995 2266671 - LINE/L1_L1MC4 chr1 2267772 2267809 + Simple_repeat_(CACACGGT)n chr1 2268863 2268919 + Simple_repeat_(CCCTTTC)n chr1 2268937 2269246 - SINE/Alu_AluJr chr1 2272819 2272880 + Simple_repeat_(CCCCG)n chr1 2275515 2275804 - SINE/Alu_AluY chr1 2277181 2277284 - SINE/Alu_AluSz6 chr1 2277285 2277630 + LTR/ERVL-MaLR_THE1A chr1 2277799 2277840 + Simple_repeat_(CACT)n chr1 2278567 2278716 - LTR/ERVL-MaLR_MLT1A0 chr1 2279575 2279647 + Low_complexity_G-rich chr1 2279718 2279852 - SINE/Alu_FRAM chr1 2279945 2279986 - DNA/hAT-Charlie_MER5A chr1 2280087 2280388 + SINE/Alu_AluSg4 chr1 2285231 2285538 + SINE/Alu_AluSq2 chr1 2285554 2285860 - SINE/Alu_AluSx1 chr1 2285871 2286171 - SINE/Alu_AluSx4 chr1 2286172 2286219 - DNA/hAT-Tip100_MER45B chr1 2286220 2286511 + SINE/Alu_AluJb chr1 2286512 2286654 - DNA/hAT-Tip100_MER45B chr1 2286832 2286982 - DNA/hAT-Tip100_MER45B chr1 2287151 2287193 + Simple_repeat_(GCCCTGG)n chr1 2287339 2287553 - SINE/Alu_AluY chr1 2287880 2287916 + Simple_repeat_(CCCTGAG)n chr1 2287976 2288285 - SINE/Alu_AluSp chr1 2288286 2288420 - SINE/Alu_AluSp chr1 2290661 2290685 + Simple_repeat_(AGG)n chr1 2292414 2292466 - LINE/L2_L2c chr1 2295377 2296110 - LINE/L1_L1MB7 chr1 2296111 2296408 + SINE/Alu_AluSx3 chr1 2296409 2296785 - LINE/L1_L1MB7 chr1 2297115 2297270 - SINE/Alu_FRAM chr1 2304296 2304339 + Simple_repeat_(CCTCGT)n chr1 2305130 2305158 + Simple_repeat_(CA)n chr1 2312560 2312623 + Simple_repeat_(CCCAGG)n chr1 2314416 2314452 + Simple_repeat_(GGCCCT)n chr1 2314825 2314863 + Simple_repeat_(CCCCCG)n chr1 2315270 2315295 + Simple_repeat_(GGGGGC)n chr1 2316828 2316867 + Simple_repeat_(CAGGC)n chr1 2317155 2317229 + Simple_repeat_(TC)n chr1 2317438 2317471 + Low_complexity_G-rich chr1 2320342 2320374 + Simple_repeat_(GGGGCT)n chr1 2321632 2321678 + Simple_repeat_(CTGCCCA)n chr1 2324860 2324904 + Low_complexity_G-rich chr1 2325081 2325240 + Simple_repeat_(TCTT)n chr1 2325242 2325305 + Simple_repeat_(CT)n chr1 2325306 2325623 - SINE/Alu_AluJr chr1 2325643 2325923 - SINE/Alu_AluJo chr1 2333402 2333432 + Simple_repeat_(GTGA)n chr1 2333610 2333645 + Simple_repeat_(GCTGAG)n chr1 2339469 2339780 + LINE/L2_L2 chr1 2340457 2340705 + LINE/L2_L2 chr1 2341414 2341719 + SINE/Alu_AluSc chr1 2342635 2342671 + Simple_repeat_(CCTCA)n chr1 2342810 2342823 - SINE/Alu_AluSx chr1 2342824 2342916 + Simple_repeat_(TATTT)n chr1 2342917 2343031 - SINE/Alu_FLAM_C chr1 2343942 2344010 + Low_complexity_G-rich chr1 2345830 2345855 + Simple_repeat_(CA)n chr1 2346284 2346351 + SINE/Alu_AluJr4 chr1 2346376 2346681 - SINE/Alu_AluSx1 chr1 2348074 2348304 - LINE/L1_L1MC4 chr1 2348452 2348504 - LINE/L1_L1MC5a chr1 2349237 2349309 + LTR/Gypsy_LTR81C chr1 2352203 2352368 + LTR/ERVL-MaLR_MLT1J chr1 2354003 2354151 + SINE/Alu_AluSx chr1 2354303 2354557 + SINE/Alu_AluJb chr1 2354718 2354781 - SINE/MIR_MIR chr1 2359262 2359324 + LINE/L2_L2b chr1 2359601 2359912 + SINE/Alu_AluSx chr1 2360303 2361275 + LINE/L1_L1MB3 chr1 2361276 2361590 + SINE/Alu_AluSx chr1 2361591 2361707 + LINE/L1_L1MB3 chr1 2361708 2361953 - DNA/TcMar-Tigger_Tigger2a chr1 2361954 2362246 + SINE/Alu_AluSx4 chr1 2362247 2362438 - DNA/TcMar-Tigger_Tigger2a chr1 2362439 2362639 + LINE/L1_L1MB3 chr1 2362640 2362883 + SINE/Alu_AluSx1 chr1 2362884 2363262 + LINE/L1_L1MB3 chr1 2363281 2363310 + Simple_repeat_(AAAG)n chr1 2363414 2363474 + LINE/L1_L1MB3 chr1 2363475 2363796 + SINE/Alu_AluJr chr1 2363797 2364147 + LINE/L1_L1MB3 chr1 2363937 2364329 + LINE/L1_L1MD chr1 2364327 2365707 - LINE/L1_L1PA3 chr1 2365700 2366486 + LINE/L1_L1PA3 chr1 2366483 2366518 + LINE/L1_L1MB3 chr1 2366519 2366834 - SINE/Alu_AluSz chr1 2366835 2366921 + LINE/L1_L1MB3 chr1 2367106 2367281 + LINE/L1_L1MB3 chr1 2367307 2367337 + Low_complexity_A-rich chr1 2367338 2367486 + SINE/Alu_FLAM_C chr1 2367493 2367647 + LINE/L1_L1MB3 chr1 2367648 2367928 - SINE/Alu_AluSx1 chr1 2367929 2368190 + LINE/L1_L1M4 chr1 2368191 2368634 - LTR/ERV1_MER66B chr1 2368635 2368782 + LINE/L1_L1M4 chr1 2368783 2369069 + SINE/Alu_AluSc chr1 2369070 2369380 + SINE/Alu_AluY chr1 2369381 2369452 + LINE/L1_L1M4 chr1 2369471 2370650 + LINE/L1_L1M4 chr1 2370662 2370922 - SINE/Alu_AluJb chr1 2370924 2371210 + SINE/Alu_AluY chr1 2371211 2371241 + Simple_repeat_(AAC)n chr1 2371242 2371254 + SINE/Alu_AluY chr1 2371256 2371331 - SINE/Alu_AluJb chr1 2371332 2371434 + LINE/L1_L1MB3 chr1 2371346 2371629 + LINE/L1_L1MB7 chr1 2371630 2371925 + SINE/Alu_AluSp chr1 2371926 2372164 + LINE/L1_L1MB7 chr1 2372222 2372297 + Simple_repeat_(CA)n chr1 2379703 2380356 + LINE/L1_L1MB3 chr1 2380358 2380487 + SINE/Alu_AluJo chr1 2380542 2380855 - SINE/Alu_AluJb chr1 2382310 2382441 + SINE/MIR_MIRb chr1 2383092 2383334 + LTR/ERVL_LTR41C chr1 2383347 2383438 + LTR/ERVL_LTR41C chr1 2383456 2383681 - LTR/ERVL_ERVL-E-int chr1 2384197 2384320 + LINE/L2_L2 chr1 2384393 2384801 + LTR/ERV1_MER34C_ chr1 2386421 2386728 - SINE/Alu_AluSx chr1 2386888 2386951 + LTR/ERVL-MaLR_MLT1H2 chr1 2387018 2387198 + LTR/ERVL-MaLR_MLT1H2 chr1 2388580 2388804 + SINE/Alu_AluJb chr1 2388830 2389142 + SINE/Alu_AluSq2 chr1 2389270 2389566 - SINE/Alu_AluSz chr1 2390751 2391025 - SINE/Alu_AluSx chr1 2392124 2392164 + Low_complexity_G-rich chr1 2392206 2392239 + Low_complexity_G-rich chr1 2393224 2393499 - LINE/L2_L2a chr1 2393539 2393763 - SINE/MIR_MIRc chr1 2393802 2393845 + Simple_repeat_(TG)n chr1 2394445 2394618 + SINE/MIR_MIRb chr1 2394635 2394777 - SINE/MIR_MIRb chr1 2395154 2395244 - SINE/MIR_MIRc chr1 2395433 2395462 - DNA/hAT-Ac_OldhAT1 chr1 2396020 2396168 - DNA/hAT-Charlie_MER58C chr1 2396665 2396735 + Low_complexity_G-rich chr1 2397440 2397491 + Simple_repeat_(CCTGG)n chr1 2397977 2398004 + Simple_repeat_(TTTA)n chr1 2398384 2398687 - SINE/Alu_AluSx chr1 2399666 2399853 - SINE/MIR_MIR chr1 2400367 2400406 + SINE/MIR_MIR1_Amn chr1 2401197 2401429 + Simple_repeat_(CCTCCCT)n chr1 2406008 2406057 + Low_complexity_G-rich chr1 2407602 2407704 - LINE/L2_L2 chr1 2407861 2407983 - LINE/L2_L2 chr1 2408015 2408049 + Simple_repeat_(GGCGAGG)n chr1 2409131 2409595 + LINE/L2_L2a chr1 2409675 2409782 + LINE/L2_L2a chr1 2410516 2410581 + LINE/L2_L2d2 chr1 2410753 2410941 + SINE/MIR_MIR chr1 2410986 2411033 + LINE/L2_L2 chr1 2411231 2411531 - SINE/Alu_AluSz chr1 2411533 2411818 - SINE/Alu_AluJo chr1 2412373 2412428 + Simple_repeat_(GCCGCGC)n chr1 2412623 2412808 + Low_complexity_G-rich chr1 2414971 2415273 - SINE/Alu_AluY chr1 2415597 2415692 + SINE/MIR_MIR chr1 2415732 2415830 - DNA/hAT-Charlie_MER5A1 chr1 2417272 2417313 - LINE/L2_L2b chr1 2417755 2417937 + SINE/MIR_MIR chr1 2418185 2418275 - DNA/hAT-Charlie_Charlie15b chr1 2420608 2420649 - SINE/MIR_MIRc chr1 2420924 2420954 - DNA/hAT-Charlie_Charlie1b chr1 2420955 2421244 - SINE/Alu_AluSx chr1 2421245 2421289 - DNA/hAT-Charlie_Charlie1b chr1 2421308 2421600 + DNA/hAT-Blackjack_MER63D chr1 2421645 2421691 - DNA/hAT-Charlie_Charlie1a chr1 2421692 2421988 + SINE/Alu_AluSx chr1 2421989 2422292 + SINE/Alu_AluSx1 chr1 2422293 2422675 - DNA/hAT-Charlie_Charlie1a chr1 2422676 2422841 - SINE/Alu_FRAM chr1 2422842 2423137 - DNA/hAT-Charlie_Charlie1a chr1 2423165 2423203 + Simple_repeat_(GTTT)n chr1 2423204 2423351 - SINE/Alu_FRAM chr1 2423356 2423399 - SINE/Alu_AluJr chr1 2423400 2423980 - LTR/ERV1_LTR26 chr1 2423981 2424025 - SINE/Alu_AluJr chr1 2424062 2424568 - LINE/L1_L1MA7 chr1 2424569 2424705 + SINE/Alu_AluSx1 chr1 2424706 2425013 + SINE/Alu_AluY chr1 2425014 2425183 + SINE/Alu_AluSx1 chr1 2425184 2425202 - LINE/L1_L1MA7 chr1 2425215 2425343 + Simple_repeat_(CA)n chr1 2425344 2425522 - LINE/L1_L1MA7 chr1 2425530 2425821 - SINE/Alu_AluSz chr1 2426030 2426132 - DNA/hAT-Charlie_Charlie1a chr1 2426222 2426297 - DNA/hAT-Charlie_Charlie1b chr1 2426965 2427018 + Simple_repeat_(TGGCAGAG)n chr1 2428028 2428065 + Simple_repeat_(CCTGGG)n chr1 2429045 2429096 + Low_complexity_G-rich chr1 2431133 2431162 + Simple_repeat_(TG)n chr1 2431245 2431371 + Simple_repeat_(GT)n chr1 2431791 2431837 + Low_complexity_G-rich chr1 2433669 2433758 - LINE/L1_L1ME4a chr1 2435455 2435492 + Simple_repeat_(GGCTGGA)n chr1 2437678 2437875 + Simple_repeat_(AC)n chr1 2439133 2439217 + SINE/MIR_MIR3 chr1 2442208 2442344 - SINE/MIR_MIR3 chr1 2443491 2443534 + Simple_repeat_(GGAGGC)n chr1 2443841 2443922 + Simple_repeat_(GCCCCC)n chr1 2444109 2444159 + Simple_repeat_(GGCACG)n chr1 2448196 2448623 - LTR/ERVL-MaLR_MLT1C2 chr1 2449209 2449499 + SINE/Alu_AluSx1 chr1 2449500 2449536 + Simple_repeat_(AACC)n chr1 2449644 2449725 + SINE/MIR_MIRb chr1 2450803 2450934 + SINE/MIR_MIR chr1 2452426 2452476 + Simple_repeat_(CCTG)n chr1 2456663 2456726 + SINE/MIR_MIR3 chr1 2457880 2457896 + Simple_repeat_(A)n chr1 2460288 2460394 + LINE/L2_L2b chr1 2460749 2460890 + SINE/MIR_MIRb chr1 2461224 2461257 + Simple_repeat_(GCAGG)n chr1 2463236 2463344 + SINE/MIR_MIR3 chr1 2463816 2463971 - SINE/MIR_MIR3 chr1 2465527 2465607 + Simple_repeat_(CCTGCCG)n chr1 2466580 2466603 + Simple_repeat_(GCCT)n chr1 2467181 2467202 + Simple_repeat_(GGGA)n chr1 2468741 2468926 + SINE/MIR_MIRb chr1 2473203 2473265 - SINE/MIR_MIR1_Amn chr1 2476233 2476286 + Low_complexity_G-rich chr1 2476450 2476482 + Low_complexity_GA-rich chr1 2477677 2477735 + Low_complexity_G-rich chr1 2478280 2478314 + Simple_repeat_(TGGCCC)n chr1 2478312 2478353 + Simple_repeat_(GGCGG)n chr1 2481005 2481115 + Simple_repeat_(CACATA)n chr1 2481143 2481184 + Simple_repeat_(CCAGCTCCTG)n chr1 2483571 2483604 + Simple_repeat_(CCCTG)n chr1 2486428 2486468 + Low_complexity_G-rich chr1 2488164 2488204 + Simple_repeat_(GCAAGAA)n chr1 2491011 2491096 + SINE/MIR_MIRc chr1 2492496 2492556 - LINE/L2_L2d chr1 2494687 2494742 + Simple_repeat_(CTCCC)n chr1 2495738 2495790 + Simple_repeat_(GGCTGGT)n chr1 2499433 2499501 - LINE/L2_L2d2 chr1 2501464 2501560 - LINE/L2_L2b chr1 2503834 2503916 + Simple_repeat_(CCTC)n chr1 2506883 2506925 + Low_complexity_G-rich chr1 2508597 2508630 + Simple_repeat_(TA)n chr1 2509792 2509858 + Low_complexity_G-rich chr1 2516793 2517009 + SINE/MIR_MIRc chr1 2518441 2518481 + Simple_repeat_(GCCCAG)n chr1 2519379 2519466 + Low_complexity_GA-rich chr1 2525516 2525721 + SINE/MIR_MIR chr1 2526358 2526451 + Simple_repeat_(GCCCGCC)n chr1 2527054 2527120 - DNA/hAT-Blackjack_MER81 chr1 2529486 2529610 + Simple_repeat_(GGCGGC)n chr1 2529732 2529811 + Simple_repeat_(CGG)n chr1 2529928 2529974 + Simple_repeat_(CTG)n chr1 2530248 2530337 + Simple_repeat_(CCGGCG)n chr1 2530594 2530638 + Simple_repeat_(CCCCT)n chr1 2533052 2533098 + Simple_repeat_(GGAGGCT)n chr1 2533376 2533479 - LINE/L1_L1MB3 chr1 2533718 2534333 + LINE/L1_L1MB3 chr1 2535101 2535247 - SINE/MIR_MIRb chr1 2535256 2535379 - SINE/MIR_MIR chr1 2536312 2536369 + Simple_repeat_(CA)n chr1 2536370 2536386 + Simple_repeat_(CATA)n chr1 2536642 2536946 - SINE/Alu_AluSq2 chr1 2536970 2537200 + Simple_repeat_(TG)n chr1 2537442 2537529 - SINE/MIR_MIRb chr1 2539008 2539100 - SINE/MIR_MIRb chr1 2539556 2539719 + SINE/MIR_MIRc chr1 2540576 2540614 + SINE/MIR_MIR3 chr1 2541261 2541402 + SINE/MIR_MIR chr1 2541474 2541585 - SINE/Alu_FLAM_A chr1 2541605 2541704 + DNA/hAT-Charlie_MER58B chr1 2542659 2542702 + Simple_repeat_(TGGCCC)n chr1 2544227 2544345 + SINE/MIR_MIRb chr1 2545594 2545682 + SINE/MIR_MIRb chr1 2546203 2546298 + Low_complexity_G-rich chr1 2546378 2546431 + Simple_repeat_(CGC)n chr1 2547735 2547797 - SINE/MIR_MIRc chr1 2548533 2548840 + SINE/Alu_AluY chr1 2549142 2549196 + LINE/L2_L2c chr1 2549204 2549271 + LINE/L2_L2b chr1 2549835 2550061 - SINE/Alu_AluSx1 chr1 2550268 2550387 - LINE/L1_L1ME2 chr1 2550717 2550897 + LTR/ERVL-MaLR_MLT1B chr1 2550900 2551364 - LINE/L1_L1ME2 chr1 2551904 2552043 - LINE/L1_L1MEc chr1 2552736 2552856 + SINE/MIR_MIR chr1 2553558 2553610 + Simple_repeat_(CCT)n chr1 2554446 2554562 - LINE/L2_L2b chr1 2555526 2555570 + Low_complexity_G-rich chr1 2556549 2556573 + Simple_repeat_(TTCTCT)n chr1 2561405 2561489 + Simple_repeat_(CTCTCCC)n chr1 2562206 2562231 + Simple_repeat_(CATT)n chr1 2562241 2562286 + Simple_repeat_(CCTGG)n chr1 2562905 2562957 + Simple_repeat_(CCCCCTC)n chr1 2563756 2563837 - LINE/L1_L1MC4a chr1 2564021 2564076 - LINE/L1_L1MC4 chr1 2565288 2565343 - SINE/MIR_MIRb chr1 2565572 2565645 - LTR/ERVL-MaLR_MLT1A0 chr1 2565646 2565941 + SINE/Alu_AluSx1 chr1 2565942 2566161 - LTR/ERVL-MaLR_MLT1A0 chr1 2566826 2566869 + Simple_repeat_(ATTT)n chr1 2566870 2567149 - SINE/Alu_AluSg4 chr1 2567154 2567250 - SINE/MIR_MIR3 chr1 2568070 2568122 + Low_complexity_GA-rich chr1 2568824 2568944 - SINE/MIR_MIR chr1 2569621 2569778 + LINE/L1_L1ME1 chr1 2570397 2570749 - LTR/ERVL-MaLR_MLT1B chr1 2570759 2571056 + SINE/Alu_AluJb chr1 2571062 2571593 + LINE/L1_L1ME1 chr1 2571600 2571726 + SINE/Alu_FLAM_A chr1 2571727 2571744 + LINE/L1_L1ME1 chr1 2571745 2571995 + SINE/Alu_AluSz chr1 2571996 2572129 + LINE/L1_L1ME1 chr1 2572130 2572430 - SINE/Alu_AluSx chr1 2572431 2573050 + LINE/L1_L1ME1 chr1 2573051 2573183 + scRNA_BC200 chr1 2573351 2574456 + LINE/L1_L1ME1 chr1 2574469 2575155 + LINE/L1_L1ME1 chr1 2575163 2575518 - LTR/ERVL-MaLR_MLT1B chr1 2576001 2576033 + Simple_repeat_(CCCAG)n chr1 2576130 2576321 - LTR/ERVL-MaLR_MLT1B chr1 2576905 2577024 + LINE/L1_L1MB4 chr1 2577231 2577523 + SINE/Alu_AluSq2 chr1 2578141 2578192 - LINE/L2_L2a chr1 2578696 2578774 + SINE/Alu_AluY chr1 2578775 2578832 + SINE/Alu_AluY chr1 2578839 2579270 + LINE/L1_L1MB3 chr1 2579271 2579575 - SINE/Alu_AluSx1 chr1 2579576 2580250 + LINE/L1_L1MB3 chr1 2580260 2580561 + SINE/Alu_AluSz chr1 2580706 2580740 + Low_complexity_G-rich chr1 2580847 2581003 - SINE/MIR_MIRc chr1 2581155 2581462 + SINE/Alu_AluSx chr1 2581466 2581930 + LINE/L1_L1MEg chr1 2581931 2582242 + SINE/Alu_AluSq2 chr1 2582243 2582288 + LINE/L1_L1MEg chr1 2582327 2582729 - LTR/ERV1_MER65A chr1 2582751 2582875 + LTR/ERVL-MaLR_MLT1A0 chr1 2582876 2583184 - SINE/Alu_AluSx chr1 2583185 2583386 + LTR/ERVL-MaLR_MLT1A0 chr1 2583533 2583965 + LTR/ERVL_LTR40c chr1 2583979 2584292 + LTR/ERVL-MaLR_MLT1I chr1 2584459 2584709 + LTR/ERV1_MER34 chr1 2584733 2584930 + LTR/ERV1_MER34 chr1 2585490 2585529 + Simple_repeat_(GCTCTGG)n chr1 2586189 2586232 + Simple_repeat_(CCCTCTA)n chr1 2586798 2586895 + Simple_repeat_(GAGCCGG)n chr1 2587017 2587065 + Low_complexity_G-rich chr1 2591111 2591488 + LTR/ERV1_LTR19A chr1 2593982 2594096 - SINE/MIR_MIRb chr1 2595016 2595220 + SINE/MIR_MIRb chr1 2598795 2598838 + Low_complexity_GA-rich chr1 2598877 2599575 + LINE/L1_L1M5 chr1 2599576 2599884 + SINE/Alu_AluSx chr1 2599885 2599965 + LINE/L1_L1M5 chr1 2599966 2600267 - SINE/Alu_AluSx chr1 2600268 2600416 + LINE/L1_L1M5 chr1 2600417 2600709 + SINE/Alu_AluJb chr1 2600710 2602019 + LINE/L1_L1M5 chr1 2602431 2602748 + LINE/L2_L2d2 chr1 2604101 2604142 + Simple_repeat_(CCCCGC)n chr1 2604926 2604992 + LINE/L2_L2d2 chr1 2605798 2605852 + Simple_repeat_(GGTGG)n chr1 2606114 2606155 + Simple_repeat_(CAGGAGTCC)n chr1 2607613 2608042 - LTR/ERV1_MER89 chr1 2608419 2608975 + Simple_repeat_(CA)n chr1 2609035 2609081 + Simple_repeat_(AC)n chr1 2610019 2610038 + SINE/MIR_MIRc chr1 2610039 2610326 - SINE/Alu_AluJr chr1 2610327 2610453 + SINE/MIR_MIRb chr1 2610906 2611024 - DNA/hAT-Charlie_Charlie22a chr1 2611051 2611089 + Simple_repeat_(GCTGGAG)n chr1 2613069 2613188 - LINE/L2_L2c chr1 2613235 2613353 + LINE/L1_L1MEf chr1 2613461 2613681 + LINE/L1_L1MCc chr1 2613710 2613880 + SINE/Alu_AluJb chr1 2613894 2615756 + LINE/L1_L1MC3 chr1 2615763 2616054 + LINE/L1_L1MEf chr1 2616055 2616372 + SINE/Alu_AluJb chr1 2616373 2616695 + LINE/L1_L1MEf chr1 2616890 2616952 + SINE/5S-Deu-L2_AmnSINE1 chr1 2616984 2617054 - SINE/MIR_MIRb chr1 2617055 2617145 + SINE/Alu_AluSz6 chr1 2617146 2617450 + SINE/Alu_AluY chr1 2617451 2617610 + SINE/Alu_AluSz6 chr1 2617611 2617754 - SINE/MIR_MIRb chr1 2618089 2618459 + LTR/ERVL-MaLR_MSTB1 chr1 2618548 2618891 + LINE/L1_L1MEd chr1 2618925 2619262 + LTR/ERVL-MaLR_MLT1H chr1 2619332 2619375 + LINE/L1_L1MEd chr1 2619376 2619681 + SINE/Alu_AluSz chr1 2619682 2620404 + LINE/L1_L1MEd chr1 2620539 2620737 + LTR/ERVL-MaLR_MLT1A chr1 2620815 2621003 + LTR/ERV1_MER4B chr1 2621004 2621302 + SINE/Alu_AluJr chr1 2621303 2621572 + LTR/ERV1_MER4B chr1 2621573 2621861 - SINE/Alu_AluSp chr1 2621862 2622049 + LTR/ERV1_MER4B chr1 2622042 2622173 + LTR/ERVL-MaLR_MLT1A chr1 2622177 2622247 + DNA/hAT-Charlie_MER5A chr1 2622256 2622416 - DNA/hAT-Charlie_MER5A1 chr1 2622423 2622602 + LINE/L1_L1MEd chr1 2622603 2622903 + SINE/Alu_AluSx1 chr1 2622906 2623179 + SINE/Alu_AluSc chr1 2623180 2623325 + LINE/L1_L1MEd chr1 2623332 2623884 + LINE/L1_L1MCc chr1 2624008 2624458 + LINE/L1_L1MCc chr1 2624743 2625128 - LTR/ERVL-MaLR_MSTA chr1 2625129 2625291 - LTR/ERVL-MaLR_MSTA-int chr1 2625292 2626288 + LTR/ERVL_HERVL-int chr1 2626407 2626613 - DNA/hAT-Charlie_MER58A chr1 2626751 2627753 + LINE/L1_L1MEd chr1 2628952 2628996 + Low_complexity_G-rich chr1 2629029 2629057 + Simple_repeat_(GGCGGA)n chr1 2629263 2629326 + Simple_repeat_(GCACGG)n chr1 2629369 2629401 + Simple_repeat_(GCA)n chr1 2630262 2631097 + Simple_repeat_(GT)n chr1 2631411 2631551 + SINE/MIR_MIR3 chr1 2632370 2632398 + Simple_repeat_(TGAA)n chr1 2635950 2636317 - LTR/ERV1_MER4A1 chr1 2636331 2636351 + Simple_repeat_(T)n chr1 2636568 2636665 + LTR/ERVL_LTR41C chr1 2636733 2637427 - LTR/ERV1_LTR27C chr1 2637771 2638501 + LTR/ERVL_ERVL-E-int chr1 2638857 2639199 - LTR/ERVL-MaLR_MLT1A0 chr1 2644125 2644162 + Simple_repeat_(CCCAA)n chr1 2645148 2645243 - SINE/MIR_MIR3 chr1 2646133 2646398 - LINE/L1_L1MEf chr1 2646399 2646685 - SINE/Alu_AluJr chr1 2646686 2647342 - LINE/L1_L1MEf chr1 2647343 2647633 + SINE/Alu_AluSx3 chr1 2647634 2647649 - LINE/L1_L1MEf chr1 2647650 2647781 - SINE/Alu_FLAM_C chr1 2647782 2648441 - LINE/L1_L1MEf chr1 2649545 2649812 - SINE/Alu_AluSg chr1 2777601 2777654 + Simple_repeat_(GCCTGA)n chr1 2778728 2779316 - LINE/L1_L1MB3 chr1 2779317 2779621 - SINE/Alu_AluSc8 chr1 2779622 2779920 - LINE/L1_L1MB3 chr1 2779928 2780119 + SINE/Alu_AluSc8 chr1 2780120 2781029 - LINE/L1_L1M2 chr1 2781030 2781099 + DNA/TcMar-Tigger_Tigger2b_Pri chr1 2781100 2781132 - LINE/L1_L1M2 chr1 2781133 2781307 + DNA/TcMar-Tigger_MER8 chr1 2781308 2782756 - LINE/L1_L1M2 chr1 2782760 2782864 - SINE/MIR_MIR3 chr1 2783167 2783264 - DNA/hAT-Charlie_MER117 chr1 2784049 2784880 - LTR/ERVK_LTR13A chr1 2784961 2785019 + Simple_repeat_(GCTCTGGGCC)n chr1 2785034 2785152 - SINE/MIR_MIR chr1 2785583 2785630 + Simple_repeat_(CTCAGGT)n chr1 2788254 2788445 + Simple_repeat_(TG)n chr1 2788812 2789105 + SINE/Alu_AluSz chr1 2789377 2789455 + SINE/MIR_MIRc chr1 2790913 2791072 + SINE/MIR_MIR chr1 2791816 2792006 - LINE/L1_L1MEb chr1 2792007 2792268 - SINE/Alu_AluJb chr1 2792269 2793044 - LINE/L1_L1MEb chr1 2793079 2793173 - LINE/L1_L1ME3G chr1 2793175 2793861 - LINE/L1_L1M5 chr1 2793871 2793960 + Simple_repeat_(TCCTTC)n chr1 2793961 2794003 - LINE/L1_L1M5 chr1 2794004 2794297 - SINE/Alu_AluJb chr1 2794298 2794891 - LINE/L1_L1M5 chr1 2794989 2795050 - LTR/ERVL-MaLR_MLT1H2 chr1 2795054 2795259 + SINE/Alu_AluJb chr1 2795262 2795681 - LTR/ERVL-MaLR_MLT1H chr1 2795743 2795910 - LTR_LTR106_Mam chr1 2795911 2796246 + LTR/ERVL-MaLR_MLT1A1 chr1 2796253 2796387 - LTR_LTR106_Mam chr1 2796448 2796593 - LINE/L1_L1MEb chr1 2797552 2797636 - SINE/MIR_MIR chr1 2799172 2799261 + LINE/L2_L2d2 chr1 2799262 2799554 + SINE/Alu_AluSq2 chr1 2799555 2799949 + LINE/L2_L2d2 chr1 2801554 2801578 + Simple_repeat_(CA)n chr1 2806333 2806387 + Low_complexity_GA-rich chr1 2807199 2807535 - LTR/ERV1_MER31B chr1 2807690 2807832 - SINE/MIR_MIR chr1 2807825 2807878 + LINE/L2_L2 chr1 2807991 2808093 + LINE/L2_L2a chr1 2808950 2809026 + Low_complexity_G-rich chr1 2811550 2811590 + Low_complexity_G-rich chr1 2812109 2812229 + SINE/Alu_FLAM_C chr1 2812479 2812914 + LTR/ERVL-MaLR_MLT1D chr1 2813099 2814487 + LTR/ERV1_LTR12C chr1 2814637 2815105 - LTR/ERVL-MaLR_MLT1H-int chr1 2815170 2815465 - SINE/Alu_AluJb chr1 2815576 2815648 + Low_complexity_G-rich chr1 2816220 2816488 + SINE/Alu_AluJb chr1 2816493 2816921 - LTR/ERVL-MaLR_MLT1H chr1 2816972 2817460 + DNA/PiggyBac_MER75 chr1 2818632 2818673 + Simple_repeat_(TGTGCC)n chr1 2819003 2819045 - SINE/MIR_MIR1_Amn chr1 2819046 2819185 + DNA/hAT-Tip100_Arthur1A chr1 2819186 2819258 - SINE/MIR_MIR1_Amn chr1 2819299 2819843 + Simple_repeat_(AC)n chr1 2821951 2821993 + SINE/MIR_MIR1_Amn chr1 2823172 2823310 + LINE/L1_L1ME4a chr1 2823312 2823711 - LTR/ERVL-MaLR_MLT1D chr1 2823715 2824095 + LTR/ERVL_MLT2B4 chr1 2824104 2824159 - LTR/ERVL-MaLR_MLT1D chr1 2824395 2824762 - LTR/ERVL-MaLR_MSTA chr1 2824892 2824941 + Simple_repeat_(GT)n chr1 2825747 2826007 - LTR/ERVL-MaLR_MLT1B chr1 2827313 2827498 + DNA/hAT_MER53 chr1 2827714 2827887 + Low_complexity_G-rich chr1 2827928 2828439 - LTR/ERVK_MER9a1 chr1 2830477 2830589 + DNA/hAT-Charlie_MER3 chr1 2831742 2831805 - LINE/L2_L2b chr1 2831951 2832079 + DNA/hAT-Charlie_MER117 chr1 2832147 2832460 + LTR/ERVL-MaLR_MLT1A1 chr1 2834440 2834481 - LINE/L1_L1MB7 chr1 2834482 2834593 + DNA/TcMar-Tigger_Tigger17c chr1 2834602 2834686 + DNA/TcMar-Tigger_Tigger17d chr1 2835050 2835095 + Low_complexity_G-rich chr1 2835096 2835105 + Simple_repeat_(GAGGTGG)n chr1 2835946 2836170 + DNA/hAT-Charlie_Charlie25 chr1 2836172 2836467 + DNA/hAT-Charlie_Charlie25 chr1 2837122 2837652 - DNA/hAT-Charlie_MER1A chr1 2837771 2838348 - LINE/L1_L1ME3A chr1 2838349 2838657 - SINE/Alu_AluSx4 chr1 2838658 2839263 - LINE/L1_L1ME3A chr1 2839264 2839574 - SINE/Alu_AluSz chr1 2839575 2839853 - LINE/L1_L1ME3A chr1 2839855 2840414 - LTR/ERV1_MER51A chr1 2840476 2840825 + LTR/ERVL_LTR52 chr1 2840828 2840971 + Simple_repeat_(TG)n chr1 2840973 2841074 + LTR/ERVL_LTR52 chr1 2841078 2841495 - LINE/L1_L1ME3A chr1 2841540 2841618 + DNA/hAT-Tip100_MER96 chr1 2841619 2841739 - LINE/L1_L1ME3A chr1 2841740 2842100 + LTR/ERVL-MaLR_MLT1A0 chr1 2842101 2842333 - LINE/L1_L1ME3A chr1 2842357 2843002 - LINE/L1_L1ME3A chr1 2843019 2843225 - LTR/ERVL_LTR40b chr1 2843242 2843528 - SINE/Alu_AluY chr1 2843530 2843850 - LINE/L1_L1ME3A chr1 2843850 2843994 - LINE/L1_L1MB7 chr1 2844002 2844140 + SINE/Alu_AluSq4 chr1 2844141 2844572 - LINE/L1_L1MB7 chr1 2844553 2844715 - LINE/L1_L1ME3A chr1 2844717 2844893 - LINE/L1_L1ME3A chr1 2844894 2844957 - LTR/ERVL-MaLR_MLT1C2 chr1 2844958 2845230 + SINE/Alu_AluYc3 chr1 2845231 2845266 + Simple_repeat_(AAAAC)n chr1 2845267 2845681 - LTR/ERVL-MaLR_MLT1C2 chr1 2845682 2845967 - LINE/L1_L1ME3A chr1 2846296 2846392 + DNA/hAT-Charlie_Charlie25 chr1 2846393 2846703 + SINE/Alu_AluSz chr1 2846704 2846831 + DNA/hAT-Charlie_Charlie25 chr1 2846848 2847056 + SINE/MIR_MIR chr1 2847057 2847209 + DNA/hAT-Charlie_Charlie25 chr1 2847416 2847527 + DNA/hAT-Blackjack_MER81 chr1 2848719 2848812 + LINE/L1_L1M3 chr1 2849692 2849954 + LINE/L1_L1MB3 chr1 2849989 2850711 + DNA/TcMar-Tigger_Tigger7 chr1 2850719 2850791 + DNA/TcMar-Tigger_Tigger7 chr1 2850862 2851078 + DNA/TcMar-Tigger_MER44A chr1 2851084 2851481 + LINE/L1_L1MB3 chr1 2851491 2851819 - LINE/L1_L1ME2 chr1 2852982 2853303 - LINE/L1_L1ME2 chr1 2853352 2853617 + SINE/Alu_AluJr chr1 2855151 2855434 - DNA/hAT-Charlie_Charlie19a chr1 2856168 2856202 + Simple_repeat_(GCAGG)n chr1 2856405 2856431 + Simple_repeat_(TCCT)n chr1 2856663 2856696 + Simple_repeat_(TCCC)n chr1 2857927 2858013 - LINE/L1_L1MD3 chr1 2858011 2858079 + LINE/L1_L1MD3 chr1 2858774 2859215 + LINE/L1_L1MC3 chr1 2859216 2859624 + Low_complexity_GA-rich chr1 2860035 2860189 + SINE/MIR_MIRc chr1 2865278 2865311 + Simple_repeat_(CAGGTGC)n chr1 2867213 2867618 - LINE/L2_L2b chr1 2868275 2868306 + Simple_repeat_(GGGGA)n chr1 2870109 2870154 - LINE/L2_L2b chr1 2870342 2870520 + LINE/L2_L2b chr1 2871126 2871228 - SINE/MIR_MIR chr1 2872595 2872888 - SINE/Alu_AluSx chr1 2872904 2873087 + Simple_repeat_(CTTT)n chr1 2878550 2878644 - SINE/MIR_MIRc chr1 2880526 2880599 - LINE/L2_L2b chr1 2880785 2880834 + LINE/L2_L2c chr1 2884060 2884162 + Simple_repeat_(GT)n chr1 2885561 2885976 - LTR/ERVL-MaLR_MLT1C2 chr1 2887397 2887460 + Simple_repeat_(GTGAGG)n chr1 2887561 2887602 + Simple_repeat_(CACCCCT)n chr1 2888794 2889008 - LTR/ERVL-MaLR_MLT1E2 chr1 2890657 2890723 - LTR/ERVL-MaLR_MLT1C2 chr1 2890761 2891150 - LTR/ERVL-MaLR_MLT1C2 chr1 2891404 2891514 + DNA/hAT_MER53 chr1 2892877 2893002 - SINE/MIR_MIR3 chr1 2893147 2893202 + Simple_repeat_(CACCC)n chr1 2893466 2893681 + Simple_repeat_(AC)n chr1 2894902 2895344 - LTR/ERVL-MaLR_MLT1C chr1 2896470 2896518 + Simple_repeat_(CA)n chr1 2896519 2896562 + Simple_repeat_(CACACATGCA)n chr1 2896563 2897217 + Simple_repeat_(CA)n chr1 2897224 2898434 - LTR/ERVL_ERVL-E-int chr1 2898441 2898753 - LTR/ERVL-MaLR_MLT1C2 chr1 2898754 2899032 + SINE/Alu_AluSx1 chr1 2899033 2899043 - LTR/ERVL-MaLR_MLT1C2 chr1 2899044 2899082 + Simple_repeat_(AAAC)n chr1 2899083 2899173 - LTR/ERVL-MaLR_MLT1C2 chr1 2900657 2900855 + DNA/hAT-Charlie_MER20 chr1 2900966 2901132 - LTR/ERVL_ERVL-B4-int chr1 2901763 2902204 + LTR/ERVL-MaLR_MLT1D chr1 2904729 2904781 + Simple_repeat_(CCTC)n chr1 2905006 2905033 + Simple_repeat_(GCCCC)n chr1 2905129 2905154 + Simple_repeat_(GGGT)n chr1 2906533 2907005 + LINE/L1_L1MC1 chr1 2907725 2907847 + LTR/ERVL-MaLR_MLT1B chr1 2907936 2908125 + LTR/ERVL-MaLR_MLT1B chr1 2909115 2909201 + LINE/L2_L2d2 chr1 2909617 2909746 + LTR/ERVL-MaLR_MLT1B chr1 2911889 2912013 + Simple_repeat_(GT)n chr1 2912399 2912546 - SINE/Alu_FRAM chr1 2915541 2916323 + Simple_repeat_(CA)n chr1 2917729 2918069 - LTR/ERVL-MaLR_MLT1C2 chr1 2918582 2918750 + SINE/MIR_MIRc chr1 2918858 2919060 - DNA/hAT-Charlie_MER107 chr1 2920801 2920891 - SINE/MIR_MIR chr1 2922380 2922444 + DNA/hAT_MER53 chr1 2922498 2922618 + DNA/hAT_MER53 chr1 2922786 2923016 - LINE/L1_L1MB8 chr1 2923516 2923560 + Simple_repeat_(CCCTTCC)n chr1 2924889 2925151 + LTR/ERVL-MaLR_MLT1D chr1 2929269 2929309 - LINE/L2_L2b chr1 2929381 2929431 - DNA/hAT_MER53 chr1 2929432 2929813 - LTR/ERVL-MaLR_MLT1B chr1 2929814 2929924 - DNA/hAT_MER53 chr1 2930144 2930237 + Simple_repeat_(CA)n chr1 2931473 2931590 - LINE/L2_L2b chr1 2932280 2932701 + Low_complexity_G-rich chr1 2932788 2932944 - SINE/MIR_MIR chr1 2934499 2934879 + LTR/ERVL-MaLR_MLT1B chr1 2937861 2938534 + Simple_repeat_(AC)n chr1 2940304 2940434 + LINE/L1_HAL1 chr1 2941141 2941603 - LINE/L1_HAL1 chr1 2941687 2941866 + SINE/MIR_MIR chr1 2941869 2941887 + Simple_repeat_(GCT)n chr1 2942235 2942330 + SINE/MIR_MIR3 chr1 2943286 2943556 - SINE/Alu_AluJr chr1 2944307 2944379 + Simple_repeat_(GT)n chr1 2944394 2944503 - LTR/ERVL-MaLR_MLT1A chr1 2944575 2945176 + Simple_repeat_(GTGAGT)n chr1 2946756 2946784 + Low_complexity_A-rich chr1 2946878 2946949 + DNA/hAT-Tip100_Arthur1C chr1 2947918 2947955 + Simple_repeat_(CTCC)n chr1 2948398 2948560 - DNA/hAT-Tip100_MER45C chr1 2950494 2950780 + LTR/ERVL-MaLR_MLT1C2 chr1 2951400 2951437 + Low_complexity_GA-rich chr1 2953356 2953581 + LINE/L1_L1MEg chr1 2953583 2953640 + LINE/L1_L1MEg chr1 2953692 2954589 + LINE/L1_L1MEg chr1 2954590 2954963 - LTR/ERVL-MaLR_THE1C chr1 2954966 2956535 - LTR/ERVL-MaLR_THE1C-int chr1 2956536 2956910 - LTR/ERVL-MaLR_THE1C chr1 2956911 2957771 + LINE/L1_L1MEg chr1 2957776 2958221 + LINE/L1_L1MEg chr1 2958222 2958509 - SINE/Alu_AluSx chr1 2958510 2958974 + LINE/L1_L1MEg chr1 2959206 2959331 - DNA/hAT-Tip100_MER91B chr1 2962367 2962687 + LTR/ERVL-MaLR_MLT1B chr1 2963408 2963694 + SINE/Alu_AluJo chr1 2963751 2964077 + DNA/hAT-Charlie_MER58B chr1 2964395 2964463 - LINE/L2_L2b chr1 2964465 2964773 + Simple_repeat_(CATC)n chr1 2964774 2964814 + Simple_repeat_(ATCC)n chr1 2964815 2965498 + Simple_repeat_(CATC)n chr1 2965501 2965749 - LINE/L2_L2a chr1 2965979 2966418 + LTR/ERVL-MaLR_MLT1C2 chr1 2967797 2967934 + SINE/Alu_AluJb chr1 2968947 2968975 + Simple_repeat_(CCTCC)n chr1 2971537 2971588 + LTR/ERVL-MaLR_MLT1K chr1 2975283 2975464 + SINE/MIR_MIRb chr1 2980090 2980196 - DNA/hAT?_MER121 chr1 2981198 2981351 - LINE/L1_L1ME1 chr1 2981404 2981636 - LINE/L1_L1ME1 chr1 2981653 2981953 - LINE/L1_L1MEg chr1 2981998 2982105 - SINE/MIR_MIRb chr1 2983908 2984099 + SINE/MIR_MIRc chr1 2984767 2984908 - SINE/MIR_MIRb chr1 2985141 2985175 + Simple_repeat_(GCAG)n chr1 2985787 2986114 + Simple_repeat_(TCCA)n chr1 2986827 2986972 + SINE/MIR_MIR chr1 2989092 2989617 + Simple_repeat_(ATCC)n chr1 2991005 2991199 - LINE/L1_L1ME3G chr1 2991227 2991514 - SINE/Alu_AluJb chr1 2991549 2991734 - SINE/Alu_AluSz6 chr1 2991736 2992960 - LINE/L1_L1ME3A chr1 2992961 2993544 + LINE/L1_L1PA12 chr1 2993545 2993662 - LINE/L1_L1ME3A chr1 2993727 2994218 - LTR/ERVL-MaLR_MLT1E3 chr1 2994375 2994817 + LTR/ERVL-MaLR_MLT1J chr1 2994848 2994984 - LINE/L1_L1ME3A chr1 2994985 2995292 + SINE/Alu_AluYg6 chr1 2995293 2995422 - LINE/L1_L1ME3A chr1 2995596 2996328 - LINE/L1_L1MC4a chr1 2996329 2997089 - LINE/L1_L1M4 chr1 2997101 2998252 - LINE/L1_L1MC4a chr1 2998267 2998456 - LINE/L1_L1MC4a chr1 2998457 2998848 - LTR/ERVL-MaLR_MSTA chr1 2998849 2998953 - LTR/ERVL_ERV3-16A3_I-int chr1 2998960 2999068 - LINE/L1_L1MC4a chr1 2999107 2999513 - LINE/L1_L1ME3A chr1 2999960 3000005 + Low_complexity_GA-rich chr1 3000529 3000742 - SINE/MIR_MIR chr1 3000839 3001206 - LTR/ERVL_LTR16A1 chr1 3001460 3001889 - LTR/ERVL_ERVL-B4-int chr1 3002414 3002759 - LTR/ERVL_ERVL-B4-int chr1 3002845 3003699 - LTR/ERVL_ERVL-B4-int chr1 3004196 3004406 + LTR/ERVL_ERVL-E-int chr1 3005930 3005954 + Simple_repeat_(GGGCA)n chr1 3006305 3006735 + LTR/ERVL-MaLR_MSTB chr1 3006736 3008389 + LTR/ERVL-MaLR_MSTB-int chr1 3008393 3008814 + LTR/ERVL-MaLR_MSTB chr1 3008815 3008879 - DNA/hAT_MER53 chr1 3008924 3009021 + LTR/ERVL-MaLR_MLT1F2 chr1 3010061 3010127 - LTR/ERVL-MaLR_THE1B chr1 3010128 3010265 + SINE/Alu_AluSx chr1 3010266 3010565 + SINE/Alu_AluYk2 chr1 3010566 3010737 + SINE/Alu_AluSx chr1 3010738 3011038 - LTR/ERVL-MaLR_THE1B chr1 3011205 3011520 - LTR/ERVL-MaLR_MLT1C2 chr1 3013281 3013457 + Simple_repeat_(TCTGTC)n chr1 3013458 3013762 + LTR/ERVL-MaLR_MLT1F2 chr1 3013982 3014365 + LTR/ERVL-MaLR_MLT1B chr1 3015615 3015771 + LTR/ERVL-MaLR_MLT1A chr1 3016313 3016710 + Low_complexity_G-rich chr1 3016746 3016776 + Simple_repeat_(TGGG)n chr1 3017314 3017352 + Low_complexity_G-rich chr1 3021351 3021368 + Simple_repeat_(TC)n chr1 3023434 3023518 + LINE/L1_L1M5 chr1 3023862 3024841 - LINE/L1_L1MDa chr1 3025152 3025433 - LINE/L1_L1MDa chr1 3025580 3025641 - SINE/MIR_MIR3 chr1 3025816 3026506 + Simple_repeat_(ATG)n chr1 3033129 3033510 + LTR/ERVL_MLT2D chr1 3034035 3034102 + LINE/L1_L1MC5 chr1 3035378 3035403 + Simple_repeat_(CCTC)n chr1 3036644 3036789 + LTR/ERV1_MER31B chr1 3037920 3038056 + LTR/ERV1_LTR37A chr1 3038364 3038520 + SINE/MIR_MIR chr1 3039215 3039256 + Simple_repeat_(CTCC)n chr1 3039400 3039408 + Simple_repeat_(CTCCTTC)n chr1 3039409 3039453 + Simple_repeat_(CCCTTC)n chr1 3039454 3039462 + Simple_repeat_(CTCCTTC)n chr1 3039607 3040065 + Simple_repeat_(GT)n chr1 3040360 3040530 - LINE/L1_L1ME3Cz chr1 3040936 3041091 - LINE/L1_L1ME3Cz chr1 3041092 3041296 + LINE/L1_L1MA4 chr1 3041297 3041984 - LINE/L1_L1ME3Cz chr1 3041985 3042296 - SINE/Alu_AluSx1 chr1 3042297 3042362 - LINE/L1_L1ME3Cz chr1 3042363 3042594 - LINE/L1_L1ME2z chr1 3042690 3042845 - SINE/Alu_AluJo chr1 3043210 3043353 - DNA/TcMar-Tigger_Tigger1 chr1 3043354 3043663 + SINE/Alu_AluSp chr1 3043664 3043984 - DNA/TcMar-Tigger_Tigger1 chr1 3043985 3044280 + SINE/Alu_AluJo chr1 3044281 3044753 - DNA/TcMar-Tigger_Tigger1 chr1 3044754 3045124 - LINE/L1_L1PA15 chr1 3045125 3045654 - DNA/TcMar-Tigger_Tigger1 chr1 3045685 3045751 - LINE/L1_L1ME3Cz chr1 3045805 3045936 + SINE/Alu_AluJr4 chr1 3045963 3047070 + LINE/L1_L1MEc chr1 3046931 3047286 + LINE/L1_L1MD chr1 3047258 3048014 + LINE/L1_L1MEc chr1 3048017 3049052 + LINE/L1_L1MEc chr1 3050644 3050720 - SINE/MIR_MIRc chr1 3051041 3052268 + Simple_repeat_(ATCC)n chr1 3052285 3052420 - LINE/L2_L2a chr1 3053172 3053631 - LTR/ERVL-MaLR_MSTA chr1 3054708 3055091 + LINE/L1_L1MB4 chr1 3055542 3055727 - SINE/Alu_AluSx3 chr1 3056169 3056436 + LINE/L1_HAL1 chr1 3056437 3056728 + SINE/Alu_AluSq2 chr1 3056729 3056868 + LINE/L1_HAL1 chr1 3056879 3057204 + LINE/L1_HAL1 chr1 3057210 3057667 + LINE/L1_HAL1 chr1 3057913 3058344 - LTR/ERVL-MaLR_MLT1D chr1 3058349 3058791 + LINE/L2_L2a chr1 3058970 3059189 - LINE/L1_L1MB8 chr1 3060486 3060644 + SINE/MIR_MIRb chr1 3061434 3061463 + Low_complexity_G-rich chr1 3063332 3063437 - SINE/MIR_MIRc chr1 3065512 3065553 + Low_complexity_G-rich chr1 3066340 3066372 + Simple_repeat_(GGCCA)n chr1 3069057 3069081 + Simple_repeat_(GGC)n chr1 3069458 3069503 + Low_complexity_G-rich chr1 3069593 3069641 + Low_complexity_G-rich chr1 3069702 3069724 + Simple_repeat_(CT)n chr1 3070503 3070554 + Simple_repeat_(CCGC)n chr1 3070990 3071059 + Simple_repeat_(TGTGCG)n chr1 3074234 3074298 - SINE/MIR_MIR chr1 3074412 3074448 + Simple_repeat_(TG)n chr1 3075090 3075122 + Simple_repeat_(GCCTG)n chr1 3085481 3085680 + SINE/MIR_MIRb chr1 3086954 3086997 + Low_complexity_A-rich chr1 3088284 3088310 + Simple_repeat_(AAT)n chr1 3088445 3088749 - SINE/Alu_AluY chr1 3088751 3088788 + Simple_repeat_(TAT)n chr1 3088791 3089077 - SINE/Alu_AluSx chr1 3089910 3089941 + Simple_repeat_(ATTC)n chr1 3095958 3095985 + Simple_repeat_(CTCC)n chr1 3099224 3099350 + SINE/MIR_MIRb chr1 3101178 3101196 + Simple_repeat_(CTC)n chr1 3102138 3102236 - SINE/MIR_MIR3 chr1 3103148 3103477 - LTR/ERVL-MaLR_MLT1B chr1 3104773 3104892 + LINE/L2_L2c chr1 3105851 3105879 + Simple_repeat_(AGGCC)n chr1 3106278 3106384 - LINE/L2_L2b chr1 3106606 3106701 - LINE/L2_L2b chr1 3106985 3107032 + Simple_repeat_(CCAGAGG)n chr1 3108035 3108132 - SINE/MIR_MIR chr1 3108262 3108361 - LINE/L1_L1M4 chr1 3108869 3109083 + SINE/Alu_AluSx1 chr1 3109084 3109116 + Simple_repeat_(AAT)n chr1 3109318 3109351 + Simple_repeat_(TCATTA)n chr1 3111516 3111585 + Low_complexity_G-rich chr1 3113276 3113332 + Low_complexity_G-rich chr1 3113464 3113507 + Low_complexity_G-rich chr1 3113577 3113765 - DNA/hAT-Charlie_MER20 chr1 3114139 3114510 + Simple_repeat_(CA)n chr1 3114511 3114560 + Simple_repeat_(CACACATG)n chr1 3114561 3114704 + Simple_repeat_(CA)n chr1 3114793 3114828 + Simple_repeat_(CCCAG)n chr1 3117951 3118172 + Simple_repeat_(GTGT)n chr1 3118187 3118248 + Simple_repeat_(GCAC)n chr1 3119251 3119286 + Simple_repeat_(GGTGCC)n chr1 3122783 3122812 + Simple_repeat_(TTTTA)n chr1 3124504 3124541 - tRNA_tRNA-Gly-GGA chr1 3129051 3129479 + Simple_repeat_(TG)n chr1 3129816 3129824 + Simple_repeat_(CTTTCT)n chr1 3129825 3129887 + Simple_repeat_(TC)n chr1 3129888 3129915 + Simple_repeat_(CTTTCT)n chr1 3130356 3130385 + Simple_repeat_(CAAACA)n chr1 3130798 3130824 + Simple_repeat_(T)n chr1 3131843 3131883 + Simple_repeat_(CCT)n chr1 3134343 3134400 + Simple_repeat_(GGCATG)n chr1 3136352 3136486 - SINE/MIR_MIR3 chr1 3140210 3140351 - Unknown_UCON16 chr1 3143465 3143488 + Simple_repeat_(TTA)n chr1 3143489 3143775 - SINE/Alu_AluSx chr1 3143776 3143786 + Simple_repeat_(TTA)n chr1 3144328 3144415 + SINE/MIR_MIRc chr1 3144556 3144664 - SINE/MIR_MIRb chr1 3145340 3145403 + Simple_repeat_(CTGGCCA)n chr1 3146427 3147223 + Simple_repeat_(GT)n chr1 3147672 3147802 + SINE/MIR_MIR3 chr1 3150152 3150238 + SINE/MIR_MIRc chr1 3150274 3150585 + SINE/Alu_AluSz chr1 3152281 3152427 + Simple_repeat_(CATC)n chr1 3157228 3157313 + DNA/hAT_MER53 chr1 3157423 3157442 + Simple_repeat_(A)n chr1 3158361 3158451 + Simple_repeat_(ATTTAA)n chr1 3158649 3158761 + Simple_repeat_(TTTC)n chr1 3158762 3159069 - SINE/Alu_AluSx1 chr1 3159276 3159654 - LTR/ERVL-MaLR_MLT1B chr1 3159862 3160035 - DNA/hAT_MER53 chr1 3160724 3160902 - SINE/MIR_MIR chr1 3161487 3161613 + DNA?_Eulor10 chr1 3162093 3162285 - LINE/L1_L1MB4 chr1 3162279 3162391 - LINE/L1_L1MB4 chr1 3163523 3163893 - LTR/ERVL-MaLR_MLT1C2 chr1 3166834 3166977 - SINE/MIR_MIRb chr1 3168760 3168889 - SINE/MIR_MIRb chr1 3169419 3169586 + SINE/MIR_MIR chr1 3172220 3172305 - SINE/MIR_MIR1_Amn chr1 3172519 3172966 + LINE/L1_L1MB3 chr1 3173983 3174404 - LTR/ERVL-MaLR_MLT1C2 chr1 3174465 3174624 + SINE/MIR_MIRc chr1 3175947 3175983 + Simple_repeat_(CCAT)n chr1 3175984 3176013 + Simple_repeat_(CAGC)n chr1 3176014 3176347 + Simple_repeat_(CCAT)n chr1 3176482 3177264 + Simple_repeat_(CCAT)n chr1 3177400 3177559 + SINE/MIR_MIRb chr1 3178746 3178880 - DNA/hAT-Charlie_MER5A chr1 3178965 3179002 + Simple_repeat_(CCTGGC)n chr1 3179306 3179431 + SINE/MIR_MIR1_Amn chr1 3179694 3179751 + Simple_repeat_(CCTGGCA)n chr1 3180147 3180203 + Simple_repeat_(TTGT)n chr1 3181917 3182035 + Simple_repeat_(CTTACACATG)n chr1 3184590 3184826 - LINE/L1_L1MB5 chr1 3185010 3185110 - LINE/L1_L1MB5 chr1 3191278 3191404 - LINE/L2_L2b chr1 3192835 3192996 + DNA/hAT-Charlie_MER5A1 chr1 3193800 3193835 + Low_complexity_G-rich chr1 3193993 3194054 - DNA/hAT-Charlie_MER5B chr1 3195813 3195838 + Simple_repeat_(TTCC)n chr1 3196648 3196741 + DNA/hAT-Charlie_MER5A chr1 3197169 3197290 - SINE/MIR_MIR3 chr1 3200337 3200641 - SINE/Alu_AluYk3 chr1 3200910 3201011 + Low_complexity_GA-rich chr1 3202749 3202892 + SINE/MIR_MIR3 chr1 3203495 3203858 - LTR/ERVL-MaLR_MLT1C chr1 3207913 3208107 + SINE/MIR_MIR1_Amn chr1 3208386 3208693 + SINE/Alu_AluSg4 chr1 3209635 3209707 + LINE/RTE-BovB_MamRTE1 chr1 3211793 3211840 + Simple_repeat_(GCGTGTGCAT)n chr1 3214415 3214650 + SINE/Alu_AluSp chr1 3221231 3221368 + SINE/MIR_MIRc chr1 3221716 3221749 + Simple_repeat_(CATGCA)n chr1 3222217 3222244 + Simple_repeat_(CCCA)n chr1 3223104 3223196 - SINE/Alu_AluSc chr1 3224440 3224537 + Simple_repeat_(CCTCTC)n chr1 3225053 3225369 - DNA/hAT?_MER121 chr1 3225531 3225574 + Simple_repeat_(TG)n chr1 3227321 3227479 + SINE/MIR_MIR chr1 3228807 3228838 + Simple_repeat_(TC)n chr1 3230730 3230778 + Simple_repeat_(CCTG)n chr1 3231099 3231204 - DNA/hAT-Charlie_MER58C chr1 3231772 3231842 + DNA/hAT-Ac_OldhAT1 chr1 3232165 3232213 + Simple_repeat_(CTCTCC)n chr1 3232230 3232447 + SINE/MIR_MIRb chr1 3233659 3233880 + LINE/L1_L1MA10 chr1 3234403 3234453 + Simple_repeat_(CCAGG)n chr1 3236181 3236216 + Low_complexity_G-rich chr1 3236717 3236886 + SINE/MIR_MIRb chr1 3238476 3238555 - DNA?_MER131 chr1 3238816 3239051 + SINE/MIR_MIRc chr1 3239669 3239924 + SINE/Alu_AluJo chr1 3239943 3240152 + Low_complexity_GA-rich chr1 3240153 3240437 + SINE/Alu_AluJr chr1 3240438 3240472 + Low_complexity_GA-rich chr1 3242169 3242195 + Simple_repeat_(GGCAG)n chr1 3243539 3243650 + LINE/L2_L2c chr1 3244532 3244691 + DNA_X30_DNA chr1 3244882 3244921 + Simple_repeat_(CA)n chr1 3249209 3249272 + Low_complexity_G-rich chr1 3250094 3250140 + Simple_repeat_(CTCCCCC)n chr1 3253209 3254244 - LINE/L1_L1PA3 chr1 3254243 3255294 + LINE/L1_L1PA3 chr1 3255846 3255918 + Simple_repeat_(CCGACA)n chr1 3256167 3256362 + LINE/L1_L1MC2 chr1 3256375 3256574 + LINE/L1_L1MC2 chr1 3256577 3256880 + SINE/Alu_AluY chr1 3257057 3257117 + SINE/MIR_MIR chr1 3257230 3257617 + LTR/ERVL-MaLR_MLT1I chr1 3257670 3257700 + Simple_repeat_(TCAT)n chr1 3257826 3257880 + DNA/TcMar-Tigger_MER2 chr1 3257924 3258032 - LINE/L2_L2b chr1 3259465 3259584 - SINE/MIR_MIRb chr1 3260415 3260545 + SINE/MIR_MIRb chr1 3260579 3260838 + Simple_repeat_(TGA)n chr1 3260892 3261122 + SINE/MIR_MIRc chr1 3262103 3262199 - SINE/MIR_MIR chr1 3264090 3264423 - LINE/L2_L2c chr1 3266665 3266977 - SINE/Alu_AluSq2 chr1 3267011 3267608 - LINE/L1_L1MB5 chr1 3269902 3269970 + Low_complexity_GA-rich chr1 3273818 3273851 + Simple_repeat_(GT)n chr1 3273883 3273934 + Simple_repeat_(GT)n chr1 3273977 3273999 + Simple_repeat_(A)n chr1 3277708 3277803 + Simple_repeat_(CA)n chr1 3280227 3280307 + Simple_repeat_(TG)n chr1 3281020 3281132 + SINE/MIR_MIR1_Amn chr1 3281433 3281512 + DNA/hAT-Tip100_MamTip2b chr1 3283368 3283401 + Simple_repeat_(CCCT)n chr1 3284821 3284855 + Low_complexity_G-rich chr1 3289505 3289757 - LINE/L2_L2d chr1 3290276 3290424 - SINE/MIR_MIR chr1 3291690 3291814 + DNA/hAT-Charlie_MER5A chr1 3293075 3293162 + LINE/L2_L2c chr1 3293928 3293963 + Simple_repeat_(GT)n chr1 3296368 3296483 - LINE/L2_L2c chr1 3296792 3297087 - DNA/TcMar-Tigger_MER47A chr1 3297281 3297583 - SINE/Alu_AluSx1 chr1 3300922 3301048 + LINE/L2_L2c chr1 3301049 3301361 + SINE/Alu_AluJr chr1 3301362 3301372 + LINE/L2_L2c chr1 3302007 3302017 + Simple_repeat_(AT)n chr1 3302018 3302990 - DNA/TcMar-Tigger_Tigger17d chr1 3302991 3303069 + Simple_repeat_(AT)n chr1 3303072 3303780 - LINE/L1_L1MB7 chr1 3305878 3305947 - LINE/L2_L2b chr1 3306658 3307347 - LINE/L1_L1MB7 chr1 3308290 3308410 + DNA/hAT-Blackjack_MER81 chr1 3309471 3309572 + SINE/MIR_MIR chr1 3310156 3310248 + SINE/MIR_MIR chr1 3310755 3311013 + Simple_repeat_(TG)n chr1 3311569 3311703 - SINE/MIR_MIRb chr1 3313306 3313359 + Simple_repeat_(AGCGGAG)n chr1 3314568 3314609 + Simple_repeat_(TC)n chr1 3314702 3314725 + Simple_repeat_(TCC)n chr1 3315119 3315203 + Simple_repeat_(CCTC)n chr1 3316266 3316301 + Simple_repeat_(GTGGGG)n chr1 3318459 3318629 + Simple_repeat_(CTAT)n chr1 3319705 3319765 - SINE/MIR_MIRc chr1 3320315 3320342 + Simple_repeat_(TG)n chr1 3321246 3321284 + Simple_repeat_(GTGAAT)n chr1 3321269 3322241 + Simple_repeat_(TG)n chr1 3325546 3325748 - LTR/ERVL-MaLR_MLT1C chr1 3326209 3326441 - LTR/ERVL-MaLR_MLT1C chr1 3327839 3327946 - SINE/MIR_MIRc chr1 3328243 3328365 - DNA/hAT-Charlie_MER20 chr1 3331406 3331463 + LINE/L2_L2b chr1 3332468 3332494 + Simple_repeat_(GGTTT)n chr1 3332556 3333124 - LINE/L1_L1MB5 chr1 3333649 3334048 + LTR/ERVL-MaLR_MSTB chr1 3334080 3334155 + SINE/MIR_MIRc chr1 3334805 3334881 - SINE/MIR_MIRb chr1 3335603 3335642 + Simple_repeat_(AC)n chr1 3337865 3338066 + Simple_repeat_(CA)n chr1 3339396 3339573 - LINE/L2_L2b chr1 3341553 3341581 + Simple_repeat_(AC)n chr1 3341582 3341990 + DNA/TcMar-Tigger_MER47B chr1 3342458 3342508 - SINE/MIR_MIR chr1 3342530 3343474 - LINE/L1_L1ME2 chr1 3343498 3343621 - LINE/L1_L1M4a1 chr1 3343622 3343940 - SINE/Alu_AluY chr1 3343941 3344054 - LINE/L1_L1M4a1 chr1 3344055 3344322 + SINE/Alu_AluJr chr1 3344323 3344681 - LINE/L1_L1M4a1 chr1 3344852 3345365 - LINE/L1_L1MEb chr1 3345451 3345633 - SINE/MIR_MIRc chr1 3350057 3350345 + SINE/Alu_AluJo chr1 3351525 3351795 + Simple_repeat_(TCTC)n chr1 3352487 3352528 + Simple_repeat_(CTTTC)n chr1 3354178 3354479 + SINE/Alu_AluSx chr1 3356586 3356796 - SINE/MIR_MIR chr1 3357717 3357926 + DNA/hAT-Charlie_MER20 chr1 3359063 3359253 - SINE/Alu_AluJb chr1 3360382 3360479 - LINE/CR1_L3 chr1 3363586 3363647 + LINE/L2_L2 chr1 3363796 3363835 + Low_complexity_GA-rich chr1 3365272 3365323 - LINE/L2_L2b chr1 3365885 3365902 + Simple_repeat_(CGCA)n chr1 3365903 3365968 + Simple_repeat_(CACACATG)n chr1 3365969 3366028 + Simple_repeat_(CACA)n chr1 3366986 3367287 + SINE/Alu_AluSq2 chr1 3370097 3370185 - LINE/L2_L2b chr1 3370447 3370694 - LINE/L2_L2b chr1 3370789 3371596 + Simple_repeat_(CCAT)n chr1 3371597 3371661 - LINE/L2_L2b chr1 3371760 3371925 + SINE/MIR_MIR chr1 3375885 3375913 + Low_complexity_GA-rich chr1 3376703 3376729 + Simple_repeat_(CT)n chr1 3377436 3377546 + Unknown_UCON80_AMi chr1 3378548 3378609 + SINE/MIR_MIR chr1 3380580 3380877 + LINE/CR1_L3 chr1 3381147 3381268 + Simple_repeat_(CACA)n chr1 3381390 3381687 - SINE/Alu_AluSx chr1 3384262 3384300 + Simple_repeat_(CCCTG)n chr1 3386155 3386288 + DNA/hAT-Charlie_Charlie15a chr1 3388270 3388321 + Simple_repeat_(CT)n chr1 3389950 3389969 + Simple_repeat_(C)n chr1 3390835 3391127 - SINE/Alu_AluSq2 chr1 3394364 3394403 + Simple_repeat_(CCGC)n chr1 3394473 3394511 + Low_complexity_G-rich chr1 3394512 3394528 + Simple_repeat_(GGAGT)n chr1 3396941 3396964 + Simple_repeat_(TTA)n chr1 3403023 3403114 + Simple_repeat_(CCTCC)n chr1 3406111 3406455 - LINE/L2_L2b chr1 3406456 3406765 + SINE/Alu_AluJr chr1 3406766 3406865 - LINE/L2_L2b chr1 3406863 3407053 - SINE/MIR_MIRb chr1 3408349 3408397 + Low_complexity_GA-rich chr1 3409647 3410190 + Simple_repeat_(TG)n chr1 3411093 3411167 + Simple_repeat_(CA)n chr1 3412052 3412118 + Low_complexity_G-rich chr1 3413123 3413161 + Simple_repeat_(ACCCC)n chr1 3416856 3416905 + LINE/L2_L2b chr1 3419395 3419490 - SINE/MIR_MIRb chr1 3421484 3421591 + LINE/L2_L2d2 chr1 3421845 3422403 + Simple_repeat_(CAGA)n chr1 3425076 3425369 - SINE/Alu_AluY chr1 3425806 3425854 + Low_complexity_G-rich chr1 3426608 3426662 + Simple_repeat_(AC)n chr1 3426697 3426749 - LINE/L1_L1PA5 chr1 3426989 3427048 + Simple_repeat_(CACATG)n chr1 3427323 3427403 - SINE/MIR_MIR3 chr1 3428736 3428993 + LTR/ERVL-MaLR_MLT1C chr1 3429066 3429267 + LTR/ERVL-MaLR_MLT1C chr1 3430924 3430982 + Low_complexity_GA-rich chr1 3431812 3431841 + Simple_repeat_(GTCT)n chr1 3436803 3436851 + Low_complexity_G-rich chr1 3437749 3437775 + Simple_repeat_(AAAT)n chr1 3438018 3438046 + Simple_repeat_(TTTTG)n chr1 3439060 3439577 - DNA/hAT-Charlie_MER1A chr1 3440827 3440936 + LINE/L2_L2b chr1 3442544 3442893 - LTR/ERV1_MER89 chr1 3442937 3443481 - LINE/L1_L1MC1 chr1 3443482 3443781 - SINE/Alu_AluSx chr1 3443782 3444279 - LINE/L1_L1MC1 chr1 3444351 3444724 + LINE/L1_L1ME1 chr1 3445154 3445315 + LINE/L1_L1ME3C chr1 3445410 3445454 + Low_complexity_GA-rich chr1 3445461 3445774 - LINE/L1_L1MEc chr1 3445775 3446053 - SINE/Alu_AluSq2 chr1 3446054 3446476 - LINE/L1_L1MEc chr1 3446477 3446780 + SINE/Alu_AluJb chr1 3446781 3448471 - LINE/L1_L1MEc chr1 3448472 3448774 + SINE/Alu_AluSg chr1 3448775 3449297 - LINE/L1_L1MEc chr1 3449298 3449605 - SINE/Alu_AluJb chr1 3449606 3449975 - LINE/L1_L1MEc chr1 3449976 3450038 + Simple_repeat_(TG)n chr1 3450039 3450049 - LINE/L1_L1MEc chr1 3450050 3450307 - SINE/Alu_AluJr chr1 3450308 3450512 - LINE/L1_L1MEc chr1 3450595 3450842 - LINE/L1_L1MEc chr1 3450855 3451021 - LINE/L1_L1MEc chr1 3451022 3451145 - SINE/Alu_AluJb chr1 3451508 3451672 - LINE/L1_L1MEc chr1 3451706 3451739 + Simple_repeat_(TA)n chr1 3451780 3451995 + SINE/MIR_MIRb chr1 3452872 3452958 - SINE/MIR_MIR1_Amn chr1 3453096 3453205 + SINE/MIR_MIR3 chr1 3454051 3454152 + SINE/MIR_MIRc chr1 3455448 3455521 + SINE/MIR_MIRc chr1 3455810 3455875 + SINE/MIR_MIR chr1 3456173 3456294 - SINE/MIR_MIR chr1 3456563 3456668 + SINE/MIR_MIRb chr1 3457040 3457098 - DNA/hAT-Charlie_MER20 chr1 3458130 3458244 - LINE/L2_L2d chr1 3458635 3459155 + LINE/L1_HAL1 chr1 3461330 3461484 - SINE/MIR_MIR chr1 3462593 3462698 - SINE/MIR_MIRc chr1 3463833 3463901 - SINE/MIR_MIRc chr1 3464685 3464762 + SINE/MIR_MIRc chr1 3465155 3465433 - LINE/L2_L2c chr1 3466274 3466413 - LINE/RTE-BovB_MamRTE1 chr1 3471528 3471560 + Low_complexity_G-rich chr1 3474805 3474857 + Simple_repeat_(CCCTA)n chr1 3474907 3474960 - SINE/MIR_MIR3 chr1 3474967 3475020 - SINE/MIR_MIR3 chr1 3475027 3475080 - SINE/MIR_MIR3 chr1 3475087 3475140 - SINE/MIR_MIR3 chr1 3475147 3475200 - SINE/MIR_MIR3 chr1 3475207 3475260 - SINE/MIR_MIR3 chr1 3475267 3475320 - SINE/MIR_MIR3 chr1 3475327 3475380 - SINE/MIR_MIR3 chr1 3475387 3475440 - SINE/MIR_MIR3 chr1 3475447 3475500 - SINE/MIR_MIR3 chr1 3475507 3475560 - SINE/MIR_MIR3 chr1 3475567 3475620 - SINE/MIR_MIR3 chr1 3475627 3475680 - SINE/MIR_MIR3 chr1 3475687 3475740 - SINE/MIR_MIR3 chr1 3475747 3475844 - SINE/MIR_MIR3 chr1 3478714 3478819 - LINE/L2_L2a chr1 3482243 3482306 + Simple_repeat_(TG)n chr1 3482411 3482517 + SINE/MIR_MIRb chr1 3483172 3483208 - SINE/MIR_MIR3 chr1 3484322 3484380 + Simple_repeat_(CGGCT)n chr1 3484792 3484915 - LINE/L1_L1MEh chr1 3485290 3485649 - LINE/L1_L1MEh chr1 3485650 3485961 + SINE/Alu_AluY chr1 3485962 3485977 - LINE/L1_L1MEh chr1 3486411 3486448 + LINE/L1_L1PA15 chr1 3486449 3486755 + SINE/Alu_AluSx chr1 3486756 3487133 + LINE/L1_L1PA15 chr1 3487134 3487180 + Simple_repeat_(AT)n chr1 3487181 3487572 + LINE/L1_L1PA15 chr1 3487977 3488149 - LINE/L1_L1MA5A chr1 3488495 3488792 - LINE/L1_L1MEh chr1 3490171 3490216 + Simple_repeat_(CCTGCA)n chr1 3491331 3491372 + Simple_repeat_(GCACCCG)n chr1 3491448 3491485 + Simple_repeat_(GCCCC)n chr1 3491753 3491792 + Low_complexity_G-rich chr1 3492905 3492997 + SINE/MIR_MIRc chr1 3500556 3500589 + Simple_repeat_(TG)n chr1 3502203 3502241 + Low_complexity_G-rich chr1 3503242 3503278 + Low_complexity_G-rich chr1 3503667 3503741 + Simple_repeat_(TG)n chr1 3503749 3503807 + Low_complexity_G-rich chr1 3507356 3507506 - LINE/CR1_L3 chr1 3508035 3508111 - DNA/hAT-Charlie_MER117 chr1 3508189 3508410 + LINE/L2_L2a chr1 3508848 3508950 + SINE/MIR_MIRc chr1 3512452 3512820 + LTR/ERVL-MaLR_THE1B chr1 3513203 3513575 - SINE/Alu_AluJo chr1 3513578 3513890 - SINE/Alu_AluJb chr1 3513967 3514267 + SINE/Alu_AluY chr1 3514877 3514913 + Simple_repeat_(GGCCA)n chr1 3518097 3518255 + LTR/ERVL-MaLR_MLT1A0 chr1 3518661 3518692 + Simple_repeat_(TGGTTC)n chr1 3519817 3519902 - SINE/MIR_MIR3 chr1 3520866 3520949 + LINE/L2_L2d2 chr1 3522584 3522630 + Low_complexity_G-rich chr1 3524750 3525098 + LTR/ERVL-MaLR_MLT1C2 chr1 3525904 3525943 + Simple_repeat_(AGGCAAC)n chr1 3526399 3526698 - SINE/Alu_AluSx1 chr1 3528445 3528945 + LTR/ERVL-MaLR_MLT1D chr1 3529710 3529750 + Simple_repeat_(GCCCCCA)n chr1 3531502 3531532 + Simple_repeat_(CCGGCC)n chr1 3531533 3531570 + Simple_repeat_(GCCCC)n chr1 3531647 3531733 + Simple_repeat_(CACAGA)n chr1 3531801 3531850 + Low_complexity_G-rich chr1 3531964 3531998 + Simple_repeat_(GTCCT)n chr1 3532837 3532964 - SINE/MIR_MIRb chr1 3534222 3534440 + DNA/hAT-Charlie_MER33 chr1 3534441 3534579 + SINE/MIR_MIR chr1 3535014 3535129 + SINE/MIR_MIR3 chr1 3535743 3535841 - SINE/MIR_MIRb chr1 3538697 3538745 + Simple_repeat_(TG)n chr1 3538977 3539028 - LINE/L2_L2d chr1 3540576 3540898 - LTR/ERVL-MaLR_MLT1A0 chr1 3542675 3542817 - SINE/MIR_MIRb chr1 3544348 3544386 + Simple_repeat_(GCACCTCT)n chr1 3550132 3550154 + LINE/L1_L1MB5 chr1 3550155 3550295 + LINE/L1_L1M3 chr1 3550317 3550550 + LINE/L1_L1MB5 chr1 3552421 3552714 + SINE/Alu_AluJr chr1 3552937 3552981 + Simple_repeat_(CAGGCC)n chr1 3554517 3554622 - LTR/ERVL-MaLR_MLT1D chr1 3554667 3554904 - LTR/ERVL-MaLR_MLT1D chr1 3555155 3555181 + Simple_repeat_(CCCA)n chr1 3556920 3557315 + LTR/ERVL-MaLR_MLT1D chr1 3558353 3558434 + SINE/Alu_AluJb chr1 3558525 3558828 + LTR/ERV1_LTR10B2 chr1 3558830 3559057 + SINE/Alu_AluJb chr1 3559376 3559542 + SINE/Alu_FRAM chr1 3559730 3560036 + SINE/Alu_AluSx3 chr1 3560150 3560567 - LINE/L1_L1MC2 chr1 3562104 3562398 + Simple_repeat_(TC)n chr1 3563461 3563576 + LINE/L2_L2b chr1 3563720 3563798 - SINE/MIR_MIR3 chr1 3563997 3564478 - LTR/ERV1_LTR21C chr1 3564991 3565137 + LINE/L2_L2b chr1 3566884 3567001 - SINE/MIR_MIR chr1 3570698 3570728 + Simple_repeat_(CTGCCC)n chr1 3574334 3574376 + Simple_repeat_(TCTGC)n chr1 3574635 3574941 - SINE/Alu_AluSx1 chr1 3575496 3575853 - LTR/ERVL-MaLR_THE1B chr1 3575926 3576084 - SINE/MIR_MIR3 chr1 3577382 3577535 + SINE/MIR_MIRb chr1 3580212 3580282 + Low_complexity_G-rich chr1 3580734 3580777 + Low_complexity_G-rich chr1 3581062 3581166 - SINE/MIR_MIRc chr1 3581901 3581944 + Simple_repeat_(CCCTC)n chr1 3582326 3582719 + LINE/L2_L2b chr1 3582747 3583023 + LINE/L2_L2b chr1 3586374 3586399 + Simple_repeat_(TG)n chr1 3587046 3587733 + LTR/ERVL_LTR62 chr1 3588948 3589362 + LTR/ERVL-MaLR_MLT1C2 chr1 3589397 3589743 + LINE/L2_L2a chr1 3589850 3590023 - DNA/hAT-Charlie_MER103C chr1 3591473 3591578 - SINE/MIR_MIRb chr1 3593412 3593484 + Simple_repeat_(CCCAGC)n chr1 3594147 3594456 + SINE/Alu_AluJo chr1 3596881 3596928 + Simple_repeat_(CCCCAG)n chr1 3597495 3597874 + LTR/ERVL-MaLR_MLT1C2 chr1 3597979 3598029 + Simple_repeat_(GAGACA)n chr1 3598030 3598040 + Low_complexity_A-rich chr1 3601496 3601685 - DNA/hAT-Charlie_MER20 chr1 3603123 3603187 - LINE/CR1_L3 chr1 3604320 3604360 + Simple_repeat_(GCAGG)n chr1 3605041 3605758 + Simple_repeat_(CACA)n chr1 3605759 3605812 + LTR/ERVL_LTR33 chr1 3606179 3606376 + DNA/hAT-Charlie_MER5A chr1 3606386 3606584 + SINE/MIR_MIR chr1 3606970 3607057 + SINE/MIR_MIRc chr1 3607510 3607551 + Simple_repeat_(GCACC)n chr1 3610470 3610502 + Simple_repeat_(CTC)n chr1 3611533 3611610 + Simple_repeat_(CCGCCC)n chr1 3611611 3611659 + Simple_repeat_(CCCCT)n chr1 3611660 3611791 + Simple_repeat_(CCGCCC)n chr1 3611929 3611967 + Simple_repeat_(GACCTCA)n chr1 3612886 3613030 - SINE/MIR_MIR chr1 3613444 3613491 + LINE/L2_L2b chr1 3613654 3613820 - DNA/hAT-Tip100_Arthur1A chr1 3617139 3617177 + Simple_repeat_(CCCT)n chr1 3617308 3617392 + LTR/ERVL-MaLR_MLT1C2 chr1 3617393 3617654 + LINE/L1_L1PA11 chr1 3617655 3617898 + LTR/ERVL-MaLR_MLT1C2 chr1 3619379 3619432 + LTR/ERVL-MaLR_MLT1C2 chr1 3619433 3619776 + LTR/ERVL-MaLR_THE1D chr1 3619777 3620647 + LTR/ERVL-MaLR_THE1D-int chr1 3620648 3621643 - LTR/ERVK_LTR13 chr1 3621644 3622155 + LTR/ERVL-MaLR_THE1D-int chr1 3622156 3622504 + LTR/ERVL-MaLR_THE1D chr1 3622505 3622858 + LTR/ERVL-MaLR_MLT1C2 chr1 3623418 3623573 + LTR/ERVL_LTR40c chr1 3623668 3623784 + LTR/ERVL_LTR40c chr1 3625278 3625358 + Simple_repeat_(GGCCGG)n chr1 3626586 3626878 - SINE/Alu_AluJb chr1 3627029 3627338 + SINE/Alu_AluSp chr1 3627748 3627884 + DNA/hAT-Charlie_MER3 chr1 3629450 3629725 + SINE/Alu_AluSz chr1 3631871 3631901 + Simple_repeat_(T)n chr1 3632931 3633053 - SINE/MIR_MIR chr1 3633967 3634086 - LINE/L2_L2 chr1 3634170 3634310 + LINE/L2_L2a chr1 3634419 3634476 + LINE/L2_L2a chr1 3634575 3634677 + LINE/L2_L2a chr1 3635539 3635830 + SINE/Alu_AluJb chr1 3637560 3637857 + SINE/Alu_AluJb chr1 3638525 3638635 + LINE/L1_L1MC5 chr1 3639012 3639042 + Simple_repeat_(TTG)n chr1 3641750 3641793 + LINE/L1_L1M5 chr1 3641794 3642106 + LINE/L1_L1M4 chr1 3642107 3642231 + SINE/Alu_FLAM_A chr1 3642259 3642457 + LINE/L1_L1M5 chr1 3642458 3642756 + SINE/Alu_AluSp chr1 3646125 3646386 + DNA/TcMar-Tigger_MER44C chr1 3646386 3646567 + DNA/TcMar-Tigger_Tigger7 chr1 3650290 3650437 + DNA/hAT_MER53 chr1 3650461 3650534 + Simple_repeat_(TATATT)n chr1 3651961 3652021 + Simple_repeat_(CCCCTGT)n chr1 3652504 3652562 + Simple_repeat_(GCCC)n chr1 3653640 3653671 + Simple_repeat_(TGTTT)n chr1 3653889 3654199 + SINE/Alu_AluSx1 chr1 3654414 3654648 + SINE/MIR_MIR chr1 3655105 3655404 + SINE/Alu_AluSx1 chr1 3655897 3656192 + SINE/Alu_AluY chr1 3656435 3656639 - SINE/MIR_MIR chr1 3656782 3657095 + DNA/hAT-Charlie_Charlie4a chr1 3657110 3657513 - LTR/ERVL-MaLR_MLT1C2 chr1 3657571 3657678 + DNA/hAT-Charlie_Charlie4a chr1 3658269 3658824 - LTR/ERV1_MER34C2 chr1 3658827 3658892 - LTR/ERV1_MER34C2 chr1 3658860 3658928 - LTR/ERV1_MER34C2 chr1 3658896 3658968 - LTR/ERV1_MER34C2 chr1 3658969 3659040 - LTR/ERV1_MER34C2 chr1 3659041 3659073 - LTR/ERV1_MER34C2 chr1 3659160 3659675 - LTR/ERV1_MER31-int chr1 3659676 3659979 - SINE/Alu_AluSx chr1 3659980 3661422 - LTR/ERV1_MER31-int chr1 3661427 3661694 + SINE/Alu_AluJb chr1 3661698 3661875 + Simple_repeat_(TA)n chr1 3661873 3661908 + Simple_repeat_(TATTTTT)n chr1 3661909 3662072 + SINE/Alu_AluJr chr1 3662279 3662728 - LTR/ERV1_MER34C2 chr1 3663060 3663141 - SINE/MIR_MIR1_Amn chr1 3663232 3663407 - LTR/ERV1_MER39 chr1 3663434 3663722 + SINE/Alu_AluYh3a3 chr1 3663775 3663854 - LTR/ERV1_MER39 chr1 3665643 3665834 - SINE/Alu_AluSx chr1 3665855 3666153 + SINE/Alu_AluSq2 chr1 3666162 3666367 - SINE/Alu_AluJo chr1 3666370 3666407 + SINE/MIR_MIR chr1 3666866 3667405 + LTR/ERVL-MaLR_MLT1D chr1 3667470 3667776 + SINE/Alu_AluSc8 chr1 3668491 3668515 - DNA/TcMar-Mariner_MADE2 chr1 3668516 3668547 + Simple_repeat_(GT)n chr1 3668548 3668601 - DNA/TcMar-Mariner_MADE2 chr1 3668604 3668708 + LINE/L1_L1MC5 chr1 3668904 3668943 + Simple_repeat_(GCAGGTG)n chr1 3669904 3669942 + Simple_repeat_(ATGTGC)n chr1 3670066 3670200 + SINE/MIR_MIR1_Amn chr1 3670383 3670692 + SINE/Alu_AluSx chr1 3670832 3670934 - LINE/L2_L2b chr1 3671313 3671350 + Simple_repeat_(TGTTCC)n chr1 3671612 3671767 + SINE/MIR_MIRb chr1 3673415 3673610 - SINE/MIR_MIR chr1 3673951 3674045 + SINE/MIR_MIR chr1 3674927 3674964 + Low_complexity_G-rich chr1 3676618 3676763 + SINE/MIR_MIR3 chr1 3676864 3677014 - DNA/hAT-Charlie_MER20 chr1 3677691 3678004 - SINE/Alu_AluJr chr1 3678750 3678881 + DNA/hAT-Charlie_MER5A chr1 3680183 3680359 - LINE/L1_L1MB5 chr1 3680372 3680467 + SINE/MIR_MIR3 chr1 3680937 3681035 + SINE/MIR_MIRb chr1 3683325 3683403 - LTR/ERVL-MaLR_MLT1K chr1 3683529 3683921 - LTR/ERVL-MaLR_MLT1K chr1 3686049 3686106 + Simple_repeat_(CTGCCC)n chr1 3686761 3686911 + SINE/MIR_MIR chr1 3687060 3687127 + Simple_repeat_(GCCTGT)n chr1 3687658 3687713 + Simple_repeat_(TCCCC)n chr1 3692045 3692204 + Simple_repeat_(TG)n chr1 3692276 3692314 + Simple_repeat_(GTGTGG)n chr1 3693015 3693094 - LINE/L2_L2b chr1 3693481 3693571 + SINE/MIR_MIRc chr1 3693952 3694083 + Satellite_MSR1 chr1 3694375 3694611 + Satellite_MSR1 chr1 3694690 3694786 + Satellite_MSR1 chr1 3694899 3694997 + Satellite_MSR1 chr1 3695747 3695974 - LINE/L2_L2b chr1 3696917 3697103 + LINE/L2_L2 chr1 3699721 3699799 + Simple_repeat_(GGAGCT)n chr1 3700527 3700793 + SINE/Alu_AluSx chr1 3701116 3701198 - LINE/L2_L2b chr1 3701467 3701502 - SINE/MIR_MIRc chr1 3701503 3701808 - SINE/Alu_AluSx1 chr1 3701809 3701950 - SINE/MIR_MIRc chr1 3702467 3702496 + Simple_repeat_(CCCCAGC)n chr1 3704165 3704426 - DNA/hAT-Charlie_MER33 chr1 3704432 3704467 - LINE/L2_L2b chr1 3705034 3705182 - SINE/Alu_FRAM chr1 3705189 3705330 - LINE/L1_L1M4 chr1 3706107 3706159 + Simple_repeat_(CCGCC)n chr1 3706225 3706352 + SINE/MIR_MIR chr1 3708429 3708629 - SINE/MIR_MIR chr1 3710726 3710831 + Simple_repeat_(CA)n chr1 3711043 3711234 + SINE/MIR_MIR chr1 3711822 3711868 + Simple_repeat_(GT)n chr1 3711869 3711881 + Simple_repeat_(GCGTGT)n chr1 3712339 3712376 + Simple_repeat_(GCTCGTG)n chr1 3712731 3712868 - SINE/MIR_MIRb chr1 3712927 3712962 + Simple_repeat_(CAGCCC)n chr1 3717251 3717304 + Simple_repeat_(CCCAG)n chr1 3717314 3717377 + Low_complexity_G-rich chr1 3719294 3719327 + LINE/L2_L2d2 chr1 3719867 3719890 - SINE/Alu_AluSx1 chr1 3719891 3719936 + Simple_repeat_(TG)n chr1 3719937 3720215 - SINE/Alu_AluSx1 chr1 3720850 3720953 + SINE/MIR_MIR3 chr1 3722661 3723183 + Satellite/telo_TAR1 chr1 3724737 3725034 + SINE/Alu_AluSz chr1 3725254 3725357 + LINE/L2_L2c chr1 3725366 3726967 + Low_complexity_G-rich chr1 3728090 3728131 + Simple_repeat_(CCTG)n chr1 3728702 3729015 + SINE/Alu_AluJo chr1 3730382 3730498 + SINE/MIR_MIRb chr1 3731887 3731986 - SINE/MIR_MIR1_Amn chr1 3732158 3732203 + Simple_repeat_(CCAGGC)n chr1 3733920 3733937 + Simple_repeat_(T)n chr1 3734989 3735017 + Simple_repeat_(GCA)n chr1 3735079 3735219 - SINE/MIR_MIRb chr1 3737578 3737875 + SINE/Alu_AluSg chr1 3740445 3740734 - SINE/Alu_AluSz chr1 3741083 3741698 + LTR/ERV1_MER51A chr1 3742201 3742339 + LTR/ERVL-MaLR_MLT1H2 chr1 3742357 3742579 + LINE/L2_L2c chr1 3743630 3743933 + SINE/Alu_AluJb chr1 3744184 3744457 + SINE/Alu_AluY chr1 3744500 3744529 + Simple_repeat_(ATAA)n chr1 3744988 3745122 + LINE/L2_L2a chr1 3745160 3745599 + LINE/L2_L2a chr1 3747455 3747508 + Simple_repeat_(GGCGCGG)n chr1 3747757 3747916 - SINE/Alu_AluSx3 chr1 3748219 3749146 - LTR/ERVK_LTR13A chr1 3749147 3749257 - RC/Helitron_Helitron3Na_Mam chr1 3749613 3749879 - DNA/TcMar-Tigger_Tigger3a chr1 3749880 3750182 - SINE/Alu_AluY chr1 3750183 3750276 - DNA/TcMar-Tigger_Tigger3a chr1 3750401 3751390 + LTR/ERVK_LTR5B chr1 3751583 3751609 + LTR/ERV1_LTR45C chr1 3751610 3751900 - SINE/Alu_AluY chr1 3751901 3752001 + LTR/ERV1_LTR45C chr1 3752017 3752276 - SINE/Alu_AluJb chr1 3752281 3752378 + LTR/ERV1_LTR45C chr1 3753307 3753634 - SINE/Alu_AluSp chr1 3754612 3754678 - SINE/MIR_MIR3 chr1 3754839 3754865 + Simple_repeat_(GGGTG)n chr1 3756009 3756058 - SINE/MIR_MIR3 chr1 3756059 3756364 + SINE/Alu_AluSg7 chr1 3756365 3756481 - SINE/MIR_MIR3 chr1 3757262 3757365 - LINE/L1_L1MEg chr1 3757394 3757452 + Simple_repeat_(T)n chr1 3757453 3757640 - SINE/Alu_AluJb chr1 3757641 3757942 + SINE/Alu_AluY chr1 3757943 3758053 - SINE/Alu_AluJb chr1 3758057 3758162 - LINE/L1_L1MEg chr1 3758174 3758191 - SINE/Alu_AluJb chr1 3758192 3758485 - SINE/Alu_AluSc chr1 3758486 3758568 - SINE/Alu_AluJb chr1 3758579 3758896 - SINE/Alu_AluJb chr1 3758929 3759229 + SINE/Alu_AluSx3 chr1 3759230 3759551 - LINE/L1_L1MEg chr1 3759773 3759821 + LINE/L1_L1M5 chr1 3759890 3760137 - LTR/ERVL-MaLR_MLT1K chr1 3760223 3760323 - LINE/L1_L1MC4 chr1 3760325 3760358 + Simple_repeat_(CT)n chr1 3760361 3760683 - LINE/L1_L1MEg chr1 3760679 3760849 - LINE/L1_L1MEg chr1 3761669 3761803 + SINE/MIR_MIR3 chr1 3761959 3762015 + Simple_repeat_(GGATGG)n chr1 3763633 3763698 + Simple_repeat_(TGCCCC)n chr1 3763908 3764070 - SINE/MIR_MIRb chr1 3764235 3764581 - LINE/L1_HAL1 chr1 3764665 3764753 - LINE/L1_HAL1 chr1 3764754 3765052 + SINE/Alu_AluSq2 chr1 3765053 3765760 - LINE/L1_HAL1 chr1 3765858 3766165 - SINE/Alu_AluJb chr1 3766179 3766504 - LINE/L1_HAL1 chr1 3766838 3767138 - SINE/Alu_AluSc chr1 3767799 3767921 - SINE/MIR_MIR chr1 3768089 3768396 - SINE/Alu_AluJr chr1 3768854 3768918 + LINE/L2_L2d2 chr1 3769534 3769675 + SINE/MIR_MIRb chr1 3770362 3770525 + SINE/MIR_MIRb chr1 3770795 3770858 + Low_complexity_GA-rich chr1 3771072 3771269 + DNA/hAT-Blackjack_MER63A chr1 3774857 3774896 + Simple_repeat_(CTCCC)n chr1 3775045 3775208 + SINE/MIR_MIRc chr1 3778785 3778809 + Simple_repeat_(CCTT)n chr1 3780139 3780433 - DNA/TcMar-Tigger_Tigger3d chr1 3780659 3780705 + SINE/Alu_Alu chr1 3780706 3780997 + SINE/Alu_AluSc chr1 3781357 3781410 + LINE/CR1_L3 chr1 3781698 3781981 + SINE/Alu_AluJo chr1 3782205 3782376 - SINE/Alu_AluSp chr1 3782377 3782593 - SINE/Alu_AluSp chr1 3782815 3783130 + srpRNA_7SLRNA chr1 3783139 3783436 + SINE/Alu_AluSq2 chr1 3784714 3785006 + SINE/Alu_AluSp chr1 3785867 3786164 - SINE/Alu_AluSx1 chr1 3786216 3786508 + SINE/Alu_AluSz chr1 3786553 3786581 + Simple_repeat_(GACCAG)n chr1 3786657 3786726 - SINE/MIR_MIR chr1 3791110 3791162 + Simple_repeat_(AGCCC)n chr1 3791453 3791755 - SINE/Alu_AluSp chr1 3791765 3792053 - SINE/Alu_AluJb chr1 3792465 3792745 - SINE/Alu_AluSc chr1 3793022 3793116 + LTR/ERV1_LTR8A chr1 3793117 3793392 - SINE/Alu_AluSp chr1 3793393 3793914 + LTR/ERV1_LTR8A chr1 3793915 3794220 + SINE/Alu_AluY chr1 3794221 3794339 + LTR/ERV1_LTR8A chr1 3794369 3794639 - SINE/Alu_AluSz chr1 3794773 3795077 + SINE/Alu_AluSx chr1 3795210 3795298 - LINE/L2_L2a chr1 3795367 3795427 + SINE/MIR_MIRb chr1 3796670 3796691 + Simple_repeat_(GGGTG)n chr1 3796692 3797417 - LTR/ERV1_MER4A chr1 3797418 3797434 + Simple_repeat_(GGGTG)n chr1 3797525 3797834 - SINE/Alu_AluSx chr1 3797844 3797868 + Simple_repeat_(TTAT)n chr1 3797869 3798149 - SINE/Alu_AluJb chr1 3798151 3798167 + LTR/ERV1_MER4D1 chr1 3798168 3798478 + SINE/Alu_AluY chr1 3798479 3798689 + LTR/ERV1_MER4D1 chr1 3798690 3798994 + SINE/Alu_AluSx1 chr1 3798995 3799228 + LTR/ERV1_MER4D1 chr1 3799234 3799265 + Low_complexity_A-rich chr1 3799275 3799580 + SINE/Alu_AluSx1 chr1 3799584 3800003 + LTR/ERV1_LTR38 chr1 3800005 3800448 + LTR/ERV1_LTR38-int chr1 3800435 3800865 + LTR/ERV1_LTR38-int chr1 3800866 3801192 - LTR/ERVL-MaLR_THE1A chr1 3801195 3801468 + LTR/ERVK_HERVK13-int chr1 3801472 3801948 - LTR/ERVK_LTR13 chr1 3801949 3803126 - LTR/ERVK_HERVK13-int chr1 3803127 3803435 - SINE/Alu_AluY chr1 3803436 3803669 - LTR/ERVK_HERVK13-int chr1 3803659 3805067 - LTR/ERVK_HERVK13-int chr1 3805059 3805231 - LTR/ERVK_HERVK13-int chr1 3805219 3805713 - LTR/ERVK_HERVK13-int chr1 3805727 3805932 - LTR/ERVK_HERVK13-int chr1 3805933 3806930 - LTR/ERVK_LTR13 chr1 3806931 3807419 + LTR/ERV1_LOR1-int chr1 3807444 3807768 - SINE/Alu_AluY chr1 3807769 3808103 - LTR/ERVL-MaLR_MSTA chr1 3808131 3808213 + LINE/L1_L1MEc chr1 3808283 3808575 - SINE/Alu_AluSx1 chr1 3808607 3808902 + SINE/Alu_AluJb chr1 3808911 3809096 - LTR/ERVL-MaLR_MLT1A1 chr1 3809097 3809388 - SINE/Alu_AluSp chr1 3809392 3809681 - SINE/Alu_AluJb chr1 3809682 3809804 - LTR/ERVL-MaLR_MLT1A1 chr1 3809861 3810309 + LTR/ERVK_LTR3B_ chr1 3810401 3810618 + SINE/Alu_AluJr chr1 3810625 3810921 + SINE/Alu_AluSx1 chr1 3811206 3811246 - SINE/MIR_MIRc chr1 3811421 3811489 + SINE/MIR_MIRb chr1 3812558 3812884 + SINE/Alu_AluSx3 chr1 3812994 3813038 + Simple_repeat_(AT)n chr1 3813240 3813543 - SINE/Alu_AluSx chr1 3813571 3813867 + SINE/Alu_AluSx3 chr1 3815690 3815972 - SINE/Alu_AluSx1 chr1 3817049 3817353 + SINE/Alu_AluSx chr1 3817714 3818094 + LTR/ERVL-MaLR_MLT1H chr1 3818129 3818341 + LINE/L2_L2 chr1 3818440 3818668 + LINE/L2_L2 chr1 3818755 3818835 - LINE/L1_L1MB2 chr1 3818842 3819057 - LINE/L1_L1M5 chr1 3819083 3819146 + LINE/L1_L1MEf chr1 3819152 3819474 + LINE/L1_L1MB7 chr1 3819459 3819629 + LINE/L1_L1MEf chr1 3819823 3820015 + LTR/ERVL-MaLR_MLT1E1 chr1 3820112 3820390 + LINE/L1_L1M8 chr1 3820609 3822159 + LINE/L1_L1MC5 chr1 3822215 3822278 + Simple_repeat_(GACA)n chr1 3822332 3822497 + LTR/ERV1_LTR78B chr1 3822731 3822918 + LTR/ERV1_LTR78B chr1 3823091 3823122 + Simple_repeat_(CACTGC)n chr1 3824039 3824097 + LTR/ERVL-MaLR_MSTA chr1 3824098 3824384 - SINE/Alu_AluSp chr1 3824385 3824423 + LTR/ERVL-MaLR_MSTA chr1 3824425 3824733 + LINE/L1_L1ME3A chr1 3825181 3825430 + LINE/L1_L1ME3A chr1 3825451 3825533 + LINE/L1_L1ME3A chr1 3826870 3827117 + SINE/Alu_AluJb chr1 3827213 3827519 - SINE/Alu_AluJb chr1 3827567 3827687 + LINE/L1_L1MB3 chr1 3827829 3827947 + LINE/L2_L2a chr1 3828518 3828676 - LINE/L1_L1MB1 chr1 3830318 3830445 + DNA/TcMar-Tigger_Tigger4a chr1 3830492 3830805 + SINE/Alu_AluY chr1 3830949 3831012 - LINE/L2_L2a chr1 3832655 3832691 - DNA/hAT-Charlie_Charlie4z chr1 3832692 3832980 - SINE/Alu_AluJb chr1 3832985 3833278 - SINE/Alu_AluSc chr1 3833279 3833386 - DNA/hAT-Charlie_Charlie4z chr1 3835380 3835670 - SINE/Alu_AluSg4 chr1 3835878 3836019 + SINE/Alu_FRAM chr1 3836020 3836323 + SINE/Alu_AluYk2 chr1 3836469 3836492 + Simple_repeat_(T)n chr1 3837834 3837918 + SINE/MIR_MIRb chr1 3837947 3838159 - SINE/MIR_MIR chr1 3838161 3838450 - SINE/Alu_AluJo chr1 3838603 3838757 - SINE/MIR_MIRc chr1 3839892 3840016 - SINE/MIR_MIRb chr1 3840231 3840537 - SINE/Alu_AluSz chr1 3840542 3840839 - SINE/Alu_AluSx3 chr1 3842082 3842379 - SINE/Alu_AluYk2 chr1 3842788 3843087 - SINE/Alu_AluSz chr1 3843377 3843651 - SINE/Alu_AluJo chr1 3843769 3844059 - SINE/Alu_AluSg chr1 3844326 3844716 + SINE/Alu_AluSq2 chr1 3845409 3845633 - SINE/Alu_AluJb chr1 3845661 3845732 + SINE/MIR_MIRc chr1 3845808 3846108 + SINE/Alu_AluSp chr1 3846875 3846922 + Low_complexity_GA-rich chr1 3847033 3847333 - DNA/hAT-Charlie_MER33 chr1 3847825 3848052 - SINE/Alu_AluSx1 chr1 3848251 3848547 + SINE/Alu_AluY chr1 3848862 3848969 - LINE/L2_L2d2 chr1 3849267 3849530 - SINE/Alu_AluJb chr1 3849919 3850195 + DNA/TcMar-Tigger_Tigger3a chr1 3851183 3851308 + LTR/ERVL_LTR79 chr1 3851663 3851810 - SINE/Alu_FRAM chr1 3852674 3853095 + LTR/ERVL-MaLR_MLT1C2 chr1 3853813 3853947 + SINE/Alu_FLAM_C chr1 3854471 3854778 - SINE/Alu_AluSz6 chr1 3854871 3855170 - SINE/Alu_AluSx chr1 3855175 3855467 - SINE/Alu_AluJr4 chr1 3856133 3856382 + SINE/Alu_AluSz chr1 3856451 3856493 + Low_complexity_A-rich chr1 3856801 3856880 + Simple_repeat_(CCGCGCC)n chr1 3857974 3858107 + SINE/MIR_MIR3 chr1 3859907 3860044 + Simple_repeat_(CTCTGT)n chr1 3860085 3860227 - SINE/Alu_FLAM_C chr1 3860234 3860528 - SINE/Alu_AluJb chr1 3860529 3860872 - LINE/L1_L1MD2 chr1 3860873 3861173 + SINE/Alu_AluSx1 chr1 3861174 3861417 - LINE/L1_L1MD2 chr1 3861418 3861727 - SINE/Alu_AluJb chr1 3861728 3861827 - LINE/L1_L1MD2 chr1 3861824 3862869 + LINE/L1_L1MB8 chr1 3862870 3863096 + SINE/Alu_AluSx1 chr1 3863097 3863960 + LINE/L1_L1MB8 chr1 3863961 3864264 - SINE/Alu_AluSc chr1 3864265 3864334 + LINE/L1_L1MB8 chr1 3864342 3864545 - LINE/L1_L1MD2 chr1 3864546 3864848 - SINE/Alu_AluJr chr1 3864849 3865216 - LINE/L1_L1MD2 chr1 3865207 3865363 - LINE/L1_L1MD2 chr1 3866253 3866537 - SINE/Alu_AluSg chr1 3866556 3866899 - LINE/L1_L1MB3 chr1 3866900 3867195 - SINE/Alu_AluY chr1 3867196 3867246 - LINE/L1_L1MB3 chr1 3867330 3867394 - LINE/L1_L1MB3 chr1 3867571 3867855 + SINE/Alu_AluJo chr1 3868571 3868741 + Simple_repeat_(CATAC)n chr1 3869069 3869360 - SINE/Alu_AluSz chr1 3869363 3869425 - SINE/MIR_MIRb chr1 3869938 3869983 - DNA/hAT-Charlie_MER5B chr1 3870035 3870139 - LTR/ERVL-MaLR_MLT1B chr1 3870534 3870655 - LINE/L2_L2b chr1 3871286 3871589 - SINE/Alu_AluSx1 chr1 3871683 3872098 - LTR/ERVL-MaLR_MSTA chr1 3872132 3872427 + SINE/Alu_AluSc chr1 3872615 3872678 + Simple_repeat_(GGCCCT)n chr1 3872996 3873158 - SINE/Alu_FRAM chr1 3873361 3873477 + DNA/TcMar-Tigger_Tigger4a chr1 3873478 3873778 - SINE/Alu_AluSp chr1 3873779 3873903 + DNA/TcMar-Tigger_Tigger4a chr1 3875081 3875222 - LINE/L1_L1ME3Cz chr1 3875249 3875334 + DNA/hAT-Charlie_MER5A1 chr1 3875408 3875746 - LINE/L1_L1ME3Cz chr1 3875776 3876077 - SINE/Alu_AluSx chr1 3876268 3876415 + DNA/hAT-Charlie_MER112 chr1 3876569 3876802 - DNA/hAT-Tip100_MER115 chr1 3877329 3877628 - SINE/Alu_AluSz chr1 3878050 3878153 + LINE/L2_L2b chr1 3878155 3878445 - SINE/Alu_AluSx1 chr1 3878726 3879375 - LINE/L1_L1MEi chr1 3879376 3879715 + LTR/ERVL-MaLR_MLT1A chr1 3879716 3879936 - LINE/L1_L1MEi chr1 3879943 3880467 + LTR/ERVL_MER77 chr1 3880550 3880598 + Simple_repeat_(CTG)n chr1 3881595 3881883 + SINE/Alu_AluSx3 chr1 3882041 3882069 + Simple_repeat_(TG)n chr1 3882070 3882353 - SINE/Alu_AluSz6 chr1 3882362 3882664 - SINE/Alu_AluSx chr1 3882667 3882861 + LINE/L1_L1ME3Cz chr1 3883005 3883304 - SINE/Alu_AluSq chr1 3883806 3883834 + Simple_repeat_(T)n chr1 3883917 3884183 - SINE/Alu_AluSp chr1 3884570 3884870 - SINE/Alu_AluSx1 chr1 3884934 3885006 - DNA/TcMar-Mariner_MADE2 chr1 3885904 3886208 - SINE/Alu_AluJo chr1 3886363 3886491 - DNA/TcMar-Tigger_Tigger15a chr1 3886966 3887047 + LTR/ERVK_LTR3A chr1 3887278 3887580 - SINE/Alu_AluY chr1 3887627 3887928 - SINE/Alu_AluSx1 chr1 3888712 3889013 + SINE/Alu_AluSx chr1 3889414 3889725 + SINE/Alu_AluSp chr1 3889971 3889998 + Simple_repeat_(CAT)n chr1 3891412 3891507 + LINE/L2_L2a chr1 3891512 3891568 - LINE/L2_L2b chr1 3893562 3893617 - LINE/L1_L1MC4a chr1 3893618 3893917 + SINE/Alu_AluJr chr1 3893918 3894114 - LINE/L1_L1MC4a chr1 3894326 3894630 + SINE/Alu_AluY chr1 3895428 3895477 - DNA/hAT-Charlie_MER5A chr1 3895874 3897669 + LINE/L1_L1M4b chr1 3897670 3897962 - SINE/Alu_AluY chr1 3897963 3898229 + LINE/L1_L1M4b chr1 3898230 3898516 + SINE/Alu_AluSx1 chr1 3898517 3898554 + Simple_repeat_(AAC)n chr1 3898555 3899340 + LINE/L1_L1M4b chr1 3900329 3900346 + Simple_repeat_(C)n chr1 3901567 3901874 - SINE/Alu_AluSz chr1 3901880 3902184 - SINE/Alu_AluY chr1 3903585 3903640 + LINE/L2_L2b chr1 3904649 3904745 + LINE/L2_L2c chr1 3905047 3905065 + Low_complexity_A-rich chr1 3905066 3905088 + Simple_repeat_(AG)n chr1 3905130 3905190 + SINE/Alu_AluYg6 chr1 3905823 3905856 + Simple_repeat_(TGGCC)n chr1 3908355 3908462 + Simple_repeat_(TC)n chr1 3908463 3908503 + Simple_repeat_(AC)n chr1 3908504 3908664 - LTR/ERVL_MER77B chr1 3908666 3908850 - LTR/ERVL-MaLR_MSTA chr1 3908879 3909380 - LTR/ERVL_MER77B chr1 3910646 3910847 - SINE/MIR_MIRc chr1 3913855 3914141 - SINE/Alu_AluSz chr1 3914142 3914280 - LINE/L2_L2a chr1 3915290 3915504 + LTR/ERVL_LTR33C chr1 3915522 3915674 - LTR/Gypsy?_LTR85c chr1 3916053 3916155 + LTR/ERVL_LTR79 chr1 3916228 3916430 + SINE/MIR_MIRc chr1 3916448 3916951 + DNA/TcMar-Tc2_Kanga1a chr1 3916952 3917249 + SINE/Alu_AluSz chr1 3917250 3917318 + DNA/TcMar-Tc2_Kanga1a chr1 3917382 3917506 - SINE/Alu_FRAM chr1 3917521 3917595 + DNA/TcMar-Tc2_Kanga1d chr1 3917603 3917686 + SINE/MIR_MIR3 chr1 3917711 3917943 - LTR/ERVL-MaLR_MLT1K chr1 3918344 3918698 + LTR/ERVL-MaLR_THE1B chr1 3920069 3920639 + Simple_repeat_(CCCT)n chr1 3920643 3920904 - SINE/Alu_AluJb chr1 3920996 3921145 + LINE/L1_L1ME2 chr1 3921209 3921264 + SINE/MIR_MIRb chr1 3921487 3921625 + LINE/L2_L2a chr1 3921872 3922056 - LINE/L1_HAL1M8 chr1 3922057 3922698 - LTR/ERV1_MER51A chr1 3922699 3922794 - LINE/L1_HAL1M8 chr1 3922795 3923256 + LTR/ERVL-MaLR_MLT1C2 chr1 3923265 3923337 - LINE/L1_HAL1M8 chr1 3923518 3923699 + SINE/MIR_MIR chr1 3923720 3923764 - SINE/MIR_MIR chr1 3924185 3924283 - LTR/ERVL_LTR105_Mam chr1 3924895 3924942 + Simple_repeat_(CCCTGA)n chr1 3926049 3926143 - SINE/MIR_MIR chr1 3926208 3926528 - LTR/ERVL-MaLR_MLT1D chr1 3926529 3926584 - LTR/ERVL_MLT2B2 chr1 3926596 3926705 + Simple_repeat_(TCTA)n chr1 3926706 3926771 + Simple_repeat_(TCCA)n chr1 3926772 3927522 + Simple_repeat_(TCTA)n chr1 3927523 3927978 - LTR/ERVL_MLT2B2 chr1 3928970 3929146 + LTR/ERVL-MaLR_MLT1C chr1 3929147 3930768 + Simple_repeat_(AC)n chr1 3930771 3931038 + LTR/ERVL-MaLR_MLT1C chr1 3931272 3931369 - SINE/MIR_MIR chr1 3931848 3932322 + LTR/ERVL_LTR40A1 chr1 3932325 3932573 + LTR/ERVL_HERVL40-int chr1 3932641 3932713 + LTR/ERVL_HERVL40-int chr1 3932733 3933030 + LTR/ERVL_HERVL40-int chr1 3933281 3933594 - SINE/Alu_AluSz chr1 3933614 3933668 + LTR/ERVL_HERVL40-int chr1 3933669 3933812 + SINE/Alu_FLAM_C chr1 3933813 3933965 + LTR/ERVL_HERVL40-int chr1 3933999 3934300 - SINE/Alu_AluSx chr1 3934313 3935087 - LINE/L1_L1MD2 chr1 3935088 3935379 - SINE/Alu_AluY chr1 3935380 3936233 - LINE/L1_L1MD2 chr1 3936251 3936550 + SINE/Alu_AluSz chr1 3936578 3936623 + Simple_repeat_(TTTA)n chr1 3936638 3936690 - SINE/Alu_Alu chr1 3936700 3937195 - LINE/L1_L1MEc chr1 3937196 3937527 - SINE/Alu_AluSx chr1 3937528 3937683 - LINE/L1_L1MEc chr1 3937684 3937988 - SINE/Alu_AluSz chr1 3937989 3938730 - LINE/L1_L1MEc chr1 3938767 3938972 - SINE/MIR_MIR chr1 3939318 3939653 - LTR/ERVL-MaLR_MLT1K chr1 3939653 3939747 - LTR/ERVL-MaLR_MLT1K chr1 3940003 3940226 - SINE/MIR_MIRb chr1 3940648 3940839 - SINE/MIR_MIRb chr1 3941365 3941392 + Simple_repeat_(T)n chr1 3941726 3942032 - SINE/Alu_AluJb chr1 3942035 3942339 - SINE/Alu_AluSz chr1 3942340 3942369 + Simple_repeat_(T)n chr1 3942395 3942748 + LTR/ERVL-MaLR_MLT1B chr1 3945547 3945732 + LINE/L1_L1M4 chr1 3946062 3946095 + Simple_repeat_(CCTTGG)n chr1 3947162 3947627 - LINE/L1_L1MA4 chr1 3948491 3948689 + SINE/MIR_MIRb chr1 3949104 3949408 + LINE/L1_L1MC1 chr1 3949409 3949952 + LTR/ERV1_MER41D chr1 3949953 3950129 + LINE/L1_L1MC1 chr1 3950130 3950440 + LTR/ERVL-MaLR_THE1B chr1 3950441 3950629 + LINE/L1_L1MC1 chr1 3950830 3951079 + SINE/Alu_AluJr chr1 3951151 3951443 - SINE/Alu_AluY chr1 3951533 3951628 + DNA/PiggyBac_MER85 chr1 3952090 3952458 + LTR/ERVL-MaLR_MLT1J1 chr1 3952643 3953030 - LTR/ERVL-MaLR_MLT1J chr1 3953095 3953469 - LTR/ERVL-MaLR_MLT1A chr1 3953625 3953861 - LTR/ERVL_LTR33B chr1 3953984 3954138 - DNA/hAT-Tip100_MamTip2b chr1 3954150 3954243 - LINE/L1_L1PA11 chr1 3954492 3954567 + LINE/L2_L2b chr1 3954789 3954915 + LTR/ERVL-MaLR_MLT1H2 chr1 3955360 3955393 + Simple_repeat_(CGCCTG)n chr1 3955684 3955767 - LTR/ERV1_MER92B chr1 3955873 3955921 - LTR/ERV1_MER92B chr1 3955922 3956539 + LTR/ERV1_LTR10E chr1 3956540 3956615 - LTR/ERV1_MER92B chr1 3956628 3956697 + LINE/L2_L2b chr1 3957076 3957157 + SINE/MIR_MIR3 chr1 3957388 3957555 + LINE/L2_L2b chr1 3957857 3957982 - SINE/MIR_MIRc chr1 3959276 3959583 - SINE/Alu_AluSx chr1 3959854 3959875 + Simple_repeat_(TG)n chr1 3960062 3960402 + LINE/L1_L1MB3 chr1 3960403 3960684 + SINE/Alu_AluSx chr1 3960685 3960725 + Simple_repeat_(AAAT)n chr1 3960726 3960745 + SINE/Alu_AluSx chr1 3960746 3961408 + LINE/L1_L1MB3 chr1 3961409 3961548 - LTR/ERV1_MER34A chr1 3961549 3961858 + SINE/Alu_AluSx1 chr1 3961859 3962273 - LTR/ERV1_MER34A chr1 3962274 3962310 + LINE/L1_L1MB3 chr1 3962418 3962456 - LTR/ERV1_MER31B chr1 3962457 3962747 - SINE/Alu_AluSx4 chr1 3962748 3962815 - LTR/ERV1_MER31B chr1 3962816 3963124 - SINE/Alu_AluSx chr1 3963125 3963482 - LTR/ERV1_MER31B chr1 3963850 3964690 - DNA/hAT-Blackjack_MER63C chr1 3965016 3965309 + SINE/Alu_AluSz chr1 3965487 3965976 + LTR/ERV1_MER66B chr1 3965996 3966068 + LINE/L2_L2b chr1 3966210 3966649 - LTR/ERVL-MaLR_MLT1C2 chr1 3966675 3966810 - LINE/L2_L2b chr1 3967269 3967406 + SINE/MIR_MIRb chr1 3967570 3967838 + LTR/ERVL-MaLR_MLT1C2 chr1 3967904 3968388 - LINE/L2_L2d2 chr1 3968661 3969080 - LTR/ERVL_LTR16E2 chr1 3969312 3969956 - LTR/ERVL_ERVL-E-int chr1 3970283 3970762 - LTR/ERVL_LTR16E1 chr1 3971708 3971890 + SINE/MIR_MIRb chr1 3972417 3972484 - SINE/MIR_MIR chr1 3972485 3972910 - LTR/ERVL-MaLR_MSTB chr1 3972911 3973070 - SINE/MIR_MIR chr1 3973095 3973115 - SINE/Alu_FLAM_C chr1 3973116 3973140 + Simple_repeat_(ATTG)n chr1 3973141 3973250 - SINE/Alu_FLAM_C chr1 3974244 3974533 + SINE/Alu_AluSz chr1 3974780 3975302 + Simple_repeat_(CA)n chr1 3975586 3975944 - LINE/L2_L2a chr1 3975945 3976307 + LTR/ERVL-MaLR_THE1D chr1 3976308 3976482 - LINE/L2_L2a chr1 3976483 3977539 - LINE/L2_L2a chr1 3977631 3978023 - LINE/L2_L2a chr1 3978304 3978479 + LTR/ERVL-MaLR_MLT1H2 chr1 3978735 3979014 - LINE/L2_L2a chr1 3980226 3980592 - LTR/ERVL-MaLR_THE1C chr1 3980599 3980666 + Simple_repeat_(CATC)n chr1 3980703 3980820 - LINE/L2_L2b chr1 3980978 3981146 - LTR/ERVL-MaLR_MLT1J chr1 3982210 3982366 + SINE/MIR_MIR3 chr1 3982661 3982705 + DNA/hAT-Charlie_MER20 chr1 3982737 3983088 + LTR/ERVL-MaLR_MLT1B chr1 3983780 3984090 - SINE/Alu_AluSz chr1 3984236 3984406 + SINE/MIR_MIR chr1 3985382 3985536 + SINE/Alu_FRAM chr1 3985537 3985568 + Simple_repeat_(ATCTAT)n chr1 3985624 3985831 + SINE/Alu_AluSx3 chr1 3985858 3985894 + Simple_repeat_(AG)n chr1 3985900 3986042 - SINE/Alu_AluJr chr1 3986093 3986154 + Simple_repeat_(AT)n chr1 3986201 3986398 - LINE/RTE-X_L5 chr1 3986827 3987339 - LTR/ERVL-MaLR_MLT1E1A chr1 3988645 3988774 + SINE/MIR_MIRb chr1 3988796 3989088 - SINE/Alu_AluSg chr1 3989350 3989856 - LINE/L1_L1PA13 chr1 3989857 3990228 + LTR/ERVL-MaLR_THE1C chr1 3990229 3991501 - LINE/L1_L1PA13 chr1 3991513 3991821 - SINE/Alu_AluSz6 chr1 3993358 3993454 - SINE/MIR_MIRc chr1 3994272 3994383 - SINE/MIR_MIRb chr1 3994484 3994630 + LTR/ERVL-MaLR_MLT1C2 chr1 3994629 3994755 - SINE/Alu_FLAM_A chr1 3994758 3995031 + LTR/ERVL-MaLR_MLT1C2 chr1 3996567 3996982 - LTR/ERVL-MaLR_MLT1C2 chr1 3997349 3997557 - SINE/MIR_MIR chr1 3997680 3997773 - SINE/Alu_FLAM_C chr1 3997787 3998083 - SINE/Alu_AluJo chr1 3998087 3998804 + DNA/TcMar-Tc2_Kanga1b chr1 3999085 3999396 - SINE/Alu_AluSx chr1 4000147 4000364 - LTR/ERVL-MaLR_MLT1A0 chr1 4001709 4001771 - DNA/hAT-Tip100_MER91B chr1 4002656 4003132 + LTR/ERVL-MaLR_MLT1D chr1 4003158 4003457 + SINE/Alu_AluSz chr1 4003546 4003667 + SINE/Alu_AluJo chr1 4003672 4003934 + SINE/Alu_AluSz6 chr1 4004087 4004150 + SINE/MIR_MIR3 chr1 4005037 4005161 - SINE/MIR_MIR chr1 4005813 4005844 + Simple_repeat_(TTTCTT)n chr1 4005845 4006127 - SINE/Alu_AluY chr1 4006674 4007020 - LTR/ERVL-MaLR_MLT1F1 chr1 4007028 4007278 + Low_complexity_GA-rich chr1 4007279 4007581 + SINE/Alu_AluY chr1 4007582 4007909 + Low_complexity_GA-rich chr1 4007916 4008110 - LTR/ERVL-MaLR_MLT1F1 chr1 4008926 4009291 + LINE/RTE-X_L4_B_Mam chr1 4009326 4009865 + LINE/RTE-X_L4_B_Mam chr1 4009871 4009979 + SINE/MIR_MIRc chr1 4011471 4011548 + DNA/hAT-Tip100_MamRep4096 chr1 4011553 4011863 + SINE/Alu_AluSz chr1 4011914 4012022 - SINE/MIR_MIR chr1 4012109 4012285 + DNA/TcMar-Tigger_Tigger2a chr1 4012286 4012574 + SINE/Alu_AluJb chr1 4012575 4012850 + DNA/TcMar-Tigger_Tigger2a chr1 4012852 4013274 + LINE/L1_L1ME5 chr1 4013284 4013501 + SINE/Alu_AluY chr1 4013592 4013841 + LINE/L1_L1ME5 chr1 4013856 4013940 - LTR/ERVL-MaLR_MSTD chr1 4013942 4014017 + LINE/L1_L1ME5 chr1 4014018 4014318 - SINE/Alu_AluSp chr1 4014319 4014839 + LINE/L1_L1ME5 chr1 4014850 4015100 - LTR/ERVL-MaLR_MLT1A1 chr1 4015101 4015404 - SINE/Alu_AluSx chr1 4015405 4015459 - LTR/ERVL-MaLR_MLT1A1 chr1 4015460 4015758 - SINE/Alu_AluSx chr1 4015759 4015806 - LTR/ERVL-MaLR_MLT1A1 chr1 4015839 4016909 + LINE/L1_L1ME5 chr1 4017087 4017245 + LINE/L1_L1ME5 chr1 4017277 4017630 + DNA/hAT-Tip100_MamRep4096 chr1 4017915 4018018 - SINE/MIR_MIR chr1 4018249 4018316 + Simple_repeat_(TGCACACACA)n chr1 4019348 4019378 + Simple_repeat_(CCACTC)n chr1 4019701 4019935 + LINE/L2_L2 chr1 4020317 4020374 + Simple_repeat_(GGCTCT)n chr1 4021846 4022122 - LTR/ERVL_MLT2D chr1 4022123 4022246 + SINE/Alu_FLAM_C chr1 4022247 4022353 - LTR/ERVL_MLT2D chr1 4022692 4022798 + DNA/hAT-Charlie_MER20 chr1 4023832 4024266 - LTR/ERVL-MaLR_MLT1C2 chr1 4024423 4024762 + LTR/ERVL-MaLR_MLT1A chr1 4024824 4025249 + Simple_repeat_(TCTA)n chr1 4025259 4025445 + LINE/L1_L1M5 chr1 4025779 4025808 + Low_complexity_A-rich chr1 4026091 4026400 - LINE/L1_L1MA9 chr1 4026436 4026743 - SINE/Alu_AluSc8 chr1 4028018 4028043 + Simple_repeat_(T)n chr1 4028522 4029206 - LINE/L1_L1ME1 chr1 4029207 4029569 + LTR/ERVL-MaLR_MSTD chr1 4029570 4030480 - LINE/L1_L1ME1 chr1 4030481 4030794 - SINE/Alu_AluSq2 chr1 4030795 4031570 - LINE/L1_L1ME1 chr1 4032008 4032029 + Simple_repeat_(G)n chr1 4032110 4032292 + LTR/Gypsy?_LTR85c chr1 4032335 4032610 + LINE/L2_L2a chr1 4032866 4033221 + LTR/ERVL-MaLR_MLT1C2 chr1 4033735 4034325 - LTR/ERVL_MER21B chr1 4034326 4039971 + LINE/L1_L1PB1 chr1 4039981 4043199 + LINE/L1_L1P2 chr1 4043235 4043314 + LTR/ERVL-MaLR_MLT1J chr1 4043423 4043650 + LTR/ERVL-MaLR_MLT1J chr1 4043746 4043885 + Simple_repeat_(CTT)n chr1 4043886 4043925 + Simple_repeat_(CCTT)n chr1 4043926 4044214 - SINE/Alu_AluY chr1 4044215 4044268 + Simple_repeat_(CTT)n chr1 4044373 4044549 + LTR/ERVL-MaLR_MLT1D chr1 4044853 4045013 - LTR/ERV1_LTR78 chr1 4045014 4045242 + LINE/L1_L1PA10 chr1 4045252 4045443 + LINE/L1_L1PB1 chr1 4045729 4045840 - LTR/ERVL-MaLR_MLT1O chr1 4045846 4045923 + LTR/ERVL_MLT2D chr1 4045924 4046282 + DNA/TcMar-Tigger_MER47A chr1 4046283 4046452 + LTR/Gypsy?_LTR85a chr1 4047254 4047297 + Low_complexity_G-rich chr1 4047465 4047600 + Low_complexity_GA-rich chr1 4047671 4047822 - DNA/TcMar-Tigger_Tigger19a chr1 4049678 4050180 + Low_complexity_GA-rich chr1 4050182 4050208 + Simple_repeat_(GTGG)n chr1 4050295 4050603 - SINE/Alu_AluJb chr1 4050719 4051027 - SINE/Alu_AluSx3 chr1 4051173 4051305 - LINE/L2_L2b chr1 4051819 4051846 + Simple_repeat_(T)n chr1 4052006 4052309 + SINE/Alu_AluY chr1 4052314 4052623 + SINE/Alu_AluSz chr1 4052945 4053098 + LINE/L2_L2c chr1 4053140 4053534 + LINE/L2_L2c chr1 4054190 4054321 + SINE/MIR_MIRc chr1 4054364 4054662 - SINE/Alu_AluSc8 chr1 4055844 4056067 + LTR/ERVL-MaLR_MLT1A0 chr1 4056068 4056419 + LTR/ERVL-MaLR_THE1D chr1 4056420 4056557 + LTR/ERVL-MaLR_MLT1A0 chr1 4056877 4056920 - SINE/MIR_MIRc chr1 4058672 4058714 + Low_complexity_G-rich chr1 4059632 4060115 + LINE/L1_L1PA3 chr1 4060995 4061468 + LTR/ERVL-MaLR_MLT1D chr1 4062558 4062608 + Simple_repeat_(CCTCCC)n chr1 4063019 4063085 + SINE/MIR_MIRb chr1 4063474 4063643 - SINE/MIR_MIRc chr1 4064206 4067619 + Simple_repeat_(CACT)n chr1 4069343 4069582 - SINE/MIR_MIRb chr1 4069697 4069930 - LINE/L1_HAL1ME chr1 4069974 4069993 + Simple_repeat_(A)n chr1 4070173 4070238 - LINE/L1_L1MC3 chr1 4070239 4070844 - LTR/ERV1_LTR24C chr1 4070845 4071337 - LINE/L1_L1MC3 chr1 4072168 4072268 - SINE/MIR_MIR chr1 4073245 4073423 + Low_complexity_GA-rich chr1 4073586 4073893 - SINE/Alu_AluYk2 chr1 4075108 4075198 - LINE/L1_L1MA1 chr1 4075276 4075318 - SINE/MIR_MIRb chr1 4075324 4075419 - LTR/ERVL-MaLR_MLT1C chr1 4075431 4075522 - SINE/MIR_MIR1_Amn chr1 4076438 4076607 - LTR/ERVL_MER77B chr1 4076680 4076858 + SINE/MIR_MIRb chr1 4078925 4079092 - LTR/ERVL_MER77 chr1 4079093 4079385 + SINE/Alu_AluJb chr1 4079386 4079492 - LTR/ERVL_MER77 chr1 4079493 4079842 - LTR/ERVL-MaLR_THE1B chr1 4079843 4080147 - LTR/ERVL_MER77 chr1 4080652 4080735 + Simple_repeat_(ATGG)n chr1 4080736 4080986 + LINE/L1_L1PA6 chr1 4080987 4081150 + Simple_repeat_(ATGG)n chr1 4081151 4081569 - LTR/ERVL-MaLR_MSTB chr1 4081570 4081603 + Simple_repeat_(ATGG)n chr1 4083648 4083729 - SINE/MIR_MIRc chr1 4083928 4084025 + DNA/hAT-Charlie_Charlie25 chr1 4084017 4084878 + DNA/hAT-Charlie_Charlie25 chr1 4084966 4085079 + DNA/hAT-Charlie_Charlie25 chr1 4085194 4085325 + DNA/hAT-Charlie_Charlie25 chr1 4085371 4085692 + DNA/hAT-Charlie_Charlie25 chr1 4087345 4087757 - LTR/ERVL-MaLR_MSTC chr1 4087802 4087859 + Simple_repeat_(TGAA)n chr1 4087899 4088072 + SINE/Alu_FRAM chr1 4088238 4088274 + Simple_repeat_(TTGGCA)n chr1 4089040 4089464 - LTR/ERVL-MaLR_MLT1C2 chr1 4090213 4090296 - SINE/MIR_MIRb chr1 4091651 4091737 + LTR/ERVL-MaLR_MLT1J1 chr1 4091902 4092072 + SINE/MIR_MIRb chr1 4092336 4092611 + SINE/Alu_AluSx chr1 4092940 4093037 + Simple_repeat_(CTCTTT)n chr1 4093066 4093416 - LTR/ERVL-MaLR_THE1B chr1 4093689 4093788 + SINE/MIR_MIRb chr1 4094163 4094222 - LINE/L2_L2a chr1 4094549 4094954 + LTR/ERVL-MaLR_MLT1C chr1 4095032 4095096 + LTR/ERVL-MaLR_MLT1C chr1 4095097 4095400 - SINE/Alu_AluYb8 chr1 4095401 4095463 + LTR/ERVL-MaLR_MLT1C chr1 4096346 4096892 - LINE/L1_L1MEc chr1 4096893 4097691 - LTR/ERVL_MER21A chr1 4097692 4098002 + SINE/Alu_AluSx chr1 4098003 4098062 - LTR/ERVL_MER21A chr1 4098063 4098480 - LTR/ERVL-MaLR_MSTB chr1 4098481 4099030 - LINE/L1_L1MEc chr1 4099043 4099092 + Simple_repeat_(TGTACA)n chr1 4099135 4099203 - LINE/L1_L1MEc chr1 4099204 4099567 + LTR/ERVL-MaLR_THE1D chr1 4099568 4099801 - LINE/L1_L1MEc chr1 4099802 4100448 + Simple_repeat_(TA)n chr1 4100449 4101424 - LINE/L1_L1MEc chr1 4102544 4105048 + LINE/L1_L1PA3 chr1 4105777 4105908 + SINE/MIR_MIRc chr1 4106082 4106143 - SINE/MIR_MIR chr1 4106153 4106484 - LTR/ERVL-MaLR_MLT1J chr1 4106487 4106686 - LTR/ERVL-MaLR_MLT1J chr1 4106769 4106839 - SINE/MIR_MIRc chr1 4108018 4108067 + Simple_repeat_(CA)n chr1 4108068 4108440 - LTR/ERVL-MaLR_MLT1B chr1 4109776 4110327 + LTR/ERVL-MaLR_MLT1K chr1 4110786 4110926 + LINE/L2_L2d2 chr1 4111458 4111672 + DNA/TcMar-Tigger_X6a_DNA chr1 4112308 4112735 + LTR/ERVL-MaLR_MLT1O chr1 4113540 4113700 - SINE/MIR_MIR3 chr1 4113791 4113967 + SINE/MIR_MIRb chr1 4113985 4114070 - SINE/MIR_MIRc chr1 4114076 4115441 - LINE/L1_L1MD2 chr1 4115899 4115953 - LTR/ERVL-MaLR_MLT1C chr1 4115954 4116480 - DNA/hAT-Charlie_MER1A chr1 4116481 4116730 - LTR/ERVL-MaLR_MLT1C chr1 4116731 4117021 - SINE/Alu_AluSc chr1 4117022 4117167 - LTR/ERVL-MaLR_MLT1C chr1 4117199 4117533 + LTR/ERVL-MaLR_MLT1I chr1 4118470 4118550 + DNA/hAT-Tip100_MamTip2b chr1 4120112 4120464 - LTR/ERVL-MaLR_MLT1C chr1 4121766 4121927 + SINE/MIR_MIR3 chr1 4123185 4123351 + LTR/ERVL-MaLR_MLT1J2 chr1 4123461 4123616 + SINE/MIR_MIRc chr1 4123918 4124213 + SINE/Alu_AluSc chr1 4124423 4124734 - SINE/Alu_AluSx1 chr1 4125026 4125146 - LINE/L2_L2d chr1 4125306 4125468 - DNA/hAT-Charlie_Charlie1a chr1 4125469 4125775 + SINE/Alu_AluSg chr1 4125776 4126785 - DNA/hAT-Charlie_Charlie1a chr1 4126829 4126960 - LTR/ERVL-MaLR_MLT1J1 chr1 4126961 4127388 + LTR/ERVL-MaLR_MSTA chr1 4127389 4127531 - LTR/ERVL-MaLR_MLT1J1 chr1 4128250 4128457 + Simple_repeat_(CTCTGT)n chr1 4129225 4129444 - SINE/Alu_AluSx chr1 4130222 4130322 - LINE/L2_L2b chr1 4130602 4130748 - DNA/hAT-Charlie_MER5A chr1 4132406 4132440 + Simple_repeat_(CCTCC)n chr1 4133445 4133531 - LINE/L2_L2a chr1 4135625 4135802 + LTR/ERVL-MaLR_MSTD chr1 4135641 4135842 + LTR/ERVL-MaLR_MSTB2 chr1 4135902 4135970 + LTR/ERVL-MaLR_MSTD chr1 4136042 4136102 + LTR/ERVL-MaLR_MSTD chr1 4136160 4136202 + LTR/ERVL-MaLR_MSTD chr1 4136204 4136300 + LTR/ERVL-MaLR_MSTD chr1 4136400 4136474 + LTR/ERVL-MaLR_MSTD chr1 4136577 4136634 + LTR/ERVL-MaLR_MSTD chr1 4136636 4136680 + LTR/ERVL-MaLR_MSTD chr1 4136823 4137198 + LTR/ERVL-MaLR_MSTD chr1 4137753 4137799 + Simple_repeat_(CCTCTG)n chr1 4137890 4137939 + Simple_repeat_(TCTGCCC)n chr1 4138761 4138860 - LTR/ERV1_MER89 chr1 4141626 4142100 + LTR/ERVL-MaLR_MLT1D chr1 4142389 4142678 - SINE/Alu_AluY chr1 4142979 4143005 + Simple_repeat_(TG)n chr1 4144120 4144315 + SINE/MIR_MIR chr1 4144324 4144933 + Simple_repeat_(TCA)n chr1 4145049 4145155 - SINE/MIR_MIR chr1 4146777 4146806 + Simple_repeat_(CA)n chr1 4147277 4147370 + LINE/L2_L2b chr1 4148051 4148356 + SINE/Alu_AluSx1 chr1 4148430 4148549 - SINE/MIR_MIR3 chr1 4148970 4149041 - DNA/hAT-Blackjack_MER81 chr1 4151038 4151085 - LINE/L1_L1MB5 chr1 4151086 4151193 - LINE/L1_L1MA8 chr1 4151194 4151310 - LINE/L1_L1MB5 chr1 4151362 4151663 + SINE/Alu_AluSc8 chr1 4151793 4152101 - SINE/Alu_AluY chr1 4153662 4153880 - LTR/ERVL-MaLR_MLT1A chr1 4153881 4154426 - LTR/ERVL_MLT2B2 chr1 4154427 4154562 - LTR/ERVL-MaLR_MLT1A chr1 4154872 4154904 + Simple_repeat_(T)n chr1 4154911 4155076 - SINE/Alu_AluJr chr1 4155077 4155106 + Simple_repeat_(ATT)n chr1 4155107 4155380 - SINE/Alu_AluSz chr1 4155381 4155520 - SINE/Alu_AluJr chr1 4155704 4156005 - SINE/Alu_AluJo chr1 4156960 4157189 + LTR/ERVL-MaLR_MLT1D chr1 4157312 4157606 + SINE/Alu_AluSx1 chr1 4157749 4157834 - LTR/ERVL-MaLR_MLT1A chr1 4158445 4158729 + SINE/Alu_AluSz chr1 4159930 4160231 + SINE/Alu_AluSg7 chr1 4160613 4160647 + Simple_repeat_(AGAG)n chr1 4160654 4160907 - LTR/ERVL_MLT2D chr1 4160908 4161208 - SINE/Alu_AluY chr1 4161209 4161304 - LTR/ERVL_MLT2D chr1 4162145 4162189 + Simple_repeat_(TCTTTC)n chr1 4162190 4162203 + Simple_repeat_(TCTGTC)n chr1 4162469 4162544 + SINE/MIR_MIRb chr1 4162578 4162644 + SINE/MIR_MIRb chr1 4162722 4162759 + Simple_repeat_(TCTT)n chr1 4162760 4162815 + Simple_repeat_(TCCT)n chr1 4162817 4163122 - SINE/Alu_AluSx chr1 4163124 4163417 - SINE/Alu_AluJo chr1 4163440 4163553 + LINE/CR1_L3 chr1 4164064 4164095 + Simple_repeat_(AACAA)n chr1 4164634 4165113 + LINE/L2_L2 chr1 4165769 4165796 + Simple_repeat_(AAAC)n chr1 4166356 4166396 + DNA/hAT-Tip100_MER91A chr1 4166398 4166505 + DNA/hAT-Tip100_MER91A chr1 4166593 4166628 + Simple_repeat_(TGTC)n chr1 4167317 4167497 + SINE/MIR_MIRb chr1 4167932 4167979 + SINE/MIR_MIRc chr1 4167980 4168493 - LTR/ERVL_MLT2A2 chr1 4168494 4168537 + SINE/MIR_MIRc chr1 4168538 4168974 - LTR/ERVL-MaLR_MLT1C2 chr1 4168975 4169028 + SINE/MIR_MIRc chr1 4169041 4169117 - LTR/ERVL-MaLR_MLT1C2 chr1 4169721 4170031 - SINE/Alu_AluSz chr1 4171547 4171723 + LINE/L2_L2b chr1 4171817 4172024 - SINE/MIR_MIRb chr1 4172959 4173003 + SINE/MIR_MIR chr1 4173006 4173079 - DNA/hAT-Charlie_MER103C chr1 4173105 4173219 + DNA/hAT-Charlie_MER103C chr1 4173261 4173392 - SINE/MIR_MIRb chr1 4173706 4174144 - LTR/ERVL-MaLR_MLT1C2 chr1 4174202 4174306 - LINE/L2_L2a chr1 4174654 4174859 - LINE/L2_L2a chr1 4174909 4175022 - SINE/MIR_MIR chr1 4175092 4175420 + LTR/ERVL-MaLR_MLT1A0 chr1 4175446 4175462 + Simple_repeat_(T)n chr1 4175980 4176054 + Simple_repeat_(TGACCTC)n chr1 4176213 4176808 + LTR/ERV1_MER4B chr1 4176809 4177002 + LINE/L2_L2b chr1 4177120 4177225 + SINE/MIR_MIR3 chr1 4177227 4177316 + LINE/L1_L1MA4 chr1 4177475 4177857 + LTR/ERV1_MER48 chr1 4178039 4178500 - LTR/ERV1_LTR7B chr1 4179333 4179783 - LTR/ERVL_MLT2A2 chr1 4180535 4180664 - LINE/L1_L1M4 chr1 4180665 4180821 - LINE/L1_L1MC1 chr1 4180822 4180841 - LINE/L1_L1M4 chr1 4180874 4180992 - LINE/L1_L1MA9 chr1 4180997 4185574 - LTR/ERV1_HERVH-int chr1 4185554 4187477 - LTR/ERV1_HERVH-int chr1 4187478 4187514 - LTR/ERV1_LTR7 chr1 4187515 4187594 - LINE/L1_L1MA9 chr1 4187595 4187959 - LTR/ERVL-MaLR_THE1B chr1 4187960 4188247 - LINE/L1_L1MA9 chr1 4188236 4188500 - LINE/L1_L1PA17 chr1 4188506 4188808 - SINE/Alu_AluSq2 chr1 4188825 4189001 - LINE/L1_L1MC3 chr1 4188859 4189004 - LINE/L1_L1MC1 chr1 4189005 4189533 - DNA/hAT-Charlie_MER1A chr1 4189540 4191793 - LINE/L1_L1MC1 chr1 4191791 4193626 - LINE/L1_L1MC1 chr1 4193627 4194009 + LTR/ERVL-MaLR_THE1C chr1 4194010 4194957 - LINE/L1_L1MC1 chr1 4194951 4195478 - LINE/L1_L1MC3 chr1 4195458 4195853 + LINE/L1_L1PB1 chr1 4195854 4196128 + SINE/Alu_AluSc chr1 4196129 4198296 + LINE/L1_L1PB1 chr1 4198297 4198649 - LTR/ERVL-MaLR_THE1B chr1 4198650 4200352 + LINE/L1_L1PB1 chr1 4200372 4200933 - LINE/L1_L1MC3 chr1 4200934 4200967 + Simple_repeat_(GT)n chr1 4200968 4201144 - LINE/L1_L1MC3 chr1 4201227 4201411 + LTR/ERV1_LTR37B chr1 4201448 4201530 - LTR/ERVL-MaLR_MLT1J2 chr1 4202146 4202442 - LTR/ERVL-MaLR_MSTD chr1 4202443 4202820 - LTR/ERVL-MaLR_THE1D chr1 4202821 4202961 - LTR/ERVL-MaLR_MSTD chr1 4203798 4204779 - LINE/L2_L2a chr1 4204778 4205002 - SINE/MIR_MIR chr1 4205029 4205213 + LTR/ERVL-MaLR_MLT1E2 chr1 4205541 4205638 + LINE/L2_L2b chr1 4205759 4206075 - LTR/ERVL-MaLR_MLT1F2 chr1 4206076 4206739 - LINE/L1_L1PB4 chr1 4206740 4206906 - LTR/ERVL-MaLR_MLT1F2 chr1 4207096 4207290 - LINE/L2_L2a chr1 4207792 4208095 - LTR/ERVL_LTR53B chr1 4208096 4208400 - SINE/Alu_AluJr chr1 4208401 4208606 - LTR/ERVL_LTR53B chr1 4208617 4208744 - DNA/hAT-Charlie_MER20 chr1 4209211 4209260 - LINE/L2_L2b chr1 4209262 4209400 + LINE/L2_L2c chr1 4209558 4209630 + LINE/L2_L2d chr1 4209631 4210162 - LTR/ERVL_MLT2A2 chr1 4210163 4210222 + LINE/L2_L2d chr1 4211147 4211423 + SINE/Alu_AluYb8 chr1 4211640 4211794 - DNA/hAT-Tip100_MER96 chr1 4212005 4212270 + LTR/ERVL-MaLR_MLT1C chr1 4212271 4212408 - LTR/ERVL-MaLR_THE1B chr1 4212409 4212702 - SINE/Alu_AluSp chr1 4212703 4212939 - LTR/ERVL-MaLR_THE1B chr1 4212940 4213057 + LTR/ERVL-MaLR_MLT1C chr1 4214033 4214191 + Simple_repeat_(TTCC)n chr1 4214194 4214251 + Simple_repeat_(TCC)n chr1 4214270 4214533 - SINE/Alu_AluJb chr1 4214833 4214906 + SINE/Alu_Alu chr1 4214948 4215384 - LTR/ERVL-MaLR_MLT1C2 chr1 4216512 4216654 + SINE/MIR_MIRc chr1 4216684 4216783 + DNA/hAT-Charlie_MER5B chr1 4217002 4217172 + LTR/ERVL-MaLR_MLT1C2 chr1 4217176 4217606 + LTR/ERVL-MaLR_MLT1C chr1 4217631 4217873 + LTR/ERVL-MaLR_MLT1C2 chr1 4218039 4218065 + Simple_repeat_(GAT)n chr1 4218087 4218483 - LTR/ERVL-MaLR_MLT1C chr1 4219416 4219704 + SINE/Alu_AluSg4 chr1 4220092 4220220 - LTR/ERVL-MaLR_MLT1C chr1 4220410 4220729 - LTR/ERVL-MaLR_MLT1C chr1 4220730 4220844 + SINE/MIR_MIR chr1 4220979 4221153 + DNA/hAT-Charlie_Charlie19a chr1 4221670 4221808 + LTR/ERVL-MaLR_MLT1I chr1 4221809 4222105 - SINE/Alu_AluSg7 chr1 4222106 4222347 + LTR/ERVL-MaLR_MLT1I chr1 4222460 4222944 + LTR/ERVL-MaLR_MLT1D chr1 4223001 4223604 - LTR/ERV1_MER4A1_ chr1 4223805 4223830 + Simple_repeat_(AT)n chr1 4223975 4224278 + SINE/Alu_AluSx chr1 4225407 4225694 - SINE/Alu_AluSz chr1 4226051 4226159 - LINE/L1_L1MB2 chr1 4226160 4226477 - SINE/Alu_AluSx1 chr1 4226478 4226529 - LINE/L1_L1MB2 chr1 4226635 4226824 - LINE/L1_L1ME3Cz chr1 4226834 4227044 - LINE/L1_L1ME3Cz chr1 4227045 4227335 - SINE/Alu_AluSg chr1 4227336 4227672 - LINE/L1_L1ME3Cz chr1 4227712 4227987 + LINE/L1_L1MC4 chr1 4228184 4228486 + LTR/ERV1_MER61B chr1 4228966 4229747 + LINE/L1_L1PB1 chr1 4229897 4230184 - LTR/ERVL-MaLR_MLT1C chr1 4230185 4230476 - SINE/Alu_AluY chr1 4230477 4230662 - LTR/ERVL-MaLR_MLT1C chr1 4230680 4230782 + LINE/L1_L1MC4 chr1 4230904 4231206 - SINE/Alu_AluSz chr1 4231385 4232361 + LINE/L1_L1MC4 chr1 4232385 4232692 + SINE/Alu_AluY chr1 4232699 4233037 + LINE/L1_L1ME3F chr1 4233079 4233215 - SINE/MIR_MIRb chr1 4234864 4235200 + LTR/ERVL-MaLR_MLT1A0 chr1 4235207 4235251 - tRNA_tRNA-Leu-TTA chr1 4235413 4235442 + LTR/ERVL-MaLR_MLT1A0-int chr1 4235443 4235796 + LTR/ERVL-MaLR_THE1B chr1 4235797 4236358 + LTR/ERVL-MaLR_MLT1A0-int chr1 4236375 4236724 + LTR/ERVL-MaLR_MLT1A0 chr1 4236809 4237583 - LINE/L1_L1ME4b chr1 4238277 4238332 - LINE/L1_L1M5 chr1 4238333 4238709 - LTR/ERVL-MaLR_THE1C chr1 4238710 4239016 - LINE/L1_L1M5 chr1 4239017 4239312 - SINE/Alu_AluJb chr1 4239313 4239929 - LINE/L1_L1M5 chr1 4239939 4240180 + LTR/ERVL-MaLR_MSTA1 chr1 4240184 4240730 + LTR/ERVL_MLT2A2 chr1 4240738 4240924 + LTR/ERVL-MaLR_MSTA1 chr1 4240925 4241126 - LINE/L1_L1M5 chr1 4241127 4241155 + Simple_repeat_(TTTTG)n chr1 4241162 4241438 - SINE/Alu_AluSz6 chr1 4241439 4242228 - LINE/L1_L1M5 chr1 4242242 4243059 - LINE/L1_L1ME2 chr1 4243060 4243384 + LTR/ERVL-MaLR_MLT1A0 chr1 4243385 4243450 - LINE/L1_L1ME2 chr1 4243451 4243475 + Simple_repeat_(CA)n chr1 4243476 4243747 - LINE/L1_L1ME2 chr1 4243747 4244339 - LINE/L1_L1MC chr1 4244340 4244384 + Simple_repeat_(TT)n chr1 4244385 4247142 - LINE/L1_L1PA3 chr1 4247127 4248103 - LINE/L1_L1MC chr1 4248104 4248418 + DNA/TcMar-Tigger_MER47A chr1 4248419 4248869 - LINE/L1_L1MC chr1 4249341 4249418 - LINE/L1_L1MC chr1 4249399 4249812 - LINE/L1_L1ME2 chr1 4249813 4250261 - LTR/ERV1_MER83 chr1 4250262 4250389 - LINE/L1_L1ME2 chr1 4250382 4251615 - LINE/L1_L1ME2 chr1 4251616 4251917 - SINE/Alu_AluSx3 chr1 4251918 4252351 - LINE/L1_L1ME2 chr1 4252379 4252435 + LINE/L2_L2a chr1 4252504 4253050 + LTR/ERVL_MLT2A2 chr1 4253273 4253478 - SINE/MIR_MIR chr1 4253509 4253527 + Simple_repeat_(AATAT)n chr1 4253528 4253835 + SINE/Alu_AluSp chr1 4253836 4253858 + Simple_repeat_(AATAT)n chr1 4254309 4254535 + LTR?_MamRep605 chr1 4254640 4255755 + LTR/ERVL_HERV16-int chr1 4255798 4255873 + LINE/L2_L2a chr1 4255925 4255959 + Simple_repeat_(CTCTGG)n chr1 4256187 4256279 + LINE/L2_L2a chr1 4256280 4256472 + DNA/hAT-Charlie_MER103C chr1 4256941 4257104 - SINE/Alu_FAM chr1 4257441 4257707 - LTR/ERVL-MaLR_THE1C chr1 4257712 4259241 + LTR/ERVL-MaLR_THE1C-int chr1 4259242 4259619 + LTR/ERVL-MaLR_THE1C chr1 4260012 4260129 + SINE/MIR_MIRb chr1 4260423 4260714 - LINE/L1_L1MEd chr1 4260755 4260778 + Simple_repeat_(TTTCA)n chr1 4260779 4261194 + LTR/ERVL-MaLR_MSTA chr1 4261260 4261440 - LINE/L2_L2a chr1 4261441 4261936 - LTR/ERV1_LOR1a chr1 4261937 4261951 - LINE/L2_L2a chr1 4261968 4262136 - LINE/L1_L1MEd chr1 4262151 4262298 - LTR/ERVL_LTR79 chr1 4262415 4262587 - LTR/ERV1_LTR9B chr1 4262589 4262728 - LINE/L1_L1MEd chr1 4262988 4263205 - LINE/L1_L1MEd chr1 4263239 4263584 - LINE/L1_L1ME3G chr1 4263706 4264225 - LINE/L2_L2a chr1 4264228 4264266 + Simple_repeat_(TG)n chr1 4264345 4264822 + Simple_repeat_(AGAT)n chr1 4264865 4264924 - LINE/L2_L2a chr1 4264925 4265319 - LTR/ERVL-MaLR_MLT1B chr1 4265320 4265559 - LINE/L2_L2a chr1 4265758 4265830 - LINE/L1_L1ME3Cz chr1 4266046 4266261 - DNA/hAT-Charlie_MER20 chr1 4266349 4266685 + LTR/ERVL-MaLR_MSTD chr1 4268019 4268216 - LTR/ERVL_LTR33A_ chr1 4269758 4269967 + LINE/L1_L1PA4 chr1 4270648 4271168 - LTR/ERVL-MaLR_MLT1K chr1 4272022 4272029 + Simple_repeat_(TCCT)n chr1 4272030 4272103 + Simple_repeat_(TTC)n chr1 4272104 4272119 + Simple_repeat_(TCCT)n chr1 4272592 4272706 + LINE/L2_L2a chr1 4272815 4274114 - LINE/L1_L1MEf chr1 4274121 4274153 + Simple_repeat_(C)n chr1 4274154 4274175 + Simple_repeat_(CCTC)n chr1 4274176 4274245 + Simple_repeat_(TCTT)n chr1 4274246 4274540 - SINE/Alu_AluSx chr1 4274627 4274659 + Simple_repeat_(TTAT)n chr1 4274954 4275152 + SINE/MIR_MIR chr1 4275174 4275516 + LTR/ERVL-MaLR_MLT1A chr1 4276670 4277069 + LTR/ERV1_MER57B1 chr1 4277226 4277255 + Simple_repeat_(GT)n chr1 4277609 4277643 + Simple_repeat_(TGAA)n chr1 4277644 4277741 + LINE/L2_L2d chr1 4279181 4279250 - SINE/MIR_MIRb chr1 4279252 4279686 - LTR/ERVL-MaLR_MLT1E1A chr1 4279678 4279735 - LTR/ERVL-MaLR_MLT1E1A chr1 4279966 4280088 - SINE/MIR_MIRb chr1 4280447 4280626 - SINE/MIR_MIRb chr1 4282083 4282199 + LINE/L2_L2d2 chr1 4282375 4282409 + Simple_repeat_(GATCA)n chr1 4282479 4282960 + LTR/ERVL-MaLR_MLT1J chr1 4283033 4283335 - SINE/Alu_AluSp chr1 4283340 4283461 - SINE/MIR_MIRb chr1 4284118 4284296 - SINE/MIR_MIR chr1 4284453 4284864 + LINE/L1_L1PA16 chr1 4284865 4285156 + SINE/Alu_AluJb chr1 4285157 4285200 + Simple_repeat_(AAAT)n chr1 4285201 4285340 + LINE/L1_L1PA16 chr1 4285609 4285801 - LTR/ERVL_LTR16C chr1 4286025 4287152 - LINE/L1_L1PA15 chr1 4287153 4287249 - SINE/Alu_AluYh7 chr1 4287250 4291645 - LINE/L1_L1PA15 chr1 4292010 4292737 - LINE/L1_L1ME3 chr1 4292738 4292864 + SINE/MIR_MIR chr1 4293755 4293892 + SINE/MIR_MIRb chr1 4295097 4295189 - DNA/hAT-Blackjack_MER81 chr1 4295189 4295256 - DNA/hAT-Blackjack_MER81 chr1 4295755 4295850 + SINE/MIR_MIR chr1 4295923 4296579 + Simple_repeat_(ATCC)n chr1 4296580 4296708 - LINE/L2_L2a chr1 4296709 4297070 - LTR/ERVL-MaLR_THE1B chr1 4297071 4297366 - LINE/L2_L2a chr1 4297442 4297905 - LINE/L2_L2a chr1 4300004 4300544 - LTR/ERVL_MER74A chr1 4300599 4300770 - SINE/MIR_MIRb chr1 4300879 4300900 + Simple_repeat_(ATTG)n chr1 4301853 4301981 - SINE/Alu_AluSg4 chr1 4301983 4302067 - DNA/hAT-Charlie_MER3 chr1 4302077 4302240 + LINE/L2_L2a chr1 4302360 4302728 + LTR/ERVL-MaLR_MLT1I chr1 4302801 4302849 - SINE/MIR_MIRc chr1 4302852 4302891 + Simple_repeat_(TCC)n chr1 4302955 4302983 + Simple_repeat_(TTCCA)n chr1 4302984 4303071 + Simple_repeat_(GTTCC)n chr1 4303072 4303312 + Simple_repeat_(TTCCA)n chr1 4304587 4304619 + Simple_repeat_(GT)n chr1 4305575 4306079 - LTR/ERV1_MER66B chr1 4306233 4306587 - LTR/ERVL-MaLR_THE1B chr1 4306989 4307097 + SINE/MIR_MIR chr1 4309213 4309573 + LINE/L1_L1PA4 chr1 4309593 4309650 - DNA/hAT-Charlie_Charlie5 chr1 4310798 4310905 + SINE/MIR_MIRb chr1 4311199 4311476 - SINE/Alu_AluSx1 chr1 4311514 4312322 - LINE/L1_L1ME2 chr1 4311726 4312417 - LINE/L1_L1MB1 chr1 4312395 4313299 - LINE/L1_L1ME2 chr1 4313309 4313598 - LTR/ERVL-MaLR_MSTA chr1 4313599 4315183 - LTR/ERVL-MaLR_MSTA-int chr1 4315184 4315551 - LTR/ERVL-MaLR_MSTA chr1 4315603 4315712 + LTR/ERVL-MaLR_MLT1H chr1 4315905 4316021 + LTR/ERVL-MaLR_MLT1H chr1 4316062 4316357 - LINE/L2_L2 chr1 4316360 4316743 - LTR/ERV1_MER89 chr1 4316758 4316815 - LINE/L2_L2 chr1 4316842 4317071 - LINE/L2_L2 chr1 4317116 4317549 + LTR/ERVL-MaLR_MLT1C chr1 4317662 4318356 - LTR/ERVL_MER21C chr1 4318357 4318487 + Simple_repeat_(ATATAC)n chr1 4318518 4318545 + Simple_repeat_(AC)n chr1 4318546 4318829 - SINE/Alu_AluSc chr1 4318830 4318839 + Simple_repeat_(AC)n chr1 4319297 4319436 + LINE/L2_L2d chr1 4320216 4320328 - DNA/hAT-Blackjack_MER81 chr1 4320360 4320410 - LINE/L2_L2c chr1 4320971 4321057 + LTR/ERVL_LTR79 chr1 4321264 4321636 - LTR/ERVL-MaLR_THE1C chr1 4322598 4322737 - LINE/L2_L2a chr1 4323771 4323812 + Simple_repeat_(GGACT)n chr1 4324846 4325080 - LINE/L2_L2b chr1 4325193 4325435 - SINE/Alu_AluJo chr1 4325438 4325600 + SINE/MIR_MIRc chr1 4325618 4325972 - LTR/ERVL_LTR16A2 chr1 4325988 4326024 + Simple_repeat_(AT)n chr1 4326025 4326320 - SINE/Alu_AluSx chr1 4326324 4326772 + LTR/ERVL_LTR53 chr1 4326795 4326909 - LTR/ERVL_LTR16A2 chr1 4326969 4327087 + LTR/ERVL-MaLR_MSTA chr1 4327202 4327524 + LTR/ERVL-MaLR_MSTA chr1 4328254 4328325 - LINE/L2_L2b chr1 4330313 4330355 + Low_complexity_GA-rich chr1 4330511 4330993 + LTR/ERVL_MLT2B4 chr1 4332280 4332376 + LTR/ERV1_MER89 chr1 4333576 4333626 + LTR/ERVL-MaLR_MLT1B chr1 4333629 4333857 - LINE/L2_L2 chr1 4333897 4334004 - SINE/MIR_MIRb chr1 4334806 4334870 - SINE/MIR_MIRc chr1 4335049 4335188 + SINE/MIR_MIRc chr1 4335216 4335286 + LINE/CR1_X17_LINE chr1 4336812 4336975 - SINE/MIR_MIR chr1 4337835 4338039 - SINE/MIR_MIR chr1 4338477 4338911 + LTR/ERVL-MaLR_THE1C chr1 4339592 4339656 - LTR/ERVL-MaLR_THE1C chr1 4339656 4340012 + LTR/ERVL-MaLR_MLT1C2 chr1 4340071 4340192 - LINE/L2_L2 chr1 4340331 4340392 + LINE/L2_L2b chr1 4340584 4340735 - DNA/hAT-Charlie_MER5A chr1 4341013 4341319 - SINE/Alu_AluJb chr1 4342339 4342371 + Simple_repeat_(GAGGCA)n chr1 4342430 4342487 - LTR/ERVL_LTR16A1 chr1 4342524 4342851 + LTR/ERVL_HERV16-int chr1 4342907 4343455 + LTR/ERV1_MER92B chr1 4343708 4343796 + LTR/ERV1_MER92B chr1 4345519 4345966 - LTR/ERVL-MaLR_MLT1C2 chr1 4345976 4346365 + LTR/ERVL-MaLR_THE1D chr1 4346784 4347156 + LTR/ERV1_MER90 chr1 4347277 4347479 + LTR/ERV1_MER90 chr1 4347486 4347619 - SINE/MIR_MIRb chr1 4347710 4347841 + Simple_repeat_(CTATAT)n chr1 4347847 4347973 - SINE/MIR_MIR chr1 4347996 4348077 + LINE/L2_L2a chr1 4348096 4348513 + LINE/L1_L1ME3Cz chr1 4348517 4348557 + Simple_repeat_(TG)n chr1 4348560 4348622 + Low_complexity_GA-rich chr1 4348727 4348948 + SINE/Alu_AluSz6 chr1 4349314 4349594 - LTR/ERVL-MaLR_MLT1C2 chr1 4349601 4350307 + LINE/L1_L1PA7 chr1 4350396 4350544 - LTR/ERVL-MaLR_MLT1C2 chr1 4350547 4350734 - LINE/L2_L2 chr1 4351892 4351955 - LTR/ERVL_LTR50 chr1 4351956 4352243 + SINE/Alu_AluSp chr1 4352244 4352554 + SINE/Alu_AluYf1 chr1 4352555 4352692 - LTR/ERVL_LTR50 chr1 4353238 4353359 - LTR/ERVL-MaLR_MLT1J1 chr1 4353372 4353531 - LTR/ERVL-MaLR_MLT1C chr1 4353534 4353565 + Simple_repeat_(GT)n chr1 4353671 4353747 + Simple_repeat_(TG)n chr1 4354495 4354730 + SINE/MIR_MIR chr1 4354733 4354832 - SINE/MIR_MIR chr1 4355053 4355206 - SINE/MIR_MIRb chr1 4357084 4357115 + Simple_repeat_(GA)n chr1 4357116 4357664 + LTR/ERV1_LTR1A2 chr1 4357665 4357860 + DNA/TcMar-Tigger_Tigger7 chr1 4358886 4359244 + LTR/ERVL-MaLR_MLT1C2 chr1 4359413 4359469 - SINE/MIR_MIRc chr1 4359863 4359892 + Simple_repeat_(A)n chr1 4360570 4360668 + DNA/hAT-Charlie_MER5A1 chr1 4361512 4361951 + LTR/ERVL-MaLR_MLT1C2 chr1 4363193 4363257 + DNA/hAT-Tip100_MamTip2b chr1 4363338 4363456 + DNA/hAT-Tip100_MamTip2b chr1 4363557 4363800 + LTR/ERVL-MaLR_MLT1J chr1 4364059 4364128 + LINE/L2_L2a chr1 4364138 4364322 + LTR/ERVL-MaLR_MSTA chr1 4364323 4364842 + LINE/L1_L1PA6 chr1 4364843 4365063 + LTR/ERVL-MaLR_MSTA chr1 4365133 4365168 + Simple_repeat_(TA)n chr1 4365887 4366248 + LTR/ERVL-MaLR_MLT1A chr1 4366528 4366950 + LTR/ERVL-MaLR_MLT1C chr1 4368462 4368580 + SINE/MIR_MIR3 chr1 4368546 4368740 - SINE/MIR_MIR chr1 4368900 4368909 + Simple_repeat_(TGA)n chr1 4368910 4368956 + Simple_repeat_(TGGTGA)n chr1 4368957 4369776 + Simple_repeat_(TGA)n chr1 4369905 4370039 - LINE/L2_L2 chr1 4370040 4370254 - DNA/hAT-Charlie_MER20 chr1 4370255 4370344 - LINE/L2_L2 chr1 4370340 4370438 + LINE/L2_L2a chr1 4370438 4371098 - LINE/L2_L2 chr1 4371099 4371438 - LTR/ERV1_MER31B chr1 4371439 4371515 - LINE/L2_L2 chr1 4372178 4372210 + Simple_repeat_(TTTGTT)n chr1 4372221 4372349 + LTR/ERVL?_LTR89B chr1 4372643 4373170 + Simple_repeat_(CCAC)n chr1 4373171 4373237 + Simple_repeat_(CCAT)n chr1 4373238 4373319 + Simple_repeat_(CCAC)n chr1 4373320 4373709 - LTR/ERVL-MaLR_MSTA chr1 4373710 4373869 + Simple_repeat_(CCAC)n chr1 4373949 4373979 + Simple_repeat_(CCAT)n chr1 4373989 4374034 - LINE/L2_L2c chr1 4374145 4374317 - LINE/L2_L2c chr1 4374395 4374498 - LINE/L2_L2c chr1 4374637 4374849 - SINE/MIR_MIR chr1 4374959 4375370 - LTR/ERVL-MaLR_MSTB chr1 4375646 4376189 + LINE/L1_L1ME2 chr1 4376193 4376233 + Simple_repeat_(TTTCT)n chr1 4376235 4376527 - SINE/Alu_AluSx3 chr1 4376538 4376647 + LINE/L1_L1ME2 chr1 4376677 4376746 + Low_complexity_GA-rich chr1 4377287 4377437 - SINE/MIR_MIRc chr1 4378700 4379024 + SINE/Alu_AluJb chr1 4379027 4379074 + Simple_repeat_(AGAAGG)n chr1 4379835 4380186 - LTR/ERVL-MaLR_MLT1B chr1 4380262 4380512 + SINE/Alu_AluSz chr1 4381071 4381113 + LTR/ERVL_MER77B chr1 4381115 4382190 - LTR/ERVK_MER11A chr1 4382299 4382448 + LTR/ERVL_MER77B chr1 4382449 4382812 - LTR/ERVL-MaLR_THE1B chr1 4382813 4383161 + LTR/ERVL_MER77B chr1 4383976 4384377 + LTR/ERVL-MaLR_MLT1C chr1 4384658 4384889 - LTR/Gypsy_LTR81AB chr1 4385003 4385020 - LTR/ERVL-MaLR_MLT1C chr1 4385021 4385408 - LTR/ERVL-MaLR_MSTA chr1 4385409 4385572 - LTR/ERVL-MaLR_MLT1C chr1 4386065 4386427 + LTR/ERVL-MaLR_THE1B chr1 4386769 4387256 - LTR/ERVL-MaLR_MLT1C chr1 4388197 4388356 + SINE/MIR_MIR chr1 4388409 4388452 + Simple_repeat_(TGCA)n chr1 4388462 4388668 - LINE/L2_L2a chr1 4388740 4389017 - LTR/ERVL-MaLR_MLT1I chr1 4389162 4389273 - SINE/MIR_MIR chr1 4389333 4389753 + LTR/ERVL-MaLR_MSTB chr1 4390573 4390771 - SINE/MIR_MIRb chr1 4391549 4391566 + LINE/L1_L1MC1 chr1 4391567 4391926 + LTR/ERVL-MaLR_THE1C chr1 4391927 4394165 + LINE/L1_L1MC1 chr1 4394168 4394294 + LINE/L1_L1ME1 chr1 4395145 4395495 + LTR/ERVL-MaLR_MLT1A0 chr1 4395961 4396094 - SINE/MIR_MIRb chr1 4398077 4398138 + LINE/CR1_L3 chr1 4398516 4398566 + Simple_repeat_(GT)n chr1 4399428 4399574 + DNA/hAT-Charlie_MER3 chr1 4400310 4401114 - LTR/ERV1_LTR1B1 chr1 4402200 4402287 + Simple_repeat_(AATTA)n chr1 4402470 4402773 - SINE/Alu_AluJr chr1 4403235 4403842 + LINE/L1_L1MC3 chr1 4403864 4404115 + LINE/L1_L1MB3 chr1 4404144 4404445 - LINE/L1_L1PA8 chr1 4404447 4404760 + LINE/L1_L1PA8 chr1 4405205 4405257 - LINE/CR1_L3b chr1 4405837 4405985 - DNA/hAT-Charlie_MER5B chr1 4407319 4407614 - SINE/Alu_AluSz chr1 4408430 4408509 - SINE/MIR_MIRb chr1 4409162 4409289 + LINE/L2_L2a chr1 4409319 4409880 - LINE/L1_L1ME3G chr1 4409909 4410159 - LTR/ERVL-MaLR_MLT1J chr1 4410464 4410849 + LTR/ERVL-MaLR_MSTB chr1 4410909 4411427 - LINE/L1_L1ME3G chr1 4411540 4411601 + Simple_repeat_(CTC)n chr1 4411694 4412409 - LTR/ERV1_MER50 chr1 4412533 4413343 - LINE/L1_L1ME3G chr1 4413402 4414557 - LINE/L1_L1ME3G chr1 4414558 4414844 + SINE/Alu_AluJb chr1 4414846 4414878 + Simple_repeat_(AATA)n chr1 4414879 4415520 - LINE/L1_L1ME3G chr1 4416229 4416263 + Simple_repeat_(GT)n chr1 4417030 4417325 + SINE/Alu_AluSx chr1 4417527 4417766 - LINE/L2_L2a chr1 4418103 4418278 + LINE/L2_L2d chr1 4418619 4418667 + Simple_repeat_(CCTC)n chr1 4419144 4419381 + SINE/MIR_MIRc chr1 4419532 4419619 - LINE/L2_L2b chr1 4419667 4420043 + LTR/ERVL-MaLR_MLT1D chr1 4420044 4420279 - LTR/ERV1_MER61D chr1 4420280 4420379 + Simple_repeat_(TA)n chr1 4420380 4420572 - LTR/ERV1_MER61D chr1 4420573 4420696 + LTR/ERVL-MaLR_MLT1D chr1 4420733 4420884 - LINE/L2_L2a chr1 4422648 4422762 - DNA/TcMar-Tigger_Tigger19b chr1 4423028 4423417 + LTR/ERVL_LTR16A1 chr1 4423466 4423603 + LTR/ERVL-MaLR_MLT1C chr1 4423839 4424045 + LTR/ERV1_MER31B chr1 4424056 4424353 + LTR/ERVL-MaLR_MLT1C chr1 4424429 4424765 + LTR/ERV1_MER61A chr1 4426313 4426381 + LINE/RTE-BovB_MamRTE1 chr1 4427308 4427529 + Simple_repeat_(AT)n chr1 4427523 4427552 + Simple_repeat_(TG)n chr1 4428808 4429672 + LTR/ERVL_MER21C chr1 4429830 4429988 + LTR/ERVL-MaLR_MLT1A0 chr1 4429989 4430121 + SINE/Alu_FLAM_C chr1 4430122 4430340 + LTR/ERVL-MaLR_MLT1A0 chr1 4430586 4430679 + LINE/L2_L2b chr1 4430764 4430837 + LTR/ERVL-MaLR_MLT1A0 chr1 4431300 4431418 - SINE/MIR_MIRc chr1 4431885 4432194 + SINE/Alu_AluSp chr1 4434210 4434274 + LINE/CR1_X7A_LINE chr1 4434389 4434438 - LINE/L1_L1MB3 chr1 4434439 4434738 - SINE/Alu_AluSx1 chr1 4434742 4435033 - SINE/Alu_AluSz chr1 4435034 4435688 - LINE/L1_L1MB3 chr1 4435689 4435881 + DNA/hAT-Charlie_MER58A chr1 4435974 4436066 + LINE/L1_L1MA7 chr1 4436427 4436549 + LTR/ERVL_LTR16E1 chr1 4436550 4436644 - SINE/MIR_MIRb chr1 4436687 4436894 - DNA/hAT-Tip100_Arthur1B chr1 4436986 4437233 + LTR/ERVL-MaLR_MLT1B chr1 4437234 4437613 - LTR/ERVL-MaLR_MSTA chr1 4437614 4437978 + LTR/ERVL-MaLR_THE1D chr1 4437979 4438002 - LTR/ERVL-MaLR_MSTA chr1 4438003 4438125 + LTR/ERVL-MaLR_MLT1B chr1 4438126 4438310 - DNA/hAT-Tip100_Arthur1 chr1 4438826 4438873 + LINE/CR1_X7B_LINE chr1 4439590 4439610 + Simple_repeat_(TC)n chr1 4439620 4439818 + LTR/ERVL-MaLR_MLT1B chr1 4439897 4440086 - LINE/L1_L1MEd chr1 4440096 4440570 + LTR/ERVL_LTR79 chr1 4440645 4441327 - LTR/ERV1_LTR8 chr1 4441367 4442155 - LINE/L1_L1MEd chr1 4442310 4442335 + Simple_repeat_(TTCTA)n chr1 4442510 4442732 - LINE/L2_L2 chr1 4443563 4443753 - LINE/L1_L1MEd chr1 4443755 4443839 + LINE/L2_L2c chr1 4443998 4444238 + DNA/hAT-Tip100?_Eutr15 chr1 4444316 4444454 + LTR/ERVL_MLT2C1 chr1 4444342 4444862 + LTR/ERVL-MaLR_MLT1E2 chr1 4444890 4445191 - SINE/Alu_AluSg chr1 4445514 4445721 + DNA/hAT-Tip100?_Eutr15 chr1 4445776 4446071 + LINE/L2_L2c chr1 4446423 4446884 + LTR/ERV1_LTR19A chr1 4446963 4447108 + DNA/hAT-Charlie_MER58A chr1 4447109 4447560 + LTR/ERV1_MER4A1 chr1 4447561 4447643 + DNA/hAT-Charlie_MER58A chr1 4447772 4447942 + SINE/MIR_MIRc chr1 4447877 4447977 + SINE/5S-Deu-L2_AmnSINE1 chr1 4448888 4449064 - LINE/L1_L1PA15 chr1 4449164 4449321 + SINE/MIR_MIRb chr1 4449405 4449475 + LINE/L2_L2c chr1 4449568 4449880 + SINE/Alu_AluSq2 chr1 4449970 4450083 - LINE/L1_L1MA8 chr1 4450084 4450526 + LTR/ERV1_MER65A chr1 4450527 4450721 - LINE/L1_L1MA8 chr1 4451401 4451722 - LTR/ERV1_MER89 chr1 4451930 4452103 + LTR/ERVL-MaLR_MLT1A0 chr1 4452112 4452480 - LINE/L1_L1MA6 chr1 4452480 4453100 + LINE/L1_L1MA6 chr1 4453101 4453410 + SINE/Alu_AluYk2 chr1 4453411 4453750 + LINE/L1_L1MA6 chr1 4453762 4453980 + LTR/ERVL-MaLR_MLT1A0 chr1 4454416 4454711 + LINE/L2_L2b chr1 4454975 4455035 + SINE/MIR_MIRc chr1 4455146 4455449 + SINE/Alu_AluJb chr1 4455543 4455993 - LINE/L1_L1MB5 chr1 4456185 4456257 + LTR/ERVL-MaLR_THE1B chr1 4456258 4456598 + LTR/ERVL-MaLR_THE1B chr1 4456734 4457027 - SINE/Alu_AluSc chr1 4457407 4457572 + SINE/Alu_FRAM chr1 4457650 4457778 + LINE/CR1_L3 chr1 4457769 4457798 - SINE/MIR_MIR chr1 4457799 4458101 - SINE/Alu_AluSg chr1 4458102 4458225 - SINE/MIR_MIR chr1 4458251 4458585 - LINE/L1_L1ME4a chr1 4458890 4458969 - SINE/MIR_MIRb chr1 4458970 4459170 + LTR/ERVL-MaLR_MLT1J chr1 4459443 4459486 - SINE/MIR_MIRc chr1 4459697 4459932 + SINE/MIR_MIRb chr1 4460970 4461080 + DNA/hAT-Tip100_ORSL-2b chr1 4462052 4462442 + LINE/L2_L2a chr1 4463498 4464005 + LTR/ERVL_LTR53B chr1 4464202 4464309 + LINE/L2_L2b chr1 4465002 4465022 + LINE/L1_L1MB8 chr1 4465023 4465385 + LTR/ERVL-MaLR_THE1B chr1 4465386 4465639 + LINE/L1_L1MB8 chr1 4465640 4465941 + SINE/Alu_AluSx3 chr1 4465942 4466290 + LINE/L1_L1MB8 chr1 4467838 4468150 - SINE/Alu_AluSx chr1 4468317 4468569 + LTR/ERVL-MaLR_MLT1B chr1 4468754 4469198 - LTR/ERVL-MaLR_MLT1C2 chr1 4469433 4469537 - DNA/TcMar-Tigger_MamRep434 chr1 4470936 4471066 + Simple_repeat_(CA)n chr1 4471067 4471123 + Simple_repeat_(GCACACACATGT)n chr1 4471124 4471276 + Simple_repeat_(CA)n chr1 4471292 4471591 + SINE/Alu_AluSq2 chr1 4471814 4472042 + DNA/TcMar-Tigger_MamRep434 chr1 4472205 4472474 - LTR/ERVL-MaLR_MLT1H2 chr1 4472476 4472852 + LTR/ERVL_MLT2B1 chr1 4472886 4473055 - SINE/Alu_AluSz chr1 4473056 4473086 + Simple_repeat_(TTTA)n chr1 4473087 4473219 - SINE/Alu_AluSz chr1 4473220 4473343 - LTR/ERVL-MaLR_MLT1H2 chr1 4473774 4474144 - LTR/ERVL-MaLR_MLT1B chr1 4474390 4474408 + Simple_repeat_(T)n chr1 4474631 4474748 - SINE/MIR_MIRb chr1 4475341 4475451 - LTR/ERVL-MaLR_MLT1K chr1 4475638 4475774 + SINE/MIR_MIR chr1 4475981 4476133 + SINE/MIR_MIR chr1 4476387 4477374 - LINE/L1_L1MCc chr1 4477465 4477917 - LINE/L1_L1MCc chr1 4477918 4478111 - SINE/MIR_MIR chr1 4478287 4478424 + SINE/MIR_MIRb chr1 4478437 4479301 - LINE/L1_L1PB2 chr1 4479382 4479416 + Simple_repeat_(TG)n chr1 4479541 4479772 - LINE/L2_L2a chr1 4479869 4480308 + LINE/L2_L2a chr1 4481436 4481581 - LTR/ERVL-MaLR_MLT1H1 chr1 4481834 4481904 - LTR/ERVL-MaLR_MLT1H1 chr1 4483451 4483620 - LINE/L2_L2c chr1 4485922 4486076 + LINE/L1_L1MB5 chr1 4487001 4487103 - LINE/L2_L2b chr1 4487136 4487581 - LTR/ERVL-MaLR_MLT1C2 chr1 4487604 4487624 + Simple_repeat_(TAA)n chr1 4487701 4487935 - LINE/L2_L2b chr1 4489676 4489731 + Low_complexity_G-rich chr1 4490040 4490086 - LINE/L1_L1PA3 chr1 4490085 4490173 + LINE/L1_L1P2 chr1 4490150 4490450 - LINE/L1_L1P3 chr1 4490483 4490692 + LINE/L2_L2a chr1 4490763 4490940 - DNA/hAT_MER53 chr1 4490982 4491740 + LINE/L2_L2b chr1 4491753 4492314 + LTR/ERVL_MER77 chr1 4492622 4492793 - LTR/ERVL-MaLR_MLT1C2 chr1 4492907 4493454 + LTR/ERVL_MLT2A2 chr1 4493455 4493580 - LTR/ERVL-MaLR_MLT1C2 chr1 4493584 4493744 + LINE/L2_L2b chr1 4494036 4494133 + LINE/L2_L2b chr1 4494536 4494640 + LTR_EUTREP8 chr1 4496147 4496898 + Simple_repeat_(CA)n chr1 4496899 4496931 + Simple_repeat_(CT)n chr1 4496932 4496950 + Simple_repeat_(CA)n chr1 4497515 4498055 - LTR/ERVL_MER21C chr1 4498056 4498348 + SINE/Alu_AluSx1 chr1 4498349 4498540 - LTR/ERVL_MER21C chr1 4499008 4499279 - SINE/Alu_AluSz chr1 4499595 4499845 + LINE/L2_L2a chr1 4500031 4500340 - SINE/Alu_AluYb8 chr1 4500500 4500575 + LINE/L2_L2b chr1 4501177 4501406 - LTR/ERVL_LTR79 chr1 4501451 4501827 - LTR/ERVL-MaLR_MLT1A chr1 4501957 4502385 + LINE/L2_L2b chr1 4502618 4503040 - LINE/CR1_CR1_Mam chr1 4503056 4503261 + LINE/L1_L1MC4 chr1 4503262 4503569 + SINE/Alu_AluSz chr1 4503570 4503869 + LINE/L1_L1MC4 chr1 4503881 4504227 + LTR/ERVL-MaLR_MLT1B chr1 4504346 4504664 + LINE/L1_L1MC4 chr1 4505216 4505524 + LTR/ERVL-MaLR_MLT1A0 chr1 4505561 4505627 - DNA/hAT-Tip100_MER45R chr1 4506696 4506922 - LINE/L1_L1MB1 chr1 4506903 4507090 + LINE/L1_L1MB1 chr1 4508107 4508565 + LINE/L1_L1PA5 chr1 4508576 4508615 + Simple_repeat_(A)n chr1 4508838 4508868 + Simple_repeat_(CTCCC)n chr1 4508869 4508932 + Simple_repeat_(TCTTT)n chr1 4508933 4509059 - SINE/Alu_AluJb chr1 4509105 4509135 + Simple_repeat_(TG)n chr1 4509254 4509302 - SINE/MIR_MIRc chr1 4510121 4510290 + LINE/CR1_X7A_LINE chr1 4510765 4510941 - DNA/hAT-Charlie_MER20 chr1 4511719 4511928 + DNA/hAT-Charlie_MER58B chr1 4512354 4512578 + Low_complexity_A-rich chr1 4515627 4515961 + LTR/ERVL-MaLR_MLT1C2 chr1 4516053 4516388 - DNA/hAT-Charlie_MER58B chr1 4518965 4519435 + LTR/ERVL-MaLR_MLT1C2 chr1 4521758 4521869 - LINE/L2_L2a chr1 4522012 4522366 - LTR/ERVL-MaLR_MLT1K chr1 4522365 4522693 + LTR/ERVL_MLT2D chr1 4522706 4522725 + Simple_repeat_(TC)n chr1 4522726 4522931 + Simple_repeat_(ATCT)n chr1 4522932 4522968 + Simple_repeat_(TC)n chr1 4522982 4523012 + Simple_repeat_(GA)n chr1 4524024 4524201 - DNA/hAT-Charlie_MER5A chr1 4524721 4524759 + Simple_repeat_(TGA)n chr1 4525025 4525172 + LINE/L2_L2c chr1 4525173 4525376 + DNA/hAT-Charlie_MER58A chr1 4525377 4525548 + LINE/L2_L2c chr1 4525905 4526535 - DNA/TcMar-Tigger_Tigger17a chr1 4526547 4526645 + DNA/hAT-Tip100_MamTip1 chr1 4527076 4527434 + LINE/L1_L1MC4a chr1 4529026 4529341 + SINE/Alu_AluSc8 chr1 4529867 4529907 + Simple_repeat_(TTGTT)n chr1 4530587 4530890 + SINE/Alu_AluSx chr1 4531333 4531703 + LTR/ERVL_MLT2D chr1 4531704 4531755 + Simple_repeat_(AC)n chr1 4531756 4531774 + LTR/ERVL_MLT2D chr1 4532041 4532145 + SINE/MIR_MIRb chr1 4533074 4533318 + SINE/Alu_AluJb chr1 4533417 4533602 + SINE/MIR_MIRb chr1 4533913 4534445 + LINE/L1_L1MC5 chr1 4534424 4534854 + LINE/L1_L1MD3 chr1 4534855 4535220 + LTR/ERVL-MaLR_THE1B chr1 4535221 4535693 + LINE/L1_L1MD3 chr1 4535691 4536769 + LINE/L1_L1MC5 chr1 4536770 4537082 + SINE/Alu_AluSq2 chr1 4537184 4537506 + LINE/L1_L1M5 chr1 4537507 4537830 + SINE/Alu_AluSx1 chr1 4537831 4538292 + LINE/L1_L1M5 chr1 4538293 4538771 + LINE/L1_L1ME3E chr1 4538772 4538803 + Simple_repeat_(ATTG)n chr1 4538804 4539375 + LINE/L1_L1ME3E chr1 4539376 4539527 + LINE/L1_L1MB7 chr1 4539511 4539843 + LINE/L1_L1MB7 chr1 4539528 4539546 + LINE/L1_L1ME3E chr1 4539844 4539870 + Simple_repeat_(TTTAG)n chr1 4539872 4540159 - SINE/Alu_AluSz chr1 4540160 4540197 + LINE/L1_L1MB7 chr1 4540312 4540789 + LINE/L1_L1ME3E chr1 4540790 4541088 + SINE/Alu_AluSx1 chr1 4541089 4541374 + LINE/L1_L1ME3E chr1 4541378 4541665 + SINE/Alu_AluJb chr1 4541750 4542230 + LINE/L1_L1M5 chr1 4542295 4542842 - DNA/TcMar-Tigger_Tigger3c chr1 4543025 4543145 + LTR/ERVL_LTR101_Mam chr1 4543401 4543541 + LTR/ERVL-MaLR_MLT1A1 chr1 4543418 4543549 + LTR/ERVL-MaLR_MLT1B chr1 4543669 4543908 + Simple_repeat_(TCTA)n chr1 4543998 4544163 + LINE/L2_L2 chr1 4544407 4544987 + Simple_repeat_(GAGACA)n chr1 4545054 4545141 + LTR/ERVL-MaLR_MLT1A1 chr1 4546916 4546952 + Simple_repeat_(TTTGT)n chr1 4552140 4552493 - LTR/ERVL-MaLR_THE1A chr1 4552546 4552605 - LINE/L2_L2a chr1 4552834 4552979 + LINE/L2_L2 chr1 4553142 4553196 + Simple_repeat_(ATTCTT)n chr1 4555383 4555694 + SINE/Alu_AluY chr1 4555870 4556000 - LTR/ERVL_LTR79 chr1 4556071 4556206 - SINE/MIR_MIRb chr1 4556268 4556779 + DNA/hAT-Charlie_Charlie1b chr1 4556926 4557784 - LINE/L1_L1PA16 chr1 4558309 4558389 - SINE/MIR_MIR3 chr1 4559055 4559384 - LINE/L2_L2b chr1 4560633 4560689 + Simple_repeat_(TATCAT)n chr1 4561333 4561370 + Simple_repeat_(GCAGCAA)n chr1 4561804 4562130 - LTR/ERVL-MaLR_MLT1G chr1 4562131 4562429 - SINE/Alu_AluSz chr1 4562430 4562668 - LTR/ERVL-MaLR_MLT1G chr1 4562671 4562787 + SINE/MIR_MIRb chr1 4562993 4563308 - SINE/Alu_AluSz chr1 4563311 4563601 - SINE/Alu_AluJr chr1 4564448 4564753 + SINE/Alu_AluSq chr1 4565395 4565759 - LTR/ERVL-MaLR_MLT1B chr1 4566767 4566878 + DNA/TcMar-Tigger_Tigger11a chr1 4567101 4567258 + SINE/MIR_MIR chr1 4567331 4567390 + LTR/ERV1_MER110 chr1 4567505 4567868 + LTR/ERVL-MaLR_THE1B chr1 4567883 4568156 + LTR/ERV1_MER110 chr1 4568803 4568897 + DNA/TcMar-Tigger_Tigger11a chr1 4569450 4569479 + Simple_repeat_(T)n chr1 4569847 4570057 + DNA/TcMar-Tigger_MamRep434 chr1 4570187 4570278 + DNA/TcMar-Tigger_MamRep434 chr1 4570283 4570503 + DNA/hAT-Charlie_MER20 chr1 4571305 4571504 + SINE/MIR_MIRb chr1 4571560 4571760 + DNA/hAT-Charlie_MER103C chr1 4571762 4572401 - LINE/L2_L2a chr1 4572463 4573419 - LINE/L1_L1MD3 chr1 4573456 4573583 - LINE/L1_L1MD3 chr1 4573950 4574003 - LINE/L1_L1MEg chr1 4574004 4574297 - SINE/Alu_AluSq4 chr1 4574298 4574453 - LINE/L1_L1MEg chr1 4574575 4574849 - LINE/L2_L2a chr1 4574876 4575036 - LINE/L2_L2a chr1 4575120 4575226 - LINE/L2_L2a chr1 4575291 4575575 - LINE/L2_L2 chr1 4575952 4576255 - SINE/Alu_AluYk2 chr1 4576847 4580808 - LINE/L1_L1PA8A chr1 4580813 4581115 - LTR/ERVL_ERVL-E-int chr1 4581116 4581421 - SINE/Alu_AluSc chr1 4581422 4581580 - LTR/ERVL_ERVL-E-int chr1 4581759 4581894 - LINE/L1_L1ME3E chr1 4582285 4582642 - LTR/ERVL-MaLR_MLT1N2 chr1 4583043 4583073 + Simple_repeat_(CT)n chr1 4583794 4583838 - LTR/ERVL_MLT2D chr1 4583839 4583872 + Simple_repeat_(AT)n chr1 4583873 4584282 - LTR/ERVL_MLT2D chr1 4584414 4584436 + Simple_repeat_(CT)n chr1 4584537 4584678 + LINE/CR1_Plat_L3 chr1 4587568 4588184 - LTR/ERVL_LTR66 chr1 4588185 4588858 - LTR/ERVL_HERVL66-int chr1 4588857 4589559 - LTR/ERVL_HERVL66-int chr1 4589540 4589899 - LTR/ERVL_HERVL66-int chr1 4589900 4590141 - LTR/ERVL_HERVL66-int chr1 4590142 4590450 - SINE/Alu_AluY chr1 4590451 4591334 - LTR/ERVL_HERVL66-int chr1 4591335 4591911 - LTR/ERVL_LTR66 chr1 4592721 4593093 + LINE/L2_L2a chr1 4593783 4593945 - LTR/ERVL-MaLR_MLT1D chr1 4594927 4594956 + Simple_repeat_(AATAA)n chr1 4595089 4595196 + LINE/L2_L2a chr1 4595569 4595679 - LINE/L2_L2b chr1 4597504 4598220 + LINE/L1_L1PA8 chr1 4599163 4599470 + SINE/Alu_AluY chr1 4599620 4599726 - SINE/MIR_MIRc chr1 4600870 4600956 - SINE/MIR_MIRc chr1 4603276 4603381 - LINE/L2_L2b chr1 4603431 4604168 + LTR/ERVL_MER54B chr1 4604848 4604903 + Simple_repeat_(ATATATA)n chr1 4605524 4606418 + LINE/L2_L2 chr1 4606556 4606887 - LTR/ERVL-MaLR_MLT1B chr1 4607040 4607363 - SINE/Alu_AluSp chr1 4607974 4608436 - LTR/ERVL-MaLR_MLT1C2 chr1 4608759 4608777 + Simple_repeat_(A)n chr1 4609419 4609573 + SINE/MIR_MIR3 chr1 4609562 4609787 + SINE/MIR_MIRb chr1 4610351 4610463 - LTR/ERVL-MaLR_MLT1K chr1 4610501 4610840 - LTR/ERVL-MaLR_MLT1C2 chr1 4610841 4611130 - SINE/Alu_AluJr chr1 4611131 4611165 - LTR/ERVL-MaLR_MLT1C2 chr1 4612235 4612286 + Simple_repeat_(CCTCT)n chr1 4613239 4613351 + LTR/ERVL_LTR41 chr1 4615911 4615984 + SINE/MIR_MIRb chr1 4616767 4616859 - SINE/MIR_MIRc chr1 4617611 4617639 + Low_complexity_G-rich chr1 4617941 4618153 + LINE/L1_L1ME2 chr1 4618283 4618328 + Simple_repeat_(TTA)n chr1 4618329 4618428 - LINE/L1_L1PA5 chr1 4618690 4618994 - SINE/Alu_AluSq2 chr1 4619200 4619901 - DNA/hAT-Blackjack_BLACKJACK chr1 4619902 4620195 + SINE/Alu_AluJb chr1 4620196 4620423 - DNA/hAT-Blackjack_BLACKJACK chr1 4620424 4620706 + SINE/Alu_AluSx chr1 4620707 4620743 + Simple_repeat_(AAAAT)n chr1 4620744 4621033 - DNA/hAT-Blackjack_BLACKJACK chr1 4621034 4621338 - SINE/Alu_AluSq2 chr1 4621339 4621415 - DNA/hAT-Blackjack_BLACKJACK chr1 4621416 4621694 - SINE/Alu_AluSx chr1 4621695 4622873 - DNA/hAT-Blackjack_BLACKJACK chr1 4623878 4624185 - LTR/ERVL-MaLR_MLT1J2 chr1 4624325 4624722 - LTR/ERVL_MER70A chr1 4624906 4625021 - SINE/MIR_MIR chr1 4625951 4626140 - LINE/L2_L2b chr1 4626409 4626572 + DNA/hAT-Blackjack_MER63A chr1 4627855 4627943 + DNA/hAT_MER53 chr1 4627869 4627965 - DNA/hAT_MER53 chr1 4628206 4628270 + DNA/hAT_MER53 chr1 4628272 4628343 + SINE/MIR_MIRb chr1 4628997 4629482 + LTR/ERVL-MaLR_MLT1E chr1 4629502 4629617 + LINE/CR1_L3 chr1 4629638 4629662 + Simple_repeat_(TCAT)n chr1 4630835 4631245 - LTR/ERVL_MER77B chr1 4631325 4631386 - LTR/ERVL_MER77B chr1 4631683 4631733 + LINE/L2_L2c chr1 4631846 4632328 - LTR/ERVL-MaLR_MLT1D chr1 4632367 4632488 + LINE/L2_L2c chr1 4632489 4632533 + Simple_repeat_(CCCACAG)n chr1 4632534 4633446 + Simple_repeat_(CACAGCA)n chr1 4633447 4633460 + Simple_repeat_(CCCACAG)n chr1 4633705 4634145 - LTR/ERV1_MER31A chr1 4635619 4635909 - SINE/Alu_AluSz chr1 4636481 4636540 + Low_complexity_A-rich chr1 4636963 4637204 - SINE/MIR_MIR chr1 4637423 4637443 + Simple_repeat_(T)n chr1 4639072 4639085 + Simple_repeat_(TAT)n chr1 4639086 4639231 - SINE/Alu_AluSz6 chr1 4639232 4639263 + Simple_repeat_(T)n chr1 4639264 4639274 - SINE/Alu_AluSz6 chr1 4639275 4639547 - SINE/Alu_AluSx1 chr1 4639548 4639677 - SINE/Alu_AluSz6 chr1 4639678 4639718 + Simple_repeat_(TAT)n chr1 4639719 4640024 - SINE/Alu_AluJb chr1 4640025 4640041 + Simple_repeat_(TAT)n chr1 4640123 4640281 - LINE/L1_L1ME3F chr1 4640264 4640411 - LINE/L1_L1ME3F chr1 4640436 4640462 + Simple_repeat_(AC)n chr1 4640482 4640660 - LINE/L1_L1ME3F chr1 4640715 4640742 + Simple_repeat_(TGAA)n chr1 4640979 4641028 + SINE/MIR_MIRb chr1 4641097 4641164 + LINE/L2_L2c chr1 4641338 4641659 - DNA/hAT-Charlie_Charlie1a chr1 4641661 4642014 - LINE/L1_L1MB7 chr1 4642078 4642134 - DNA/hAT-Charlie_Charlie1a chr1 4642135 4642332 - LINE/L1_L1MB7 chr1 4642429 4642808 + LTR/ERVL-MaLR_MLT1B chr1 4642831 4642884 - DNA/hAT-Blackjack_BLACKJACK chr1 4643422 4643488 + LINE/L2_L2d2 chr1 4643613 4643716 - LTR/ERVL_LTR33B chr1 4643747 4643877 + LINE/L2_L2c chr1 4644005 4644160 - SINE/MIR_MIRb chr1 4645069 4645360 - SINE/Alu_AluSx chr1 4645485 4646003 - LTR/ERVL-MaLR_MLT1J chr1 4646588 4646650 + SINE/MIR_MIRb chr1 4647039 4647789 + Simple_repeat_(TGG)n chr1 4647791 4648014 - LINE/L1_L1MA4 chr1 4648403 4648741 + LTR/ERVL-MaLR_MLT1I chr1 4651360 4651417 + Simple_repeat_(GT)n chr1 4651598 4651882 + SINE/Alu_AluSx chr1 4651883 4651919 + Simple_repeat_(AAT)n chr1 4652197 4652623 + DNA/hAT-Charlie_Charlie4a chr1 4652992 4653169 - SINE/MIR_MIRb chr1 4653751 4653783 + Simple_repeat_(T)n chr1 4654351 4654384 + Low_complexity_G-rich chr1 4655793 4655857 + Low_complexity_G-rich chr1 4659674 4659723 + LINE/L1_L1MD1 chr1 4659724 4660241 + LTR/ERV1_MER66B chr1 4660242 4660560 + LINE/L1_L1MD1 chr1 4661338 4661577 - SINE/MIR_MIRb chr1 4661637 4661686 + Low_complexity_A-rich chr1 4662047 4662080 + Simple_repeat_(TG)n chr1 4662081 4662582 - LINE/L1_L1MB7 chr1 4663146 4663278 - SINE/Alu_AluJo chr1 4663711 4663882 + DNA/hAT-Charlie_MER117 chr1 4663997 4664219 - SINE/MIR_MIR chr1 4665734 4665996 - LINE/L2_L2c chr1 4667402 4667729 - DNA/hAT-Charlie_MER1B chr1 4667933 4668224 + SINE/Alu_AluSz chr1 4668323 4668383 + Simple_repeat_(TG)n chr1 4668463 4668623 - SINE/MIR_MIR chr1 4669049 4669298 - LINE/L1_L1MB5 chr1 4669299 4669661 - LTR/ERVL-MaLR_THE1D chr1 4669662 4669934 - LINE/L1_L1MB5 chr1 4669949 4670088 + SINE/MIR_MIRc chr1 4671087 4671387 + SINE/Alu_AluSx1 chr1 4672434 4672551 - LTR/Gypsy_MamGypsy2-I chr1 4672729 4673283 + LTR/ERVL-MaLR_MLT1E3 chr1 4673399 4673427 + Simple_repeat_(TCTT)n chr1 4673921 4674140 - DNA/hAT_MER53 chr1 4674278 4674319 + DNA/hAT-Charlie_MER58A chr1 4674362 4674522 + DNA/hAT-Charlie_MER58A chr1 4675139 4675751 - LTR/ERVL_MER77B chr1 4676875 4677173 + SINE/Alu_AluSx chr1 4678331 4678795 - LTR/ERVL-MaLR_MLT1F2 chr1 4678827 4679002 - SINE/MIR_MIR chr1 4679146 4679395 + LINE/L2_L2a chr1 4679472 4679775 - LTR/ERVL-MaLR_MLT1C2 chr1 4679776 4679808 - LTR/ERVL_MLT2B4 chr1 4679809 4679852 + Simple_repeat_(GT)n chr1 4679853 4680328 - LTR/ERVL_MLT2B4 chr1 4680329 4680465 - LTR/ERVL-MaLR_MLT1C2 chr1 4681741 4681930 + SINE/MIR_MIRb chr1 4681943 4682084 - SINE/MIR_MIR chr1 4683088 4683276 + SINE/MIR_MIR chr1 4683300 4683483 - DNA/hAT-Charlie_MER5A chr1 4683983 4684027 + Low_complexity_GA-rich chr1 4684672 4684780 - SINE/MIR_MIRc chr1 4686211 4686340 - LTR/ERVL-MaLR_MLT1C2 chr1 4686377 4686485 + SINE/MIR_MIRc chr1 4687792 4687833 + Simple_repeat_(CATCCATT)n chr1 4688108 4688138 + Simple_repeat_(GCTGG)n chr1 4688297 4688325 + Simple_repeat_(AG)n chr1 4688491 4688797 + SINE/Alu_AluSx chr1 4688891 4688964 - SINE/MIR_MIR1_Amn chr1 4689692 4689728 + Simple_repeat_(CATGAG)n chr1 4690588 4690733 - SINE/MIR_MIRb chr1 4692149 4692199 - SINE/MIR_MIR chr1 4692631 4692805 + LINE/L2_L2b chr1 4693090 4693169 - SINE/MIR_MIRc chr1 4693610 4693974 - LTR/ERVL-MaLR_MSTD chr1 4694334 4694411 - SINE/MIR_MIRb chr1 4694419 4694543 - LINE/L2_L2b chr1 4695024 4695145 + DNA/hAT-Charlie_MER20 chr1 4695542 4695886 - LTR/ERVL-MaLR_MLT1B chr1 4696215 4696318 + LTR_LTR90B chr1 4697270 4697463 + LINE/CR1_L3 chr1 4697608 4697720 + SINE/MIR_MIRc chr1 4697835 4697865 + Simple_repeat_(CTGGAC)n chr1 4698540 4698658 - LINE/CR1_L3 chr1 4699790 4699938 + SINE/MIR_MIR chr1 4700324 4700474 - DNA/hAT-Charlie_MER5B chr1 4701302 4701419 - SINE/MIR_MIRb chr1 4701570 4701724 - DNA/hAT-Charlie_MER20 chr1 4701906 4701932 + Simple_repeat_(CCTTC)n chr1 4704158 4705175 - LINE/L1_L1PA3 chr1 4705171 4705376 + LINE/L1_L1PA3 chr1 4705396 4705429 + Simple_repeat_(T)n chr1 4705441 4706060 + LINE/L1_L1MC3 chr1 4707278 4707955 - LINE/L1_L1MB7 chr1 4707964 4708265 - SINE/Alu_AluSc chr1 4709400 4709442 + Simple_repeat_(TGGTGAGGCC)n chr1 4710011 4710506 + Simple_repeat_(CA)n chr1 4710430 4710519 + Simple_repeat_(CACG)n chr1 4710928 4711115 - DNA/TcMar-Tigger_Tigger19b chr1 4712520 4712555 + Simple_repeat_(CCA)n chr1 4713131 4713275 + LINE/CR1_L3b chr1 4714897 4715020 + LINE/CR1_L3b chr1 4715248 4715403 + SINE/MIR_MIR chr1 4715404 4715699 + SINE/Alu_AluSp chr1 4715700 4715801 + SINE/MIR_MIR chr1 4716276 4716478 + SINE/MIR_MIR chr1 4716489 4716801 + Simple_repeat_(TGATGA)n chr1 4718477 4718780 - SINE/Alu_AluSc chr1 4719209 4719541 - LINE/L2_L2d2 chr1 4719618 4719709 + SINE/MIR_MIR3 chr1 4720447 4720659 + SINE/MIR_MIRb chr1 4721200 4721256 - SINE/MIR_MIR3 chr1 4721740 4722245 - DNA/hAT-Charlie_MER20B chr1 4722516 4722821 - LTR/ERVL-MaLR_MLT1C2 chr1 4723108 4723924 - LINE/L2_L2 chr1 4725643 4726002 - LTR/ERVL-MaLR_MLT1B chr1 4726627 4726719 - DNA/hAT-Charlie_MER5A chr1 4727632 4727732 + LTR/ERVL-MaLR_MLT1F2 chr1 4727774 4728150 + LTR/ERVL-MaLR_MLT1E1A chr1 4729514 4729811 - LTR/ERVL-MaLR_MLT1B chr1 4729814 4729959 - LTR/ERVL-MaLR_MLT1C2 chr1 4731019 4731068 + Simple_repeat_(TTTGTT)n chr1 4731069 4731380 - SINE/Alu_AluSx chr1 4731381 4731393 + Simple_repeat_(TTTGTT)n chr1 4732673 4732872 + SINE/MIR_MIRc chr1 4733423 4733448 + Simple_repeat_(CATT)n chr1 4733458 4733556 - LINE/L2_L2b chr1 4734300 4734482 - SINE/MIR_MIRb chr1 4735986 4736162 + SINE/MIR_MIR chr1 4736305 4736729 + LINE/L2_L2d2 chr1 4737258 4737283 + Simple_repeat_(TGAGCC)n chr1 4737612 4737904 + SINE/Alu_AluJo chr1 4737905 4737935 + Simple_repeat_(AAAACC)n chr1 4738457 4738867 - LINE/L2_L2 chr1 4740014 4740503 + LINE/L1_L1MB3 chr1 4740504 4740805 + SINE/Alu_AluY chr1 4740806 4740863 + LINE/L1_L1MB3 chr1 4741214 4741575 - LTR/ERVL-MaLR_MLT1C2 chr1 4741604 4742509 + LINE/L1_L1MC4 chr1 4743539 4743571 + Simple_repeat_(CATC)n chr1 4744592 4744625 + Simple_repeat_(CACATG)n chr1 4744626 4744653 + Simple_repeat_(CA)n chr1 4744654 4744668 + Simple_repeat_(CACATG)n chr1 4745850 4745989 + SINE/MIR_MIRb chr1 4745990 4746445 - LTR/ERVL-MaLR_MLT1C2 chr1 4746446 4746554 + SINE/MIR_MIRb chr1 4747517 4747692 + LINE/L2_L2 chr1 4747708 4748017 + SINE/Alu_AluSx chr1 4748466 4748777 + SINE/Alu_AluSx chr1 4748778 4749015 + LTR/ERVL-MaLR_MLT1A1 chr1 4749262 4749425 + SINE/MIR_MIRc chr1 4750961 4751100 - SINE/MIR_MIRb chr1 4751492 4751608 - LINE/L2_L2b chr1 4752134 4752231 + Low_complexity_GA-rich chr1 4752506 4752541 + LINE/L2_L2c chr1 4753314 4753406 - SINE/MIR_MIR3 chr1 4753449 4753491 + Simple_repeat_(AT)n chr1 4753503 4753573 - LINE/L1_L1MEg chr1 4753574 4753871 - SINE/Alu_AluSp chr1 4753872 4754063 - LINE/L1_L1MEg chr1 4754064 4754385 + DNA/hAT-Charlie_MER58B chr1 4754386 4754453 - LINE/L1_L1MEg chr1 4754654 4754682 + Simple_repeat_(TTTG)n chr1 4755618 4756134 - LTR/ERV1_MER31A chr1 4756694 4756837 + DNA/hAT-Charlie_MER103C chr1 4757328 4757445 - LTR/ERVL-MaLR_MLT1H chr1 4757611 4757818 + LINE/L2_L2a chr1 4757854 4758231 + LTR/ERVL-MaLR_THE1C chr1 4758320 4758702 - LTR/ERVL-MaLR_THE1D chr1 4760604 4760666 - DNA/hAT-Ac_OldhAT1 chr1 4760902 4760968 - DNA/hAT-Charlie_MER58B chr1 4761501 4761539 + Simple_repeat_(CCATGG)n chr1 4761565 4762023 - LTR/ERVL_MLT2A2 chr1 4762024 4762144 - LTR/ERVL_HERVL-int chr1 4762231 4762751 + LTR/ERVL_MER77 chr1 4762760 4762880 + SINE/MIR_MIR chr1 4763103 4763233 - DNA/hAT-Charlie_MER20 chr1 4763477 4763710 + LTR/ERVL_MLT2B2 chr1 4763711 4763925 + Simple_repeat_(CTCCCT)n chr1 4763926 4763935 + Simple_repeat_(CTCC)n chr1 4763941 4764223 + LTR/ERVL_MLT2B2 chr1 4766109 4766321 + DNA/hAT-Charlie_MER58A chr1 4767188 4767768 + Simple_repeat_(CATCAT)n chr1 4768209 4768268 + LINE/L2_L2b chr1 4774594 4774630 + Simple_repeat_(CCCTC)n chr1 4775029 4775075 + Low_complexity_A-rich chr1 4776205 4776270 - SINE/MIR_MIR1_Amn chr1 4776354 4776485 - SINE/MIR_MIRb chr1 4778019 4778069 + Simple_repeat_(TTTA)n chr1 4778572 4778649 + Simple_repeat_(TC)n chr1 4779124 4779182 - LINE/L2_L2b chr1 4779230 4779264 + Low_complexity_GA-rich chr1 4779324 4779427 - SINE/Alu_AluYc chr1 4779819 4779862 - LINE/L1_L1MB3 chr1 4779863 4780157 + SINE/Alu_AluSx chr1 4780158 4780395 - LINE/L1_L1MB3 chr1 4780595 4780846 - SINE/Alu_AluSx chr1 4781338 4781469 - DNA/hAT-Charlie_MER5A chr1 4782566 4782603 + Simple_repeat_(AAAAC)n chr1 4782757 4782786 + Simple_repeat_(AGAA)n chr1 4783197 4783247 + Simple_repeat_(CA)n chr1 4783373 4783444 - SINE/tRNA-RTE_MamSINE1 chr1 4783472 4783544 + Simple_repeat_(TA)n chr1 4783545 4783598 + Simple_repeat_(TG)n chr1 4785431 4785497 + SINE/MIR_MIRc chr1 4787111 4787329 - LINE/L2_L2b chr1 4789788 4789816 + Simple_repeat_(TTTTG)n chr1 4790917 4790960 + Simple_repeat_(TTTTTG)n chr1 4792501 4792549 + Low_complexity_A-rich chr1 4793143 4793341 + DNA/hAT-Charlie_MER20 chr1 4793839 4794131 - SINE/Alu_AluJr chr1 4795419 4795620 + DNA/hAT-Charlie_MER20 chr1 4795795 4796261 - LTR/ERVL-MaLR_MLT1C chr1 4798679 4799027 + LTR/ERVL-MaLR_THE1B chr1 4799571 4799604 + Simple_repeat_(CT)n chr1 4799865 4800027 - SINE/MIR_MIR chr1 4800231 4800383 - LINE/L2_L2d chr1 4800884 4801176 + LTR/ERVL_LTR16D chr1 4802209 4802603 + LTR/ERVL-MaLR_MLT1A1 chr1 4804113 4804341 - LTR/ERVL-MaLR_MLT1F chr1 4804376 4804513 + SINE/MIR_MIRb chr1 4804516 4804833 + LINE/CR1_L3 chr1 4804908 4805171 - LINE/L2_L2b chr1 4805172 4805605 + DNA/TcMar-Tigger_Tigger2a chr1 4805606 4805746 - LINE/L2_L2b chr1 4805889 4806189 - LINE/L2_L2b chr1 4806287 4806852 + LINE/L1_L1ME1 chr1 4806853 4807156 + SINE/Alu_AluJb chr1 4807157 4808280 + LINE/L1_L1ME1 chr1 4808281 4808697 + LTR/ERVL-MaLR_MSTA chr1 4808698 4809066 + LINE/L1_L1ME1 chr1 4809068 4809368 + LINE/L1_L1MC1 chr1 4809370 4811540 + LINE/L1_L1MC1 chr1 4811545 4811694 + LINE/L1_L1ME1 chr1 4811695 4811897 - DNA/hAT-Blackjack_MER63A chr1 4811898 4813308 + LINE/L1_L1ME1 chr1 4813309 4813348 + Simple_repeat_(AC)n chr1 4813349 4815782 + LINE/L1_L1ME1 chr1 4815783 4816080 - SINE/Alu_AluJr chr1 4816393 4817073 + LTR/ERV1_LTR8 chr1 4817228 4817770 + LTR/ERVL_MLT2A2 chr1 4817789 4817868 + LINE/L1_L1ME3B chr1 4817869 4818344 - LTR/ERV1_MER31A chr1 4818345 4818669 + LINE/L1_L1ME3B chr1 4818670 4819104 - LTR/ERV1_LTR24 chr1 4819105 4819404 + LINE/L1_L1ME3B chr1 4819405 4819985 - LTR/ERV1_LTR24B chr1 4820001 4820135 + LINE/L1_L1ME3B chr1 4820138 4820702 + LTR/ERV1_MER34C2 chr1 4821492 4821577 - DNA/hAT-Charlie_Charlie4z chr1 4821711 4821739 + Low_complexity_A-rich chr1 4821767 4822482 + LTR/ERV1_MER50 chr1 4822661 4822686 - LTR/ERV1_MER4D chr1 4822687 4823119 + LTR/ERV1_MER57A1 chr1 4823120 4823235 - LTR/ERV1_MER4D chr1 4823260 4823478 - LTR/ERV1_MER50 chr1 4823481 4823559 + Simple_repeat_(TATATACGTA)n chr1 4823562 4823571 + Simple_repeat_(ACATAC)n chr1 4823572 4823594 + Simple_repeat_(AC)n chr1 4823595 4823602 + Simple_repeat_(ACATAC)n chr1 4823616 4823719 - SINE/Alu_AluSp chr1 4823724 4824023 - SINE/Alu_AluSq chr1 4824025 4824801 - LTR/ERV1_MER4D chr1 4825399 4825932 + LTR/ERV1_MER41B chr1 4826762 4827349 + LTR/ERV1_LTR10C chr1 4827629 4827650 + Simple_repeat_(AC)n chr1 4828460 4828566 + LTR/ERVL-MaLR_MLT1J-int chr1 4828796 4829070 + LTR/ERVL-MaLR_MLT1J-int chr1 4830944 4831054 + SINE/MIR_MIRb chr1 4831330 4831499 - DNA/hAT-Tip100_MER45A chr1 4831569 4831672 - SINE/MIR_MIRb chr1 4831818 4831996 + SINE/MIR_MIR chr1 4832330 4832376 + LINE/L2_L2a chr1 4832379 4832429 + DNA/TcMar-Mariner_MADE1 chr1 4832442 4832496 + LINE/L2_L2a chr1 4832772 4833007 - LTR/ERVL-MaLR_MLT1A0 chr1 4833008 4833294 - SINE/Alu_AluSx chr1 4833295 4833452 - LTR/ERVL-MaLR_MLT1A0 chr1 4833552 4833909 - LTR/ERVL-MaLR_THE1B chr1 4834102 4834565 + LTR/ERVL-MaLR_MLT1C chr1 4834570 4834896 + LTR/ERVL-MaLR_MLT1I chr1 4834908 4835005 - LTR/ERVL_MLT2A1 chr1 4835006 4835033 + Simple_repeat_(CT)n chr1 4835034 4835439 - LTR/ERVL_MLT2A1 chr1 4835440 4835717 - LTR/ERVL_HERVL-int chr1 4837005 4837103 + LTR/ERVL-MaLR_MLT1E chr1 4837090 4837534 + LTR/ERVL-MaLR_MLT1E chr1 4837549 4837824 - SINE/Alu_AluJb chr1 4838807 4839163 - LTR/ERVL-MaLR_THE1B chr1 4839164 4840636 - LTR/ERVL-MaLR_THE1B-int chr1 4840637 4840999 - LTR/ERVL-MaLR_THE1B chr1 4841063 4841662 + LTR/ERVL-MaLR_MLT1E1 chr1 4841746 4842391 - LTR/ERV1_LTR48B chr1 4842395 4842489 - LTR/ERV1_MER34-int chr1 4842491 4846118 - LTR/ERV1_LTR49-int chr1 4846152 4846413 - LTR/ERV1_LTR49-int chr1 4846414 4846619 - LINE/L1_L1PA7 chr1 4846620 4847271 - LTR/ERV1_LTR49-int chr1 4847272 4847339 - tRNA_tRNA-Glu-GAA chr1 4847340 4848467 - LTR/ERV1_LTR49-int chr1 4848473 4849144 - LTR/ERV1_LTR48B chr1 4849149 4849204 + Simple_repeat_(TATC)n chr1 4849205 4849263 + Simple_repeat_(CATC)n chr1 4849267 4849325 - LTR/ERVL-MaLR_MLT1E1A chr1 4849428 4849497 - LTR/ERVL-MaLR_MLT1E1A chr1 4849726 4849880 + SINE/MIR_MIRb chr1 4851004 4851273 - LTR/ERVL-MaLR_MLT1K chr1 4851359 4851569 + SINE/MIR_MIR chr1 4852342 4852455 + SINE/MIR_MIRb chr1 4852547 4852578 + Simple_repeat_(CATT)n chr1 4853034 4853182 + SINE/MIR_MIRc chr1 4853208 4853245 + Simple_repeat_(TCC)n chr1 4853246 4853638 + LTR/ERVL-MaLR_MSTA chr1 4853639 4853663 + Simple_repeat_(TCC)n chr1 4854944 4854988 - LTR/ERVL_MLT2C1 chr1 4854990 4855040 + Simple_repeat_(TATATC)n chr1 4855164 4855463 + SINE/Alu_AluSp chr1 4855464 4855504 - DNA/TcMar-Mariner_MADE1 chr1 4855524 4855685 + Simple_repeat_(TATATC)n chr1 4855690 4856050 + LTR/ERVL-MaLR_MSTD chr1 4856290 4856661 - LTR/ERVL_MLT2C1 chr1 4856930 4857408 - LTR/ERVL_MER70B chr1 4857446 4857808 + LTR/ERVL-MaLR_THE1B chr1 4857810 4859388 + LTR/ERVL-MaLR_THE1B-int chr1 4859391 4859749 + LTR/ERVL-MaLR_THE1B chr1 4859773 4860751 - LINE/L1_L1PA5 chr1 4860763 4864082 - LINE/L1_L1PA4 chr1 4864083 4864391 - SINE/Alu_AluYk3 chr1 4864392 4867230 - LINE/L1_L1PA4 chr1 4867253 4867338 - SINE/MIR_MIR chr1 4867448 4867629 + SINE/MIR_MIRc chr1 4868299 4868487 + Simple_repeat_(CTC)n chr1 4869946 4870047 - SINE/MIR_MIRb chr1 4870136 4870556 + LTR/ERVL-MaLR_MLT1C chr1 4871465 4871656 + Simple_repeat_(AC)n chr1 4872883 4872922 + Simple_repeat_(TCTTTC)n chr1 4874259 4874486 - LTR/ERVL-MaLR_MLT1A0 chr1 4874487 4874862 - LTR/ERVL-MaLR_MSTB1 chr1 4874867 4876096 - LINE/L1_L1PA6 chr1 4876099 4876146 - LTR/ERVL-MaLR_MSTB1 chr1 4876149 4876285 - LTR/ERVL-MaLR_MLT1A0 chr1 4876324 4876348 + Simple_repeat_(TCAT)n chr1 4877458 4877810 + LTR/ERVL-MaLR_THE1B chr1 4878401 4878912 + LTR/ERVL_MLT2A2 chr1 4879436 4879467 + Simple_repeat_(TTTTG)n chr1 4880362 4880558 + SINE/MIR_MIR chr1 4882039 4882197 + LINE/L1_L1ME4c chr1 4882200 4882227 + Simple_repeat_(GT)n chr1 4882408 4882614 - SINE/MIR_MIR chr1 4882793 4883912 + LINE/L1_HAL1 chr1 4883989 4884229 - SINE/MIR_MIR chr1 4884256 4885289 + LINE/L1_HAL1 chr1 4885290 4885586 + SINE/Alu_AluSg chr1 4885587 4885658 + LINE/L1_HAL1 chr1 4885692 4885795 + LINE/L1_HAL1b chr1 4886135 4886199 + LINE/L1_L1PA8 chr1 4886216 4886295 + LINE/L1_HAL1 chr1 4886363 4887160 - LINE/L1_L1PA13 chr1 4887161 4887460 + LINE/L1_L1PREC2 chr1 4887462 4887518 + LINE/L1_HAL1 chr1 4887551 4887759 + LINE/L1_L1ME4a chr1 4888264 4888630 - LTR/ERVL-MaLR_THE1B chr1 4888909 4889109 + DNA/hAT-Blackjack_MER63A chr1 4889682 4889705 + Simple_repeat_(A)n chr1 4889924 4889982 - LINE/L2_L2b chr1 4889938 4890075 - SINE/MIR_MIRb chr1 4890090 4890274 + LINE/L2_L2a chr1 4890342 4890750 - LTR/ERVL_LTR101_Mam chr1 4890955 4891310 - LTR/ERVL-MaLR_MSTD chr1 4891320 4891896 + LINE/L1_L1ME2 chr1 4892021 4892077 + LTR/ERVL-MaLR_MLT1H chr1 4892078 4892157 - DNA/TcMar-Mariner_MADE1 chr1 4892158 4892283 + LTR/ERVL-MaLR_MLT1H chr1 4892392 4892521 + LTR/ERVL-MaLR_MLT1H chr1 4893057 4893362 + SINE/Alu_AluY chr1 4893492 4893943 + LINE/L1_L1MB3 chr1 4893976 4894267 + SINE/Alu_AluSz chr1 4895325 4895360 + Simple_repeat_(CA)n chr1 4895913 4896101 + LTR/ERVL-MaLR_MLT1B chr1 4896102 4896398 + SINE/Alu_AluSg7 chr1 4896399 4896609 + LTR/ERVL-MaLR_MLT1B chr1 4896757 4897004 + DNA/hAT?_MER121B chr1 4897608 4897971 + LTR/ERVL-MaLR_THE1D chr1 4898065 4898227 + LTR/ERVL_LTR79 chr1 4898948 4899033 - LINE/L2_L2c chr1 4899260 4899604 - LTR/ERVL-MaLR_MLT1B chr1 4899670 4899881 + LINE/L2_L2a chr1 4900791 4900873 + SINE/MIR_MIRb chr1 4900874 4901239 - LTR/ERVL-MaLR_THE1B chr1 4901240 4902811 - LTR/ERVL-MaLR_THE1B-int chr1 4902812 4903166 - LTR/ERVL-MaLR_THE1B chr1 4903167 4903313 + SINE/MIR_MIRb chr1 4903695 4904043 - LINE/L1_L1MEi chr1 4904044 4904197 - LINE/L1_L1PA8 chr1 4904198 4904927 - LINE/L1_L1MEi chr1 4905402 4905458 + Low_complexity_A-rich chr1 4905892 4906260 - LTR/ERVL-MaLR_THE1C chr1 4906557 4906601 + Simple_repeat_(ATATGT)n chr1 4906705 4907011 + SINE/Alu_AluSc chr1 4907037 4907392 - LTR/ERVL-MaLR_MLT1A0 chr1 4908975 4909153 - SINE/MIR_MIR chr1 4909154 4909456 + SINE/Alu_AluSg chr1 4909457 4909548 - SINE/MIR_MIR chr1 4910104 4910755 - LTR/ERVL_MER21C chr1 4911666 4912015 - LTR/ERVL-MaLR_MLT1A chr1 4912297 4912598 - SINE/Alu_AluSx3 chr1 4913701 4914140 - LTR/ERVL-MaLR_MLT1C chr1 4914777 4914797 + Simple_repeat_(AAAC)n chr1 4914869 4915290 + LINE/L1_L1ME4a chr1 4915312 4915543 + LINE/L1_L1ME4a chr1 4915918 4916027 + Simple_repeat_(GT)n chr1 4916050 4916097 + Simple_repeat_(TG)n chr1 4916126 4916204 + SINE/tRNA_LFSINE_Vert chr1 4916724 4916774 + Simple_repeat_(TG)n chr1 4916925 4917554 + DNA/TcMar-Tigger_Tigger9b chr1 4917760 4918029 + LTR/ERVL-MaLR_MLT1D chr1 4918030 4918394 - LTR/ERVL-MaLR_THE1B chr1 4918395 4919949 - LTR/ERVL-MaLR_THE1B-int chr1 4919950 4920315 - LTR/ERVL-MaLR_THE1B chr1 4920316 4920537 + LTR/ERVL-MaLR_MLT1D chr1 4921023 4921099 + LINE/L2_UCON49 chr1 4921598 4921716 + DNA/TcMar-Tigger_Tigger9b chr1 4921725 4921766 + Simple_repeat_(ATTA)n chr1 4921878 4922080 + DNA/TcMar-Tc2_Kanga2_a chr1 4922113 4922701 + LTR/ERVK_HERVK13-int chr1 4922722 4922767 + DNA/TcMar-Tc2_Kanga2_a chr1 4922768 4923136 - LTR/ERVL-MaLR_THE1C chr1 4923137 4923290 + DNA/TcMar-Tc2_Kanga2_a chr1 4924214 4924385 + LTR/ERVL_MER77 chr1 4924387 4924852 - LTR/ERVL_MLT2B1 chr1 4924853 4925209 + LTR/ERVL_MER77 chr1 4925210 4925538 - LINE/L1_L1PA13 chr1 4925539 4925619 + LTR/ERVL_MER77 chr1 4926405 4926494 - DNA/hAT-Charlie_MER117 chr1 4926610 4926626 + Simple_repeat_(T)n chr1 4926847 4927226 + LTR/ERVL-MaLR_MLT1B chr1 4927611 4927641 + Low_complexity_A-rich chr1 4927865 4928363 - LTR_MamRep1527 chr1 4928379 4928419 + Simple_repeat_(TGATTA)n chr1 4929444 4929807 - LTR/ERVL-MaLR_MLT1B chr1 4930011 4930134 - SINE/MIR_MIRb chr1 4930312 4930606 - SINE/Alu_AluSp chr1 4930641 4931166 - LTR/ERVL_MLT2B2 chr1 4931672 4931740 + LINE/L2_L2a chr1 4932075 4932105 + Simple_repeat_(TA)n chr1 4933653 4933976 + SINE/Alu_AluSx1 chr1 4933977 4934266 + SINE/Alu_AluSx3 chr1 4934275 4934936 - LINE/L1_L1MB7 chr1 4935576 4935745 - SINE/MIR_MIRc chr1 4936978 4937118 + SINE/MIR_MIRc chr1 4937666 4937696 + Simple_repeat_(GAGG)n chr1 4937975 4938084 - SINE/Alu_AluSp chr1 4938562 4938654 - SINE/MIR_MIRb chr1 4939807 4940002 - LINE/L2_L2b chr1 4941292 4941379 - SINE/MIR_MIRb chr1 4941878 4942082 - SINE/MIR_MIRb chr1 4942080 4942179 - SINE/MIR_MIRc chr1 4942745 4943187 - LTR/ERV1_LTR19A chr1 4943282 4943300 + Simple_repeat_(A)n chr1 4943723 4943791 + Simple_repeat_(GATA)n chr1 4943843 4943871 + Simple_repeat_(ATCT)n chr1 4944218 4944534 - SINE/Alu_AluJr chr1 4946148 4946779 + Simple_repeat_(TG)n chr1 4948757 4948820 - LINE/RTE-BovB_MamRTE1 chr1 4949829 4949918 + LTR/ERVL_LTR79 chr1 4949931 4950072 - LINE/L1_L1PA10 chr1 4950035 4950524 + LINE/L1_L1PA10 chr1 4950532 4950558 + Simple_repeat_(AAAAT)n chr1 4951809 4951976 + DNA/hAT-Charlie_MER5A chr1 4952303 4952415 - LINE/L2_L2a chr1 4953003 4953943 + LINE/L1_L1MEd chr1 4953948 4953997 + LINE/L2_L2a chr1 4954273 4954419 - SINE/MIR_MIR chr1 4954420 4955119 + LINE/L1_L1MEd chr1 4955956 4956112 - SINE/MIR_MIRb chr1 4958449 4958495 - DNA/hAT-Charlie_MER5B chr1 4958497 4958799 + SINE/Alu_AluJr chr1 4958901 4959326 - LTR/ERVL-MaLR_MLT1C2 chr1 4959547 4959723 - LTR/ERVL-MaLR_MLT1E2 chr1 4959870 4960014 + Low_complexity_GA-rich chr1 4960222 4960283 + LTR/Gypsy_MamGypLTR1a chr1 4960908 4960967 + Simple_repeat_(CTCTGC)n chr1 4961124 4961160 + Simple_repeat_(CTTCCT)n chr1 4961720 4961771 - SINE/MIR_MIR chr1 4961772 4962078 + SINE/Alu_AluSc chr1 4962079 4962161 - SINE/MIR_MIR chr1 4964307 4964391 + Simple_repeat_(TCTC)n chr1 4965920 4966352 - DNA/hAT-Charlie_Charlie15b chr1 4966854 4967026 - SINE/MIR_MIRb chr1 4967086 4967429 + LINE/L2_L2a chr1 4967491 4967803 + LTR/ERVL_MLT2D chr1 4967804 4968278 - LTR/ERV1_MER4A chr1 4968279 4968628 + LTR/ERVL-MaLR_THE1B chr1 4968629 4968782 - LTR/ERV1_MER4A chr1 4968783 4968879 + LTR/ERVL_MLT2D chr1 4968897 4968931 + Simple_repeat_(ATGA)n chr1 4969138 4969253 + Low_complexity_G-rich chr1 4969295 4969444 + SINE/MIR_MIRb chr1 4969570 4969832 - LINE/L2_L2b chr1 4969957 4970055 + LINE/CR1_L3 chr1 4971539 4971679 - SINE/MIR_MIRb chr1 4971748 4971907 - SINE/MIR_MIRb chr1 4972551 4972851 + SINE/Alu_AluJr chr1 4974497 4974798 + SINE/Alu_AluY chr1 4977427 4977577 + SINE/MIR_MIRb chr1 4977589 4978092 - LTR/ERVL_LTR42 chr1 4978126 4978199 - SINE/MIR_MIR chr1 4978752 4978825 + SINE/MIR_MIR3 chr1 4979724 4979808 - SINE/MIR_MIR chr1 4979814 4980035 - LINE/L2_L2a chr1 4980157 4980466 - LINE/L2_L2a chr1 4980512 4980581 - LINE/L2_L2a chr1 4980822 4980850 + Low_complexity_A-rich chr1 4981105 4981182 - SINE/MIR_MIRc chr1 4981915 4982278 - LTR/ERVL-MaLR_THE1B chr1 4983870 4984199 - LTR/ERV1_MER31B chr1 4985623 4985729 + LINE/L2_L2d chr1 4985974 4986268 - SINE/Alu_AluSp chr1 4986761 4986786 + Simple_repeat_(TTGTT)n chr1 4987738 4987867 + SINE/MIR_MIR chr1 4987868 4987915 + Simple_repeat_(TG)n chr1 4987916 4987938 + SINE/MIR_MIRb chr1 4988176 4988225 - SINE/MIR_MIRc chr1 4988553 4988621 - LINE/RTE-BovB_MamRTE1 chr1 4989097 4989188 - LINE/L2_L2a chr1 4989341 4989776 + LTR/ERV1_MER39 chr1 4990039 4990460 + LINE/L2_L2 chr1 4990556 4990946 - LINE/L1_L1ME3B chr1 4991039 4991467 - LINE/L1_L1ME3B chr1 4991627 4991740 + LTR/ERVL-MaLR_MLT1H2 chr1 4992135 4992892 - LINE/L1_L1ME3Cz chr1 4992939 4993356 - LINE/L1_L1MEi chr1 4993772 4995068 + LINE/L1_L1MD1 chr1 4995069 4995542 + LTR/ERVL-MaLR_MLT1C chr1 4995543 4996209 + LINE/L1_L1MD1 chr1 4996210 4996347 + Simple_repeat_(TTTC)n chr1 4996350 4996648 - SINE/Alu_AluSp chr1 4996649 4997262 + LINE/L1_L1MD1 chr1 4997263 4997564 - SINE/Alu_AluSx1 chr1 4997565 4997632 + LINE/L1_L1MD1 chr1 4998150 4998514 - LTR/ERVL-MaLR_THE1B chr1 4998631 4998960 + LTR/ERVL_LTR16E2 chr1 4998961 4999149 - DNA/hAT_MER53 chr1 4999150 4999301 + LTR/ERVL_LTR16E2 chr1 5000007 5000065 + Simple_repeat_(GT)n chr1 5001591 5001710 + LINE/L2_L2b chr1 5002174 5002252 - SINE/Alu_FRAM chr1 5002454 5002546 + LINE/L2_L2a chr1 5003256 5003563 - SINE/Alu_AluJr chr1 5003616 5004134 - LTR/ERVL-MaLR_MLT1F2 chr1 5004138 5004363 + LINE/L2_L2a chr1 5004364 5004722 - LTR/ERVL-MaLR_THE1B chr1 5004723 5005567 + LINE/L2_L2a chr1 5005919 5005955 + Low_complexity_G-rich chr1 5005972 5006178 + SINE/MIR_MIR chr1 5006338 5006862 - DNA/hAT-Charlie_MER1A chr1 5008232 5008276 + Simple_repeat_(TG)n chr1 5008388 5008430 + Simple_repeat_(TGTGTG)n chr1 5008758 5008789 + Simple_repeat_(GT)n chr1 5008982 5009014 + Simple_repeat_(GGAT)n chr1 5009079 5009130 + Simple_repeat_(GATG)n chr1 5011175 5011311 + DNA/TcMar-Tigger_Tigger23a chr1 5011413 5011465 + Simple_repeat_(A)n chr1 5012811 5013171 + LTR/ERVL-MaLR_MLT1B chr1 5013224 5013452 - SINE/MIR_MIR chr1 5014192 5014355 - SINE/MIR_MIRb chr1 5014833 5015249 + LTR/ERVL-MaLR_MSTA chr1 5015289 5015495 - SINE/MIR_MIRb chr1 5016740 5017042 - SINE/Alu_AluJb chr1 5017184 5017429 + LINE/L1_L1ME3E chr1 5017449 5017860 - LINE/L2_L2a chr1 5017968 5018045 - LINE/L2_L2a chr1 5018079 5018354 + LINE/L1_L1ME3E chr1 5018506 5018541 - SINE/Alu_Alu chr1 5018550 5018767 + SINE/Alu_AluSx3 chr1 5018887 5019273 + LTR/ERVL-MaLR_THE1D chr1 5019385 5019741 - LTR/ERVL-MaLR_MLT1A0 chr1 5019922 5019953 + Low_complexity_A-rich chr1 5020008 5020040 + Simple_repeat_(AAAC)n chr1 5020869 5020972 + SINE/MIR_MIRb chr1 5021473 5021553 + Simple_repeat_(ATAT)n chr1 5021595 5021924 - SINE/Alu_AluSz chr1 5021925 5021940 + Simple_repeat_(TTTC)n chr1 5022036 5022470 - LTR/ERV1_MER83 chr1 5022499 5022535 + Simple_repeat_(AC)n chr1 5022541 5022751 - SINE/MIR_MIR chr1 5023296 5023395 + SINE/Alu_AluSx3 chr1 5023406 5023475 - DNA/hAT-Tip100_MER91A chr1 5023703 5023991 + SINE/Alu_AluJb chr1 5023997 5024420 + LINE/L1_L1M5 chr1 5024421 5024704 + SINE/Alu_AluSz chr1 5024705 5024739 + Simple_repeat_(AAAT)n chr1 5024740 5024934 + LINE/L1_L1M5 chr1 5024967 5025109 - SINE/MIR_MIR chr1 5025425 5025749 - LINE/L1_L1ME3Cz chr1 5026331 5026871 - LINE/L1_L1ME3Cz chr1 5026884 5026918 + Low_complexity_A-rich chr1 5026932 5026996 + Simple_repeat_(ATATTT)n chr1 5027039 5027199 - LINE/L1_L1MEj chr1 5027721 5028036 + LTR/ERVL-MaLR_MLT1H chr1 5028293 5028387 - LINE/L1_L1MEj chr1 5028437 5028516 + LTR/ERVL-MaLR_MLT1H chr1 5028582 5028738 - LINE/L1_L1ME3Cz chr1 5028848 5029018 - LINE/L1_L1MEj chr1 5029343 5030119 - LINE/L1_L1MEg chr1 5030126 5030253 - LINE/L1_L1MEj chr1 5030320 5030584 + LINE/L1_L1MB3 chr1 5030590 5030618 + Simple_repeat_(AGCT)n chr1 5030611 5030737 + Simple_repeat_(GATG)n chr1 5030741 5031238 - LTR/ERVL_LTR33 chr1 5031239 5031338 - LINE/L2_L2a chr1 5031346 5031375 + Simple_repeat_(ATTT)n chr1 5031521 5031736 - DNA/hAT-Charlie_MER58A chr1 5031737 5032057 - LINE/L1_L1MEj chr1 5033770 5034098 - LTR/ERVL-MaLR_THE1D chr1 5034294 5034317 + Simple_repeat_(A)n chr1 5034485 5035298 - LTR/ERVL_MER21B chr1 5037486 5037647 + LTR/ERVL-MaLR_MLT1B chr1 5037648 5037683 + Simple_repeat_(TC)n chr1 5037684 5037886 + LTR/ERVL-MaLR_MLT1B chr1 5038095 5038464 - LTR/ERVL-MaLR_THE1D chr1 5038564 5038913 - LINE/L1_L1MC5a chr1 5038990 5039591 + LINE/L2_L2 chr1 5039612 5039800 + SINE/MIR_MIR chr1 5040121 5040159 + LTR/ERVL-MaLR_MLT1K chr1 5040160 5040529 + LTR/ERVL-MaLR_THE1B chr1 5040530 5040595 + LTR/ERVL-MaLR_MLT1K chr1 5040625 5040822 + LTR/ERVL-MaLR_MLT1K chr1 5040829 5041140 - LINE/RTE-X_L4_A_Mam chr1 5041354 5041726 + LTR/ERVL_LTR16E1 chr1 5041944 5042187 + LTR/ERVL_MER74B chr1 5042188 5042557 - LTR/ERVL-MaLR_THE1B chr1 5042558 5042841 + LTR/ERVL_MER74B chr1 5042905 5043106 + SINE/MIR_MIRb chr1 5043242 5043262 + Simple_repeat_(TGAA)n chr1 5044122 5044217 + DNA/hAT_MamRep38 chr1 5044221 5044795 - LINE/L1_L1M4a1 chr1 5044796 5045235 + LTR/ERV1_LTR7 chr1 5045238 5048268 + LTR/ERV1_HERVH-int chr1 5048248 5048394 + LTR/ERV1_HERVH-int chr1 5048384 5052649 + LTR/ERV1_HERVH-int chr1 5052650 5053098 + LTR/ERV1_LTR7 chr1 5053165 5053378 - LINE/L1_L1M4a1 chr1 5053398 5053651 + LINE/L1_L1M5 chr1 5053656 5054861 + LINE/L1_L1M4 chr1 5054857 5056829 + LINE/L1_L1PB3 chr1 5056853 5058532 + LINE/L1_L1M4b chr1 5058349 5058750 + LINE/L1_L1MB8 chr1 5058751 5058882 + SINE/Alu_FLAM_C chr1 5058883 5059253 + LINE/L1_L1MB8 chr1 5059241 5059708 + LINE/L1_L1MB8 chr1 5059709 5060069 - LTR/ERVL-MaLR_THE1B chr1 5060070 5061025 + LINE/L1_L1M4 chr1 5061026 5061402 + LTR/ERV1_MER92B chr1 5061421 5062367 + LINE/L1_L1MB8 chr1 5062381 5062660 + LINE/L1_L1M4b chr1 5062695 5063261 - LINE/L1_L1M5 chr1 5063294 5063629 - SINE/Alu_AluSc5 chr1 5063638 5063780 + LINE/L1_L1ME3A chr1 5063784 5064204 - LTR/ERV1_LTR23 chr1 5064206 5064346 - LTR/ERV1_ERV24_Prim-int chr1 5064391 5064565 - LTR/ERV1_LTR23-int chr1 5064567 5064646 + LINE/L1_L1ME3A chr1 5064722 5064778 + LINE/L1_L1ME3A chr1 5064779 5065417 + DNA/TcMar-Tigger_Tigger17a chr1 5065418 5065862 + LINE/L1_L1ME3A chr1 5066046 5066408 - LTR/ERV1_MER34B chr1 5066409 5066763 + LTR/ERVL-MaLR_THE1A chr1 5066764 5066962 - LTR/ERV1_MER34B chr1 5067496 5067596 + LINE/L2_L2a chr1 5068438 5068799 - LTR/ERVL-MaLR_THE1B chr1 5068815 5069341 - LTR/ERV1_LTR54 chr1 5069348 5069441 + LTR/ERV1_LTR37B chr1 5069448 5069497 - DNA/hAT-Charlie_MER3 chr1 5069498 5069550 + Simple_repeat_(TTAA)n chr1 5069551 5069682 - DNA/hAT-Charlie_MER3 chr1 5069686 5069985 + LTR/ERV1_LTR37B chr1 5070410 5070698 + LTR/ERV1_MER31A chr1 5070699 5070994 - SINE/Alu_AluSx chr1 5070995 5071196 + LTR/ERV1_MER31A chr1 5071246 5071657 + LTR/ERV1_MER31-int chr1 5071798 5072098 + LTR/ERV1_MER31-int chr1 5071917 5072232 + LTR/ERV1_MER65-int chr1 5072233 5072274 + Simple_repeat_(GTTT)n chr1 5072275 5072337 + LTR/ERV1_MER65-int chr1 5072426 5073530 + LTR/ERV1_MER31-int chr1 5073531 5073830 + SINE/Alu_AluSg chr1 5073831 5075301 + LTR/ERV1_MER31-int chr1 5075302 5075383 + LTR/ERV1_MER31A chr1 5075384 5075877 + Simple_repeat_(TA)n chr1 5075975 5076074 + Simple_repeat_(TA)n chr1 5076075 5076093 + LTR/ERV1_MER31A chr1 5076094 5076152 + Simple_repeat_(GATA)n chr1 5076153 5076523 + LTR/ERV1_MER31A chr1 5076751 5076811 - LINE/CR1_L3 chr1 5076961 5077134 - SINE/MIR_MIRb chr1 5077950 5078383 + LTR/ERVL-MaLR_MLT1C2 chr1 5079656 5079847 + LTR/ERV1_MER34C_ chr1 5079851 5079882 + Simple_repeat_(TTTTC)n chr1 5079886 5080015 - SINE/Alu_FLAM_A chr1 5080020 5080140 - SINE/MIR_MIR chr1 5080235 5080388 + DNA/hAT-Charlie_MER113 chr1 5080467 5080679 + LINE/L1_L1MC5 chr1 5080680 5080976 + SINE/Alu_AluSx chr1 5080977 5081149 + LINE/L1_L1MC5 chr1 5081217 5081496 + LTR/ERV1_MER34C chr1 5082089 5082142 + Simple_repeat_(TTGCC)n chr1 5082320 5082433 - LTR/ERVL_MER77B chr1 5082519 5082879 + LTR/ERVL_MER77B chr1 5082880 5083395 - LTR/ERVL-MaLR_THE1B chr1 5083396 5083616 + LTR/ERVL_MER77B chr1 5083675 5083750 + Simple_repeat_(TC)n chr1 5084120 5084394 - SINE/Alu_AluJr chr1 5085843 5086152 + LTR/ERVL-MaLR_MLT1B chr1 5086153 5086235 + DNA/PiggyBac_MER75A chr1 5086236 5086286 + LTR/ERVL-MaLR_MLT1B chr1 5086326 5086361 + Simple_repeat_(AC)n chr1 5086410 5086436 + Simple_repeat_(TGA)n chr1 5086870 5087177 - LTR/ERVL_LTR16A chr1 5087182 5087347 - LINE/L1_L1ME3Cz chr1 5087349 5088095 + LINE/L1_L1PA17 chr1 5088096 5088402 + SINE/Alu_AluSp chr1 5088403 5088718 + LINE/L1_L1PA17 chr1 5088743 5088798 + Simple_repeat_(AT)n chr1 5088799 5089014 + DNA/TcMar-Tigger_Tigger2 chr1 5089015 5089192 - LINE/L1_L1PA4 chr1 5089193 5089411 + DNA/TcMar-Tigger_Tigger2 chr1 5089412 5089686 + SINE/Alu_AluJr chr1 5089687 5089756 + DNA/TcMar-Tigger_Tigger2 chr1 5089758 5089802 + Simple_repeat_(AT)n chr1 5090918 5091082 + SINE/MIR_MIR chr1 5091095 5091199 + DNA/TcMar-Tigger_Tigger15a chr1 5091649 5091850 + LINE/L1_L1ME3A chr1 5091862 5092626 - LTR/ERVL_MER21C chr1 5092671 5093806 + LINE/L1_L1ME3A chr1 5093896 5094267 + LINE/L1_L1ME3A chr1 5094373 5094668 + LINE/L1_L1ME3A chr1 5094669 5095855 + LINE/L1_L1ME3A chr1 5095865 5096008 + SINE/Alu_AluYc chr1 5096086 5096290 + LINE/L1_L1M5 chr1 5096301 5097033 + LTR/ERVL_MER21C chr1 5097167 5097218 - LTR/ERVL-MaLR_MLT1J chr1 5097219 5097290 - LTR/ERVL-MaLR_MSTA chr1 5097291 5097610 + SINE/Alu_AluSq2 chr1 5097611 5097927 - LTR/ERVL-MaLR_MSTA chr1 5097928 5098034 - LTR/ERVL-MaLR_MLT1J chr1 5098125 5098327 - LTR/ERVL-MaLR_MLT1J chr1 5098485 5098783 + SINE/Alu_AluY chr1 5099066 5099167 + SINE/MIR_MIRc chr1 5100039 5100091 + Simple_repeat_(CAT)n chr1 5100286 5100458 + LINE/L1_L1M6 chr1 5100731 5100968 + LINE/L1_L1M6 chr1 5101280 5101665 + DNA/TcMar-Tigger_MER44D chr1 5101666 5102018 + LTR/ERVL-MaLR_THE1B chr1 5102019 5102123 + DNA/TcMar-Tigger_MER44D chr1 5102153 5102397 + LINE/L1_L1M6 chr1 5102440 5102657 - SINE/Alu_AluSp chr1 5102658 5103022 + LINE/L1_L1M6 chr1 5103023 5103416 + LTR/ERVL-MaLR_MSTA chr1 5103417 5103511 + LINE/L1_L1M6 chr1 5103560 5103847 + SINE/Alu_AluSx1 chr1 5103848 5103867 + Simple_repeat_(CA)n chr1 5103918 5104517 + LINE/L1_L1M6 chr1 5104518 5104671 - LTR/ERVL_LTR52 chr1 5104672 5104969 - SINE/Alu_AluJb chr1 5104970 5105198 - LTR/ERVL_LTR52 chr1 5105199 5105457 + LINE/L1_L1M6 chr1 5105682 5105941 - LTR/ERVL-MaLR_MLT1I chr1 5106000 5106107 - LTR/ERVL_LTR52 chr1 5106322 5106514 + LINE/L1_L1M6 chr1 5106522 5106704 + DNA/hAT-Blackjack_MER63A chr1 5106752 5107176 + LINE/L1_L1ME3E chr1 5107186 5107341 + LINE/L1_L1M6 chr1 5107430 5107553 + LINE/L1_L1M6 chr1 5108278 5108649 - LTR/ERV1_MER31A chr1 5108693 5108836 + DNA/hAT-Charlie_Charlie15b chr1 5109035 5109100 + Low_complexity_GA-rich chr1 5109601 5109703 + LTR/ERV1?_LTR103b_Mam chr1 5109859 5110137 + DNA/hAT-Charlie_Charlie15b chr1 5110371 5111147 + LTR/ERV1_LTR39 chr1 5111263 5111394 - LINE/L2_L2c chr1 5111938 5112189 + SINE/MIR_MIR chr1 5112289 5112530 + Low_complexity_G-rich chr1 5112615 5113174 - LTR/ERV1_MER4B chr1 5113181 5113524 - LINE/L2_L2c chr1 5113903 5113967 + LINE/L2_L2c chr1 5113968 5114310 - LTR/ERVL-MaLR_THE1B chr1 5114311 5114346 + LINE/L2_L2c chr1 5114347 5114486 - LTR/ERV1_LTR37A chr1 5114487 5116526 + LTR/ERV1_HERVH-int chr1 5116521 5116670 + LTR/ERV1_HERVH-int chr1 5116670 5117165 + LTR/ERV1_HERVH-int chr1 5117168 5117829 + LTR/ERV1_HERVH-int chr1 5117861 5118062 + LTR/ERV1_HERVH-int chr1 5118061 5118439 + LTR/ERV1_HERVH-int chr1 5118440 5118888 + LTR/ERV1_LTR7 chr1 5118898 5119059 - LTR/ERVL-MaLR_THE1C chr1 5119097 5119481 - LTR/ERV1_LTR37A chr1 5120221 5120499 + SINE/Alu_AluSg chr1 5120609 5121087 + Simple_repeat_(TCCA)n chr1 5121104 5121194 - LINE/L2_L2b chr1 5121201 5121292 + LINE/L2_L2a chr1 5121355 5121833 + LINE/L2_L2a chr1 5121875 5121916 + DNA/hAT-Charlie_MER20 chr1 5121917 5122490 + LTR/ERV1_MER34A1 chr1 5122491 5122687 + DNA/hAT-Charlie_MER20 chr1 5122722 5123320 - LINE/L1_L1ME2 chr1 5123321 5123416 - LINE/L1_L1PA14 chr1 5123417 5123496 - LINE/L1_L1ME2 chr1 5124132 5124446 + SINE/Alu_AluSz6 chr1 5124531 5124599 - LINE/L2_L2b chr1 5124747 5124863 + DNA/hAT-Charlie_MER33 chr1 5124911 5124971 + Simple_repeat_(ATAATCT)n chr1 5124986 5125196 + DNA/hAT-Charlie_MER33 chr1 5125360 5125572 - LTR/ERVL_LTR79 chr1 5125633 5126228 - LTR/ERVL-MaLR_MLT1E2 chr1 5126276 5127032 + LTR/ERV1_LTR8 chr1 5127082 5127196 + SINE/MIR_MIR chr1 5127337 5127379 + LINE/CR1_L3 chr1 5128009 5128315 - SINE/Alu_AluSx1 chr1 5128374 5128405 + Simple_repeat_(TTTTGC)n chr1 5128563 5128841 + LTR/ERVL-MaLR_MLT1I chr1 5129100 5129377 + SINE/Alu_AluSz chr1 5129378 5129411 + Simple_repeat_(TAA)n chr1 5129483 5129506 + Simple_repeat_(TGAT)n chr1 5129604 5129708 + Simple_repeat_(AAT)n chr1 5131265 5131422 + LTR/ERVL-MaLR_MLT1A chr1 5131423 5131683 + SINE/Alu_AluSx chr1 5131684 5131714 + LTR/ERVL-MaLR_MLT1A chr1 5131715 5131744 + Simple_repeat_(TC)n chr1 5131745 5131962 + LTR/ERVL-MaLR_MLT1A chr1 5132185 5132689 - LINE/RTE-X_L4_C_Mam chr1 5133169 5133326 + SINE/MIR_MIRb chr1 5134604 5134915 - SINE/Alu_AluSz chr1 5135712 5135867 - DNA/hAT-Charlie_MER5B chr1 5135905 5136005 - DNA/hAT-Charlie_MER5B chr1 5136220 5136319 - DNA/hAT-Tip100_Arthur1C chr1 5137418 5137516 + LINE/L2_L2b chr1 5137582 5137788 + SINE/MIR_MIR chr1 5138891 5139242 + DNA/hAT-Charlie_MER113 chr1 5139729 5139808 + SINE/MIR_MIRc chr1 5140870 5140969 - DNA/hAT-Charlie_MER117 chr1 5141210 5141300 - SINE/MIR_MIR1_Amn chr1 5141867 5142217 - LTR/ERVL-MaLR_MLT1A chr1 5142342 5142413 - DNA/hAT-Charlie_MER5B chr1 5144188 5144211 + Simple_repeat_(A)n chr1 5144640 5144718 - DNA/hAT-Charlie_MER5B chr1 5144839 5144875 - LINE/L2_L2b chr1 5144999 5145486 - LINE/L1_L1PA16 chr1 5145491 5145895 + LTR/ERVL-MaLR_MLT1C2 chr1 5146480 5146554 + Low_complexity_GA-rich chr1 5146792 5146852 + Simple_repeat_(ATTC)n chr1 5146969 5147151 - LINE/CR1_L3 chr1 5147351 5147498 + SINE/MIR_MIRb chr1 5148993 5149247 + LINE/L1_L1MC4a chr1 5149548 5149607 + LINE/L2_L2d2 chr1 5150159 5150374 + SINE/MIR_MIRc chr1 5151018 5151231 - SINE/MIR_MIR chr1 5151813 5151856 + Simple_repeat_(CA)n chr1 5152457 5152819 + LTR/ERVL-MaLR_THE1C chr1 5152820 5154349 + LTR/ERVL-MaLR_THE1C-int chr1 5154355 5154657 + LTR/ERVL-MaLR_THE1C chr1 5154658 5154953 - SINE/Alu_AluY chr1 5154954 5155022 + LTR/ERVL-MaLR_THE1C chr1 5155522 5155896 - LTR/ERVL-MaLR_MLT1A1 chr1 5156661 5156739 + Low_complexity_GA-rich chr1 5158263 5158534 - LTR/ERVL-MaLR_MLT1B chr1 5158781 5158823 + Simple_repeat_(AC)n chr1 5158824 5159201 - LTR/ERVL-MaLR_MLT1A chr1 5159203 5159253 + Simple_repeat_(AC)n chr1 5159287 5159645 - LTR/ERVL_MLT2B3 chr1 5159646 5159941 + SINE/Alu_AluY chr1 5159942 5160163 - LTR/ERVL_MLT2B3 chr1 5160177 5160334 + Simple_repeat_(ACACAT)n chr1 5161160 5161178 + Simple_repeat_(T)n chr1 5161249 5161279 + Simple_repeat_(TG)n chr1 5162107 5162192 - LTR/ERVL_LTR33 chr1 5162217 5162431 - LTR/ERVL_LTR33 chr1 5162739 5163042 - SINE/Alu_AluSx1 chr1 5163622 5163767 + LINE/L1_L1ME1 chr1 5163768 5163936 - LINE/L1_L1PA10 chr1 5163937 5163984 + LINE/L1_L1PA8A chr1 5163998 5164079 + DNA/hAT-Charlie_Chap1a_Mam chr1 5164080 5164392 - LTR/ERVL-MaLR_MLT1J2 chr1 5165142 5165678 + LTR/ERVL_MER74B chr1 5166057 5166196 - SINE/MIR_MIRc chr1 5166426 5166520 - LINE/L2_L2b chr1 5166630 5167078 - LTR/ERV1_MER4A1 chr1 5167324 5167535 - LINE/L2_L2b chr1 5168352 5168652 + SINE/Alu_AluSp chr1 5168714 5168926 + SINE/MIR_MIR chr1 5169611 5170132 - LTR/ERVL-MaLR_MLT1F2 chr1 5171348 5171808 - LINE/L2_L2a chr1 5172308 5172536 + LINE/RTE-X_L4_C_Mam chr1 5172668 5172715 + Simple_repeat_(AACTG)n chr1 5172930 5173074 + LINE/RTE-X_L4_C_Mam chr1 5173766 5173878 + LINE/RTE-X_L4_C_Mam chr1 5174280 5174513 + LINE/L1_L1ME4a chr1 5174565 5174802 - LINE/L1_L1ME3E chr1 5174851 5174953 - LINE/L1_L1M5 chr1 5174954 5175311 + LTR/ERVL-MaLR_MLT1A0 chr1 5175312 5175428 - LINE/L1_L1M5 chr1 5175478 5175530 + Simple_repeat_(TT)n chr1 5175531 5175825 - SINE/Alu_AluYf1 chr1 5175826 5175842 + Simple_repeat_(TT)n chr1 5178420 5178722 + SINE/Alu_AluSg chr1 5179621 5179975 - LTR/ERVL-MaLR_MLT1B chr1 5179992 5180051 + DNA/hAT-Charlie_MER5B chr1 5180061 5180594 - LINE/L1_L1ME1 chr1 5180595 5180944 - LTR/ERVL-MaLR_MLT1A0 chr1 5180945 5181846 - LINE/L1_L1ME1 chr1 5181924 5183108 - LINE/L1_L1PA3 chr1 5183126 5183839 - LINE/L1_L1ME1 chr1 5184622 5184713 - LINE/L2_L2c chr1 5184919 5185222 + SINE/Alu_AluSg chr1 5185990 5186048 + LINE/L2_L2a chr1 5187431 5187562 + DNA/hAT-Tip100?_MamRep1879 chr1 5188217 5188269 + Low_complexity_GA-rich chr1 5188433 5189234 + LINE/L2_L2d2 chr1 5189694 5189848 + LINE/L2_L2d2 chr1 5190173 5190208 + Simple_repeat_(TCTTA)n chr1 5190605 5190642 + Simple_repeat_(TGTGTTT)n chr1 5190643 5190933 - SINE/Alu_AluSz6 chr1 5191303 5191372 + Simple_repeat_(AT)n chr1 5192647 5192775 + DNA/PiggyBac_MER85 chr1 5192811 5192841 + Simple_repeat_(CA)n chr1 5193009 5193317 + SINE/Alu_AluSz6 chr1 5193769 5193857 - LINE/L2_L2a chr1 5193866 5194062 - LTR/ERVL_LTR79 chr1 5194252 5194344 - LINE/L2_L2a chr1 5194759 5194870 - DNA/PiggyBac_MER85 chr1 5195663 5195975 - SINE/Alu_AluSz6 chr1 5195979 5196452 - LTR/ERVL-MaLR_MLT1D chr1 5196581 5196784 + LTR/ERVL-MaLR_MLT1N2 chr1 5196822 5196970 + LTR/ERVL-MaLR_MLT1N2 chr1 5197032 5197103 + LTR/ERVL-MaLR_MSTB1 chr1 5197436 5197848 + LTR/ERVL-MaLR_MSTB chr1 5197878 5198001 + DNA/hAT-Blackjack_MER94 chr1 5198626 5198845 - DNA/hAT-Charlie_MER107 chr1 5199318 5199659 + LTR/ERVL-MaLR_MLT1K chr1 5199719 5199805 - LTR/ERVL_LTR33 chr1 5200276 5200390 + LINE/L2_L2d2 chr1 5202812 5202836 - LINE/L2_L2b chr1 5202951 5203074 - DNA/TcMar-Tigger_X12_DNA chr1 5203206 5203245 - LTR/ERVL-MaLR_MSTA chr1 5203246 5203543 + SINE/Alu_AluSx chr1 5203544 5203917 - LTR/ERVL-MaLR_MSTA chr1 5203919 5204495 - LTR/ERVL-MaLR_MLT1E1 chr1 5204565 5204700 - SINE/MIR_MIRb chr1 5204805 5204981 + LTR/ERVL-MaLR_MLT1L chr1 5205043 5205395 - LTR/ERVL-MaLR_THE1B chr1 5205690 5205703 + Low_complexity_GA-rich chr1 5205704 5205750 + Low_complexity_A-rich chr1 5205751 5205770 + Low_complexity_GA-rich chr1 5206722 5206943 - LINE/L2_L2d2 chr1 5207335 5207539 + LINE/L1_L1MEf chr1 5207983 5208255 + LINE/L1_L1MEf chr1 5208256 5208601 - DNA/TcMar-Tigger_MER47A chr1 5208602 5209627 + LINE/L1_L1MEf chr1 5209712 5209899 + LTR/ERVL-MaLR_MLT1J chr1 5209980 5210094 + LTR/ERVL-MaLR_MLT1J chr1 5210265 5210381 - LINE/L2_L2b chr1 5210453 5210759 - SINE/Alu_AluY chr1 5210893 5211311 + LINE/L1_L1MEb chr1 5211327 5212365 + LINE/L1_L1MEb chr1 5212366 5212415 + Simple_repeat_(TAAA)n chr1 5212416 5214090 + LINE/L1_L1MEb chr1 5214111 5214488 - LINE/L1_L1PB1 chr1 5214489 5214651 + Simple_repeat_(AT)n chr1 5214658 5215135 - LINE/L1_L1ME4a chr1 5215130 5215999 + LINE/L1_L1ME2z chr1 5216062 5216599 + LINE/L1_L1MB1 chr1 5216657 5216721 - LTR/ERVL_MLT2B4 chr1 5216749 5216988 + Simple_repeat_(AT)n chr1 5217002 5217335 - LTR/ERVL_MLT2B4 chr1 5217525 5218230 + LINE/L2_L2a chr1 5218293 5218568 - LTR/ERVL_LTR16B2 chr1 5218582 5218681 - LTR/ERVL_LTR16B2 chr1 5218720 5218844 + LINE/L2_L2a chr1 5218852 5219214 + LTR/ERVL-MaLR_THE1B chr1 5221320 5221523 + SINE/MIR_MIRb chr1 5222258 5222301 + Simple_repeat_(TTTC)n chr1 5222302 5222591 - SINE/Alu_AluJb chr1 5224217 5224336 - SINE/MIR_MIRb chr1 5224385 5224604 - SINE/Alu_AluSc chr1 5224622 5224667 + DNA/hAT-Tip100_MER45B chr1 5224806 5225026 - LINE/L1_L1MB5 chr1 5225027 5225233 + LTR/ERVL_MLT2B2 chr1 5225234 5225330 + Simple_repeat_(CTCTCTCCCT)n chr1 5225331 5225585 + LTR/ERVL_MLT2B2 chr1 5225595 5225631 + Simple_repeat_(CA)n chr1 5225670 5225743 - LINE/L1_L1MB5 chr1 5225917 5225949 + Simple_repeat_(TTGCTG)n chr1 5226000 5226063 - SINE/MIR_MIRc chr1 5227273 5227421 + SINE/MIR_MIRb chr1 5227623 5227671 + Simple_repeat_(TTTAT)n chr1 5227672 5227944 - SINE/Alu_AluSg chr1 5228421 5228806 - LINE/L1_HAL1ME chr1 5228967 5229006 + Simple_repeat_(TT)n chr1 5229007 5229300 + SINE/Alu_AluSx1 chr1 5229301 5229319 + Simple_repeat_(TT)n chr1 5229587 5230710 + LINE/L1_L1ME3F chr1 5230711 5230955 + SINE/Alu_AluJr chr1 5230956 5231273 + LINE/L1_L1ME3F chr1 5232913 5233331 + LTR/ERV1_MER39 chr1 5233332 5233685 + LTR/ERVL-MaLR_THE1D chr1 5233686 5233892 + LTR/ERV1_MER39 chr1 5233894 5234872 + LINE/L2_L2a chr1 5235204 5235362 + DNA/hAT-Charlie_MER5A1 chr1 5236132 5236905 + LINE/L1_L1MEh chr1 5237002 5237490 + LINE/L1_L1ME5 chr1 5237556 5237634 + SINE/MIR_MIR3 chr1 5237707 5237975 + LINE/L1_L1ME5 chr1 5237988 5238378 + LINE/L1_L1ME5 chr1 5238380 5238934 - LINE/L2_L2 chr1 5238968 5239155 + LINE/L1_L1ME5 chr1 5239779 5239801 + Simple_repeat_(T)n chr1 5239837 5240245 - LTR/ERVL-MaLR_MLT1H2 chr1 5240976 5241104 + SINE/MIR_MIR chr1 5242431 5242493 - LINE/L2_L2c chr1 5242494 5242795 - SINE/Alu_AluY chr1 5242906 5243025 - DNA/hAT-Charlie_MER5A1 chr1 5246029 5246756 + Simple_repeat_(CTCC)n chr1 5246806 5247728 - LINE/L1_L1MB5 chr1 5247729 5248023 + SINE/Alu_AluJr chr1 5248024 5249031 - LINE/L1_L1MB5 chr1 5249032 5249072 + Simple_repeat_(TG)n chr1 5249073 5249142 - LINE/L1_L1MB5 chr1 5249218 5249431 + LINE/L2_L2b chr1 5249660 5250082 + DNA/hAT-Charlie_Charlie22a chr1 5250286 5250396 - LTR/ERVL_LTR33A_ chr1 5250904 5250921 + Simple_repeat_(AC)n chr1 5250922 5251159 - LINE/L1_L1MA2 chr1 5251160 5251522 + LTR/ERVL-MaLR_THE1B chr1 5251523 5251621 - LINE/L1_L1MA2 chr1 5251677 5251964 + SINE/Alu_AluY chr1 5251967 5251990 + Simple_repeat_(ATC)n chr1 5253475 5253560 + DNA/hAT-Charlie_Charlie22a chr1 5253587 5253899 - SINE/Alu_AluSc8 chr1 5253901 5254018 + DNA/hAT-Charlie_Charlie22a chr1 5255510 5255728 - LINE/L2_L2a chr1 5255843 5256225 - LINE/L2_L2a chr1 5256232 5256330 + LTR/ERVL-MaLR_MSTD chr1 5256339 5257285 - LINE/L1_L1M5 chr1 5257288 5257614 + LTR/ERVL_MER74A chr1 5257615 5257974 + LTR/ERVL-MaLR_THE1B chr1 5257975 5258172 + LTR/ERVL_MER74A chr1 5258173 5258240 - LINE/L1_L1MCc chr1 5258236 5258257 - LINE/L1_L1MCc chr1 5258258 5258763 - LINE/L1_L1MEf chr1 5258764 5258861 - LINE/L1_L1MCc chr1 5258876 5259199 + DNA/hAT-Charlie_MER1B chr1 5259200 5259364 - LINE/L1_L1MCc chr1 5259365 5259794 - LTR/ERVL-MaLR_MLT1C chr1 5259795 5260279 - LINE/L1_L1MCc chr1 5260409 5260969 - LINE/L1_L1MCc chr1 5260974 5261158 - SINE/MIR_MIR chr1 5261268 5261579 + SINE/Alu_AluSz chr1 5261617 5261687 - SINE/Alu_Alu chr1 5261688 5261810 + SINE/Alu_AluSx3 chr1 5262492 5262670 + LTR/ERVL-MaLR_MSTB chr1 5262671 5262954 + SINE/Alu_AluSg chr1 5262955 5263205 + LTR/ERVL-MaLR_MSTB chr1 5263449 5263499 + Simple_repeat_(TG)n chr1 5263531 5263901 + LTR/ERVL-MaLR_THE1B chr1 5265003 5265259 + SINE/Alu_AluSg chr1 5265974 5266112 - LINE/L2_L2c chr1 5266766 5266901 + SINE/MIR_MIRb chr1 5266917 5266955 + Simple_repeat_(TAT)n chr1 5267243 5267333 + LTR/ERVL-MaLR_MLT1N2 chr1 5267406 5267446 + Simple_repeat_(CT)n chr1 5267963 5268143 + SINE/MIR_MIRc chr1 5268690 5268897 - SINE/MIR_MIR chr1 5268999 5269308 + LTR/ERVL-MaLR_MLT1D chr1 5269309 5269657 - LTR/ERVL-MaLR_MLT1A0 chr1 5269658 5269825 + LTR/ERVL-MaLR_MLT1D chr1 5270185 5270207 + Simple_repeat_(CCTT)n chr1 5270265 5270429 + LINE/L2_L2b chr1 5270642 5270671 + Low_complexity_GA-rich chr1 5271738 5272098 + LTR/ERVL-MaLR_THE1D chr1 5273027 5273057 + Simple_repeat_(TG)n chr1 5273064 5273179 + LINE/L2_L2c chr1 5273657 5273861 + SINE/MIR_MIRb chr1 5274923 5275041 - SINE/MIR_MIR1_Amn chr1 5276311 5276699 + LTR/ERVL_MLT2D chr1 5276700 5276737 + Simple_repeat_(CA)n chr1 5276738 5276786 + LTR/ERVL_MLT2D chr1 5277152 5277474 + LTR/ERVL-MaLR_MLT1E1 chr1 5277604 5277712 + LINE/L2_L2a chr1 5279126 5279166 + Simple_repeat_(TG)n chr1 5279528 5279837 - SINE/Alu_AluY chr1 5279846 5280154 - SINE/Alu_AluSx chr1 5280337 5280510 - LINE/L2_L2c chr1 5280510 5281054 + LINE/L2_L2a chr1 5281187 5281406 + SINE/MIR_MIRb chr1 5282175 5282648 + LTR/ERVL-MaLR_MLT1C chr1 5283637 5284157 - LINE/L2_L2c chr1 5284158 5284435 + SINE/Alu_AluJo chr1 5284436 5284522 - LINE/L2_L2c chr1 5284523 5285025 - LTR/ERVL_MLT2B1 chr1 5285026 5285650 - LINE/L2_L2c chr1 5285731 5286420 - LINE/L2_L2c chr1 5286489 5287050 - LTR/ERVL-MaLR_MLT1E3 chr1 5288215 5288370 + SINE/MIR_MIR chr1 5288481 5288786 - SINE/Alu_AluY chr1 5289618 5290149 + Simple_repeat_(ATGG)n chr1 5290150 5290722 + LTR/ERV1_MER34B chr1 5290723 5290803 + Simple_repeat_(GATG)n chr1 5290804 5290967 + Simple_repeat_(ATGG)n chr1 5290968 5291040 + Simple_repeat_(GATG)n chr1 5291668 5291847 - SINE/MIR_MIRb chr1 5292433 5292791 - LTR/ERVL-MaLR_THE1B chr1 5292909 5293066 - DNA/hAT-Tip100_Zaphod3 chr1 5295182 5295208 + Simple_repeat_(GTT)n chr1 5295214 5295381 - SINE/Alu_AluSp chr1 5295383 5295702 - SINE/Alu_AluY chr1 5297983 5298015 + Simple_repeat_(AGAGAC)n chr1 5298074 5298152 + LTR/ERVL-MaLR_MLT1I chr1 5298334 5298428 + LTR/ERVL-MaLR_MLT1I chr1 5298429 5298491 + LTR/ERVL-MaLR_MLT1I chr1 5298569 5298678 + SINE/MIR_MIRb chr1 5298747 5298856 - LINE/L2_L2 chr1 5298926 5299456 + LTR/ERVL-MaLR_MLT1G chr1 5299497 5299701 - LINE/L2_L2 chr1 5299720 5300210 + LINE/L1_L1ME3A chr1 5301032 5301446 - LTR/ERVL-MaLR_MSTB chr1 5302012 5302046 + Simple_repeat_(ATAA)n chr1 5302047 5302224 + Low_complexity_A-rich chr1 5302233 5302367 - SINE/MIR_MIRc chr1 5302380 5302420 - LINE/L2_L2d chr1 5302470 5302624 + DNA/hAT-Charlie_MER113A chr1 5302625 5303085 + LINE/L1_L1M2 chr1 5303086 5303157 + Simple_repeat_(GATA)n chr1 5303175 5303463 - DNA/TcMar-Tigger_Tigger3a chr1 5303518 5303992 + LINE/L1_L1MA10 chr1 5305329 5305394 + LTR/ERVL-MaLR_MLT1H2 chr1 5305550 5306003 - LTR/ERVL_MLT2B1 chr1 5306101 5306302 - SINE/MIR_MIR chr1 5306357 5306412 + Simple_repeat_(ACTATT)n chr1 5306413 5306446 + Simple_repeat_(TGTTT)n chr1 5306447 5306726 - SINE/Alu_AluSg chr1 5306727 5306847 + Simple_repeat_(ACTATT)n chr1 5306997 5307066 + LINE/L2_L2b chr1 5307395 5307528 - DNA/TcMar-Tigger_Tigger19a chr1 5307755 5308067 + SINE/Alu_AluSx chr1 5308769 5309062 - SINE/Alu_AluSg chr1 5309214 5309510 - SINE/Alu_AluSx chr1 5310363 5310783 - LTR/ERVL-MaLR_MSTD chr1 5312414 5312565 + LINE/L2_L2a chr1 5312778 5312973 - SINE/MIR_MIR chr1 5313603 5313964 - DNA/hAT-Charlie_MER58D chr1 5313965 5314584 - LTR/ERVL-MaLR_MLT1D chr1 5314618 5314742 - SINE/MIR_MIR chr1 5314968 5315192 + LINE/L2_L2a chr1 5315194 5315581 + Low_complexity_GA-rich chr1 5315641 5315757 + DNA/hAT-Tip100_MamTip2 chr1 5316039 5316108 - SINE/MIR_MIR chr1 5316109 5316410 - SINE/Alu_AluSq2 chr1 5316411 5316457 - SINE/MIR_MIR chr1 5316715 5316818 - DNA/hAT-Charlie_MER113 chr1 5316820 5317126 + SINE/Alu_AluSx1 chr1 5318850 5319050 - SINE/MIR_MIR chr1 5319068 5319144 - LINE/L2_L2c chr1 5319180 5319379 + LINE/L1_L1MEc chr1 5319372 5320000 + LINE/L1_L1MEc chr1 5320007 5320192 + Simple_repeat_(AT)n chr1 5320195 5320958 + LINE/L1_L1MEc chr1 5321107 5321689 - LINE/L2_L2c chr1 5321720 5322020 + SINE/Alu_AluY chr1 5322615 5322818 + SINE/MIR_MIR chr1 5322820 5322963 + LINE/L1_L1ME4a chr1 5322969 5323052 + LINE/L2_L2a chr1 5323055 5323441 + LINE/L1_L1ME4a chr1 5324598 5324792 + LTR/ERVL-MaLR_MLT1K chr1 5324834 5325020 + SINE/MIR_MIR chr1 5325064 5325275 + LTR/ERVL-MaLR_MLT1K chr1 5325901 5326092 - SINE/MIR_MIRb chr1 5326477 5326535 + Simple_repeat_(CTC)n chr1 5327098 5327737 - LTR/ERVL_MER77B chr1 5329540 5329626 + SINE/MIR_MIRc chr1 5329627 5329986 - LTR/ERVL-MaLR_MLT1A chr1 5329987 5330065 + SINE/MIR_MIRc chr1 5330789 5330945 - LTR/ERVL_LTR16E1 chr1 5331666 5331968 - SINE/Alu_AluSc5 chr1 5332433 5332509 + LINE/L2_L2d2 chr1 5332548 5332797 - DNA/hAT-Tip100_MER45B chr1 5332798 5333110 - SINE/Alu_AluSg7 chr1 5333111 5333208 - DNA/hAT-Tip100_MER45B chr1 5333243 5333486 - DNA/hAT-Tip100_MER45B chr1 5333860 5333890 + Simple_repeat_(ATTC)n chr1 5334749 5334909 - LINE/L2_L2a chr1 5335140 5335556 - LTR/ERVL-MaLR_MLT1C2 chr1 5336425 5336649 + SINE/MIR_MIRc chr1 5337014 5337303 + SINE/Alu_AluJb chr1 5337872 5338031 - SINE/MIR_MIR3 chr1 5338236 5338316 + Simple_repeat_(TC)n chr1 5338320 5338729 - LTR/ERVL-MaLR_MLT1K chr1 5338834 5339170 + LINE/L1_L1ME4c chr1 5339220 5339528 + SINE/Alu_AluSc chr1 5339769 5340175 + LTR/ERVL-MaLR_MLT1C2 chr1 5340482 5340623 - DNA/hAT-Tip100?_MamRep1879 chr1 5340920 5341107 + LTR/ERVL_LTR33A chr1 5341173 5341357 + LINE/L2_L2a chr1 5342130 5342323 + SINE/MIR_MIR chr1 5342506 5342614 - DNA/hAT-Charlie_MER33 chr1 5342620 5342663 + DNA/TcMar-Mariner_MADE1 chr1 5342679 5342871 - DNA/hAT-Charlie_MER33 chr1 5343533 5343665 + SINE/MIR_MIRb chr1 5343874 5344103 + SINE/MIR_MIR chr1 5344147 5344271 - SINE/MIR_MIR3 chr1 5344427 5344595 - LTR/ERV1_LTR78B chr1 5344658 5344768 - DNA/hAT-Tip100_MER91A chr1 5344807 5344862 - DNA/hAT-Tip100_MER91A chr1 5345038 5345348 - SINE/Alu_AluSq2 chr1 5345930 5346126 - LTR/ERV1_LTR78B chr1 5346410 5346625 - LTR/ERV1_LTR78B chr1 5347122 5347156 + SINE/MIR_MIR3 chr1 5347157 5347389 + Simple_repeat_(TGA)n chr1 5347413 5347557 - SINE/MIR_MIR chr1 5348486 5348759 - SINE/Alu_AluSg chr1 5348760 5348827 + LTR/ERVL-MaLR_MLT1C2 chr1 5350414 5350578 + SINE/MIR_MIR chr1 5350730 5351105 + LTR/ERVL-MaLR_THE1B chr1 5351751 5352060 + SINE/Alu_AluSx1 chr1 5352209 5352295 + DNA/hAT-Charlie_MER5A chr1 5353099 5353196 - LINE/L1_L1MEf chr1 5353197 5353347 - SINE/Alu_AluSx chr1 5353348 5355205 - LINE/L1_L1MEf chr1 5355183 5355282 - LINE/L1_L1MD3 chr1 5355288 5355466 + DNA/hAT-Charlie_MER58A chr1 5355467 5355857 + LTR/ERVL-MaLR_MLT1A0 chr1 5355858 5355890 + DNA/hAT-Charlie_MER58A chr1 5355933 5356342 - LINE/L1_L1MD3 chr1 5356343 5356706 - LTR/ERVL-MaLR_THE1B chr1 5356707 5357114 - LINE/L1_L1MD3 chr1 5357092 5357471 - LINE/L1_L1MEf chr1 5357909 5358290 - LINE/L1_L1MEf chr1 5358516 5358662 - SINE/MIR_MIR chr1 5358665 5359028 + LTR/ERVL-MaLR_MSTB chr1 5359029 5359328 + SINE/Alu_AluSx3 chr1 5359329 5359355 + LTR/ERVL-MaLR_MSTB chr1 5359356 5359749 + LTR/ERVL-MaLR_THE1B chr1 5359750 5359791 + LTR/ERVL-MaLR_MSTB chr1 5359793 5359826 - SINE/MIR_MIRc chr1 5359961 5360444 - LTR/ERV1_MER31A chr1 5360973 5361022 + SINE/MIR_MIRc chr1 5361023 5361390 + LTR/ERVL-MaLR_THE1C chr1 5361391 5361452 + SINE/MIR_MIRc chr1 5362306 5362556 - DNA/TcMar-Tigger_Tigger4a chr1 5363041 5363202 - LINE/L2_L2a chr1 5364059 5364328 - LTR/ERVL-MaLR_MLT1K chr1 5364466 5365194 + LINE/L2_L2a chr1 5365195 5365727 - LTR/ERV1_MER41A chr1 5365728 5366331 + LINE/L2_L2a chr1 5366503 5366584 + DNA/hAT-Ac_OldhAT1 chr1 5367092 5367209 + LTR/ERV1_MER34C_ chr1 5367192 5367376 + LTR/ERV1_MER34C_ chr1 5367952 5368809 + LTR/ERVL_MER21C chr1 5369009 5369106 - LTR/ERVL-MaLR_MSTA chr1 5369517 5369821 + SINE/Alu_AluJb chr1 5370360 5370655 + LTR/ERV1_MER89 chr1 5370656 5371241 + LTR/ERVL_MLT2B3 chr1 5371242 5371463 + LTR/ERV1_MER89 chr1 5371808 5372085 + LTR/Gypsy_LTR81B chr1 5372438 5372598 + LTR/ERVL-MaLR_THE1B chr1 5372599 5372892 - SINE/Alu_AluSz chr1 5372893 5373093 + LTR/ERVL-MaLR_THE1B chr1 5373461 5374205 - LTR/ERV1_LTR8B chr1 5374484 5374614 - LINE/L2_L2a chr1 5374857 5374886 + Simple_repeat_(CA)n chr1 5374936 5374967 - LINE/CR1_L3 chr1 5375003 5375370 + Simple_repeat_(ACATAC)n chr1 5375645 5375676 + LINE/CR1_L3 chr1 5376301 5376360 + LINE/L2_L2b chr1 5376450 5376809 - LTR/ERVL-MaLR_MLT1E1A chr1 5377375 5377563 - SINE/MIR_MIRb chr1 5377852 5377877 + Simple_repeat_(TCCT)n chr1 5378702 5378731 - LINE/L2_L2a chr1 5378819 5378992 + LINE/L1_L1P4 chr1 5380177 5380512 - LTR/ERVL_LTR33A_ chr1 5380617 5380731 - LINE/L2_L2d chr1 5382089 5382315 - DNA/TcMar-Tigger_Tigger19a chr1 5383001 5383125 - SINE/MIR_MIRb chr1 5383522 5383757 - SINE/MIR_MIRb chr1 5383796 5384076 + SINE/Alu_AluSx chr1 5384096 5384253 + Simple_repeat_(ATAT)n chr1 5384343 5384559 - DNA/hAT-Charlie_Charlie29b chr1 5385524 5385883 - LTR/ERVL-MaLR_MLT1B chr1 5386696 5386827 - LINE/L1_L1M4b chr1 5386832 5387785 + Simple_repeat_(TG)n chr1 5387786 5387864 - LTR/ERVL-MaLR_MLT1C2 chr1 5391067 5391094 + Simple_repeat_(GGATG)n chr1 5391940 5391964 + Simple_repeat_(CA)n chr1 5391979 5392204 + SINE/MIR_MIR chr1 5392543 5392752 - DNA/hAT-Charlie_MER5C chr1 5393169 5393751 - LTR/ERV1_MER92B chr1 5393855 5393946 + SINE/MIR_MIRb chr1 5394640 5394850 + Low_complexity_GA-rich chr1 5395232 5395361 - Unknown_UCON63 chr1 5395866 5396153 - LTR/ERVL-MaLR_MLT1H chr1 5396623 5396879 + SINE/MIR_MIRc chr1 5397011 5397327 - LINE/L2_L2a chr1 5397323 5397425 - SINE/MIR_MIR3 chr1 5398816 5399108 + LINE/L2_L2a chr1 5399113 5399490 + Simple_repeat_(TGGA)n chr1 5399708 5399802 + LINE/L2_L2c chr1 5400675 5401039 - LTR/ERVL-MaLR_THE1B chr1 5401269 5401364 - LINE/L1_L1PA16 chr1 5401659 5401745 - LINE/L2_L2a chr1 5401763 5401901 + LTR/ERV1_MER31A chr1 5401979 5402327 + LINE/L2_L2a chr1 5402969 5403304 - LTR/ERVL-MaLR_MLT1A chr1 5403898 5403955 + Simple_repeat_(AGC)n chr1 5405428 5405951 + LTR/ERVL_MLT2B3 chr1 5405962 5406291 + DNA/TcMar-Tigger_MER2 chr1 5406292 5406961 + Simple_repeat_(AGAT)n chr1 5406963 5407018 + LTR/ERVL_MLT2B3 chr1 5407743 5407895 - SINE/MIR_MIR3 chr1 5407965 5408144 - DNA/hAT-Charlie_MER20 chr1 5408840 5409212 + LTR/ERVL-MaLR_THE1D chr1 5409332 5409395 - LINE/L2_L2c chr1 5410082 5410189 + LINE/L2_L2c chr1 5411870 5412350 - LTR/ERVL-MaLR_MLT1H chr1 5412760 5413057 + SINE/Alu_AluSx chr1 5413058 5413155 - SINE/MIR_MIR3 chr1 5414334 5414468 + LINE/L2_L2c chr1 5414578 5414716 + DNA/hAT-Charlie_MER5B chr1 5415125 5415345 + SINE/MIR_MIRb chr1 5416032 5416167 + Simple_repeat_(CCTT)n chr1 5416168 5416298 - SINE/Alu_FLAM_A chr1 5416318 5416607 + SINE/Alu_AluJb chr1 5416828 5416906 + DNA/TcMar-Mariner_MADE1 chr1 5417317 5417608 + SINE/Alu_AluSx chr1 5417609 5417662 - SINE/MIR_MIRc chr1 5417670 5417734 - LTR/ERVL-MaLR_MLT1H chr1 5417806 5417839 + Simple_repeat_(CCCCTT)n chr1 5419160 5419369 - LTR/ERVL-MaLR_MLT1H chr1 5420612 5420977 - LTR/ERVL_LTR33B chr1 5422418 5422888 + LTR/ERVL-MaLR_MLT1C chr1 5424061 5424204 - LINE/L2_L2a chr1 5424200 5424452 - SINE/MIR_MIR chr1 5425953 5425982 + Simple_repeat_(GACCCA)n chr1 5426036 5426078 - LINE/L1_L1MB5 chr1 5426099 5426247 - LINE/L1_L1PA16 chr1 5426254 5426286 + LINE/L1_L1P4 chr1 5426289 5426371 - LINE/L1_L1MB5 chr1 5426372 5426720 - SINE/Alu_AluSx chr1 5426721 5427197 - LINE/L1_L1MB5 chr1 5427517 5427554 - SINE/MIR_MIRc chr1 5428718 5428983 + Low_complexity_GA-rich chr1 5433083 5433169 - SINE/MIR_MIR chr1 5433170 5433342 + DNA/hAT_MER53 chr1 5433746 5433783 + Simple_repeat_(TG)n chr1 5434818 5435013 + LINE/L2_L2a chr1 5435057 5435101 + Simple_repeat_(ATTC)n chr1 5435410 5435579 - SINE/MIR_MIR3 chr1 5435748 5435784 + Low_complexity_GA-rich chr1 5436348 5436482 + SINE/Alu_AluJo chr1 5437333 5437460 + LTR/ERVL-MaLR_MLT1C2 chr1 5437461 5437814 - LTR/ERVL-MaLR_THE1B chr1 5437815 5439462 - LTR/ERVL-MaLR_THE1B-int chr1 5439463 5439818 - LTR/ERVL-MaLR_THE1B chr1 5439819 5440137 + LTR/ERVL-MaLR_MLT1C2 chr1 5440199 5440237 + Simple_repeat_(TCATGC)n chr1 5440610 5440949 - LINE/L1_L1MA10 chr1 5440950 5441258 + SINE/Alu_AluJb chr1 5441259 5441468 - LINE/L1_L1MA10 chr1 5441495 5441531 + Simple_repeat_(CAGGAGC)n chr1 5442291 5442730 + LTR/ERVL-MaLR_MLT1D chr1 5442873 5443031 + SINE/MIR_MIRb chr1 5443032 5443507 + LTR/ERVL_MLT2B4 chr1 5443508 5443569 + SINE/MIR_MIRb chr1 5444213 5444258 + Simple_repeat_(AAAAGA)n chr1 5444865 5444971 - LINE/L1_L1MC4 chr1 5444989 5445208 - LINE/L1_L1MC4 chr1 5445222 5445355 - LINE/L1_L1MB1 chr1 5445372 5445665 - LTR/ERVL-MaLR_MLT1A1 chr1 5445860 5446219 - LINE/L1_L1MC4 chr1 5446220 5446599 - LTR/ERVL-MaLR_MSTA chr1 5446600 5446943 - LINE/L1_L1MC4 chr1 5447093 5447138 + SINE/MIR_MIR3 chr1 5448081 5448453 + LINE/L1_L1ME2 chr1 5448832 5449050 - SINE/MIR_MIRc chr1 5449154 5449643 + LTR/ERVL-MaLR_MLT1D chr1 5451158 5451454 + LTR/ERV1_MER61A chr1 5451750 5452086 + LTR/ERV1_MER61A chr1 5452134 5452698 - LTR/ERV1_MER4E chr1 5452755 5452863 - DNA/hAT-Blackjack_MER81 chr1 5453851 5454105 - LINE/L2_L2b chr1 5454778 5454933 - SINE/MIR_MIRb chr1 5455164 5455228 - LINE/L2_L2b chr1 5455316 5455426 + LINE/L1_L1MA8 chr1 5456043 5456196 + LINE/L2_L2a chr1 5456300 5456398 + SINE/MIR_MIRc chr1 5456737 5456866 + LTR/ERVL_LTR79 chr1 5456883 5456897 - LINE/L1_L1PA3 chr1 5456898 5456924 + Simple_repeat_(ATTT)n chr1 5456925 5457263 - LINE/L1_L1PA3 chr1 5457264 5457330 - LTR/ERVL-MaLR_MLT1D chr1 5457347 5457497 - SINE/Alu_AluJb chr1 5458148 5458164 + Simple_repeat_(T)n chr1 5458334 5458577 + Simple_repeat_(CCCT)n chr1 5459269 5459686 - LTR/ERV1_MER57A1 chr1 5459818 5460028 + SINE/MIR_MIR chr1 5460413 5460599 - DNA/hAT-Charlie_MER3 chr1 5460678 5460756 + LINE/L2_L2a chr1 5460757 5461196 + LTR/ERVL-MaLR_MLT1D chr1 5461197 5461245 + LINE/L2_L2a chr1 5462177 5462660 + LTR/ERVL-MaLR_MLT1F chr1 5463470 5463732 + LTR/ERVL-MaLR_MLT1A chr1 5463733 5464062 - LTR/ERVL-MaLR_THE1A chr1 5464063 5464197 + LTR/ERVL-MaLR_MLT1A chr1 5464859 5465114 - SINE/Alu_AluYa5 chr1 5465507 5465872 + LTR/ERVL-MaLR_MLT1B chr1 5465874 5466042 - LINE/L2_L2c chr1 5466191 5466247 - LINE/L2_L2d2 chr1 5466249 5466451 - LTR/ERVL-MaLR_MLT1J chr1 5466698 5466921 + LTR/ERVL-MaLR_MLT1A1 chr1 5466933 5467091 + LTR/ERVL-MaLR_MLT1A0 chr1 5467184 5467371 + LTR/ERVL-MaLR_MLT1A0 chr1 5467373 5467472 + SINE/MIR_MIR3 chr1 5467574 5467713 + LTR/ERVL-MaLR_MLT1C2 chr1 5467854 5468432 + LTR/ERVL-MaLR_MLT1C2-int chr1 5468464 5468863 + LTR/ERVL-MaLR_MLT1C2 chr1 5468949 5469450 + LTR/ERVL-MaLR_MLT1C2-int chr1 5469465 5470022 + LTR/ERVL_MLT2B3 chr1 5470027 5470144 + LTR/ERVL-MaLR_MLT1C2 chr1 5470942 5471142 - SINE/MIR_MIRb chr1 5471343 5471383 + Simple_repeat_(CA)n chr1 5471400 5471795 - LINE/L1_L1ME4a chr1 5471793 5472351 - LINE/L1_L1MEi chr1 5472526 5472604 + Simple_repeat_(GT)n chr1 5472778 5472943 - SINE/MIR_MIRb chr1 5473672 5473722 + LTR/ERVL-MaLR_MLT1A0 chr1 5473723 5474017 - SINE/Alu_AluSx chr1 5474018 5474343 + LTR/ERVL-MaLR_MLT1A0 chr1 5475319 5475738 + LTR/ERVL-MaLR_MLT1C2 chr1 5475742 5476313 + DNA/TcMar-Tigger_Tigger13a chr1 5476624 5477153 + LTR/ERVL_LTR16D2 chr1 5477356 5477471 - LINE/CR1_L3 chr1 5478828 5479027 - DNA/hAT-Tip100_ORSL chr1 5479111 5479144 + Simple_repeat_(CTG)n chr1 5479517 5479586 - SINE/MIR_MIR chr1 5479608 5479727 - SINE/MIR_MIRb chr1 5480436 5480633 + SINE/MIR_MIRb chr1 5480897 5481059 - SINE/MIR_MIR chr1 5481543 5481848 + LTR/ERVL-MaLR_MLT1H chr1 5482645 5482996 + LTR/ERVL-MaLR_THE1D chr1 5483005 5483303 - LTR/ERVL-MaLR_MLT1C2 chr1 5484294 5484579 - LINE/L2_L2b chr1 5484604 5484708 + SINE/MIR_MIR chr1 5484807 5484846 + Simple_repeat_(ATGA)n chr1 5487628 5487991 + LINE/L2_L2a chr1 5488000 5488442 + Simple_repeat_(ATCC)n chr1 5488743 5488801 + DNA/hAT-Tip100_MER45C chr1 5488850 5489604 - DNA/hAT-Tip100_MER45B chr1 5489605 5490381 + LINE/L1_L1PA13 chr1 5490382 5490508 - DNA/hAT-Tip100_MER45R chr1 5490701 5490753 + Simple_repeat_(CA)n chr1 5491270 5491385 - DNA/TcMar-Mariner_MARNA chr1 5491439 5491669 + SINE/MIR_MIR chr1 5491815 5492009 + DNA/hAT-Charlie_MER3 chr1 5491856 5492033 - DNA/hAT-Charlie_MER3 chr1 5492052 5492223 - DNA/hAT-Charlie_MER103C chr1 5492332 5492638 + SINE/Alu_AluSc8 chr1 5492717 5492773 - LINE/L2_L2b chr1 5496696 5497128 - LTR/ERVL-MaLR_MLT1C2 chr1 5498362 5498506 - SINE/MIR_MIRb chr1 5499216 5499284 + Simple_repeat_(GATA)n chr1 5499291 5499787 + Simple_repeat_(ATAG)n chr1 5499906 5500136 - LINE/L2_L2b chr1 5500254 5500554 + SINE/Alu_AluY chr1 5500864 5501005 + Simple_repeat_(ATCT)n chr1 5502063 5502295 + SINE/MIR_MIRb chr1 5503059 5503104 + Low_complexity_A-rich chr1 5503534 5504045 + LTR/ERVL_MER74A chr1 5504824 5505017 + LTR/ERVL-MaLR_MLT1I chr1 5505018 5505327 + LTR/ERVL-MaLR_MLT1A0 chr1 5505328 5505455 + LTR/ERVL-MaLR_MLT1I chr1 5507802 5507965 + LTR/ERVL-MaLR_MSTB chr1 5507966 5508273 + SINE/Alu_AluJb chr1 5508274 5508505 + LTR/ERVL-MaLR_MSTB chr1 5508949 5509116 + DNA/hAT-Charlie_MER5B chr1 5509128 5509273 + DNA/hAT-Charlie_MER5A chr1 5509816 5509845 + Simple_repeat_(GGAGGAG)n chr1 5510601 5510816 - SINE/MIR_MIR chr1 5511431 5511540 + DNA/hAT-Ac_OldhAT1 chr1 5511620 5511643 + Simple_repeat_(TG)n chr1 5511643 5511693 + Low_complexity_GA-rich chr1 5512228 5512263 + LINE/CR1_L3b chr1 5512512 5512604 + LINE/L2_L2c chr1 5512638 5513148 + LTR/ERVL-MaLR_MLT1E2 chr1 5513191 5513245 + LTR/ERVL_LTR16B1 chr1 5514085 5514336 + LINE/L2_L2a chr1 5514473 5514580 + LINE/L2_L2a chr1 5515087 5515496 - LTR/ERVL-MaLR_MSTB1 chr1 5516113 5516307 + SINE/MIR_MIRb chr1 5516541 5516593 + Simple_repeat_(CA)n chr1 5516652 5516798 + DNA/hAT-Charlie_MER5B chr1 5517150 5517415 + LINE/L1_L1MC5 chr1 5517438 5517692 + LINE/L1_L1MA7 chr1 5517704 5517837 - LINE/L1_L1ME3Cz chr1 5517956 5517999 + Simple_repeat_(CTCCTCA)n chr1 5518155 5518239 - LTR/ERVL-MaLR_MLT1B chr1 5518245 5518649 + LTR/ERV1_MER83 chr1 5518698 5518732 - LTR/ERVL-MaLR_MLT1B chr1 5518733 5519096 - LTR/ERVL-MaLR_THE1B chr1 5519097 5520699 - LTR/ERVL-MaLR_THE1B-int chr1 5520702 5521066 - LTR/ERVL-MaLR_THE1B chr1 5521067 5521330 - LTR/ERVL-MaLR_MLT1B chr1 5521771 5522081 - SINE/Alu_AluSx3 chr1 5523042 5523210 - SINE/MIR_MIRb chr1 5523289 5523588 + SINE/Alu_AluSx4 chr1 5523984 5524095 + SINE/MIR_MIRb chr1 5524315 5524558 + LTR/ERVL-MaLR_MLT1L chr1 5524895 5525024 - SINE/MIR_MIRb chr1 5525379 5525713 + LINE/L1_L1ME4a chr1 5527298 5527394 - LINE/L2_L2b chr1 5527541 5527660 - DNA/hAT-Tip100_MamTip2b chr1 5527647 5527818 + SINE/MIR_MIR3 chr1 5528281 5528777 - LINE/L2_L2b chr1 5528784 5529203 + LTR/ERVL-MaLR_MSTA chr1 5529204 5530809 + LTR/ERVL-MaLR_MSTA-int chr1 5530812 5531229 + LTR/ERVL-MaLR_MSTA chr1 5531258 5531415 - DNA/hAT-Tip100_Zaphod3 chr1 5531436 5532788 - LINE/L2_L2b chr1 5532791 5533294 - LTR/ERVL-MaLR_MLT1G1 chr1 5533344 5533623 - LTR/ERVL-MaLR_MLT1G1-int chr1 5533624 5534197 - LTR/ERV1_LTR24B chr1 5534198 5534342 - LTR/ERVL-MaLR_MLT1G1-int chr1 5534355 5534442 - LINE/L2_L2 chr1 5534447 5534515 - LTR/ERVL-MaLR_MLT1G1-int chr1 5534524 5534923 - LTR/ERVL-MaLR_MLT1G1 chr1 5534978 5535693 - LINE/L2_L2 chr1 5536279 5536449 + SINE/MIR_MIR chr1 5536458 5536834 + LTR/ERVL-MaLR_MLT1A chr1 5537140 5537207 - LTR/ERVL_LTR84b chr1 5537229 5537360 - LINE/L2_L2a chr1 5537361 5537538 - LINE/L2_L2c chr1 5537856 5538375 + LINE/L1_L1MA6 chr1 5538376 5538612 - LTR/ERV1_MER66B chr1 5538613 5538913 - SINE/Alu_AluJb chr1 5538914 5539184 - LTR/ERV1_MER66B chr1 5539185 5539328 + LINE/L1_L1MA6 chr1 5539692 5541295 - LINE/L1_L1M2 chr1 5541279 5543108 + LINE/L1_L1MA5 chr1 5543182 5544182 + LINE/L1_L1MB3 chr1 5544183 5544555 + LINE/L1_L1PA6 chr1 5544556 5547120 + LINE/L1_L1MB3 chr1 5547121 5549820 + DNA/TcMar-Tigger_Tigger2 chr1 5549821 5551264 + LINE/L1_L1MB3 chr1 5551407 5551580 + SINE/MIR_MIRc chr1 5551957 5552080 - SINE/MIR_MIRc chr1 5552618 5552933 - LTR/ERVL_LTR16A2 chr1 5553425 5553604 + LINE/L1_L1MA1 chr1 5553696 5554064 - LTR/ERVL-MaLR_THE1B chr1 5554516 5554686 - LTR/ERVL-MaLR_MLT1D chr1 5554873 5555139 - LTR/ERVL-MaLR_MLT1D chr1 5555140 5555654 - LTR/ERVL_MLT2B2 chr1 5555655 5555731 - LTR/ERVL-MaLR_MLT1D chr1 5556727 5556837 - SINE/MIR_MIRb chr1 5556998 5557107 - LINE/L2_L2a chr1 5557272 5557343 - SINE/MIR_MIR3 chr1 5557374 5557571 - SINE/MIR_MIRb chr1 5557573 5557752 + LINE/L1_L1ME1 chr1 5557753 5558064 - SINE/Alu_AluYf1 chr1 5558065 5559604 + LINE/L1_L1ME1 chr1 5559604 5559736 + LINE/L1_L1ME1 chr1 5559765 5560414 - LINE/L1_L1ME1 chr1 5560426 5560559 + LINE/L1_L1ME1 chr1 5560560 5560618 + Simple_repeat_(AT)n chr1 5560629 5561186 + LINE/L1_L1ME1 chr1 5561204 5561659 - LTR/ERVL_MER54A chr1 5561660 5561861 - LTR/ERVL-MaLR_MSTA chr1 5561862 5561888 + Simple_repeat_(TGG)n chr1 5561889 5562092 - LTR/ERVL-MaLR_MSTA chr1 5562093 5563274 - LTR/ERVL-MaLR_MSTA-int chr1 5563275 5563366 + SINE/Alu_AluSx3 chr1 5563367 5563394 + Simple_repeat_(AAAG)n chr1 5563395 5563555 + LINE/L1_L1ME1 chr1 5564864 5564979 - SINE/MIR_MIRc chr1 5565951 5566155 - SINE/MIR_MIRb chr1 5566185 5566404 - SINE/MIR_MIRc chr1 5566502 5566705 + SINE/MIR_MIRb chr1 5567664 5567837 - LINE/L1_L1ME3Cz chr1 5567857 5568255 + LTR/ERVL-MaLR_MSTA chr1 5568290 5568594 - LINE/L1_L1M5 chr1 5568977 5569473 - LINE/L1_L1ME3Cz chr1 5569569 5570696 - LINE/L1_L1ME3Cz chr1 5571177 5571425 - LTR/ERV1_LTR78 chr1 5571557 5572283 + LTR/ERV1_LTR1C chr1 5572499 5572886 + LTR/ERVL-MaLR_MSTA chr1 5573005 5573064 - LTR/ERV1_LTR78 chr1 5573123 5573272 + LINE/L2_L2a chr1 5573273 5573582 - SINE/Alu_AluSx1 chr1 5573583 5573600 + LINE/L2_L2a chr1 5573601 5574610 - LTR/ERVK_LTR5B chr1 5574611 5575393 + LINE/L2_L2a chr1 5576501 5576667 + SINE/MIR_MIR chr1 5577511 5578011 - LTR/ERVL-MaLR_THE1A-int chr1 5578125 5578157 + Simple_repeat_(TGTC)n chr1 5578158 5578315 + Simple_repeat_(CT)n chr1 5578316 5578362 + Simple_repeat_(TCTTT)n chr1 5578363 5578489 + Simple_repeat_(CT)n chr1 5578853 5578997 - LTR/ERVL-MaLR_THE1A chr1 5578998 5579267 - LTR/ERVL-MaLR_THE1A chr1 5579335 5579417 - LTR/ERVL_ERVL-E-int chr1 5579526 5579588 - LTR/ERVL-MaLR_THE1A chr1 5579589 5579833 - LTR/ERVL-MaLR_THE1C chr1 5579861 5580040 + SINE/MIR_MIRb chr1 5580327 5580950 - LTR/ERVL_ERVL-E-int chr1 5581090 5581178 + Simple_repeat_(TG)n chr1 5581397 5581831 + LTR/ERVL-MaLR_MLT1C2 chr1 5582240 5582352 - LTR/ERVL_ERVL-E-int chr1 5582389 5582418 + Simple_repeat_(TGTT)n chr1 5582601 5582793 + LTR/ERVL-MaLR_MSTD chr1 5582794 5582853 + Simple_repeat_(CT)n chr1 5582854 5583024 + LTR/ERVL-MaLR_MSTD chr1 5583311 5583415 + LTR/ERVL-MaLR_MLT1J chr1 5583731 5583754 + Simple_repeat_(ACAC)n chr1 5583795 5584105 + SINE/Alu_AluSc5 chr1 5584427 5584813 - LTR/ERVL-MaLR_MSTA chr1 5584849 5585142 - SINE/Alu_AluSx1 chr1 5585507 5585801 - LTR?_MamRep605 chr1 5585819 5585910 - LINE/L2_L2a chr1 5585888 5586332 - LINE/L2_L2a chr1 5586333 5586416 - LTR/ERVL_LTR79 chr1 5586506 5586635 - LTR/ERVL_LTR79 chr1 5586679 5586861 - LINE/L2_L2a chr1 5586900 5587031 + LTR/ERVL_LTR79 chr1 5587032 5587412 + LTR/ERVL-MaLR_MLT1A1 chr1 5587413 5587800 + LTR/ERVL_LTR79 chr1 5587846 5588075 - LTR?_MamRep605 chr1 5588433 5588462 + Simple_repeat_(TA)n chr1 5588550 5591159 + LINE/L1_L1M3 chr1 5591406 5591713 - SINE/Alu_AluSx chr1 5591761 5591955 + SINE/MIR_MIR chr1 5591958 5592008 + Simple_repeat_(TATACA)n chr1 5592010 5592048 + Simple_repeat_(AC)n chr1 5592092 5592472 + LTR/ERVL-MaLR_MLT1D chr1 5592473 5592777 - SINE/Alu_AluSp chr1 5592778 5592885 + LTR/ERVL-MaLR_MLT1D chr1 5593197 5593286 + LTR_LTR91 chr1 5593376 5593414 + Simple_repeat_(TC)n chr1 5593623 5593673 + Low_complexity_G-rich chr1 5594670 5594874 - SINE/MIR_MIRb chr1 5595136 5595212 + LTR/ERVL-MaLR_MLT1D chr1 5595213 5595445 + SINE/Alu_AluJb chr1 5595446 5595803 + LTR/ERVL-MaLR_MLT1D chr1 5596164 5596476 + SINE/Alu_AluYk3 chr1 5596998 5597365 - LTR/ERVL-MaLR_MLT1B chr1 5598695 5599298 - DNA/hAT-Tip100_MER115 chr1 5599932 5600227 - SINE/Alu_AluY chr1 5600298 5600324 + Simple_repeat_(GCTCA)n chr1 5600961 5600997 + Simple_repeat_(TTCA)n chr1 5601242 5601524 + DNA/hAT-Charlie_Charlie15b chr1 5602058 5602215 + DNA/hAT-Charlie_Charlie15b chr1 5602362 5602441 - LINE/L1_L1MC5a chr1 5602378 5602457 - LINE/L1_L1ME3Cz chr1 5602594 5602956 - LTR/ERVL-MaLR_THE1C chr1 5602974 5603033 + SINE/MIR_MIR chr1 5603034 5603292 - LINE/L1_L1MC5a chr1 5603296 5604518 + Simple_repeat_(CT)n chr1 5604564 5605382 - LINE/L1_L1MC5a chr1 5605474 5605784 - SINE/Alu_AluSx1 chr1 5605820 5605905 - LINE/L1_L1ME2 chr1 5605906 5606335 - DNA/TcMar-Tigger_Tigger2a chr1 5606342 5606405 + Simple_repeat_(TA)n chr1 5606455 5607414 - LINE/L1_L1ME2 chr1 5607415 5607625 - DNA/hAT-Charlie_MER3 chr1 5607627 5607937 - LINE/L1_L1MC5a chr1 5607938 5609354 - LINE/L1_L1MEf chr1 5609355 5609379 + Simple_repeat_(AC)n chr1 5609380 5609710 - LINE/L1_L1MEf chr1 5609712 5609811 + LINE/L1_L1PA5 chr1 5609846 5610077 - LINE/L1_L1M5 chr1 5610078 5610371 + SINE/Alu_AluSx chr1 5610372 5610385 - LINE/L1_L1M5 chr1 5610386 5610410 - LINE/L1_L1M5 chr1 5610414 5611297 - LINE/L1_L1M5 chr1 5612302 5612467 - LINE/L2_L2d chr1 5612596 5612865 - LINE/L1_L1MB7 chr1 5612866 5613336 + LTR/ERVL-MaLR_MLT1C chr1 5613337 5613792 - LINE/L1_L1MB7 chr1 5614505 5614815 + SINE/Alu_AluSx chr1 5614989 5615082 - LINE/L2_L2c chr1 5616679 5616865 - LINE/L2_L2c chr1 5618687 5619051 - LINE/L2_L2a chr1 5619054 5619436 - LTR/ERVL-MaLR_MLT1C2 chr1 5619450 5619925 - LTR/ERV1_MER31A chr1 5619966 5620203 - LINE/L2_L2a chr1 5620423 5620453 + Simple_repeat_(TGGTGG)n chr1 5620480 5620496 + Simple_repeat_(A)n chr1 5621453 5621700 + SINE/Alu_AluSx chr1 5622293 5622486 - LINE/L1_L1ME4c chr1 5622584 5622685 - LINE/L2_L2d chr1 5622800 5622822 + Simple_repeat_(TG)n chr1 5623881 5623971 + DNA/TcMar-Tigger_X6a_DNA chr1 5623998 5624058 + LINE/L1_L1ME1 chr1 5624076 5624240 - LTR/ERVL_MLT2B4 chr1 5624258 5624751 + LINE/L1_L1ME1 chr1 5624757 5625011 + LINE/L1_L1ME1 chr1 5625021 5625372 + LINE/L1_L1ME1 chr1 5625373 5625674 + SINE/Alu_AluSz chr1 5625675 5627213 + LINE/L1_L1ME1 chr1 5627214 5627606 - LTR/ERVL-MaLR_MLT1B chr1 5627608 5627667 + LINE/L1_L1MA10 chr1 5628496 5628634 + LINE/L2_L2a chr1 5630073 5630238 - LINE/CR1_L3 chr1 5630366 5630472 - LINE/CR1_L3 chr1 5630478 5630532 - LINE/CR1_L3 chr1 5630558 5631142 - LINE/L1_L1PA10 chr1 5631143 5631710 + LINE/L1_L1PA10 chr1 5631711 5631878 - LINE/CR1_L3 chr1 5632746 5632827 + Simple_repeat_(TGCTGCA)n chr1 5633195 5633276 + Simple_repeat_(ATTTAT)n chr1 5633318 5633536 - LINE/L1_L1MD2 chr1 5634043 5634091 + Simple_repeat_(TGTGTGTGCA)n chr1 5634437 5634554 - LINE/L1_L1MD2 chr1 5635928 5636034 + LINE/L2_L2b chr1 5636815 5636912 + SINE/MIR_MIR chr1 5637090 5637416 + LTR/ERVL-MaLR_MLT1C2 chr1 5637417 5637948 - DNA/hAT-Charlie_MER1A chr1 5637949 5638093 + LTR/ERVL-MaLR_MLT1C2 chr1 5639599 5639667 - LINE/L2_L2a chr1 5639668 5639957 + SINE/Alu_AluSz6 chr1 5639958 5639980 - LINE/L2_L2a chr1 5640488 5640531 - LTR/ERVL-MaLR_MSTA chr1 5640532 5640827 + SINE/Alu_AluSx chr1 5640828 5641186 - LTR/ERVL-MaLR_MSTA chr1 5641187 5641417 - LINE/L1_L1ME4b chr1 5641476 5642813 + LINE/L1_L1PA7 chr1 5642927 5642956 - LINE/L2_L2c chr1 5643017 5643147 + DNA/hAT-Charlie_MER33 chr1 5643214 5643365 + LINE/L1_L1PA8 chr1 5644208 5644405 + LINE/L1_L1MC2 chr1 5645450 5645825 + LTR/ERVL-MaLR_THE1C chr1 5646622 5646640 - LINE/L2_L2b chr1 5646641 5646723 - LINE/L2_L2a chr1 5646861 5646908 + Simple_repeat_(GGCA)n chr1 5647135 5647199 - LINE/L2_L2b chr1 5647223 5647756 + LTR/ERVL_MER77B chr1 5647853 5647922 - LINE/L2_L2b chr1 5647926 5647996 - SINE/tRNA-RTE_MamSINE1 chr1 5648151 5648345 - LINE/L2_L2b chr1 5648348 5648551 - DNA/hAT-Charlie_MER58A chr1 5648780 5648949 - LINE/CR1_L3 chr1 5648963 5649198 + SINE/MIR_MIR chr1 5649235 5649331 - LINE/CR1_L3 chr1 5649637 5649741 - SINE/MIR_MIR chr1 5649729 5649838 + SINE/MIR_MIRb chr1 5649869 5650019 - SINE/MIR_MIRb chr1 5650937 5651074 + SINE/MIR_MIRb chr1 5651792 5651993 - SINE/MIR_MIRb chr1 5652142 5652293 - SINE/MIR_MIRb chr1 5652992 5653060 + Simple_repeat_(AG)n chr1 5653829 5653861 + Simple_repeat_(TTTTG)n chr1 5653914 5654060 + LINE/L1_L1ME4b chr1 5654061 5654806 + LINE/L1_L1PA4 chr1 5654807 5655319 + LINE/L1_L1ME4b chr1 5655576 5655753 - LINE/L1_L1M4 chr1 5655799 5656319 + Low_complexity_GA-rich chr1 5656549 5656958 + LTR/ERVL-MaLR_MSTD chr1 5657680 5657837 + SINE/MIR_MIR chr1 5657825 5658017 - SINE/MIR_MIR chr1 5658616 5658781 - LTR/ERVL-MaLR_MLT1I chr1 5659204 5659660 - LTR/ERVL-MaLR_MLT1C chr1 5659931 5660003 + SINE/MIR_MIRb chr1 5660820 5660920 - SINE/MIR_MIRc chr1 5661179 5661184 + Simple_repeat_(TTGGTTT)n chr1 5661185 5661224 + Simple_repeat_(TTTTG)n chr1 5664259 5664433 - SINE/MIR_MIR chr1 5665470 5665541 + LINE/L2_L2b chr1 5665877 5666038 + SINE/MIR_MIRb chr1 5666042 5666116 - LTR/ERVL-MaLR_MLT1I chr1 5666117 5666428 - SINE/Alu_AluSc8 chr1 5666429 5666630 - LTR/ERVL-MaLR_MLT1I chr1 5666655 5666956 + SINE/Alu_AluSx chr1 5667116 5667277 + LTR/ERVL_LTR41B chr1 5668236 5668525 + LTR/ERVL_LTR41B chr1 5668773 5668919 - SINE/MIR_MIR3 chr1 5668999 5669352 + LTR/ERVL-MaLR_THE1A chr1 5669434 5669724 + SINE/Alu_AluSp chr1 5669730 5669822 + LINE/L1_L1ME3Cz chr1 5670027 5670066 + DNA/hAT-Charlie_MER5A chr1 5670139 5670520 + LINE/L1_L1ME3Cz chr1 5670535 5670637 + LINE/L1_L1ME4a chr1 5671687 5672049 + LTR/ERVL-MaLR_MLT1A0 chr1 5672112 5672169 - LINE/L2_L2a chr1 5672114 5672200 - LINE/L2_L2a chr1 5672886 5673090 - LINE/L1_L1MEc chr1 5673091 5673474 + LTR/ERVL-MaLR_MSTA chr1 5673475 5673768 - SINE/Alu_AluSc8 chr1 5673769 5673815 + LTR/ERVL-MaLR_MSTA chr1 5673816 5674316 - LINE/L1_L1MEc chr1 5674317 5674624 - SINE/Alu_AluJb chr1 5674625 5674661 - LINE/L1_L1MEc chr1 5674662 5674690 + Simple_repeat_(T)n chr1 5674694 5674973 - SINE/Alu_AluSz6 chr1 5674974 5676196 - LINE/L1_L1MEc chr1 5676248 5676543 - LTR_LTR91A chr1 5676867 5676925 + SINE/tRNA_LFSINE_Vert chr1 5677144 5677361 - SINE/MIR_MIR chr1 5677550 5677763 - LINE/L2_L2a chr1 5678446 5678542 - LINE/L2_L2a chr1 5678651 5678678 + Simple_repeat_(GGTA)n chr1 5678719 5679300 + Simple_repeat_(TGGA)n chr1 5679388 5679549 + DNA/hAT-Tip100_MER96 chr1 5679734 5679840 + SINE/MIR_MIR3 chr1 5679991 5680264 - LINE/L2_L2b chr1 5681187 5681827 - LTR/ERVL-MaLR_MLT1E chr1 5681916 5682134 + SINE/MIR_MIRb chr1 5682469 5682528 + LINE/L1_L1ME4a chr1 5684395 5684533 + DNA/hAT-Charlie_MER5A1 chr1 5684698 5684820 + LINE/L2_L2b chr1 5686202 5686334 + LTR/ERVL-MaLR_MLT1C2 chr1 5686322 5686383 + LTR/ERVL-MaLR_MLT1C2 chr1 5686371 5686653 + LTR/ERVL-MaLR_MLT1C2 chr1 5686663 5686831 + LTR/ERVL-MaLR_MLT1C2-int chr1 5687140 5687761 + LTR/ERVL-MaLR_MLT1C2-int chr1 5688387 5688418 + Simple_repeat_(TG)n chr1 5688421 5688442 + Simple_repeat_(AG)n chr1 5688740 5688834 + Simple_repeat_(GT)n chr1 5688910 5689091 + SINE/MIR_MIRb chr1 5690509 5690855 + LTR/ERVL-MaLR_MLT1C2 chr1 5691216 5691497 + LINE/L1_L1PB1 chr1 5691644 5691830 - DNA/hAT-Charlie_MER20 chr1 5692615 5692715 + LINE/L2_L2b chr1 5693307 5693364 + Simple_repeat_(CACACA)n chr1 5693490 5693513 + Simple_repeat_(AGAA)n chr1 5694068 5694455 + LINE/L1_L1MC3 chr1 5694456 5694743 + SINE/Alu_AluJb chr1 5694746 5695051 + SINE/Alu_AluSg chr1 5695058 5695286 + SINE/Alu_AluJb chr1 5695287 5695977 + LINE/L1_L1MC3 chr1 5695978 5696040 + Simple_repeat_(AGAT)n chr1 5696041 5696493 + LINE/L1_L1MC3 chr1 5696494 5696555 + SINE/Alu_FRAM chr1 5696556 5697326 + LINE/L1_L1MC3 chr1 5697400 5697758 + LTR/ERVL-MaLR_MLT1A1 chr1 5697769 5697941 - SINE/Alu_AluSx chr1 5698092 5698212 - SINE/Alu_AluSx chr1 5698733 5698915 - SINE/MIR_MIRb chr1 5699487 5699538 - LTR/ERVL-MaLR_MLT1E3 chr1 5699610 5699697 + LINE/L1_L1MD chr1 5699698 5699942 - DNA/TcMar-Tigger_MER8 chr1 5699943 5700382 + LINE/L1_L1MD chr1 5700383 5700683 + SINE/Alu_AluSx1 chr1 5700684 5700790 + LINE/L1_L1MD chr1 5700791 5700890 + LINE/L1_L1MA8 chr1 5700891 5701177 + LINE/L1_L1MD chr1 5701179 5701354 + SINE/Alu_AluSx chr1 5701355 5701413 + LINE/L1_L1MD chr1 5701414 5701720 + SINE/Alu_AluSq2 chr1 5701731 5701811 + Simple_repeat_(AAGA)n chr1 5701815 5701828 + LINE/L1_L1MD chr1 5701829 5701913 + Simple_repeat_(AGGA)n chr1 5701914 5702479 + LINE/L1_L1MD chr1 5702678 5702710 - LINE/L1_L1MB3 chr1 5702711 5703020 - SINE/Alu_AluSp chr1 5703021 5703364 - LINE/L1_L1MB3 chr1 5703607 5703920 + SINE/Alu_AluSq10 chr1 5703955 5704265 + SINE/Alu_AluY chr1 5704976 5705118 + DNA/hAT-Charlie_MER5A chr1 5705247 5705468 + LTR/ERV1_LTR78 chr1 5705831 5706163 + LTR/ERV1_LTR78 chr1 5706511 5706803 + LTR/ERVL-MaLR_MLT1J chr1 5707329 5707750 + LTR/ERVL-MaLR_MLT1C chr1 5707917 5707946 + Simple_repeat_(ATGA)n chr1 5710178 5710591 + LTR/ERVL-MaLR_MLT1K chr1 5710642 5710673 + Low_complexity_A-rich chr1 5710674 5710890 + Low_complexity_GA-rich chr1 5711374 5711455 - SINE/MIR_MIRb chr1 5712460 5712540 - SINE/MIR_MIR3 chr1 5712967 5713049 + LINE/L2_L2b chr1 5713415 5713492 + SINE/MIR_MIRb chr1 5714374 5714463 + SINE/MIR_MIR3 chr1 5714595 5715428 + LINE/L1_L1PA11 chr1 5715453 5715642 - SINE/MIR_MIR chr1 5715663 5716114 - LTR/ERVL-MaLR_MLT1D chr1 5716180 5716292 - SINE/MIR_MIRb chr1 5716518 5717004 - LTR/ERVL_MER74A chr1 5717036 5717210 - DNA/hAT-Tip100_MER91A chr1 5718381 5718659 + SINE/Alu_AluSg chr1 5718756 5718911 - SINE/MIR_MIR3 chr1 5719854 5719984 - SINE/MIR_MIRb chr1 5720449 5720528 + LINE/L2_L2c chr1 5720620 5720707 + LINE/L2_L2c chr1 5720765 5720967 - SINE/MIR_MIRb chr1 5721842 5722228 + LTR/ERVL-MaLR_MLT1K chr1 5722266 5722395 - SINE/Alu_AluJo chr1 5722402 5722457 + Simple_repeat_(AT)n chr1 5722458 5722514 - SINE/Alu_Alu chr1 5722520 5722660 - SINE/Alu_AluSx chr1 5722883 5722966 - DNA/hAT-Tip100_MER96 chr1 5723052 5723521 - LINE/L1_L1MA8 chr1 5723523 5723601 - SINE/Alu_AluJb chr1 5723607 5723678 + LTR/ERVL_MLT2A2 chr1 5723666 5724009 + LTR/ERVL-MaLR_THE1B chr1 5724010 5724186 - SINE/Alu_AluJb chr1 5724187 5724410 - LINE/L1_L1MA8 chr1 5724421 5724539 + SINE/MIR_MIR3 chr1 5724809 5724898 - LINE/L1_L1P1 chr1 5725491 5725633 + SINE/MIR_MIRb chr1 5726357 5726388 + Low_complexity_G-rich chr1 5726563 5726753 - DNA/hAT-Charlie_MER20 chr1 5726918 5726949 + Simple_repeat_(CCCCTG)n chr1 5726988 5727235 + LINE/L1_L1PA3 chr1 5727312 5727373 - LINE/L2_L2b chr1 5727503 5727570 + LINE/L2_L2b chr1 5728053 5728116 + LINE/L2_L2b chr1 5729097 5729175 - SINE/MIR_MIR chr1 5729176 5729681 + LTR/ERVL_LTR40a chr1 5729682 5729769 - SINE/MIR_MIR chr1 5730149 5730245 + LINE/L2_L2a chr1 5730492 5730524 + Simple_repeat_(TGAG)n chr1 5730525 5730552 + Simple_repeat_(CAGT)n chr1 5730711 5730821 - SINE/MIR_MIRc chr1 5731450 5732500 - LINE/L1_L1PA17 chr1 5732544 5732569 + Simple_repeat_(TAT)n chr1 5732570 5733028 - LINE/L1_L1PA2 chr1 5733029 5733409 + LINE/L1_L1PA2 chr1 5734063 5734110 + Simple_repeat_(CT)n chr1 5734987 5735047 + LINE/Jockey_X12_LINE chr1 5735551 5735608 + LINE/L2_L2c chr1 5735614 5735847 + LINE/L1_L1M5 chr1 5735848 5736178 - LTR/ERVL-MaLR_MLT1I chr1 5736193 5736261 + Low_complexity_A-rich chr1 5736262 5736292 + Simple_repeat_(AGAT)n chr1 5736293 5736338 + Simple_repeat_(GATG)n chr1 5736339 5736423 + Simple_repeat_(AGAT)n chr1 5736425 5736473 - LINE/L1_HAL1b chr1 5736485 5736633 + Simple_repeat_(ATAG)n chr1 5736655 5736679 + LINE/L2_L2a chr1 5736680 5736944 - SINE/Alu_AluSx3 chr1 5736945 5737007 + LINE/L2_L2a chr1 5737021 5737486 + Simple_repeat_(TGGA)n chr1 5737624 5737904 + Simple_repeat_(TGGA)n chr1 5737924 5738009 + LINE/L2_L2a chr1 5738023 5738157 + Simple_repeat_(TGGA)n chr1 5738157 5738210 + Simple_repeat_(GAGCAA)n chr1 5738415 5738510 + Simple_repeat_(TCTC)n chr1 5738580 5738813 - LINE/L2_L2c chr1 5738828 5739189 - LTR/ERVL-MaLR_MLT1J2 chr1 5739563 5739629 - LINE/L2_L2a chr1 5739814 5739867 + Simple_repeat_(GCCAGA)n chr1 5740514 5740677 - DNA/hAT-Charlie_MER105 chr1 5741079 5741216 + DNA/hAT-Charlie_MER5A1 chr1 5741441 5741563 + Simple_repeat_(CA)n chr1 5742494 5742729 + LTR/ERV1_LTR19B chr1 5744059 5744092 + Simple_repeat_(TTTT)n chr1 5744092 5744340 + Simple_repeat_(TCCC)n chr1 5744645 5744927 + Simple_repeat_(AC)n chr1 5745492 5745654 - SINE/MIR_MIRb chr1 5746348 5746392 + Simple_repeat_(GGACCT)n chr1 5747515 5747921 + Low_complexity_GA-rich chr1 5747922 5748140 + SINE/MIR_MIRb chr1 5748106 5748147 + LINE/L2_L2d chr1 5749355 5749484 - LINE/L2_L2b chr1 5749570 5749624 + Low_complexity_GA-rich chr1 5749630 5749865 + LTR/ERV1_HERVIP10FH-int chr1 5749888 5750217 + Simple_repeat_(AGAGAC)n chr1 5750546 5750601 + Simple_repeat_(TCTG)n chr1 5751148 5751296 - SINE/MIR_MIR chr1 5751490 5751802 + LTR/ERVL-MaLR_MLT1B chr1 5753306 5753447 - SINE/MIR_MIR chr1 5753591 5753669 - SINE/MIR_MIR3 chr1 5754430 5754735 - SINE/Alu_AluSx chr1 5754743 5754894 - DNA/hAT-Charlie_Charlie16a chr1 5754970 5755187 + SINE/MIR_MIR chr1 5756024 5756445 - LTR/ERVL-MaLR_MLT1D chr1 5756446 5756777 - LTR/ERVL-MaLR_MLT1A1 chr1 5756778 5756851 - LTR/ERVL-MaLR_MLT1D chr1 5756876 5757053 - SINE/MIR_MIR chr1 5757492 5757749 - LINE/L1_L1ME4a chr1 5757767 5757791 + Simple_repeat_(CTC)n chr1 5757895 5758053 + Simple_repeat_(CA)n chr1 5759410 5759545 + LTR/ERVL_LTR52 chr1 5759576 5759648 + LTR/ERVL_LTR52 chr1 5759649 5759929 + DNA/hAT-Tip100_FordPrefect_a chr1 5760102 5760393 - LINE/L1_L1MD1 chr1 5760394 5760777 + LTR/ERVL-MaLR_THE1D chr1 5760778 5760813 - LINE/L1_L1MD1 chr1 5760822 5761100 - LINE/L1_L1MD chr1 5761067 5762497 - LINE/L1_L1MD1 chr1 5762498 5762807 + SINE/Alu_AluJr chr1 5762808 5763356 - LINE/L1_L1MD1 chr1 5763359 5763429 + LINE/L2_L2a chr1 5763430 5763801 + LTR/ERVL-MaLR_THE1C chr1 5763802 5763828 + LINE/L2_L2a chr1 5764698 5764824 - LINE/L1_L1MB8 chr1 5765180 5765361 + SINE/MIR_MIRb chr1 5766870 5767906 - LTR/ERVK_MER11C chr1 5767908 5767959 + Simple_repeat_(CTGCCACCT)n chr1 5768064 5768499 - LTR/ERVL-MaLR_MLT1C2 chr1 5768532 5768610 + DNA/hAT-Charlie_MER5A1 chr1 5768693 5768829 + LTR/ERVL_LTR16C chr1 5769993 5770181 + SINE/MIR_MIR chr1 5770311 5770618 + SINE/Alu_AluSz chr1 5770868 5770966 - SINE/MIR_MIR chr1 5771871 5772130 + SINE/MIR_MIR chr1 5772135 5772189 + Simple_repeat_(TAT)n chr1 5772216 5772404 - SINE/MIR_MIRb chr1 5772437 5772588 - DNA/hAT-Charlie_MER102c chr1 5772595 5772895 + SINE/Alu_AluJr chr1 5772898 5772987 - DNA/hAT-Charlie_MER102c chr1 5773045 5773324 + SINE/Alu_AluSx1 chr1 5773325 5773372 + Simple_repeat_(AAAT)n chr1 5773417 5773455 + Simple_repeat_(AGGACCC)n chr1 5773656 5773802 - SINE/MIR_MIR3 chr1 5774388 5774502 + SINE/tRNA_LFSINE_Vert chr1 5774991 5775217 + LINE/L1_L1MD2 chr1 5775017 5775292 + LINE/L1_L1MB3 chr1 5775293 5775690 - LTR/ERVL-MaLR_MSTA chr1 5775699 5775793 + LINE/L1_L1MD2 chr1 5775853 5776031 - LINE/L2_L2b chr1 5776873 5777186 + SINE/Alu_AluSx chr1 5777562 5777942 - LTR/ERVL-MaLR_MLT1E1 chr1 5777943 5778313 + LTR/ERVL-MaLR_THE1C chr1 5778314 5778530 - LTR/ERVL-MaLR_MLT1E1 chr1 5778533 5779019 + LTR/ERVL-MaLR_MLT1D chr1 5779080 5779456 - LTR/ERVL-MaLR_MLT1C2 chr1 5779875 5780109 + SINE/MIR_MIR chr1 5780206 5780355 + LTR/Gypsy?_LTR85b chr1 5780376 5780543 - SINE/MIR_MIRb chr1 5781764 5781861 - SINE/Alu_AluSz6 chr1 5781862 5782457 + LTR/ERV1_LTR71B chr1 5782458 5782650 - SINE/Alu_AluSz6 chr1 5782834 5783161 + LINE/L1_L1MB5 chr1 5783269 5783372 + SINE/MIR_MIR chr1 5783374 5784030 + LINE/L1_L1PA5 chr1 5784031 5784056 + Simple_repeat_(AATA)n chr1 5784057 5784148 + SINE/MIR_MIR chr1 5784304 5784522 + SINE/Alu_AluSq2 chr1 5784526 5784688 + SINE/Alu_AluSg4 chr1 5785178 5785200 + Simple_repeat_(A)n chr1 5785315 5785664 + LINE/L2_L2a chr1 5785676 5785740 + Simple_repeat_(GATG)n chr1 5785746 5785804 + Simple_repeat_(CATT)n chr1 5785855 5785905 - LINE/CR1_L3 chr1 5786080 5786320 - DNA/hAT-Charlie_MER5C chr1 5786529 5786636 - LINE/L2_L2a chr1 5786690 5786791 - LINE/L2_L2a chr1 5787004 5787068 + Low_complexity_GA-rich chr1 5787141 5787356 + SINE/MIR_MIRb chr1 5787566 5787610 - LINE/CR1_L3 chr1 5787915 5788059 + Simple_repeat_(CATG)n chr1 5788303 5788599 + SINE/Alu_AluSx chr1 5788963 5789138 + LINE/L1_L1MC4 chr1 5789165 5790242 + LTR/ERVK_MER11C chr1 5790339 5790406 + LINE/L1_L1MC5a chr1 5790407 5791081 + LTR/ERVL-MaLR_THE1C-int chr1 5791082 5791465 + LTR/ERVL-MaLR_THE1C chr1 5792566 5792619 + LINE/L2_L2c chr1 5792639 5792794 - LINE/L2_L2d2 chr1 5792907 5793057 - LTR/ERVL_LTR79 chr1 5793527 5793661 - LINE/L2_L2d2 chr1 5794420 5794478 + Simple_repeat_(CACATG)n chr1 5794479 5794669 + Simple_repeat_(CATG)n chr1 5794670 5794696 + Simple_repeat_(CACATG)n chr1 5794944 5795218 - LTR/ERVL_LTR16D1 chr1 5795249 5795342 - LTR/ERVL_LTR16D1 chr1 5797088 5797159 + DNA/hAT-Charlie_MER5A chr1 5798942 5799229 + LINE/L1_L1MB5 chr1 5799230 5799533 + SINE/Alu_AluY chr1 5799534 5799788 + LINE/L1_L1MB5 chr1 5800504 5800529 + Simple_repeat_(A)n chr1 5801052 5801513 + LTR/ERVL-MaLR_MLT1C2 chr1 5802388 5802633 + SINE/MIR_MIR chr1 5802653 5802778 + SINE/MIR_MIRb chr1 5803253 5803292 + Simple_repeat_(AC)n chr1 5803797 5804628 + Simple_repeat_(TCCA)n chr1 5805909 5806062 + SINE/Alu_FRAM chr1 5806068 5806161 + Simple_repeat_(AT)n chr1 5806172 5806204 + Simple_repeat_(AC)n chr1 5806564 5806701 - DNA/hAT-Charlie_MER58A chr1 5806835 5807197 + LINE/L1_L1PA8 chr1 5807546 5807631 + Simple_repeat_(ATGG)n chr1 5809792 5810042 + LINE/L2_L2a chr1 5810056 5810125 - LINE/L2_L2d2 chr1 5810219 5810318 - SINE/MIR_MIRb chr1 5810928 5811005 + SINE/MIR_MIRc chr1 5811186 5811280 + Simple_repeat_(CA)n chr1 5811997 5812306 + SINE/Alu_AluSc8 chr1 5813020 5813244 + SINE/MIR_MIRb chr1 5813366 5813501 - LTR/ERVL-MaLR_MLT1J2 chr1 5813502 5813604 + LINE/L1_L1MA3 chr1 5813608 5813781 + Simple_repeat_(GGA)n chr1 5813872 5814084 + DNA/hAT-Charlie_MER113 chr1 5814804 5814868 + DNA/TcMar-Mariner_MADE1 chr1 5814869 5815168 - SINE/Alu_AluY chr1 5815169 5815194 + DNA/TcMar-Mariner_MADE1 chr1 5815582 5815819 + LTR/ERV1_LTR37B chr1 5815876 5815954 - DNA/TcMar-Mariner_MADE1 chr1 5815973 5816184 - SINE/MIR_MIR chr1 5816162 5816222 - LINE/L1_X9_LINE chr1 5817625 5817796 + LTR/ERVL_LTR67B chr1 5817814 5817988 + LTR/ERVL_LTR67B chr1 5818199 5818448 + LTR/ERV1_MER50 chr1 5818449 5818823 + LTR/ERVL-MaLR_THE1B chr1 5818824 5819290 + LTR/ERV1_MER50 chr1 5819299 5819615 + LTR/ERVL_ERVL-E-int chr1 5819649 5819787 - LTR/ERVL-MaLR_MLT1H chr1 5819816 5820105 - LTR/ERVL-MaLR_MLT1H chr1 5820207 5820594 + LTR/ERVL_ERVL-E-int chr1 5820723 5821047 + LTR/ERVL_ERVL-E-int chr1 5821051 5821328 + SINE/Alu_AluSz chr1 5821343 5821490 + Low_complexity_GA-rich chr1 5821599 5822379 + LTR/ERVL_ERVL-E-int chr1 5822505 5823227 + LTR/ERVL_ERVL-E-int chr1 5823250 5823272 + LTR/ERV1_MER4B chr1 5823273 5823579 + SINE/Alu_AluSq2 chr1 5823580 5823821 + LTR/ERV1_MER4B chr1 5823822 5824121 + SINE/Alu_AluSx1 chr1 5824122 5824340 + LTR/ERV1_MER4B chr1 5824341 5825868 + LINE/L1_L1PA3 chr1 5825869 5825906 + Simple_repeat_(AATAA)n chr1 5825909 5826042 + LTR/ERV1_MER4B chr1 5826044 5826358 + LTR/ERVL_ERVL-E-int chr1 5826692 5826839 + LTR/ERVL_LTR67B chr1 5826943 5827075 + LTR/ERVL_LTR67B chr1 5827663 5828150 - LTR/ERVL-MaLR_MLT1K chr1 5828521 5828630 - SINE/tRNA-RTE_MamSINE1 chr1 5828586 5828674 - LINE/RTE-BovB_MamRTE1 chr1 5828897 5829200 - SINE/Alu_AluSx chr1 5829419 5829880 + Simple_repeat_(GATGGT)n chr1 5830006 5830147 - SINE/MIR_MIR3 chr1 5830854 5830886 + Simple_repeat_(CTCC)n chr1 5831304 5831428 - SINE/MIR_MIR chr1 5832083 5832177 + LTR/ERVL_MER21C chr1 5832249 5832348 + Simple_repeat_(GCTGGGG)n chr1 5832349 5832938 + LTR/ERVL_MER21C chr1 5834585 5834637 + Simple_repeat_(CCCGCC)n chr1 5835573 5835785 - SINE/Alu_AluJb chr1 5835859 5836102 - SINE/Alu_AluJr chr1 5837231 5837267 + Simple_repeat_(GGCAGT)n chr1 5837476 5837665 + DNA/hAT-Charlie_Charlie18a chr1 5837666 5837963 + SINE/Alu_AluY chr1 5837964 5838038 + DNA/hAT-Charlie_Charlie18a chr1 5839057 5839565 + LTR/ERV1_MER51E chr1 5839897 5839954 + Simple_repeat_(CATACA)n chr1 5840012 5840034 + Simple_repeat_(AG)n chr1 5840044 5840079 + LTR/ERVL_MLT2D chr1 5840086 5840176 - LINE/L1_L1MB7 chr1 5841036 5841071 + Low_complexity_G-rich chr1 5841179 5841343 + LINE/L2_L2b chr1 5841403 5841707 + SINE/Alu_AluSx1 chr1 5843720 5843860 + SINE/MIR_MIRc chr1 5844403 5844786 - LTR/ERVL_MLT2D chr1 5845265 5845444 - SINE/MIR_MIRb chr1 5845554 5845599 - SINE/MIR_MIRc chr1 5846299 5846431 + SINE/MIR_MIRc chr1 5846774 5846846 + DNA/hAT-Tip100_MER91C chr1 5846877 5847096 + SINE/MIR_MIRb chr1 5847666 5847803 + LTR/ERVL-MaLR_MLT1A0 chr1 5847804 5847924 + SINE/Alu_AluJb chr1 5847925 5848218 - SINE/Alu_AluY chr1 5848219 5848430 + SINE/Alu_AluJb chr1 5848431 5848631 + LTR/ERVL-MaLR_MLT1A0 chr1 5848682 5848737 - LINE/L2_L2b chr1 5849683 5849865 + Simple_repeat_(AC)n chr1 5851596 5851629 + Simple_repeat_(CATT)n chr1 5851650 5852027 + LTR/ERVL-MaLR_MSTA chr1 5852122 5852253 - LINE/L2_L2a chr1 5852313 5852676 - LTR/ERVL-MaLR_MLT1H chr1 5853064 5853422 - LINE/L2_L2a chr1 5853731 5853857 - DNA/hAT-Charlie_Charlie4z chr1 5853882 5854254 - LTR/ERVL-MaLR_THE1B chr1 5854866 5855070 + LTR/ERVL-MaLR_MLT1K chr1 5856575 5856988 - LTR/ERVL-MaLR_MLT1C chr1 5860977 5861290 + LTR/ERVL-MaLR_MLT1J chr1 5861308 5861503 + LINE/L2_L2 chr1 5865899 5865985 + LINE/CR1_L3b chr1 5866316 5866347 + Simple_repeat_(CCT)n chr1 5868490 5869402 + Simple_repeat_(ACAC)n chr1 5869404 5869616 + LINE/L1_L1MC5a chr1 5869791 5871262 + LINE/L1_L1MC4 chr1 5871236 5871600 + LINE/L1_L1MC5a chr1 5871871 5871971 + LINE/L1_L1MC5a chr1 5873134 5873164 + Simple_repeat_(CCCTG)n chr1 5874457 5874495 + Simple_repeat_(CCTGCA)n chr1 5876223 5876252 + Simple_repeat_(GGGA)n chr1 5876285 5876300 + Simple_repeat_(CCCTG)n chr1 5876301 5876338 + Simple_repeat_(CCCT)n chr1 5876774 5876922 + DNA/hAT-Charlie_MER5B chr1 5878874 5879015 + SINE/MIR_MIR3 chr1 5879778 5879901 + Simple_repeat_(CA)n chr1 5880003 5880049 + Simple_repeat_(CA)n chr1 5881419 5881685 + LINE/CR1_L3 chr1 5881684 5881735 - LINE/L2_L2b chr1 5882790 5883001 - LINE/CR1_L3 chr1 5883076 5883173 - LINE/L2_L2c chr1 5883398 5883573 - LINE/CR1_L3 chr1 5885667 5885699 + Simple_repeat_(GGCCGA)n chr1 5887959 5887997 + Simple_repeat_(GGCACAG)n chr1 5892197 5892291 + LINE/L1_L1P2 chr1 5892400 5892528 + LINE/L2_L2b chr1 5893081 5893162 + LINE/L1_L1ME3A chr1 5893163 5894170 - LTR/ERVK_LTR13 chr1 5894171 5894253 + LINE/L1_L1ME3A chr1 5894256 5894471 + SINE/Alu_AluJb chr1 5894485 5894795 + LINE/L1_L1ME3A chr1 5894893 5895342 + LINE/L1_L1PA4 chr1 5895343 5895529 + SINE/Alu_AluSg4 chr1 5895739 5895881 + LINE/L1_L1MC5a chr1 5896002 5897909 + LINE/L1_L1MC5a chr1 5898830 5898858 + Simple_repeat_(CA)n chr1 5898859 5899594 + LINE/L1_L1ME3A chr1 5899591 5900758 + LINE/L1_L1MC2 chr1 5900759 5901064 + SINE/Alu_AluJb chr1 5901065 5901208 + LINE/L1_L1MC2 chr1 5902164 5902187 + Simple_repeat_(CTAA)n chr1 5902546 5903451 - LINE/L1_L1M5 chr1 5903502 5904534 + LINE/L1_L1MC5 chr1 5905886 5906067 + DNA/hAT_MER53 chr1 5907734 5907903 + SINE/MIR_MIR chr1 5912257 5912567 + SINE/Alu_AluY chr1 5912955 5913171 + SINE/Alu_AluSx chr1 5914139 5914446 + SINE/Alu_AluJb chr1 5915203 5915232 + Simple_repeat_(GGCCTTG)n chr1 5916728 5916854 + SINE/Alu_AluSx chr1 5917264 5917611 + LTR/ERVL-MaLR_MLT1K chr1 5917612 5917695 - LINE/L2_L2c chr1 5917743 5918090 + LTR/ERVL-MaLR_MLT1I chr1 5918515 5918863 + LINE/L1_L1MD chr1 5918991 5919198 + DNA/hAT-Charlie_MER58A chr1 5919309 5919427 - LINE/L1_L1MC1 chr1 5919467 5919553 + LINE/L2_L2a chr1 5919738 5919950 - SINE/Alu_AluJr chr1 5919951 5920230 - SINE/Alu_AluSc8 chr1 5920231 5920360 - SINE/Alu_AluJr chr1 5920367 5920447 + LTR/ERVL_LTR86A1 chr1 5920653 5921012 - LINE/L1_L1M5 chr1 5921013 5921056 - SINE/MIR_MIR chr1 5921070 5921746 - LINE/L1_L1ME3Cz chr1 5921785 5921902 - LINE/L1_L1ME3Cz chr1 5921903 5922363 + LTR/ERVL-MaLR_MLT1D chr1 5922364 5922537 - LINE/L1_L1ME3Cz chr1 5922564 5922706 + SINE/Alu_AluJr4 chr1 5922853 5923018 + SINE/Alu_FRAM chr1 5923221 5924421 + LINE/L1_L1MEc chr1 5924424 5924535 + LINE/L1_L1M8 chr1 5924652 5924955 - SINE/Alu_AluJo chr1 5925138 5925430 + DNA/TcMar-Tigger_MER2 chr1 5925604 5925914 - SINE/Alu_AluY chr1 5926418 5926569 + DNA/TcMar-Tigger_MER2 chr1 5928103 5928619 - LINE/L1_L1MD2 chr1 5928608 5931261 - LINE/L1_L1MD2 chr1 5931262 5931457 - DNA/TcMar-Tigger_Tigger1 chr1 5931458 5931758 + SINE/Alu_AluSx1 chr1 5931759 5931797 - DNA/TcMar-Tigger_Tigger1 chr1 5931798 5932085 + SINE/Alu_AluJr chr1 5932086 5932390 - DNA/TcMar-Tigger_Tigger1 chr1 5935594 5935887 + SINE/Alu_AluSx1 chr1 5936582 5936639 + Low_complexity_A-rich chr1 5936769 5937125 + LTR/ERVL-MaLR_MLT1C2 chr1 5937234 5937396 + LTR/ERVL-MaLR_MLT1C2 chr1 5938160 5938197 + Simple_repeat_(GGCTCAG)n chr1 5938651 5939155 - DNA/PiggyBac_MER75 chr1 5940094 5940458 + LTR/ERVL-MaLR_MSTD chr1 5940459 5940557 + LINE/L1_L1MD1 chr1 5940659 5941616 + LINE/L1_L1M5 chr1 5941629 5942249 + LINE/L1_L1MC4 chr1 5942250 5942551 + SINE/Alu_AluSc8 chr1 5942552 5942679 + LINE/L1_L1MC4 chr1 5943754 5944046 + LINE/L1_L1MC4 chr1 5944056 5944161 + LINE/L1_L1MC5 chr1 5944688 5944981 + SINE/Alu_AluSz chr1 5945777 5945969 + DNA/TcMar-Tigger_Tigger7 chr1 5945984 5946206 + DNA/TcMar-Tigger_MER44A chr1 5946549 5946979 + LTR/ERVL_MER21B chr1 5947419 5947503 - SINE/Alu_FLAM_C chr1 5949344 5949370 + Simple_repeat_(AC)n chr1 5949410 5949545 - LINE/L2_L2a chr1 5950104 5950140 - snRNA_U2 chr1 5950512 5950744 + LTR/ERVL-MaLR_MLT1H2 chr1 5950842 5951137 + LINE/L1_L1ME3B chr1 5951334 5951416 - LTR/ERVL_LTR83 chr1 5952561 5952611 + Simple_repeat_(CCCA)n chr1 5953093 5953403 - SINE/Alu_AluSx4 chr1 5953954 5954421 + LINE/L1_L1ME4a chr1 5954505 5956017 + LINE/L1_L1MA6 chr1 5956553 5956628 + SINE/MIR_MIRb chr1 5956792 5956973 - SINE/MIR_MIRb chr1 5957009 5957035 + Simple_repeat_(TCAC)n chr1 5957137 5957232 + DNA/TcMar-Tigger_X26_DNA chr1 5957952 5958028 + LINE/L2_L2b chr1 5958032 5958056 + Simple_repeat_(TGAA)n chr1 5958264 5958412 + SINE/Alu_AluSz chr1 5958413 5958709 + SINE/Alu_AluSx chr1 5958710 5958876 + SINE/Alu_AluSz chr1 5960902 5961436 + LINE/L1_L1MB3 chr1 5962179 5962343 - SINE/Alu_FRAM chr1 5962883 5962966 - LINE/CR1_L3 chr1 5963139 5963410 + SINE/Alu_AluSx chr1 5963680 5963987 - SINE/Alu_AluSx1 chr1 5964033 5964245 + LINE/L1_L1ME4b chr1 5964246 5964764 + LTR/ERV1_LTR75_1 chr1 5964765 5964839 + LINE/L1_L1ME4b chr1 5964844 5964942 + Simple_repeat_(TA)n chr1 5964943 5965231 - SINE/Alu_AluJr chr1 5965232 5965275 + LINE/L1_L1ME4b chr1 5965554 5966187 + LTR/ERV1_MER72 chr1 5966246 5966546 - SINE/Alu_AluSz chr1 5967691 5967918 + DNA/hAT-Charlie_MER20 chr1 5967971 5968046 + LINE/L2_L2b chr1 5968043 5968245 - SINE/MIR_MIRb chr1 5968321 5968615 + SINE/Alu_AluSz6 chr1 5968742 5968802 + SINE/Alu_AluSx chr1 5968803 5969041 + SINE/Alu_AluSz6 chr1 5970222 5970520 + SINE/Alu_AluSz chr1 5971253 5971275 + Simple_repeat_(CAA)n chr1 5972396 5972461 + DNA/hAT-Charlie_MER20 chr1 5972514 5972592 + LINE/L2_L2a chr1 5972745 5972874 - LINE/L1_L1PB1 chr1 5972875 5973184 - SINE/Alu_AluSz chr1 5973185 5973214 - LINE/L1_L1PB1 chr1 5973216 5973308 - SINE/MIR_MIRc chr1 5973411 5973598 + SINE/Alu_AluJr chr1 5976218 5976368 + SINE/MIR_MIRb chr1 5977258 5977652 + LINE/L2_L2c chr1 5977758 5977843 + SINE/MIR_MIRc chr1 5977849 5977930 + Low_complexity_A-rich chr1 5977853 5977929 + Low_complexity_GA-rich chr1 5978032 5978121 + DNA/hAT-Charlie_Charlie15a chr1 5978737 5978911 + LTR/ERVL-MaLR_MLT1K chr1 5979184 5979511 - LINE/L2_L2b chr1 5979539 5979813 - SINE/Alu_AluJb chr1 5979819 5980351 + LTR/ERVL_MER74A chr1 5980352 5980590 - LINE/L2_L2b chr1 5980887 5980940 + Simple_repeat_(CCT)n chr1 5980955 5981096 + LINE/L2_L2a chr1 5981152 5981177 + Simple_repeat_(CCT)n chr1 5981323 5981426 + LINE/L2_L2c chr1 5981687 5982031 - LINE/L1_L1M5 chr1 5982034 5982211 - LINE/L1_L1M5 chr1 5982212 5982535 + DNA/TcMar-Tigger_Tigger3a chr1 5982536 5983246 - LINE/L1_L1M5 chr1 5983247 5983721 + LTR/ERVL_MER68 chr1 5983722 5983962 - LINE/L1_L1M5 chr1 5983975 5984253 + LINE/L1_L1ME4b chr1 5984254 5984550 + SINE/Alu_AluSx chr1 5984551 5984776 + LINE/L1_L1ME4b chr1 5984948 5985046 + SINE/MIR_MIR chr1 5985051 5985124 + LINE/L2_L2d2 chr1 5985771 5986083 - DNA/TcMar-Tigger_MER47A chr1 5986511 5986712 + LINE/L1_L1ME4b chr1 5986827 5986903 + DNA/hAT-Tip100_MER91A chr1 5987346 5987506 + DNA/hAT-Charlie_Charlie1a chr1 5987550 5988662 + DNA/hAT-Charlie_Charlie1 chr1 5989217 5989332 + LTR/ERVL-MaLR_MLT1D chr1 5989373 5989501 + LINE/L2_L2c chr1 5990098 5990137 + Simple_repeat_(TGCTCCT)n chr1 5990447 5990470 + Simple_repeat_(GAA)n chr1 5990471 5990635 - SINE/MIR_MIRb chr1 5990895 5991003 - LINE/L2_L2c chr1 5991195 5991291 - LINE/L2_L2a chr1 5991414 5991535 + DNA/hAT-Charlie_Charlie1a chr1 5991922 5991958 + Simple_repeat_(CAGGGGG)n chr1 5993052 5993102 + Simple_repeat_(CCCTGCC)n chr1 5995290 5995400 - SINE/MIR_MIR3 chr1 5995715 5995894 - SINE/MIR_MIRb chr1 5996566 5996685 + DNA/TcMar-Tigger_Tigger19a chr1 5997094 5997128 + LINE/L2_L2b chr1 5997317 5997359 + Low_complexity_GA-rich chr1 5998012 5998237 - LINE/L2_L2d2 chr1 5998798 5998912 - SINE/MIR_MIRc chr1 6000652 6000675 + Simple_repeat_(A)n chr1 6001297 6001357 + Simple_repeat_(CA)n chr1 6001831 6001884 - SINE/MIR_MIR3 chr1 6001948 6002069 + LINE/L2_L2b chr1 6003010 6003075 - SINE/MIR_MIRc chr1 6003559 6003802 - LINE/L1_L1ME4c chr1 6004228 6004359 - SINE/Alu_FLAM_A chr1 6004389 6004809 - LTR/ERVL-MaLR_MLT1C2 chr1 6007441 6007480 + Simple_repeat_(AGCCGG)n chr1 6008340 6008637 + SINE/Alu_AluSx chr1 6008642 6008727 + LINE/CR1_L3 chr1 6009799 6009858 - DNA/hAT-Charlie_MER113 chr1 6009859 6010176 - SINE/Alu_AluSz chr1 6010177 6010311 - DNA/hAT-Charlie_MER113 chr1 6010316 6010444 + SINE/Alu_FLAM_A chr1 6010881 6010979 + SINE/MIR_MIRb chr1 6011959 6012814 + Simple_repeat_(GGAGGT)n chr1 6013156 6013498 - LINE/L2_L2b chr1 6013505 6013838 + LINE/L2_L2 chr1 6013925 6014153 + SINE/MIR_MIR chr1 6015017 6015329 - SINE/Alu_AluSx1 chr1 6015352 6015561 + DNA/hAT-Charlie_MER58A chr1 6015683 6015708 + Simple_repeat_(TTGG)n chr1 6015717 6016001 - SINE/Alu_AluJb chr1 6017262 6017406 - SINE/Alu_AluSz chr1 6017407 6017439 + Simple_repeat_(TG)n chr1 6017440 6017521 - SINE/Alu_AluSz chr1 6017522 6017803 + SINE/Alu_AluSx1 chr1 6017804 6017854 - SINE/Alu_AluSz chr1 6017923 6018236 - SINE/Alu_AluJr chr1 6018238 6018358 - DNA/hAT-Charlie_MER58B chr1 6018363 6018468 + LTR/ERVL-MaLR_MLT1A chr1 6018469 6018627 - SINE/Alu_AluJb chr1 6018629 6018877 + LTR/ERVL-MaLR_MLT1A chr1 6018885 6019009 - DNA/hAT-Charlie_MER58B chr1 6019010 6019320 + SINE/Alu_AluJr chr1 6019321 6019395 - DNA/hAT-Charlie_MER58B chr1 6019398 6019591 + LINE/CR1_L3 chr1 6019766 6020052 + LINE/CR1_L3 chr1 6020657 6020942 - SINE/Alu_AluSx1 chr1 6020988 6021275 - SINE/Alu_AluJb chr1 6021413 6021494 - LINE/L1_L1MB3 chr1 6022519 6022874 - LINE/L1_L1MB3 chr1 6022875 6023181 - SINE/Alu_AluSx1 chr1 6023182 6023762 - LINE/L1_L1MB3 chr1 6023763 6023939 - SINE/Alu_AluJr chr1 6023940 6024240 - SINE/Alu_AluSq2 chr1 6024241 6024383 - SINE/Alu_AluJr chr1 6024384 6024417 - LINE/L1_L1MB3 chr1 6024798 6024816 + SINE/MIR_MIRb chr1 6024817 6024942 + SINE/MIR_MIR3 chr1 6025726 6025848 + SINE/MIR_MIRb chr1 6027220 6027274 + Simple_repeat_(GT)n chr1 6028548 6028669 - LINE/L2_L2a chr1 6029034 6029303 + SINE/Alu_AluSc8 chr1 6029661 6029772 - SINE/MIR_MIRc chr1 6030945 6031009 - LTR/ERVL-MaLR_MLT1H chr1 6031414 6031473 - SINE/MIR_MIR3 chr1 6031605 6031909 + SINE/Alu_AluJr chr1 6032280 6032298 + Low_complexity_A-rich chr1 6032299 6032601 + SINE/Alu_AluSc chr1 6032602 6032671 + Low_complexity_A-rich chr1 6032679 6033195 + LINE/L1_L1MC4 chr1 6033196 6033515 + Simple_repeat_(TGTGTG)n chr1 6033747 6033886 + SINE/MIR_MIR chr1 6034809 6035972 - LINE/L2_L2a chr1 6036021 6036220 - LINE/L2_L2-3_Crp chr1 6036221 6036520 + SINE/Alu_AluSz6 chr1 6036527 6036853 - LINE/L2_L2a chr1 6036864 6037060 - DNA/hAT-Charlie_MER58A chr1 6037094 6037203 + DNA/hAT-Charlie_MER5A chr1 6037804 6037860 - SINE/Alu_AluYb8 chr1 6037871 6038176 - SINE/Alu_AluY chr1 6038280 6038594 - SINE/Alu_AluJr chr1 6038947 6039114 - LINE/L2_L2a chr1 6039514 6040029 - LTR/ERVL-MaLR_MLT1F2 chr1 6040351 6040383 + Simple_repeat_(TGTC)n chr1 6041776 6041815 + Simple_repeat_(CT)n chr1 6042477 6042548 + SINE/MIR_MIR1_Amn chr1 6042824 6042854 + Simple_repeat_(C)n chr1 6043828 6043957 - LTR/ERVL-MaLR_MLT1H chr1 6044001 6044243 - LTR/ERVL-MaLR_MLT1H chr1 6044333 6044585 - LINE/L2_L2d2 chr1 6047667 6047783 - LINE/L2_L2a chr1 6048368 6048534 - SINE/MIR_MIRb chr1 6049242 6049484 - LINE/L2_L2a chr1 6049750 6049834 - LINE/L2_L2b chr1 6050490 6050698 - LINE/L1_L1MC5 chr1 6051822 6052136 + SINE/Alu_AluSx chr1 6052140 6052442 + SINE/Alu_AluJo chr1 6052769 6052920 - DNA/hAT_MER53 chr1 6053259 6053661 - LINE/L2_L2a chr1 6053739 6053799 + LTR/ERVL-MaLR_MLT1C2 chr1 6053800 6054072 + SINE/Alu_AluJb chr1 6054073 6054247 + LTR/ERVL-MaLR_MLT1C2 chr1 6054248 6054348 - LTR/ERV1_MER51A chr1 6054349 6054583 + SINE/Alu_AluSx1 chr1 6054584 6055032 - LTR/ERV1_MER51A chr1 6055033 6055207 + LTR/ERVL-MaLR_MLT1C2 chr1 6055354 6055651 - SINE/Alu_AluSx chr1 6056054 6056344 - SINE/Alu_AluSz chr1 6056599 6056890 + LINE/L2_L2a chr1 6057303 6057491 - LINE/L2_L2a chr1 6057572 6057868 - LTR/ERVL-MaLR_MLT1C2 chr1 6057869 6058173 + SINE/Alu_AluSz6 chr1 6058174 6058329 - LTR/ERVL-MaLR_MLT1C2 chr1 6058382 6058519 + SINE/MIR_MIRb chr1 6059179 6059489 - SINE/Alu_AluJo chr1 6060620 6060922 + SINE/Alu_AluSc chr1 6061494 6061676 - DNA/hAT-Charlie_MER20 chr1 6062032 6062327 + SINE/Alu_AluSp chr1 6062482 6062580 - LINE/L2_L2b chr1 6062799 6063030 - LINE/L1_L1MB8 chr1 6063031 6063315 - SINE/Alu_AluSx3 chr1 6063316 6063409 - LINE/L1_L1MB8 chr1 6063410 6063704 - SINE/Alu_AluSx chr1 6063705 6063923 - LINE/L1_L1MB8 chr1 6064221 6064285 - DNA/hAT-Charlie_MER5A chr1 6067749 6068042 + SINE/Alu_AluSz chr1 6068356 6068406 + SINE/MIR_MIRb chr1 6068407 6068474 + SINE/MIR_MIRb chr1 6069094 6069141 + LINE/RTE-BovB_MamRTE1 chr1 6070655 6070864 - SINE/MIR_MIR chr1 6073256 6073298 + Simple_repeat_(AC)n chr1 6074678 6074974 + SINE/Alu_AluSx1 chr1 6075109 6075678 + LINE/CR1_L3 chr1 6075720 6075930 - LINE/L1_L1MB4 chr1 6075938 6076110 + DNA/hAT-Charlie_MER58A chr1 6076510 6076597 - SINE/MIR_MIRb chr1 6076909 6077200 + SINE/Alu_AluSg4 chr1 6077897 6078416 - LTR/ERVL-MaLR_MLT1D chr1 6078622 6078743 - LINE/L2_L2 chr1 6078797 6078887 - DNA/hAT-Charlie_MER5A1 chr1 6078899 6079114 - LINE/L2_L2 chr1 6079271 6079535 + SINE/Alu_AluSz chr1 6079546 6079892 + LINE/L1_L1MB3 chr1 6079929 6080074 + LINE/L1_L1MC4a chr1 6080667 6080750 + DNA/hAT-Charlie_MER102c chr1 6081475 6081887 - LTR/ERVL-MaLR_MLT1C2 chr1 6081893 6081954 - LINE/L2_L2b chr1 6084035 6084133 + Unknown_UCON88 chr1 6084382 6084423 + Simple_repeat_(CA)n chr1 6084536 6084827 + SINE/Alu_AluSx1 chr1 6085655 6085834 + SINE/MIR_MIR3 chr1 6088048 6088219 - SINE/Alu_AluJo chr1 6088220 6088546 - SINE/Alu_AluJb chr1 6088547 6088682 - SINE/Alu_AluJo chr1 6088704 6088732 + Simple_repeat_(AAT)n chr1 6089244 6089407 + SINE/MIR_MIR chr1 6089701 6090001 - SINE/Alu_AluSq chr1 6093107 6093127 + Simple_repeat_(CTTC)n chr1 6094614 6094844 + SINE/MIR_MIRb chr1 6094870 6095098 - SINE/MIR_MIR chr1 6097435 6098169 - LINE/L2_L2a chr1 6101701 6101748 + Simple_repeat_(CACCCCC)n chr1 6110794 6110947 + LTR/ERVL-MaLR_MLT1C2 chr1 6110948 6111251 + SINE/Alu_AluSz chr1 6111252 6111556 + SINE/Alu_AluSz chr1 6111557 6111711 + LTR/ERVL-MaLR_MLT1C2 chr1 6112304 6112434 - LINE/L2_L2c chr1 6113143 6113432 + SINE/Alu_AluJb chr1 6113968 6114266 + SINE/Alu_AluSg chr1 6114388 6114659 - DNA/hAT-Charlie_MER30 chr1 6114783 6115025 + SINE/Alu_AluSx1 chr1 6115031 6115237 + SINE/Alu_AluYk3 chr1 6115311 6115374 + LTR/ERVL-MaLR_MLT1E2 chr1 6115433 6115906 + LTR/ERVL-MaLR_MLT1E2 chr1 6116035 6116266 + LINE/L2_L2a chr1 6116288 6117376 + LINE/L1_L1MEd chr1 6117332 6117430 + LINE/L1_L1MB7 chr1 6117431 6117550 + SINE/Alu_FLAM_A chr1 6117551 6118113 + LINE/L1_L1MB7 chr1 6118114 6118403 + SINE/Alu_AluJb chr1 6118404 6118702 + LINE/L1_L1MB7 chr1 6118703 6118997 - SINE/Alu_AluSq chr1 6118998 6119070 + LINE/L1_L1MB7 chr1 6119149 6119211 - SINE/Alu_AluY chr1 6119212 6119397 - SINE/Alu_AluY chr1 6119400 6119705 + LINE/L1_L1MEd chr1 6119865 6120160 - SINE/Alu_AluSx chr1 6120161 6120358 + LINE/L1_L1MEd chr1 6120390 6120652 + SINE/Alu_AluSz chr1 6120692 6120916 + SINE/Alu_AluSc chr1 6121434 6121480 + Simple_repeat_(CCCCACA)n chr1 6121850 6121949 + SINE/MIR_MIR3 chr1 6122039 6122157 - SINE/MIR_MIRb chr1 6122308 6122787 + LINE/L1_L1MB7 chr1 6122788 6123097 + SINE/Alu_AluSq2 chr1 6123098 6123431 + LINE/L1_L1MB7 chr1 6123432 6123728 - SINE/Alu_AluSx chr1 6123729 6123792 + LINE/L1_L1MB7 chr1 6125474 6125512 + Simple_repeat_(GGAAGCA)n chr1 6126814 6126987 + LINE/L2_L2d2 chr1 6126960 6127069 - LINE/L2_L2c chr1 6127206 6127506 + SINE/Alu_AluSx chr1 6129932 6130016 + SINE/MIR_MIR3 chr1 6131107 6131343 + LTR/ERVL_LTR79 chr1 6132234 6132874 - LINE/L1_L1MEi chr1 6132881 6132910 + Simple_repeat_(TC)n chr1 6132911 6133057 - SINE/Alu_FRAM chr1 6133058 6133163 - LINE/L1_L1MEi chr1 6133587 6133709 + LINE/L2_L2b chr1 6135607 6135735 - SINE/MIR_MIRb chr1 6135739 6136039 + SINE/Alu_AluSx chr1 6137123 6137406 - SINE/Alu_AluJb chr1 6137509 6137632 - SINE/Alu_AluJr chr1 6137635 6137722 - SINE/MIR_MIRb chr1 6137727 6137868 + DNA/hAT-Tip100_MER45C chr1 6137958 6138093 + DNA/hAT-Tip100_MER45C chr1 6138094 6138398 + SINE/Alu_AluSz6 chr1 6138399 6138514 + DNA/hAT-Tip100_MER45C chr1 6138703 6139216 + LINE/L1_L1MB2 chr1 6139219 6139251 + Simple_repeat_(TGT)n chr1 6139256 6139535 - SINE/Alu_AluSq2 chr1 6139539 6139736 - SINE/Alu_AluJb chr1 6139737 6139778 + LINE/L1_L1MB2 chr1 6140121 6140427 + SINE/Alu_AluSc chr1 6140428 6140491 + DNA/hAT-Charlie_MER5A1 chr1 6140528 6140631 - SINE/MIR_MIRb chr1 6140641 6140718 + LTR/ERVL-MaLR_MLT1C2 chr1 6140719 6140967 + SINE/Alu_AluJb chr1 6140968 6141001 + Simple_repeat_(AAT)n chr1 6141002 6141289 + SINE/Alu_AluSx chr1 6141290 6141326 + Simple_repeat_(AAT)n chr1 6141334 6141634 + SINE/Alu_AluSz chr1 6141635 6141971 + LTR/ERVL-MaLR_MLT1C2 chr1 6142668 6142988 + LINE/L2_L2b chr1 6143057 6143332 - SINE/Alu_AluJo chr1 6144289 6144370 - SINE/MIR_MIR3 chr1 6145237 6145313 - SINE/MIR_MIR chr1 6145670 6145824 + SINE/MIR_MIR chr1 6147569 6147718 + SINE/MIR_MIR1_Amn chr1 6147838 6147874 + Low_complexity_GA-rich chr1 6148079 6148142 + Simple_repeat_(AC)n chr1 6148364 6148584 + SINE/MIR_MIRb chr1 6148996 6149077 + Simple_repeat_(TCC)n chr1 6149586 6150660 + Simple_repeat_(GATG)n chr1 6151315 6151688 + LINE/L2_L2b chr1 6152180 6152253 - SINE/MIR_MIR3 chr1 6152359 6152411 + Simple_repeat_(CA)n chr1 6152595 6152731 - SINE/MIR_MIRc chr1 6153548 6153827 + SINE/Alu_AluSx chr1 6153828 6153856 + Simple_repeat_(CAAAA)n chr1 6154758 6154789 + Simple_repeat_(GCC)n chr1 6156255 6156558 + SINE/Alu_AluSz chr1 6156911 6157084 + SINE/MIR_MIRc chr1 6157955 6158039 - DNA/hAT-Charlie_MER113A chr1 6158520 6158585 + SINE/Alu_AluSc chr1 6158586 6158643 + SINE/Alu_AluYg6 chr1 6158646 6158725 + SINE/Alu_AluSc chr1 6158726 6159030 + SINE/Alu_AluYa5 chr1 6159031 6159227 + SINE/Alu_AluSc chr1 6159228 6159259 + Simple_repeat_(CA)n chr1 6159343 6159438 + Simple_repeat_(CAT)n chr1 6161100 6161164 + Low_complexity_GA-rich chr1 6161946 6162055 + SINE/MIR_MIRc chr1 6162100 6162416 + SINE/Alu_AluSp chr1 6162952 6162985 + Low_complexity_G-rich chr1 6163338 6163370 + Simple_repeat_(CCTGC)n chr1 6164835 6164940 + Low_complexity_GA-rich chr1 6164999 6165038 + Low_complexity_GA-rich chr1 6166758 6166887 + LINE/L2_L2 chr1 6167071 6167100 + Simple_repeat_(CACCT)n chr1 6167241 6167392 + SINE/MIR_MIRc chr1 6167483 6167583 + SINE/MIR_MIRc chr1 6167805 6167855 + Low_complexity_GA-rich chr1 6168422 6168532 + SINE/MIR_MIRc chr1 6168738 6169046 + SINE/Alu_AluSx1 chr1 6169452 6169562 + LTR/ERV1_LTR78 chr1 6170091 6170150 - SINE/MIR_MIR chr1 6172330 6172504 - SINE/Alu_AluJb chr1 6172505 6172614 - SINE/MIR_MIR chr1 6173102 6173403 - SINE/Alu_AluY chr1 6173539 6173661 + SINE/MIR_MIRb chr1 6173891 6173957 + DNA/hAT-Charlie_Charlie32a chr1 6174228 6174285 + Low_complexity_GA-rich chr1 6174363 6175681 + Simple_repeat_(GATG)n chr1 6175682 6175727 + Simple_repeat_(ATGG)n chr1 6175728 6176060 + Simple_repeat_(GATG)n chr1 6176647 6176883 - SINE/MIR_MIRb chr1 6177519 6177640 + SINE/Alu_FLAM_A chr1 6177701 6178102 - LINE/L2_L2b chr1 6179534 6179591 + Low_complexity_G-rich chr1 6179838 6179901 + Simple_repeat_(GGCCC)n chr1 6180288 6180335 + Simple_repeat_(GGC)n chr1 6182058 6182366 - SINE/Alu_AluSc chr1 6182645 6182950 + SINE/Alu_AluSx1 chr1 6187032 6187329 + SINE/Alu_AluSz chr1 6187333 6187642 + SINE/Alu_AluSc8 chr1 6188058 6188331 - SINE/Alu_AluSz6 chr1 6188408 6188530 + SINE/Alu_AluJo chr1 6188793 6189084 - SINE/Alu_AluSp chr1 6189415 6189578 + SINE/Alu_AluJb chr1 6189638 6189942 + SINE/Alu_AluSq2 chr1 6190376 6190652 - SINE/Alu_AluSp chr1 6190941 6191077 + SINE/Alu_AluSx chr1 6191078 6191385 + SINE/Alu_AluSx3 chr1 6191386 6191702 + SINE/Alu_AluYk2 chr1 6191716 6192015 + SINE/Alu_AluSx chr1 6192051 6192346 - SINE/Alu_AluJr chr1 6192416 6192712 + SINE/Alu_AluSx1 chr1 6193146 6193323 - SINE/Alu_AluJb chr1 6193324 6193621 - SINE/Alu_AluSp chr1 6193622 6193750 - SINE/Alu_AluJb chr1 6193942 6194226 + SINE/Alu_AluSx chr1 6194383 6194576 + SINE/MIR_MIR1_Amn chr1 6194605 6194893 + SINE/Alu_AluJr chr1 6194894 6195158 + SINE/Alu_AluY chr1 6195164 6195464 + SINE/Alu_AluYk2 chr1 6195468 6195779 + SINE/Alu_AluSx chr1 6195781 6196092 + SINE/Alu_AluSp chr1 6197030 6197328 - SINE/Alu_AluSx3 chr1 6197987 6198124 - LINE/CR1_X7A_LINE chr1 6199328 6199386 + Low_complexity_G-rich chr1 6200028 6200112 + LINE/L2_L2b chr1 6200116 6200185 + LINE/L2_L2b chr1 6200200 6200331 - DNA/hAT-Charlie_MER5A1 chr1 6200396 6200505 + LINE/L2_L2b chr1 6200566 6201073 + LTR/ERVL_LTR50 chr1 6201074 6201140 + LINE/L2_L2b chr1 6201141 6201305 - SINE/Alu_FRAM chr1 6201306 6201391 + LINE/L2_L2b chr1 6201393 6201729 - SINE/Alu_AluJb chr1 6201916 6202014 + Simple_repeat_(CACCCC)n chr1 6202015 6202161 + LINE/L2_L2b chr1 6202047 6202164 + LINE/L2_L2b chr1 6202165 6202470 - SINE/Alu_AluJr chr1 6202471 6202516 + LINE/L2_L2b chr1 6202595 6202732 - SINE/Alu_AluSx chr1 6202733 6203056 - SINE/Alu_AluSx3 chr1 6203057 6203192 - SINE/Alu_AluSx chr1 6203293 6203332 + LINE/L2_L2b chr1 6203338 6203358 + Simple_repeat_(ATGA)n chr1 6203447 6203531 + SINE/MIR_MIR chr1 6203574 6203877 + SINE/Alu_AluSp chr1 6203977 6204272 + SINE/Alu_AluY chr1 6204303 6204378 + LINE/L2_L2d2 chr1 6204379 6204490 + SINE/Alu_AluJo chr1 6204529 6204556 + Simple_repeat_(A)n chr1 6204557 6204825 + SINE/Alu_AluSx3 chr1 6204853 6204964 - LINE/L2_L2b chr1 6205242 6205324 - SINE/MIR_MIR3 chr1 6205477 6205750 - SINE/Alu_AluSz chr1 6206904 6207004 - SINE/MIR_MIR3 chr1 6207656 6207778 - SINE/MIR_MIRc chr1 6208326 6208606 - SINE/Alu_AluSx1 chr1 6208675 6208723 + SINE/MIR_MIRc chr1 6209039 6209074 + Low_complexity_G-rich chr1 6209589 6209768 - LINE/L2_L2b chr1 6209837 6209882 + Simple_repeat_(GCAGGTG)n chr1 6211643 6211704 - DNA/hAT-Charlie_MER5B chr1 6213239 6213489 + SINE/Alu_AluSx chr1 6213986 6214394 - LINE/L1_HAL1 chr1 6214399 6214618 - SINE/Alu_AluSx chr1 6214624 6214926 - SINE/Alu_AluSc chr1 6214945 6215011 - LINE/L1_HAL1 chr1 6215017 6215327 - SINE/Alu_AluSp chr1 6215333 6215466 - SINE/Alu_FLAM_C chr1 6215474 6215663 - LINE/L1_HAL1 chr1 6215668 6215823 - SINE/Alu_AluSx chr1 6215829 6215967 - LINE/L1_HAL1 chr1 6216209 6216308 + LINE/L1_L1ME3A chr1 6216419 6216560 + LINE/L2_L2a chr1 6216561 6216866 - SINE/Alu_AluSc8 chr1 6216867 6217164 - SINE/Alu_AluJb chr1 6217165 6217720 + LINE/L2_L2a chr1 6217849 6217922 + LINE/L2_L2a chr1 6218052 6218171 + LINE/L2_L2a chr1 6219490 6219793 - SINE/Alu_AluSx1 chr1 6219798 6220097 - SINE/Alu_AluSp chr1 6220302 6220356 - LINE/L2_L2b chr1 6222139 6222443 + SINE/Alu_AluSz chr1 6223099 6223406 - SINE/Alu_AluSx3 chr1 6223653 6223847 - SINE/MIR_MIRb chr1 6225863 6226084 - SINE/Alu_AluJb chr1 6226113 6226408 + SINE/Alu_AluSx1 chr1 6226625 6226744 - SINE/MIR_MIR chr1 6226745 6226874 - SINE/Alu_FLAM_A chr1 6226875 6226968 - SINE/MIR_MIR chr1 6227526 6227566 + Simple_repeat_(TCAAGAA)n chr1 6227574 6227878 + SINE/Alu_AluY chr1 6227886 6228151 + SINE/Alu_AluJo chr1 6228152 6228338 - SINE/Alu_AluJr chr1 6228339 6228641 - SINE/Alu_AluY chr1 6228642 6228700 - SINE/Alu_AluJr chr1 6228745 6229058 + SINE/Alu_AluSx chr1 6229064 6229196 + SINE/Alu_AluSz chr1 6229197 6229509 + SINE/Alu_AluSp chr1 6229510 6229637 + SINE/Alu_AluSz chr1 6229638 6229782 + SINE/Alu_AluJb chr1 6229784 6229824 + Simple_repeat_(AC)n chr1 6229864 6230011 - SINE/Alu_AluSz6 chr1 6230022 6230118 + SINE/Alu_AluJb chr1 6230119 6230394 - SINE/Alu_AluSz6 chr1 6230395 6230608 + SINE/Alu_AluJb chr1 6230609 6230919 + SINE/Alu_AluSc8 chr1 6230920 6231200 + SINE/Alu_AluSz chr1 6231220 6231326 - SINE/tRNA-RTE_MamSINE1 chr1 6231430 6231557 + SINE/Alu_FLAM_C chr1 6231768 6231864 + LINE/L1_L1MB7 chr1 6232555 6232847 - SINE/Alu_AluSp chr1 6232860 6233153 - SINE/Alu_AluSg chr1 6233834 6234173 - SINE/Alu_AluSg chr1 6234355 6234442 + SINE/tRNA_LFSINE_Vert chr1 6234433 6234518 + LINE/CR1_X17_LINE chr1 6235075 6235301 + SINE/MIR_MIRb chr1 6235667 6235714 + Simple_repeat_(CGGC)n chr1 6236099 6236123 + Simple_repeat_(CCCG)n chr1 6236269 6236561 + SINE/Alu_AluSz chr1 6236785 6237076 - SINE/Alu_AluYk2 chr1 6237227 6237367 - SINE/Alu_FLAM_C chr1 6237368 6237455 + LINE/L1_L1ME4a chr1 6237456 6237750 - SINE/Alu_AluSq2 chr1 6237751 6237771 + LINE/L1_L1ME4a chr1 6238225 6238385 + SINE/MIR_MIRc chr1 6238419 6238584 - SINE/Alu_FRAM chr1 6239723 6240032 - SINE/Alu_AluSg chr1 6240863 6240890 + Simple_repeat_(T)n chr1 6241774 6241810 + Low_complexity_G-rich chr1 6242456 6242480 + Simple_repeat_(GCCC)n chr1 6244716 6244767 + SINE/Alu_FLAM_A chr1 6244934 6245081 + Simple_repeat_(CCCTC)n chr1 6245813 6245836 + Simple_repeat_(CCCG)n chr1 6246494 6246664 - DNA/hAT-Tip100_MamTip1 chr1 6248718 6248781 - SINE/MIR_MIR3 chr1 6248721 6248790 - SINE/MIR_MIR chr1 6249419 6249450 + Simple_repeat_(GCC)n chr1 6250630 6250651 + Simple_repeat_(GGGT)n chr1 6250737 6250943 + SINE/MIR_MIR chr1 6251808 6251960 - SINE/Alu_AluJb chr1 6251964 6252030 - SINE/MIR_MIRc chr1 6252164 6252295 + SINE/MIR_MIR chr1 6253110 6253429 + SINE/Alu_AluSq2 chr1 6253474 6253655 + SINE/MIR_MIRb chr1 6255104 6255201 + LTR/ERVL-MaLR_MLT1A1 chr1 6255202 6255475 - SINE/Alu_AluY chr1 6255485 6255642 - SINE/Alu_AluJr chr1 6255643 6255950 - SINE/Alu_AluSx3 chr1 6255951 6256094 - SINE/Alu_AluJr chr1 6256095 6256355 + LTR/ERVL-MaLR_MLT1A1 chr1 6256356 6256669 - SINE/Alu_AluSz6 chr1 6256670 6256801 - SINE/Alu_FLAM_A chr1 6256802 6256874 + LTR/ERVL-MaLR_MLT1A1 chr1 6258143 6258428 - SINE/Alu_AluSq2 chr1 6258979 6259272 + SINE/Alu_AluJb chr1 6259349 6259469 - SINE/MIR_MIRb chr1 6259542 6259658 - SINE/MIR_MIRc chr1 6260844 6260893 + Simple_repeat_(GGCTCC)n chr1 6260992 6261091 + Simple_repeat_(CGCTC)n chr1 6261138 6261280 + Simple_repeat_(CCGC)n chr1 6261453 6261506 - LINE/L2_L2c chr1 6261791 6261850 + Simple_repeat_(CCT)n chr1 6262858 6262915 - SINE/MIR_MIRc chr1 6265544 6265616 + DNA/hAT-Blackjack_MER94 chr1 6266418 6266570 - DNA/hAT-Charlie_MER5A1 chr1 6267462 6267487 + Simple_repeat_(TGCC)n chr1 6267565 6267692 - DNA/hAT-Charlie_MER103C chr1 6267880 6267936 + SINE/MIR_MIRb chr1 6267967 6268261 + LINE/L1_L1MB8 chr1 6269071 6269233 - SINE/MIR_MIR chr1 6270718 6270773 + Simple_repeat_(GT)n chr1 6272053 6272105 - SINE/MIR_MIRb chr1 6272178 6272248 + LINE/L2_L2b chr1 6272249 6272279 + Simple_repeat_(GAAT)n chr1 6275451 6275743 + SINE/Alu_AluSg chr1 6277509 6277865 + LINE/L2_L2a chr1 6277895 6278034 - SINE/MIR_MIR chr1 6278197 6278255 - LINE/L2_L2b chr1 6279590 6279679 + LINE/L2_L2b chr1 6281495 6281740 - LINE/CR1_L3 chr1 6283171 6283463 - SINE/Alu_AluSz6 chr1 6283842 6284011 + SINE/Alu_AluJb chr1 6284295 6284602 + SINE/Alu_AluSp chr1 6284881 6285106 + LINE/L2_L2c chr1 6286522 6286621 + SINE/MIR_MIRb chr1 6286949 6287084 - SINE/Alu_AluY chr1 6287287 6287512 + SINE/MIR_MIR chr1 6287724 6287748 + Simple_repeat_(A)n chr1 6289088 6289399 - SINE/Alu_AluSx1 chr1 6289402 6289673 - SINE/Alu_AluJb chr1 6289696 6289827 - SINE/Alu_FLAM_A chr1 6289894 6290324 + LTR/ERVL-MaLR_MLT1D chr1 6291174 6291531 + LTR/ERVL-MaLR_MLT1C2 chr1 6292667 6292859 - SINE/Alu_AluSg4 chr1 6292880 6293188 - SINE/Alu_AluY chr1 6293513 6293713 + SINE/Alu_AluJr chr1 6295272 6295906 + LINE/L1_L1MB8 chr1 6295907 6296192 - SINE/Alu_AluJr chr1 6296193 6296250 + LINE/L1_L1MB8 chr1 6296421 6296722 - SINE/Alu_AluSx3 chr1 6296739 6296875 - SINE/Alu_AluJo chr1 6296912 6296991 + DNA/TcMar-Mariner_MADE2 chr1 6297046 6297337 - SINE/Alu_AluSx chr1 6297568 6297929 + LINE/L1_L1MC5a chr1 6298136 6298432 - SINE/Alu_AluSx1 chr1 6299079 6299240 + SINE/MIR_MIRb chr1 6299641 6299660 + Simple_repeat_(AG)n chr1 6299666 6299691 + Simple_repeat_(GT)n chr1 6300865 6301030 - SINE/MIR_MIR chr1 6301544 6301627 + LINE/L2_L2b chr1 6301929 6302452 + LINE/CR1_L3 chr1 6304318 6304353 + Simple_repeat_(A)n chr1 6304394 6306236 - Retroposon/SVA_SVA_D chr1 6306668 6306775 + LINE/L2_L2c chr1 6307140 6307221 + LINE/L2_L2b chr1 6307353 6307437 - LINE/L2_L2a chr1 6310131 6310307 - LINE/L2_L2 chr1 6310616 6310718 + DNA/hAT-Charlie_MER5A chr1 6310722 6311037 - SINE/Alu_AluSx1 chr1 6311845 6311979 - DNA/hAT-Charlie_MER5B chr1 6312197 6312457 + LINE/L1_L1MA1 chr1 6312458 6312767 - SINE/Alu_AluSx1 chr1 6312768 6312853 + LINE/L1_L1MA1 chr1 6312977 6313060 + DNA/hAT-Charlie_MER5A chr1 6313717 6313851 + LTR/ERVL-MaLR_MLT1J chr1 6313897 6314058 + LINE/L1_HAL1 chr1 6315470 6315783 + SINE/Alu_AluY chr1 6316528 6316815 + SINE/Alu_AluSz chr1 6316861 6317104 - LINE/L2_L2c chr1 6317155 6317441 - DNA/hAT-Charlie_MER113A chr1 6317723 6318031 - SINE/Alu_AluSx1 chr1 6318065 6318355 - SINE/Alu_AluJr chr1 6319197 6319492 - SINE/Alu_AluSz chr1 6320018 6320246 + SINE/MIR_MIR chr1 6320346 6320643 - LTR/ERVL-MaLR_MLT1D chr1 6320732 6321518 - LTR/ERVL-MaLR_MLT1D-int chr1 6321519 6321801 - SINE/Alu_AluYc chr1 6321802 6321907 - LTR/ERVL-MaLR_MLT1D-int chr1 6322446 6322813 - LTR/ERVL-MaLR_MLT1B chr1 6322849 6323159 + SINE/Alu_AluSq2 chr1 6323436 6323737 + SINE/Alu_AluSx chr1 6323738 6323770 + Simple_repeat_(AT)n chr1 6324715 6324897 - LTR/ERVL-MaLR_MLT1D chr1 6325118 6325417 + SINE/Alu_AluY chr1 6326644 6326958 + SINE/Alu_AluSz chr1 6327764 6328071 - SINE/Alu_AluSc chr1 6328379 6328706 + SINE/Alu_AluSx1 chr1 6330054 6330088 + Simple_repeat_(GT)n chr1 6330094 6330168 + Simple_repeat_(AT)n chr1 6331197 6331583 + LTR/ERVL-MaLR_MLT1C2 chr1 6332535 6332850 + SINE/Alu_AluY chr1 6333704 6333735 + Simple_repeat_(GCCCCCAA)n chr1 6334707 6334842 - SINE/MIR_MIR3 chr1 6334905 6335048 + SINE/Alu_AluSz chr1 6335049 6335352 + SINE/Alu_AluSp chr1 6335353 6335508 + SINE/Alu_AluSz chr1 6336065 6336358 + SINE/Alu_AluSc8 chr1 6336699 6336762 - LINE/L2_L2a chr1 6336826 6336900 + Low_complexity_GA-rich chr1 6337456 6337480 + Simple_repeat_(TCAT)n chr1 6337693 6338027 + SINE/Alu_AluSx chr1 6338247 6338300 + LINE/RTE-BovB_MamRTE1 chr1 6338755 6338794 + Simple_repeat_(GGCAGG)n chr1 6339148 6339319 + SINE/MIR_MIR chr1 6339612 6339661 + Simple_repeat_(CAGCC)n chr1 6339740 6339890 - SINE/Alu_AluYa8 chr1 6339905 6339953 - SINE/Alu_Alu chr1 6339954 6340265 - SINE/Alu_AluY chr1 6340446 6340567 - LINE/L1_L1ME4a chr1 6340754 6341057 + SINE/Alu_AluSx chr1 6341147 6341432 - SINE/Alu_AluSx chr1 6341461 6341752 + SINE/Alu_AluY chr1 6341765 6341881 - LINE/L1_L1ME4b chr1 6342025 6342394 - LTR/ERVL-MaLR_MLT1A0 chr1 6342458 6342639 - SINE/Alu_AluJr chr1 6342673 6342789 - LINE/L1_L1ME4a chr1 6343305 6343410 + DNA/hAT-Charlie_MER5A chr1 6344484 6344793 + SINE/Alu_AluSg chr1 6345344 6345550 + SINE/MIR_MIRb chr1 6345731 6345894 + SINE/MIR_MIRb chr1 6346106 6346378 - SINE/Alu_AluJb chr1 6346384 6347047 - LINE/L2_L2a chr1 6347325 6347483 + SINE/MIR_MIRc chr1 6347484 6347790 + SINE/Alu_AluSz chr1 6347791 6347892 + SINE/MIR_MIRc chr1 6348313 6348364 + Simple_repeat_(CA)n chr1 6348420 6348783 + LTR/ERVL-MaLR_MLT1A0 chr1 6350842 6350953 + SINE/MIR_MIRc chr1 6352345 6352382 + Simple_repeat_(CCCT)n chr1 6352591 6352820 - SINE/Alu_AluYj4 chr1 6352869 6353161 - SINE/Alu_AluSx chr1 6353205 6353333 + SINE/Alu_AluJb chr1 6353351 6353657 + SINE/Alu_AluSz chr1 6354191 6354471 - LINE/L2_L2b chr1 6355194 6355514 + LINE/L1_L1ME4b chr1 6355989 6356211 + LTR/ERVL-MaLR_MLT1C2 chr1 6356637 6356932 + SINE/Alu_AluSx3 chr1 6356957 6357250 + SINE/Alu_AluSp chr1 6357863 6358168 - SINE/Alu_AluSx chr1 6359421 6359477 + Simple_repeat_(CTCCCCT)n chr1 6360978 6361503 + LINE/L1_L1MB7 chr1 6361517 6361822 + SINE/Alu_AluSx chr1 6362149 6362453 + SINE/Alu_AluSx1 chr1 6362768 6362900 + SINE/Alu_AluJo chr1 6362927 6363907 + LTR/ERVK_LTR13 chr1 6363944 6364069 + SINE/Alu_AluJb chr1 6364070 6364259 + SINE/Alu_AluSq chr1 6364260 6364556 + SINE/Alu_AluSx chr1 6364557 6364567 + SINE/Alu_AluSq chr1 6364568 6364873 + SINE/Alu_AluSc chr1 6364874 6365043 + SINE/Alu_AluSq chr1 6365044 6365285 + LINE/L1_L1ME1 chr1 6365295 6365490 - DNA/TcMar-Tigger_Tigger1 chr1 6365491 6365783 + SINE/Alu_AluY chr1 6365784 6365898 - DNA/TcMar-Tigger_Tigger1 chr1 6365899 6366171 - SINE/Alu_AluSx chr1 6366172 6366300 - DNA/TcMar-Tigger_Tigger1 chr1 6366301 6366607 + SINE/Alu_AluJr chr1 6366608 6366659 - DNA/TcMar-Tigger_Tigger1 chr1 6366660 6366886 - SINE/Alu_AluSc8 chr1 6366887 6366916 - DNA/TcMar-Tigger_Tigger1 chr1 6366917 6367214 + SINE/Alu_AluY chr1 6367215 6367282 - DNA/TcMar-Tigger_Tigger1 chr1 6367283 6368326 - LTR/ERV1_MER52D chr1 6368489 6368927 - LTR/ERV1_MER52D chr1 6368926 6368992 - DNA/TcMar-Tigger_Tigger1 chr1 6368993 6369280 - SINE/Alu_AluSx chr1 6369282 6369394 - DNA/TcMar-Tigger_Tigger1 chr1 6369395 6369573 - SINE/Alu_AluSx3 chr1 6369574 6369859 - SINE/Alu_AluY chr1 6369861 6369958 - DNA/TcMar-Tigger_Tigger1 chr1 6369959 6370366 + LTR/ERVL-MaLR_MSTB1 chr1 6370367 6370457 - DNA/TcMar-Tigger_Tigger1 chr1 6370458 6370506 + Simple_repeat_(TAT)n chr1 6370507 6370792 - SINE/Alu_AluSx chr1 6370793 6370803 - DNA/TcMar-Tigger_Tigger1 chr1 6370804 6371108 - SINE/Alu_AluSg4 chr1 6371109 6371362 - DNA/TcMar-Tigger_Tigger1 chr1 6371363 6371661 - SINE/Alu_AluSz chr1 6371662 6371762 - DNA/TcMar-Tigger_Tigger1 chr1 6371763 6372065 + SINE/Alu_AluSz chr1 6372066 6372547 - DNA/TcMar-Tigger_Tigger1 chr1 6372548 6372849 - SINE/Alu_AluSz chr1 6372850 6373256 - DNA/TcMar-Tigger_Tigger1 chr1 6373257 6373550 - SINE/Alu_AluSx3 chr1 6373551 6373582 - DNA/TcMar-Tigger_Tigger1 chr1 6373583 6373882 + SINE/Alu_AluSq2 chr1 6373883 6373921 - DNA/TcMar-Tigger_Tigger1 chr1 6374618 6374717 + SINE/MIR_MIR3 chr1 6374938 6374995 + LINE/L1_L1MC chr1 6374996 6375283 + SINE/Alu_AluY chr1 6375284 6375385 + LINE/L1_L1MC chr1 6375386 6375546 + SINE/Alu_AluYh3a3 chr1 6375547 6375654 + SINE/Alu_AluJb chr1 6375655 6375953 + SINE/Alu_AluYj4 chr1 6375954 6375973 + SINE/Alu_AluJb chr1 6375974 6376283 + SINE/Alu_AluSq2 chr1 6376284 6376437 + SINE/Alu_AluJb chr1 6376447 6376746 + SINE/Alu_AluSp chr1 6376747 6376880 + SINE/Alu_AluYh3a3 chr1 6376881 6377201 + LINE/L1_L1MC chr1 6377254 6377388 + SINE/Alu_AluJo chr1 6377789 6378063 + SINE/Alu_AluY chr1 6378068 6378097 + Simple_repeat_(AAAC)n chr1 6378746 6378769 + Simple_repeat_(AGG)n chr1 6378900 6379193 - SINE/Alu_AluY chr1 6379367 6379464 + LINE/L1_L1MB8 chr1 6379465 6379753 - SINE/Alu_AluJr chr1 6379785 6380054 + SINE/Alu_AluSz chr1 6380057 6380108 + LINE/L1_L1MB8 chr1 6380113 6380317 + LINE/L1_L1MB3 chr1 6380318 6380610 + SINE/Alu_AluSg chr1 6380611 6381046 + LINE/L1_L1MB3 chr1 6381047 6381125 + LINE/L1_L1MA5 chr1 6381126 6381161 + LINE/L1_L1M chr1 6381203 6381305 + Simple_repeat_(AT)n chr1 6381343 6381851 + LINE/L1_L1MB3 chr1 6381854 6382130 + SINE/Alu_AluYk2 chr1 6382132 6382428 + SINE/Alu_AluSx4 chr1 6382531 6382825 + SINE/Alu_AluSz chr1 6382873 6383177 - SINE/Alu_AluSq2 chr1 6383190 6383486 - SINE/Alu_AluY chr1 6383540 6383708 - SINE/Alu_AluJo chr1 6383709 6384003 - SINE/Alu_AluY chr1 6384004 6384064 - SINE/Alu_AluJo chr1 6384099 6384507 + LINE/L1_L1MB8 chr1 6384508 6384625 - LTR/ERVL_MER21B chr1 6384637 6384836 - LTR/ERVL_MER21B chr1 6384838 6384950 + LINE/L1_L1MB8 chr1 6384973 6385059 + SINE/MIR_MIRc chr1 6386330 6386577 - LINE/L1_L1MC5 chr1 6386578 6386892 + SINE/Alu_AluJr chr1 6386893 6386940 - LINE/L1_L1MC5 chr1 6387106 6387332 + LINE/L1_L1ME1 chr1 6387279 6387434 - LINE/L1_L1MC5 chr1 6387928 6388107 - SINE/Alu_AluSq2 chr1 6388109 6388408 - SINE/Alu_AluY chr1 6388457 6388756 + SINE/Alu_AluSx1 chr1 6388757 6389065 + SINE/Alu_AluY chr1 6389128 6389238 - LINE/L2_L2a chr1 6389255 6389402 + DNA/TcMar-Tigger_Tigger19b chr1 6389494 6389573 - DNA/hAT-Charlie_MER3 chr1 6389575 6389803 + SINE/Alu_AluSx1 chr1 6389880 6389989 + LINE/L2_L2c chr1 6390289 6390589 + SINE/Alu_AluSx chr1 6390654 6390960 + SINE/Alu_AluSx1 chr1 6391014 6391208 + SINE/Alu_AluSx chr1 6391218 6391518 + SINE/Alu_AluSx chr1 6391653 6391866 + SINE/MIR_MIRc chr1 6392868 6392937 + Low_complexity_G-rich chr1 6393307 6393340 + Simple_repeat_(GGC)n chr1 6393473 6393525 + Simple_repeat_(CCGCC)n chr1 6393526 6393533 + Simple_repeat_(CGCGC)n chr1 6394457 6394553 + LINE/L2_X15_LINE chr1 6394582 6394892 - SINE/Alu_AluY chr1 6394893 6395050 - SINE/Alu_AluSx3 chr1 6395075 6396293 - LTR/ERV1_LTR12E chr1 6396295 6396407 - SINE/Alu_AluSx chr1 6396516 6396553 + SINE/MIR_MIRb chr1 6396554 6396869 - SINE/Alu_AluJb chr1 6396870 6396931 + SINE/MIR_MIRb chr1 6397113 6397272 + SINE/Alu_FAM chr1 6397755 6398157 + LINE/L2_L2a chr1 6398158 6398447 - SINE/Alu_AluJb chr1 6398448 6398464 + LINE/L2_L2a chr1 6398465 6398769 - SINE/Alu_AluSx1 chr1 6398770 6399550 + LINE/L2_L2a chr1 6399651 6399685 + Simple_repeat_(TATA)n chr1 6399686 6399986 + SINE/Alu_AluY chr1 6399987 6400005 + Simple_repeat_(TATA)n chr1 6400017 6400305 + SINE/Alu_AluSg chr1 6400306 6400528 - SINE/Alu_AluJb chr1 6400576 6400879 + SINE/Alu_AluY chr1 6400912 6401169 + SINE/Alu_AluSc8 chr1 6401234 6401472 + LINE/L1_L1ME3B chr1 6401505 6401813 - SINE/Alu_AluSc8 chr1 6402115 6402257 + SINE/Alu_AluJr chr1 6402258 6402559 + SINE/Alu_AluY chr1 6402560 6402736 + SINE/Alu_AluJr chr1 6403236 6403494 + SINE/Alu_AluJb chr1 6403518 6403816 - SINE/Alu_AluSp chr1 6403817 6404119 - SINE/Alu_AluJr chr1 6404426 6404707 + SINE/Alu_AluY chr1 6404708 6404758 + Simple_repeat_(AAAT)n chr1 6405483 6405737 + SINE/MIR_MIR chr1 6405770 6405822 - LINE/L2_L2b chr1 6405887 6406050 - LINE/L1_L1ME3A chr1 6406055 6406124 - LINE/L1_L1ME3A chr1 6406146 6406451 - SINE/Alu_AluY chr1 6406457 6406767 - SINE/Alu_AluSx1 chr1 6406771 6407042 - LINE/L1_L1MC chr1 6407043 6407297 - SINE/Alu_AluSz chr1 6407298 6407318 - LINE/L1_L1MC chr1 6407323 6407401 - SINE/Alu_AluJb chr1 6407432 6407738 - SINE/Alu_AluSp chr1 6407794 6408085 + SINE/Alu_AluSx3 chr1 6408086 6408239 - LINE/L1_L1M5 chr1 6408240 6408537 + SINE/Alu_AluSp chr1 6408538 6408579 - LINE/L1_L1M5 chr1 6408598 6408780 - SINE/Alu_AluSp chr1 6408806 6409130 - SINE/Alu_AluSq2 chr1 6409140 6409535 - LINE/L1_L1MD chr1 6409537 6409828 - SINE/Alu_AluJb chr1 6409907 6410005 + SINE/Alu_AluJr4 chr1 6410006 6410316 - SINE/Alu_AluY chr1 6410317 6410511 + SINE/Alu_AluJr4 chr1 6410523 6410584 + LINE/L1_L1MA4 chr1 6410663 6410967 - SINE/Alu_AluSz chr1 6410999 6411736 + LTR/ERVL_HERV16-int chr1 6411750 6411797 - LINE/L1_L1MB3 chr1 6411798 6412098 + SINE/Alu_AluSq2 chr1 6412099 6412113 - LINE/L1_L1MB3 chr1 6412114 6412407 - SINE/Alu_AluJr chr1 6412418 6412720 - SINE/Alu_AluY chr1 6412721 6412883 - LINE/L1_L1MB3 chr1 6412885 6412949 + LTR/ERVL_HERV16-int chr1 6413161 6413461 + DNA/hAT-Tip100_Arthur1C chr1 6413463 6413752 - SINE/Alu_AluSx1 chr1 6413775 6413915 - SINE/Alu_AluSz chr1 6413950 6413981 + Simple_repeat_(TGTTT)n chr1 6413982 6414260 - SINE/Alu_AluSx4 chr1 6414733 6414793 + Simple_repeat_(ACACAC)n chr1 6414928 6415215 + SINE/Alu_AluJr chr1 6415219 6415432 + DNA/TcMar-Tigger_Tigger1 chr1 6415777 6416077 - SINE/Alu_AluSx1 chr1 6417038 6417139 - LINE/L2_L2b chr1 6417585 6417602 + Simple_repeat_(AAAG)n chr1 6417603 6417883 - SINE/Alu_AluSx chr1 6418908 6418974 + Low_complexity_G-rich chr1 6419541 6419594 + Simple_repeat_(GCCCCCC)n chr1 6419733 6419774 + Simple_repeat_(GTCCCCA)n chr1 6420405 6420509 - SINE/MIR_MIR3 chr1 6421157 6421274 - SINE/MIR_MIRb chr1 6421991 6422119 + SINE/MIR_MIR chr1 6422120 6422429 + SINE/Alu_AluSq2 chr1 6422432 6422741 + SINE/Alu_AluY chr1 6422742 6422883 + SINE/MIR_MIR chr1 6424316 6424397 + LINE/L2_L2b chr1 6424672 6424738 + Low_complexity_G-rich chr1 6424777 6424816 + Simple_repeat_(GAGCG)n chr1 6425829 6426095 - LINE/L2_L2b chr1 6428000 6428075 - SINE/MIR_MIRb chr1 6429095 6429118 + Simple_repeat_(GGGA)n chr1 6430614 6430798 + SINE/Alu_AluSp chr1 6431327 6431657 + SINE/Alu_AluSx chr1 6431659 6431710 + Low_complexity_A-rich chr1 6431753 6431812 - SINE/MIR_MIRb chr1 6431814 6431922 - LINE/L2_L2a chr1 6431952 6432027 + SINE/MIR_MIR3 chr1 6432842 6433149 + SINE/Alu_AluSg chr1 6433163 6433456 + SINE/Alu_AluSg chr1 6433458 6433626 + SINE/Alu_AluSx3 chr1 6433700 6433903 + LINE/L2_L2a chr1 6433915 6434202 - SINE/Alu_AluSx chr1 6434390 6434463 + LINE/L2_L2a chr1 6434463 6434541 + SINE/MIR_MIR3 chr1 6434743 6435010 - LINE/L2_L2a chr1 6435819 6436033 - SINE/MIR_MIRb chr1 6436172 6436423 + SINE/MIR_MIRb chr1 6436424 6436452 + Simple_repeat_(ATT)n chr1 6436483 6436520 + Simple_repeat_(TTAT)n chr1 6436521 6436789 - SINE/Alu_AluJo chr1 6437113 6437149 - SINE/MIR_MIR chr1 6438311 6438452 - SINE/MIR_MIRb chr1 6438607 6438678 + SINE/MIR_MIRb chr1 6438830 6439135 + SINE/Alu_AluSz chr1 6439203 6439395 + DNA/hAT_MER53 chr1 6439401 6439607 - LINE/L2_L2b chr1 6439735 6440018 + SINE/Alu_AluSx3 chr1 6440450 6440569 + Low_complexity_G-rich chr1 6440584 6440623 + Simple_repeat_(GCCCCCC)n chr1 6441091 6441207 - SINE/MIR_MIRb chr1 6441588 6441674 - SINE/MIR_MIRc chr1 6441874 6442077 + LINE/CR1_L3 chr1 6442195 6442272 - SINE/MIR_MIRb chr1 6442289 6442587 + SINE/Alu_AluSc chr1 6442588 6442888 + SINE/Alu_AluSc chr1 6442897 6443188 + SINE/Alu_AluSq2 chr1 6443221 6443362 - SINE/MIR_MIR chr1 6444306 6444422 - SINE/MIR_MIR3 chr1 6445036 6445120 + Simple_repeat_(CA)n chr1 6448958 6449035 + Simple_repeat_(GCC)n chr1 6449323 6449387 + SINE/MIR_MIR3 chr1 6452071 6452094 + Simple_repeat_(AGG)n chr1 6452150 6452180 + Simple_repeat_(CCCAAC)n chr1 6452203 6452453 - SINE/Alu_AluYc chr1 6452550 6452712 - DNA/hAT-Charlie_MER5A1 chr1 6452714 6452845 + SINE/MIR_MIRb chr1 6452888 6453193 - SINE/Alu_AluSx1 chr1 6453376 6453607 - LINE/L2_L2c chr1 6454982 6455021 + Simple_repeat_(GC)n chr1 6455463 6455496 + Simple_repeat_(CGGCAG)n chr1 6456001 6456044 + Simple_repeat_(CGC)n chr1 6456578 6456623 + Simple_repeat_(AGTGTGC)n chr1 6457182 6457199 + Simple_repeat_(AGG)n chr1 6457309 6457373 - LINE/CR1_L3 chr1 6457680 6457889 - DNA/hAT-Charlie_MER33 chr1 6457890 6458029 + SINE/Alu_AluJb chr1 6458030 6458316 - SINE/Alu_AluSz chr1 6458317 6458615 - SINE/Alu_AluSq chr1 6458616 6458648 + SINE/Alu_AluJb chr1 6458649 6458947 + SINE/Alu_AluSx chr1 6458948 6458958 + SINE/Alu_AluJb chr1 6458959 6459258 + SINE/Alu_AluSx chr1 6459259 6459419 + SINE/Alu_AluJb chr1 6459420 6459541 - DNA/hAT-Charlie_MER33 chr1 6460349 6460460 - Unknown_UCON88 chr1 6462334 6462388 + Simple_repeat_(CTCCTGC)n chr1 6463328 6463444 + LINE/L2_L2a chr1 6463445 6463755 + SINE/Alu_AluSc8 chr1 6463971 6464056 - SINE/Alu_AluJb chr1 6466068 6466102 + Simple_repeat_(CCG)n chr1 6466398 6466503 + SINE/MIR_MIR3 chr1 6466516 6466812 + SINE/Alu_AluSz chr1 6468702 6468890 - DNA/hAT-Charlie_MER117 chr1 6469123 6469175 + Simple_repeat_(TCC)n chr1 6470550 6470607 + Simple_repeat_(CGCTGC)n chr1 6470919 6470985 + Simple_repeat_(GCCCC)n chr1 6471128 6471358 - SINE/MIR_MIRb chr1 6471686 6471744 + Simple_repeat_(CCCCGC)n chr1 6472076 6472210 + LINE/L2_L2b chr1 6473041 6473073 + Simple_repeat_(TCCTCA)n chr1 6473655 6473744 + LINE/CR1_L3 chr1 6474257 6474307 + Simple_repeat_(CCTGC)n chr1 6474647 6474687 + Simple_repeat_(GCCCCA)n chr1 6475951 6475969 + Simple_repeat_(CTC)n chr1 6476189 6476492 - SINE/Alu_AluSz chr1 6476579 6476654 + SINE/MIR_MIR chr1 6476749 6476937 - SINE/Alu_AluJb chr1 6477360 6477472 - Unknown_MamRep564 chr1 6477784 6477874 + LINE/L2_L2a chr1 6477875 6478182 - SINE/Alu_AluSx3 chr1 6478190 6478468 - SINE/Alu_AluJb chr1 6478469 6478602 + LINE/L2_L2a chr1 6478630 6478941 - SINE/Alu_AluSx chr1 6478944 6479275 - LINE/L1_L1MC4 chr1 6479276 6479585 - SINE/Alu_AluY chr1 6479586 6479615 - LINE/L1_L1MC4 chr1 6479616 6479909 - SINE/Alu_AluJo chr1 6479910 6480041 - LINE/L1_L1MC4 chr1 6480086 6480170 - LINE/L1_L1MC4 chr1 6480171 6480247 + SINE/Alu_AluSz6 chr1 6480248 6480541 + SINE/Alu_AluSx chr1 6480542 6480576 + SINE/Alu_AluSz6 chr1 6480579 6480646 - LINE/L1_L1MC4 chr1 6480651 6480942 - SINE/Alu_AluSz chr1 6480950 6481249 + LINE/L2_L2a chr1 6481250 6481524 + SINE/Alu_AluSp chr1 6481525 6481579 + Simple_repeat_(AAAT)n chr1 6481582 6481883 + SINE/Alu_AluSc8 chr1 6481884 6482169 + LINE/L2_L2a chr1 6482506 6482662 - SINE/MIR_MIRb chr1 6482699 6483006 - SINE/Alu_AluSx chr1 6483466 6483681 + SINE/Alu_AluJr chr1 6484695 6484741 + Simple_repeat_(CCTG)n chr1 6485289 6485320 + Simple_repeat_(CCCCG)n chr1 6486704 6486773 - SINE/MIR_MIRc chr1 6487108 6487416 - SINE/Alu_AluSx1 chr1 6487490 6487914 + LINE/L2_L2a chr1 6488149 6488320 - SINE/MIR_MIR chr1 6488777 6488806 + Simple_repeat_(AGCCCC)n chr1 6489662 6489795 + SINE/MIR_MIR3 chr1 6491749 6492004 - LINE/CR1_L3 chr1 6492017 6492217 + SINE/MIR_MIRb chr1 6492605 6492774 - SINE/MIR_MIRb chr1 6493266 6493377 + LINE/L2_L2a chr1 6493419 6493653 + SINE/MIR_MIRb chr1 6493668 6493959 - SINE/Alu_AluSz chr1 6493962 6494118 - SINE/Alu_AluJb chr1 6494119 6494430 - SINE/Alu_AluSq2 chr1 6494431 6494563 - SINE/Alu_AluJb chr1 6494654 6494802 + LINE/L2_L2d chr1 6495388 6495440 + Low_complexity_G-rich chr1 6495477 6495647 + SINE/MIR_MIR chr1 6495708 6495929 + SINE/MIR_MIRb chr1 6496143 6496255 + SINE/MIR_MIRc chr1 6497843 6498089 - LINE/L1_L1ME3A chr1 6498465 6498574 - LINE/L2_L2b chr1 6499387 6499475 + Simple_repeat_(CTCCTGG)n chr1 6499808 6500092 - SINE/Alu_AluJr chr1 6500953 6501273 + DNA/hAT-Charlie_Charlie8 chr1 6501291 6501467 + LINE/L2_L2d2 chr1 6501522 6501747 - SINE/MIR_MIR chr1 6503131 6503405 + SINE/Alu_AluJo chr1 6503406 6503709 - SINE/Alu_AluSq2 chr1 6503710 6503737 + SINE/Alu_AluJo chr1 6504559 6504868 - SINE/Alu_AluY chr1 6505570 6505616 + SINE/MIR_MIR chr1 6506798 6506832 + Simple_repeat_(CTGCGCT)n chr1 6507070 6507205 + Unknown_MamRep564 chr1 6507223 6507329 - SINE/MIR_MIRb chr1 6508735 6508949 - SINE/MIR_MIRb chr1 6509187 6509226 + Low_complexity_G-rich chr1 6509697 6509873 + SINE/MIR_MIRb chr1 6510311 6510604 + SINE/Alu_AluSg7 chr1 6510805 6511100 + SINE/Alu_AluSx1 chr1 6511360 6511501 + SINE/MIR_MIRb chr1 6512029 6512177 - SINE/MIR_MIRb chr1 6512757 6512920 + SINE/MIR_MIR chr1 6513171 6513210 + Low_complexity_G-rich chr1 6514041 6514177 + SINE/Alu_AluJo chr1 6514178 6514479 + SINE/Alu_AluJb chr1 6514481 6514783 + SINE/Alu_AluSg chr1 6514786 6515079 + SINE/Alu_AluSx chr1 6515172 6515242 + LINE/L1_L1MEg chr1 6515246 6515537 + SINE/Alu_AluSc8 chr1 6515545 6515676 + SINE/Alu_AluJr4 chr1 6515956 6516147 + LINE/L1_L1ME4b chr1 6516149 6516272 + SINE/Alu_FLAM_A chr1 6516273 6516375 + LINE/L1_L1ME4b chr1 6516376 6516677 + SINE/Alu_AluSx1 chr1 6516678 6516699 + LINE/L1_L1ME4b chr1 6516719 6516906 + Simple_repeat_(AT)n chr1 6516906 6516926 + Simple_repeat_(AC)n chr1 6516966 6517325 + SINE/Alu_AluSx1 chr1 6517944 6518231 - SINE/Alu_AluY chr1 6518279 6518588 + SINE/Alu_AluSp chr1 6518701 6518773 - SINE/MIR_MIR3 chr1 6518847 6519140 - SINE/Alu_AluY chr1 6520308 6520398 - DNA/hAT-Ac_OldhAT1 chr1 6520517 6520639 - SINE/Alu_FLAM_C chr1 6520859 6520879 + Simple_repeat_(AAC)n chr1 6520880 6521189 + SINE/Alu_AluSx chr1 6521190 6521203 + Simple_repeat_(AAC)n chr1 6521808 6522027 - SINE/MIR_MIRb chr1 6522064 6522237 + SINE/Alu_AluSq4 chr1 6522270 6522575 + SINE/Alu_AluSx1 chr1 6522604 6522905 + SINE/Alu_AluSg chr1 6522912 6523186 + SINE/Alu_AluSx4 chr1 6523299 6523603 + SINE/Alu_AluSq chr1 6524739 6525038 - SINE/Alu_AluYk2 chr1 6525145 6525301 - SINE/Alu_FAM chr1 6526964 6527268 + SINE/Alu_AluSx1 chr1 6527367 6527648 + SINE/Alu_AluSg chr1 6527654 6527953 + SINE/Alu_AluSg chr1 6528302 6528819 - LINE/L2_L2 chr1 6529295 6529309 + Simple_repeat_(TAAAAAA)n chr1 6529310 6529637 + SINE/Alu_AluSg chr1 6529644 6529955 + SINE/Alu_AluSz chr1 6529957 6530146 + SINE/Alu_AluSx3 chr1 6530172 6530477 + SINE/Alu_AluSq2 chr1 6530668 6530840 + SINE/MIR_MIR chr1 6530861 6531069 + SINE/MIR_MIR chr1 6531214 6531511 - SINE/Alu_AluSc8 chr1 6531697 6531915 + SINE/MIR_MIR chr1 6532891 6533195 + SINE/Alu_AluSz6 chr1 6533682 6533803 - LINE/L2_L2a chr1 6533844 6533881 - SINE/MIR_MIR chr1 6533882 6534186 - SINE/Alu_AluSp chr1 6534187 6534349 - SINE/MIR_MIR chr1 6534626 6534791 + LINE/L1_L1MC4a chr1 6534792 6535107 - SINE/Alu_AluSz chr1 6535108 6535431 + LINE/L1_L1MC4a chr1 6535646 6535943 + SINE/Alu_AluSq2 chr1 6536000 6536064 + SINE/Alu_AluJr4 chr1 6536065 6536345 + SINE/Alu_AluY chr1 6536346 6536381 + Simple_repeat_(AAATA)n chr1 6536382 6536545 + SINE/Alu_AluJr4 chr1 6536546 6536830 + SINE/Alu_AluJb chr1 6536872 6537019 + SINE/Alu_FRAM chr1 6537023 6537840 + LINE/L1_L1MB4 chr1 6537841 6537959 + SINE/Alu_AluY chr1 6537960 6538011 + SINE/Alu_AluY chr1 6538065 6538390 + LINE/L1_L1MB4 chr1 6538391 6538690 + SINE/Alu_AluSx1 chr1 6538701 6538993 + SINE/Alu_AluSx chr1 6538994 6539479 + LINE/L1_L1MB4 chr1 6539480 6539783 - SINE/Alu_AluSz chr1 6539784 6539969 + LINE/L1_L1MB4 chr1 6539993 6540118 - SINE/Alu_AluSq chr1 6540119 6540421 - SINE/Alu_AluY chr1 6540422 6540523 - SINE/Alu_AluSq chr1 6540545 6540841 + SINE/Alu_AluSx chr1 6540854 6540934 + snRNA_U6 chr1 6541012 6541182 - SINE/Alu_AluSx3 chr1 6541183 6541473 - SINE/Alu_AluSx chr1 6541476 6541554 + LINE/L1_L1MD3 chr1 6542164 6542461 - SINE/Alu_AluYm1 chr1 6542466 6542755 - SINE/Alu_AluSc8 chr1 6542756 6542879 - SINE/Alu_AluSz chr1 6542904 6543193 - SINE/Alu_AluJr chr1 6543194 6543492 - SINE/Alu_AluY chr1 6543495 6543693 - SINE/Alu_AluJb chr1 6543699 6543843 + SINE/Alu_AluJr chr1 6543844 6544133 + SINE/Alu_AluSx chr1 6544134 6544295 + SINE/Alu_AluJr chr1 6544309 6544513 + SINE/Alu_AluJo chr1 6544514 6544544 + Simple_repeat_(CACA)n chr1 6544544 6544565 + Simple_repeat_(C)n chr1 6545282 6545359 + LINE/L2_L2 chr1 6545499 6545605 + LINE/L1_HAL1ME chr1 6545768 6545948 + SINE/Alu_AluSz6 chr1 6545950 6546385 + LINE/L1_HAL1ME chr1 6546409 6547472 + LINE/L2_L2 chr1 6547579 6547866 + SINE/Alu_AluJb chr1 6548139 6548446 - SINE/Alu_AluSz chr1 6548782 6548799 + SINE/Alu_AluSg4 chr1 6548800 6549107 + SINE/Alu_AluSx chr1 6549108 6549263 + SINE/Alu_AluSg4 chr1 6549266 6549291 + Simple_repeat_(AACC)n chr1 6549873 6550032 + SINE/MIR_MIR chr1 6550038 6550330 - SINE/Alu_AluY chr1 6550682 6550982 - SINE/Alu_AluSp chr1 6551030 6551281 + SINE/Alu_AluJb chr1 6551298 6551617 + SINE/Alu_AluSz6 chr1 6551646 6551749 + LINE/L1_L1PA11 chr1 6551750 6552058 + SINE/Alu_AluY chr1 6552059 6552285 + LINE/L1_L1PA11 chr1 6552286 6552343 - SINE/Alu_FLAM_C chr1 6552344 6552476 - LINE/L1_L1PA10 chr1 6552480 6552582 - SINE/Alu_AluSp chr1 6552588 6552768 - SINE/Alu_AluSx chr1 6552810 6553111 - SINE/Alu_AluSx chr1 6553327 6553519 + SINE/MIR_MIR chr1 6553562 6553870 + SINE/Alu_AluSc8 chr1 6554193 6554272 + Low_complexity_G-rich chr1 6555862 6556170 - SINE/Alu_AluYk2 chr1 6556374 6556385 - SINE/Alu_AluSx chr1 6556386 6556410 + Simple_repeat_(TCTA)n chr1 6556411 6556701 - SINE/Alu_AluSx chr1 6556730 6557031 + SINE/Alu_AluSg chr1 6557223 6557363 + LINE/CR1_L3 chr1 6557421 6557729 - SINE/Alu_AluJr chr1 6557746 6557867 - SINE/Alu_FLAM_A chr1 6558037 6558328 - SINE/Alu_AluJo chr1 6558329 6558418 + LTR/ERVL_MLT2D chr1 6558419 6558437 - SINE/Alu_AluSz chr1 6558438 6558726 + SINE/Alu_AluSx1 chr1 6558727 6558885 - SINE/Alu_AluSz chr1 6558886 6559192 - SINE/Alu_AluY chr1 6559193 6559329 - SINE/Alu_AluSz chr1 6559330 6559374 + LTR/ERVL_MLT2D chr1 6559377 6559673 + SINE/Alu_AluSx1 chr1 6559686 6560017 + SINE/Alu_AluSq2 chr1 6560018 6560296 + SINE/Alu_AluSp chr1 6560297 6560439 - LTR/ERV1_LTR21B chr1 6560626 6560700 - LTR/ERV1_HERVFH21-int chr1 6560701 6561007 + SINE/Alu_AluY chr1 6561008 6561429 - LTR/ERV1_HERVFH21-int chr1 6561430 6561729 + SINE/Alu_AluYh7 chr1 6561730 6561831 - LTR/ERV1_HERVFH21-int chr1 6561832 6562169 - LTR/ERVK_LTR14B chr1 6562170 6562467 - SINE/Alu_AluY chr1 6562468 6562581 - LTR/ERVK_LTR14B chr1 6562582 6563794 - LTR/ERV1_HERVFH21-int chr1 6564127 6565087 - LTR/ERV1_HERVFH21-int chr1 6565082 6565604 - LTR/ERV1_HERVFH21-int chr1 6565605 6565720 - SINE/Alu_AluSx3 chr1 6565721 6566609 - LTR/ERV1_HERVFH21-int chr1 6566610 6566893 - SINE/Alu_AluYk4 chr1 6566894 6567279 - LTR/ERV1_HERVFH21-int chr1 6567280 6567579 + SINE/Alu_AluY chr1 6567580 6568168 - LTR/ERV1_HERVFH21-int chr1 6568169 6568482 + SINE/Alu_AluYk2 chr1 6568483 6570024 - LTR/ERV1_HERVFH21-int chr1 6570025 6570051 + Simple_repeat_(GA)n chr1 6570052 6570109 - LTR/ERV1_HERVFH21-int chr1 6570110 6570283 - LTR/ERV1_LTR21B chr1 6570452 6570671 + LTR/ERVL_MLT2D chr1 6570787 6570846 + LINE/CR1_L3 chr1 6571548 6571819 + SINE/Alu_AluSz chr1 6572266 6572556 - SINE/Alu_AluJo chr1 6573001 6573167 + LINE/L2_L2b chr1 6573179 6573473 + SINE/Alu_AluSg chr1 6573534 6573640 - DNA/hAT-Charlie_MER1A chr1 6573641 6573652 - SINE/Alu_AluYg6 chr1 6573653 6573679 + Simple_repeat_(TTTATT)n chr1 6573680 6573962 - SINE/Alu_AluYg6 chr1 6573963 6574290 - DNA/hAT-Charlie_MER1A chr1 6574296 6574546 + LINE/L2_L2b chr1 6574548 6574615 + LINE/L2_L2b chr1 6575511 6575685 - SINE/Alu_AluSx1 chr1 6575686 6575993 - SINE/Alu_AluY chr1 6575994 6576115 - SINE/Alu_AluSx1 chr1 6577254 6577539 + SINE/Alu_AluSz chr1 6577543 6577852 + SINE/Alu_AluSq2 chr1 6578764 6578827 + Simple_repeat_(TGC)n chr1 6581376 6581679 + SINE/Alu_AluJb chr1 6581962 6581993 + Low_complexity_G-rich chr1 6582838 6582969 - SINE/Alu_AluJo chr1 6582981 6583288 - SINE/Alu_AluSc8 chr1 6583293 6583598 - SINE/Alu_AluSg4 chr1 6583601 6583767 - SINE/Alu_AluSx4 chr1 6583768 6584081 - SINE/Alu_AluSx3 chr1 6584082 6584213 - SINE/Alu_AluSx4 chr1 6584220 6584382 - DNA/hAT-Charlie_MER3 chr1 6584890 6585567 - LINE/L2_L2a chr1 6588207 6588234 + Simple_repeat_(CCCTC)n chr1 6589901 6590073 + SINE/Alu_AluSz6 chr1 6590422 6590539 - SINE/Alu_AluJo chr1 6593897 6594016 - SINE/MIR_MIRc chr1 6593985 6594034 - SINE/MIR_MIR3 chr1 6594421 6594717 + SINE/Alu_AluY chr1 6594748 6595069 + SINE/Alu_AluJr chr1 6595083 6595244 + LINE/L2_L2a chr1 6595278 6595357 + SINE/MIR_MIR chr1 6596118 6596458 + SINE/Alu_AluSx chr1 6596498 6596662 - DNA/hAT-Charlie_Cheshire chr1 6596666 6596690 + Simple_repeat_(TGA)n chr1 6598150 6598285 + SINE/Alu_AluJr4 chr1 6598289 6598598 + SINE/Alu_AluSp chr1 6598601 6598911 + SINE/Alu_AluY chr1 6598926 6598971 + SINE/MIR_MIRc chr1 6599949 6600046 + LINE/L1_L1ME3A chr1 6600047 6600342 - SINE/Alu_AluSz chr1 6600343 6600377 + LINE/L1_L1ME3A chr1 6602163 6602214 + Simple_repeat_(GCGGC)n chr1 6604741 6604960 + SINE/Alu_AluSx chr1 6604961 6605166 + SINE/Alu_AluJr chr1 6605459 6605548 - SINE/Alu_AluSc chr1 6605551 6605663 + SINE/Alu_AluSx chr1 6605850 6606142 - SINE/Alu_AluSx chr1 6606151 6606445 - SINE/Alu_AluJb chr1 6606474 6606683 + SINE/Alu_AluSx chr1 6606847 6607134 - SINE/Alu_AluSx chr1 6607256 6607549 - SINE/Alu_AluSx1 chr1 6607568 6607862 - SINE/Alu_AluSx4 chr1 6608137 6608272 + SINE/Alu_AluJb chr1 6608273 6608552 + SINE/Alu_AluY chr1 6608553 6608708 + SINE/Alu_AluJb chr1 6608709 6608845 + SINE/Alu_AluSx chr1 6608846 6609156 + SINE/Alu_AluSc chr1 6609157 6609353 + SINE/Alu_AluSx chr1 6609874 6610143 - SINE/Alu_AluSc chr1 6610410 6610708 + SINE/Alu_AluSp chr1 6610900 6611090 + SINE/Alu_AluJo chr1 6611094 6611183 - SINE/Alu_AluSx chr1 6611188 6611289 - SINE/Alu_AluSx chr1 6611431 6611723 + SINE/Alu_AluSc chr1 6612051 6612080 + Simple_repeat_(AAC)n chr1 6612118 6612152 - SINE/MIR_MIRc chr1 6612154 6612450 - SINE/Alu_AluSg chr1 6613790 6613845 + Simple_repeat_(CCG)n chr1 6613962 6614061 + Simple_repeat_(CCGCCC)n chr1 6614872 6614923 + Low_complexity_G-rich chr1 6616026 6616325 - SINE/Alu_AluSg chr1 6617108 6617377 - SINE/Alu_AluSx chr1 6617437 6617727 - SINE/Alu_AluSx3 chr1 6617802 6617963 - SINE/MIR_MIR chr1 6618142 6618432 - SINE/Alu_AluSx1 chr1 6618434 6618555 - SINE/Alu_AluJo chr1 6618634 6618946 - SINE/Alu_AluSx1 chr1 6619353 6619638 - SINE/Alu_AluSx chr1 6619992 6620030 + Low_complexity_GA-rich chr1 6620605 6620737 + SINE/Alu_AluJo chr1 6620738 6621049 + SINE/Alu_AluSx3 chr1 6621050 6621215 + SINE/Alu_AluJo chr1 6624265 6624572 - SINE/Alu_AluSq2 chr1 6625131 6625182 + Simple_repeat_(GCCCC)n chr1 6625797 6625907 + LINE/L2_L2a chr1 6625914 6626026 - LINE/L1_L1ME3A chr1 6626027 6626323 + SINE/Alu_AluJo chr1 6626324 6626426 - LINE/L1_L1ME3A chr1 6626425 6626555 - LINE/L1_L1ME3A chr1 6626556 6626865 + SINE/Alu_AluSx chr1 6626866 6626956 - LINE/L1_L1ME3A chr1 6627030 6627258 + LINE/L2_L2a chr1 6627373 6627663 - SINE/Alu_AluSx chr1 6627980 6628257 + LINE/L2_L2a chr1 6628920 6629157 + SINE/Alu_AluSx1 chr1 6629394 6629730 + LINE/L2_L2b chr1 6630574 6630869 - SINE/Alu_AluSx1 chr1 6630878 6631008 - SINE/Alu_FLAM_C chr1 6631009 6631316 - SINE/Alu_AluJr chr1 6631346 6631554 + SINE/Alu_AluSz chr1 6631555 6631622 + SINE/Alu_AluSx chr1 6631623 6631900 + SINE/Alu_AluSx1 chr1 6631901 6632065 + SINE/Alu_AluSx3 chr1 6632067 6632227 + SINE/Alu_AluSx chr1 6633627 6633926 + SINE/Alu_AluSz chr1 6636609 6636700 + LINE/L2_L2b chr1 6636851 6637149 - SINE/Alu_AluJb chr1 6637817 6637991 + DNA/hAT-Charlie_MER102a chr1 6638691 6638811 - SINE/Alu_AluJo chr1 6639022 6639276 + LINE/RTE-X_L4_C_Mam chr1 6639329 6639631 - SINE/Alu_AluSx chr1 6639725 6639885 - SINE/MIR_MIR chr1 6640064 6640089 + Simple_repeat_(A)n chr1 6640472 6640546 + LINE/L1_L1MEb chr1 6640550 6640799 + SINE/Alu_AluSx chr1 6640800 6641016 + LINE/L1_L1MEf chr1 6641104 6641391 + SINE/Alu_AluSq2 chr1 6641407 6641433 + Simple_repeat_(AC)n chr1 6641538 6641658 - SINE/Alu_AluJb chr1 6641899 6642557 - LINE/L2_L2a chr1 6642558 6642686 + SINE/Alu_AluJr chr1 6642691 6643277 - LINE/L2_L2a chr1 6643278 6643574 - SINE/Alu_AluY chr1 6643575 6643601 - LINE/L2_L2a chr1 6643879 6644114 - SINE/Alu_AluY chr1 6644129 6644433 - SINE/Alu_AluSx chr1 6644833 6644929 + LINE/L2_L2c chr1 6645282 6645423 + DNA/hAT-Charlie_Charlie8 chr1 6646484 6646587 + DNA/hAT-Charlie_MER5B chr1 6646886 6647070 + SINE/Alu_AluJo chr1 6647079 6647382 - SINE/Alu_AluSq chr1 6647390 6647452 + DNA/TcMar-Tigger_Tigger5b chr1 6647538 6647843 + SINE/Alu_AluSc chr1 6647873 6648045 + LTR?_MamRep605b chr1 6648064 6648140 - DNA/hAT-Charlie_MER5A chr1 6648157 6648455 - SINE/Alu_AluSx1 chr1 6648460 6648755 - SINE/Alu_AluJb chr1 6649004 6649168 - SINE/Alu_AluJb chr1 6649186 6649468 - SINE/Alu_AluYk2 chr1 6649713 6649934 - SINE/Alu_AluSx chr1 6650004 6650272 + SINE/Alu_AluSx chr1 6650295 6650589 + SINE/Alu_AluJo chr1 6650592 6650900 + SINE/Alu_AluSx chr1 6652431 6652653 - SINE/Alu_AluSq2 chr1 6654777 6655095 - SINE/Alu_AluSc chr1 6655159 6655526 + DNA/hAT-Blackjack_BLACKJACK chr1 6655541 6655819 + SINE/Alu_AluSx chr1 6655822 6656122 + SINE/Alu_AluSg chr1 6656123 6656597 + DNA/hAT-Blackjack_BLACKJACK chr1 6656598 6656924 + SINE/Alu_AluJb chr1 6656925 6657027 + DNA/hAT-Blackjack_BLACKJACK chr1 6657028 6657588 + LTR/ERVL-MaLR_MLT1H1 chr1 6657656 6657944 - SINE/Alu_AluY chr1 6657982 6658151 - DNA/TcMar-Tigger_Tigger1 chr1 6658159 6658461 + LINE/L1_HAL1 chr1 6658491 6658649 + LINE/L1_HAL1 chr1 6659112 6659214 - LINE/CR1_L3 chr1 6659247 6659477 + LINE/L2_L2a chr1 6659478 6659769 + SINE/Alu_AluSq chr1 6659776 6660089 + SINE/Alu_AluY chr1 6660090 6660161 + LINE/L2_L2a chr1 6660162 6660455 - SINE/Alu_AluSx chr1 6660456 6660628 + LINE/L2_L2a chr1 6660725 6660970 - LINE/CR1_L3 chr1 6661026 6661132 - LINE/L1_L1MA9 chr1 6661945 6662118 - SINE/Alu_AluJb chr1 6662119 6662149 + Simple_repeat_(TTGTTTT)n chr1 6662150 6662180 - SINE/Alu_AluJb chr1 6662190 6662463 - SINE/Alu_AluSq chr1 6662464 6663020 + LTR/ERV1_LTR12 chr1 6663022 6663057 - SINE/Alu_Alu chr1 6663864 6663995 - SINE/MIR_MIRc chr1 6664003 6664036 + Simple_repeat_(TG)n chr1 6664233 6664537 - SINE/Alu_AluY chr1 6664657 6664796 - SINE/MIR_MIRc chr1 6665073 6665364 - SINE/Alu_AluJr4 chr1 6665847 6665881 + Simple_repeat_(CACCTC)n chr1 6666375 6666701 - SINE/Alu_AluSx chr1 6667733 6667755 + Simple_repeat_(TC)n chr1 6668133 6668437 - SINE/Alu_AluYm1 chr1 6668610 6668731 - SINE/Alu_AluJr chr1 6668905 6669215 + SINE/Alu_AluSx1 chr1 6669228 6669512 + SINE/Alu_AluSq2 chr1 6669514 6669577 + Simple_repeat_(AGAAA)n chr1 6669957 6670207 - SINE/Alu_AluY chr1 6671195 6671230 + Simple_repeat_(ATTT)n chr1 6671250 6671535 - SINE/Alu_AluY chr1 6671537 6671818 - SINE/Alu_AluY chr1 6671823 6672134 - SINE/Alu_AluSz chr1 6672953 6673225 + SINE/Alu_AluSx1 chr1 6673226 6673302 + SINE/Alu_FLAM_C chr1 6673395 6673500 + SINE/MIR_MIR1_Amn chr1 6673502 6673831 - LINE/L1_L1MD3 chr1 6674279 6674579 + SINE/Alu_AluSx chr1 6674580 6674739 + SINE/Alu_AluJb chr1 6675410 6675722 - SINE/Alu_AluJb chr1 6676132 6676387 - DNA/hAT-Charlie_MER102c chr1 6676398 6676666 + SINE/Alu_AluJr chr1 6676667 6676702 + Simple_repeat_(AC)n chr1 6676892 6677183 + SINE/Alu_AluSp chr1 6677293 6677594 - SINE/Alu_AluJr chr1 6677656 6677804 - SINE/MIR_MIR3 chr1 6678116 6678243 - SINE/MIR_MIR3 chr1 6678673 6678947 - SINE/Alu_AluJr chr1 6680206 6680354 - LINE/L1_L1MB4 chr1 6682164 6682183 + Simple_repeat_(A)n chr1 6682646 6682915 + SINE/Alu_AluJb chr1 6683264 6683490 + SINE/MIR_MIRb chr1 6683469 6683632 + SINE/MIR_MIRb chr1 6683747 6683826 + LINE/L1_L1ME3A chr1 6684079 6684381 - SINE/Alu_AluSx chr1 6684847 6684985 + LINE/L1_L1M4 chr1 6684986 6685165 + SINE/Alu_AluJr chr1 6685235 6685408 + DNA/hAT-Charlie_Charlie1 chr1 6685409 6685826 + LTR/ERV1_MER101 chr1 6685827 6686113 + DNA/hAT-Charlie_Charlie1 chr1 6686106 6686219 + DNA/hAT-Charlie_Charlie1b chr1 6686322 6686374 + DNA/hAT-Charlie_Charlie1b chr1 6686375 6686736 - LTR/ERVL-MaLR_MLT1I chr1 6687355 6687656 - SINE/Alu_AluY chr1 6687960 6688004 + Low_complexity_A-rich chr1 6688874 6688998 - LINE/L2_L2b chr1 6688999 6689065 - LINE/L2_L2a chr1 6689041 6689414 - LINE/L2_L2a chr1 6689516 6689789 + SINE/Alu_AluSp chr1 6689920 6690071 - SINE/MIR_MIRb chr1 6690290 6690522 - SINE/MIR_MIRb chr1 6690645 6690846 + SINE/Alu_AluYk3 chr1 6691068 6691251 + LINE/CR1_X7C_LINE chr1 6691494 6691700 - LINE/CR1_CR1_Mam chr1 6692610 6692910 - SINE/Alu_AluSp chr1 6692913 6693213 - SINE/Alu_AluSx chr1 6693335 6693399 + Simple_repeat_(ATTTTT)n chr1 6693400 6693681 - SINE/Alu_AluSg chr1 6693682 6693703 + Simple_repeat_(ATTTTT)n chr1 6693718 6693818 - SINE/Alu_AluJb chr1 6693819 6693834 + LTR/ERV1_LTR36 chr1 6693835 6694136 - SINE/Alu_AluSx1 chr1 6694137 6694304 + LTR/ERV1_LTR36 chr1 6694340 6694611 + LTR/ERV1_LTR36 chr1 6694684 6694996 + SINE/Alu_AluY chr1 6694999 6695162 + SINE/Alu_AluYc chr1 6695163 6695317 - SINE/Alu_FLAM_C chr1 6695351 6695509 - SINE/MIR_MIR chr1 6695510 6695824 + SINE/Alu_AluSx3 chr1 6695825 6695919 - SINE/MIR_MIR chr1 6695957 6696077 - SINE/MIR_MIRb chr1 6697325 6697691 - DNA/hAT-Charlie_MER1B chr1 6697810 6698088 - SINE/Alu_AluYm1 chr1 6698660 6698923 + SINE/Alu_AluJo chr1 6698932 6698989 + Simple_repeat_(TA)n chr1 6699024 6699313 + SINE/Alu_AluJb chr1 6699644 6699765 + LINE/L1_L1PA8 chr1 6699931 6700411 + LTR/ERV1_LTR7B chr1 6700472 6700707 + SINE/MIR_MIR chr1 6702134 6702431 - SINE/Alu_AluSx chr1 6703462 6703768 - SINE/Alu_AluSx4 chr1 6703838 6704155 - SINE/Alu_AluY chr1 6704501 6704552 - LINE/CR1_L3 chr1 6704662 6704972 - SINE/Alu_AluJr chr1 6705173 6705474 - SINE/Alu_AluSz6 chr1 6705477 6705771 - SINE/Alu_AluSx1 chr1 6705942 6706019 - DNA/hAT_MER53 chr1 6706026 6706324 + SINE/Alu_AluJb chr1 6706326 6706344 + Simple_repeat_(AT)n chr1 6706340 6706371 + Simple_repeat_(AC)n chr1 6706345 6706372 + Simple_repeat_(ATACAC)n chr1 6706424 6706599 - SINE/Alu_AluSx chr1 6707611 6708464 - LINE/L2_L2a chr1 6708507 6708777 - SINE/Alu_AluSx chr1 6708961 6709001 + Simple_repeat_(TC)n chr1 6709002 6709155 - SINE/Alu_FRAM chr1 6709247 6709338 + LINE/L2_L2d2 chr1 6709343 6709609 - LINE/L1_L1MEg chr1 6709612 6709769 - LINE/L1_L1MEg chr1 6709781 6709800 + Simple_repeat_(T)n chr1 6709831 6710135 + SINE/Alu_AluSx1 chr1 6710137 6710292 - LTR/ERV1_MER65A chr1 6710293 6710602 + SINE/Alu_AluSx chr1 6710603 6710889 - LTR/ERV1_MER65A chr1 6710899 6711136 - LINE/L1_L1ME1 chr1 6711149 6711555 - LINE/L1_HAL1 chr1 6711556 6711866 - SINE/Alu_AluSx1 chr1 6711867 6711891 - LINE/L1_HAL1 chr1 6712002 6712048 - LINE/L1_HAL1 chr1 6712049 6712356 - SINE/Alu_AluY chr1 6712357 6712641 - LINE/L1_HAL1 chr1 6712711 6712861 - LINE/L1_HAL1 chr1 6712989 6713287 + SINE/Alu_AluY chr1 6713298 6713606 + SINE/Alu_AluSx1 chr1 6713742 6713820 + DNA/TcMar-Tigger_Tigger7 chr1 6713821 6714118 + SINE/Alu_AluJb chr1 6714119 6714158 + DNA/TcMar-Tigger_Tigger7 chr1 6714159 6714458 - SINE/Alu_AluY chr1 6714459 6714530 + DNA/TcMar-Tigger_MER44A chr1 6714508 6716330 - Retroposon/SVA_SVA_D chr1 6716334 6716770 + DNA/TcMar-Tigger_MER44B chr1 6717019 6717234 - LTR/ERVL-MaLR_MLT1A0 chr1 6717242 6717406 - SINE/Alu_FRAM chr1 6717429 6717564 + SINE/Alu_AluSx chr1 6717565 6717858 + SINE/Alu_AluJr chr1 6717867 6717911 + Simple_repeat_(GTTTT)n chr1 6717952 6718119 + LINE/L1_L1MB2 chr1 6718433 6718644 - DNA/hAT-Charlie_MER20 chr1 6718762 6718784 - SINE/MIR_MIRb chr1 6718785 6718893 + DNA/hAT-Charlie_MER30 chr1 6718894 6719182 - SINE/Alu_AluSx chr1 6719183 6719299 + DNA/hAT-Charlie_MER30 chr1 6719300 6719449 - SINE/MIR_MIRb chr1 6719450 6719753 + SINE/Alu_AluJb chr1 6719754 6719821 - SINE/MIR_MIRb chr1 6720341 6720522 - SINE/Alu_AluSq2 chr1 6720523 6720812 - SINE/Alu_AluSx3 chr1 6720813 6720963 - SINE/Alu_AluSq2 chr1 6721054 6721359 - SINE/Alu_AluJo chr1 6722025 6722134 - SINE/Alu_AluJr chr1 6722135 6722443 - SINE/Alu_AluSx chr1 6722444 6722581 - SINE/Alu_AluJr chr1 6723052 6723318 - SINE/Alu_AluSc8 chr1 6723371 6723800 + LTR/ERVL-MaLR_MLT1C2 chr1 6724049 6724433 - LTR/ERVL-MaLR_MSTA chr1 6724443 6724466 + Simple_repeat_(T)n chr1 6724516 6724582 + DNA_MER126 chr1 6724787 6724888 + DNA_MER130 chr1 6725273 6725393 - DNA/hAT-Blackjack_MER94 chr1 6725396 6725691 - SINE/Alu_AluSx1 chr1 6725775 6725903 + LINE/L1_L1MB3 chr1 6725914 6726497 + LINE/L2_L2d chr1 6726328 6726595 + LINE/L2_L2a chr1 6726596 6726892 + SINE/Alu_AluSz chr1 6726893 6727074 + LINE/L2_L2a chr1 6727075 6727396 + SINE/Alu_AluSx1 chr1 6727397 6727492 + LINE/L2_L2a chr1 6727636 6727866 + DNA/hAT-Charlie_MER20 chr1 6727933 6728054 + LTR/ERVL-MaLR_MLT1C2 chr1 6728049 6728227 + SINE/Alu_AluSp chr1 6728230 6728264 + LTR/ERVL-MaLR_MLT1C2 chr1 6728265 6728558 - SINE/Alu_AluSc chr1 6728559 6728812 + LTR/ERVL-MaLR_MLT1C2 chr1 6728818 6729075 + Simple_repeat_(AT)n chr1 6729080 6729355 - SINE/Alu_AluJb chr1 6729361 6729427 + LTR/ERVL-MaLR_MLT1C2 chr1 6729949 6729993 + Simple_repeat_(ATAA)n chr1 6730326 6730349 + Simple_repeat_(T)n chr1 6730441 6730465 + Simple_repeat_(A)n chr1 6730924 6731124 + LINE/L2_L2d chr1 6731237 6731340 + Simple_repeat_(ATATT)n chr1 6731368 6731529 + LTR/ERVL-MaLR_MLT1B chr1 6731530 6731838 + SINE/Alu_AluSx chr1 6731938 6732108 - Unknown_UCON22 chr1 6732367 6732447 + Simple_repeat_(TTTC)n chr1 6732448 6732731 - SINE/Alu_AluY chr1 6733045 6733185 + SINE/Alu_FRAM chr1 6733191 6733220 + Simple_repeat_(CA)n chr1 6733394 6733599 + SINE/Alu_AluSg chr1 6733600 6733906 - SINE/Alu_AluSx chr1 6733916 6734083 - SINE/Alu_AluJr chr1 6734084 6734317 + SINE/Alu_AluSx chr1 6734318 6734418 - SINE/Alu_AluJr chr1 6735091 6735129 + Simple_repeat_(TCATC)n chr1 6735347 6735359 + Low_complexity_A-rich chr1 6735360 6735693 + SINE/Alu_AluY chr1 6735974 6736037 - LTR/ERVL-MaLR_MLT1M chr1 6736059 6736275 - LINE/L1_L1PA16 chr1 6736276 6736577 - SINE/Alu_AluJr chr1 6736578 6736745 - LINE/L1_L1PA16 chr1 6736746 6736978 + SINE/Alu_AluSz chr1 6736979 6737011 - LINE/L1_L1PA16 chr1 6737025 6737100 - LTR/ERVL-MaLR_MLT1M chr1 6737101 6737413 - SINE/Alu_AluSz chr1 6737414 6737538 - LTR/ERVL-MaLR_MLT1M chr1 6737821 6738131 + LTR/ERVL-MaLR_MLT1L chr1 6738260 6738310 + DNA/hAT-Tip100_MER45A chr1 6738311 6738600 - SINE/Alu_AluJo chr1 6738601 6738678 + DNA/hAT-Tip100_MER45A chr1 6739035 6739191 + Simple_repeat_(CACA)n chr1 6739232 6739505 + SINE/Alu_AluSx chr1 6739588 6739606 + Simple_repeat_(TG)n chr1 6740086 6740383 - SINE/Alu_AluJb chr1 6740403 6740530 - SINE/Alu_AluJo chr1 6740542 6740585 + Simple_repeat_(TG)n chr1 6741046 6741333 - SINE/Alu_AluSx1 chr1 6741343 6741377 - SINE/MIR_MIRb chr1 6741378 6741814 - LTR/ERVL-MaLR_MLT1D chr1 6741815 6742125 + SINE/Alu_AluSz chr1 6742126 6742218 - LTR/ERVL-MaLR_MLT1D chr1 6742219 6742337 - SINE/MIR_MIRb chr1 6742452 6742514 - LTR/ERVL_MLT2A1 chr1 6742543 6742740 + Simple_repeat_(TATATA)n chr1 6742745 6743159 - LTR/ERVL_MLT2A1 chr1 6743255 6743295 + Simple_repeat_(CTCCCAA)n chr1 6743342 6743501 + DNA/hAT-Charlie_MER5A1 chr1 6744232 6744535 - SINE/Alu_AluSx3 chr1 6744536 6744838 - SINE/Alu_AluJb chr1 6744854 6744907 - LINE/L1_L1MB3 chr1 6744908 6745195 - SINE/Alu_AluSp chr1 6745196 6745496 - LINE/L1_L1MB3 chr1 6745528 6745818 + SINE/Alu_AluSx1 chr1 6745842 6746152 + SINE/Alu_AluJr chr1 6746769 6746800 + Simple_repeat_(T)n chr1 6747413 6747711 + SINE/Alu_AluSz chr1 6747739 6747769 + Simple_repeat_(TATC)n chr1 6747770 6748324 + LTR/ERV1_LTR24B chr1 6748480 6748628 + LTR/ERVL-MaLR_THE1C chr1 6748615 6748666 - SINE/Alu_FRAM chr1 6748677 6748729 + Simple_repeat_(CTC)n chr1 6748739 6748794 - SINE/Alu_FRAM chr1 6748792 6749028 + LTR/ERVL-MaLR_THE1C chr1 6749155 6749403 - LINE/L1_L1MB7 chr1 6749405 6749465 + Simple_repeat_(TCC)n chr1 6749466 6749572 - SINE/Alu_AluJb chr1 6749572 6749661 - SINE/Alu_AluJb chr1 6749663 6749751 - SINE/Alu_AluJb chr1 6749753 6749838 - SINE/Alu_AluJb chr1 6749842 6750136 - SINE/Alu_AluJr chr1 6750137 6750605 - LINE/L1_L1MB7 chr1 6750611 6750709 + LTR/ERV1_LTR24B chr1 6750710 6750803 - LINE/L1_L1MB7 chr1 6750816 6751129 - SINE/Alu_AluJr chr1 6751140 6751451 - SINE/Alu_AluSx chr1 6751503 6751740 - LINE/L1_L1MB7 chr1 6751741 6751787 + Simple_repeat_(TG)n chr1 6751788 6751835 - LINE/L1_L1MB7 chr1 6751837 6751886 + Simple_repeat_(TCCCCC)n chr1 6751887 6751996 - SINE/Alu_AluSg7 chr1 6751998 6752290 + SINE/Alu_AluSq2 chr1 6752295 6752737 + LTR/ERV1_LTR71A chr1 6752738 6752824 + snRNA_U13_ chr1 6752985 6753048 + SINE/Alu_AluYm1 chr1 6753054 6753100 - SINE/Alu_Alu chr1 6753102 6753131 - LTR/ERV1_LTR71A chr1 6753165 6753408 - LINE/L1_HAL1ME chr1 6753753 6754053 - SINE/Alu_AluJo chr1 6755004 6755308 + SINE/Alu_AluSx3 chr1 6755334 6755619 - SINE/Alu_AluSx chr1 6755905 6756206 + SINE/Alu_AluSx chr1 6756774 6756834 - SINE/MIR_MIR3 chr1 6756876 6757216 + SINE/Alu_AluJr chr1 6757228 6757332 + LINE/L2_L2 chr1 6757346 6757660 - SINE/Alu_AluSg chr1 6757661 6757785 - SINE/MIR_MIRc chr1 6758070 6758235 + DNA/hAT-Tip100_MER96 chr1 6758356 6758665 - SINE/Alu_AluSx chr1 6759005 6759140 + SINE/Alu_AluJr4 chr1 6759142 6759270 + SINE/Alu_AluJr chr1 6759271 6759571 + SINE/Alu_AluY chr1 6759572 6759736 + SINE/Alu_AluJr chr1 6759825 6760122 + SINE/Alu_AluSx1 chr1 6760311 6760453 + SINE/Alu_AluJb chr1 6760454 6760759 + SINE/Alu_AluSz chr1 6760760 6760926 + SINE/Alu_AluJb chr1 6760929 6761236 + SINE/Alu_AluSp chr1 6761237 6761324 - LINE/Penelope_Penelope1_Vert chr1 6761663 6761790 - SINE/Alu_FLAM_C chr1 6761805 6762103 - SINE/Alu_AluSx chr1 6762110 6762179 - DNA/hAT-Charlie_MER5A chr1 6762697 6763013 - SINE/Alu_AluYm1 chr1 6763017 6763114 - SINE/Alu_AluJo chr1 6763385 6763704 - SINE/Alu_AluJb chr1 6763791 6764087 - LINE/L1_L1MB3 chr1 6764088 6764375 - SINE/Alu_AluSp chr1 6764376 6764787 - LINE/L1_L1MB3 chr1 6764788 6765071 - SINE/Alu_AluSz chr1 6765072 6765118 - LINE/L1_L1MB3 chr1 6765477 6765628 - SINE/MIR_MIRb chr1 6765629 6766007 + LTR/ERVL-MaLR_MSTA chr1 6766008 6766108 - SINE/MIR_MIRb chr1 6766109 6766323 + LINE/L2_L2a chr1 6766695 6766987 - SINE/Alu_AluSz6 chr1 6766999 6767290 - SINE/Alu_AluSx1 chr1 6767522 6767838 - SINE/Alu_AluJr chr1 6767843 6768032 - LINE/L1_L1ME4b chr1 6768105 6768271 - DNA/hAT-Charlie_Charlie16a chr1 6768300 6768449 - SINE/MIR_MIRc chr1 6768713 6769021 + SINE/Alu_AluSz chr1 6769039 6769163 + LTR/ERVL-MaLR_MLT1B chr1 6769183 6769224 + Low_complexity_A-rich chr1 6769228 6769379 - SINE/Alu_FRAM chr1 6769455 6769566 - LINE/L2_L2a chr1 6769680 6769809 + SINE/Alu_AluSx chr1 6769810 6770123 + SINE/Alu_AluY chr1 6770124 6770305 + SINE/Alu_AluSx chr1 6770317 6770488 + SINE/Alu_FRAM chr1 6771247 6771355 + DNA/hAT-Ac_OldhAT1 chr1 6772877 6773116 + SINE/Alu_AluSx1 chr1 6773119 6773168 + LINE/L2_L2a chr1 6773169 6773474 - SINE/Alu_AluJr chr1 6773478 6773493 + LINE/L2_L2a chr1 6773494 6773655 - SINE/Alu_FRAM chr1 6773656 6773718 + LINE/L2_L2a chr1 6773719 6774017 + SINE/Alu_AluJr chr1 6774018 6774246 + LINE/L2_L2a chr1 6774211 6774309 + LINE/CR1_L3 chr1 6774395 6774728 + LTR/ERV1_LTR37B chr1 6774738 6775040 + SINE/Alu_AluSx4 chr1 6775065 6775103 + LINE/L2_L2a chr1 6775104 6775407 - SINE/Alu_AluSz chr1 6775408 6775809 + LINE/L2_L2a chr1 6775819 6775994 + DNA/hAT-Charlie_MER58A chr1 6776004 6776116 + LTR/ERVL-MaLR_MLT1D chr1 6776117 6776391 + SINE/Alu_AluSz6 chr1 6776392 6776760 + LTR/ERVL-MaLR_MLT1D chr1 6776761 6777153 + LINE/L2_L2a chr1 6777154 6777465 + SINE/Alu_AluSx chr1 6777466 6777685 + LINE/L2_L2a chr1 6777762 6777938 - LINE/L1_L1ME4b chr1 6777939 6778249 - SINE/Alu_AluSz6 chr1 6778250 6778285 - LINE/L1_L1ME4b chr1 6778641 6778744 - DNA/hAT-Tip100?_MamRep1879 chr1 6778896 6779213 + SINE/Alu_AluSc chr1 6779233 6779303 - DNA/TcMar-Tigger_Tigger19b chr1 6779698 6779999 - SINE/Alu_AluSz6 chr1 6780241 6780313 + LINE/L1_L1MD1 chr1 6780460 6780756 - SINE/Alu_AluSg chr1 6781228 6781280 - DNA/hAT-Charlie_Charlie1b chr1 6781290 6781564 + SINE/Alu_AluSg chr1 6781567 6781774 + SINE/Alu_AluJb chr1 6781775 6782040 - DNA/hAT-Charlie_Charlie1b chr1 6782041 6782324 + SINE/Alu_AluJr chr1 6782325 6782408 - DNA/hAT-Charlie_Charlie1b chr1 6782620 6782932 + SINE/Alu_AluSp chr1 6783031 6783328 - SINE/Alu_AluSq2 chr1 6783459 6783842 - LINE/L1_L1ME4c chr1 6784676 6784720 + Simple_repeat_(GCCGGC)n chr1 6787467 6787521 + Simple_repeat_(TACATA)n chr1 6788159 6788184 + Simple_repeat_(CTCC)n chr1 6789679 6789740 - LINE/L2_L2c chr1 6789805 6790128 - SINE/Alu_AluSz chr1 6790351 6790375 + Simple_repeat_(AG)n chr1 6790376 6790409 + Simple_repeat_(GT)n chr1 6790570 6790696 + LINE/RTE-X_L4_C_Mam chr1 6790746 6790831 + LINE/RTE-X_L4_A_Mam chr1 6791113 6791131 + Simple_repeat_(T)n chr1 6791162 6791204 + Simple_repeat_(CCTTT)n chr1 6791964 6792257 - SINE/Alu_AluSz chr1 6793016 6793089 + Simple_repeat_(AT)n chr1 6794883 6794921 + Simple_repeat_(TTTTTTTG)n chr1 6795185 6795494 - SINE/Alu_AluJr chr1 6796133 6796436 - SINE/Alu_AluJb chr1 6797080 6797359 - SINE/Alu_AluJb chr1 6797369 6797683 - SINE/Alu_AluSx chr1 6797997 6798287 - SINE/Alu_AluSq10 chr1 6798434 6798584 - SINE/Alu_AluYh7 chr1 6798585 6798887 - SINE/Alu_AluYb8 chr1 6799022 6799037 + Simple_repeat_(ACTATTT)n chr1 6799038 6799078 + Simple_repeat_(TATT)n chr1 6799086 6799367 - SINE/Alu_AluJr4 chr1 6799618 6799680 + LINE/CR1_L3 chr1 6799701 6799874 - DNA/TcMar-Tigger_MER44A chr1 6799953 6800117 + SINE/tRNA_LFSINE_Vert chr1 6800123 6800429 + SINE/Alu_AluJo chr1 6801569 6801681 + LINE/L1_L1MB3 chr1 6802723 6803048 - SINE/Alu_AluJr chr1 6803300 6803338 + Low_complexity_A-rich chr1 6803444 6803653 + DNA/TcMar-Tigger_Tigger15a chr1 6803705 6803900 - SINE/Alu_AluJr4 chr1 6803901 6804176 + SINE/Alu_AluSq2 chr1 6804177 6804317 - SINE/Alu_AluJr4 chr1 6804325 6804737 - LINE/L1_L1MB7 chr1 6804738 6804854 - SINE/Alu_AluJo chr1 6804855 6805512 - LINE/L1_L1MB7 chr1 6805513 6805809 - SINE/Alu_AluJr chr1 6805810 6806452 - LINE/L1_L1MB7 chr1 6806700 6806931 - DNA/TcMar-Tigger_Tigger14a chr1 6806942 6807209 - LINE/L2_L2b chr1 6807473 6807737 + SINE/Alu_AluSc8 chr1 6807974 6808100 - LINE/L2_L2b chr1 6808495 6808740 + LINE/L2_L2c chr1 6808869 6808988 - LINE/L2_L2b chr1 6809015 6809326 - SINE/Alu_AluSq2 chr1 6809453 6809557 - LTR/ERVL_LTR33 chr1 6810043 6810524 - LTR/ERVL-MaLR_MLT1F2 chr1 6810525 6810813 + SINE/Alu_AluY chr1 6810814 6810871 - LTR/ERVL-MaLR_MLT1F2 chr1 6811178 6811265 + SINE/MIR_MIRc chr1 6811267 6811813 - DNA/TcMar-Tigger_Tigger15a chr1 6811847 6811979 + DNA/hAT-Charlie_Charlie32a chr1 6812240 6812398 + SINE/MIR_MIRc chr1 6812835 6812967 + LINE/L2_X24_LINE chr1 6813613 6813775 - SINE/Alu_FRAM chr1 6813985 6814076 - LINE/L2_L2a chr1 6814270 6814640 + LTR/ERVL-MaLR_MLT1J2 chr1 6814947 6815024 - LINE/L2_L2d2 chr1 6815207 6815503 - SINE/Alu_AluJo chr1 6815744 6815901 + DNA/hAT-Charlie_MER5A1 chr1 6815916 6815979 - SINE/MIR_MIRc chr1 6815980 6816137 - DNA/hAT-Charlie_MER5B chr1 6816138 6816201 - SINE/MIR_MIRc chr1 6816259 6816451 + LINE/L2_L2a chr1 6816604 6816662 - SINE/MIR_MIRb chr1 6817639 6817796 - SINE/Alu_FRAM chr1 6818058 6818366 + SINE/Alu_AluSz chr1 6818440 6818625 - SINE/MIR_MIRc chr1 6818651 6818932 - SINE/Alu_AluJb chr1 6818946 6819116 - SINE/Alu_FRAM chr1 6819711 6820142 - LINE/L1_L1MC4 chr1 6820181 6820209 - DNA/hAT-Charlie_MER103C chr1 6820317 6820413 + SINE/MIR_MIRc chr1 6820567 6820772 + SINE/MIR_MIRb chr1 6822795 6822845 + Simple_repeat_(CA)n chr1 6823922 6824058 - SINE/MIR_MIRc chr1 6826897 6827077 - DNA/hAT-Charlie_Charlie7a chr1 6827741 6828177 - DNA/TcMar-Tigger_MamRep137 chr1 6828287 6828589 - SINE/Alu_AluSc chr1 6828595 6828708 - SINE/MIR_MIRb chr1 6828891 6829202 - SINE/Alu_AluSz6 chr1 6829540 6829764 - SINE/MIR_MIR chr1 6830025 6830343 - SINE/Alu_AluY chr1 6830347 6830641 - SINE/Alu_AluSx chr1 6830642 6830783 - LINE/L1_L1ME1 chr1 6830807 6831117 - SINE/Alu_AluY chr1 6832080 6832374 - SINE/Alu_AluSz chr1 6833020 6833042 + Simple_repeat_(CAG)n chr1 6833207 6833284 - DNA/hAT-Charlie_Charlie29a chr1 6833286 6833335 + LINE/L2_L2a chr1 6833338 6833912 - DNA/hAT-Charlie_Charlie29a chr1 6833919 6834016 + SINE/MIR_MIRc chr1 6834142 6834246 + Simple_repeat_(TTTA)n chr1 6834247 6834287 + Simple_repeat_(T)n chr1 6834638 6834930 - SINE/Alu_AluJr chr1 6834942 6835021 - LINE/L1_L1MB7 chr1 6835473 6835616 - DNA/hAT-Charlie_MER5A chr1 6836574 6836619 + Simple_repeat_(TG)n chr1 6836620 6836631 + Simple_repeat_(CATGTG)n chr1 6836941 6837254 - SINE/Alu_AluY chr1 6837911 6838014 + LINE/L2_L2a chr1 6838774 6839056 - SINE/Alu_AluJr chr1 6839101 6839407 + SINE/Alu_AluSc8 chr1 6839441 6839918 - LINE/L2_L2a chr1 6839919 6840209 + SINE/Alu_AluSp chr1 6840210 6840867 - LINE/L2_L2a chr1 6840885 6841383 - LTR/ERVL-MaLR_MLT1D chr1 6841394 6841600 - LINE/L2_L2a chr1 6842047 6842107 + Simple_repeat_(TCTCC)n chr1 6842387 6842558 + DNA/hAT-Charlie_MER5B chr1 6842639 6842954 + SINE/Alu_AluSx chr1 6843581 6843779 - SINE/Alu_AluSx chr1 6843842 6844037 + LINE/L1_L1ME3A chr1 6844032 6844381 + LINE/L1_L1ME3A chr1 6844391 6844714 + SINE/Alu_AluJb chr1 6844726 6844899 + LINE/L1_L1ME3B chr1 6844901 6845462 - LTR/ERVL_LTR33C chr1 6845475 6845574 + LINE/L1_L1MB7 chr1 6845827 6846175 - LTR/ERVL-MaLR_THE1B chr1 6846679 6846822 - LTR/ERV1_MER31B chr1 6847006 6847244 - SINE/Alu_AluSz6 chr1 6847460 6847538 - LINE/L2_L2 chr1 6847539 6847707 - SINE/Alu_AluSx chr1 6847708 6848010 - SINE/Alu_AluSx3 chr1 6848011 6848136 - SINE/Alu_AluSx chr1 6848137 6848162 - LINE/L2_L2 chr1 6848163 6848448 - SINE/Alu_AluJr chr1 6848449 6848647 - LINE/L2_L2 chr1 6848677 6848852 - LTR/ERV1_MER34A chr1 6849051 6849136 - LTR/ERV1_MER34A chr1 6849636 6849663 + Simple_repeat_(TA)n chr1 6849753 6850056 + SINE/Alu_AluSz6 chr1 6850511 6851567 + LINE/L1_L1MEg chr1 6851597 6851732 + SINE/Alu_AluSz6 chr1 6851733 6852029 + SINE/Alu_AluSz chr1 6852030 6852237 + LINE/L1_L1MEg chr1 6852258 6853058 - LTR/ERVL_MER21C chr1 6853069 6854099 + LINE/L1_L1MEg chr1 6854100 6854507 + DNA/TcMar-Tigger_Tigger3a chr1 6854508 6854676 + LINE/L1_L1MEg chr1 6854699 6855200 + LINE/L1_L1MEg chr1 6855214 6855251 - LTR/ERVL_LTR41 chr1 6855252 6855679 - LTR/ERVL-MaLR_MSTA chr1 6855680 6856200 - LTR/ERVL_LTR41 chr1 6856349 6856488 - LINE/L2_L2b chr1 6856487 6856538 - LINE/L2_L2c chr1 6856584 6857364 - LINE/L2_L2c chr1 6857671 6857804 - LINE/L2_L2a chr1 6857928 6858048 + LINE/L2_L2a chr1 6858434 6858472 + Simple_repeat_(GTG)n chr1 6859301 6859332 + Simple_repeat_(TG)n chr1 6859508 6859810 + SINE/Alu_AluJb chr1 6859821 6860084 - LINE/L1_L1M5 chr1 6860241 6860535 - LINE/L1_L1M5 chr1 6860628 6860939 + SINE/Alu_AluSz chr1 6860965 6861255 - SINE/Alu_AluSz chr1 6861441 6861536 - DNA/hAT-Charlie_Charlie17a chr1 6861710 6861958 - LINE/L1_L1MB3 chr1 6861959 6862272 - SINE/Alu_AluSx1 chr1 6862273 6862488 - LINE/L1_L1MB3 chr1 6863009 6863340 - LINE/L1_L1MA9 chr1 6863351 6863611 + LINE/L2_L2b chr1 6863625 6864195 - LINE/L1_L1MC3 chr1 6864216 6864959 + LINE/L2_L2b chr1 6864980 6865104 - SINE/Alu_AluJo chr1 6865317 6865381 - LINE/L2_L2b chr1 6865462 6865648 + SINE/MIR_MIRb chr1 6865662 6865872 - SINE/MIR_MIRb chr1 6865922 6866018 + DNA/hAT-Tip100_MamTip2 chr1 6866148 6866335 + DNA/hAT-Charlie_MER5A chr1 6866737 6866776 + DNA/hAT-Blackjack_MER94B chr1 6867052 6867223 - LINE/L1_L1MA10 chr1 6867796 6868109 - SINE/Alu_AluJb chr1 6868230 6868302 + SINE/Alu_AluSx3 chr1 6868320 6868353 + Simple_repeat_(AAAAC)n chr1 6871847 6871881 + Simple_repeat_(ATTTA)n chr1 6872228 6872263 + Simple_repeat_(CAC)n chr1 6874058 6874144 + SINE/MIR_MIRc chr1 6874236 6874481 + SINE/MIR_MIR chr1 6875000 6875192 - SINE/MIR_MIRb chr1 6875291 6875584 - SINE/Alu_AluSx3 chr1 6875712 6875767 + DNA/hAT-Charlie_MER5B chr1 6876366 6876662 - SINE/Alu_AluSq2 chr1 6876942 6877020 - SINE/MIR_MIR3 chr1 6877897 6877967 + LINE/L2_L2b chr1 6879601 6879926 - SINE/Alu_AluJo chr1 6880076 6880373 - SINE/Alu_AluJr chr1 6880384 6880689 - SINE/Alu_AluJb chr1 6880791 6880948 - LINE/L2_L2a chr1 6880949 6881183 - SINE/MIR_MIR chr1 6881184 6881685 - LINE/L2_L2a chr1 6881686 6881994 + SINE/Alu_AluSx3 chr1 6881995 6882033 - LINE/L2_L2a chr1 6882034 6882354 + DNA/hAT-Charlie_MER58B chr1 6882355 6882961 - LINE/L2_L2a chr1 6882962 6883191 - SINE/Alu_AluSz6 chr1 6883192 6883466 - LINE/L2_L2a chr1 6883467 6883766 - LINE/L2_L2a chr1 6883767 6884053 - DNA/hAT-Charlie_MER33 chr1 6884054 6884192 - LINE/L2_L2a chr1 6884292 6884336 + Simple_repeat_(AC)n chr1 6887949 6888203 + LINE/L2_L2a chr1 6888808 6888837 + Simple_repeat_(T)n chr1 6889803 6889836 + Simple_repeat_(TTATT)n chr1 6890564 6890871 - SINE/Alu_AluJr chr1 6890895 6891092 + SINE/MIR_MIRc chr1 6891174 6891248 - LINE/L1_L1MD3 chr1 6891792 6892002 - SINE/MIR_MIRc chr1 6892585 6892614 + Simple_repeat_(T)n chr1 6892999 6893289 + SINE/Alu_AluSx3 chr1 6893957 6893988 + Simple_repeat_(CTTTC)n chr1 6895260 6895991 + LINE/L2_L2a chr1 6897146 6897227 - LINE/L2_L2b chr1 6898272 6898575 + SINE/Alu_AluYm1 chr1 6898881 6898913 + Simple_repeat_(T)n chr1 6899555 6899594 + Simple_repeat_(CA)n chr1 6900507 6901761 + LINE/L1_L1ME1 chr1 6901762 6902039 + SINE/Alu_AluSx1 chr1 6902040 6902073 + Simple_repeat_(CA)n chr1 6902074 6902095 + SINE/Alu_AluSx1 chr1 6902096 6902676 + LINE/L1_L1ME1 chr1 6902667 6903409 + LINE/L1_L1ME2 chr1 6903889 6904031 + LINE/L2_L2 chr1 6904104 6904533 + LINE/L2_L2b chr1 6904541 6904733 - SINE/Alu_AluJr chr1 6904734 6904765 + Simple_repeat_(T)n chr1 6904766 6904892 - SINE/Alu_AluJr chr1 6905182 6905494 - SINE/Alu_AluSx chr1 6907968 6908057 + LINE/L2_L2b chr1 6911715 6911775 + SINE/MIR_MIRb chr1 6911841 6911905 + LINE/L2_L2b chr1 6911975 6912023 + Simple_repeat_(TTCCC)n chr1 6913027 6913092 + SINE/MIR_MIR1_Amn chr1 6913720 6913924 - LINE/L2_L2d2 chr1 6914041 6914102 + LINE/L2_L2a chr1 6914103 6914401 - SINE/Alu_AluSq2 chr1 6914402 6914482 + LINE/L2_L2a chr1 6916622 6916738 - LINE/L2_L2b chr1 6916778 6917195 - LINE/L2_L2b chr1 6917389 6917749 - LINE/L2_L2b chr1 6918384 6918587 - DNA?_Eulor2A chr1 6919562 6919612 - DNA/hAT-Charlie_Charlie18a chr1 6919613 6919973 - LTR/ERVL-MaLR_THE1B chr1 6919974 6921562 - LTR/ERVL-MaLR_THE1B-int chr1 6921563 6921928 - LTR/ERVL-MaLR_THE1B chr1 6921929 6922155 - DNA/hAT-Charlie_Charlie18a chr1 6922218 6922285 - SINE/MIR_MIRc chr1 6922296 6922797 - LTR/ERVL-MaLR_MLT1F1 chr1 6923394 6923454 - LTR/ERVL-MaLR_MLT1M chr1 6924113 6924342 - LINE/L2_L2a chr1 6925324 6925491 - LTR/Gypsy_LTR81 chr1 6925507 6925800 - LTR/Gypsy_LTR81B chr1 6926014 6926157 + SINE/MIR_MIR chr1 6926284 6926715 + Simple_repeat_(CCTT)n chr1 6926720 6927039 - SINE/Alu_AluJo chr1 6927072 6927332 - SINE/Alu_AluJr chr1 6927333 6927849 - DNA/hAT-Charlie_MER119 chr1 6927851 6928217 - LTR/Gypsy_LTR81B chr1 6928615 6928832 + LINE/L2_L2b chr1 6928854 6928973 + LINE/L2_L2b chr1 6929033 6929167 - DNA/hAT-Charlie_Charlie18a chr1 6929168 6929351 - SINE/Alu_AluSx3 chr1 6929352 6929662 - SINE/Alu_AluY chr1 6929663 6929796 - SINE/Alu_AluSx3 chr1 6929797 6929891 - DNA/hAT-Charlie_Charlie18a chr1 6930093 6930122 + LINE/L2_L2c chr1 6931593 6931677 - SINE/MIR_MIR chr1 6932016 6933068 - LINE/L1_L1ME1 chr1 6933069 6933223 - SINE/Alu_FAM chr1 6933224 6933273 - LINE/L1_L1ME1 chr1 6933274 6933559 - SINE/Alu_AluSz chr1 6933560 6934037 - LINE/L1_L1ME1 chr1 6934660 6934701 + Simple_repeat_(CCTCC)n chr1 6935813 6935956 - SINE/MIR_MIR3 chr1 6936711 6937006 + SINE/Alu_AluY chr1 6937033 6937918 + Simple_repeat_(CACCAT)n chr1 6937928 6938055 - SINE/MIR_MIRb chr1 6938001 6938081 - SINE/MIR_MIRc chr1 6938982 6939049 - LINE/L2_L2a chr1 6939809 6939948 + LINE/L2_L2a chr1 6939956 6940000 + LINE/L2_L2c chr1 6940116 6940583 - LTR/ERVL_MLT2B1 chr1 6941076 6941167 - SINE/MIR_MIR3 chr1 6941475 6941619 - LTR/ERVL_LTR16C chr1 6942064 6942088 + Simple_repeat_(T)n chr1 6942388 6942688 + SINE/Alu_AluJr chr1 6943448 6943537 + LINE/L2_L2a chr1 6943538 6943566 - LINE/L1_L1MB3 chr1 6943567 6943876 + SINE/Alu_AluSg chr1 6943877 6944328 - LINE/L1_L1MB3 chr1 6944329 6944736 + LINE/L2_L2a chr1 6944737 6944852 + DNA/TcMar-Tigger_Tigger5b chr1 6944845 6945011 + DNA/TcMar-Tigger_Tigger5b chr1 6945019 6945162 + LINE/L2_L2a chr1 6945165 6945357 - LINE/L1_L1MB8 chr1 6945358 6945645 - SINE/Alu_AluJb chr1 6945646 6946180 - LINE/L1_L1MB8 chr1 6946181 6946481 - SINE/Alu_AluJr chr1 6946482 6947377 - LINE/L1_L1MB8 chr1 6947378 6947681 - SINE/Alu_AluSz chr1 6947682 6948076 - LINE/L1_L1MB8 chr1 6948103 6948208 - SINE/MIR_MIRb chr1 6948426 6948570 - SINE/MIR_MIRc chr1 6948621 6948847 - SINE/MIR_MIRc chr1 6949613 6949726 + SINE/MIR_MIRc chr1 6949719 6949859 - SINE/MIR_MIR3 chr1 6949840 6950029 - SINE/MIR_MIRb chr1 6950462 6950558 - LTR/ERVL-MaLR_MLT1I chr1 6951286 6951478 + SINE/MIR_MIRb chr1 6951497 6951594 + SINE/MIR_MIR chr1 6952149 6952456 + SINE/Alu_AluY chr1 6952554 6952861 + SINE/Alu_AluSx1 chr1 6952868 6953151 - LINE/L1_L1ME4a chr1 6953754 6953773 + Simple_repeat_(T)n chr1 6954134 6954155 + Simple_repeat_(GCCA)n chr1 6954611 6954698 - SINE/MIR_MIRc chr1 6954907 6955124 - LINE/L1_X9_LINE chr1 6955286 6955324 + Simple_repeat_(CTCTCCC)n chr1 6955435 6955528 + SINE/MIR_MIRb chr1 6957303 6957833 - LTR/ERVL-MaLR_MLT1D chr1 6959620 6959823 - SINE/MIR_MIRb chr1 6959920 6960133 - SINE/MIR_MIR chr1 6960509 6960772 + LINE/L2_L2c chr1 6960828 6961031 - SINE/MIR_MIR chr1 6961186 6961281 - LTR/ERVL-MaLR_MLT1N2 chr1 6962345 6962390 + Simple_repeat_(CCTCC)n chr1 6963256 6963287 + Simple_repeat_(C)n chr1 6965174 6965332 + Simple_repeat_(GT)n chr1 6966166 6966588 - LINE/L1_L1MB8 chr1 6966612 6966911 - SINE/Alu_AluSx chr1 6966960 6967261 + SINE/Alu_AluSx chr1 6967881 6968053 + Unknown_UCON20 chr1 6968468 6968944 - LINE/L2_L2c chr1 6969584 6969644 + SINE/MIR_MIR1_Amn chr1 6969787 6969934 - LINE/L1_L1MD1 chr1 6969945 6970121 - LINE/L1_L1MD2 chr1 6970701 6970773 - LINE/L2_L2b chr1 6972777 6972826 - LINE/L1_L1MB1 chr1 6972827 6972866 + Simple_repeat_(TTTTCC)n chr1 6972867 6973151 - SINE/Alu_AluSx1 chr1 6973152 6973678 - LINE/L1_L1MB1 chr1 6975073 6975190 - SINE/MIR_MIR chr1 6975212 6975247 - LINE/L2_L2d2 chr1 6975248 6975308 + LINE/Penelope_Penelope1_Vert chr1 6975630 6976023 - LINE/L1_L1MB8 chr1 6976758 6977121 + LTR/ERVL-MaLR_THE1B chr1 6977332 6977639 - SINE/Alu_AluSc8 chr1 6977640 6977803 - SINE/MIR_MIRb chr1 6977838 6978351 + LINE/L1_L1MA2 chr1 6978352 6978640 + SINE/Alu_AluSx1 chr1 6978651 6978702 + Simple_repeat_(AT)n chr1 6978815 6978867 - LINE/L2_L2c chr1 6980410 6980524 - SINE/MIR_MIR3 chr1 6980525 6980893 + LTR/ERVL-MaLR_THE1B chr1 6980894 6980942 - SINE/MIR_MIRc chr1 6980967 6981263 - DNA/TcMar-Tigger_MER46C chr1 6981429 6981735 - SINE/Alu_AluJr chr1 6981738 6982013 - SINE/Alu_AluJo chr1 6983294 6983668 + LINE/L2_L2c chr1 6983681 6984239 + Simple_repeat_(GATG)n chr1 6984361 6984647 + SINE/Alu_AluSz chr1 6987082 6987163 - DNA/hAT-Charlie_MER5B chr1 6987164 6987464 - SINE/Alu_AluSx chr1 6987465 6987559 - DNA/hAT-Charlie_MER5B chr1 6990012 6990186 - LTR/ERVL-MaLR_MLT1J chr1 6990196 6990291 - LINE/L1_L1M5 chr1 6990297 6990772 - LINE/L1_L1ME2z chr1 6990774 6990806 + Simple_repeat_(TC)n chr1 6990807 6990832 + Simple_repeat_(AT)n chr1 6990844 6990977 - LINE/L1_L1PB3 chr1 6990982 6991708 - LINE/L1_L1ME2z chr1 6991709 6992000 - SINE/Alu_AluSz6 chr1 6992001 6992062 - LINE/L1_L1ME2z chr1 6992063 6992371 - SINE/Alu_AluJo chr1 6992372 6992507 - LINE/L1_L1ME2z chr1 6992514 6992883 - LTR/ERVL-MaLR_MSTA chr1 6992884 6994651 - LINE/L1_L1ME2z chr1 6994652 6994968 - SINE/Alu_AluJb chr1 6994969 6995171 - LINE/L1_L1ME2z chr1 6995181 6995234 - DNA/hAT-Charlie_MER20 chr1 6995282 6995601 - SINE/Alu_AluSc chr1 6995631 6996156 - LTR/ERVL_MER74A chr1 6996161 6996689 - LINE/L1_L1ME2z chr1 6996690 6996721 + Simple_repeat_(AAAG)n chr1 6996722 6996900 - LINE/L1_L1ME2z chr1 6996899 6997470 - LINE/L1_L1MEh chr1 6997468 6997647 - LINE/L1_L1MEh chr1 6997648 6997958 - SINE/Alu_AluSq chr1 6997959 6998068 - LINE/L1_L1MEh chr1 6998079 6998324 - SINE/Alu_AluSx chr1 6998352 6998539 + LINE/L2_L2 chr1 6998621 6998763 + LINE/L2_L2a chr1 6998883 6998904 - LINE/L1_L1MEh chr1 6998905 6999064 - LINE/L1_L1MB3 chr1 6999065 6999222 - LINE/L1_L1MEh chr1 6999240 6999383 - LTR/ERVL-MaLR_MSTA chr1 6999385 6999659 - SINE/Alu_AluSz6 chr1 6999674 6999748 - LINE/L1_L1MEh chr1 7000072 7000104 + Simple_repeat_(GCTGGT)n chr1 7000863 7000904 - LINE/CR1_L3 chr1 7001021 7001062 + Simple_repeat_(ATATTT)n chr1 7001227 7001323 + LINE/L2_L2a chr1 7001365 7001830 - LINE/CR1_L3 chr1 7001886 7002192 - SINE/Alu_AluSx3 chr1 7002323 7002419 + SINE/MIR_MIRc chr1 7002447 7002784 - LINE/CR1_CR1-3_Croc chr1 7002807 7002906 - LINE/CR1_L3 chr1 7003008 7003216 - DNA/hAT-Charlie_MER20 chr1 7004230 7004256 + Simple_repeat_(TTCTT)n chr1 7004547 7004765 - LINE/L1_L1MC4 chr1 7004766 7005071 - SINE/Alu_AluSz chr1 7005072 7005194 - LINE/L1_L1MC4 chr1 7005434 7005485 + Simple_repeat_(TTCTTCC)n chr1 7005492 7005642 - LINE/L2_L2b chr1 7007005 7007028 + Simple_repeat_(A)n chr1 7007837 7007876 + SINE/MIR_MIRb chr1 7008189 7008301 - LINE/L2_L2d chr1 7010191 7010300 - SINE/MIR_MIR3 chr1 7010516 7010583 + SINE/MIR_MIRb chr1 7010620 7010688 + SINE/MIR_MIRb chr1 7010704 7010739 + Simple_repeat_(CCACCC)n chr1 7011200 7011344 - SINE/Alu_AluSp chr1 7011542 7011829 + LINE/L2_L2a chr1 7011835 7011945 + LINE/L2_L2c chr1 7011996 7013203 + LINE/L2_L2c chr1 7013204 7013512 - SINE/Alu_AluSz6 chr1 7013513 7013568 + LINE/L2_L2 chr1 7014954 7015002 + LINE/CR1_L3 chr1 7015647 7016091 - LTR/ERVL-MaLR_MSTA chr1 7016092 7016271 - LINE/L1_L1MD2 chr1 7016309 7016436 + LINE/L2_L2c chr1 7016553 7016863 + SINE/Alu_AluSz chr1 7017174 7017379 + SINE/MIR_MIR3 chr1 7017963 7018131 - SINE/Alu_AluJb chr1 7019543 7019736 + Unknown_MamRep564 chr1 7020194 7020556 + DNA/TcMar-Tigger_Tigger3a chr1 7021089 7021171 - LINE/RTE-BovB_MamRTE1 chr1 7021577 7021740 + LINE/L1_L1MA10 chr1 7021979 7022086 - SINE/MIR_MIRc chr1 7023204 7023745 + LINE/L1_L1MEd chr1 7023746 7024057 + SINE/Alu_AluSx1 chr1 7024058 7024383 + LINE/L1_L1MEd chr1 7025531 7025848 - LINE/L2_L2b chr1 7025849 7026143 + SINE/Alu_AluJo chr1 7026144 7026273 - LINE/L2_L2b chr1 7026636 7026927 - SINE/Alu_AluSc chr1 7027893 7028196 - SINE/Alu_AluSx chr1 7028791 7028941 - LINE/L2_L2a chr1 7028964 7029171 + SINE/MIR_MIRb chr1 7029220 7029514 + SINE/Alu_AluYk4 chr1 7029597 7029615 + Simple_repeat_(TC)n chr1 7029654 7030576 - DNA/hAT-Charlie_Charlie1a chr1 7030590 7030839 - LINE/L1_L1MEc chr1 7030840 7031001 - SINE/Alu_AluY chr1 7031002 7031027 + Simple_repeat_(T)n chr1 7031028 7031120 - SINE/Alu_AluY chr1 7031121 7031546 - LINE/L1_L1MEc chr1 7031547 7031840 - SINE/Alu_AluSc8 chr1 7031841 7031955 - LINE/L1_L1MEc chr1 7031956 7031981 + Simple_repeat_(TTAT)n chr1 7031982 7032264 - SINE/Alu_AluSx1 chr1 7032265 7033585 - LINE/L1_L1MEc chr1 7033586 7033893 - SINE/Alu_AluSz chr1 7033894 7034149 - LINE/L1_L1MEc chr1 7034150 7034452 - SINE/Alu_AluSg7 chr1 7034453 7034872 - LINE/L1_L1MEc chr1 7034873 7035119 - DNA/hAT-Charlie_Charlie1a chr1 7035120 7035413 + SINE/Alu_AluSp chr1 7035414 7035733 - DNA/hAT-Charlie_Charlie1a chr1 7035734 7036283 + LINE/L1_L1ME3C chr1 7036463 7036644 - SINE/MIR_MIRb chr1 7037276 7037323 - DNA/hAT-Ac_OldhAT1 chr1 7037558 7037850 + SINE/Alu_AluY chr1 7037980 7038168 + SINE/MIR_MIR chr1 7038169 7038415 + LINE/L1_L1ME4b chr1 7038455 7038699 - LINE/L1_L1ME2z chr1 7038758 7039069 + SINE/Alu_AluSx1 chr1 7039246 7039266 + Simple_repeat_(TCC)n chr1 7040724 7041021 - SINE/Alu_AluSc chr1 7043340 7043385 + Simple_repeat_(TG)n chr1 7045087 7045249 - SINE/MIR_MIRb chr1 7045351 7045457 - LINE/CR1_L3b chr1 7046552 7046648 - LINE/L2_L2c chr1 7046689 7046891 + SINE/MIR_MIR chr1 7046939 7047280 + LTR/ERVL-MaLR_MLT1I chr1 7047316 7047442 - LINE/L2_L2c chr1 7047475 7047574 + Simple_repeat_(TTCTCCC)n chr1 7047701 7047722 + Simple_repeat_(CT)n chr1 7049448 7049730 - SINE/Alu_AluSx1 chr1 7049808 7049926 - LINE/L2_L2a chr1 7050003 7050184 - LINE/L2_L2a chr1 7051816 7053369 + LINE/L2_L2a chr1 7053398 7053751 + LINE/L2_L2a chr1 7053758 7053851 + LINE/L2_L2a chr1 7053979 7054002 + Simple_repeat_(TGC)n chr1 7054664 7054775 + SINE/MIR_MIRb chr1 7054776 7055200 - LTR/ERVL-MaLR_MSTA chr1 7055201 7055291 + SINE/MIR_MIRb chr1 7056300 7056334 + Simple_repeat_(ATATTT)n chr1 7056564 7056676 - LINE/L2_L2c chr1 7058512 7058557 + LINE/L2_L2b chr1 7058859 7058896 + Simple_repeat_(AC)n chr1 7059336 7059646 + SINE/Alu_AluJo chr1 7059795 7059930 - SINE/MIR_MIRb chr1 7060143 7060546 - LTR/ERVL-MaLR_MLT1B chr1 7061402 7061495 - DNA/hAT_MamRep38 chr1 7061680 7061728 + Low_complexity_G-rich chr1 7061924 7062216 - SINE/Alu_AluSx1 chr1 7062683 7063078 - LTR/ERVL-MaLR_MLT1C2 chr1 7063088 7063214 + LINE/L2_L2b chr1 7063289 7063467 + DNA/hAT-Charlie_MER103C chr1 7063729 7063867 - LINE/L2_L2b chr1 7063873 7064041 - LTR/ERVL-MaLR_MLT1A chr1 7064053 7064058 + Simple_repeat_(CCTTCT)n chr1 7064059 7064237 + Simple_repeat_(CCT)n chr1 7064238 7064291 + Simple_repeat_(CCTTCT)n chr1 7064295 7064454 - LTR/ERVL-MaLR_MLT1A chr1 7064530 7064762 - LINE/L2_L2b chr1 7064836 7065196 - LINE/L2_L2b chr1 7065224 7065270 + Low_complexity_G-rich chr1 7065528 7065652 - LINE/L2_L2b chr1 7067419 7067443 + Simple_repeat_(TTC)n chr1 7067512 7067615 - LINE/L2_L2a chr1 7067684 7068008 + LTR/ERVL-MaLR_MLT1L chr1 7068023 7068171 + LTR/ERVL-MaLR_MLT1L chr1 7068277 7068399 + LINE/L2_L2d2 chr1 7069199 7069369 - SINE/MIR_MIRb chr1 7070311 7070441 + SINE/MIR_MIRc chr1 7071108 7071131 + Simple_repeat_(CCT)n chr1 7071896 7072031 - SINE/tRNA-RTE_MamSINE1 chr1 7072309 7072414 + SINE/MIR_MIRb chr1 7072435 7073520 - LINE/L2_L2a chr1 7073523 7073861 - LINE/L2_L2-3_Crp chr1 7073882 7074032 + SINE/MIR_MIR chr1 7074374 7074622 - LINE/L1_L1ME4a chr1 7074688 7074814 - SINE/MIR_MIR chr1 7075272 7075459 - DNA/hAT-Charlie_MER112 chr1 7075663 7075962 - SINE/Alu_AluSx chr1 7077225 7077244 + Simple_repeat_(T)n chr1 7077947 7078392 - LTR/ERVL_LTR86A2 chr1 7078597 7079301 + LINE/L1_L1MEc chr1 7079316 7079472 + LINE/L1_L1MEc chr1 7079473 7079771 - SINE/Alu_AluSz chr1 7079772 7079954 + LINE/L1_L1MEc chr1 7079964 7080093 + LINE/L1_L1ME1 chr1 7080140 7080229 - SINE/MIR_MIR chr1 7080531 7080751 - SINE/Alu_AluSx1 chr1 7080762 7081026 - LTR/ERVL_LTR41C chr1 7081207 7081244 + Simple_repeat_(AATGAT)n chr1 7081276 7081438 - LTR/ERVL_LTR41C chr1 7081621 7081934 - DNA/hAT-Charlie_MER102b chr1 7082157 7082440 + SINE/Alu_AluSx1 chr1 7082441 7082492 + Simple_repeat_(ATAG)n chr1 7082577 7083025 + LINE/L2_L2b chr1 7084149 7084202 + Simple_repeat_(AT)n chr1 7084738 7084986 + DNA/hAT-Charlie_MER33 chr1 7084987 7085134 - LINE/L1_L1PA8 chr1 7085135 7085198 + DNA/hAT-Charlie_MER33 chr1 7086427 7086919 - LINE/L1_L1MB5 chr1 7086971 7087118 + SINE/MIR_MIR3 chr1 7087575 7087777 + SINE/MIR_MIR chr1 7088414 7088535 + LINE/L2_L2c chr1 7089192 7089364 - SINE/MIR_MIRb chr1 7089533 7089593 + Simple_repeat_(CCCAT)n chr1 7089597 7089928 - LINE/L2_L2b chr1 7092646 7093011 - LTR/ERVL-MaLR_MLT1I chr1 7093150 7093323 - DNA/hAT-Charlie_MER5A chr1 7093735 7093943 + SINE/MIR_MIRb chr1 7094965 7095300 + LINE/L2_L2a chr1 7095473 7095582 - SINE/MIR_MIRb chr1 7096027 7096056 + Simple_repeat_(TTAA)n chr1 7096082 7096143 - LINE/CR1_X5B_LINE chr1 7097535 7097634 - LINE/CR1_X5B_LINE chr1 7097673 7098028 + LINE/L1_L1MB3 chr1 7098100 7098159 + Simple_repeat_(TGTGTG)n chr1 7099065 7099350 - SINE/Alu_AluSx1 chr1 7099359 7099610 - LINE/L1_HAL1 chr1 7099696 7100336 - LINE/L1_HAL1 chr1 7101799 7102009 + Simple_repeat_(CA)n chr1 7102019 7102070 + Simple_repeat_(AC)n chr1 7102913 7103000 - LINE/L2_L2b chr1 7103281 7103309 - SINE/MIR_MIR1_Amn chr1 7103325 7104392 + Simple_repeat_(CA)n chr1 7105115 7105172 + Simple_repeat_(TTTATTAA)n chr1 7105661 7105951 + SINE/Alu_AluJb chr1 7106159 7106226 - SINE/MIR_MIRc chr1 7106443 7106588 - SINE/MIR_MIRc chr1 7106744 7106819 + LINE/CR1_X20_LINE chr1 7107081 7107206 - LINE/L2_L2d2 chr1 7107250 7107302 + Simple_repeat_(GT)n chr1 7107803 7107857 - SINE/MIR_MIRb chr1 7107997 7108160 + DNA/hAT-Charlie_MER20 chr1 7108203 7108379 + SINE/MIR_MIR chr1 7108555 7108686 - LINE/L2_L2 chr1 7108734 7109302 - LTR/ERVL-MaLR_MLT1F2 chr1 7109329 7109471 + SINE/MIR_MIRb chr1 7109503 7109649 - DNA/hAT-Charlie_MER117 chr1 7110868 7111348 - LTR/ERVL-MaLR_MLT1D chr1 7111458 7111603 + SINE/MIR_MIRb chr1 7111604 7111912 + SINE/Alu_AluSc8 chr1 7111913 7111968 + SINE/MIR_MIRb chr1 7112258 7112320 + LINE/L2_L2d2 chr1 7112410 7112549 - SINE/MIR_MIRc chr1 7114260 7114348 - LTR/ERVL_MLT2B4 chr1 7114349 7114577 - DNA/hAT-Charlie_MER30 chr1 7114578 7116055 - LTR/ERVL_MLT2B4 chr1 7116236 7116638 - DNA/TcMar-Tigger_Tigger13a chr1 7116639 7116951 - SINE/Alu_AluYm1 chr1 7116952 7116977 - DNA/TcMar-Tigger_Tigger13a chr1 7116978 7117270 - SINE/Alu_AluSq chr1 7117271 7117433 - DNA/TcMar-Tigger_Tigger13a chr1 7117451 7117559 - DNA/hAT-Blackjack_MER63B chr1 7117824 7118046 - DNA/hAT-Tip100_MamTip2b chr1 7118291 7118514 - SINE/Alu_AluJr chr1 7119775 7119967 - DNA/hAT-Charlie_MER105 chr1 7119979 7120130 - SINE/MIR_MIRb chr1 7120131 7120696 - LTR/ERVL-MaLR_MLT1F2 chr1 7120697 7120733 - SINE/MIR_MIRb chr1 7122816 7123044 + LINE/L2_L2d2 chr1 7123101 7123491 - LTR/ERVL-MaLR_MLT1B chr1 7124325 7124468 + LINE/L2_L2d2 chr1 7124377 7124640 + LINE/L2_L2c chr1 7124700 7124779 + LINE/L2_L2d2 chr1 7124782 7124995 - DNA/hAT-Tip100_MamTip2 chr1 7125288 7125311 + Simple_repeat_(ATTC)n chr1 7125316 7125744 - LINE/L2_L2b chr1 7125885 7126252 - LTR/ERVL-MaLR_THE1C chr1 7126769 7127068 - SINE/Alu_AluY chr1 7127248 7127270 + Simple_repeat_(T)n chr1 7127594 7127782 - LINE/L2_L2c chr1 7127783 7128361 + LTR/ERVL_MER68 chr1 7128362 7128606 - LINE/L2_L2c chr1 7128660 7128820 - SINE/Alu_AluSx chr1 7128832 7129137 - SINE/Alu_AluSz chr1 7129138 7129237 - SINE/MIR_MIRb chr1 7129465 7129619 + SINE/MIR_MIR chr1 7129634 7129913 - LINE/L1_L1ME1 chr1 7129920 7129955 + Simple_repeat_(TG)n chr1 7129956 7130238 - SINE/Alu_AluSz chr1 7130239 7130950 - LINE/L1_L1ME1 chr1 7130951 7131251 - SINE/Alu_AluSc chr1 7131252 7131773 - LINE/L1_L1ME1 chr1 7131774 7132047 + SINE/Alu_AluSx chr1 7132049 7132082 + Simple_repeat_(AC)n chr1 7132083 7132265 - LINE/L1_L1ME1 chr1 7132294 7132490 - SINE/Alu_AluJr chr1 7132564 7132660 + SINE/Alu_AluJo chr1 7132667 7132796 - LINE/L1_L1MC4a chr1 7132795 7133546 - LINE/L1_L1ME1 chr1 7133544 7133680 - LINE/L1_L1ME1 chr1 7133741 7133828 + LINE/L2_L2 chr1 7133906 7134283 - LINE/L1_L1PA16 chr1 7134284 7134607 - SINE/Alu_AluJb chr1 7134608 7134677 - LINE/L1_L1PA16 chr1 7134686 7134823 + LINE/L1_L1P4 chr1 7134801 7135104 + LINE/L1_L1P4 chr1 7135105 7135417 + SINE/Alu_AluSq chr1 7135418 7135524 + LINE/L1_L1P4 chr1 7135525 7135832 + LINE/L2_L2 chr1 7135846 7135937 - LTR/ERVL-MaLR_MLT1J2 chr1 7135942 7137304 + LINE/L2_L2 chr1 7137434 7137976 + LINE/L2_L2a chr1 7137977 7138291 - SINE/Alu_AluJr chr1 7138292 7138309 + LINE/L2_L2a chr1 7138714 7139001 + SINE/Alu_AluSz chr1 7139002 7139379 + Simple_repeat_(TATA)n chr1 7139959 7140217 - SINE/MIR_MIRb chr1 7141634 7141712 + SINE/MIR_MIRb chr1 7141853 7141977 - SINE/MIR_MIR3 chr1 7142462 7142876 + LTR/ERVL-MaLR_MSTB1 chr1 7143691 7144029 - DNA/TcMar-Tc2_Kanga2_a chr1 7144058 7144494 - LTR/ERVL-MaLR_MLT1B chr1 7144495 7144865 - DNA/TcMar-Tc2_Kanga2_a chr1 7144870 7145048 - SINE/MIR_MIR chr1 7145864 7146062 + SINE/MIR_MIRb chr1 7146019 7146096 - SINE/MIR_MIRb chr1 7146722 7147139 + Simple_repeat_(CA)n chr1 7147140 7147439 + SINE/Alu_AluY chr1 7147440 7148412 + Simple_repeat_(CA)n chr1 7150475 7150661 - DNA/hAT-Charlie_MER20 chr1 7153883 7154050 + SINE/MIR_MIR3 chr1 7154516 7154677 - DNA/hAT-Tip100_MamTip2b chr1 7155531 7155722 - SINE/Alu_AluJr chr1 7155891 7155973 - LINE/L2_L2c chr1 7155974 7156297 + SINE/Alu_AluSp chr1 7156298 7156503 - LINE/L2_L2c chr1 7156526 7156604 - LINE/L2_L2d2 chr1 7157006 7157061 + LINE/L1_L1ME2z chr1 7157062 7157373 + SINE/Alu_AluY chr1 7157374 7157466 + LINE/L1_L1ME2z chr1 7157467 7157877 + LINE/L1_L1ME2z chr1 7157878 7158176 + SINE/Alu_AluY chr1 7158177 7158785 + LINE/L1_L1ME2z chr1 7158779 7159062 + LINE/L1_L1ME3F chr1 7159534 7159848 - SINE/Alu_AluJr chr1 7159885 7159952 + LINE/L2_L2a chr1 7159965 7160250 + SINE/Alu_AluSq2 chr1 7160382 7161366 + LINE/L2_L2b chr1 7161374 7161404 - LINE/L2_L2a chr1 7161405 7161766 + LTR/ERVL-MaLR_THE1B chr1 7161767 7162291 - LINE/L2_L2a chr1 7162302 7162926 - LINE/L1_L1MB8 chr1 7162958 7163021 - LINE/L2_L2a chr1 7163063 7163693 - LINE/L2_L2a chr1 7163839 7164028 + LINE/L2_L2b chr1 7165237 7165427 - LINE/L1_L1ME3Cz chr1 7165428 7165731 - SINE/Alu_AluSx chr1 7165732 7166073 - LINE/L1_L1ME3Cz chr1 7166074 7166365 - SINE/Alu_AluY chr1 7166366 7166536 - LINE/L1_L1ME3Cz chr1 7166530 7166808 - LINE/L1_L1ME3Cz chr1 7166809 7167108 - SINE/Alu_AluSx1 chr1 7167109 7167761 - LINE/L1_L1ME3Cz chr1 7167762 7168034 - SINE/Alu_AluJr chr1 7168035 7168392 - LINE/L1_L1ME3Cz chr1 7168393 7168702 - SINE/Alu_AluSx chr1 7168703 7169514 - LINE/L1_L1ME3Cz chr1 7169515 7169826 - SINE/Alu_AluSz chr1 7169827 7170297 - LINE/L1_L1ME3Cz chr1 7170298 7170743 - LINE/L1_L1PA8 chr1 7170744 7171091 - LINE/L1_L1ME3Cz chr1 7171294 7171434 + LINE/L2_L2c chr1 7171402 7171522 + LINE/L2_L2c chr1 7171603 7172034 - LINE/L1_L1MB5 chr1 7172154 7172449 - SINE/Alu_AluSx1 chr1 7173394 7173687 - SINE/Alu_AluSp chr1 7175044 7175091 + Simple_repeat_(GT)n chr1 7176828 7176995 - LTR/ERVL-MaLR_MLT1J1 chr1 7179144 7179174 + LINE/L2_L2d2 chr1 7179200 7180084 + LINE/L1_L1ME3G chr1 7180085 7180370 + SINE/Alu_AluSq2 chr1 7180371 7180511 + LINE/L1_L1ME3G chr1 7180512 7180811 - SINE/Alu_AluJb chr1 7180812 7182258 + LINE/L1_L1ME3G chr1 7182259 7182513 + SINE/Alu_AluJb chr1 7182514 7182846 + LINE/L1_L1ME3G chr1 7182708 7183002 + LINE/L1_L1MC4a chr1 7183003 7183399 - LTR/ERVL-MaLR_MSTB chr1 7183400 7183953 + LINE/L1_L1MC4a chr1 7183954 7184519 + LINE/L1_L1MA2 chr1 7184520 7186317 + LINE/L1_L1MC4a chr1 7186328 7186428 + SINE/MIR_MIRb chr1 7186522 7186614 + SINE/MIR_MIR3 chr1 7186712 7186763 - LINE/L2_L2a chr1 7187234 7187534 + DNA/hAT-Charlie_Charlie18a chr1 7187895 7188264 - LTR/ERVL-MaLR_THE1D chr1 7188298 7189020 - LINE/L1_L1MB3 chr1 7189148 7189362 + LINE/L1_L1ME3G chr1 7189346 7190289 + LINE/L1_L1PB1 chr1 7190334 7190429 + Simple_repeat_(AT)n chr1 7190767 7190929 - SINE/MIR_MIRb chr1 7191854 7192013 + SINE/MIR_MIR3 chr1 7192694 7192807 - SINE/MIR_MIRb chr1 7193069 7193366 + SINE/Alu_AluSx1 chr1 7195210 7195504 + LTR/ERVL-MaLR_MLT1I chr1 7195737 7196030 + SINE/Alu_AluSx chr1 7196035 7196395 + LTR/ERVL-MaLR_MSTA chr1 7197052 7197185 - LINE/L2_L2d2 chr1 7197629 7197678 + Simple_repeat_(CA)n chr1 7197692 7197843 - SINE/MIR_MIRb chr1 7198549 7198783 + LINE/L2_L2a chr1 7198908 7199137 + LINE/L2_L2a chr1 7199893 7200032 - LINE/L1_L1ME3D chr1 7200037 7200870 - LINE/L1_L1MC4 chr1 7200876 7201790 - LINE/L1_L1MB7 chr1 7201659 7203817 - LINE/L1_L1M4 chr1 7203818 7204137 - SINE/Alu_AluY chr1 7204138 7204809 - LINE/L1_L1M4 chr1 7204810 7205128 - SINE/Alu_AluY chr1 7205129 7205402 - SINE/Alu_AluSp chr1 7205403 7205694 - LINE/L1_L1M4 chr1 7205684 7206445 - LINE/L1_L1MB7 chr1 7206523 7206681 - LINE/L1_L1ME3D chr1 7206666 7206824 - LINE/L1_L1M5 chr1 7207145 7207278 - LINE/L2_L2b chr1 7208051 7208203 - SINE/tRNA-RTE_MamSINE1 chr1 7208343 7208515 + LINE/L2_L2a chr1 7208647 7208822 - LINE/L2_L2b chr1 7209090 7209107 + Simple_repeat_(TTG)n chr1 7209811 7210973 - LINE/L1_L1MC4 chr1 7210971 7211584 - LINE/L1_L1ME2z chr1 7211584 7212536 - LINE/L1_L1MC4 chr1 7212525 7213608 - LINE/L1_L1MD1 chr1 7213609 7214347 - LINE/L1_L1PA4 chr1 7214348 7214360 - LINE/L1_L1MD1 chr1 7214361 7214665 - SINE/Alu_AluY chr1 7214666 7215083 - LINE/L1_L1MD1 chr1 7215206 7215390 - LINE/L1_L1MD1 chr1 7215391 7215688 - SINE/Alu_AluSx chr1 7215689 7215958 - LINE/L1_L1MD1 chr1 7215959 7216341 - LTR/ERVL-MaLR_MSTA chr1 7216342 7216544 - LINE/L1_L1MD1 chr1 7216545 7216802 - SINE/Alu_AluSz6 chr1 7216803 7217043 - LINE/L1_L1MD1 chr1 7217044 7217409 + LTR/ERVL-MaLR_THE1C chr1 7217410 7218260 - LINE/L1_L1MD1 chr1 7218229 7218971 - LINE/L1_L1MD1 chr1 7219090 7219182 - DNA/TcMar-Tigger_MamRep434 chr1 7219360 7219524 + snRNA_U1 chr1 7219543 7219572 + Simple_repeat_(A)n chr1 7219589 7219753 + LTR/ERVL-MaLR_MLT1A chr1 7219754 7219888 + SINE/Alu_FLAM_C chr1 7219889 7220101 + LTR/ERVL-MaLR_MLT1A chr1 7221256 7221306 + Simple_repeat_(TCAT)n chr1 7221644 7221802 + SINE/MIR_MIRb chr1 7221907 7221949 + Simple_repeat_(AC)n chr1 7222769 7222977 + SINE/MIR_MIRb chr1 7223147 7223173 + Simple_repeat_(CCAC)n chr1 7223323 7223408 + Simple_repeat_(TGTG)n chr1 7223454 7223521 - LINE/L2_L2c chr1 7225092 7225383 - SINE/Alu_AluYk2 chr1 7226457 7226805 - LINE/L1_L1ME4a chr1 7226827 7227132 - SINE/Alu_AluY chr1 7227318 7227628 - SINE/Alu_AluSp chr1 7228322 7228384 + SINE/Alu_Alu chr1 7229406 7229663 + Low_complexity_G-rich chr1 7230136 7230166 + Simple_repeat_(GCCCG)n chr1 7231328 7231351 + Simple_repeat_(TG)n chr1 7231351 7231374 + Simple_repeat_(GA)n chr1 7231375 7231672 - SINE/Alu_AluSx1 chr1 7231766 7231906 + SINE/MIR_MIRb chr1 7232479 7232655 + LINE/L2_L2a chr1 7233639 7233770 + LINE/L2_L2c chr1 7233767 7233870 + LINE/L2_L2c chr1 7234455 7234573 + LINE/L2_L2c chr1 7234789 7235086 - SINE/Alu_AluSx1 chr1 7235245 7235317 + SINE/MIR_MIRb chr1 7235345 7235643 + SINE/Alu_AluSx1 chr1 7236538 7236733 + SINE/MIR_MIRb chr1 7237943 7238041 - SINE/MIR_MIRb chr1 7238058 7238156 - LINE/L1_L1MC5 chr1 7238277 7238467 + SINE/MIR_MIR chr1 7238589 7238601 - LINE/L2_L2b chr1 7238602 7238896 + SINE/Alu_AluSx chr1 7238897 7238999 - LINE/L2_L2b chr1 7239003 7239051 + SINE/MIR_MIR chr1 7239065 7239635 - LINE/L1_L1MC5 chr1 7239636 7239660 + Simple_repeat_(T)n chr1 7239661 7239687 - LINE/L1_L1MC5 chr1 7239688 7240082 - LTR/ERVL-MaLR_MSTD chr1 7240083 7240518 - LINE/L1_L1MC5 chr1 7240519 7240805 - SINE/Alu_AluSz chr1 7240806 7240840 - LINE/L1_L1MC5 chr1 7240840 7242380 + LINE/L1_L1M4 chr1 7242377 7242647 - LINE/L1_L1M5 chr1 7242648 7242766 + SINE/Alu_AluY chr1 7242767 7242810 + Simple_repeat_(AAAT)n chr1 7242811 7242991 + SINE/Alu_AluY chr1 7242992 7243332 - LINE/L1_L1M5 chr1 7243327 7243810 - LINE/L1_L1PA4 chr1 7243812 7244905 + LINE/L1_L1PA4 chr1 7244906 7244938 + Simple_repeat_(ATAA)n chr1 7245757 7246110 - LTR/ERVL_LTR16C chr1 7246242 7246296 + SINE/MIR_MIR chr1 7246314 7246354 + Simple_repeat_(TATAGA)n chr1 7246671 7246938 - SINE/Alu_AluSx1 chr1 7251588 7251698 - LINE/L2_L2d chr1 7251768 7251852 - LINE/L2_L2d chr1 7251854 7251897 + Simple_repeat_(TG)n chr1 7251903 7252350 - LINE/L1_L1ME3Cz chr1 7253582 7253772 - SINE/MIR_MIRb chr1 7255032 7255299 + LINE/L1_L1PA7 chr1 7255302 7255340 + Low_complexity_A-rich chr1 7256278 7256584 + SINE/Alu_AluY chr1 7256660 7256812 - LTR/ERVL-MaLR_MLT1A0 chr1 7256882 7257130 - LTR/ERVL-MaLR_MLT1A0 chr1 7257181 7257311 - SINE/MIR_MIRb chr1 7257677 7257811 + DNA/hAT_MER53 chr1 7259366 7259527 + DNA/hAT-Charlie_MER5A1 chr1 7259694 7259870 - LINE/CR1_L3b chr1 7259871 7259986 + LINE/L2_L2a chr1 7260015 7260210 - DNA/hAT-Charlie_MER58A chr1 7262280 7262570 + SINE/Alu_AluSq2 chr1 7262764 7262813 + LINE/L2_L2c chr1 7264151 7264290 - DNA/hAT-Charlie_MER5A1 chr1 7265163 7265304 + DNA/hAT-Charlie_MER33 chr1 7265305 7265613 - SINE/Alu_AluSx1 chr1 7265614 7265783 + DNA/hAT-Charlie_MER33 chr1 7266464 7266926 - LINE/L2_L2a chr1 7267949 7269012 + LINE/L1_L1MB7 chr1 7269013 7269583 - LTR/ERVL-MaLR_MLT1F2 chr1 7269584 7270194 + LINE/L1_L1MEc chr1 7270195 7270243 + Simple_repeat_(ATATATACAC)n chr1 7270245 7270275 + Simple_repeat_(AC)n chr1 7270276 7270291 + LINE/L1_L1MEc chr1 7270292 7270595 - SINE/Alu_AluSq2 chr1 7270596 7270699 + LINE/L1_L1MEc chr1 7270700 7270822 + SINE/Alu_FLAM_A chr1 7270823 7270895 + LINE/L1_L1MEc chr1 7270907 7271010 + LINE/L1_L1M4 chr1 7271011 7271026 + LINE/L1_L1MB7 chr1 7271027 7271423 + LTR/ERVL-MaLR_MLT1B chr1 7271431 7271565 + LINE/L1_L1MB7 chr1 7271566 7271614 + Simple_repeat_(AGAT)n chr1 7271615 7272075 + LINE/L1_L1MB7 chr1 7272074 7272873 + LINE/L1_L1MB7 chr1 7272872 7273761 + LINE/L1_L1MB7 chr1 7273747 7274859 + LINE/L1_L1MEc chr1 7274860 7275175 + SINE/Alu_AluSx1 chr1 7275176 7276278 + LINE/L1_L1MEc chr1 7276279 7276304 + Simple_repeat_(AT)n chr1 7276305 7276606 - SINE/Alu_AluSc8 chr1 7276607 7277355 + LINE/L1_L1MEc chr1 7277457 7277492 + Simple_repeat_(GT)n chr1 7277752 7277774 + Simple_repeat_(CA)n chr1 7277905 7277948 + Simple_repeat_(GACATTA)n chr1 7277993 7278030 - LINE/L2_L2a chr1 7278031 7278198 - LINE/L1_L1MC5a chr1 7278475 7278644 - SINE/MIR_MIRb chr1 7278700 7279142 - LINE/L2_L2c chr1 7281800 7281844 + Simple_repeat_(TG)n chr1 7282022 7282084 - SINE/MIR_MIR3 chr1 7282446 7282624 + LINE/L2_L2b chr1 7282958 7283063 + LINE/L1_L1M5 chr1 7283368 7283814 + LINE/L2_L2b chr1 7284127 7284179 + Simple_repeat_(TTC)n chr1 7284180 7284216 + Simple_repeat_(ATT)n chr1 7284217 7284499 - SINE/Alu_AluSg chr1 7284695 7284813 + LINE/L2_L2b chr1 7286327 7286504 + DNA/hAT-Charlie_Charlie19a chr1 7286777 7286948 + SINE/MIR_MIRb chr1 7287575 7287602 + Simple_repeat_(CTT)n chr1 7287624 7288030 - LTR/ERVL-MaLR_MLT1J2 chr1 7288036 7288205 - SINE/MIR_MIR chr1 7288204 7288282 - LINE/L2_L2d2 chr1 7288735 7288777 + Simple_repeat_(GAGCAGA)n chr1 7288867 7289264 - LTR/ERVL-MaLR_MSTA chr1 7289472 7289501 + SINE/MIR_MIR3 chr1 7289546 7290095 + LTR/ERVL-MaLR_MLT1C2 chr1 7290521 7290696 - LINE/Dong-R4_Mam_R4 chr1 7290701 7290731 + Simple_repeat_(ATTATC)n chr1 7290888 7291014 - SINE/MIR_MIR3 chr1 7291385 7291477 + SINE/MIR_MIRb chr1 7294615 7294809 + LTR?_MamRep605 chr1 7295049 7295266 + SINE/MIR_MIR chr1 7295388 7295510 + LINE/L2_L2d chr1 7295632 7295809 - LINE/L2_L2a chr1 7295810 7296181 - LTR/ERVL-MaLR_MLT1F2 chr1 7296212 7296376 - SINE/MIR_MIR chr1 7296416 7297028 - LINE/L2_L2a chr1 7297225 7297378 - LINE/L1_L1MEc chr1 7297554 7297933 - LINE/L1_L1MEc chr1 7298704 7298758 - LINE/L2_L2a chr1 7299016 7299107 + SINE/MIR_MIR3 chr1 7299837 7299956 - DNA/hAT-Tip100_Arthur1A chr1 7300395 7300691 + SINE/Alu_AluSq chr1 7302984 7303161 + SINE/MIR_MIR chr1 7303573 7303898 - LINE/L2_L2a chr1 7303907 7304245 + LINE/L1_L1MB4 chr1 7304273 7305365 - LINE/L1_L1ME4b chr1 7305461 7306774 - LINE/L1_L1ME1 chr1 7306775 7307083 + DNA/TcMar-Tigger_MER2 chr1 7307084 7309287 - LINE/L1_L1ME1 chr1 7309288 7309598 - SINE/Alu_AluSc8 chr1 7309599 7310314 - LINE/L1_L1ME1 chr1 7310315 7310535 - DNA/hAT-Charlie_MER58A chr1 7310536 7310577 - LINE/L1_L1ME1 chr1 7310586 7310745 + Simple_repeat_(TTTC)n chr1 7310746 7311030 - SINE/Alu_AluSz6 chr1 7311031 7312633 - LINE/L1_L1MEf chr1 7312637 7313678 - LINE/L1_L1ME1 chr1 7314044 7314241 - LINE/CR1_L3 chr1 7314673 7314776 - Unknown_MamRep564 chr1 7315442 7315557 + SINE/MIR_MIRb chr1 7315593 7315863 - LTR/ERVL-MaLR_MLT1C2 chr1 7315864 7316247 - LTR/ERVL-MaLR_THE1C chr1 7316248 7316420 - LTR/ERVL-MaLR_MLT1C2 chr1 7316437 7316518 + SINE/MIR_MIRb chr1 7316609 7316875 - LINE/L2_L2c chr1 7317271 7317331 + Simple_repeat_(TTAGCTG)n chr1 7317342 7317502 + SINE/MIR_MIR chr1 7318771 7318873 + LINE/L2_L2a chr1 7319050 7319138 - LINE/L2_L2c chr1 7319231 7319618 + LTR/ERVL-MaLR_MSTA chr1 7319671 7319782 - LINE/L2_L2a chr1 7319799 7320308 - LINE/L1_L1MEd chr1 7320314 7320649 - LTR/ERVL_ERV3-16A3_I-int chr1 7320650 7320674 + Simple_repeat_(T)n chr1 7320675 7321218 - LTR/ERVL_ERV3-16A3_I-int chr1 7321252 7321715 - LTR/ERVL_ERV3-16A3_I-int chr1 7321844 7322250 - LTR/ERVL_ERV3-16A3_I-int chr1 7322335 7322506 - DNA/hAT-Charlie_MER58A chr1 7322550 7322603 + LINE/L1_L1M5 chr1 7322599 7322937 - LINE/L1_L1MEd chr1 7322944 7322958 + Simple_repeat_(CCCTC)n chr1 7322959 7323004 + Simple_repeat_(CCCTTTT)n chr1 7323005 7323023 + Simple_repeat_(CCCTC)n chr1 7323024 7323304 - LINE/L1_L1MEd chr1 7323326 7323481 + LINE/L2_L2a chr1 7323488 7323660 - SINE/Alu_AluSx chr1 7323708 7323837 + LINE/L2_L2a chr1 7323845 7324487 - LINE/L1_L1MEd chr1 7324611 7324822 + SINE/Alu_AluSx1 chr1 7324829 7325082 - LINE/L1_L1MEd chr1 7325085 7325846 - LINE/L2_L2a chr1 7325847 7326158 - SINE/Alu_AluSz chr1 7326159 7326298 - DNA/hAT-Tip100_Arthur1A chr1 7326299 7326378 - LINE/L2_L2a chr1 7326379 7326822 + LTR/ERVL-MaLR_MLT1C2 chr1 7326826 7327134 - LINE/L2_L2a chr1 7327135 7327432 + SINE/Alu_AluYc3 chr1 7327433 7327704 - LINE/L2_L2a chr1 7327707 7328215 - LINE/L1_L1MEd chr1 7328216 7328524 + SINE/Alu_AluJb chr1 7328525 7329008 - LINE/L1_L1MEd chr1 7330151 7330326 - SINE/MIR_MIRb chr1 7330961 7331257 + SINE/Alu_AluSx1 chr1 7331363 7331490 - DNA/hAT-Charlie_Charlie1b chr1 7331488 7331621 - DNA/hAT-Charlie_Charlie1 chr1 7332812 7332843 + Simple_repeat_(CA)n chr1 7335140 7335170 + Simple_repeat_(G)n chr1 7336110 7336135 + Simple_repeat_(GCCTG)n chr1 7337327 7337498 + LTR/ERVL_MLT2C2 chr1 7337699 7337980 + LTR/ERVL_MLT2C2 chr1 7337986 7338052 + Simple_repeat_(TA)n chr1 7338065 7338121 + DNA/MULE-MuDR_Ricksha_c chr1 7338502 7338717 - LINE/L2_L2a chr1 7338724 7338799 + LINE/L1_L1MB3 chr1 7338800 7339159 - LTR/ERVL-MaLR_MSTA chr1 7339186 7339313 + LINE/L1_L1MB8 chr1 7339330 7339397 - LINE/L2_L2a chr1 7339391 7339522 - SINE/MIR_MIRb chr1 7339592 7339895 - SINE/Alu_AluSz chr1 7340027 7340244 - DNA/hAT-Charlie_MER58A chr1 7340534 7340609 + Simple_repeat_(CTTC)n chr1 7340654 7340710 + Simple_repeat_(ATCC)n chr1 7340717 7340771 + Simple_repeat_(CATC)n chr1 7340780 7340885 - LINE/L2_L2b chr1 7341983 7342100 - SINE/MIR_MIR chr1 7342609 7342766 - SINE/MIR_MIR3 chr1 7343537 7344024 - LINE/L1_L1ME3F chr1 7344043 7344080 - LTR/ERVL-MaLR_MLT1G3 chr1 7344225 7344470 - LTR/ERVL-MaLR_MLT1H1 chr1 7344491 7344748 - LINE/L1_L1ME3F chr1 7344749 7345050 - SINE/Alu_AluY chr1 7345051 7345390 - LINE/L1_L1ME3F chr1 7346192 7346379 + SINE/MIR_MIR3 chr1 7347528 7347983 - LTR/ERVL-MaLR_MLT1C2 chr1 7349495 7349833 - LTR/ERVL-MaLR_MLT1B chr1 7350502 7350661 - LINE/CR1_L3 chr1 7350723 7350751 + Simple_repeat_(GT)n chr1 7352114 7352216 + Low_complexity_GA-rich chr1 7353101 7353177 + SINE/MIR_MIRc chr1 7353206 7353412 + LINE/L2_L2c chr1 7353413 7353723 - SINE/Alu_AluSp chr1 7353724 7353742 + LINE/L2_L2a chr1 7353750 7354137 - LINE/L1_L1PA15 chr1 7354138 7354448 - SINE/Alu_AluYm1 chr1 7354449 7354866 - LINE/L1_L1PA15 chr1 7354920 7355234 + SINE/Alu_AluSx1 chr1 7355235 7355414 + SINE/Alu_AluJb chr1 7355432 7355957 + LINE/L2_L2a chr1 7356351 7356400 + LINE/L2_L2c chr1 7356720 7356925 + SINE/MIR_MIRb chr1 7357413 7357559 + SINE/MIR_MIRb chr1 7359849 7360269 - LTR/ERVL-MaLR_MLT1C chr1 7360402 7360629 + SINE/MIR_MIRb chr1 7361250 7361371 - LINE/L2_L2b chr1 7361389 7361479 - LINE/L2_L2b chr1 7362030 7362126 - DNA_X9a_DNA chr1 7363935 7364117 - SINE/MIR_MIR3 chr1 7364141 7364226 - LINE/L2_L2a chr1 7364235 7364422 - LTR/ERVL-MaLR_MLT1A0 chr1 7364446 7364528 - SINE/Alu_AluJb chr1 7364862 7364900 + Simple_repeat_(TGAA)n chr1 7365937 7366138 + LINE/L2_L2b chr1 7368636 7368778 + LINE/L2_L2b chr1 7369346 7369899 - LTR/ERVL_MER77B chr1 7370691 7370816 - DNA/TcMar-Tc2_Kanga2_a chr1 7370901 7371210 - SINE/Alu_AluY chr1 7371240 7371559 - SINE/Alu_AluSx1 chr1 7371802 7371900 + DNA/hAT-Charlie_MER20 chr1 7371905 7371996 - SINE/MIR_MIR3 chr1 7372656 7372872 - SINE/MIR_MIR chr1 7373101 7373289 - DNA/hAT-Charlie_MER20 chr1 7374339 7374446 + LINE/CR1_L3 chr1 7374800 7374898 - SINE/MIR_MIRb chr1 7376195 7376229 + Simple_repeat_(TTTA)n chr1 7376623 7376744 - SINE/MIR_MIRb chr1 7377187 7377298 - LINE/L2_L2c chr1 7377307 7377590 - LTR/ERVL-MaLR_MLT1K chr1 7377609 7377674 + DNA/hAT-Charlie_MER58A chr1 7377675 7377946 + SINE/Alu_AluSz chr1 7377947 7378102 + DNA/hAT-Charlie_MER58A chr1 7378327 7378775 + LINE/L1_L1MB5 chr1 7380295 7380595 + SINE/Alu_AluSp chr1 7380921 7380967 - SINE/MIR_MIRb chr1 7381063 7381139 - LTR/ERVL-MaLR_MLT1J chr1 7381153 7381920 - LINE/L1_L1PA3 chr1 7381974 7382282 - LTR/ERVL-MaLR_MLT1J chr1 7382636 7382709 - SINE/MIR_MIRc chr1 7382826 7382874 + Simple_repeat_(TAGA)n chr1 7382942 7383090 - LINE/L2_L2a chr1 7383375 7383628 + SINE/MIR_MIR chr1 7383632 7383846 + Simple_repeat_(GAT)n chr1 7383856 7383953 - LINE/L2_L2a chr1 7385093 7385303 - SINE/Alu_AluSc8 chr1 7386604 7386788 + SINE/MIR_MIRb chr1 7386944 7387192 + LINE/L2_L2 chr1 7388048 7388194 + SINE/MIR_MIRb chr1 7390492 7390606 + SINE/MIR_MIR chr1 7391001 7391295 - SINE/Alu_AluSp chr1 7391329 7391358 + Simple_repeat_(GT)n chr1 7391359 7391470 - SINE/MIR_MIRb chr1 7391498 7392141 - LINE/L1_L1ME1 chr1 7392142 7392258 + SINE/Alu_AluJr chr1 7392259 7392554 - SINE/Alu_AluSp chr1 7392555 7392596 + SINE/Alu_AluJr chr1 7392597 7392869 + SINE/Alu_AluSx1 chr1 7392871 7392897 + Simple_repeat_(ATCA)n chr1 7392898 7393069 + SINE/Alu_AluJr chr1 7393070 7393246 - LINE/L1_L1ME1 chr1 7393255 7393534 - SINE/Alu_AluJb chr1 7393563 7394811 - LINE/L1_L1MA6 chr1 7394812 7394869 + Simple_repeat_(TG)n chr1 7394870 7395182 - SINE/Alu_AluSq chr1 7395183 7396016 - LINE/L1_L1MA6 chr1 7396017 7396443 + LTR/ERVL-MaLR_MSTB1 chr1 7396444 7398193 - LINE/L1_L1MA6 chr1 7398194 7398238 + Simple_repeat_(TA)n chr1 7398239 7399042 - LINE/L1_L1MA6 chr1 7399043 7399483 + LTR/ERVL-MaLR_MSTB1 chr1 7399484 7400790 - LINE/L1_L1MA6 chr1 7400876 7401828 - LINE/L1_L1ME1 chr1 7401896 7402053 + SINE/MIR_MIR3 chr1 7402992 7403072 - LINE/L2_L2b chr1 7403073 7403366 + SINE/Alu_AluSq chr1 7403367 7403659 - LINE/L2_L2b chr1 7403761 7403980 - SINE/MIR_MIR chr1 7403995 7404124 - SINE/MIR_MIRb chr1 7404907 7404964 + Simple_repeat_(TTTT)n chr1 7405354 7405671 - SINE/Alu_AluJb chr1 7405684 7405772 + SINE/MIR_MIRb chr1 7406560 7406666 + Simple_repeat_(AC)n chr1 7408242 7408341 + LINE/L2_L2b chr1 7408755 7408829 - SINE/MIR_MIR3 chr1 7409617 7409730 - SINE/MIR_MIRb chr1 7410898 7411021 + Simple_repeat_(TG)n chr1 7411336 7411556 + SINE/Alu_AluSc chr1 7411670 7411714 + LINE/L2_L2c chr1 7411758 7417922 - LINE/L1_L1PA4 chr1 7418061 7418193 + LINE/L2_L2 chr1 7418194 7418497 - SINE/Alu_AluSx1 chr1 7418498 7419034 + LINE/L2_L2 chr1 7419101 7419143 + LINE/L2_L2a chr1 7419144 7419456 - SINE/Alu_AluSq chr1 7419457 7420515 + LINE/L2_L2a chr1 7421141 7421453 - SINE/Alu_AluSq chr1 7421480 7421679 + SINE/MIR_MIR chr1 7422570 7422740 - SINE/MIR_MIRb chr1 7423189 7423493 + SINE/Alu_AluSq2 chr1 7423645 7423723 + SINE/MIR_MIRc chr1 7425943 7426007 - LINE/L2_L2a chr1 7428224 7428355 + LINE/L2_L2a chr1 7428463 7428673 + LINE/L2_L2b chr1 7428815 7428905 + LINE/L2_L2b chr1 7429101 7429324 + SINE/MIR_MIRb chr1 7429331 7429685 + Simple_repeat_(ATG)n chr1 7429694 7430078 - LTR/ERVL-MaLR_MSTA chr1 7430089 7430590 + Simple_repeat_(TGA)n chr1 7432018 7432099 - LINE/L2_L2d2 chr1 7432159 7432360 - SINE/MIR_MIRb chr1 7433546 7433851 + LINE/L1_L1MD1 chr1 7434452 7434481 + Simple_repeat_(CATT)n chr1 7434483 7434581 - LINE/L2_L2b chr1 7434582 7434759 - LTR/ERVL-MaLR_MLT1A1 chr1 7434760 7435075 + SINE/Alu_AluJb chr1 7435076 7435246 - LTR/ERVL-MaLR_MLT1A1 chr1 7435247 7435292 - LINE/L2_L2b chr1 7437266 7437643 - LTR/ERVL-MaLR_MLT1C chr1 7437799 7438160 - LINE/L2_L2a chr1 7438758 7438963 + SINE/MIR_MIRb chr1 7439876 7439943 + SINE/MIR_MIR3 chr1 7440715 7440924 + SINE/MIR_MIRb chr1 7441365 7441491 - LINE/L2_L2d2 chr1 7441598 7441719 - LINE/L2_L2d2 chr1 7442561 7442773 + SINE/MIR_MIRb chr1 7442774 7442906 - LINE/L2_L2b chr1 7442933 7443372 + LINE/L2_L2b chr1 7444297 7444336 - LINE/L2_L2d chr1 7444847 7445132 - LINE/L2_L2b chr1 7445877 7446069 + SINE/MIR_MIR3 chr1 7446564 7446650 - SINE/MIR_MIRb chr1 7446717 7446795 + LINE/CR1_L3 chr1 7447041 7447185 - DNA/hAT-Charlie_MER20 chr1 7447419 7447449 + Low_complexity_G-rich chr1 7447843 7447976 - SINE/MIR_MIRb chr1 7448517 7448877 - LTR/ERVL-MaLR_MLT1A0 chr1 7449489 7449799 + SINE/Alu_AluSz chr1 7450152 7450269 + SINE/MIR_MIRb chr1 7451540 7451619 - LINE/L2_L2b chr1 7451652 7451756 - LINE/L2_L2b chr1 7452074 7452199 + LINE/L2_L2b chr1 7452371 7452928 - LINE/L1_L1MB5 chr1 7454362 7454563 - SINE/MIR_MIRb chr1 7454940 7455055 - SINE/MIR_MIRb chr1 7456147 7456324 + Low_complexity_GA-rich chr1 7456350 7456426 + DNA/hAT-Tip100_MamTip2b chr1 7456981 7457026 + Simple_repeat_(CCTC)n chr1 7458257 7458409 + SINE/MIR_MIRb chr1 7459133 7459263 + SINE/MIR_MIRb chr1 7460183 7460467 + LINE/L2_L2c chr1 7461402 7461568 + DNA/hAT-Charlie_MER3 chr1 7462129 7462262 - SINE/Alu_AluSp chr1 7462265 7462554 - SINE/Alu_AluSx chr1 7462558 7462717 + DNA/hAT-Charlie_MER58B chr1 7463300 7463673 + Low_complexity_GA-rich chr1 7464324 7464542 + LINE/CR1_CR1-16_AMi chr1 7465340 7465604 - DNA/TcMar-Tigger_Tigger15a chr1 7467168 7467238 + DNA/TcMar-Mariner_MARNA chr1 7467455 7467549 + DNA/hAT-Charlie_Charlie4z chr1 7467770 7467798 + Simple_repeat_(CTTC)n chr1 7471159 7471227 + Simple_repeat_(CTGT)n chr1 7471518 7471671 - SINE/MIR_MIR3 chr1 7472549 7472649 - SINE/MIR_MIR chr1 7474133 7474216 + SINE/MIR_MIRc chr1 7474223 7474306 + SINE/MIR_MIRc chr1 7474313 7474504 + SINE/MIR_MIRb chr1 7475105 7475135 + Simple_repeat_(TTTC)n chr1 7475713 7475841 - SINE/MIR_MIRc chr1 7475858 7475897 - LINE/L2_L2d chr1 7476347 7476460 - LINE/L2_L2d chr1 7477940 7478094 + SINE/MIR_MIRb chr1 7479638 7479685 + Simple_repeat_(CCAGC)n chr1 7480417 7480438 + Simple_repeat_(GA)n chr1 7480439 7481323 + LINE/L2_L2a chr1 7481324 7481514 + LINE/L2_L2b chr1 7481558 7482148 - LINE/L1_L1ME2z chr1 7482153 7482316 + LINE/L2_L2a chr1 7483173 7483327 - LINE/L2_L2 chr1 7484305 7484452 + SINE/MIR_MIRb chr1 7484508 7484802 + SINE/Alu_AluY chr1 7485690 7485842 - SINE/MIR_MIR3 chr1 7485866 7486086 + DNA/TcMar-Tigger_Tigger19a chr1 7486190 7486393 - DNA/hAT-Blackjack_MER63A chr1 7486905 7487090 + SINE/MIR_MIR chr1 7487336 7487519 - DNA/hAT-Tip100_MER96B chr1 7487600 7487634 + Simple_repeat_(GCTGAG)n chr1 7487635 7487692 + LINE/L2_L2b chr1 7488479 7488986 + Simple_repeat_(CA)n chr1 7489497 7489613 - LINE/L2_L2c chr1 7490370 7490663 + SINE/Alu_AluSx chr1 7492549 7493514 + Simple_repeat_(ACAC)n chr1 7494522 7494826 + SINE/Alu_AluSz6 chr1 7497394 7497450 + SINE/MIR_MIRb chr1 7498157 7500460 + Simple_repeat_(TG)n chr1 7501550 7501714 + DNA/hAT_MER53 chr1 7502970 7503065 + LINE/L2_L2b chr1 7506406 7507273 + Simple_repeat_(CACTCA)n chr1 7508020 7508232 - SINE/MIR_MIRb chr1 7508553 7508637 + LINE/L2_L2b chr1 7508638 7508748 + Low_complexity_GA-rich chr1 7509576 7509696 + DNA?_MER131 chr1 7509993 7510027 + Low_complexity_A-rich chr1 7510239 7510293 - DNA/hAT-Charlie_MER5B chr1 7511205 7511436 + SINE/MIR_MIRb chr1 7512135 7512406 - LINE/L2_L2a chr1 7512770 7513280 - LINE/L2_L2a chr1 7513366 7513640 - LTR/ERVL-MaLR_MLT1C chr1 7513641 7513927 + SINE/Alu_AluSp chr1 7513928 7514126 - LTR/ERVL-MaLR_MLT1C chr1 7515337 7515450 + SINE/MIR_MIR chr1 7516539 7516604 + LINE/CR1_Plat_L3 chr1 7516733 7516890 + LINE/CR1_L3b chr1 7516891 7517052 - LINE/L1_L1ME4a chr1 7517065 7517327 - LINE/L1_L1ME4a chr1 7517346 7517505 + SINE/MIR_MIRb chr1 7519777 7520040 + LINE/L2_L2 chr1 7520201 7520263 + Simple_repeat_(CCT)n chr1 7520899 7520939 + Simple_repeat_(CACA)n chr1 7520985 7521281 + DNA/hAT-Charlie_Charlie18a chr1 7521285 7521531 - LINE/L1_L1MD1 chr1 7521547 7521842 - SINE/Alu_AluSg chr1 7521845 7522863 - LINE/L1_L1MD1 chr1 7522864 7523166 - SINE/Alu_AluSq chr1 7523167 7523596 - LINE/L1_L1MD1 chr1 7523597 7523908 + SINE/Alu_AluY chr1 7523909 7523920 - LINE/L1_L1MD1 chr1 7523921 7524225 + SINE/Alu_AluSx4 chr1 7524226 7524668 - LINE/L1_L1MD1 chr1 7524945 7524986 + Simple_repeat_(CATT)n chr1 7525951 7526115 + DNA/hAT-Tip100_MER96B chr1 7526158 7526191 + Simple_repeat_(AAGCAG)n chr1 7527136 7527291 - SINE/MIR_MIRb chr1 7528298 7528426 - LINE/L1_L1ME4a chr1 7528789 7528856 + Simple_repeat_(GAAT)n chr1 7529001 7529131 + SINE/MIR_MIRc chr1 7529434 7529620 - SINE/MIR_MIR chr1 7529634 7529847 + SINE/MIR_MIR chr1 7530544 7530658 - SINE/MIR_MIRb chr1 7530808 7531116 - SINE/Alu_AluSq2 chr1 7531154 7531207 + Low_complexity_G-rich chr1 7531309 7531431 + SINE/MIR_MIR chr1 7532292 7532563 - SINE/Alu_AluJr chr1 7532787 7532828 + Low_complexity_A-rich chr1 7532982 7533072 - LTR/ERVL_LTR79 chr1 7533631 7533944 + SINE/Alu_AluSz chr1 7534919 7534933 + Low_complexity_GA-rich chr1 7534934 7534999 + Low_complexity_G-rich chr1 7538038 7538097 + DNA/hAT-Tip100_Arthur1A chr1 7538098 7538299 - DNA/hAT-Blackjack_MER63A chr1 7538300 7538390 + DNA/hAT-Tip100_Arthur1A chr1 7538391 7538677 + SINE/Alu_AluJb chr1 7538678 7538716 + DNA/hAT-Tip100_Arthur1A chr1 7539101 7539501 + DNA/hAT-Charlie_Charlie8 chr1 7539513 7539590 - DNA/hAT-Tip100_MER91C chr1 7539905 7540226 + LINE/L2_L2c chr1 7540229 7540261 + Simple_repeat_(ATGG)n chr1 7541122 7541188 + Simple_repeat_(CCCT)n chr1 7541864 7542020 + LINE/L2_L2a chr1 7542117 7542423 - LINE/L1_L1ME4a chr1 7542424 7542829 - SINE/Alu_AluSz chr1 7542839 7542880 + Simple_repeat_(GT)n chr1 7542881 7543166 - SINE/Alu_AluY chr1 7543167 7543349 - LINE/L1_L1ME4a chr1 7543350 7543867 + DNA/PiggyBac_MER75 chr1 7543901 7544057 + DNA/hAT-Tip100_MER45A chr1 7544063 7544415 - LTR/ERVL-MaLR_THE1A chr1 7544615 7545054 - LTR/ERVL-MaLR_MSTA1 chr1 7545241 7545643 - LINE/L1_L1MC4 chr1 7545668 7545943 - LINE/L1_L1PA10 chr1 7545944 7546255 - SINE/Alu_AluY chr1 7546256 7546839 - LINE/L1_L1PA10 chr1 7546821 7546995 - LINE/L1_L1MC4 chr1 7546999 7547347 - LINE/L1_L1MC4 chr1 7547349 7547390 + Simple_repeat_(CA)n chr1 7547392 7547589 + DNA/TcMar-Tigger_MER2 chr1 7547591 7547775 + DNA/hAT-Charlie_MER30 chr1 7547914 7548037 + DNA/hAT-Charlie_MER117 chr1 7549608 7549832 - LINE/L2_L2a chr1 7549825 7549892 - LINE/L2_L2b chr1 7550061 7550117 + Low_complexity_G-rich chr1 7551303 7551372 + Simple_repeat_(TGTG)n chr1 7551941 7552113 - SINE/MIR_MIRb chr1 7552118 7552187 - SINE/MIR_MIR3 chr1 7553100 7553308 + Simple_repeat_(TGAA)n chr1 7553392 7553500 - LINE/L2_L2c chr1 7554316 7554366 - LINE/L2_L2b chr1 7554428 7554583 - DNA/hAT-Charlie_MER117 chr1 7555447 7555475 + Simple_repeat_(CCT)n chr1 7555895 7556414 - LTR/ERVL_MER74A chr1 7556890 7556996 - SINE/MIR_MIR chr1 7557027 7557325 + SINE/Alu_AluSx3 chr1 7557397 7557500 + SINE/MIR_MIR3 chr1 7557667 7557765 - LINE/L2_L2b chr1 7557987 7558023 + Low_complexity_G-rich chr1 7560450 7560483 + SINE/MIR_MIR3 chr1 7563701 7563772 - SINE/MIR_MIRc chr1 7563824 7563973 - LINE/L2_L2a chr1 7564559 7564658 + Simple_repeat_(TGCGTG)n chr1 7564904 7564991 + Low_complexity_GA-rich chr1 7565736 7566113 - LTR/ERVL-MaLR_MLT1A chr1 7566174 7566272 + SINE/MIR_MIR1_Amn chr1 7567618 7567748 + DNA/hAT-Charlie_MER5A1 chr1 7567819 7568059 + SINE/MIR_MIR chr1 7568066 7569832 + Simple_repeat_(TCA)n chr1 7570812 7570993 + SINE/MIR_MIRc chr1 7571077 7571321 - LTR/ERVL-MaLR_MLT1A0 chr1 7571344 7572660 - LINE/L1_L1PREC2 chr1 7572663 7572793 - LTR/ERVL-MaLR_MLT1A0 chr1 7573287 7573358 - SINE/MIR_MIRb chr1 7573444 7573755 - LTR/ERVL-MaLR_MLT1C2 chr1 7573756 7574056 - SINE/Alu_AluSx chr1 7574057 7574099 - LTR/ERVL-MaLR_MLT1C2 chr1 7574210 7574251 + Simple_repeat_(GAGCTGG)n chr1 7575041 7575180 + SINE/MIR_MIRb chr1 7575480 7575616 + SINE/MIR_MIR3 chr1 7575651 7575768 + DNA/hAT-Charlie_MER117 chr1 7576001 7576025 + Simple_repeat_(GTTTT)n chr1 7576026 7576309 - SINE/Alu_AluSx1 chr1 7576325 7576685 + LTR/ERVL-MaLR_MLT1A0 chr1 7577697 7577902 - DNA/hAT-Charlie_MER58A chr1 7577960 7578134 + DNA/hAT-Charlie_MER103C chr1 7578603 7578939 - LTR/ERVL-MaLR_MSTD chr1 7579394 7579537 - SINE/MIR_MIR chr1 7579538 7579859 - SINE/Alu_AluSx1 chr1 7579860 7579877 - SINE/MIR_MIR chr1 7580069 7580169 - LINE/L2_L2b chr1 7580874 7581018 + SINE/MIR_MIR chr1 7581766 7581932 + SINE/MIR_MIRb chr1 7582496 7582883 - LTR/ERVL-MaLR_MLT1J chr1 7583383 7583535 + LINE/CR1_L3 chr1 7584118 7584571 - LTR/ERV1_MER31B chr1 7584574 7584691 - LINE/L2_L2b chr1 7584755 7584940 + DNA/hAT-Charlie_MER20 chr1 7584944 7585214 - DNA/TcMar-Tigger_MER46C chr1 7585217 7585606 - LINE/L2_L2a chr1 7587003 7587049 + Simple_repeat_(ACC)n chr1 7590891 7590935 + Simple_repeat_(TTACTT)n chr1 7591624 7591885 + LINE/L2_L2b chr1 7591886 7592198 - SINE/Alu_AluSq2 chr1 7592199 7592256 + LINE/L2_L2b chr1 7592828 7593043 - SINE/MIR_MIRb chr1 7593236 7593380 - LINE/L2_L2a chr1 7593453 7593489 + SINE/Alu_Alu chr1 7593490 7593790 - SINE/Alu_AluSz chr1 7593791 7594062 + SINE/Alu_AluSx chr1 7594064 7594245 + Low_complexity_GA-rich chr1 7594247 7595093 - LINE/L2_L2a chr1 7595577 7595701 - SINE/MIR_MIRc chr1 7597150 7597361 + SINE/MIR_MIRb chr1 7597525 7597815 - LTR/ERVL_MLT2D chr1 7597817 7597936 - LINE/L1_L1PA8 chr1 7597938 7600485 - LINE/L1_L1PA4 chr1 7600486 7602002 - Retroposon/SVA_SVA_D chr1 7602003 7602080 - LINE/L1_L1PA4 chr1 7602081 7602176 + Simple_repeat_(TTCC)n chr1 7602177 7605817 - LINE/L1_L1PA4 chr1 7605818 7605919 - LTR/ERVL_MLT2D chr1 7606890 7607046 + LINE/L2_L2c chr1 7607056 7607726 + Simple_repeat_(GGAT)n chr1 7607727 7608036 + LINE/CR1_L3 chr1 7609673 7609878 + SINE/MIR_MIRb chr1 7609890 7609964 + SINE/MIR_MIR3 chr1 7610254 7610409 + LINE/L2_L2b chr1 7610960 7611158 + SINE/MIR_MIRb chr1 7611224 7611389 - DNA/hAT-Tip100_MER96B chr1 7611877 7612631 - LTR/ERV1_LTR1F chr1 7613295 7613339 - LINE/L2_L2d chr1 7615036 7615080 + Simple_repeat_(TTCA)n chr1 7615373 7615584 + SINE/MIR_MIRb chr1 7615813 7616041 + SINE/MIR_MIRc chr1 7617371 7617461 + LINE/CR1_L3b chr1 7617837 7617948 + SINE/MIR_MIRb chr1 7618162 7618272 + LINE/L2_L2c chr1 7618725 7618805 + Unknown_UCON57 chr1 7619230 7619601 - LTR/ERVL-MaLR_MLT1L chr1 7619653 7619903 - SINE/Alu_AluSz chr1 7620171 7620367 + DNA/hAT-Charlie_MER20 chr1 7620603 7620703 + LINE/L2_L2c chr1 7622141 7622405 - SINE/MIR_MIRb chr1 7622742 7622842 - LINE/L2_L2b chr1 7622905 7623266 + LINE/L2_L2a chr1 7623267 7623293 + Simple_repeat_(TGAA)n chr1 7623294 7623306 + LINE/L2_L2a chr1 7623308 7623477 - SINE/MIR_MIRb chr1 7623573 7623775 - SINE/Alu_AluSx chr1 7624211 7624330 + DNA/hAT-Charlie_MER117 chr1 7624676 7625006 - LINE/L2_L2d2 chr1 7626557 7626669 - DNA/TcMar-Tigger_Tigger19b chr1 7626644 7626841 + SINE/MIR_MIR3 chr1 7627447 7627683 - SINE/MIR_MIR chr1 7627722 7627894 + SINE/MIR_MIR chr1 7628023 7628400 - LTR/ERVL-MaLR_MLT1K chr1 7628456 7628596 + SINE/MIR_MIRc chr1 7629354 7629669 - LINE/CR1_X7A_LINE chr1 7630004 7630105 - LINE/CR1_CR1_Mam chr1 7631991 7632044 + Simple_repeat_(GCCCAGTGCC)n chr1 7634108 7634598 - LINE/L2_L2 chr1 7634793 7634818 + Simple_repeat_(TGG)n chr1 7635326 7635627 + SINE/Alu_AluY chr1 7636311 7636379 + DNA/hAT-Charlie_Charlie24 chr1 7636435 7636726 + SINE/Alu_AluSq2 chr1 7636973 7637078 - LINE/L2_L2c chr1 7637508 7637625 + SINE/MIR_MIRc chr1 7637639 7637905 + SINE/MIR_MIRc chr1 7638067 7638103 + Simple_repeat_(TTCA)n chr1 7638359 7638655 + SINE/Alu_AluSx chr1 7638986 7639278 - SINE/Alu_AluY chr1 7639492 7639786 + SINE/Alu_AluSz chr1 7640081 7640291 + DNA/hAT-Charlie_MER20 chr1 7640713 7640801 - SINE/MIR_MIRc chr1 7642666 7642811 + SINE/MIR_MIRb chr1 7644414 7644788 - LTR/ERVL-MaLR_MLT1J chr1 7644754 7644794 - LTR/ERVL_LTR67B chr1 7646177 7646230 + Simple_repeat_(GGTGAG)n chr1 7647804 7648110 + SINE/Alu_AluJr chr1 7648112 7648416 + SINE/Alu_AluSx1 chr1 7648744 7648878 - DNA/hAT-Charlie_MER5A chr1 7651733 7651843 - SINE/MIR_MIR3 chr1 7652817 7652999 + SINE/MIR_MIRb chr1 7653022 7653185 - DNA/hAT-Charlie_MER5A chr1 7653250 7653553 - SINE/Alu_AluJb chr1 7653751 7653889 - LINE/L2_L2a chr1 7653921 7654032 - DNA/hAT-Charlie_MER5A chr1 7654099 7654408 + SINE/Alu_AluSz chr1 7654470 7655796 + Simple_repeat_(AC)n chr1 7655894 7655986 + LTR/ERVL-MaLR_MSTD chr1 7656019 7656192 + LTR/ERVL-MaLR_MSTD chr1 7656200 7656564 - LTR/ERVL-MaLR_MLT1B chr1 7658366 7658455 + DNA/hAT-Charlie_MER5B chr1 7658498 7658636 + SINE/MIR_MIRb chr1 7659253 7659558 + SINE/Alu_AluSg4 chr1 7659825 7659986 - SINE/MIR_MIRb chr1 7659988 7660072 + SINE/Alu_AluJb chr1 7660073 7660366 - SINE/Alu_AluSp chr1 7660367 7660557 + SINE/Alu_AluJb chr1 7661808 7661840 + Simple_repeat_(CAGCCC)n chr1 7663283 7663338 + Simple_repeat_(TG)n chr1 7665432 7665731 + SINE/Alu_AluJb chr1 7665886 7666191 + SINE/Alu_AluSq2 chr1 7666868 7666904 + Simple_repeat_(TGTTT)n chr1 7666905 7667188 - SINE/Alu_AluSq2 chr1 7667407 7667471 + SINE/MIR_MIR3 chr1 7668098 7668261 + SINE/MIR_MIR3 chr1 7668389 7668443 + Simple_repeat_(AC)n chr1 7669085 7669194 - SINE/MIR_MIRc chr1 7671104 7671177 + SINE/MIR_MIR3 chr1 7671433 7671573 - LTR/ERVL_LTR33 chr1 7671611 7671717 - DNA/hAT-Charlie_Charlie4z chr1 7671725 7671959 + LINE/L2_L2b chr1 7672414 7672716 - SINE/Alu_AluSz chr1 7673234 7673406 + SINE/MIR_MIRb chr1 7673804 7673958 - LINE/L2_L2b chr1 7674498 7674805 + SINE/Alu_AluSx1 chr1 7674840 7675502 - LINE/L2_L2a chr1 7675673 7675823 - LINE/L2_L2a chr1 7676894 7677007 - LINE/L2_L2b chr1 7677322 7677530 - LINE/L2_L2b chr1 7680221 7680259 + Simple_repeat_(CCCTC)n chr1 7680843 7680861 + Simple_repeat_(CG)n chr1 7680862 7680892 + Simple_repeat_(CAG)n chr1 7681149 7681261 + LINE/L2_L2b chr1 7681320 7681430 - LINE/L2_L2b chr1 7682546 7682587 + Simple_repeat_(TGGCCTG)n chr1 7682890 7683202 + SINE/Alu_AluY chr1 7683273 7683380 + DNA/hAT-Charlie_Charlie16 chr1 7684367 7684795 - LTR/ERVL-MaLR_MLT1C2 chr1 7685215 7685305 - LINE/L2_L2d chr1 7686103 7687456 - LINE/L2_L2a chr1 7688005 7688304 - SINE/Alu_AluSz chr1 7688980 7689282 + SINE/Alu_AluSq2 chr1 7689293 7689592 + SINE/Alu_AluJb chr1 7689665 7689797 - SINE/MIR_MIR3 chr1 7689880 7690179 + SINE/Alu_AluSq chr1 7690767 7690846 - SINE/MIR_MIR chr1 7690931 7691530 - LINE/L2_L2b chr1 7691724 7691767 + Low_complexity_GA-rich chr1 7691851 7691953 + LINE/L2_L2b chr1 7691954 7692008 + Simple_repeat_(AGGG)n chr1 7692504 7692550 + Simple_repeat_(TGAA)n chr1 7693072 7693429 - LTR/ERVL-MaLR_THE1B chr1 7693430 7694107 - LTR/ERVL_MER77B chr1 7694682 7694719 + Simple_repeat_(CTCC)n chr1 7696185 7696493 - SINE/Alu_AluSx3 chr1 7697240 7697469 - SINE/MIR_MIRb chr1 7697874 7697969 - LTR/ERVL-MaLR_MLT1H2 chr1 7698180 7698402 - DNA/hAT-Charlie_MER30 chr1 7698420 7698569 - SINE/MIR_MIR1_Amn chr1 7699054 7699088 + Simple_repeat_(TTCTA)n chr1 7699111 7700313 - LINE/L1_L1MB8 chr1 7700471 7700852 - LTR/ERVL-MaLR_MLT1I chr1 7700958 7701044 + SINE/Alu_AluJb chr1 7701127 7701328 + SINE/MIR_MIRb chr1 7701415 7701719 - SINE/Alu_AluJb chr1 7702333 7702360 + Simple_repeat_(AAAC)n chr1 7702542 7702641 - LINE/L2_L2c chr1 7702689 7702857 - LINE/L1_L1ME3A chr1 7702881 7702980 - SINE/MIR_MIR chr1 7703136 7703376 - LINE/L2_L2c chr1 7704012 7704112 + Simple_repeat_(GCCTCCG)n chr1 7704248 7704282 + Simple_repeat_(GGCGG)n chr1 7705483 7705571 + Low_complexity_G-rich chr1 7706884 7707183 - SINE/Alu_AluSg chr1 7707388 7707660 - SINE/Alu_AluJb chr1 7708049 7708349 + SINE/Alu_AluJb chr1 7708350 7708511 + SINE/Alu_AluJr chr1 7709715 7709832 + SINE/MIR_MIR3 chr1 7709845 7709975 + DNA/hAT-Charlie_MER112 chr1 7710265 7710327 - LTR/ERVL-MaLR_MLT1A0 chr1 7710328 7710625 + SINE/Alu_AluJr chr1 7710626 7711013 - LTR/ERVL-MaLR_MLT1K chr1 7711029 7711372 - LTR/ERVL-MaLR_MLT1A0 chr1 7711873 7711992 - DNA/TcMar-Tigger_Tigger16a chr1 7712327 7712617 - SINE/Alu_AluJb chr1 7712707 7713064 - LTR/ERVL-MaLR_THE1B chr1 7713133 7713227 - DNA/TcMar-Tigger_Tigger16a chr1 7713462 7713673 - DNA/hAT-Charlie_MER103C chr1 7713720 7713839 + DNA/hAT-Charlie_Charlie4z chr1 7714322 7714528 + LINE/RTE-X_L4_A_Mam chr1 7714529 7714821 - SINE/Alu_AluSx chr1 7714822 7714906 + LINE/RTE-X_L4_A_Mam chr1 7715433 7715591 - SINE/Alu_FRAM chr1 7716026 7716329 - SINE/Alu_AluJr chr1 7716748 7717459 + LINE/L1_L1MA8 chr1 7717464 7717749 + SINE/Alu_AluJb chr1 7717925 7718040 - LINE/L2_L2b chr1 7718246 7718282 + LINE/L2_L2b chr1 7718555 7718843 - SINE/Alu_AluSx chr1 7720370 7720675 - SINE/Alu_AluSx1 chr1 7721090 7721141 - LINE/L2_L2c chr1 7721232 7721460 + SINE/MIR_MIRb chr1 7721725 7721784 - SINE/Alu_Alu chr1 7721785 7721911 - SINE/Alu_AluSx chr1 7722802 7722930 + SINE/Alu_FLAM_C chr1 7722938 7723819 + LINE/L1_L1MC3 chr1 7723820 7724118 - SINE/Alu_AluSx1 chr1 7724119 7724459 + LINE/L1_L1MC3 chr1 7724551 7724837 + SINE/Alu_AluSc8 chr1 7724843 7725025 - SINE/MIR_MIRb chr1 7725664 7725827 + DNA/hAT-Charlie_MER5A chr1 7726258 7726307 - LINE/L2_L2d chr1 7727122 7727421 - SINE/Alu_AluY chr1 7727490 7727588 + LINE/L2_L2a chr1 7729115 7729415 - SINE/Alu_AluSz chr1 7729683 7729827 + SINE/MIR_MIR chr1 7730127 7730273 - SINE/MIR_MIR chr1 7730263 7730456 + LINE/L2_L2b chr1 7730615 7730901 + SINE/Alu_AluSz chr1 7730903 7730929 + Simple_repeat_(TC)n chr1 7730929 7730998 + Simple_repeat_(TA)n chr1 7731292 7731600 + SINE/Alu_AluSg chr1 7731604 7731881 + SINE/Alu_AluJb chr1 7732073 7732107 + Simple_repeat_(TAATT)n chr1 7732539 7732579 + Simple_repeat_(GAGCGG)n chr1 7732912 7733225 + SINE/Alu_AluJr chr1 7733950 7734246 - SINE/Alu_AluSx chr1 7735203 7735215 + Simple_repeat_(AGA)n chr1 7735216 7735464 + SINE/Alu_AluSz chr1 7735465 7735478 + Simple_repeat_(AGA)n chr1 7735765 7736074 - SINE/Alu_AluSx1 chr1 7736075 7736204 - SINE/Alu_AluJo chr1 7739478 7739838 - LTR/ERVL-MaLR_THE1B chr1 7739858 7739934 - LTR/ERVL-MaLR_THE1B chr1 7739954 7740032 - LTR/ERVL-MaLR_THE1B chr1 7740052 7740130 - LTR/ERVL-MaLR_THE1B chr1 7740190 7740405 - DNA/hAT-Charlie_MER58A chr1 7740691 7740748 + DNA/hAT-Charlie_Charlie5 chr1 7740788 7740919 + LINE/L1_HAL1ME chr1 7741070 7741179 + LINE/L1_L1MEj chr1 7741282 7741574 + SINE/Alu_AluY chr1 7741613 7741852 + SINE/Alu_AluSx1 chr1 7741853 7741936 - SINE/Alu_AluYj4 chr1 7741942 7741961 + Simple_repeat_(T)n chr1 7741997 7742044 + Low_complexity_A-rich chr1 7742124 7742147 + Simple_repeat_(TA)n chr1 7742148 7742225 + LINE/L1_L1M5 chr1 7742357 7742423 + Simple_repeat_(AT)n chr1 7742429 7742595 + LINE/L1_L1M5 chr1 7742838 7743153 - SINE/Alu_AluSx chr1 7743159 7743192 + Simple_repeat_(TATTTT)n chr1 7743246 7743287 - LINE/L1_L1ME1 chr1 7743315 7743664 - LINE/L1_L1ME1 chr1 7743815 7744126 - SINE/Alu_AluY chr1 7744129 7744431 - SINE/Alu_AluSz chr1 7744766 7744820 + LINE/CR1_L3 chr1 7745245 7745542 + SINE/Alu_AluSx1 chr1 7746216 7746286 - DNA/hAT-Charlie_Charlie15b chr1 7746287 7746424 - LINE/L1_L1MB4 chr1 7746425 7746511 - DNA/hAT-Charlie_Charlie15b chr1 7746634 7746936 - SINE/Alu_AluSz chr1 7747199 7747369 + DNA/hAT-Charlie_Charlie4z chr1 7747907 7748211 - SINE/Alu_AluSz chr1 7748239 7748273 + Simple_repeat_(AAAC)n chr1 7748284 7748395 + SINE/Alu_FLAM_A chr1 7748904 7749017 - SINE/MIR_MIRb chr1 7751703 7751946 - DNA/PiggyBac_MER75B chr1 7755044 7755352 + SINE/Alu_AluSz chr1 7756567 7756847 + SINE/Alu_AluJb chr1 7756848 7756875 + Simple_repeat_(AAACA)n chr1 7756900 7756933 + Simple_repeat_(AATTT)n chr1 7757192 7757408 - DNA/hAT-Charlie_MER58A chr1 7757485 7757755 + SINE/Alu_AluSz chr1 7758654 7758955 + SINE/Alu_AluYk4 chr1 7759032 7759148 + SINE/Alu_AluJb chr1 7759149 7759174 + Simple_repeat_(AGAAA)n chr1 7759503 7759538 + Simple_repeat_(T)n chr1 7761595 7761655 + DNA/hAT-Blackjack_MER94B chr1 7761883 7762133 + SINE/Alu_AluJr4 chr1 7763019 7763202 + SINE/MIR_MIRb chr1 7764677 7764973 + SINE/Alu_AluSz chr1 7765746 7766045 + SINE/Alu_AluY chr1 7766578 7766614 + Simple_repeat_(CACACACG)n chr1 7767045 7767075 + Simple_repeat_(TGTT)n chr1 7768114 7768131 + Simple_repeat_(A)n chr1 7771353 7771380 + Simple_repeat_(CGC)n chr1 7772065 7772249 + SINE/MIR_MIRb chr1 7772373 7772488 - LINE/L2_L2d2 chr1 7772607 7772911 + SINE/Alu_AluSp chr1 7773986 7774364 + LINE/RTE-X_L5 chr1 7774393 7775338 - LINE/L1_L1MB5 chr1 7775339 7775648 - SINE/Alu_AluSx1 chr1 7775649 7776715 - LINE/L1_L1MB5 chr1 7776817 7777110 - SINE/Alu_AluSx1 chr1 7778148 7778168 + Simple_repeat_(ATC)n chr1 7778281 7778549 + SINE/Alu_AluJr chr1 7778717 7778913 + SINE/MIR_MIR chr1 7778914 7779224 + SINE/Alu_AluY chr1 7779225 7779254 + SINE/MIR_MIR chr1 7779260 7779324 + Simple_repeat_(AT)n chr1 7781963 7782067 + LINE/L2_L2b chr1 7782319 7782647 + LINE/L2_L2b chr1 7783034 7783324 + SINE/Alu_AluSz chr1 7783810 7783867 + Simple_repeat_(CCTCT)n chr1 7785975 7786261 + SINE/Alu_AluY chr1 7787660 7787712 - LINE/L2_L2d chr1 7788622 7788924 + SINE/Alu_AluSq2 chr1 7789141 7789158 + Simple_repeat_(AT)n chr1 7789206 7789240 - LINE/L1_L1MB2 chr1 7789241 7789610 + LTR/ERVL-MaLR_THE1B chr1 7789611 7789715 - LINE/L1_L1MB2 chr1 7789972 7790052 - LINE/L2_L2b chr1 7790172 7790545 - LTR/ERVL-MaLR_THE1C chr1 7790546 7792079 - LTR/ERVL-MaLR_THE1C-int chr1 7792080 7792441 - LTR/ERVL-MaLR_THE1C chr1 7793225 7793537 + LINE/L1_L1ME4c chr1 7794105 7794201 - LINE/L2_L2d2 chr1 7794207 7794509 + SINE/Alu_AluSc chr1 7794828 7794852 + Simple_repeat_(TATAT)n chr1 7794853 7794922 + Simple_repeat_(TA)n chr1 7794923 7794936 + Simple_repeat_(TATAT)n chr1 7795232 7795675 - LINE/L2_L2a chr1 7795755 7795982 - LINE/L1_L1MC5 chr1 7795986 7796302 - LINE/L1_L1MC5a chr1 7796315 7796614 - SINE/Alu_AluSz chr1 7796647 7796727 - LINE/L2_L2a chr1 7796783 7796817 - snRNA_U2 chr1 7796818 7797147 - LINE/L2_L2a chr1 7797151 7797352 - LINE/L2_L2a chr1 7797353 7797661 + SINE/Alu_AluSx chr1 7797662 7797731 - LINE/L2_L2a chr1 7799152 7799323 - DNA/TcMar-Tigger_Tigger9b chr1 7799324 7799630 + SINE/Alu_AluSp chr1 7799631 7799691 - DNA/TcMar-Tigger_Tigger9b chr1 7799681 7799759 - DNA/TcMar-Tigger_Tigger9b chr1 7799760 7800067 - SINE/Alu_AluJr chr1 7800068 7800216 - DNA/TcMar-Tigger_Tigger9b chr1 7800217 7800496 - SINE/Alu_AluSz6 chr1 7800497 7800536 - DNA/TcMar-Tigger_Tigger9b chr1 7800680 7800851 + SINE/Alu_AluSg4 chr1 7801641 7801886 + DNA/hAT-Charlie_Charlie18a chr1 7802262 7802564 - SINE/Alu_AluSx chr1 7802580 7802629 + Simple_repeat_(TTATGCA)n chr1 7802710 7802856 - DNA/hAT-Charlie_MER5A chr1 7803307 7803595 + SINE/Alu_AluSx chr1 7804038 7804366 + LINE/L1_L1ME4a chr1 7804399 7804699 - SINE/Alu_AluJb chr1 7804874 7805167 - SINE/Alu_AluSz chr1 7805991 7806190 - LINE/L2_L2b chr1 7806215 7806387 - SINE/MIR_MIRc chr1 7806487 7806516 + Simple_repeat_(TACA)n chr1 7806517 7806813 + SINE/Alu_AluJr chr1 7806814 7806860 + Simple_repeat_(TA)n chr1 7807041 7807226 - LINE/L2_L2a chr1 7807508 7807698 + SINE/MIR_MIRb chr1 7807752 7807861 + SINE/MIR_MIR chr1 7807951 7808259 + SINE/Alu_AluSz chr1 7810721 7810755 + Simple_repeat_(ATAGTA)n chr1 7811417 7811806 - LTR/ERVL-MaLR_MLT1A1 chr1 7812345 7812649 + SINE/Alu_AluY chr1 7813287 7813477 + LINE/L1_L1MD3 chr1 7813519 7814981 + LINE/L1_L1MD3 chr1 7815016 7815127 + SINE/Alu_FLAM_A chr1 7815228 7815554 + LINE/L1_L1MD3 chr1 7815556 7815703 + LINE/L1_L1MD chr1 7815704 7816017 + SINE/Alu_AluSc8 chr1 7816018 7816321 + LINE/L1_L1MD chr1 7816297 7816368 + LINE/L1_L1MD3 chr1 7816306 7816369 + LINE/L1_L1MC chr1 7816374 7816413 + rRNA_5S chr1 7816414 7817732 + LINE/L1_L1MD3 chr1 7817753 7818336 + LINE/L1_L1MD3 chr1 7818446 7818642 + LINE/L2_L2 chr1 7818655 7818838 - DNA/hAT-Charlie_MER3 chr1 7818850 7819037 - LINE/L1_L1MC5a chr1 7819041 7819175 - LINE/L1_L1ME3G chr1 7819532 7819679 - LINE/L2_L2d2 chr1 7819680 7819910 + DNA/hAT-Charlie_MER30 chr1 7819911 7819966 - LINE/L2_L2d2 chr1 7820855 7820937 + SINE/MIR_MIR chr1 7821292 7821388 - SINE/MIR_MIRb chr1 7821442 7821859 - LTR/ERVL_LTR86A1 chr1 7821938 7822078 - SINE/MIR_MIR chr1 7822124 7822254 + SINE/Alu_AluJr4 chr1 7822259 7822552 - SINE/Alu_AluSx1 chr1 7822553 7822776 + LINE/L1_L1M4b chr1 7822777 7823077 + SINE/Alu_AluJr chr1 7823078 7823361 + LINE/L1_L1M4b chr1 7823362 7823661 + SINE/Alu_AluSp chr1 7823662 7824364 + LINE/L1_L1M4b chr1 7824361 7824512 - LINE/L1_L1MEc chr1 7824500 7824902 - LINE/L1_L1MEc chr1 7824926 7825116 - LINE/L1_L1MEc chr1 7825283 7825322 + LINE/L1_L1MA4 chr1 7825350 7825596 + LINE/L1_L1MC4a chr1 7825610 7825920 + SINE/Alu_AluSq2 chr1 7825922 7826042 + SINE/Alu_AluSx3 chr1 7826071 7826179 + LINE/L1_L1MC4a chr1 7826414 7826461 + Simple_repeat_(AATAA)n chr1 7828625 7828802 + SINE/MIR_MIR chr1 7828785 7828938 + LINE/L2_L2a chr1 7829117 7829316 - DNA/TcMar-Tigger_Tigger7 chr1 7829316 7829781 - DNA/TcMar-Tigger_MER44C chr1 7830295 7832366 - LINE/L1_L1ME1 chr1 7832367 7832672 - SINE/Alu_AluSx3 chr1 7832673 7832837 - LINE/L1_L1ME1 chr1 7832838 7833001 - SINE/Alu_FRAM chr1 7833002 7833913 - LINE/L1_L1ME1 chr1 7833914 7834219 - SINE/Alu_AluSx3 chr1 7834220 7834433 - LINE/L1_L1ME1 chr1 7834450 7834757 - SINE/Alu_AluJr chr1 7835097 7835404 - LINE/L2_L2 chr1 7835989 7836190 - SINE/Alu_AluSx chr1 7836191 7836478 - SINE/Alu_AluSx chr1 7837245 7837387 - SINE/MIR_MIRb chr1 7837697 7837729 + Simple_repeat_(GTGGTA)n chr1 7837792 7838038 + SINE/Alu_AluJb chr1 7838190 7838404 - LINE/L1_L1MB3 chr1 7838405 7838687 - SINE/Alu_AluSz chr1 7838688 7838759 - LINE/L1_L1MB3 chr1 7838768 7840320 - LINE/L1_L1M4c chr1 7840321 7840616 - SINE/Alu_AluJr chr1 7840617 7840903 - SINE/Alu_AluJo chr1 7840904 7841020 - LINE/L1_L1M4c chr1 7841021 7841250 - DNA/hAT-Charlie_MER30 chr1 7841251 7841388 - LINE/L1_L1M4c chr1 7841392 7841698 - LINE/L1_L1M4c chr1 7841908 7842177 - DNA/TcMar-Tigger_Tigger3a chr1 7842205 7842220 + Simple_repeat_(TAA)n chr1 7842221 7842516 + SINE/Alu_AluSg chr1 7842517 7842555 + Simple_repeat_(TAA)n chr1 7844030 7844075 + Simple_repeat_(T)n chr1 7845386 7845435 + DNA/PiggyBac_MER85 chr1 7845600 7845828 - SINE/MIR_MIR chr1 7846088 7846389 - SINE/Alu_AluYm1 chr1 7847024 7847354 - SINE/Alu_AluSz6 chr1 7847355 7847367 + Simple_repeat_(TTTTA)n chr1 7847881 7847922 + Simple_repeat_(AAC)n chr1 7848167 7848456 + SINE/Alu_AluSx1 chr1 7848476 7848531 + Simple_repeat_(TGTAT)n chr1 7848532 7848816 - SINE/Alu_AluSz chr1 7848878 7849014 - DNA/hAT-Charlie_MER5A1 chr1 7849228 7849510 - SINE/Alu_AluSq10 chr1 7849971 7850268 - SINE/Alu_AluSx1 chr1 7850341 7850486 - SINE/MIR_MIR chr1 7853725 7853921 + LINE/L2_L2 chr1 7853948 7854063 + SINE/Alu_AluJb chr1 7854064 7854369 + SINE/Alu_AluSg chr1 7854370 7854540 + SINE/Alu_AluJb chr1 7854554 7854654 + LINE/L1_L1M5 chr1 7854654 7854844 + LINE/L1_L1MA8 chr1 7854848 7854878 + Simple_repeat_(TAA)n chr1 7854879 7855173 + SINE/Alu_AluSq2 chr1 7855215 7855296 + LINE/L1_L1MD chr1 7855307 7855566 + SINE/Alu_AluSx1 chr1 7855598 7855682 - SINE/Alu_AluJo chr1 7855683 7855717 + Simple_repeat_(ATT)n chr1 7855718 7855998 - SINE/Alu_AluJr chr1 7855999 7856129 - SINE/Alu_AluJo chr1 7856159 7856199 + LINE/L2_L2a chr1 7856230 7856466 - LINE/L2_L2a chr1 7856780 7857057 + SINE/Alu_AluSx1 chr1 7857082 7857144 + Simple_repeat_(GGAA)n chr1 7857215 7857303 + Simple_repeat_(CCCTCTG)n chr1 7857577 7857870 + SINE/Alu_AluJb chr1 7858585 7858881 - SINE/Alu_AluSz6 chr1 7859452 7859575 - LINE/L2_L2a chr1 7859728 7860006 + SINE/Alu_AluJb chr1 7860077 7860555 - LINE/L2_L2b chr1 7860562 7860743 - SINE/Alu_AluJo chr1 7860761 7861061 + SINE/Alu_AluSz chr1 7861078 7861335 - LINE/L2_L2b chr1 7861338 7861520 - LINE/L2_L2b chr1 7861622 7861678 - SINE/MIR_MIR chr1 7861916 7862224 - SINE/Alu_AluSx1 chr1 7862339 7862412 - LTR/ERVL-MaLR_MSTB1 chr1 7862413 7862490 + DNA/TcMar-Mariner_MADE1 chr1 7862491 7862818 - LTR/ERVL-MaLR_MSTB1 chr1 7862834 7862974 + LINE/L2_L2a chr1 7862975 7863127 + Simple_repeat_(TATTG)n chr1 7863128 7863410 - SINE/Alu_AluSp chr1 7863411 7863641 + LINE/L2_L2a chr1 7863678 7863712 + Simple_repeat_(TATTT)n chr1 7863738 7864106 - LTR/ERVL-MaLR_MLT1H chr1 7864114 7864281 - LTR/ERVL-MaLR_MLT1B chr1 7864282 7864878 + LTR/ERV1_LTR48B chr1 7864879 7864929 - LTR/ERVL-MaLR_MLT1B chr1 7865500 7865655 - LTR/ERVL-MaLR_MLT1B chr1 7865656 7865953 + SINE/Alu_AluJr chr1 7865954 7866024 - LTR/ERVL-MaLR_MLT1B chr1 7866861 7867169 - SINE/Alu_AluSp chr1 7867267 7867594 + LTR/ERVL-MaLR_MLT1B chr1 7867595 7867900 + SINE/Alu_AluSx1 chr1 7867901 7867979 + LTR/ERVL-MaLR_MLT1B chr1 7868738 7869079 + LTR/ERVL-MaLR_MLT1A chr1 7870102 7870485 + LTR/ERVL-MaLR_MLT1B chr1 7870799 7871295 - LTR/ERV1_MER90a chr1 7871527 7872019 + DNA/TcMar-Tigger_Tigger1 chr1 7872020 7872329 + SINE/Alu_AluSc8 chr1 7872330 7873924 + DNA/TcMar-Tigger_Tigger1 chr1 7873961 7874694 + LINE/L1_L1MA4A chr1 7874695 7875062 + LTR/ERVL-MaLR_THE1D chr1 7875063 7875373 - SINE/Alu_AluY chr1 7875374 7875418 + LTR/ERVL-MaLR_THE1D chr1 7875419 7876849 + LINE/L1_L1MA4A chr1 7876850 7877156 + SINE/Alu_AluSc chr1 7877157 7877592 + LINE/L1_L1MA4A chr1 7877593 7877886 + SINE/Alu_AluSg chr1 7877887 7878594 + LINE/L1_L1MA4A chr1 7878602 7878708 + SINE/Alu_FLAM_A chr1 7878721 7879459 + LINE/L1_L1MA4A chr1 7879460 7879761 + SINE/Alu_AluSz6 chr1 7879762 7879986 + LINE/L1_L1MA4A chr1 7879987 7880280 + SINE/Alu_AluJr chr1 7880281 7882054 + LINE/L1_L1MA4A chr1 7882055 7882350 + SINE/Alu_AluJr chr1 7882351 7883679 + LINE/L1_L1MA4A chr1 7883691 7883842 + DNA/TcMar-Tigger_Tigger1 chr1 7883843 7884077 - SINE/Alu_AluSp chr1 7884078 7884168 + DNA/TcMar-Tigger_Tigger1 chr1 7884169 7884231 + LINE/L2_L2c chr1 7884303 7884608 - SINE/Alu_AluJb chr1 7884690 7885164 + Simple_repeat_(CATC)n chr1 7885887 7885934 + Simple_repeat_(GGT)n chr1 7886771 7887070 + SINE/Alu_AluSg4 chr1 7887489 7887778 + SINE/Alu_AluJr chr1 7888828 7889014 + SINE/MIR_MIR chr1 7889142 7889472 + SINE/Alu_AluJb chr1 7889523 7889783 + SINE/Alu_AluSq2 chr1 7889785 7890076 + SINE/Alu_AluY chr1 7890077 7890141 + SINE/Alu_Alu chr1 7890456 7890582 + LINE/L1_L1ME2 chr1 7890648 7890812 + SINE/Alu_AluJr chr1 7890845 7890861 + Simple_repeat_(A)n chr1 7890862 7891143 + LINE/L1_L1ME1 chr1 7891129 7891288 + LINE/L1_L1MA8 chr1 7891289 7891524 + SINE/Alu_AluSx1 chr1 7891525 7891599 + Simple_repeat_(AAGA)n chr1 7891600 7891763 + LINE/L1_L1MA8 chr1 7891783 7891987 + LINE/L1_L1ME2 chr1 7892157 7892469 - LTR/ERVL-MaLR_MLT1D chr1 7892470 7892786 - SINE/Alu_AluSx1 chr1 7892787 7892978 - LTR/ERVL-MaLR_MLT1D chr1 7893003 7893096 + LINE/L2_L2c chr1 7893231 7893519 + SINE/Alu_AluSz chr1 7893923 7894007 - SINE/MIR_MIR3 chr1 7894129 7894173 + Simple_repeat_(TTTCTT)n chr1 7894174 7894473 - SINE/Alu_AluJb chr1 7894700 7894938 + SINE/Alu_AluSg chr1 7895087 7895394 + SINE/Alu_AluSq2 chr1 7896199 7896319 + LINE/L1_L1M5 chr1 7896335 7896397 + LINE/L1_L1M4 chr1 7896465 7896486 + Simple_repeat_(TG)n chr1 7896703 7896996 - SINE/Alu_AluSx chr1 7897196 7897591 - LINE/L1_L1M5 chr1 7897592 7897886 - SINE/Alu_AluSq2 chr1 7897887 7898215 - LINE/L1_L1M5 chr1 7898216 7898471 - LTR/ERVL_LTR42 chr1 7898472 7898741 - SINE/Alu_AluY chr1 7898742 7898886 - LTR/ERVL_LTR42 chr1 7898887 7899259 - LTR/ERVL-MaLR_MSTA chr1 7899260 7899329 - LTR/ERVL_LTR42 chr1 7899336 7899415 - LINE/L1_L1M2 chr1 7899513 7899672 - SINE/Alu_AluJb chr1 7899722 7900114 - LTR/ERVL-MaLR_MLT1F1 chr1 7900115 7900419 + SINE/Alu_AluJb chr1 7900420 7900449 + Simple_repeat_(GGAA)n chr1 7900495 7900793 + SINE/Alu_AluSx1 chr1 7900816 7901165 - LINE/L1_L1M5 chr1 7901189 7901380 - LINE/L2_L2 chr1 7901467 7901521 - SINE/MIR_MIRb chr1 7901524 7901606 + Simple_repeat_(AT)n chr1 7901607 7901631 + Simple_repeat_(TG)n chr1 7901637 7901718 - SINE/MIR_MIRb chr1 7901868 7902029 - LINE/L2_L2a chr1 7902600 7902720 - SINE/Alu_AluJr chr1 7903371 7903393 + Simple_repeat_(TTAA)n chr1 7903394 7903688 - SINE/Alu_AluSx1 chr1 7903689 7903708 + Simple_repeat_(TTAA)n chr1 7904216 7904500 + SINE/Alu_AluJb chr1 7904623 7904925 + SINE/Alu_AluSx chr1 7904945 7905011 - LINE/L1_L1ME3B chr1 7905025 7905136 + LTR/ERVL_MLT2D chr1 7905137 7905442 - SINE/Alu_AluSp chr1 7905443 7905456 + LTR/ERVL_MLT2D chr1 7905457 7905535 - DNA/TcMar-Mariner_MADE1 chr1 7905536 7905705 + LTR/ERVL_MLT2D chr1 7906400 7906510 + Simple_repeat_(AAGA)n chr1 7906676 7906761 + Simple_repeat_(AATACC)n chr1 7906969 7907267 + SINE/Alu_AluSx chr1 7907366 7907657 + SINE/Alu_AluJr chr1 7907880 7908020 + SINE/Alu_AluSx chr1 7908021 7908315 + SINE/Alu_AluSx chr1 7908316 7908480 + SINE/Alu_AluSx chr1 7908663 7908808 - LTR/ERVL-MaLR_THE1B chr1 7908809 7909090 - SINE/Alu_AluSx4 chr1 7909091 7909231 - LTR/ERVL-MaLR_THE1B chr1 7909276 7909572 + SINE/Alu_AluSx chr1 7909623 7909913 - SINE/Alu_AluSx1 chr1 7910088 7910705 + LTR/ERV1_MER39B chr1 7910714 7911006 - SINE/Alu_AluJo chr1 7911375 7911569 - SINE/MIR_MIRb chr1 7911621 7911917 + SINE/Alu_AluSx1 chr1 7912117 7912451 + LTR/ERV1_MER101 chr1 7912452 7912712 - SINE/Alu_AluSx3 chr1 7912866 7913475 + LTR/ERV1_LTR36 chr1 7913585 7913718 - SINE/Alu_FLAM_C chr1 7913729 7913759 + Simple_repeat_(T)n chr1 7913760 7914013 - SINE/Alu_AluYk2 chr1 7914840 7915053 - SINE/Alu_AluJb chr1 7915064 7915373 - SINE/Alu_AluSz6 chr1 7915384 7915685 - SINE/Alu_AluSx chr1 7916793 7916931 + LINE/L1_L1M5 chr1 7916932 7917230 - SINE/Alu_AluSx chr1 7917231 7917750 + LINE/L1_L1M5 chr1 7917768 7917874 + SINE/Alu_AluJo chr1 7917956 7918001 + Simple_repeat_(ATATAT)n chr1 7918002 7918285 - SINE/Alu_AluSq2 chr1 7918290 7918383 + LINE/L1_L1M5 chr1 7918384 7918666 - SINE/Alu_AluJb chr1 7918667 7918959 + LINE/L1_L1M5 chr1 7919098 7919205 + LTR/ERVL-MaLR_MLT1I chr1 7919226 7919521 + SINE/Alu_AluSx chr1 7919576 7919736 + SINE/Alu_FRAM chr1 7919784 7919894 + LINE/L1_L1ME3F chr1 7920305 7920322 + Simple_repeat_(T)n chr1 7920376 7920663 + SINE/Alu_AluJb chr1 7921055 7921338 + SINE/Alu_AluSq2 chr1 7921339 7921382 + Simple_repeat_(AAAAC)n chr1 7921386 7921674 + SINE/Alu_AluSg7 chr1 7921677 7921707 + Simple_repeat_(ATA)n chr1 7922363 7922529 + LINE/L1_L1MD2 chr1 7922530 7922829 + SINE/Alu_AluSx chr1 7922830 7922894 + LINE/L1_L1MD2 chr1 7922895 7923200 - SINE/Alu_AluSx4 chr1 7923201 7923556 + LINE/L1_L1MD2 chr1 7923559 7923849 + SINE/Alu_AluJr chr1 7923859 7923978 + LINE/L1_L1MD2 chr1 7923981 7924294 + DNA/TcMar-Tigger_Tigger3b chr1 7924295 7924335 + Simple_repeat_(AT)n chr1 7924345 7924621 + SINE/Alu_AluSx chr1 7924625 7924659 + Simple_repeat_(CAAA)n chr1 7924660 7925031 + DNA/TcMar-Tigger_Tigger3b chr1 7925032 7925315 + SINE/Alu_AluSx1 chr1 7925316 7925441 + DNA/TcMar-Tigger_Tigger3b chr1 7925442 7925921 - DNA/hAT-Charlie_MER1A chr1 7925922 7926199 - SINE/Alu_AluSx chr1 7926211 7926474 + DNA/TcMar-Tigger_Tigger3c chr1 7926476 7926940 + LINE/L1_L1MD2 chr1 7926946 7927073 + SINE/Alu_FLAM_C chr1 7927085 7927511 + LTR/ERVK_LTR3A chr1 7927528 7927679 + SINE/Alu_AluJb chr1 7927684 7928554 + LINE/L1_L1MD2 chr1 7928555 7928609 + LTR/ERV1_MER65A chr1 7928610 7928908 + SINE/Alu_AluSz6 chr1 7928909 7929136 + LTR/ERV1_MER65A chr1 7929137 7929466 - SINE/Alu_AluSx chr1 7929467 7929600 + LTR/ERV1_MER65A chr1 7929601 7929947 + LINE/L1_L1MD2 chr1 7929968 7930267 - SINE/Alu_AluSp chr1 7930279 7930348 - tRNA_tRNA-Glu-GAG_ chr1 7930501 7930787 + SINE/Alu_AluJo chr1 7930835 7931778 + LINE/L1_L1ME3B chr1 7931862 7932153 + SINE/Alu_AluSp chr1 7932654 7932761 + Simple_repeat_(CACA)n chr1 7932833 7933053 - DNA/hAT-Charlie_MER20 chr1 7933455 7933722 + SINE/Alu_AluSx1 chr1 7933735 7934039 + SINE/Alu_AluSx1 chr1 7934094 7934412 + SINE/Alu_AluJr chr1 7934429 7934736 + SINE/Alu_AluSg chr1 7935438 7935547 + SINE/MIR_MIRb chr1 7935550 7935768 + SINE/Alu_AluJr chr1 7935820 7935960 + SINE/MIR_MIRb chr1 7935998 7936089 + LINE/L2_L2d2 chr1 7936135 7936418 + SINE/Alu_AluSq2 chr1 7936419 7936456 + Simple_repeat_(AAAC)n chr1 7936645 7936924 - DNA/hAT-Charlie_MER5C chr1 7937176 7937462 - SINE/Alu_AluJb chr1 7939009 7939037 + Simple_repeat_(AATAACAA)n chr1 7939038 7939336 + SINE/Alu_AluSx chr1 7939337 7939370 + Simple_repeat_(AATAACAA)n chr1 7939592 7939758 - SINE/MIR_MIR chr1 7941633 7941921 + SINE/Alu_AluSq chr1 7941922 7942192 + Low_complexity_GA-rich chr1 7942207 7942241 + Simple_repeat_(GGA)n chr1 7942786 7942956 + SINE/MIR_MIRb chr1 7943600 7943898 + SINE/Alu_AluJb chr1 7944266 7944553 + SINE/Alu_AluY chr1 7944554 7944600 + Simple_repeat_(AAAT)n chr1 7945176 7945294 - DNA/hAT-Tip100_Zaphod chr1 7945305 7945631 + DNA/hAT-Tip100_Zaphod chr1 7945690 7945905 - SINE/Alu_AluSz6 chr1 7945908 7946400 + DNA/hAT-Tip100_Zaphod chr1 7946397 7946508 + DNA/hAT-Tip100_Zaphod chr1 7946509 7946803 - SINE/Alu_AluSx chr1 7946804 7947060 + DNA/hAT-Tip100_Zaphod chr1 7947069 7947112 + LINE/L2_L2b chr1 7947279 7947498 - SINE/MIR_MIR chr1 7947502 7947981 - DNA/hAT-Charlie_Charlie4a chr1 7948248 7948332 - SINE/MIR_MIRb chr1 7948389 7948671 + SINE/Alu_AluSx chr1 7948718 7948783 - SINE/MIR_MIR chr1 7948806 7949049 - SINE/MIR_MIR chr1 7949421 7949534 - DNA/hAT-Charlie_Charlie30a chr1 7950214 7950510 - SINE/Alu_AluSx1 chr1 7950512 7950804 + LINE/L2_L2a chr1 7950924 7951022 - LINE/L2_L2c chr1 7951023 7951331 + SINE/Alu_AluSg7 chr1 7951332 7951500 - LINE/L2_L2c chr1 7952052 7952087 + Simple_repeat_(CCTCTC)n chr1 7952098 7953558 + Retroposon/SVA_SVA_D chr1 7953559 7953580 + Simple_repeat_(A)n chr1 7953587 7953725 + SINE/Alu_AluJo chr1 7954837 7954969 - LINE/L2_L2a chr1 7955018 7955331 - SINE/Alu_AluSq2 chr1 7955618 7955748 + SINE/Alu_FLAM_A chr1 7955999 7956136 + SINE/MIR_MIRc chr1 7956137 7956278 + LINE/L1_HAL1 chr1 7956390 7956452 - LTR/ERVL-MaLR_MLT1I chr1 7956914 7957196 + SINE/Alu_AluSz chr1 7957471 7957738 - SINE/Alu_AluJr chr1 7957794 7957838 + SINE/MIR_MIRc chr1 7957869 7958180 + SINE/Alu_AluSz chr1 7958190 7958486 - SINE/Alu_AluSc chr1 7958540 7958851 + SINE/Alu_AluSx1 chr1 7958881 7959190 + SINE/Alu_AluSx1 chr1 7959191 7959777 + LINE/L1_L1MA4 chr1 7959778 7960082 + SINE/Alu_AluSz6 chr1 7960083 7960445 + LINE/L1_L1MA4 chr1 7960455 7960624 - LTR/ERV1_MER84 chr1 7960625 7960739 + LTR/ERV1_MER84-int chr1 7960740 7960847 - LTR/ERV1_MER84 chr1 7960848 7961086 + LINE/L1_L1MA4 chr1 7961129 7961208 - LINE/L2_L2a chr1 7961691 7961714 + Simple_repeat_(GCGT)n chr1 7962741 7962759 + Simple_repeat_(T)n chr1 7962962 7963082 - LINE/L1_L1ME3Cz chr1 7963095 7963358 - SINE/Alu_AluJb chr1 7963373 7963674 - SINE/Alu_AluSz chr1 7963712 7963814 - LINE/L1_L1ME3Cz chr1 7963820 7964112 - SINE/Alu_AluSx3 chr1 7964212 7964413 - SINE/MIR_MIR chr1 7964435 7964513 - LINE/L1_L1ME3Cz chr1 7964525 7964797 - LINE/L1_L1ME3Cz chr1 7964860 7964975 - DNA/hAT-Blackjack_MER94 chr1 7965096 7965241 + SINE/Alu_FRAM chr1 7965631 7965705 + Simple_repeat_(GTGG)n chr1 7965802 7965980 - SINE/Alu_AluJb chr1 7966416 7966720 + SINE/Alu_AluJr chr1 7966797 7967487 - LINE/L1_L1MA5A chr1 7967638 7967928 + SINE/Alu_AluJr chr1 7968028 7968329 + SINE/Alu_AluSg4 chr1 7968519 7968816 - SINE/Alu_AluSx chr1 7969031 7969241 - LINE/L1_L1ME3G chr1 7969585 7969877 - SINE/Alu_AluSg chr1 7969909 7970209 + SINE/Alu_AluJr chr1 7971651 7971962 + SINE/Alu_AluSq2 chr1 7971972 7972007 + Simple_repeat_(GAAT)n chr1 7972180 7972469 + SINE/Alu_AluJr chr1 7972575 7972624 - DNA/TcMar-Mariner_MADE1 chr1 7972627 7972757 + SINE/Alu_AluSx chr1 7972758 7973055 + SINE/Alu_AluSz chr1 7973060 7973141 + DNA/hAT-Charlie_Charlie1a chr1 7973491 7973743 + SINE/Alu_AluSq2 chr1 7973744 7973925 + SINE/Alu_AluSx chr1 7974028 7974345 + SINE/Alu_AluJr chr1 7974348 7974641 + SINE/Alu_AluY chr1 7974848 7975153 - SINE/Alu_AluSq chr1 7976711 7977019 - SINE/Alu_AluY chr1 7977458 7977583 - SINE/Alu_FLAM_C chr1 7977751 7977985 - SINE/Alu_AluSg chr1 7977986 7978295 - SINE/Alu_AluSg chr1 7978296 7978375 - SINE/Alu_AluSg chr1 7978393 7978549 - SINE/Alu_AluSx chr1 7978562 7978864 + SINE/Alu_AluJb chr1 7978889 7979183 - LINE/L1_L1ME3G chr1 7979227 7979446 - LINE/L1_L1MC1 chr1 7979516 7979800 - SINE/Alu_AluJr chr1 7979876 7980177 + SINE/Alu_AluY chr1 7980451 7980565 - SINE/MIR_MIRc chr1 7980868 7980997 - SINE/Alu_FLAM_C chr1 7981131 7981271 + SINE/MIR_MIR chr1 7981334 7981432 - SINE/MIR_MIRb chr1 7981434 7981642 - DNA/hAT-Charlie_MER58A chr1 7981656 7981957 - SINE/Alu_AluY chr1 7981966 7982285 - SINE/Alu_AluSx1 chr1 7982878 7982982 - scRNA_HY1 chr1 7983142 7983237 - LINE/L2_L2c chr1 7983330 7983435 + LINE/L2_L2 chr1 7985505 7985811 + SINE/Alu_AluJr chr1 7986368 7986800 - LINE/L2_L2a chr1 7986872 7986916 + Simple_repeat_(GCAG)n chr1 7986970 7987277 - SINE/Alu_AluSz6 chr1 7987409 7987707 - SINE/Alu_AluSz chr1 7987708 7987995 - SINE/Alu_AluJb chr1 7988054 7988349 - SINE/Alu_AluSx1 chr1 7988375 7988673 + SINE/Alu_AluSg4 chr1 7990085 7990695 + Simple_repeat_(CATCAC)n chr1 7990873 7991180 - SINE/Alu_AluSg chr1 7991539 7991836 - SINE/Alu_AluSq2 chr1 7992472 7992502 + Simple_repeat_(TCCC)n chr1 7992653 7992970 + SINE/Alu_AluSq2 chr1 7992975 7993116 - LINE/L1_L1ME3G chr1 7993120 7993382 - SINE/Alu_AluJb chr1 7993414 7993763 - LINE/L1_L1MB3 chr1 7993764 7994063 - SINE/Alu_AluSz chr1 7994064 7994286 - LINE/L1_L1MB3 chr1 7994287 7994399 - LINE/L1_L1MB3 chr1 7994413 7994475 - snRNA_U6 chr1 7994521 7994812 - SINE/Alu_AluSz chr1 7994920 7994929 + Simple_repeat_(TCTT)n chr1 7994930 7995265 + Simple_repeat_(TTTCC)n chr1 7995266 7995285 + Simple_repeat_(TCTT)n chr1 7995342 7995404 - SINE/Alu_FRAM chr1 7995404 7995544 - LTR/ERV1_MER34A chr1 7995564 7995614 + SINE/Alu_Alu chr1 7995616 7995654 + Simple_repeat_(AATA)n chr1 7995660 7995792 - LINE/L1_L1MEd chr1 7995793 7996094 - SINE/Alu_AluY chr1 7996095 7996302 - LINE/L1_L1MEd chr1 7996303 7996599 - SINE/Alu_AluY chr1 7996600 7996626 - LINE/L1_L1MEd chr1 7996627 7997030 - LTR/ERVL-MaLR_MSTB chr1 7997031 7997142 - LINE/L1_L1MEd chr1 7997140 7997407 - LINE/L1_L1MB5 chr1 7997419 7997553 - LINE/L1_L1MB5 chr1 7997554 7997877 - SINE/Alu_AluSx chr1 7997879 7998134 + LTR/ERVL-MaLR_MSTB1 chr1 7998135 7998249 - LINE/L1_L1MEd chr1 7998250 7998557 - SINE/Alu_AluSz chr1 7998558 7998669 - LINE/L1_L1MEd chr1 7998671 7998867 - LTR/ERVL-MaLR_MSTD chr1 7998868 7999168 + SINE/Alu_AluSx1 chr1 7999169 7999327 - LTR/ERVL-MaLR_MSTD chr1 7999329 7999633 - SINE/Alu_AluJb chr1 7999851 7999949 + LINE/L1_L1MA2 chr1 8000033 8000327 - SINE/Alu_AluSx chr1 8000394 8000572 - LINE/L1_HAL1ME chr1 8000573 8000889 - SINE/Alu_AluSp chr1 8000890 8001267 - LINE/L1_HAL1ME chr1 8001283 8001583 + SINE/Alu_AluSc8 chr1 8001588 8001879 - SINE/Alu_AluSc chr1 8001891 8002187 - SINE/Alu_AluSg chr1 8002657 8002764 - LINE/L1_HAL1ME chr1 8002765 8003066 - SINE/Alu_AluSz chr1 8003067 8003213 - LINE/L1_HAL1ME chr1 8005614 8005649 + Simple_repeat_(CTTC)n chr1 8005650 8005921 - SINE/Alu_AluSg4 chr1 8005922 8006226 - SINE/Alu_AluSz chr1 8007006 8007302 - SINE/Alu_AluSg chr1 8007443 8007482 + Simple_repeat_(CT)n chr1 8007498 8007803 - SINE/Alu_AluSp chr1 8007938 8008074 + LINE/L1_L1MC4a chr1 8008335 8008627 - SINE/Alu_AluSp chr1 8008811 8009107 + SINE/Alu_AluSx1 chr1 8009322 8009629 + SINE/Alu_AluSp chr1 8010287 8010477 + LINE/L2_L2 chr1 8010953 8011084 - SINE/MIR_MIR chr1 8016902 8016924 + Simple_repeat_(T)n chr1 8016946 8017073 + LINE/L2_L2c chr1 8018367 8018387 + Simple_repeat_(G)n chr1 8018844 8019008 + LINE/L2_L2d chr1 8019343 8019410 + LINE/L2_L2d chr1 8020075 8020096 + Simple_repeat_(A)n chr1 8020524 8020548 + Simple_repeat_(AAAC)n chr1 8020553 8020844 - SINE/Alu_AluSp chr1 8021655 8021899 - SINE/MIR_MIR chr1 8022379 8022538 - SINE/MIR_MIRc chr1 8022668 8022754 - SINE/MIR_MIR1_Amn chr1 8022803 8023141 - LINE/L2_L2a chr1 8023274 8023578 - SINE/Alu_AluJb chr1 8024460 8024564 - LINE/L2_L2c chr1 8026245 8026295 + Simple_repeat_(TCGCGC)n chr1 8026302 8026330 + Simple_repeat_(CCG)n chr1 8027983 8028094 - DNA/hAT-Tip100_MARE4 chr1 8029387 8029419 + Simple_repeat_(TTATAT)n chr1 8029740 8029765 + Simple_repeat_(AC)n chr1 8029793 8030085 - SINE/Alu_AluSc chr1 8030278 8030585 - SINE/Alu_AluSp chr1 8030920 8031225 - SINE/Alu_AluSq2 chr1 8031697 8031899 + SINE/MIR_MIR chr1 8031900 8032200 - SINE/Alu_AluSz6 chr1 8032201 8032213 + SINE/MIR_MIR chr1 8032827 8033116 + SINE/Alu_AluSq chr1 8033119 8033307 - LINE/L2_L2c chr1 8033774 8034074 - SINE/Alu_AluSx1 chr1 8034389 8034692 + SINE/Alu_AluJb chr1 8034819 8035145 - SINE/Alu_AluJo chr1 8035316 8035489 - LINE/L2_L2 chr1 8035538 8035701 - LINE/L2_L2a chr1 8035702 8036083 + LTR/ERVL_MLT2A1 chr1 8036084 8036125 + Simple_repeat_(AT)n chr1 8036126 8036165 + LTR/ERVL_MLT2A1 chr1 8036166 8036428 - LINE/L2_L2a chr1 8036428 8036558 - LINE/L2_L2a chr1 8036559 8036872 + SINE/Alu_AluJb chr1 8036873 8037022 - LINE/L2_L2a chr1 8037668 8037998 - SINE/Alu_AluSx1 chr1 8038253 8038288 - LINE/L2_L2b chr1 8038621 8038661 + LINE/L1_L1ME4c chr1 8038662 8038954 + SINE/Alu_AluSz chr1 8038955 8039130 + LINE/L1_L1ME4c chr1 8039147 8039353 - SINE/MIR_MIR chr1 8039396 8039496 + LINE/L1_L1PREC2 chr1 8039546 8040094 - LTR/ERVL-MaLR_MLT1K chr1 8040469 8040876 - LTR/ERVL_LTR16A chr1 8040924 8041122 - LINE/L1_L1MEd chr1 8041123 8041435 - SINE/Alu_AluSz6 chr1 8041436 8041933 - LINE/L1_L1MEd chr1 8041979 8042189 - SINE/Alu_AluSc chr1 8042191 8042563 - LINE/L1_L1MEd chr1 8042556 8042789 - LINE/L1_L1PB4 chr1 8042809 8043096 - LINE/L1_L1MEd chr1 8043097 8043141 + Simple_repeat_(TC)n chr1 8043143 8043432 - SINE/Alu_AluSq chr1 8043433 8043535 - LINE/L1_L1MEd chr1 8043674 8043883 - DNA/hAT-Charlie_MER58A chr1 8043884 8044095 - LINE/L1_L1MEd chr1 8044097 8044353 - SINE/Alu_AluSc chr1 8044354 8044477 - LINE/L1_L1MC3 chr1 8044681 8044845 + SINE/Alu_FAM chr1 8044928 8045196 - SINE/Alu_AluSx chr1 8045230 8045483 + SINE/Alu_AluY chr1 8045769 8046071 + SINE/Alu_AluSp chr1 8046605 8046906 + SINE/Alu_AluSz chr1 8046944 8046962 + Simple_repeat_(AG)n chr1 8048230 8048260 + Low_complexity_A-rich chr1 8048478 8048815 + LINE/L1_L1MA3 chr1 8049122 8049865 - LINE/L1_L1PA5 chr1 8050307 8050394 + LINE/L2_L2b chr1 8050395 8050704 - SINE/Alu_AluSx chr1 8050705 8050715 + LINE/L2_L2b chr1 8050716 8050754 - LTR/ERVL-MaLR_MLT1C2 chr1 8050755 8051030 + SINE/Alu_AluSx4 chr1 8051033 8051061 + Simple_repeat_(CAAA)n chr1 8051062 8051446 - LTR/ERVL-MaLR_MLT1C2 chr1 8051447 8051947 + LINE/L2_L2b chr1 8052014 8052309 - SINE/Alu_AluSx1 chr1 8052310 8052493 - SINE/Alu_AluJb chr1 8052494 8052790 - SINE/Alu_AluSx1 chr1 8052791 8052925 - SINE/Alu_AluJb chr1 8052937 8053089 - LINE/L1_L1MB8 chr1 8053169 8053460 + SINE/Alu_AluSx chr1 8053467 8053770 + LINE/L2_L2b chr1 8054282 8054568 - SINE/Alu_AluJo chr1 8054790 8054823 + Simple_repeat_(TTTGTT)n chr1 8054921 8055179 + SINE/Alu_AluSq2 chr1 8055421 8055746 - SINE/Alu_AluSx1 chr1 8055747 8055761 + Simple_repeat_(TTTTC)n chr1 8055766 8056069 - SINE/Alu_AluJr chr1 8056070 8056083 + Simple_repeat_(TTT)n chr1 8056248 8056467 - LINE/L2_L2a chr1 8056468 8056768 - SINE/Alu_AluSx chr1 8056769 8056973 - LINE/L2_L2a chr1 8057949 8057964 - SINE/Alu_AluSp chr1 8057965 8058023 + Simple_repeat_(TTCC)n chr1 8058028 8058070 + Simple_repeat_(TCTT)n chr1 8058071 8058358 - SINE/Alu_AluSp chr1 8058487 8058643 + LINE/L2_L2c chr1 8058660 8058964 + SINE/Alu_AluSq2 chr1 8059349 8059658 - SINE/Alu_AluJb chr1 8059760 8059882 - SINE/Alu_AluJr chr1 8059935 8060035 - LINE/L2_L2a chr1 8060155 8060201 + Simple_repeat_(TAATAG)n chr1 8060202 8060294 + SINE/Alu_AluJb chr1 8060295 8060608 + SINE/Alu_AluSq2 chr1 8060609 8060808 + SINE/Alu_AluJb chr1 8060809 8060823 + Simple_repeat_(TAATAG)n chr1 8060826 8061107 + SINE/Alu_AluSx chr1 8061108 8061145 + Simple_repeat_(AAT)n chr1 8061146 8061354 - SINE/MIR_MIR chr1 8061816 8062070 + DNA/hAT-Charlie_Charlie10 chr1 8062101 8062147 + Low_complexity_A-rich chr1 8062268 8062569 - SINE/Alu_AluSz chr1 8062599 8062742 - SINE/Alu_AluJo chr1 8062743 8062767 + Simple_repeat_(T)n chr1 8062768 8062885 - SINE/Alu_AluJo chr1 8063044 8063215 + SINE/Alu_AluJb chr1 8063345 8063560 - SINE/MIR_MIRb chr1 8064108 8064400 - SINE/Alu_AluSz6 chr1 8064809 8065118 + SINE/Alu_AluSx1 chr1 8065383 8065738 - LTR/ERVL-MaLR_THE1B chr1 8065824 8066136 - SINE/Alu_AluSp chr1 8066259 8066416 + LINE/L2_L2 chr1 8066678 8066709 + Simple_repeat_(CTC)n chr1 8066712 8066886 - SINE/Alu_AluSx chr1 8066887 8067192 - SINE/Alu_AluSz6 chr1 8067298 8067501 + SINE/MIR_MIR chr1 8067729 8068033 - SINE/Alu_AluSz chr1 8068231 8068508 + SINE/Alu_AluSx chr1 8068509 8068566 + Simple_repeat_(CA)n chr1 8068611 8068974 - LINE/L1_L1M5 chr1 8069034 8069345 - SINE/Alu_AluSq2 chr1 8069458 8069479 + Simple_repeat_(ATT)n chr1 8069480 8070166 - LINE/L1_L1PA16 chr1 8070167 8070266 - SINE/Alu_FRAM chr1 8071268 8071577 - SINE/Alu_AluSx chr1 8071848 8072090 + SINE/MIR_MIRc chr1 8072092 8072270 - SINE/Alu_AluSz chr1 8072277 8072329 + LINE/L2_L2d chr1 8072446 8072558 - LINE/L2_L2a chr1 8072820 8073112 - SINE/Alu_AluSz chr1 8073259 8073315 - LINE/L2_L2a chr1 8073457 8073763 - SINE/Alu_AluSz chr1 8073854 8073988 - DNA/TcMar-Tigger_Tigger15a chr1 8074155 8074472 + SINE/Alu_AluSz chr1 8074485 8074579 + SINE/Alu_FLAM_C chr1 8074583 8074876 + SINE/Alu_AluSx chr1 8074877 8074900 + Simple_repeat_(CAA)n chr1 8075287 8075586 - SINE/Alu_AluSx chr1 8075720 8076126 - LTR/ERV1_MER34C2 chr1 8076139 8076463 + LTR/ERV1_LTR9D chr1 8076464 8076762 + SINE/Alu_AluSx chr1 8076763 8076940 + LTR/ERV1_LTR9D chr1 8076941 8077034 + SINE/Alu_AluSx3 chr1 8078156 8078215 + LINE/L2_L2c chr1 8079152 8079209 - Retroposon/SVA_SVA_F chr1 8079159 8079221 + SINE/Alu_AluSx3 chr1 8079303 8079572 + SINE/Alu_AluSx chr1 8079706 8080010 + SINE/Alu_AluY chr1 8080262 8080570 - LINE/L2_L2b chr1 8080765 8080859 - LINE/L2_L2b chr1 8081335 8081402 - SINE/MIR_MIRb chr1 8081606 8081915 - SINE/Alu_AluSx chr1 8082021 8082322 + SINE/Alu_AluJr chr1 8082728 8083036 + SINE/Alu_AluSz chr1 8083346 8083390 + Simple_repeat_(TAT)n chr1 8084634 8084794 - LINE/L1_L1ME2z chr1 8084817 8085024 - SINE/Alu_AluJb chr1 8085027 8085587 - LINE/L1_L1MC4a chr1 8085588 8085882 - SINE/Alu_AluSq2 chr1 8085883 8087196 - LINE/L1_L1MC4a chr1 8087197 8087495 - SINE/Alu_AluSx1 chr1 8087496 8087544 - LINE/L1_L1MC4a chr1 8087549 8087767 - SINE/Alu_AluY chr1 8087803 8088040 - LINE/L1_L1ME2z chr1 8088041 8088315 + SINE/Alu_AluSq2 chr1 8088316 8088509 - LINE/L1_L1ME2z chr1 8088510 8088822 - SINE/Alu_AluSx chr1 8088823 8088926 - LINE/L1_L1ME2z chr1 8089094 8089217 - SINE/Alu_AluJb chr1 8089234 8089403 + Simple_repeat_(GATA)n chr1 8089515 8089599 - LINE/L2_L2a chr1 8090787 8091095 + SINE/Alu_AluSz6 chr1 8091099 8091394 - LINE/L2_L2a chr1 8091395 8091658 - SINE/Alu_AluJb chr1 8091659 8091709 - LINE/L2_L2a chr1 8092030 8092319 + SINE/Alu_AluSz chr1 8092320 8092404 + SINE/MIR_MIRc chr1 8093163 8093480 + SINE/Alu_AluJr chr1 8093503 8093745 - LINE/L2_L2c chr1 8095614 8095815 - SINE/MIR_MIRb chr1 8097988 8098295 - LINE/L1_L1MC1 chr1 8098334 8098369 + Simple_repeat_(ATCTGAA)n chr1 8098495 8098546 + DNA/TcMar-Tigger_MamRep434 chr1 8098626 8098709 + LINE/L1_L1ME1 chr1 8098710 8099022 - SINE/Alu_AluSx3 chr1 8099023 8099651 + LINE/L1_L1ME1 chr1 8099652 8099936 - SINE/Alu_AluSz6 chr1 8099937 8100281 + LINE/L1_L1ME1 chr1 8100282 8100579 - SINE/Alu_AluSx1 chr1 8100580 8100649 + LINE/L1_L1ME1 chr1 8100650 8101130 - LTR/ERVL_MER68 chr1 8101131 8101147 - SINE/Alu_AluSx1 chr1 8101148 8101182 + Simple_repeat_(TTA)n chr1 8101183 8101472 - SINE/Alu_AluSx1 chr1 8101473 8101534 - LTR/ERVL_MER68 chr1 8101535 8102926 + LINE/L1_L1ME1 chr1 8102929 8103449 + LINE/L1_L1ME1 chr1 8103450 8103755 + SINE/Alu_AluSx1 chr1 8103756 8103888 + LINE/L1_L1ME1 chr1 8103889 8104184 - SINE/Alu_AluSx chr1 8104185 8104318 + LINE/L1_L1ME1 chr1 8104318 8104532 - LINE/L1_L1MB2 chr1 8104909 8105197 - SINE/Alu_AluJb chr1 8105551 8105698 + LINE/CR1_CR1_Mam chr1 8105942 8106236 + SINE/Alu_AluSx1 chr1 8106311 8106570 - DNA/hAT-Charlie_Charlie26a chr1 8106914 8107270 + LINE/L1_L1MB7 chr1 8107281 8107375 + LTR/ERVL-MaLR_MLT1A0 chr1 8107378 8107548 + SINE/Alu_AluSx chr1 8107552 8108168 + LINE/L1_L1M5 chr1 8108169 8108466 + SINE/Alu_AluJb chr1 8108467 8108760 + LINE/L1_L1M5 chr1 8108761 8109591 + DNA/TcMar-Tigger_Tigger1 chr1 8109592 8109888 - SINE/Alu_AluSx chr1 8109889 8111362 + DNA/TcMar-Tigger_Tigger1 chr1 8111363 8111646 - SINE/Alu_AluSx chr1 8111647 8111830 + DNA/TcMar-Tigger_Tigger1 chr1 8111831 8112091 + LINE/L1_L1M5 chr1 8112092 8112398 + SINE/Alu_AluY chr1 8112399 8112731 + LINE/L1_L1M5 chr1 8112732 8113021 + SINE/Alu_AluJr chr1 8113022 8113272 + LINE/L1_L1M5 chr1 8113386 8113594 - LTR/ERVL-MaLR_MLT1E3 chr1 8113595 8113892 - LINE/L1_L1PA11 chr1 8113893 8114318 - LTR/ERVL-MaLR_MLT1E3 chr1 8114436 8114741 + SINE/Alu_AluSx chr1 8114751 8115060 + LINE/L1_L1MC2 chr1 8115066 8115344 + SINE/Alu_AluJr chr1 8115867 8116019 - LTR/Gypsy_LTR104_Mam chr1 8116132 8116189 + DNA/hAT-Tip100_MamTip1 chr1 8116252 8116533 + SINE/Alu_AluSz6 chr1 8117349 8117381 + Simple_repeat_(AAAT)n chr1 8117759 8118063 - SINE/Alu_AluSc8 chr1 8118719 8119028 + SINE/Alu_AluSx chr1 8119209 8119424 - SINE/MIR_MIR chr1 8119520 8119827 - SINE/Alu_AluSz chr1 8119866 8120352 + LTR/ERV1_MER34A chr1 8121701 8122011 + SINE/Alu_AluJb chr1 8122077 8122321 - LINE/L2_L2b chr1 8122526 8122630 - LINE/L1_L1MC4 chr1 8122631 8123171 - LTR/ERV1_MER41A chr1 8123172 8123386 - LINE/L1_L1MC4 chr1 8123387 8123866 - LTR/ERV1_LTR8 chr1 8123867 8124179 - SINE/Alu_AluSp chr1 8124180 8124444 - LTR/ERV1_LTR8 chr1 8124445 8124691 - LINE/L1_L1MC4 chr1 8124700 8125057 - LINE/L1_L1MC4 chr1 8125058 8125355 + SINE/Alu_AluSx chr1 8125356 8125523 - LINE/L1_L1MC4 chr1 8125977 8126018 - LTR/ERVL-MaLR_THE1B chr1 8126019 8126322 + SINE/Alu_AluSq10 chr1 8126323 8126631 - LTR/ERVL-MaLR_THE1B chr1 8126755 8127117 + LTR/ERVL-MaLR_MLT1A0 chr1 8127173 8127249 + Low_complexity_A-rich chr1 8127694 8128012 - SINE/Alu_AluJr chr1 8128163 8128463 - SINE/Alu_AluSx1 chr1 8128640 8128937 + SINE/Alu_AluSx chr1 8129047 8129280 - LTR/ERVL-MaLR_MSTA chr1 8129281 8129583 - SINE/Alu_AluSx chr1 8129584 8129762 - LTR/ERVL-MaLR_MSTA chr1 8129949 8130246 - SINE/Alu_AluJr chr1 8130321 8130622 + SINE/Alu_AluSz chr1 8130637 8130940 + SINE/Alu_AluSz chr1 8131353 8131642 - SINE/Alu_AluSx chr1 8131688 8131837 + LINE/L1_L1MA10 chr1 8132081 8132158 - LTR/ERV1_MER34A chr1 8132159 8132479 + SINE/Alu_AluJb chr1 8132480 8132900 - LTR/ERV1_MER34A chr1 8132901 8132989 + SINE/Alu_AluSx3 chr1 8133044 8133176 - SINE/Alu_AluJb chr1 8133837 8133889 + DNA/hAT-Ac_OldhAT1 chr1 8134456 8134726 - LINE/L1_L1MC5a chr1 8134745 8135050 - LTR/ERVL_LTR33C chr1 8135063 8135295 - LINE/L1_L1MC5a chr1 8135349 8135651 - SINE/Alu_AluSz chr1 8135925 8136400 - LTR/ERVL_LTR86A1 chr1 8136530 8136830 + SINE/Alu_AluJb chr1 8137138 8137442 + SINE/Alu_AluY chr1 8137724 8138126 - LINE/RTE-X_L4_C_Mam chr1 8138384 8138512 + DNA/hAT-Charlie_Charlie21a chr1 8138513 8138815 - SINE/Alu_AluJb chr1 8138816 8138865 + DNA/hAT-Charlie_Charlie21a chr1 8138866 8138992 + SINE/Alu_FLAM_C chr1 8138993 8139287 + DNA/hAT-Charlie_Charlie21a chr1 8139288 8139617 + SINE/Alu_AluSz6 chr1 8139618 8139806 + DNA/hAT-Charlie_Charlie21a chr1 8139813 8139831 + Simple_repeat_(GTT)n chr1 8140519 8140598 + LTR/ERVL-MaLR_MLT1J chr1 8140634 8140782 + SINE/Alu_AluSx3 chr1 8140783 8140825 + Simple_repeat_(AAAT)n chr1 8140828 8141139 + SINE/Alu_AluSq2 chr1 8141259 8141374 + LTR/ERVL-MaLR_MLT1J chr1 8141375 8141658 + SINE/Alu_AluJr chr1 8141659 8141689 + LTR/ERVL-MaLR_MLT1J chr1 8141708 8141925 - DNA/hAT-Tip100_Zaphod chr1 8141997 8142292 - SINE/Alu_AluSc chr1 8142419 8142452 + Simple_repeat_(T)n chr1 8142469 8142773 + SINE/Alu_AluJb chr1 8143696 8143989 + SINE/Alu_AluSz chr1 8143991 8144353 - LINE/L1_L1MA4 chr1 8144577 8144607 + Simple_repeat_(GT)n chr1 8144610 8144791 + LTR/ERVL-MaLR_MLT1B chr1 8144928 8145238 + SINE/Alu_AluY chr1 8145305 8145616 + SINE/Alu_AluJb chr1 8145647 8145812 + LTR/ERVL-MaLR_MLT1B chr1 8146088 8146289 - SINE/MIR_MIRc chr1 8146599 8146886 - SINE/Alu_AluJb chr1 8146936 8147246 + SINE/Alu_AluY chr1 8147598 8147664 + DNA/hAT-Charlie_MER5B chr1 8147811 8147998 + LTR/ERVL_LTR79 chr1 8148003 8148508 + LINE/L1_HAL1 chr1 8148509 8148806 + SINE/Alu_AluSz chr1 8148807 8148885 + LINE/L1_HAL1 chr1 8148886 8149053 + LINE/L1_L1PA10 chr1 8149054 8149088 + LINE/L1_HAL1 chr1 8149542 8149672 - SINE/Alu_FLAM_C chr1 8149846 8150151 - SINE/Alu_AluSz chr1 8150310 8150433 + LINE/L1_L1MC2 chr1 8150434 8150623 + SINE/Alu_AluJo chr1 8150639 8150675 + Simple_repeat_(A)n chr1 8150677 8151082 + LINE/L1_L1MC2 chr1 8151083 8151382 + SINE/Alu_AluSz chr1 8151383 8151506 + LINE/L1_L1MC2 chr1 8151649 8151734 - LINE/RTE-X_L4_C_Mam chr1 8151749 8152032 - SINE/Alu_AluJb chr1 8152366 8152664 + SINE/Alu_AluSz chr1 8152768 8152918 - SINE/MIR_MIR3 chr1 8153060 8153812 - LTR/ERV1_LTR12 chr1 8154340 8154668 - SINE/Alu_AluJb chr1 8155577 8155745 - LINE/RTE-X_L4_B_Mam chr1 8155771 8155815 + Low_complexity_GA-rich chr1 8155889 8156059 - SINE/Alu_AluJb chr1 8156424 8156728 + SINE/Alu_AluSx1 chr1 8156783 8157473 + LTR/ERVL_MER21C chr1 8157624 8157756 + SINE/Alu_FLAM_C chr1 8157759 8158036 + Low_complexity_GA-rich chr1 8158159 8158259 - SINE/MIR_MIR3 chr1 8158475 8159018 + LTR/Gypsy_MamGypLTR2c chr1 8159025 8159227 - LTR/ERVL_MLT2C2 chr1 8159228 8159523 - SINE/Alu_AluJb chr1 8159524 8159536 - LTR/ERVL_MLT2C2 chr1 8159537 8159548 - LINE/L1_L1MB3 chr1 8159549 8159905 - LTR/ERVL-MaLR_MLT1A0 chr1 8159906 8160043 - LINE/L1_L1MB3 chr1 8160044 8160085 - LTR/ERVL_MLT2C2 chr1 8160086 8160389 - SINE/Alu_AluSp chr1 8160392 8160703 - SINE/Alu_AluSg4 chr1 8160704 8160904 - LTR/ERVL_MLT2C2 chr1 8161214 8161518 - SINE/Alu_AluSq2 chr1 8161967 8162003 + Simple_repeat_(CTTTCTC)n chr1 8162855 8162889 - LINE/L1_L1MB7 chr1 8162890 8163178 - SINE/Alu_AluJb chr1 8163179 8163331 - LINE/L1_L1MB7 chr1 8163336 8163402 + SINE/Alu_AluSp chr1 8163467 8163514 + Simple_repeat_(TTA)n chr1 8163517 8163603 - LINE/L2_L2a chr1 8163991 8164263 - LINE/L1_L1MB8 chr1 8164264 8164564 - SINE/Alu_AluY chr1 8164565 8164743 - LINE/L1_L1MB8 chr1 8164896 8164936 + LTR/ERVL-MaLR_MLT1I chr1 8164937 8165107 - SINE/Alu_AluJr chr1 8165108 8165366 - SINE/Alu_AluSc8 chr1 8165367 8165504 - SINE/Alu_AluJr chr1 8165505 8165635 + LTR/ERVL-MaLR_MLT1I chr1 8165636 8166011 + LTR/ERVL-MaLR_THE1D chr1 8166012 8166107 + LTR/ERVL-MaLR_MLT1I chr1 8166815 8166929 + SINE/Alu_AluJb chr1 8167512 8167550 + Simple_repeat_(CA)n chr1 8167597 8167889 + SINE/Alu_AluSx1 chr1 8168499 8168634 + SINE/Alu_AluSp chr1 8168635 8168925 + SINE/Alu_AluSg chr1 8168926 8169098 + SINE/Alu_AluSp chr1 8169108 8169368 + SINE/Alu_AluSx1 chr1 8169782 8169850 + Low_complexity_GA-rich chr1 8170034 8170058 + Simple_repeat_(A)n chr1 8170228 8170262 + Simple_repeat_(GGCCAG)n chr1 8170796 8170999 - SINE/Alu_AluSx1 chr1 8171000 8171295 - SINE/Alu_AluY chr1 8171296 8171384 - SINE/Alu_AluSx1 chr1 8171557 8171683 + SINE/Alu_AluJo chr1 8172011 8172390 - LINE/L2_L2b chr1 8172623 8172784 - SINE/Alu_FRAM chr1 8172785 8173102 - SINE/Alu_AluSx chr1 8173113 8173175 + DNA/TcMar-Tigger_Tigger13a chr1 8173185 8173345 - SINE/Alu_AluSz chr1 8173346 8173644 - SINE/Alu_AluY chr1 8173645 8173778 - SINE/Alu_AluSz chr1 8174063 8174362 + SINE/Alu_AluSz chr1 8174366 8174515 - LINE/L2_L2d2 chr1 8174741 8174916 - LINE/L1_L1ME3F chr1 8174947 8175086 + SINE/Alu_AluJb chr1 8175095 8175347 + SINE/Alu_AluSx chr1 8175365 8175652 + SINE/Alu_AluJo chr1 8175728 8175845 - LINE/L1_L1ME2 chr1 8175846 8176125 + SINE/Alu_AluSg chr1 8176126 8176143 - LINE/L1_L1ME2 chr1 8176144 8176442 + SINE/Alu_AluJb chr1 8176443 8176757 - LINE/L1_L1ME2 chr1 8176758 8176917 - SINE/Alu_AluJb chr1 8176987 8177270 + SINE/Alu_AluSx3 chr1 8177273 8177566 + SINE/Alu_AluSg4 chr1 8177605 8177732 - LINE/L1_L1ME3F chr1 8177727 8178010 - LINE/L1_L1M5 chr1 8178011 8178318 + SINE/Alu_AluSq chr1 8178319 8178476 - LINE/L1_L1M5 chr1 8178637 8179329 - LTR/ERVL_MER77B chr1 8179334 8180414 - LINE/L1_L1M5 chr1 8180415 8180715 + SINE/Alu_AluSc8 chr1 8180716 8180795 - LINE/L1_L1M5 chr1 8180796 8180870 - SINE/Alu_AluSq2 chr1 8180871 8181058 - SINE/Alu_AluSx1 chr1 8181059 8181126 - LINE/L1_L1M5 chr1 8181127 8181461 - SINE/Alu_AluSx chr1 8181462 8181776 - LINE/L1_L1M5 chr1 8182052 8182240 + SINE/MIR_MIR chr1 8183039 8183339 + SINE/Alu_AluSq10 chr1 8183385 8183593 + SINE/MIR_MIR chr1 8183731 8184042 - SINE/Alu_AluSq2 chr1 8184537 8184639 + LINE/L1_L1MC5a chr1 8184728 8184854 + SINE/Alu_FLAM_C chr1 8184855 8185227 - LTR/ERVL-MaLR_THE1C chr1 8185263 8185423 + SINE/Alu_AluJb chr1 8185528 8185705 - SINE/MIR_MIR chr1 8185713 8186018 + DNA/hAT-Ac_EuthAT-2 chr1 8186036 8186257 + SINE/Alu_AluJr chr1 8186263 8186695 + DNA/hAT-Ac_EuthAT-2 chr1 8186696 8187014 + SINE/Alu_AluJb chr1 8187015 8187172 + DNA/hAT-Ac_EuthAT-2 chr1 8187174 8187335 - SINE/Alu_AluSx chr1 8187336 8187664 - SINE/Alu_AluSz chr1 8187717 8188005 + SINE/Alu_AluSx chr1 8188030 8188340 + SINE/Alu_AluSg chr1 8188619 8188769 + LINE/CR1_L3b chr1 8188772 8188799 + Simple_repeat_(TTTA)n chr1 8188817 8188945 + SINE/Alu_FLAM_C chr1 8188960 8189248 - SINE/Alu_AluJb chr1 8189286 8189507 + SINE/Alu_AluSx chr1 8190392 8190703 + SINE/Alu_AluSx1 chr1 8190734 8191030 - SINE/Alu_AluJb chr1 8191071 8191177 + SINE/Alu_FRAM chr1 8191181 8191205 + Simple_repeat_(AAAT)n chr1 8191498 8191827 + LTR/ERVL-MaLR_MSTA chr1 8191828 8192136 - SINE/Alu_AluSx chr1 8192137 8192175 + LTR/ERVL-MaLR_MSTA chr1 8192246 8192546 + SINE/Alu_AluSg4 chr1 8192935 8193223 + SINE/Alu_AluSx1 chr1 8193461 8193743 + SINE/Alu_AluSx chr1 8193744 8193779 + Simple_repeat_(AAAT)n chr1 8193780 8193791 + SINE/Alu_AluSx chr1 8193814 8193966 + DNA/hAT-Tip100_ORSL-2b chr1 8194038 8194160 + SINE/tRNA-RTE_MamSINE1 chr1 8194185 8194288 + LINE/CR1_L3b chr1 8195230 8195329 - LINE/L2_L2d2 chr1 8195339 8195467 + SINE/Alu_AluSz chr1 8195472 8195765 + SINE/Alu_AluSx chr1 8195768 8196078 + SINE/Alu_AluSg chr1 8196453 8196533 - LINE/L2_L2d2 chr1 8196599 8196672 - LINE/L2_L2a chr1 8196679 8196979 + SINE/Alu_AluJb chr1 8196980 8197053 + Low_complexity_GA-rich chr1 8197062 8197235 - SINE/Alu_AluJb chr1 8197294 8197379 - LINE/L1_L1MC4 chr1 8197428 8197449 + Simple_repeat_(AT)n chr1 8197457 8197737 + SINE/Alu_AluJr chr1 8197738 8197844 - LINE/L1_L1MC4 chr1 8198128 8198365 + SINE/MIR_MIR chr1 8198366 8198661 + SINE/Alu_AluSz chr1 8198681 8198729 + Simple_repeat_(CCT)n chr1 8198730 8198787 + LINE/L2_L2d2 chr1 8198965 8199251 - SINE/Alu_AluY chr1 8199458 8199617 - SINE/MIR_MIRb chr1 8199631 8199940 + SINE/Alu_AluSp chr1 8200991 8201290 - SINE/Alu_AluJb chr1 8202685 8202802 + LINE/CR1_L3 chr1 8202750 8202815 - LINE/RTE-BovB_MamRTE1 chr1 8202880 8203119 - SINE/MIR_MIR chr1 8203646 8203752 + SINE/MIR_MIRb chr1 8203998 8204309 - SINE/Alu_AluSx chr1 8204357 8204447 + SINE/MIR_MIR3 chr1 8205169 8205470 - SINE/Alu_AluSc8 chr1 8205473 8205773 - SINE/Alu_AluSx chr1 8206122 8206394 - SINE/Alu_AluSg chr1 8206434 8206594 - snRNA_U1 chr1 8206645 8206784 + SINE/Alu_FLAM_C chr1 8207162 8207453 + SINE/Alu_AluSz chr1 8207590 8207902 - SINE/Alu_AluSq2 chr1 8208183 8208476 - SINE/Alu_AluJr chr1 8208600 8208695 - SINE/MIR_MIR3 chr1 8208913 8209063 - DNA/hAT-Tip100_MER45C chr1 8209187 8209256 - DNA/hAT-Tip100_MER45C chr1 8209257 8209547 + SINE/Alu_AluSx chr1 8209548 8209648 - DNA/hAT-Tip100_MER45C chr1 8209730 8209866 + SINE/Alu_AluJb chr1 8209869 8209905 + Simple_repeat_(GAAA)n chr1 8210056 8210370 - SINE/Alu_AluJb chr1 8210777 8211067 + SINE/Alu_AluY chr1 8211080 8211260 - SINE/MIR_MIR chr1 8211530 8211666 + SINE/Alu_AluSp chr1 8212671 8212737 - LINE/L2_L2c chr1 8213049 8213202 - LINE/L2_L2c chr1 8213273 8213461 - SINE/MIR_MIRb chr1 8213818 8214011 - SINE/MIR_MIR chr1 8214095 8214406 - SINE/Alu_AluSx chr1 8214500 8214625 + LINE/L1_L1ME3G chr1 8214629 8214739 - LINE/L2_L2c chr1 8214803 8214952 + DNA/hAT-Charlie_MER5A chr1 8215097 8215180 + LINE/L1_L1PB1 chr1 8215875 8215941 + Simple_repeat_(GAGACA)n chr1 8216027 8216262 + LINE/L1_L1MC4 chr1 8216378 8216428 + Simple_repeat_(T)n chr1 8216435 8216692 - SINE/Alu_AluSg chr1 8216701 8216813 - SINE/Alu_AluJb chr1 8217064 8217105 + Low_complexity_G-rich chr1 8217345 8217400 + Simple_repeat_(GGCCGG)n chr1 8217833 8217985 + SINE/Alu_AluJb chr1 8218015 8218185 + DNA/hAT-Tip100_MER96B chr1 8218187 8218333 - SINE/Alu_AluYc chr1 8219039 8219200 - SINE/Alu_AluJb chr1 8219230 8219280 + Low_complexity_A-rich chr1 8219307 8219423 + SINE/Alu_AluJr chr1 8219437 8219551 + Simple_repeat_(TATATA)n chr1 8219884 8220182 + SINE/Alu_AluSx chr1 8220183 8220228 + Low_complexity_A-rich chr1 8220563 8220867 + SINE/Alu_AluJo chr1 8221189 8221223 + Simple_repeat_(TTAT)n chr1 8221224 8221487 - SINE/Alu_AluJo chr1 8221860 8222149 - SINE/Alu_AluSz6 chr1 8222169 8222483 - SINE/Alu_AluSp chr1 8222596 8222825 + DNA/TcMar-Tigger_MamRep434 chr1 8222826 8223232 + DNA/hAT-Charlie_MER1B chr1 8223260 8223283 + LTR/ERV1_MER34A chr1 8223284 8223600 + SINE/Alu_AluSx1 chr1 8223601 8224049 + LTR/ERV1_MER34A chr1 8224050 8224353 + SINE/Alu_AluSz chr1 8224354 8224455 + LTR/ERV1_MER34A chr1 8224641 8224932 + SINE/Alu_AluSx3 chr1 8225391 8225561 + LINE/CR1_L3 chr1 8225564 8225669 - LINE/L2_L2b chr1 8225693 8225968 + SINE/Alu_AluSx1 chr1 8225970 8225999 + Simple_repeat_(AACA)n chr1 8226022 8226132 + DNA/hAT-Charlie_MER30 chr1 8226230 8226517 + SINE/Alu_AluSq2 chr1 8226545 8226594 - LTR/ERVL-MaLR_MLT1D chr1 8226603 8226713 - LTR/ERV1_MER34A chr1 8226787 8227428 + LINE/CR1_L3 chr1 8227666 8228027 + LTR/ERVL-MaLR_THE1B chr1 8228088 8228541 - LINE/L2_L2a chr1 8228616 8228894 + SINE/Alu_AluSx1 chr1 8228895 8228967 + Simple_repeat_(AAAG)n chr1 8228968 8229019 + Simple_repeat_(GGGA)n chr1 8229018 8229181 + Simple_repeat_(GAAA)n chr1 8229188 8229484 + SINE/Alu_AluSq2 chr1 8229531 8229788 + LINE/CR1_L3 chr1 8229789 8230082 + SINE/Alu_AluSp chr1 8230083 8230318 + LINE/CR1_L3 chr1 8230693 8231012 - SINE/Alu_AluSx3 chr1 8231343 8231378 - LINE/L1_L1MD2 chr1 8231379 8231684 + SINE/Alu_AluSx1 chr1 8231685 8231796 - LINE/L1_L1MD2 chr1 8231871 8231957 + LTR/ERVL-MaLR_MLT1N2 chr1 8232328 8232382 + Low_complexity_GA-rich chr1 8232723 8232853 - SINE/MIR_MIR chr1 8232905 8233045 - LINE/L1_L1MA5 chr1 8233075 8233385 - SINE/Alu_AluSp chr1 8233613 8233655 + Simple_repeat_(AT)n chr1 8233665 8233750 + Simple_repeat_(ATGTAT)n chr1 8233776 8233827 + Simple_repeat_(AT)n chr1 8233828 8234130 - SINE/Alu_AluSp chr1 8234272 8234407 - SINE/Alu_AluSp chr1 8234409 8234682 - SINE/Alu_AluSx1 chr1 8235189 8235331 - LINE/L2_L2b chr1 8235332 8235388 - LINE/L2_L2a chr1 8235517 8235797 + SINE/Alu_AluSz chr1 8235798 8235832 + Simple_repeat_(AAAT)n chr1 8235841 8236257 - LINE/L2_L2a chr1 8236270 8236334 + Simple_repeat_(CTTT)n chr1 8236335 8236388 + Simple_repeat_(CCTC)n chr1 8236392 8236443 + Simple_repeat_(CT)n chr1 8236456 8236705 - SINE/Alu_AluJo chr1 8236709 8237005 - SINE/Alu_AluJr chr1 8237257 8237542 + SINE/Alu_AluSq2 chr1 8237702 8237714 + Simple_repeat_(TTTTTA)n chr1 8237715 8237731 + Simple_repeat_(TTA)n chr1 8237732 8237737 + Simple_repeat_(TTTTTA)n chr1 8237757 8237895 - SINE/Alu_AluJb chr1 8238915 8238971 + Low_complexity_G-rich chr1 8239061 8239318 - SINE/Alu_AluSx chr1 8239339 8239429 - SINE/Alu_AluSp chr1 8239651 8239751 - SINE/Alu_FLAM_C chr1 8239980 8240262 - SINE/Alu_AluSc8 chr1 8240312 8240377 + SINE/Alu_AluJo chr1 8240414 8240433 - LINE/L2_L2 chr1 8240434 8240742 - SINE/Alu_AluSx chr1 8240746 8241065 - SINE/Alu_AluY chr1 8241069 8241377 - SINE/Alu_AluY chr1 8241378 8241434 - LINE/L2_L2 chr1 8241674 8241725 + Simple_repeat_(CAGG)n chr1 8242074 8242205 + SINE/Alu_AluSx chr1 8242214 8242517 + SINE/Alu_AluSx3 chr1 8242563 8242666 + LINE/L1_L1ME4b chr1 8242681 8243010 - SINE/Alu_AluSx chr1 8243013 8243036 + Simple_repeat_(TATT)n chr1 8243037 8243196 + SINE/Alu_AluSc chr1 8243201 8243240 + Simple_repeat_(AGGGG)n chr1 8243242 8243330 + Simple_repeat_(AGAA)n chr1 8243460 8243553 - SINE/Alu_AluJb chr1 8243580 8243763 + SINE/Alu_AluYk3 chr1 8243764 8244052 + SINE/Alu_AluSx chr1 8244053 8244431 + LINE/L1_L1ME3Cz chr1 8244432 8244646 + DNA/hAT-Charlie_MER58A chr1 8244647 8244869 + LINE/L1_L1ME3Cz chr1 8244870 8245174 + SINE/Alu_AluSp chr1 8245175 8245321 + LINE/L1_L1ME3Cz chr1 8245328 8245628 + SINE/Alu_AluSx chr1 8245630 8245931 + SINE/Alu_AluSx3 chr1 8245992 8246069 + LINE/L1_L1MB7 chr1 8246070 8246378 + SINE/Alu_AluSx1 chr1 8246379 8246729 + LINE/L1_L1MB7 chr1 8246934 8247266 - SINE/Alu_AluSx chr1 8247473 8247775 + SINE/Alu_AluSp chr1 8248185 8248487 + SINE/Alu_AluJb chr1 8248590 8248890 - SINE/Alu_AluSx chr1 8249066 8249326 + SINE/Alu_AluJo chr1 8249327 8249647 + SINE/Alu_AluSx1 chr1 8249782 8249875 - DNA/hAT-Tip100_MER115 chr1 8249893 8250469 + LTR/ERV1_LTR29 chr1 8250743 8250833 - DNA/hAT-Tip100_MER115 chr1 8250912 8251194 - SINE/Alu_AluJb chr1 8251411 8251631 - LINE/L1_L1ME4a chr1 8251634 8251766 - LINE/L1_L1ME4a chr1 8251786 8251873 + SINE/MIR_MIR chr1 8251956 8252041 + DNA/hAT-Tip100_MER45A chr1 8252042 8252325 - SINE/Alu_AluSx chr1 8252326 8252396 + DNA/hAT-Tip100_MER45A chr1 8252400 8252593 - DNA/hAT-Charlie_MER58A chr1 8252599 8252717 + SINE/MIR_MIR chr1 8252732 8253126 - LTR/ERVL-MaLR_MSTB chr1 8253132 8253425 - SINE/Alu_AluSp chr1 8253452 8253734 + SINE/Alu_AluSp chr1 8253740 8254138 - LINE/L1_L1MB3 chr1 8254139 8254397 - SINE/Alu_AluSx chr1 8254408 8254548 - LINE/L1_L1ME4a chr1 8254598 8254913 - SINE/Alu_AluSq2 chr1 8254914 8255013 - LINE/L1_L1ME4a chr1 8255014 8255302 - SINE/Alu_AluY chr1 8255303 8255372 - LINE/L1_L1ME4a chr1 8255374 8255437 + Simple_repeat_(TTCT)n chr1 8255438 8255492 + Simple_repeat_(CT)n chr1 8255493 8255788 - SINE/Alu_AluSp chr1 8255794 8255830 - LINE/L1_L1ME4a chr1 8255842 8256002 - SINE/Alu_FRAM chr1 8256218 8256653 - LINE/L2_L2c chr1 8256801 8257090 - SINE/Alu_AluSg chr1 8257163 8257379 - LINE/L2_L2a chr1 8257411 8257979 - LINE/L2_L2a chr1 8257980 8258055 - LINE/L2_L2 chr1 8258100 8258394 - SINE/Alu_AluY chr1 8258552 8258858 + SINE/Alu_AluSx3 chr1 8260047 8260103 + Retroposon/SVA_SVA_B chr1 8260104 8260237 - SINE/Alu_AluJr4 chr1 8260238 8260251 + Retroposon/SVA_SVA_B chr1 8260403 8260466 + Simple_repeat_(CATTTTA)n chr1 8260545 8260584 - scRNA_HY1 chr1 8260640 8260923 - SINE/Alu_AluSq2 chr1 8260924 8260998 - SINE/Alu_AluSp chr1 8261074 8261157 - LINE/CR1_X7A_LINE chr1 8261179 8261284 + LINE/L2_L2a chr1 8261338 8261358 - SINE/Alu_AluJb chr1 8261359 8261405 + Simple_repeat_(TTCC)n chr1 8261406 8261700 - SINE/Alu_AluJb chr1 8261717 8262018 - SINE/Alu_AluY chr1 8262055 8262363 + SINE/Alu_AluY chr1 8262471 8262771 + SINE/Alu_AluSq2 chr1 8262829 8262877 + Simple_repeat_(TTC)n chr1 8262878 8263028 - SINE/Alu_AluJb chr1 8263309 8263348 + Simple_repeat_(AC)n chr1 8263476 8263774 - SINE/Alu_AluSx1 chr1 8263780 8263881 + DNA/hAT-Tip100_MamTip2b chr1 8263863 8263927 - SINE/MIR_MIR chr1 8264037 8264201 + SINE/MIR_MIR3 chr1 8264335 8264367 + Simple_repeat_(AC)n chr1 8264380 8264665 - SINE/Alu_AluSp chr1 8264671 8264788 + SINE/MIR_MIRc chr1 8264851 8264895 + Simple_repeat_(TTG)n chr1 8264896 8265178 - SINE/Alu_AluSz6 chr1 8265224 8265351 + SINE/Alu_FLAM_C chr1 8265352 8265377 + Simple_repeat_(GT)n chr1 8265382 8265613 - LINE/L2_L2c chr1 8265615 8265780 + LTR/ERVL-MaLR_MLT1C chr1 8265790 8266027 + SINE/Alu_AluSc chr1 8266029 8266202 - SINE/Alu_AluSx chr1 8266247 8266552 - SINE/Alu_AluSq2 chr1 8266627 8267197 - LTR/ERV1_LTR24C chr1 8267198 8267339 - SINE/Alu_FLAM_C chr1 8267340 8267369 - LTR/ERV1_LTR24C chr1 8267713 8268085 - LTR/ERVL-MaLR_THE1C chr1 8268239 8268406 - SINE/Alu_AluSq2 chr1 8268407 8268707 - SINE/Alu_AluSp chr1 8269173 8269472 - SINE/Alu_AluJb chr1 8269705 8270254 + LINE/L2_L2 chr1 8270264 8270576 - SINE/Alu_AluSq chr1 8271149 8271305 - LTR/Gypsy_MamGypLTR1b chr1 8271583 8271856 + SINE/Alu_AluJb chr1 8271910 8272182 - SINE/Alu_AluSx1 chr1 8272207 8272513 - LTR/ERVL_MER21A chr1 8272514 8272545 + Simple_repeat_(GT)n chr1 8272546 8272563 - LTR/ERVL_MER21A chr1 8272601 8272744 + Simple_repeat_(AT)n chr1 8272783 8273059 - SINE/Alu_AluSx chr1 8273672 8273897 + SINE/Alu_AluJb chr1 8274944 8275221 + SINE/Alu_AluJb chr1 8275222 8275513 - SINE/Alu_AluY chr1 8275615 8275712 + LTR/ERVL_ERVL-E-int chr1 8275713 8276030 - SINE/Alu_AluSg chr1 8276031 8276307 + LTR/ERVL_ERVL-E-int chr1 8276398 8276856 + LTR/ERVL_MLT2B4 chr1 8276857 8277261 + Simple_repeat_(AC)n chr1 8277266 8277412 - SINE/Alu_AluJb chr1 8277415 8277605 + SINE/Alu_AluSx1 chr1 8277626 8277837 - SINE/Alu_AluJo chr1 8277854 8278160 - SINE/Alu_AluJb chr1 8278291 8278448 + SINE/Alu_FRAM chr1 8278582 8278883 - SINE/Alu_AluSx1 chr1 8279116 8279401 - SINE/Alu_AluY chr1 8279415 8279477 - LINE/L1_L1MA8 chr1 8279478 8279787 + SINE/Alu_AluSx chr1 8279804 8279863 - SINE/Alu_Alu chr1 8279864 8280175 - SINE/Alu_AluSz chr1 8280209 8280341 + SINE/Alu_AluSz chr1 8280342 8280648 + SINE/Alu_AluSg7 chr1 8280649 8280702 + SINE/Alu_AluSz chr1 8280729 8280835 - LINE/L1_L1MA8 chr1 8280836 8280966 - SINE/Alu_FLAM_A chr1 8280967 8281379 - LINE/L1_L1MA8 chr1 8281380 8281752 + LTR/ERVL-MaLR_THE1C chr1 8281753 8281837 - LINE/L1_L1MA8 chr1 8281884 8281936 - LTR/ERVL-MaLR_MLT1J chr1 8282427 8282658 - LINE/L2_L2 chr1 8284424 8284721 - SINE/Alu_AluSz chr1 8284830 8285111 + LTR/ERVL_LTR16E2 chr1 8285303 8285441 + SINE/MIR_MIRb chr1 8285443 8285575 + LINE/L1_L1M5 chr1 8285576 8285884 + SINE/Alu_AluSg chr1 8285885 8286305 + LINE/L1_L1M5 chr1 8286331 8286505 + LINE/L1_L1M5 chr1 8286506 8286809 + SINE/Alu_AluJb chr1 8286810 8286998 + LINE/L1_L1M5 chr1 8286999 8287293 - SINE/Alu_AluSc chr1 8287294 8287484 + LINE/L1_L1M5 chr1 8287485 8287795 + SINE/Alu_AluSx chr1 8287828 8287906 + DNA/TcMar-Mariner_MADE2 chr1 8287910 8288035 + LINE/L1_L1M5 chr1 8288039 8288160 + LINE/L1_L1MC5a chr1 8288152 8288254 - LINE/L1_L1M5 chr1 8288257 8288331 - SINE/Alu_AluSq2 chr1 8288333 8288556 - LINE/L1_L1M5 chr1 8288557 8288866 - SINE/Alu_AluSx chr1 8288867 8288900 - LINE/L1_L1M5 chr1 8288901 8289214 + SINE/Alu_AluJb chr1 8289215 8289631 - LINE/L1_L1M5 chr1 8289632 8289950 - SINE/Alu_AluSz6 chr1 8289951 8289970 - LINE/L1_L1M5 chr1 8289971 8290262 + SINE/Alu_AluSg4 chr1 8290263 8290463 - LINE/L1_L1M5 chr1 8290464 8290767 - SINE/Alu_AluSx3 chr1 8290768 8291083 - LINE/L1_L1M5 chr1 8291084 8291390 - SINE/Alu_AluSz chr1 8291394 8291700 - SINE/Alu_AluSg4 chr1 8291701 8291922 - LINE/L1_L1M5 chr1 8292163 8292263 - snRNA_U6 chr1 8292264 8292352 - LINE/L1_L1M5 chr1 8292380 8292663 - SINE/Alu_AluSc8 chr1 8292664 8292968 - SINE/Alu_AluSz chr1 8293453 8293597 + LINE/L1_L1M5 chr1 8293649 8293682 + Simple_repeat_(AATAGAA)n chr1 8293793 8294098 + SINE/Alu_AluJr chr1 8295653 8295820 - SINE/Alu_AluSq chr1 8295871 8295913 + Simple_repeat_(ATATTAA)n chr1 8296300 8296916 - LTR/ERV1_MER41B chr1 8297066 8297187 + LINE/CR1_L3b chr1 8297437 8297554 - LTR/ERVL-MaLR_MLT1J2 chr1 8297660 8297963 - SINE/Alu_AluJb chr1 8298482 8298780 + SINE/Alu_AluSq chr1 8299050 8299082 + Simple_repeat_(TA)n chr1 8299083 8299376 - SINE/Alu_AluSx1 chr1 8299718 8300016 - SINE/Alu_AluSz chr1 8300365 8300562 + SINE/MIR_MIR1_Amn chr1 8303481 8303582 + LTR/ERV1_MER39 chr1 8303583 8303858 + SINE/Alu_AluSx3 chr1 8303863 8303900 + Simple_repeat_(CAAA)n chr1 8304243 8304418 - SINE/Alu_AluSz chr1 8304419 8304721 - SINE/Alu_AluSz chr1 8304722 8304734 - SINE/Alu_AluSz chr1 8304828 8305009 - SINE/Alu_AluJr chr1 8305010 8305062 - LINE/L2_L2 chr1 8305065 8305363 + SINE/Alu_AluSx3 chr1 8305378 8305687 + SINE/Alu_AluSp chr1 8305995 8306102 + LINE/L1_L1ME3A chr1 8306103 8306412 + SINE/Alu_AluSz chr1 8306413 8306436 + LINE/L1_L1ME3A chr1 8306437 8306748 + SINE/Alu_AluJb chr1 8306749 8306932 + LINE/L1_L1ME3A chr1 8306933 8307243 - SINE/Alu_AluSx1 chr1 8307244 8307312 + LINE/L1_L1ME3A chr1 8307249 8307327 + LINE/L1_L1PB chr1 8307353 8307430 - LTR/ERVL-MaLR_MLT1B chr1 8307431 8307549 - SINE/Alu_FLAM_C chr1 8307550 8307607 - LTR/ERVL-MaLR_MLT1B chr1 8307608 8307989 - LTR/ERVL-MaLR_MSTA chr1 8307990 8308207 - LTR/ERVL-MaLR_MLT1B chr1 8308209 8308339 + LINE/L1_L1ME3A chr1 8308340 8308591 + SINE/Alu_AluSx1 chr1 8308592 8308644 + Simple_repeat_(AAGG)n chr1 8308645 8309000 + LINE/L1_L1ME3A chr1 8309005 8309203 - SINE/Alu_AluSx chr1 8309218 8309331 + LINE/L1_L1ME3A chr1 8309351 8309644 - SINE/Alu_AluJb chr1 8309652 8309817 - LINE/L1_L1M5 chr1 8309848 8310100 + LINE/L1_L1ME3A chr1 8310101 8310410 + SINE/Alu_AluSz6 chr1 8310411 8310835 + LINE/L1_L1ME3A chr1 8310916 8310992 - DNA/hAT-Charlie_Charlie10a chr1 8310992 8311040 + SINE/Alu_Alu chr1 8311077 8311281 + LTR/ERVL-MaLR_MLT1B chr1 8311299 8311400 + LTR/ERVL_LTR16E1 chr1 8311401 8311705 + LTR/ERVL-MaLR_MLT1A0 chr1 8311705 8311858 + LTR/ERVL_LTR16E1 chr1 8311863 8312101 + LINE/L1_L1ME3A chr1 8312516 8312608 + DNA/hAT_MER53 chr1 8312851 8312992 - DNA/TcMar-Tigger_Tigger16b chr1 8313065 8313158 - LINE/L2_L2d chr1 8313268 8313395 + Simple_repeat_(GT)n chr1 8313505 8313803 - SINE/Alu_AluJb chr1 8314190 8314275 + SINE/MIR_MIRb chr1 8315097 8315148 + Simple_repeat_(CTCCCT)n chr1 8315346 8315480 - LTR/Gypsy_LTR81B chr1 8315531 8315684 + LTR/ERVL-MaLR_MLT1J2 chr1 8315726 8315871 + LTR/ERVL-MaLR_MLT1J2 chr1 8316838 8316991 + LINE/L2_L2a chr1 8316992 8317283 - SINE/Alu_AluSx1 chr1 8317284 8317471 + LINE/L2_L2a chr1 8317478 8317583 + SINE/Alu_AluJo chr1 8317607 8317724 + LINE/L2_L2a chr1 8317840 8317878 + DNA/hAT-Tip100_ORSL chr1 8318276 8318308 + Simple_repeat_(CCGAGA)n chr1 8318496 8318543 + Simple_repeat_(CCTGGTCA)n chr1 8318999 8319298 + SINE/Alu_AluSx chr1 8319707 8320017 - SINE/Alu_AluSx1 chr1 8320266 8320349 + DNA/hAT-Tip100_MamTip2 chr1 8320361 8320640 + SINE/Alu_AluJb chr1 8320658 8320692 + Simple_repeat_(TC)n chr1 8320693 8320734 + Simple_repeat_(AC)n chr1 8321624 8322438 + Simple_repeat_(GATG)n chr1 8323211 8323511 + SINE/Alu_AluSg chr1 8326279 8326696 - DNA/TcMar-Tigger_Tigger17a chr1 8326697 8327001 + SINE/Alu_AluSp chr1 8327002 8327224 - DNA/TcMar-Tigger_Tigger17a chr1 8327295 8327366 + LINE/L1_L1MC chr1 8327508 8327652 + SINE/Alu_FLAM_C chr1 8327749 8328058 + SINE/Alu_AluSz chr1 8328067 8328185 + scRNA_HY1 chr1 8328557 8328866 + SINE/Alu_AluSq chr1 8331094 8331212 + SINE/Alu_AluJb chr1 8331483 8331657 + SINE/Alu_AluJb chr1 8331795 8332091 - SINE/Alu_AluY chr1 8332512 8332810 - SINE/Alu_AluSx1 chr1 8333217 8333296 + Simple_repeat_(CTTTT)n chr1 8333317 8333436 - SINE/Alu_AluJb chr1 8333437 8333734 - SINE/Alu_AluJb chr1 8334465 8334726 + SINE/Alu_AluJr chr1 8336130 8336428 + SINE/Alu_AluSg chr1 8336446 8336780 + LINE/L1_L1ME4a chr1 8337034 8337396 - LTR/ERVL-MaLR_THE1B chr1 8337428 8337716 - SINE/Alu_AluY chr1 8338556 8338849 - DNA/hAT-Charlie_MER58B chr1 8339766 8339860 - DNA/hAT-Tip100_MER45A chr1 8340064 8340151 + DNA/hAT-Tip100_MER96B chr1 8340152 8340461 - SINE/Alu_AluSx3 chr1 8340462 8340489 + DNA/hAT-Tip100_MER96B chr1 8340913 8341218 + SINE/Alu_AluSc8 chr1 8341254 8341573 + SINE/Alu_AluSx1 chr1 8341574 8341629 - DNA/hAT-Tip100_MER96B chr1 8341641 8341903 + SINE/Alu_AluSz chr1 8341912 8341925 + Simple_repeat_(TAAA)n chr1 8341926 8342233 + SINE/Alu_AluY chr1 8342273 8342634 - LINE/L1_L1MB7 chr1 8342699 8342928 + SINE/Alu_AluJr chr1 8344286 8344477 + DNA/hAT-Charlie_MER20 chr1 8344560 8344609 + Simple_repeat_(ATTC)n chr1 8344848 8344892 + Simple_repeat_(ATCC)n chr1 8345194 8345314 - LINE/L2_L2a chr1 8346564 8346670 + LINE/L2_L2d2 chr1 8346671 8346795 - SINE/MIR_MIRb chr1 8347300 8347602 + SINE/Alu_AluSc8 chr1 8347670 8347822 - SINE/MIR_MIR chr1 8349649 8349730 + Simple_repeat_(TCCA)n chr1 8350361 8350633 + LTR/ERVL_LTR86C chr1 8350636 8350659 + Simple_repeat_(C)n chr1 8350662 8350872 + LTR/ERVL_LTR86C chr1 8355661 8355693 + Simple_repeat_(GGCACA)n chr1 8355999 8356031 + Simple_repeat_(AATG)n chr1 8356778 8357242 + LINE/L2_L2a chr1 8358199 8358262 + Simple_repeat_(GGTGGA)n chr1 8358580 8358637 + Simple_repeat_(GCTCCC)n chr1 8359783 8359812 + Simple_repeat_(CGCTCC)n chr1 8362060 8362400 + DNA/TcMar-Tigger_MER2 chr1 8363784 8363830 - LINE/L2_L2 chr1 8366220 8366344 - LINE/L2_L2a chr1 8366905 8366954 + Simple_repeat_(A)n chr1 8368188 8368499 + SINE/Alu_AluY chr1 8368780 8368915 + SINE/Alu_FLAM_C chr1 8368920 8369235 + SINE/Alu_AluJr chr1 8369468 8369513 + snRNA_U5 chr1 8369514 8369550 + Simple_repeat_(A)n chr1 8369639 8369800 + LINE/L1_L1ME1 chr1 8369801 8370089 - SINE/Alu_AluY chr1 8370090 8370520 + LINE/L1_L1ME1 chr1 8372508 8372543 + Simple_repeat_(GT)n chr1 8375119 8375188 + LINE/L2_L2d2 chr1 8375227 8375362 - DNA/hAT-Charlie_Charlie18a chr1 8375898 8376063 - SINE/MIR_MIR chr1 8376548 8376585 + Simple_repeat_(CCTCACTA)n chr1 8376586 8376592 + Simple_repeat_(CCTA)n chr1 8377048 8377095 + Low_complexity_A-rich chr1 8378394 8378769 + LTR/ERVL-MaLR_MLT1B chr1 8380341 8380646 - SINE/Alu_AluSx4 chr1 8381253 8381377 + LINE/L2_L2b chr1 8381622 8381651 + Simple_repeat_(AAAAC)n chr1 8382519 8382697 - DNA/hAT-Charlie_MER105 chr1 8382700 8382773 + SINE/MIR_MIR chr1 8382750 8382806 - SINE/MIR_MIR chr1 8382777 8382875 + SINE/MIR_MIRc chr1 8383579 8383860 + SINE/Alu_AluSg chr1 8383861 8383898 + Simple_repeat_(AAAAT)n chr1 8385071 8385195 + LINE/L2_L2-2_Mam chr1 8385694 8385723 + Simple_repeat_(CAAA)n chr1 8385731 8385993 + SINE/Alu_AluY chr1 8385994 8386023 + Simple_repeat_(AT)n chr1 8386024 8386051 + Simple_repeat_(T)n chr1 8386559 8386714 + LINE/L1_L1M chr1 8386933 8388317 + LINE/L1_L1ME3E chr1 8388318 8388618 - SINE/Alu_AluYb8 chr1 8388619 8388737 + LINE/L1_L1ME3E chr1 8390726 8391217 + LINE/L2_L2c chr1 8391184 8391359 + SINE/MIR_MIR chr1 8391394 8391753 + LINE/L2_L2c chr1 8391754 8392047 + SINE/Alu_AluSx1 chr1 8392048 8392155 + LINE/L2_L2c chr1 8392283 8392327 + Low_complexity_A-rich chr1 8392405 8392444 + Simple_repeat_(TCTA)n chr1 8394421 8394567 - SINE/MIR_MIRb chr1 8394809 8394978 + LINE/L2_L2c chr1 8394983 8395016 + Low_complexity_A-rich chr1 8395194 8395493 + SINE/Alu_AluSx3 chr1 8396663 8396817 - LINE/L1_L1MB5 chr1 8397103 8397344 + SINE/MIR_MIR chr1 8397303 8397514 - SINE/MIR_MIRb chr1 8397888 8398287 - LINE/L2_L2c chr1 8398408 8398624 + LINE/L2_L2d2 chr1 8398855 8399864 - LINE/L1_L1ME3Cz chr1 8399899 8400037 - SINE/Alu_FLAM_C chr1 8400223 8400255 + Simple_repeat_(TG)n chr1 8400256 8400273 + Simple_repeat_(AT)n chr1 8400274 8400551 - SINE/Alu_AluJr chr1 8400552 8400588 + Simple_repeat_(AT)n chr1 8400635 8400716 - LINE/L1_L1M3 chr1 8400733 8401036 + SINE/Alu_AluJb chr1 8401039 8401072 + Simple_repeat_(AT)n chr1 8401236 8401332 + LTR/ERVL-MaLR_MLT1G chr1 8401337 8401465 + SINE/Alu_AluSx3 chr1 8401480 8401772 + SINE/Alu_AluJb chr1 8401773 8401858 + LTR/ERVL-MaLR_MLT1G chr1 8401859 8402166 - SINE/Alu_AluSc chr1 8402167 8402532 + LTR/ERVL-MaLR_MLT1G chr1 8402555 8402898 - LINE/L1_L1ME3Cz chr1 8402899 8403196 - SINE/Alu_AluSx4 chr1 8403197 8403449 - LINE/L1_L1ME3Cz chr1 8403450 8403665 - SINE/Alu_AluSx1 chr1 8403666 8403813 - LINE/L1_L1ME3Cz chr1 8403826 8404127 - SINE/Alu_AluSc chr1 8404148 8404444 + SINE/Alu_AluYc3 chr1 8404445 8404546 - SINE/Alu_AluJr chr1 8405021 8405052 + Low_complexity_G-rich chr1 8405099 8405640 + LINE/L1_L1ME4a chr1 8405641 8405934 - SINE/Alu_AluSc5 chr1 8405935 8405972 + LINE/L1_L1ME4a chr1 8406104 8406356 - SINE/Alu_AluJb chr1 8406432 8406646 + LINE/L1_L1MC4 chr1 8407623 8407763 + SINE/MIR_MIR chr1 8409972 8409999 + Simple_repeat_(T)n chr1 8410404 8410556 + LINE/CR1_L3 chr1 8411319 8411417 - SINE/Alu_FLAM_A chr1 8411478 8411727 + LTR/ERVL-MaLR_MLT1H1 chr1 8413339 8413643 - SINE/Alu_AluJb chr1 8413775 8414077 + SINE/Alu_AluSp chr1 8414265 8414567 + SINE/Alu_AluSc chr1 8416304 8416615 + SINE/Alu_AluY chr1 8416814 8416967 - DNA/hAT-Charlie_MER5A1 chr1 8417677 8418004 + LINE/L2_L2d2 chr1 8419655 8419824 - SINE/MIR_MIRb chr1 8420185 8420307 + SINE/Alu_FLAM_A chr1 8423870 8423905 + Simple_repeat_(GGCCCC)n chr1 8424659 8424711 - LINE/L2_L2c chr1 8425838 8426034 - SINE/MIR_MIR chr1 8426174 8426468 + SINE/Alu_AluY chr1 8427029 8427153 - SINE/Alu_FLAM_C chr1 8427161 8427214 - LINE/L2_L2c chr1 8427271 8427537 + LINE/L2_L2 chr1 8427638 8427659 + Simple_repeat_(A)n chr1 8428786 8428819 + Simple_repeat_(AATAAA)n chr1 8429095 8429218 - SINE/MIR_MIR chr1 8430756 8430906 - SINE/MIR_MIRb chr1 8431119 8431637 + DNA/hAT-Charlie_MER1A chr1 8431638 8431766 - DNA/TcMar-Tigger_Tigger3b chr1 8432982 8433014 + Simple_repeat_(TACATA)n chr1 8433550 8433859 - SINE/Alu_AluYb8 chr1 8433861 8433941 - SINE/5S-Deu-L2_AmnSINE1 chr1 8434331 8434408 - SINE/MIR_MIR3 chr1 8434476 8435076 + LTR/ERVL-MaLR_MLT1G3 chr1 8435277 8435382 + LINE/L2_L2a chr1 8436039 8436345 + SINE/Alu_AluSg chr1 8436630 8436931 - DNA/hAT-Charlie_MER33 chr1 8437977 8438289 - SINE/Alu_AluJr4 chr1 8438435 8438507 - LINE/L1_L1MC1 chr1 8439792 8440092 + SINE/Alu_AluSq2 chr1 8440241 8440306 - DNA/hAT-Charlie_Charlie4a chr1 8440314 8440605 + SINE/Alu_AluSc5 chr1 8440606 8440914 + SINE/Alu_AluSx chr1 8440924 8440977 - DNA/hAT-Charlie_Charlie4a chr1 8441286 8441494 + DNA/hAT-Charlie_Charlie2a chr1 8441541 8441761 + DNA/hAT-Charlie_Charlie2b chr1 8441809 8442103 + DNA/hAT-Charlie_Charlie2b chr1 8442104 8442405 + SINE/Alu_AluSx chr1 8442406 8442430 + DNA/hAT-Charlie_Charlie2b chr1 8442431 8442637 + LTR/ERVL-MaLR_MSTA chr1 8442641 8443180 + LTR/ERVL-MaLR_MSTA-int chr1 8443181 8443479 + SINE/Alu_AluSx3 chr1 8443480 8444555 + LTR/ERVL-MaLR_MSTA-int chr1 8444558 8444918 + LTR/ERVL-MaLR_MSTA chr1 8444936 8445115 + DNA/hAT-Charlie_Charlie2b chr1 8445686 8445950 - LINE/L1_L1PA7 chr1 8445951 8446675 - LINE/L1_L1P2 chr1 8446676 8446978 - SINE/Alu_AluY chr1 8446979 8447654 - LINE/L1_L1P2 chr1 8448477 8448886 + SINE/Alu_AluSg chr1 8449982 8450157 + LINE/L2_L2a chr1 8451150 8451449 + SINE/Alu_AluSz chr1 8451722 8451827 - DNA/hAT-Charlie_MER5B chr1 8452103 8452198 + LINE/L2_L2d chr1 8453524 8453817 + SINE/Alu_AluSz chr1 8454459 8454489 + Simple_repeat_(TTTTCC)n chr1 8454526 8454834 + SINE/Alu_AluSg chr1 8454845 8454969 - SINE/MIR_MIR3 chr1 8455079 8455132 + Simple_repeat_(CCAGGAG)n chr1 8455232 8455310 - SINE/Alu_AluJb chr1 8455733 8455820 - LINE/L2_L2c chr1 8456682 8457087 + LINE/L2_L2a chr1 8457602 8457900 - SINE/Alu_AluSx chr1 8458578 8458604 + Simple_repeat_(CA)n chr1 8461760 8461806 + Simple_repeat_(AGG)n chr1 8461840 8462067 - SINE/Alu_AluJr chr1 8462749 8462760 + Simple_repeat_(TAA)n chr1 8462761 8462881 + SINE/Alu_FLAM_A chr1 8462882 8462915 + Simple_repeat_(TAA)n chr1 8464287 8464828 + LINE/L2_L2a chr1 8464895 8464945 + LINE/L2_L2a chr1 8465406 8465535 + SINE/Alu_AluJr chr1 8466288 8466517 - SINE/MIR_MIR chr1 8467117 8467179 - LINE/L2_L2a chr1 8467288 8467424 - LINE/L2_L2c chr1 8467503 8467550 + LINE/L2_L2b chr1 8467636 8467943 - SINE/Alu_AluY chr1 8468172 8468231 + Simple_repeat_(TTA)n chr1 8468606 8468926 + SINE/Alu_AluJb chr1 8468940 8469130 + SINE/MIR_MIR chr1 8469337 8469655 + SINE/Alu_AluSg4 chr1 8470263 8470552 - SINE/Alu_AluSx1 chr1 8470814 8471116 - SINE/Alu_AluSc chr1 8471234 8471303 - LINE/L2_L2a chr1 8471312 8471512 - SINE/Alu_AluJb chr1 8471518 8471813 - SINE/Alu_AluJo chr1 8471824 8472111 - SINE/Alu_AluSp chr1 8473332 8473411 - LINE/L2_L2a chr1 8473729 8473865 + SINE/MIR_MIRc chr1 8475093 8475390 + SINE/Alu_AluSz chr1 8475396 8475711 + SINE/Alu_AluSz chr1 8476131 8476304 - SINE/MIR_MIRb chr1 8476335 8476448 + DNA/hAT-Tip100?_MamRep1879 chr1 8477933 8478230 + DNA/TcMar-Tigger_Tigger15a chr1 8478351 8478389 + Low_complexity_GA-rich chr1 8479104 8479227 + SINE/Alu_AluJr chr1 8479375 8479832 + LTR/ERVL-MaLR_MLT1D chr1 8480121 8480435 - SINE/Alu_AluSg chr1 8480439 8480735 - SINE/Alu_AluSx chr1 8481121 8481413 - SINE/Alu_AluSc chr1 8481531 8481627 + SINE/MIR_MIR chr1 8481685 8482011 + LINE/CR1_L3 chr1 8482028 8482181 + LINE/L1_L1MB8 chr1 8482400 8482707 + SINE/Alu_AluY chr1 8483607 8483678 - LINE/L2_L2a chr1 8484410 8484428 + Low_complexity_A-rich chr1 8484429 8484722 - SINE/Alu_AluSx chr1 8484723 8484734 + Low_complexity_A-rich chr1 8485080 8485369 + SINE/Alu_AluSp chr1 8485410 8485796 + LINE/L1_L1MC5 chr1 8485797 8486097 - SINE/Alu_AluSc chr1 8486098 8486730 + LINE/L1_L1MC5 chr1 8486756 8486790 + Simple_repeat_(A)n chr1 8486791 8487300 + LINE/L1_L1MC5 chr1 8487301 8487594 + SINE/Alu_AluJb chr1 8487595 8487959 + LINE/L1_L1MC5 chr1 8487960 8488102 + SINE/Alu_FLAM_A chr1 8488103 8488233 + LINE/L1_L1MC5 chr1 8488234 8488542 - SINE/Alu_AluSq2 chr1 8488543 8489062 + LINE/L1_L1MC5 chr1 8489063 8489381 + SINE/Alu_AluJr4 chr1 8489382 8489587 + LINE/L1_L1MC5 chr1 8489449 8489768 + LINE/L1_L1ME1 chr1 8489769 8490069 + SINE/Alu_AluY chr1 8490070 8490083 + LINE/L1_L1ME1 chr1 8490084 8490373 + SINE/Alu_AluSc chr1 8490374 8490993 + LINE/L1_L1ME1 chr1 8491110 8491158 + SINE/Alu_AluJb chr1 8491159 8491461 + SINE/Alu_AluSp chr1 8491462 8491642 + SINE/Alu_AluJb chr1 8492476 8492617 + SINE/Alu_AluJr chr1 8492618 8492918 + SINE/Alu_AluJb chr1 8492919 8493103 + SINE/Alu_AluJr chr1 8493211 8493263 + SINE/MIR_MIR3 chr1 8493705 8493742 + Simple_repeat_(AAGAAAA)n chr1 8493904 8494122 + LINE/L1_L1MC5 chr1 8494123 8494291 - LINE/L1_L1MC5 chr1 8494367 8494444 + SINE/MIR_MIRb chr1 8494445 8494736 + SINE/Alu_AluYf1 chr1 8494737 8494831 + SINE/MIR_MIRb chr1 8495313 8495609 - SINE/Alu_AluSx chr1 8495893 8496169 + SINE/Alu_AluJr chr1 8496282 8496518 + SINE/Alu_AluJr chr1 8496873 8497056 - LINE/L1_L1PA8 chr1 8498050 8498078 + Simple_repeat_(ATTAA)n chr1 8498079 8498374 + SINE/Alu_AluSg7 chr1 8498375 8498391 + Simple_repeat_(ATTAA)n chr1 8498414 8498723 + SINE/Alu_AluSx chr1 8498733 8498769 + Simple_repeat_(AC)n chr1 8498780 8498972 + LINE/L1_L1ME3 chr1 8499259 8499425 - DNA/hAT-Charlie_MER33 chr1 8499426 8499655 - DNA/hAT-Charlie_Charlie8 chr1 8499838 8500124 + SINE/Alu_AluSx chr1 8500126 8500165 + Simple_repeat_(AAC)n chr1 8500314 8500391 + LINE/L1_L1M chr1 8500395 8501501 + Retroposon/SVA_SVA_D chr1 8501392 8503087 + Retroposon/SVA_SVA_E chr1 8503088 8503130 + Simple_repeat_(AAAT)n chr1 8503145 8503661 + LINE/L1_L1MC5 chr1 8503756 8504571 + LINE/L1_L1MC5 chr1 8504572 8504868 + SINE/Alu_AluSx3 chr1 8504869 8505543 + LINE/L1_L1MC5 chr1 8505544 8505818 - SINE/Alu_AluJb chr1 8506401 8506429 + Simple_repeat_(TTCAC)n chr1 8506639 8506763 + LTR/ERVL-MaLR_MLT1L chr1 8506793 8506949 - DNA/TcMar-Tc2_MER104 chr1 8507127 8507301 + SINE/Alu_AluJb chr1 8507416 8507708 - SINE/Alu_AluSx chr1 8507731 8508037 - SINE/Alu_AluSx1 chr1 8508069 8508472 + LINE/L1_L1MB3 chr1 8508923 8509246 - SINE/Alu_AluSx1 chr1 8509400 8509458 + Simple_repeat_(CCAT)n chr1 8509459 8509524 - LINE/L2_L2a chr1 8509526 8509814 + SINE/Alu_AluJb chr1 8510032 8510121 + Low_complexity_GA-rich chr1 8510923 8511049 - SINE/Alu_FLAM_C chr1 8511767 8511793 + Simple_repeat_(T)n chr1 8512015 8512332 - SINE/Alu_AluYb8 chr1 8512400 8512449 - DNA/hAT-Charlie_Charlie1 chr1 8513136 8513177 + Low_complexity_A-rich chr1 8513192 8513524 - DNA/hAT-Charlie_Charlie1 chr1 8513525 8513806 + SINE/Alu_AluSq2 chr1 8513807 8514406 - DNA/hAT-Charlie_Charlie1 chr1 8514448 8514760 + SINE/Alu_AluSq2 chr1 8514761 8514791 + Simple_repeat_(AAGAA)n chr1 8514787 8514804 + Simple_repeat_(AAT)n chr1 8514920 8514982 - SINE/MIR_MIR chr1 8515081 8515095 + Simple_repeat_(CAA)n chr1 8515096 8515373 + SINE/Alu_AluJo chr1 8515376 8515642 + SINE/Alu_AluSx1 chr1 8515643 8515667 + Simple_repeat_(CAA)n chr1 8515935 8516249 + SINE/Alu_AluSx1 chr1 8516293 8516372 - SINE/MIR_MIR chr1 8516385 8516406 + Simple_repeat_(TAA)n chr1 8516415 8516660 - SINE/MIR_MIRb chr1 8516825 8517142 + LINE/L1_L1MD2 chr1 8517132 8517198 + LINE/L1_L1MD3 chr1 8518716 8518862 - DNA/hAT-Charlie_Charlie16 chr1 8518873 8518930 + LTR/ERV1_MER66C chr1 8518932 8518968 + Low_complexity_A-rich chr1 8519037 8519094 + SINE/MIR_MIRb chr1 8519095 8519440 + SINE/Alu_AluJo chr1 8519441 8519610 + SINE/MIR_MIRb chr1 8519744 8519836 + LINE/L2_L2b chr1 8520055 8520097 + Low_complexity_A-rich chr1 8520184 8520318 - SINE/MIR_MIR3 chr1 8520755 8520776 + Simple_repeat_(A)n chr1 8521177 8521196 + Simple_repeat_(A)n chr1 8521338 8521359 - Retroposon/SVA_SVA_A chr1 8521360 8521504 + SINE/Alu_FLAM_C chr1 8521580 8521795 - SINE/MIR_MIRb chr1 8521805 8521936 - LINE/L2_L2b chr1 8522591 8522889 + SINE/Alu_AluSq2 chr1 8522900 8523214 + SINE/Alu_AluJb chr1 8526287 8526310 + Simple_repeat_(T)n chr1 8526620 8526668 + Low_complexity_A-rich chr1 8528083 8528559 + LINE/L1_L1MC4 chr1 8528560 8528787 + DNA/TcMar-Tigger_Tigger4a chr1 8528788 8528822 + LINE/L1_L1MC4 chr1 8528874 8529046 - DNA/TcMar-Tigger_Tigger4b chr1 8529099 8529188 + LINE/L1_L1MC4 chr1 8529285 8529599 - SINE/Alu_AluY chr1 8529753 8529771 + Simple_repeat_(TCC)n chr1 8530675 8530990 - SINE/Alu_AluYb8 chr1 8531008 8531106 + Simple_repeat_(TCTA)n chr1 8531108 8531135 + Simple_repeat_(TCCA)n chr1 8531177 8531496 + SINE/Alu_AluSz chr1 8531624 8531809 - LINE/L1_L1MEd chr1 8531810 8532073 + LINE/L1_L1MA2 chr1 8532074 8532357 - LINE/L1_L1MEd chr1 8532440 8532595 - SINE/Alu_AluSz chr1 8532596 8532894 - SINE/Alu_AluSp chr1 8532895 8533031 - SINE/Alu_AluSz chr1 8533047 8533323 + LTR/ERVL-MaLR_MLT1J chr1 8533332 8533572 - LINE/L1_L1MEd chr1 8534282 8534589 + LINE/L1_L1MC4 chr1 8534586 8534690 + LINE/L1_L1MC4 chr1 8534692 8534956 + LINE/L1_L1MC4 chr1 8534958 8535033 + SINE/Alu_FLAM_A chr1 8535088 8535156 + SINE/Alu_FAM chr1 8535167 8535514 + LINE/L1_L1MC4 chr1 8535753 8535793 + Low_complexity_GA-rich chr1 8535827 8536121 + SINE/Alu_AluSc chr1 8536218 8536362 - SINE/MIR_MIRc chr1 8536922 8537219 + SINE/Alu_AluJb chr1 8537258 8537585 - DNA/TcMar-Tigger_MER46C chr1 8539394 8539703 - SINE/Alu_AluSg chr1 8540070 8540104 + Simple_repeat_(TTAT)n chr1 8540113 8540412 - SINE/Alu_AluJb chr1 8541133 8541161 + Simple_repeat_(AC)n chr1 8542132 8542160 + Simple_repeat_(CACA)n chr1 8542541 8542657 + SINE/Alu_AluJr chr1 8542658 8542805 - SINE/Alu_AluJr4 chr1 8542927 8543068 + DNA/hAT-Charlie_MER3 chr1 8543069 8543115 - DNA/hAT-Charlie_MER3 chr1 8543595 8543778 - DNA/hAT-Charlie_MER20 chr1 8543953 8544074 - LINE/L1_L1M5 chr1 8544175 8544233 - LINE/L1_L1M5 chr1 8544351 8544744 + LINE/L1_L1ME2 chr1 8545014 8545358 - DNA/hAT-Charlie_Charlie2a chr1 8545442 8545543 - DNA/hAT-Charlie_Charlie2a chr1 8545635 8545682 + Simple_repeat_(AATAATA)n chr1 8545683 8545982 - SINE/Alu_AluSc8 chr1 8545983 8546278 - SINE/Alu_AluJb chr1 8546279 8546328 + Simple_repeat_(AATAATA)n chr1 8546566 8546873 + SINE/Alu_AluSx1 chr1 8546906 8547244 + LINE/L1_L1MC4a chr1 8547526 8548702 + LINE/L1_L1MC4a chr1 8548703 8549001 + SINE/Alu_AluSg7 chr1 8549002 8550194 + LINE/L1_L1MC4a chr1 8550210 8550327 + LINE/L2_L2b chr1 8550523 8550549 + Simple_repeat_(TGT)n chr1 8550550 8550847 - SINE/Alu_AluSx1 chr1 8551709 8551779 + SINE/MIR_MIR chr1 8551792 8551889 - SINE/MIR_MIRc chr1 8553896 8554194 + SINE/Alu_AluSz chr1 8554405 8554707 + SINE/Alu_AluJo chr1 8555123 8555383 + DNA/hAT-Charlie_MER58B chr1 8555924 8556034 - LINE/L1_L1MC4 chr1 8556094 8556150 - SINE/Alu_AluJo chr1 8557030 8557068 + Low_complexity_A-rich chr1 8557109 8557249 + SINE/MIR_MIR3 chr1 8558784 8559098 - SINE/Alu_AluSx chr1 8559120 8559304 + SINE/Alu_AluSp chr1 8559916 8559971 + LTR/ERV1_MER34C_ chr1 8560174 8560389 - DNA/hAT-Charlie_MER20 chr1 8560475 8560696 + SINE/MIR_MIR chr1 8561908 8562083 + SINE/MIR_MIR chr1 8562478 8562505 + Simple_repeat_(GTTT)n chr1 8562507 8562669 - SINE/Alu_AluJb chr1 8564223 8564525 - DNA/TcMar-Tigger_Tigger4b chr1 8564629 8564795 + LINE/L1_L1MA7 chr1 8564796 8564871 + Simple_repeat_(TG)n chr1 8564872 8565341 + LINE/L1_L1MA7 chr1 8565427 8565730 + SINE/Alu_AluSq chr1 8566340 8566644 + SINE/Alu_AluSg7 chr1 8566793 8567088 - SINE/Alu_AluSc8 chr1 8567770 8567820 + SINE/MIR_MIR3 chr1 8567951 8568384 + LTR/ERVL_MLT2B3 chr1 8568385 8568761 + LTR/ERVL-MaLR_MSTC chr1 8568762 8568918 + LTR/ERVL_MLT2B3 chr1 8568925 8569049 + LINE/CR1_L3 chr1 8569252 8569273 + Simple_repeat_(A)n chr1 8569610 8569905 + SINE/Alu_AluJr chr1 8569943 8570056 - DNA/hAT-Charlie_Charlie1b chr1 8570057 8570359 + SINE/Alu_AluSc chr1 8570360 8570698 - DNA/hAT-Charlie_Charlie1b chr1 8572122 8572251 - LINE/L1_L1ME1 chr1 8572600 8572738 - LINE/L2_L2a chr1 8573268 8573622 - LINE/L1_L1ME3C chr1 8573743 8573816 - LINE/L1_L1M5 chr1 8573817 8574105 - SINE/Alu_AluJb chr1 8574106 8574340 - LINE/L1_L1M5 chr1 8574341 8574595 - SINE/Alu_AluY chr1 8574596 8574924 + LINE/L1_L1ME3A chr1 8575006 8575091 + LINE/L1_L1ME4b chr1 8575092 8575291 + LINE/L1_L1MC4 chr1 8575384 8575534 - SINE/Alu_AluJr chr1 8575535 8575562 + Simple_repeat_(AT)n chr1 8575563 8575688 - SINE/Alu_AluJr chr1 8575820 8576225 + LINE/L1_L1MC4 chr1 8576389 8576448 + DNA/TcMar-Tc2_Kanga11a chr1 8576647 8576886 + LINE/L1_L1MC chr1 8576889 8577190 + SINE/Alu_AluY chr1 8577302 8577329 + Simple_repeat_(CT)n chr1 8577814 8578106 + SINE/Alu_AluSx1 chr1 8579722 8580024 + SINE/Alu_AluSx chr1 8580625 8580688 - LINE/L1_L1ME2z chr1 8580723 8580991 - SINE/Alu_AluJb chr1 8581029 8581707 - LINE/L1_L1ME2 chr1 8582214 8582533 - SINE/Alu_AluJr chr1 8582569 8582739 + SINE/Alu_AluSq chr1 8583179 8584275 + LINE/L1_HAL1M8 chr1 8584276 8584551 + SINE/Alu_AluJo chr1 8584552 8584867 + LINE/L1_HAL1M8 chr1 8584868 8585164 + SINE/Alu_AluSz chr1 8585165 8586228 + LINE/L1_HAL1M8 chr1 8586232 8586491 + LINE/L1_L1ME3B chr1 8587197 8587316 + SINE/MIR_MIRb chr1 8588007 8588126 - LINE/L2_L2a chr1 8588453 8588608 + LTR/ERVL-MaLR_MLT1M chr1 8588842 8589147 + SINE/Alu_AluSx1 chr1 8589158 8589456 + SINE/Alu_AluSx chr1 8589540 8589660 - LINE/L2_L2b chr1 8589668 8590140 + LTR/ERVL_LTR42 chr1 8590377 8590482 - LINE/L2_L2d chr1 8591429 8591452 + Simple_repeat_(A)n chr1 8592267 8592571 - SINE/Alu_AluJb chr1 8592605 8592726 + SINE/Alu_FLAM_C chr1 8593102 8593168 + LTR/ERVL-MaLR_MLT1L chr1 8593263 8593631 + LTR/ERVL-MaLR_THE1B chr1 8593702 8593815 - DNA/hAT-Charlie_MER5C1 chr1 8594098 8594264 - SINE/MIR_MIRc chr1 8594737 8594875 - LINE/RTE-BovB_MamRTE1 chr1 8594876 8595175 + SINE/Alu_AluJr chr1 8595176 8595215 - LINE/RTE-BovB_MamRTE1 chr1 8595311 8595360 + Simple_repeat_(ATATTAA)n chr1 8596057 8596198 + DNA/hAT-Charlie_MER58B chr1 8596530 8596811 - SINE/Alu_AluJr chr1 8597091 8597391 - SINE/Alu_AluJb chr1 8597491 8597547 + Simple_repeat_(AATGAA)n chr1 8597527 8597560 + Simple_repeat_(AATTAA)n chr1 8599948 8600303 + LTR/ERVL-MaLR_THE1B chr1 8600751 8601016 - SINE/Alu_AluY chr1 8601018 8601317 - SINE/Alu_AluSz chr1 8601627 8601672 + Simple_repeat_(CA)n chr1 8601737 8601797 + Simple_repeat_(AC)n chr1 8602111 8602414 + SINE/Alu_AluSc chr1 8602515 8602536 + Simple_repeat_(A)n chr1 8602871 8603009 - LINE/L2_L2c chr1 8603218 8603634 + DNA/hAT-Charlie_Charlie29a chr1 8603741 8603966 + SINE/Alu_AluJb chr1 8604053 8604139 + DNA/hAT-Charlie_MER5B chr1 8604128 8604226 - SINE/MIR_MIRb chr1 8604280 8604366 + LINE/L1_L1MC4a chr1 8604574 8604676 + Simple_repeat_(AAGG)n chr1 8605136 8605431 - SINE/Alu_AluSc8 chr1 8605461 8605771 + SINE/Alu_AluSx chr1 8605846 8606115 - SINE/Alu_AluJo chr1 8607079 8607378 + SINE/Alu_AluJb chr1 8607535 8607725 - SINE/Alu_AluJr chr1 8607726 8608025 - SINE/Alu_AluSx1 chr1 8608026 8608187 - SINE/Alu_AluJr chr1 8608244 8608520 + SINE/Alu_AluJb chr1 8608804 8608936 + SINE/Alu_AluSz chr1 8608937 8609242 + SINE/Alu_AluSx chr1 8609860 8610067 - SINE/Alu_AluJb chr1 8610177 8610295 - DNA/hAT-Charlie_Charlie4z chr1 8610309 8610619 + SINE/Alu_AluSg chr1 8610653 8610831 - SINE/MIR_MIR chr1 8610915 8611214 + SINE/Alu_AluSc8 chr1 8611223 8611530 + SINE/Alu_AluSz chr1 8611901 8611929 + Simple_repeat_(CCTC)n chr1 8613280 8613651 + LINE/L2_L2d chr1 8615778 8615804 + Simple_repeat_(AC)n chr1 8617071 8617360 + SINE/Alu_AluSq2 chr1 8618076 8618161 - LINE/L2_L2a chr1 8618219 8618414 - SINE/MIR_MIRb chr1 8619893 8620017 + LINE/L2_L2d chr1 8620516 8620605 - LTR/ERVL-MaLR_MLT1M chr1 8621113 8621212 - DNA/hAT-Blackjack_MER94 chr1 8621871 8621911 + Simple_repeat_(AATAAGA)n chr1 8622487 8622511 + Simple_repeat_(A)n chr1 8623030 8623169 - DNA/hAT-Charlie_Charlie23a chr1 8623170 8623453 + SINE/Alu_AluSz chr1 8623454 8623671 - DNA/hAT-Charlie_Charlie23a chr1 8624657 8624721 - SINE/MIR_MIR chr1 8625125 8625162 + Simple_repeat_(TTTG)n chr1 8625164 8625258 - SINE/Alu_FLAM_C chr1 8625468 8625490 + Simple_repeat_(TCAT)n chr1 8625491 8625605 - SINE/Alu_AluJo chr1 8625606 8625621 + Simple_repeat_(TCAT)n chr1 8626417 8626881 + LTR/ERV1_LTR19A chr1 8627342 8627363 + Simple_repeat_(A)n chr1 8627366 8627612 + SINE/Alu_AluSx chr1 8630606 8630717 + SINE/Alu_FRAM chr1 8630725 8630763 + Simple_repeat_(CATA)n chr1 8631254 8631417 + SINE/Alu_FRAM chr1 8632051 8632119 - DNA?_MER131 chr1 8633138 8633422 + SINE/Alu_AluJr chr1 8633444 8633477 + Simple_repeat_(CA)n chr1 8633645 8633950 + SINE/Alu_AluJb chr1 8634653 8634694 + Simple_repeat_(GTT)n chr1 8634746 8635034 - SINE/Alu_AluY chr1 8635793 8635919 - LINE/L2_L2c chr1 8635938 8635980 + Simple_repeat_(TTATC)n chr1 8635981 8636287 - SINE/Alu_AluSp chr1 8636333 8636383 + LTR/ERVL-MaLR_MLT1J chr1 8636388 8636543 + SINE/Alu_FRAM chr1 8636546 8636596 + Simple_repeat_(AAT)n chr1 8636610 8636815 + LTR/ERVL-MaLR_MLT1J chr1 8638046 8638502 - DNA/hAT-Charlie_Charlie4a chr1 8638784 8639072 - SINE/Alu_AluY chr1 8640006 8640175 + SINE/Alu_AluSx chr1 8641956 8642145 + LTR?_MamRep1151 chr1 8642194 8642346 - SINE/MIR_MIRb chr1 8642377 8642803 + LTR?_MamRep1151 chr1 8642968 8643107 - LINE/L2_L2a chr1 8643079 8643222 - LINE/L2_L2 chr1 8643230 8643391 + LINE/RTE-X_L4_C_Mam chr1 8643411 8643550 - DNA/TcMar-Tigger_Tigger9a chr1 8644694 8644794 - SINE/Alu_FLAM_A chr1 8645008 8645139 + SINE/MIR_MIRb chr1 8645339 8645406 - LINE/CR1_L3 chr1 8645512 8645644 - DNA/hAT-Charlie_MER5A1 chr1 8646157 8646248 + LTR/ERVL-MaLR_MLT1C chr1 8646249 8646566 + SINE/Alu_AluJr chr1 8646567 8646838 + LTR/ERVL-MaLR_MLT1C chr1 8646839 8647091 + SINE/Alu_AluJo chr1 8647092 8647220 + LTR/ERVL-MaLR_MLT1C chr1 8647642 8647678 + Simple_repeat_(TG)n chr1 8647964 8648223 - LINE/L1_L1ME3A chr1 8648245 8648531 - SINE/Alu_AluJb chr1 8649448 8649746 + SINE/Alu_AluSz chr1 8650011 8650085 - LINE/L2_L2a chr1 8650489 8650616 + SINE/Alu_AluSx chr1 8650617 8650910 + SINE/Alu_AluSc chr1 8650911 8651106 + SINE/Alu_AluSx chr1 8651132 8651404 + SINE/Alu_AluJr4 chr1 8651581 8651740 + SINE/Alu_AluJb chr1 8651820 8651979 - SINE/Alu_FRAM chr1 8652469 8652837 - LTR/ERVL-MaLR_THE1C chr1 8652900 8652996 - SINE/Alu_FAM chr1 8653106 8653208 + DNA/hAT-Charlie_MER103C chr1 8653426 8653716 + SINE/Alu_AluYc chr1 8654058 8654335 - SINE/Alu_AluJr chr1 8654626 8654913 - SINE/Alu_AluJb chr1 8655211 8655505 - SINE/Alu_AluSx chr1 8656198 8656236 + Simple_repeat_(TC)n chr1 8656263 8656288 + Simple_repeat_(TCTTTG)n chr1 8656831 8656959 + SINE/Alu_AluJr chr1 8657021 8657336 + SINE/Alu_AluY chr1 8657565 8657856 + SINE/Alu_AluSx chr1 8658200 8658317 - LINE/L2_L2c chr1 8658477 8658783 + SINE/Alu_AluSc8 chr1 8659275 8659578 + SINE/Alu_AluSx1 chr1 8660145 8660393 - DNA/TcMar-Tigger_Tigger19a chr1 8660410 8660581 - SINE/MIR_MIRc chr1 8660893 8661881 - LINE/L2_L2a chr1 8662393 8662718 + SINE/Alu_AluJr chr1 8662774 8662993 + SINE/MIR_MIRc chr1 8663490 8663610 + LINE/L2_L2b chr1 8663641 8663710 - LINE/L1_L1MB8 chr1 8663758 8664111 + LINE/L2_L2b chr1 8664177 8664547 + LINE/L2_L2b chr1 8664563 8664672 - SINE/Alu_AluJr4 chr1 8664673 8665425 + LINE/L2_L2b chr1 8665521 8665609 - LINE/L2_L2d2 chr1 8665714 8665798 - SINE/MIR_MIR chr1 8665902 8666078 - SINE/MIR_MIR chr1 8667393 8667702 + SINE/Alu_AluSx chr1 8667850 8668070 - DNA/hAT-Charlie_MER20 chr1 8668559 8668912 + LINE/L1_L1MB8 chr1 8668925 8668968 + Simple_repeat_(TG)n chr1 8668995 8669024 + LINE/L1_L1MB7 chr1 8669025 8669082 + Simple_repeat_(TG)n chr1 8669083 8669398 - SINE/Alu_AluJb chr1 8669399 8669466 + LINE/L1_L1MB7 chr1 8670355 8671175 - LINE/L2_L2a chr1 8671176 8671988 + DNA/hAT-Charlie_Charlie17 chr1 8671989 8672123 + SINE/Alu_AluJr chr1 8672126 8672173 + DNA/hAT-Charlie_Charlie17 chr1 8672174 8672473 - SINE/Alu_AluJr chr1 8672474 8672601 + DNA/hAT-Charlie_Charlie17 chr1 8673096 8673395 - SINE/Alu_AluSq2 chr1 8673767 8674040 + SINE/Alu_AluJb chr1 8674731 8674766 + Simple_repeat_(CATACTA)n chr1 8675376 8675726 + SINE/Alu_AluSq2 chr1 8675727 8675787 + Simple_repeat_(ATAC)n chr1 8675788 8676014 - DNA/TcMar-Tigger_Tigger4a chr1 8677148 8677473 + SINE/Alu_AluSx chr1 8677658 8677840 + SINE/MIR_MIRb chr1 8678364 8678722 + SINE/Alu_AluSc8 chr1 8678918 8679116 - DNA/hAT-Charlie_MER3 chr1 8683032 8683058 + Simple_repeat_(A)n chr1 8683661 8683945 + SINE/Alu_AluSp chr1 8683946 8683968 + Simple_repeat_(TCAA)n chr1 8684834 8684875 + Simple_repeat_(TCAT)n chr1 8684876 8685157 - SINE/Alu_AluJo chr1 8685440 8685629 + SINE/Alu_AluSc8 chr1 8685828 8685980 - LINE/L2_L2a chr1 8685981 8686282 - SINE/Alu_AluJb chr1 8686283 8686468 - LINE/L2_L2a chr1 8686469 8686764 + SINE/Alu_AluSq2 chr1 8686765 8687068 - LINE/L2_L2a chr1 8687075 8687183 - LINE/L2_L2-3_Crp chr1 8687228 8687489 - LINE/L2_L2a chr1 8688027 8688159 + DNA/TcMar-Tigger_MER47A chr1 8688160 8688283 + SINE/Alu_AluJr chr1 8688284 8688573 + SINE/Alu_AluSx chr1 8688574 8688674 + SINE/Alu_AluJr chr1 8688699 8688745 + Simple_repeat_(CATC)n chr1 8688752 8688966 + DNA/TcMar-Tigger_Tigger5b chr1 8688967 8689044 + LINE/L1_L1MB8 chr1 8689809 8689943 + SINE/MIR_MIRc chr1 8690072 8690456 - LTR/ERVL-MaLR_MLT1A chr1 8690763 8690873 - LINE/RTE-X_L4_A_Mam chr1 8690874 8691195 - SINE/Alu_AluSc chr1 8691196 8691393 - LINE/RTE-X_L4_A_Mam chr1 8691540 8691697 + DNA/hAT-Charlie_Charlie4z chr1 8692223 8692344 - DNA/TcMar-Tigger_MER2 chr1 8692345 8692648 - SINE/Alu_AluSx1 chr1 8692649 8692711 - DNA/TcMar-Tigger_MER2 chr1 8693061 8693926 + LINE/L1_L1MC2 chr1 8694030 8694047 + Simple_repeat_(A)n chr1 8694113 8694478 + LINE/L1_L1P4 chr1 8694479 8694772 + SINE/Alu_AluSx4 chr1 8694773 8695467 + LINE/L1_L1P4 chr1 8695468 8695771 + SINE/Alu_AluSx1 chr1 8695772 8696330 + LINE/L1_L1P4 chr1 8696331 8696620 + SINE/Alu_AluSx chr1 8696628 8696932 + SINE/Alu_AluSx1 chr1 8697240 8697288 + Low_complexity_A-rich chr1 8697327 8697353 + Simple_repeat_(ATAA)n chr1 8697397 8697687 - SINE/Alu_AluY chr1 8697967 8698014 + Low_complexity_A-rich chr1 8698017 8698081 - LTR/ERV1_MER41B chr1 8698674 8698817 - SINE/Alu_FLAM_C chr1 8698913 8698995 - SINE/Alu_AluJb chr1 8699046 8699159 + LINE/L2_L2a chr1 8699128 8699361 - SINE/MIR_MIR chr1 8700027 8700328 + SINE/Alu_AluSg chr1 8701288 8701331 + Simple_repeat_(AC)n chr1 8703038 8703092 + Simple_repeat_(GGCCGCT)n chr1 8704474 8704644 - SINE/MIR_MIR1_Amn chr1 8705454 8705575 + LINE/L2_L2b chr1 8705835 8706141 + SINE/Alu_AluY chr1 8706173 8706291 + SINE/Alu_FLAM_A chr1 8707812 8708115 + DNA/TcMar-Tigger_MER44A chr1 8708136 8708300 + LTR/ERVL-MaLR_THE1B chr1 8708301 8708625 - SINE/Alu_AluSx3 chr1 8708626 8708821 + LTR/ERVL-MaLR_THE1B chr1 8708906 8709218 - SINE/Alu_AluY chr1 8709285 8709416 - SINE/Alu_FLAM_C chr1 8709662 8709904 - LINE/L1_L1MC5a chr1 8710515 8710814 - SINE/Alu_AluYf1 chr1 8710897 8711186 + SINE/Alu_AluSz6 chr1 8713282 8713319 + Simple_repeat_(ATATTA)n chr1 8713452 8713739 + SINE/Alu_AluSq chr1 8713740 8713770 + Simple_repeat_(TAA)n chr1 8714124 8714169 + Simple_repeat_(TTTATA)n chr1 8714385 8714881 + LINE/L1_HAL1 chr1 8714882 8715171 - SINE/Alu_AluSc chr1 8715172 8715229 + LINE/L1_HAL1 chr1 8715230 8715513 + SINE/Alu_AluSq2 chr1 8715514 8715626 + LINE/L1_HAL1 chr1 8715627 8715901 + SINE/Alu_AluJb chr1 8715902 8715998 + LINE/L1_HAL1 chr1 8715999 8716167 + SINE/Alu_FRAM chr1 8716168 8716181 + LINE/L1_HAL1 chr1 8716182 8716493 + SINE/Alu_AluJb chr1 8716494 8716596 + LINE/L1_HAL1 chr1 8716603 8716629 + Low_complexity_A-rich chr1 8718691 8718912 + SINE/MIR_MIR chr1 8719830 8719955 + LINE/L2_L2c chr1 8719974 8720018 - LINE/L2_L2a chr1 8720019 8720283 + SINE/Alu_AluSc chr1 8720284 8720461 - LINE/L2_L2a chr1 8720492 8720577 + Low_complexity_GA-rich chr1 8720775 8721063 + SINE/Alu_AluSz chr1 8721311 8721612 - SINE/Alu_AluSx1 chr1 8721906 8722020 + DNA/TcMar-Tigger_Tigger1 chr1 8722013 8722197 + LTR/ERV1_LTR54 chr1 8722219 8722361 + LINE/L2_L2 chr1 8722372 8722523 - SINE/MIR_MIR chr1 8722541 8722901 + DNA/TcMar-Tigger_MER2 chr1 8724565 8724848 + SINE/Alu_AluSg chr1 8725284 8725577 + SINE/Alu_AluSx1 chr1 8725698 8725871 - DNA/hAT?_MER121 chr1 8726136 8726452 - SINE/Alu_AluSg chr1 8726806 8727104 - SINE/Alu_AluSx chr1 8727166 8727440 - SINE/Alu_AluSp chr1 8727484 8727791 - SINE/Alu_AluSc chr1 8728886 8729171 + SINE/Alu_AluSp chr1 8729186 8729217 + DNA/hAT-Charlie_MER58A chr1 8729218 8729522 - SINE/Alu_AluSg chr1 8729523 8729662 + DNA/hAT-Charlie_MER58A chr1 8730254 8730325 + SINE/MIR_MIR3 chr1 8730335 8730642 - SINE/Alu_AluSx chr1 8730646 8730737 - SINE/Alu_AluJb chr1 8730738 8730779 - SINE/Alu_Alu chr1 8730829 8730901 - DNA/hAT-Charlie_MER5A chr1 8730914 8731774 + LINE/L1_L1MCb chr1 8731775 8731822 + Simple_repeat_(TGTT)n chr1 8731827 8732102 - SINE/Alu_AluYj4 chr1 8732107 8732802 + LINE/L1_L1MC2 chr1 8732803 8733120 - SINE/Alu_AluYk3 chr1 8733121 8733166 + LINE/L1_L1MC2 chr1 8733193 8733314 - DNA/hAT-Charlie_MER5A chr1 8733376 8733791 + LTR/ERVL-MaLR_MLT1F chr1 8733829 8734132 + SINE/Alu_AluSx1 chr1 8734561 8734723 + SINE/MIR_MIR chr1 8734721 8734918 + LINE/L2_L2c chr1 8735008 8735107 - SINE/MIR_MIR chr1 8735354 8735577 - LINE/L1_L1ME4b chr1 8735578 8735955 + LTR/ERVL-MaLR_MSTA chr1 8735956 8736024 - LINE/L1_L1ME4b chr1 8736081 8736177 + LINE/L1_L1ME2z chr1 8736189 8736224 + Simple_repeat_(GTTT)n chr1 8736225 8736506 - SINE/Alu_AluY chr1 8736852 8736881 + Simple_repeat_(A)n chr1 8737612 8737734 - SINE/Alu_AluJo chr1 8738090 8738240 + LINE/L2_L2c chr1 8738271 8738484 - SINE/Alu_AluSz chr1 8738641 8738725 - SINE/MIR_MIR chr1 8739037 8739455 - LTR/ERVL_LTR41 chr1 8739503 8739677 - LTR/ERVL_LTR41 chr1 8739691 8739823 + LINE/L2_L2b chr1 8739824 8739900 + Simple_repeat_(TAATGA)n chr1 8739940 8740059 + LINE/L2_L2b chr1 8740178 8741224 - DNA/TcMar-Tigger_Tigger3b chr1 8741234 8741376 + LINE/L2_L2b chr1 8741383 8741775 + LTR/ERVL-MaLR_THE1D chr1 8742551 8742886 + SINE/Alu_AluJb chr1 8743115 8743283 + SINE/Alu_AluJr4 chr1 8743286 8743560 - SINE/Alu_AluSz chr1 8743685 8744052 + LINE/L2_L2a chr1 8745058 8745417 + LTR/ERVL-MaLR_THE1D chr1 8745788 8746083 + SINE/Alu_AluJo chr1 8746370 8746472 + LTR/ERVL-MaLR_MLT1K chr1 8746476 8746650 - DNA/TcMar-Tigger_MER47A chr1 8746812 8746937 + Low_complexity_GA-rich chr1 8747013 8747300 - SINE/Alu_AluY chr1 8747787 8748077 - SINE/Alu_AluSz chr1 8748083 8748303 - SINE/MIR_MIRc chr1 8748972 8749199 - SINE/MIR_MIR chr1 8749271 8749869 - LINE/L2_L2a chr1 8749870 8750172 + SINE/Alu_AluSz6 chr1 8750173 8750226 - LINE/L2_L2a chr1 8750229 8750280 + LINE/L2_L2a chr1 8751277 8751354 - SINE/MIR_MIRb chr1 8751778 8751796 + Simple_repeat_(A)n chr1 8751982 8752194 - SINE/MIR_MIR chr1 8755433 8755570 - DNA/hAT-Tip100_Arthur1A chr1 8755783 8755916 + SINE/Alu_AluJo chr1 8756015 8756128 + SINE/Alu_AluJo chr1 8756810 8757111 + SINE/Alu_AluSp chr1 8757814 8757891 + LINE/L1_L1MA8 chr1 8757892 8757930 + Simple_repeat_(AC)n chr1 8757931 8758091 + LINE/L1_L1MA8 chr1 8758092 8758380 - SINE/Alu_AluSg chr1 8758388 8758639 - SINE/Alu_AluJo chr1 8758640 8758973 + LINE/L1_L1MA8 chr1 8758993 8759022 - Retroposon/SVA_SVA_C chr1 8759023 8759126 + SINE/Alu_FLAM_A chr1 8759839 8759877 + Simple_repeat_(AC)n chr1 8760026 8760326 - SINE/Alu_AluSq chr1 8760618 8760665 - SINE/Alu_FRAM chr1 8760855 8761110 - SINE/MIR_MIR chr1 8761636 8761947 + SINE/Alu_AluSp chr1 8762089 8762384 + LINE/L2_L2c chr1 8763352 8763655 + SINE/Alu_AluSz chr1 8763659 8763955 + SINE/Alu_AluSc chr1 8764569 8764658 - LINE/L2_L2b chr1 8764721 8765526 + LINE/L1_L1MB8 chr1 8765537 8765672 + SINE/Alu_AluSx chr1 8765673 8765963 + SINE/Alu_AluSc5 chr1 8765964 8766140 + SINE/Alu_AluSx chr1 8766259 8766576 + SINE/Alu_AluSz chr1 8766577 8766594 + Simple_repeat_(AG)n chr1 8767184 8767269 - LINE/L2_L2b chr1 8767321 8767632 + SINE/Alu_AluJb chr1 8767700 8767998 + SINE/Alu_AluSx3 chr1 8768439 8768635 + DNA/hAT-Charlie_MER3 chr1 8768944 8769128 + DNA/hAT-Charlie_MER58A chr1 8769490 8769777 - SINE/Alu_AluJb chr1 8769784 8770089 - SINE/Alu_AluSx chr1 8770466 8770539 + SINE/MIR_MIR chr1 8770688 8771068 + LINE/L1_L1ME3G chr1 8771088 8771127 + rRNA_LSU-rRNA_Hsa chr1 8771242 8771538 + SINE/Alu_AluSz chr1 8771629 8771939 + SINE/Alu_AluY chr1 8772064 8772158 + LINE/L1_L1ME4a chr1 8772493 8772800 + SINE/Alu_AluSx1 chr1 8772807 8773106 + SINE/Alu_AluSz6 chr1 8773412 8773544 + SINE/Alu_AluJo chr1 8773545 8773831 + SINE/Alu_AluSg chr1 8774348 8774653 - SINE/Alu_AluSp chr1 8774937 8774951 - SINE/Alu_AluSx chr1 8774952 8775258 - SINE/Alu_AluSc chr1 8775290 8775602 + SINE/Alu_AluJr chr1 8775702 8775733 + Low_complexity_A-rich chr1 8775788 8775994 - SINE/MIR_MIR chr1 8776302 8776596 - SINE/Alu_AluSx chr1 8776711 8777036 - SINE/Alu_AluJb chr1 8777534 8777834 - SINE/Alu_AluY chr1 8777947 8778055 + SINE/MIR_MIRb chr1 8778558 8778836 + SINE/Alu_AluJb chr1 8778853 8779142 - SINE/Alu_AluSz chr1 8779248 8779380 + SINE/Alu_AluSg chr1 8779381 8779678 + SINE/Alu_AluSx1 chr1 8779679 8779778 + LINE/L2_L2a chr1 8779912 8780091 + SINE/Alu_AluSz chr1 8780122 8780232 + SINE/MIR_MIR3 chr1 8780940 8781075 + SINE/Alu_AluJb chr1 8781076 8781369 + SINE/Alu_AluSc chr1 8781370 8781552 + SINE/Alu_AluJb chr1 8781555 8781736 + SINE/MIR_MIR chr1 8781903 8781962 + DNA/hAT-Charlie_Charlie4z chr1 8782012 8782623 + LINE/L2_L2a chr1 8782624 8782919 + SINE/Alu_AluSx1 chr1 8782920 8783051 + LINE/L2_L2a chr1 8783052 8783340 + SINE/Alu_AluJb chr1 8783341 8783552 + LINE/L2_L2a chr1 8783556 8783653 + SINE/Alu_AluJo chr1 8783675 8783753 + LINE/L2_L2a chr1 8783832 8783891 + LTR/ERVL-MaLR_MLT1C chr1 8783900 8784100 + LTR/ERVL-MaLR_MLT1C chr1 8784101 8784556 + LINE/L2_L2a chr1 8784570 8784653 + DNA/hAT-Charlie_Charlie24 chr1 8785632 8785921 - SINE/Alu_AluSq2 chr1 8785987 8786117 + LINE/L2_L2a chr1 8786957 8787368 + LINE/L2_L2a chr1 8787547 8787849 + SINE/Alu_AluSx1 chr1 8787899 8788109 + SINE/Alu_AluJb chr1 8788363 8788662 - SINE/Alu_AluSg chr1 8788728 8789052 - LINE/L2_L2b chr1 8789146 8789162 + Simple_repeat_(TA)n chr1 8789163 8789297 + SINE/Alu_FLAM_C chr1 8789298 8789325 + Simple_repeat_(TA)n chr1 8789675 8789902 + LINE/L2_L2d chr1 8790604 8790885 - SINE/Alu_AluSz6 chr1 8791301 8791492 + LINE/L2_L2d chr1 8791940 8792117 + SINE/Alu_AluJb chr1 8792875 8793185 + SINE/Alu_AluSx1 chr1 8793801 8794078 + SINE/Alu_AluSz6 chr1 8794116 8794381 + SINE/Alu_AluYf1 chr1 8794615 8794747 + SINE/Alu_AluJo chr1 8794748 8794824 + SINE/Alu_AluJr chr1 8794847 8795030 - SINE/Alu_AluJb chr1 8795047 8795323 - SINE/Alu_AluSg chr1 8795330 8795475 - SINE/Alu_FLAM_C chr1 8795701 8796008 + SINE/Alu_AluSp chr1 8796565 8796665 - scRNA_HY1 chr1 8797092 8797390 + SINE/Alu_AluSz chr1 8797977 8798101 + SINE/Alu_AluJr chr1 8798113 8798428 + SINE/Alu_AluSx1 chr1 8798656 8798674 + Simple_repeat_(AAG)n chr1 8798690 8798991 - SINE/Alu_AluSx chr1 8798992 8799293 - SINE/Alu_AluSq2 chr1 8799554 8799657 + SINE/Alu_AluJr chr1 8799658 8799795 + DNA/hAT-Charlie_MER30 chr1 8799796 8800113 - SINE/Alu_AluSx1 chr1 8800114 8800198 + DNA/hAT-Charlie_MER30 chr1 8800199 8800272 + SINE/Alu_AluJr chr1 8800288 8800368 + LINE/L1_L1MB7 chr1 8800471 8800531 + LINE/L1_L1ME4b chr1 8800537 8800674 + SINE/Alu_FRAM chr1 8800684 8800970 + SINE/Alu_AluSz chr1 8800977 8801267 + SINE/Alu_AluJb chr1 8801271 8801568 - SINE/Alu_AluSp chr1 8801610 8801970 + LINE/L1_L1ME4b chr1 8802078 8802108 + Simple_repeat_(TATATT)n chr1 8802438 8802749 + LINE/L2_L2d2 chr1 8802988 8803159 + DNA/TcMar-Tc1_X32_DNA chr1 8803195 8803492 + SINE/Alu_AluSc chr1 8803586 8803861 - SINE/Alu_AluJb chr1 8804452 8804926 + LTR/ERVL-MaLR_MLT1J chr1 8804997 8805170 - SINE/Alu_AluSg4 chr1 8805231 8805367 + SINE/Alu_AluSx chr1 8805368 8805677 + SINE/Alu_AluSx1 chr1 8805678 8805836 + SINE/Alu_AluSx chr1 8805842 8806151 - SINE/Alu_AluSq2 chr1 8806248 8806524 + SINE/Alu_AluSx1 chr1 8806525 8806542 + Simple_repeat_(TAA)n chr1 8806623 8806723 - LINE/L2_L2a chr1 8807057 8807352 - SINE/Alu_AluJr chr1 8807998 8808170 + SINE/Alu_AluJb chr1 8808445 8808659 - SINE/MIR_MIR chr1 8808856 8809160 + SINE/Alu_AluY chr1 8809382 8809472 + DNA/TcMar-Tigger_X26_DNA chr1 8809543 8809710 + DNA/TcMar-Tc2_MER104 chr1 8809979 8810022 + Simple_repeat_(TTG)n chr1 8810023 8810299 - SINE/Alu_AluSx1 chr1 8810300 8810325 + Low_complexity_A-rich chr1 8810326 8810643 + SINE/Alu_AluJb chr1 8810891 8810930 + Simple_repeat_(GGCA)n chr1 8811325 8811616 + SINE/Alu_AluJo chr1 8812079 8812134 + LTR/ERVL-MaLR_MLT1M chr1 8812366 8812651 + SINE/Alu_AluSz chr1 8812652 8812667 + Simple_repeat_(TAA)n chr1 8813607 8813914 - SINE/Alu_AluSz chr1 8815830 8815872 + Simple_repeat_(TG)n chr1 8817827 8817854 + Simple_repeat_(T)n chr1 8818008 8818060 + Simple_repeat_(CCGCCCC)n chr1 8818901 8819198 - SINE/Alu_AluSp chr1 8819554 8819788 - SINE/Alu_AluSx chr1 8820164 8820249 - SINE/MIR_MIR chr1 8820250 8820558 - SINE/Alu_AluSp chr1 8820559 8820625 - SINE/MIR_MIR chr1 8821020 8821254 + SINE/Alu_AluJb chr1 8821482 8821585 - LINE/L1_L1ME3F chr1 8822285 8822578 + SINE/Alu_AluSp chr1 8822715 8823111 + SINE/5S-Deu-L2_AmnSINE1 chr1 8823363 8823397 + Low_complexity_A-rich chr1 8823398 8823431 + Simple_repeat_(A)n chr1 8823614 8823914 + SINE/Alu_AluSx1 chr1 8823964 8824086 + LINE/L1_L1MC2 chr1 8824088 8824210 + SINE/Alu_AluJr chr1 8824247 8824539 + LINE/L1_L1MC2 chr1 8824540 8824702 + SINE/Alu_FRAM chr1 8824703 8824989 + SINE/Alu_AluSz chr1 8824992 8825301 + SINE/Alu_AluSx3 chr1 8825302 8825487 + LINE/L1_L1MC2 chr1 8825488 8825794 + SINE/Alu_AluSq2 chr1 8826105 8826275 - LTR/ERVL-MaLR_MLT1M chr1 8826431 8826565 - DNA/TcMar-Tigger_MER44A chr1 8826566 8826881 + SINE/Alu_AluSg chr1 8826882 8826924 - DNA/TcMar-Tigger_MER44A chr1 8826925 8827217 + SINE/Alu_AluJo chr1 8827218 8827264 - DNA/TcMar-Tigger_MER44A chr1 8827265 8827383 + SINE/Alu_AluJb chr1 8827384 8827690 + SINE/Alu_AluSx chr1 8827691 8827848 + SINE/Alu_AluJb chr1 8827849 8827982 - DNA/TcMar-Tigger_MER44A chr1 8828040 8828331 + SINE/Alu_AluSz6 chr1 8828332 8828510 + SINE/Alu_AluJb chr1 8828681 8828980 + SINE/Alu_AluSx1 chr1 8829090 8829376 + SINE/Alu_AluY chr1 8829524 8830207 + LINE/L2_L2 chr1 8830208 8830514 + SINE/Alu_AluSp chr1 8830515 8830730 + LINE/L2_L2 chr1 8830741 8831031 + SINE/Alu_AluSz6 chr1 8831443 8831596 + LINE/L1_L1MCa chr1 8831597 8831893 - SINE/Alu_AluSp chr1 8831894 8831935 + LINE/L1_L1MCa chr1 8831936 8832049 + DNA/TcMar-Tigger_Tigger3a chr1 8832052 8832230 + SINE/Alu_AluSx chr1 8832234 8832273 + LINE/L1_L1MCa chr1 8832274 8832551 + SINE/Alu_AluJb chr1 8832552 8832609 + LINE/L1_L1MCa chr1 8832610 8832918 - SINE/Alu_AluSx chr1 8832919 8832947 + LINE/L1_L1MCa chr1 8832948 8833237 + SINE/Alu_AluJb chr1 8833238 8833264 + Simple_repeat_(AAAAT)n chr1 8833266 8833575 + SINE/Alu_AluSx3 chr1 8833576 8833679 + LINE/L1_L1MCa chr1 8833663 8833801 + LINE/L1_L1ME3Cz chr1 8833802 8834102 + SINE/Alu_AluY chr1 8834103 8834214 + LINE/L1_L1ME3Cz chr1 8834233 8834517 - SINE/Alu_AluSx chr1 8834550 8834844 - SINE/Alu_AluSx3 chr1 8834845 8835026 - SINE/Alu_AluJr chr1 8835034 8835167 + SINE/Alu_AluJr chr1 8835168 8835466 + SINE/Alu_AluSq2 chr1 8835467 8835617 + SINE/Alu_AluJr chr1 8835636 8835947 - SINE/Alu_AluSq2 chr1 8836789 8837098 + SINE/Alu_AluSx1 chr1 8838832 8838969 + SINE/Alu_AluJb chr1 8838970 8839257 + SINE/Alu_AluSz chr1 8839258 8839444 + SINE/Alu_AluJb chr1 8839450 8839716 + SINE/Alu_AluSz chr1 8839763 8839984 + LINE/L2_L2 chr1 8839985 8840343 + LTR/ERVL-MaLR_THE1B chr1 8840344 8840709 + LINE/L2_L2 chr1 8840710 8841002 + SINE/Alu_AluSx chr1 8841003 8841288 + LINE/L2_L2 chr1 8841296 8841346 + LTR/ERVL-MaLR_MLT1C2 chr1 8841355 8841497 + LTR/ERVL-MaLR_MLT1A0 chr1 8841498 8841629 + SINE/Alu_FRAM chr1 8841630 8841798 + LINE/L1_L1PA8 chr1 8841799 8841809 + SINE/Alu_FRAM chr1 8841838 8842021 + LTR/ERVL-MaLR_MLT1A0 chr1 8842025 8842372 - SINE/Alu_AluY chr1 8842378 8842541 - SINE/Alu_AluSx chr1 8842542 8842848 - SINE/Alu_AluSc8 chr1 8842849 8842984 - SINE/Alu_AluSx chr1 8842996 8843317 + LTR/ERVL-MaLR_MLT1C2 chr1 8843318 8843628 - SINE/Alu_AluSx chr1 8843629 8843694 + LTR/ERVL-MaLR_MLT1C2 chr1 8843970 8844072 + Simple_repeat_(TTCT)n chr1 8844073 8844149 + Simple_repeat_(CTCT)n chr1 8844150 8844435 - SINE/Alu_AluSx1 chr1 8845265 8845334 - SINE/MIR_MIRc chr1 8845341 8845738 + LTR/ERVL-MaLR_MLT1K chr1 8846241 8846263 + Simple_repeat_(GAAA)n chr1 8846607 8846670 - LINE/RTE-BovB_MamRTE1 chr1 8846679 8846841 - SINE/Alu_AluSx1 chr1 8846842 8847140 - SINE/Alu_AluSx1 chr1 8847141 8847280 - SINE/Alu_AluSx1 chr1 8847632 8847655 + Simple_repeat_(GGCT)n chr1 8848406 8848715 + SINE/Alu_AluSx1 chr1 8848997 8849036 + Simple_repeat_(CAGC)n chr1 8849183 8849211 + Simple_repeat_(AAAT)n chr1 8849666 8849982 - SINE/Alu_AluSx chr1 8850065 8850322 + LINE/L1_L1ME1 chr1 8850323 8850639 + SINE/Alu_AluJo chr1 8850640 8850783 + LINE/L1_L1ME1 chr1 8850784 8851087 + SINE/Alu_AluSx1 chr1 8851093 8851329 + SINE/Alu_AluJr chr1 8851330 8851385 + LINE/L1_L1ME1 chr1 8851386 8851661 + SINE/Alu_AluJb chr1 8851662 8851780 + LINE/L1_L1ME1 chr1 8851781 8852083 + SINE/Alu_AluSx chr1 8852084 8852365 + LINE/L1_L1ME1 chr1 8852373 8852508 + SINE/Alu_FRAM chr1 8852529 8852571 + Simple_repeat_(AAAG)n chr1 8852608 8852764 - SINE/Alu_FRAM chr1 8852887 8853077 - LINE/L2_L2a chr1 8853300 8853607 + SINE/Alu_AluYk2 chr1 8853608 8853831 + LINE/L1_L1M3 chr1 8853832 8853982 + SINE/Alu_AluJo chr1 8853983 8854039 + SINE/Alu_Alu chr1 8854345 8854422 + SINE/tRNA_LFSINE_Vert chr1 8854808 8854873 - DNA/hAT-Charlie_MER102a chr1 8854874 8855016 + SINE/Alu_AluJr chr1 8855017 8855328 + SINE/Alu_AluSg chr1 8855329 8855475 + SINE/Alu_AluJr chr1 8855476 8855544 - DNA/hAT-Charlie_MER102a chr1 8855557 8855669 + LINE/L1_L1ME1 chr1 8855670 8855845 + LTR/ERVL-MaLR_THE1C chr1 8855846 8856158 - SINE/Alu_AluSx chr1 8856159 8856381 + LTR/ERVL-MaLR_THE1C chr1 8856382 8856441 + LINE/L1_L1ME1 chr1 8856522 8856794 - SINE/Alu_AluSz6 chr1 8856934 8857035 - SINE/MIR_MIR1_Amn chr1 8857375 8857539 - DNA/hAT-Charlie_MER5B chr1 8857560 8857715 - SINE/Alu_AluJr chr1 8857723 8858011 + SINE/Alu_AluSx chr1 8858594 8858733 + LTR/ERVL_ERVL-B4-int chr1 8858736 8858901 + LTR/ERVL_MLT2C1 chr1 8858902 8859194 - SINE/Alu_AluSx chr1 8859195 8859355 + LTR/ERVL_MLT2C1 chr1 8859371 8859525 - SINE/Alu_AluJb chr1 8859711 8859880 - SINE/Alu_AluSz6 chr1 8859881 8860176 - SINE/Alu_AluSg4 chr1 8860177 8860301 - SINE/Alu_AluSz6 chr1 8861881 8861901 + Simple_repeat_(A)n chr1 8861910 8862164 + SINE/Alu_AluSx1 chr1 8862186 8862281 + SINE/Alu_FRAM chr1 8863636 8863721 - SINE/Alu_AluJo chr1 8864328 8864605 - SINE/Alu_AluJr chr1 8864690 8864756 + SINE/MIR_MIRc chr1 8864755 8864898 + SINE/MIR_MIR chr1 8865993 8866104 + SINE/Alu_AluSx3 chr1 8866677 8866804 - SINE/Alu_FLAM_A chr1 8866870 8867005 - SINE/MIR_MIR chr1 8867553 8867829 - SINE/Alu_AluY chr1 8868473 8868671 - DNA/TcMar-Mariner_MARNA chr1 8868677 8868987 - SINE/Alu_AluSz6 chr1 8869559 8869859 - SINE/Alu_AluSz chr1 8869969 8870073 + DNA/hAT-Charlie_Charlie15a chr1 8872238 8872275 - DNA/hAT-Charlie_MER5A1 chr1 8872388 8872680 - SINE/Alu_AluSx1 chr1 8873165 8873331 + LINE/L1_L1MC1 chr1 8873709 8873742 + Low_complexity_G-rich chr1 8874227 8874251 + Simple_repeat_(CTTA)n chr1 8874294 8874606 + SINE/Alu_AluSx4 chr1 8875142 8875266 + DNA/PiggyBac_MER85 chr1 8875298 8875332 + Low_complexity_A-rich chr1 8875789 8875920 - SINE/Alu_FLAM_C chr1 8876276 8876332 + DNA_MARE8 chr1 8876434 8876513 - LINE/L1_L1ME2z chr1 8876616 8876691 + SINE/Alu_AluY chr1 8876692 8876816 + SINE/Alu_AluY chr1 8876845 8876992 - SINE/Alu_AluSq2 chr1 8876993 8877251 - SINE/Alu_AluYk2 chr1 8877252 8877391 - SINE/Alu_AluSq2 chr1 8877574 8877891 + SINE/Alu_AluSp chr1 8878056 8878108 + Simple_repeat_(GCGCGG)n chr1 8879289 8879343 + Simple_repeat_(CGCCTG)n chr1 8881105 8881414 + SINE/Alu_AluSg chr1 8881437 8881733 + SINE/Alu_AluJr chr1 8881737 8882044 + SINE/Alu_AluSq2 chr1 8882061 8882361 + SINE/Alu_AluSx chr1 8882406 8882454 - LINE/L1_L1ME3F chr1 8882455 8882760 - SINE/Alu_AluY chr1 8882784 8883086 - SINE/Alu_AluSx3 chr1 8883089 8883394 - SINE/Alu_AluSc8 chr1 8883429 8883525 - snRNA_U6 chr1 8883979 8884278 - SINE/Alu_AluSp chr1 8884536 8884842 + SINE/Alu_AluSx chr1 8884845 8884923 - SINE/MIR_MIR chr1 8885404 8885691 + SINE/Alu_AluSx1 chr1 8885820 8885993 + LINE/CR1_L3 chr1 8886031 8886332 + SINE/Alu_AluSx1 chr1 8886333 8886591 + LTR/ERVL-MaLR_MLT1A0 chr1 8886659 8886710 - LTR/ERVL-MaLR_MLT1H2 chr1 8886719 8886796 + DNA/hAT-Charlie_MER58A chr1 8886797 8887012 - SINE/Alu_AluJb chr1 8887013 8887146 + DNA/hAT-Charlie_MER58A chr1 8887148 8887327 - LTR/ERVL-MaLR_MLT1H2 chr1 8887389 8887403 - LINE/L2_L2 chr1 8887404 8887711 - SINE/Alu_AluSg chr1 8887712 8888002 - SINE/Alu_AluJr chr1 8888003 8888126 - LINE/L2_L2 chr1 8888131 8888425 + SINE/Alu_AluJo chr1 8888429 8888721 + SINE/Alu_AluSq2 chr1 8888724 8889031 + SINE/Alu_AluY chr1 8889070 8889373 + SINE/Alu_AluJb chr1 8889506 8889651 + LINE/CR1_L3 chr1 8889788 8889820 + Simple_repeat_(AC)n chr1 8889809 8889865 + Simple_repeat_(AT)n chr1 8890293 8890333 - SINE/Alu_Alu chr1 8890334 8890352 + Simple_repeat_(A)n chr1 8890363 8890566 + LTR/ERV1_MER51B chr1 8890572 8890876 - SINE/Alu_AluY chr1 8890878 8891064 + SINE/Alu_AluJb chr1 8891075 8891372 - SINE/Alu_AluSp chr1 8891378 8891674 - LTR/ERV1_LTR8B chr1 8891763 8891915 + LTR/ERV1_MER34A chr1 8892030 8892216 - LINE/L2_L2a chr1 8892220 8892746 + LTR/ERV1_LTR8 chr1 8892747 8892860 + SINE/Alu_AluSx3 chr1 8892861 8893161 + SINE/Alu_AluY chr1 8893162 8893353 + SINE/Alu_AluSx3 chr1 8893354 8893407 + LTR/ERV1_LTR8 chr1 8894395 8894464 - DNA/TcMar-Tigger_Tigger19b chr1 8894611 8894666 + LTR/ERV1_LTR8 chr1 8894670 8894719 + Simple_repeat_(TTTTTAA)n chr1 8894721 8895019 - SINE/Alu_AluSz chr1 8895020 8895109 + LTR/ERV1_LTR8 chr1 8895286 8895313 + Simple_repeat_(TTTAT)n chr1 8895340 8895466 + SINE/Alu_AluSq2 chr1 8895494 8895792 + SINE/Alu_AluSc8 chr1 8895877 8898307 + LTR/ERV1_HERV9-int chr1 8898308 8899114 + LTR/ERV1_LTR12 chr1 8899118 8899373 - SINE/Alu_AluJr chr1 8899405 8899713 - SINE/Alu_AluSq10 chr1 8899717 8900019 - SINE/Alu_AluSx1 chr1 8900021 8900074 + LTR/ERV1_MER34A1 chr1 8900086 8900167 - LTR/ERV1_MER41B chr1 8900168 8900469 + SINE/Alu_AluSz chr1 8900470 8900648 - LTR/ERV1_MER41B chr1 8900649 8900943 - SINE/Alu_AluSg chr1 8900944 8901025 - LTR/ERV1_MER41B chr1 8901033 8901204 - SINE/Alu_AluSz chr1 8901205 8901268 + Simple_repeat_(TTTTA)n chr1 8901269 8901551 - SINE/Alu_AluSq2 chr1 8901552 8901694 - SINE/Alu_AluSz chr1 8901715 8902004 + LTR/ERVL_LTR47A chr1 8902023 8902316 - SINE/Alu_AluSx chr1 8902453 8902708 + LTR/ERVL_MER21A chr1 8902689 8902875 + DNA/hAT-Charlie_MER30 chr1 8902876 8903241 + LTR/ERVL_MER21A chr1 8903242 8903555 + SINE/Alu_AluSz chr1 8903556 8903844 + LTR/ERVL_MER21A chr1 8904386 8904427 + Simple_repeat_(TCTCC)n chr1 8904428 8904676 - SINE/Alu_AluJr chr1 8904810 8904997 + LINE/L1_L1MEd chr1 8904998 8905021 + Simple_repeat_(TTAA)n chr1 8905022 8905292 - SINE/Alu_AluSx chr1 8905338 8905368 + Simple_repeat_(TCTTTC)n chr1 8905371 8905683 - SINE/Alu_AluSx1 chr1 8905684 8905695 + Simple_repeat_(GTCT)n chr1 8906048 8906238 + LINE/L1_L1MEd chr1 8906273 8906584 - SINE/Alu_AluSx1 chr1 8906589 8906892 - SINE/Alu_AluY chr1 8906901 8907209 - SINE/Alu_AluYa5 chr1 8907213 8907343 - SINE/Alu_AluJo chr1 8907810 8907995 - LTR/ERVK_HERVK9-int chr1 8907992 8908629 - LTR/ERVK_HERVK9-int chr1 8908630 8908879 - LTR/ERVK_MER9a3 chr1 8908903 8908986 - SINE/Alu_FLAM_C chr1 8908992 8909231 + LTR/ERVL_ERV3-16A3_LTR chr1 8909232 8909357 - LINE/L1_L1PB2 chr1 8909358 8909370 + LTR/ERVL_ERV3-16A3_LTR chr1 8909371 8909679 - SINE/Alu_AluSc chr1 8909680 8909741 + LTR/ERVL_ERV3-16A3_LTR chr1 8909925 8910033 - LINE/L2_L2 chr1 8910049 8910211 - SINE/Alu_AluSx chr1 8910285 8910646 - LINE/L2_L2 chr1 8910797 8911044 + SINE/Alu_AluJb chr1 8911294 8911341 + Simple_repeat_(TATTT)n chr1 8911898 8911938 + Simple_repeat_(AAC)n chr1 8912173 8912788 + Retroposon/SVA_SVA_D chr1 8912768 8912866 + LTR/ERV1_MER41A chr1 8912867 8912889 + Simple_repeat_(A)n chr1 8913009 8913343 - SINE/Alu_AluSz chr1 8913370 8913417 + Simple_repeat_(TG)n chr1 8913418 8913553 - SINE/Alu_FRAM chr1 8914093 8914399 - SINE/Alu_AluSx chr1 8914528 8914770 - LTR/ERVL_LTR82A chr1 8914876 8915170 + SINE/Alu_AluSz6 chr1 8915207 8915365 + DNA/TcMar-Tigger_Tigger15a chr1 8915856 8916425 + LINE/L1_L1MB5 chr1 8916433 8916725 + SINE/Alu_AluSq2 chr1 8916739 8917043 + SINE/Alu_AluSz chr1 8917049 8917347 + SINE/Alu_AluSc5 chr1 8917348 8917469 + SINE/Alu_FLAM_C chr1 8917793 8917928 + SINE/Alu_AluSz chr1 8917929 8918241 + SINE/Alu_AluSx chr1 8918545 8918866 + SINE/Alu_AluJr chr1 8918957 8919260 + SINE/Alu_AluSq chr1 8919592 8919868 - SINE/Alu_AluSz chr1 8919873 8920172 - SINE/Alu_AluSz6 chr1 8920330 8920454 - SINE/Alu_FLAM_A chr1 8920509 8920804 + SINE/Alu_AluSz6 chr1 8920807 8920892 + SINE/Alu_AluSp chr1 8921052 8921231 + LTR/ERV1_LTR21A chr1 8921232 8924068 + LTR/ERV1_HERVFH21-int chr1 8924069 8924337 - SINE/Alu_AluSg4 chr1 8924338 8925978 + LTR/ERV1_HERVFH21-int chr1 8926497 8927320 + LTR/ERV1_HERVFH21-int chr1 8927489 8927668 + LTR/ERV1_LTR21A chr1 8927669 8927887 + SINE/Alu_AluSx1 chr1 8927939 8928250 - SINE/Alu_AluY chr1 8928345 8928476 + SINE/Alu_FLAM_A chr1 8928520 8928574 + LINE/L2_L2 chr1 8928622 8928694 - LTR/ERVL-MaLR_MLT1L chr1 8928695 8929000 + SINE/Alu_AluSg7 chr1 8929001 8929245 - LTR/ERVL-MaLR_MLT1L chr1 8929246 8929414 - SINE/Alu_AluJr chr1 8929415 8929697 - SINE/Alu_AluSz chr1 8929698 8929841 - SINE/Alu_AluJr chr1 8929842 8929957 - LTR/ERVL-MaLR_MLT1L chr1 8930094 8930119 + Simple_repeat_(TCAT)n chr1 8930121 8930262 - LINE/L1_L1M5 chr1 8930263 8930386 - DNA/hAT-Charlie_MER58B chr1 8930439 8930688 - LINE/L1_L1M5 chr1 8930679 8930729 - LINE/L1_L1MB2 chr1 8930730 8931028 - SINE/Alu_AluSq2 chr1 8931029 8931128 - LINE/L1_L1MB2 chr1 8931141 8931440 - SINE/Alu_AluSz chr1 8931441 8931757 - LINE/L1_L1MB4 chr1 8931836 8932148 - SINE/Alu_AluSx1 chr1 8932150 8932199 + Simple_repeat_(TATTT)n chr1 8932200 8932499 - SINE/Alu_AluSx chr1 8932508 8932602 - SINE/Alu_AluJo chr1 8932673 8932715 + SINE/Alu_FLAM_C chr1 8932819 8933070 - LINE/L1_L1ME3A chr1 8933071 8933358 - SINE/Alu_AluSz chr1 8933359 8933572 - LINE/L1_L1ME3A chr1 8933573 8933938 - LTR/ERVL-MaLR_THE1B chr1 8933939 8934261 - LINE/L1_L1ME3A chr1 8934269 8934387 - SINE/Alu_AluJr4 chr1 8934435 8934737 - SINE/Alu_AluY chr1 8934773 8935071 - SINE/Alu_AluSx chr1 8935084 8935376 - SINE/Alu_AluSc8 chr1 8935388 8935415 + Simple_repeat_(A)n chr1 8935455 8935498 - LINE/L1_L1ME3A chr1 8935499 8935786 + SINE/Alu_AluJb chr1 8935787 8935866 - LINE/L1_L1ME3A chr1 8935859 8935934 - LINE/L1_L1MC3 chr1 8935963 8936028 - LINE/L1_L1ME3A chr1 8936098 8936383 + SINE/Alu_AluJb chr1 8936390 8936672 + SINE/Alu_AluSx3 chr1 8936673 8936701 + Simple_repeat_(AAAT)n chr1 8936702 8936712 + SINE/Alu_AluSx3 chr1 8936755 8937055 - SINE/Alu_AluSx chr1 8937058 8937154 + LTR/ERVL_HERVL40-int chr1 8937181 8937342 - SINE/Alu_AluSz chr1 8937343 8937649 - SINE/Alu_AluSx4 chr1 8937650 8937794 - SINE/Alu_AluSz chr1 8938090 8938281 + LTR/ERVL-MaLR_MLT1D chr1 8938289 8938749 + LTR/ERV1_LTR77 chr1 8938750 8939344 - LTR/ERV1_LTR61 chr1 8939345 8939497 + LTR/ERV1_LTR77 chr1 8939524 8939754 + LTR/ERVL-MaLR_MLT1D chr1 8939983 8940137 + LTR/ERV1_LTR77 chr1 8940877 8941189 + SINE/Alu_AluSq2 chr1 8941263 8941393 - DNA/hAT_MamRep38 chr1 8941518 8941545 + Simple_repeat_(TTTC)n chr1 8941555 8941750 - SINE/Alu_AluSx chr1 8941797 8942015 + DNA/TcMar-Tigger_Tigger15a chr1 8942114 8942423 - SINE/Alu_AluSx chr1 8942846 8943023 + SINE/Alu_AluJb chr1 8943797 8943914 - LTR/ERVL-MaLR_MLT1M chr1 8944101 8944178 + SINE/MIR_MIR3 chr1 8944186 8944288 - SINE/MIR_MIR chr1 8944289 8944589 + SINE/Alu_AluJb chr1 8944590 8944610 - SINE/MIR_MIR chr1 8944636 8944761 - SINE/MIR_MIR chr1 8946031 8946331 - SINE/Alu_AluSx1 chr1 8946794 8947103 - SINE/Alu_AluSz6 chr1 8947851 8948147 - SINE/Alu_AluSp chr1 8948688 8948985 + SINE/Alu_AluSx chr1 8949982 8950283 - SINE/Alu_AluSz chr1 8950348 8950524 - SINE/MIR_MIR3 chr1 8950587 8950883 + SINE/Alu_AluJb chr1 8950895 8950934 + SINE/MIR_MIRc chr1 8950935 8951246 + SINE/Alu_AluSq2 chr1 8951247 8951308 + SINE/MIR_MIRc chr1 8951827 8952119 - SINE/Alu_AluSz chr1 8952146 8952436 - SINE/Alu_AluSz chr1 8952437 8952660 - DNA/hAT-Tip100_ORSL-2b chr1 8952664 8952956 - SINE/Alu_AluSz chr1 8952958 8953341 - LINE/L1_L1MC1 chr1 8953342 8953619 + SINE/Alu_AluJb chr1 8953620 8953660 + Simple_repeat_(TCAA)n chr1 8953661 8953730 - LINE/L1_L1MC1 chr1 8953731 8954173 + LTR/ERV1_MER41A chr1 8954174 8954464 - SINE/Alu_AluSz6 chr1 8954465 8954588 + LTR/ERV1_MER41A chr1 8954589 8954734 - LINE/L1_L1MC1 chr1 8954741 8954874 - SINE/Alu_AluSx chr1 8954882 8954939 + Low_complexity_A-rich chr1 8954944 8955047 - DNA/hAT-Charlie_MER5C1 chr1 8955097 8955383 + SINE/Alu_AluJb chr1 8955942 8956234 + SINE/Alu_AluSz chr1 8956379 8956688 + SINE/Alu_AluSx1 chr1 8957336 8957640 - SINE/Alu_AluSx1 chr1 8957667 8957946 + SINE/Alu_AluJr chr1 8958014 8958058 - LINE/L2_L2b chr1 8958165 8958465 - SINE/Alu_AluSq chr1 8959311 8959613 - SINE/Alu_AluSq2 chr1 8959650 8959950 + SINE/Alu_AluSx1 chr1 8959951 8960245 + SINE/Alu_AluSx chr1 8960250 8960743 + LINE/L1_L1M4a1 chr1 8960744 8960790 + Simple_repeat_(AC)n chr1 8960791 8961679 + LINE/L1_L1M4a1 chr1 8961958 8962272 + SINE/Alu_AluSx1 chr1 8963111 8963548 + LINE/L2_L2 chr1 8963549 8963840 - SINE/Alu_AluJb chr1 8963841 8964236 + LINE/L2_L2 chr1 8964237 8964545 - SINE/Alu_AluSc chr1 8964546 8964692 + LINE/L2_L2 chr1 8964962 8965265 + SINE/Alu_AluSx chr1 8965267 8965605 - LINE/L1_L1MB3 chr1 8965606 8965900 + SINE/Alu_AluSx1 chr1 8965901 8966104 - LINE/L1_L1MB3 chr1 8966105 8966409 - SINE/Alu_AluSx chr1 8966410 8966463 - LINE/L1_L1MB3 chr1 8966999 8967122 - SINE/Alu_AluJr chr1 8967292 8967499 - SINE/MIR_MIRb chr1 8967964 8968265 - SINE/Alu_AluSz chr1 8968354 8968733 + LINE/L1_L1MEd chr1 8968734 8969030 + SINE/Alu_AluSx1 chr1 8969035 8969333 + SINE/Alu_AluSx3 chr1 8969334 8969927 + LINE/L1_L1MEd chr1 8969928 8970235 + SINE/Alu_AluSx1 chr1 8970236 8970274 + LINE/L1_L1MEd chr1 8970502 8970800 - SINE/Alu_AluSz chr1 8971111 8971311 - LTR/ERVL_LTR101_Mam chr1 8971312 8971610 - SINE/Alu_AluSx1 chr1 8971611 8971739 - LTR/ERVL_LTR101_Mam chr1 8971795 8972040 - LTR/ERVL_LTR101_Mam chr1 8972050 8972350 - SINE/Alu_AluJr chr1 8972393 8972714 + SINE/Alu_AluYf1 chr1 8973031 8973318 - SINE/Alu_AluSx chr1 8973699 8973787 + Simple_repeat_(TTTC)n chr1 8973788 8973819 + Simple_repeat_(TC)n chr1 8973820 8974124 - SINE/Alu_AluJb chr1 8975264 8975571 - SINE/Alu_AluSc chr1 8975572 8975862 - SINE/Alu_AluSx chr1 8976207 8976380 - SINE/Alu_AluJb chr1 8976604 8976720 - SINE/MIR_MIRb chr1 8976735 8977236 - LINE/L2_L2c chr1 8977420 8977700 + SINE/Alu_AluSc chr1 8977701 8977771 + Simple_repeat_(AAAG)n chr1 8977780 8977914 + LINE/L1_L1MC5a chr1 8977915 8978209 - SINE/Alu_AluSx chr1 8978210 8978355 + LINE/L1_L1MC5a chr1 8978356 8978666 + SINE/Alu_AluYk2 chr1 8978667 8979196 + LINE/L1_L1MC5a chr1 8979197 8979493 + SINE/Alu_AluJb chr1 8979494 8979556 + LINE/L1_L1MC5a chr1 8979557 8979874 - srpRNA_7SLRNA chr1 8979875 8979992 + LINE/L1_L1MC5a chr1 8979993 8980287 - SINE/Alu_AluSz chr1 8980288 8980384 + LINE/L1_L1MC5a chr1 8980385 8980519 + SINE/Alu_FLAM_C chr1 8980520 8980863 + LINE/L1_L1MC5a chr1 8980886 8981263 - LTR/ERVL-MaLR_MLT1C2 chr1 8981264 8981467 + Simple_repeat_(TTCC)n chr1 8981476 8981780 - SINE/Alu_AluJb chr1 8981781 8981852 - LTR/ERVL-MaLR_MLT1C2 chr1 8981858 8982097 + LINE/L1_L1MC5a chr1 8982160 8982284 + LINE/L2_L2a chr1 8982285 8982826 + LINE/L1_L1ME3C chr1 8982827 8983137 + SINE/Alu_AluSc chr1 8983138 8983165 + LINE/L1_L1ME3C chr1 8983166 8983482 + SINE/Alu_AluJb chr1 8983483 8983574 + LINE/L1_L1ME3C chr1 8983575 8983928 + DNA/TcMar-Tigger_Tigger3a chr1 8983929 8983980 + LINE/L1_L1ME3C chr1 8983995 8984268 + SINE/Alu_AluJb chr1 8984271 8984485 + Low_complexity_GA-rich chr1 8984612 8984681 + LINE/L2_L2b chr1 8984747 8985041 - SINE/Alu_AluSx chr1 8985042 8985254 + LINE/L2_L2b chr1 8985749 8985868 + SINE/Alu_AluJo chr1 8985929 8986085 - SINE/MIR_MIRb chr1 8986457 8986752 + SINE/Alu_AluJr chr1 8987005 8987301 - SINE/Alu_AluJo chr1 8987318 8987334 + Simple_repeat_(AAAACT)n chr1 8987335 8987645 + SINE/Alu_AluSx chr1 8987646 8987668 + Simple_repeat_(AAAACT)n chr1 8988262 8988327 - SINE/MIR_MIR3 chr1 8989398 8989684 + SINE/Alu_AluSx1 chr1 8990319 8990618 - SINE/Alu_AluSz chr1 8990626 8990860 + LINE/L1_L1M6 chr1 8990885 8991154 + SINE/Alu_AluJr chr1 8991220 8991484 - SINE/Alu_AluSz chr1 8991815 8992183 + LINE/L2_L2 chr1 8992201 8992487 - SINE/Alu_AluJr chr1 8992491 8992548 + Simple_repeat_(TCTT)n chr1 8992601 8992880 - LTR/ERVL-MaLR_MLT1K chr1 8993013 8993351 - LTR/ERVL-MaLR_THE1B chr1 8993352 8993614 + LINE/L1_L1MC5a chr1 8993630 8993926 - SINE/Alu_AluSc8 chr1 8994535 8994758 - SINE/MIR_MIRb chr1 8994759 8995070 - SINE/Alu_AluSx1 chr1 8995071 8995108 - SINE/MIR_MIR1_Amn chr1 8995167 8995463 + SINE/Alu_AluY chr1 8995465 8995753 + SINE/Alu_AluSq4 chr1 8995754 8996067 - SINE/Alu_AluJo chr1 8996090 8996787 - LINE/L1_L1ME1 chr1 8996788 8997077 - SINE/Alu_AluSp chr1 8997078 8997156 - LINE/L1_L1ME1 chr1 8997079 8997159 - LINE/L1_L1MD chr1 8997160 8997326 + SINE/Alu_FRAM chr1 8997327 8997397 - LINE/L1_L1MD chr1 8997398 8997697 - SINE/Alu_AluSq2 chr1 8997698 8998121 - LINE/L1_L1MD chr1 8998122 8998428 + SINE/Alu_AluSx1 chr1 8998429 8998761 - LINE/L1_L1MD chr1 8998738 8999159 - LINE/L1_L1ME1 chr1 8999169 8999296 - SINE/Alu_AluSz chr1 8999297 8999419 - LINE/L1_L1ME1 chr1 9000005 9000075 - SINE/MIR_MIRc chr1 9000176 9000325 + SINE/Alu_FRAM chr1 9000337 9000648 + SINE/Alu_AluSx chr1 9000746 9000784 + Simple_repeat_(GT)n chr1 9000839 9001087 + LTR/ERVL-MaLR_MLT1G chr1 9001088 9001283 - LINE/L2_L2c chr1 9001420 9001716 - SINE/Alu_AluSx chr1 9001820 9002808 + LTR/ERVK_LTR5B chr1 9002809 9002935 - SINE/tRNA-RTE_MamSINE1 chr1 9003577 9003880 + SINE/Alu_AluSx chr1 9004080 9004349 + SINE/Alu_AluJb chr1 9005346 9005503 - DNA/hAT-Charlie_MER106B chr1 9005580 9005811 + SINE/Alu_AluSz chr1 9006095 9006173 - SINE/MIR_MIRb chr1 9006265 9006551 - SINE/Alu_AluSx1 chr1 9007213 9007271 - LINE/L2_L2b chr1 9007644 9007714 - SINE/MIR_MIR chr1 9007735 9007902 - SINE/Alu_AluSx3 chr1 9008262 9008328 - LINE/L2_L2c chr1 9008604 9008894 - SINE/Alu_AluSx chr1 9009454 9009757 - SINE/Alu_AluJb chr1 9009825 9010013 + DNA/hAT-Charlie_MER20B chr1 9010346 9010651 - SINE/Alu_AluSq chr1 9010825 9011364 + LTR/ERVL_MER74A chr1 9011372 9011508 + SINE/Alu_AluJo chr1 9011707 9011740 + Simple_repeat_(TTCTAC)n chr1 9011741 9012048 - SINE/Alu_AluSx chr1 9012049 9012083 + Simple_repeat_(TTCTAC)n chr1 9012879 9013190 - SINE/Alu_AluSx1 chr1 9013196 9013329 - SINE/Alu_AluSz chr1 9013759 9013944 + SINE/MIR_MIR3 chr1 9015021 9015073 + Simple_repeat_(CCCTGA)n chr1 9015721 9016021 - SINE/Alu_AluJr chr1 9016323 9016601 + SINE/Alu_AluJb chr1 9016602 9016716 + Low_complexity_GA-rich chr1 9016859 9017090 + LTR/ERV1_MER39B chr1 9017091 9017321 + SINE/Alu_AluSx chr1 9017322 9017355 + Simple_repeat_(AAC)n chr1 9017356 9017726 + LTR/ERV1_MER39B chr1 9017729 9017832 + LINE/L2_L2 chr1 9017868 9018179 + LINE/L2_L2 chr1 9018900 9019025 + SINE/Alu_FLAM_C chr1 9019466 9019641 + LTR/ERVL-MaLR_MLT1A chr1 9019642 9019952 + SINE/Alu_AluSz chr1 9019953 9020112 + LTR/ERVL-MaLR_MLT1A chr1 9020116 9020413 + LINE/CR1_L3 chr1 9020669 9020758 - SINE/Alu_AluSx chr1 9020759 9021091 + LTR/ERVL-MaLR_THE1B chr1 9021092 9021309 - SINE/Alu_AluSx chr1 9022590 9022841 - SINE/MIR_MIRb chr1 9023326 9023618 + SINE/Alu_AluY chr1 9023628 9023749 + SINE/Alu_AluJr chr1 9023839 9024163 - SINE/Alu_AluSx1 chr1 9024176 9024332 + SINE/MIR_MIRb chr1 9024451 9024666 + LINE/L2_L2 chr1 9025260 9025646 - LINE/L2_L2a chr1 9025838 9025875 + Simple_repeat_(TGCCCCC)n chr1 9026012 9026050 + Low_complexity_G-rich chr1 9026729 9026886 - SINE/Alu_AluSz chr1 9026887 9027197 - SINE/Alu_AluY chr1 9027198 9027333 - SINE/Alu_AluSz chr1 9027340 9027574 - LINE/L1_L1PA14 chr1 9027908 9028215 + SINE/Alu_AluJb chr1 9028341 9028574 - SINE/Alu_AluJb chr1 9028650 9028879 - SINE/Alu_AluSx chr1 9028885 9029009 - SINE/Alu_AluSz chr1 9029013 9029258 - LTR/ERVL-MaLR_MLT1D-int chr1 9029261 9029396 - LTR/ERVL-MaLR_MLT1D chr1 9029392 9029548 + LTR/ERVL-MaLR_MSTD chr1 9029770 9030081 + SINE/Alu_AluJb chr1 9030385 9030490 + DNA/hAT-Charlie_Charlie18a chr1 9030538 9030554 + Simple_repeat_(TA)n chr1 9030560 9030657 - SINE/Alu_FLAM_C chr1 9030881 9030978 - LINE/L2_L2a chr1 9031057 9032419 - LTR/ERV1_MER52D chr1 9032500 9032585 - DNA/hAT-Charlie_Charlie7a chr1 9032593 9032881 - SINE/Alu_AluY chr1 9033221 9033245 + Simple_repeat_(TTAT)n chr1 9033247 9033533 - SINE/Alu_AluJr chr1 9033627 9033930 + LINE/L2_L2a chr1 9034012 9034081 + LINE/L2_L2a chr1 9034087 9034246 + LINE/L2_L2 chr1 9034741 9034982 - LINE/L1_L1MC3 chr1 9035064 9035270 - SINE/MIR_MIR chr1 9035707 9036068 - LTR/ERVL-MaLR_THE1B chr1 9036181 9036385 - SINE/Alu_AluJb chr1 9036386 9036462 - SINE/Alu_AluSx3 chr1 9036463 9036536 - LINE/L2_L2a chr1 9036559 9036869 + SINE/Alu_AluY chr1 9038837 9038900 + Simple_repeat_(CAG)n chr1 9039489 9039533 + Low_complexity_G-rich chr1 9039726 9039790 + Simple_repeat_(GCCCCGA)n chr1 9040766 9040815 + LINE/L2_L2c chr1 9040863 9041079 + SINE/MIR_MIR chr1 9041346 9041467 + SINE/MIR_MIRb chr1 9042276 9042435 + SINE/Alu_AluSz chr1 9042448 9042736 + Simple_repeat_(TG)n chr1 9043037 9043090 - SINE/MIR_MIRc chr1 9043450 9043572 - LINE/L2_L2 chr1 9043724 9044021 - SINE/Alu_AluSq2 chr1 9044048 9044355 + SINE/Alu_AluSz chr1 9044543 9044854 - SINE/Alu_AluSz chr1 9044973 9045261 + DNA/hAT-Charlie_Charlie1b chr1 9045236 9045399 + DNA/hAT-Charlie_Charlie1b chr1 9045606 9045893 + SINE/Alu_AluSq2 chr1 9046591 9046661 - DNA/hAT-Charlie_MER112 chr1 9046666 9046706 + Simple_repeat_(GT)n chr1 9046784 9046804 - SINE/Alu_FLAM_A chr1 9046805 9047163 + LTR/ERVL-MaLR_THE1D chr1 9047164 9047259 - SINE/Alu_FLAM_A chr1 9047261 9047346 - DNA/hAT-Charlie_MER112 chr1 9048062 9048226 + LINE/L1_L1M5 chr1 9048227 9048541 + SINE/Alu_AluSx1 chr1 9048542 9048654 + LINE/L1_L1M5 chr1 9048655 9048956 - SINE/Alu_AluSz6 chr1 9048957 9049094 + LINE/L1_L1M5 chr1 9049099 9049342 + LINE/L1_L1MEd chr1 9049353 9049441 + LINE/L1_L1MC chr1 9049442 9049729 + SINE/Alu_AluSx chr1 9049730 9049855 + LINE/L1_L1MC chr1 9049856 9049989 + SINE/Alu_AluJr chr1 9049990 9050304 + SINE/Alu_AluSx chr1 9050305 9050486 + SINE/Alu_AluJr chr1 9050487 9050730 + LINE/L1_L1MC chr1 9050723 9050992 + LINE/L1_L1MEd chr1 9051180 9051284 + Low_complexity_A-rich chr1 9051286 9052003 + LINE/L1_L1MC1 chr1 9052004 9052305 + SINE/Alu_AluSc chr1 9052306 9052730 + LINE/L1_L1MC1 chr1 9052736 9052974 + SINE/Alu_AluJr chr1 9052975 9053016 + Simple_repeat_(AT)n chr1 9053042 9053581 + Simple_repeat_(TA)n chr1 9053592 9053899 + SINE/Alu_AluSx1 chr1 9053909 9054295 + LINE/L1_L1MEd chr1 9054285 9054513 + LINE/L1_L1MEd chr1 9054643 9054924 + SINE/Alu_AluJo chr1 9055091 9055236 + SINE/MIR_MIR chr1 9055237 9055539 + SINE/Alu_AluSx3 chr1 9055540 9055622 + SINE/MIR_MIR chr1 9055634 9055936 + SINE/Alu_AluSz chr1 9056182 9056483 - SINE/Alu_AluSq2 chr1 9056539 9056664 + SINE/MIR_MIR3 chr1 9057010 9057315 + SINE/Alu_AluSx chr1 9058407 9058610 - DNA/hAT-Charlie_MER20 chr1 9058850 9059026 + SINE/MIR_MIRb chr1 9059133 9059442 - SINE/Alu_AluY chr1 9059537 9059833 - SINE/Alu_AluJr chr1 9059986 9060207 + Simple_repeat_(AC)n chr1 9060220 9060743 + Simple_repeat_(AC)n chr1 9060810 9060926 - LINE/L2_L2a chr1 9061004 9061309 + SINE/Alu_AluSz chr1 9061320 9061619 + SINE/Alu_AluJr chr1 9061764 9061790 + Simple_repeat_(CAGCA)n chr1 9062117 9062486 - LINE/L2_L2b chr1 9062590 9062897 + SINE/Alu_AluSz chr1 9063057 9063106 + LINE/L1_L1M4 chr1 9063124 9063196 - LINE/L1_L1MA8 chr1 9063232 9063287 - SINE/Alu_FLAM_C chr1 9063288 9063357 - SINE/Alu_AluJr chr1 9063358 9063671 - SINE/Alu_AluSz chr1 9063682 9063994 - SINE/Alu_AluY chr1 9063995 9064122 - SINE/Alu_AluJr chr1 9064237 9064259 + Simple_repeat_(TCTT)n chr1 9064262 9064349 - LINE/L2_L2b chr1 9064554 9064769 + LTR/ERV1_LTR56 chr1 9064770 9065071 + SINE/Alu_AluSg chr1 9065072 9065301 + LTR/ERV1_LTR56 chr1 9065309 9065475 + DNA/hAT-Tip100_MamTip2 chr1 9065592 9065715 + SINE/MIR_MIR chr1 9065716 9065953 + SINE/Alu_AluJo chr1 9065954 9066056 + SINE/MIR_MIR chr1 9066087 9066153 - SINE/MIR_MIR chr1 9066233 9066534 - SINE/Alu_AluJb chr1 9066572 9066609 + Simple_repeat_(CTTTTT)n chr1 9066699 9067009 + SINE/Alu_AluJr chr1 9067471 9067674 + SINE/MIR_MIRc chr1 9067675 9067869 + DNA/hAT-Charlie_MER20 chr1 9067908 9068209 + SINE/Alu_AluSx chr1 9068236 9068449 - SINE/MIR_MIR chr1 9068450 9068713 - SINE/Alu_AluSq2 chr1 9068714 9068739 - SINE/MIR_MIR chr1 9068785 9069031 + DNA/hAT-Charlie_Charlie23a chr1 9070073 9070380 - SINE/Alu_AluSp chr1 9070834 9070853 + Simple_repeat_(GT)n chr1 9071128 9071403 + SINE/Alu_AluSx1 chr1 9072304 9072408 + SINE/Alu_FRAM chr1 9072469 9072640 - LINE/L2_L2 chr1 9072674 9072991 + SINE/Alu_AluSz chr1 9073258 9073639 + LTR/ERVL-MaLR_MLT1J chr1 9073725 9073805 + DNA/TcMar-Tigger_Tigger9a chr1 9073806 9074100 + SINE/Alu_AluSx1 chr1 9074101 9074223 + DNA/TcMar-Tigger_Tigger9a chr1 9074224 9074681 - LTR/ERV1_MER4C chr1 9074682 9074762 + DNA/TcMar-Tigger_Tigger9a chr1 9074763 9075046 + SINE/Alu_AluJo chr1 9075047 9075063 + DNA/TcMar-Tigger_Tigger9a chr1 9075064 9075558 + LTR/ERV1_MER83 chr1 9075559 9075665 + DNA/TcMar-Tigger_Tigger9a chr1 9075666 9075959 - SINE/Alu_AluSq10 chr1 9075961 9076255 - SINE/Alu_AluSz chr1 9076256 9076343 + DNA/TcMar-Tigger_Tigger9a chr1 9076347 9076540 + DNA/TcMar-Tigger_Tigger9a chr1 9076644 9076786 - LTR/ERV1_LTR23 chr1 9076787 9077086 - SINE/Alu_AluSq chr1 9077087 9077117 - LTR/ERV1_LTR23 chr1 9077118 9077426 + SINE/Alu_AluSz chr1 9077461 9077737 + SINE/Alu_AluSq2 chr1 9077738 9077827 + Low_complexity_G-rich chr1 9077842 9078453 - LTR/ERV1_MER41B chr1 9078454 9078596 + LINE/L1_L1PB2 chr1 9078609 9078867 - LTR/ERV1_LTR23 chr1 9079472 9079786 - SINE/Alu_AluSx chr1 9079802 9080658 - LINE/L1_L1MB2 chr1 9080751 9080831 + LTR/ERV1_LTR24C chr1 9080879 9080934 + LTR/ERV1_LTR24C chr1 9080962 9081293 + SINE/Alu_AluJb chr1 9081317 9081360 + LINE/L1_L1MB5 chr1 9081361 9081483 + SINE/Alu_FLAM_A chr1 9081484 9081799 + LINE/L1_L1MB5 chr1 9081800 9082099 + SINE/Alu_AluSz6 chr1 9082100 9082432 + LINE/L1_L1MB5 chr1 9082433 9082563 + SINE/Alu_FLAM_A chr1 9082564 9082700 + LINE/L1_L1MB5 chr1 9082886 9083086 + LINE/L1_L1MB5 chr1 9083097 9083135 + SINE/Alu_AluYb8 chr1 9083140 9083290 - LTR/ERV1_LTR37A chr1 9083318 9083541 + LTR/ERV1_MER41E chr1 9083542 9083848 + SINE/Alu_AluSq2 chr1 9083849 9083862 + LTR/ERV1_MER41E chr1 9083863 9084163 + SINE/Alu_AluYe6 chr1 9084164 9084532 + LTR/ERV1_MER41E chr1 9084787 9084933 + DNA/hAT-Charlie_MER5A1 chr1 9085147 9085738 - LTR/ERV1_MER4E chr1 9086204 9086376 - LTR/ERV1_LTR8B chr1 9086377 9086689 - SINE/Alu_AluSz chr1 9086690 9086798 - LTR/ERV1_LTR8B chr1 9086800 9087193 + LTR/ERV1_LTR19A chr1 9087194 9087503 - SINE/Alu_AluY chr1 9087504 9087529 + LTR/ERV1_LTR19A chr1 9087530 9087918 + LTR/ERV1_HERVFH19-int chr1 9087912 9088502 + LTR/ERV1_HERVFH19-int chr1 9088503 9088810 + SINE/Alu_AluSc8 chr1 9088811 9094338 + LTR/ERV1_HERVFH19-int chr1 9094339 9094628 + SINE/Alu_AluSp chr1 9094629 9095599 + LTR/ERV1_HERVFH19-int chr1 9095600 9096029 + LTR/ERV1_LTR19A chr1 9096030 9096489 - LTR/ERV1_LTR8B chr1 9096518 9096579 - SINE/MIR_MIRc chr1 9096593 9096904 + SINE/Alu_AluSx chr1 9096915 9096950 + snRNA_U2 chr1 9097059 9097391 + LTR/ERV1_MER110A chr1 9097392 9097704 + SINE/Alu_AluSx3 chr1 9097705 9097744 + LTR/ERV1_MER110A chr1 9097745 9098423 + LTR/ERV1_LTR8 chr1 9098424 9098482 + LTR/ERV1_MER110A chr1 9098490 9098550 + DNA/hAT-Tip100_MER45R chr1 9098564 9098603 + SINE/MIR_MIR chr1 9099013 9099039 + Simple_repeat_(GGTGT)n chr1 9099422 9099536 - SINE/Alu_AluSx3 chr1 9099922 9099970 + LTR/ERV1_LTR8 chr1 9100017 9100059 + DNA/hAT-Charlie_MER5A chr1 9100513 9100542 + Low_complexity_A-rich chr1 9100810 9101032 + SINE/Alu_AluJb chr1 9101052 9101355 - SINE/Alu_AluSx chr1 9102128 9102435 + SINE/Alu_AluSx chr1 9102913 9103072 - SINE/Alu_FRAM chr1 9103077 9103123 + SINE/Alu_AluYd8 chr1 9103170 9103463 - SINE/Alu_AluSp chr1 9104719 9104997 + SINE/Alu_AluSx1 chr1 9105035 9105069 + Simple_repeat_(AC)n chr1 9105804 9106059 + LINE/L2_L2a chr1 9106066 9106198 - SINE/Alu_FLAM_C chr1 9106202 9106681 + LINE/L2_L2a chr1 9106682 9106963 + SINE/Alu_AluSz chr1 9106964 9106997 + Simple_repeat_(AAAT)n chr1 9106998 9107155 + LINE/L2_L2a chr1 9107357 9107501 + SINE/Alu_AluJr4 chr1 9107503 9107646 + SINE/Alu_AluSz chr1 9107647 9107942 + SINE/Alu_AluSz6 chr1 9107943 9108100 + SINE/Alu_AluSz chr1 9108258 9108280 + Simple_repeat_(CCT)n chr1 9108647 9108795 - SINE/Alu_AluJb chr1 9108796 9109075 - SINE/Alu_AluYc chr1 9109080 9109382 - SINE/Alu_AluSx1 chr1 9109387 9109693 - SINE/Alu_AluSz chr1 9110241 9110550 + SINE/Alu_AluSz chr1 9111002 9111137 + SINE/MIR_MIRb chr1 9111648 9111744 - DNA/hAT-Tip100_MER115 chr1 9111762 9111988 + SINE/Alu_AluSx chr1 9112141 9112438 - DNA/hAT-Tip100_MER115 chr1 9112459 9112752 - SINE/Alu_AluSx chr1 9112883 9113126 + SINE/MIR_MIR chr1 9113662 9113952 + SINE/Alu_AluSz chr1 9113957 9114000 + Simple_repeat_(AC)n chr1 9114039 9114345 + SINE/Alu_AluSx chr1 9114769 9114950 + SINE/Alu_AluSx chr1 9115155 9115787 - DNA/hAT-Blackjack_MER63C chr1 9115788 9115997 + DNA/hAT-Charlie_MER58A chr1 9115998 9116134 - DNA/hAT-Blackjack_MER63C chr1 9116135 9116376 + Simple_repeat_(TA)n chr1 9116377 9116485 - DNA/hAT-Blackjack_MER63C chr1 9116486 9116745 + SINE/Alu_AluSx3 chr1 9116749 9117040 - SINE/Alu_AluJr chr1 9117075 9117177 - SINE/Alu_AluJb chr1 9117149 9117250 - DNA/hAT-Blackjack_MER63C chr1 9117479 9117793 + SINE/Alu_AluSx1 chr1 9117910 9117963 + LINE/CR1_L3 chr1 9118335 9118391 + LINE/CR1_L3 chr1 9118580 9118755 + LTR/ERVL-MaLR_MLT1B chr1 9118756 9119031 + SINE/Alu_AluJb chr1 9119032 9119332 - SINE/Alu_AluSx1 chr1 9119333 9119580 + LTR/ERVL-MaLR_MLT1B chr1 9121051 9121353 + SINE/Alu_AluSx chr1 9121356 9121662 + SINE/Alu_AluSz chr1 9122387 9122874 + LINE/L1_L1MC4 chr1 9122875 9123175 + SINE/Alu_AluSp chr1 9123176 9123218 + LINE/L1_L1MC4 chr1 9123222 9123833 + Simple_repeat_(AT)n chr1 9123834 9124129 - SINE/Alu_AluJb chr1 9124130 9124143 + Simple_repeat_(AT)n chr1 9124203 9124259 + SINE/MIR_MIRc chr1 9124260 9125131 + LTR/ERVK_MER11D chr1 9125132 9125162 + SINE/MIR_MIRc chr1 9125210 9125377 - SINE/Alu_AluJb chr1 9125605 9125871 - LINE/L1_L1ME4c chr1 9125968 9126254 - SINE/Alu_AluSz chr1 9126907 9127178 - SINE/Alu_AluJr chr1 9129441 9129508 + Simple_repeat_(GCCCAGA)n chr1 9129586 9129620 + Simple_repeat_(GCCCCC)n chr1 9130110 9130486 - LINE/L1_L1MB7 chr1 9130487 9130786 - SINE/Alu_AluSx chr1 9130787 9131081 - LINE/L1_L1MB7 chr1 9131082 9131230 - SINE/Alu_AluJr chr1 9131231 9131532 - SINE/Alu_AluSx chr1 9131533 9131651 - SINE/Alu_AluJr chr1 9131652 9131875 - LINE/L1_L1MB7 chr1 9131876 9132185 - SINE/Alu_AluSx1 chr1 9132186 9132263 - LINE/L1_L1MB3 chr1 9132264 9132474 - LINE/L1_L1MB7 chr1 9132461 9132550 - LINE/L1_L1MB7 chr1 9132551 9132843 - SINE/Alu_AluSx1 chr1 9132844 9133110 - LINE/L1_L1MB7 chr1 9133186 9133311 + SINE/Alu_AluJr chr1 9133316 9133625 + SINE/Alu_AluSq2 chr1 9133638 9133762 - LINE/L1_L1ME3B chr1 9133800 9133888 - SINE/Alu_FLAM_A chr1 9133902 9134208 - LINE/L1_L1ME3B chr1 9134697 9134719 + Simple_repeat_(T)n chr1 9135795 9135857 - LTR/ERVL_MLT2A2 chr1 9135858 9136157 - SINE/Alu_AluSx chr1 9136158 9136177 - LTR/ERVL_MLT2A2 chr1 9136178 9136479 - SINE/Alu_AluY chr1 9136480 9136678 - LTR/ERVL_MLT2A2 chr1 9136661 9136719 - SINE/Alu_Alu chr1 9136721 9136867 - SINE/Alu_AluSx chr1 9136869 9137072 - LTR/ERVL_MLT2A2 chr1 9137661 9137972 - SINE/Alu_AluSq chr1 9137974 9138067 + LTR/ERVL-MaLR_MLT1E3 chr1 9138070 9138230 - SINE/Alu_AluSx chr1 9138231 9138524 - SINE/Alu_AluSz chr1 9138561 9138821 + SINE/Alu_AluSx3 chr1 9138874 9139164 + SINE/Alu_AluJr chr1 9139219 9139546 + SINE/Alu_AluJr4 chr1 9139597 9139778 - LINE/L2_L2 chr1 9139791 9140044 + SINE/Alu_AluSx1 chr1 9140045 9140082 - SINE/Alu_Alu chr1 9140083 9140510 - LINE/L1_L1MB4 chr1 9140791 9140927 + SINE/Alu_AluJb chr1 9140928 9141227 + SINE/Alu_AluSq2 chr1 9141228 9141405 + SINE/Alu_AluJb chr1 9141421 9141547 + LINE/L2_L2a chr1 9141751 9141913 - SINE/Alu_FRAM chr1 9141941 9142295 + DNA/hAT-Charlie_Charlie4a chr1 9142540 9142559 + Simple_repeat_(TTCC)n chr1 9142560 9142871 - SINE/Alu_AluSz chr1 9142872 9143081 + Simple_repeat_(TTCC)n chr1 9143082 9143164 - LINE/L1_L1MC5a chr1 9143740 9144050 - SINE/Alu_AluSx1 chr1 9144064 9144109 - SINE/Alu_AluJo chr1 9144105 9144157 - SINE/Alu_AluYg6 chr1 9144158 9144304 + LTR/ERVL-MaLR_MLT1B chr1 9144305 9144610 + SINE/Alu_AluJb chr1 9144611 9144691 + LTR/ERVL-MaLR_MLT1B chr1 9144697 9144920 + SINE/Alu_AluSz chr1 9145235 9145350 - DNA/hAT-Tip100_MER45A chr1 9145696 9146002 + SINE/Alu_AluJr chr1 9146287 9146365 + Simple_repeat_(CTC)n chr1 9146413 9146698 + SINE/Alu_AluSz chr1 9146707 9146900 + SINE/Alu_AluSz chr1 9147076 9147129 - LTR/ERVL-MaLR_MLT1K chr1 9147502 9147813 - SINE/Alu_AluSq2 chr1 9148016 9148316 - LINE/L1_L1MD3 chr1 9148293 9148465 - LINE/L1_L1MD2 chr1 9148954 9149200 + SINE/Alu_AluSx1 chr1 9149208 9149226 + Simple_repeat_(A)n chr1 9149257 9149536 - SINE/Alu_AluJb chr1 9151246 9151461 + SINE/MIR_MIR chr1 9151878 9152122 + SINE/Alu_AluJr chr1 9152127 9152267 + SINE/Alu_AluJr chr1 9152268 9152296 - Retroposon/SVA_SVA_A chr1 9152477 9152864 - LTR/ERVL_MLT2C1 chr1 9152913 9153045 - SINE/Alu_AluJo chr1 9153459 9153760 + SINE/Alu_AluSz chr1 9153838 9153938 - DNA/hAT-Charlie_MER5A chr1 9154735 9154880 + SINE/Alu_FRAM chr1 9154887 9154945 + SINE/Alu_AluJo chr1 9156367 9156680 + SINE/Alu_AluSx1 chr1 9157860 9158132 + SINE/Alu_AluJr chr1 9158135 9158422 + SINE/Alu_AluSq2 chr1 9158423 9158462 + Simple_repeat_(AAAT)n chr1 9158704 9159016 + SINE/Alu_AluSx chr1 9159543 9159568 + Simple_repeat_(A)n chr1 9159686 9159981 - SINE/Alu_AluSq2 chr1 9160008 9160094 + Simple_repeat_(ATAC)n chr1 9160163 9160563 + LTR/ERVL-MaLR_MLT1L chr1 9160564 9160886 - SINE/Alu_AluJb chr1 9160887 9160969 + LTR/ERVL-MaLR_MLT1L chr1 9161957 9161995 + Simple_repeat_(A)n chr1 9163421 9163553 + SINE/Alu_AluSp chr1 9164260 9164313 + DNA/hAT-Charlie_MER20 chr1 9164314 9164617 + SINE/Alu_AluSz chr1 9164618 9164778 + DNA/hAT-Charlie_MER20 chr1 9165143 9165189 - SINE/MIR_MIRc chr1 9165672 9165853 - SINE/Alu_AluJo chr1 9165854 9166155 - SINE/Alu_AluSx chr1 9166156 9166302 - SINE/Alu_AluJo chr1 9167031 9167339 + SINE/Alu_AluSx chr1 9167352 9167660 + SINE/Alu_AluSg chr1 9168118 9168412 + SINE/Alu_AluJr chr1 9168509 9168546 + Simple_repeat_(GT)n chr1 9168573 9168705 + SINE/Alu_FLAM_C chr1 9169302 9169602 - SINE/Alu_AluSx chr1 9169623 9169705 - DNA/hAT-Tip100_MamTip2 chr1 9169764 9170057 + LINE/L2_L2c chr1 9170251 9170554 + SINE/Alu_AluSc8 chr1 9170609 9170899 - SINE/Alu_AluSx1 chr1 9170938 9171047 - DNA/TcMar-Tigger_Tigger16a chr1 9171288 9171676 + LTR/ERVL-MaLR_MLT1C2 chr1 9171776 9171815 + SINE/Alu_AluYj4 chr1 9171816 9172099 + SINE/Alu_AluJb chr1 9172403 9172696 - SINE/Alu_AluSx chr1 9172698 9172927 - LTR/ERVL-MaLR_MLT1C2 chr1 9172937 9173115 + DNA/TcMar-Tigger_Tigger15a chr1 9173115 9173225 + DNA/TcMar-Tigger_Tigger10 chr1 9173226 9173481 - SINE/Alu_AluJb chr1 9173482 9173506 + DNA/TcMar-Tigger_Tigger10 chr1 9173512 9173792 + SINE/Alu_AluSz chr1 9173793 9173822 + Simple_repeat_(AAAAT)n chr1 9174708 9175275 + LTR/ERVL-MaLR_MLT1E1A chr1 9175369 9175616 + SINE/MIR_MIR chr1 9175758 9175829 - SINE/MIR_MIRc chr1 9175837 9175912 + SINE/Alu_AluJr chr1 9175913 9176212 - SINE/Alu_AluSz chr1 9176314 9176542 + SINE/Alu_AluJb chr1 9176592 9176707 - DNA/hAT-Tip100_hAT-N1a_Mam chr1 9177062 9177182 - DNA/TcMar-Tigger_MER2 chr1 9177190 9177308 - SINE/Alu_AluSz chr1 9177311 9177604 - SINE/Alu_AluSq2 chr1 9177606 9177704 - DNA/TcMar-Tigger_MER2 chr1 9177705 9177836 - SINE/Alu_FLAM_C chr1 9177837 9177952 - DNA/TcMar-Tigger_MER2 chr1 9178172 9178476 - SINE/Alu_AluSz chr1 9179306 9179614 + SINE/Alu_AluJr4 chr1 9179784 9179994 - SINE/MIR_MIRb chr1 9180261 9180353 + LINE/L2_L2a chr1 9180389 9180431 + LINE/L2_L2a chr1 9181574 9181768 - SINE/MIR_MIRb chr1 9184200 9184506 - SINE/Alu_AluSq2 chr1 9184610 9184811 - SINE/MIR_MIRb chr1 9184902 9184935 + Simple_repeat_(GAAT)n chr1 9185713 9186000 - SINE/Alu_AluSx1 chr1 9186185 9186397 + SINE/Alu_AluJr chr1 9186440 9186562 + LINE/L1_L1MB7 chr1 9187373 9187592 - SINE/MIR_MIRb chr1 9187784 9187840 - LTR/Gypsy_MamGypLTR3a chr1 9187842 9188007 - SINE/Alu_AluSg4 chr1 9188008 9188321 - SINE/Alu_AluSx chr1 9188457 9188579 - DNA/hAT-Charlie_MER3 chr1 9188756 9189070 + LTR/ERVL?_Eutr10 chr1 9189071 9189379 + SINE/Alu_AluSz chr1 9189380 9189522 + LTR/ERVL?_Eutr10 chr1 9189587 9189662 + LTR/ERVL?_Eutr10 chr1 9189663 9190365 + LINE/L2_L2a chr1 9190366 9190663 - SINE/Alu_AluJr chr1 9190664 9190759 + LINE/L2_L2a chr1 9190767 9191145 + LINE/L2_L2a chr1 9191224 9191440 + LTR/ERV1_LTR26E chr1 9191889 9191923 + Simple_repeat_(AAAGAA)n chr1 9192193 9192497 + SINE/Alu_AluSx1 chr1 9193578 9193765 + DNA/hAT-Tip100_MamRep4096 chr1 9193866 9193923 + Simple_repeat_(AGAT)n chr1 9193924 9194227 - SINE/Alu_AluSz chr1 9194533 9194660 - SINE/Alu_AluJr chr1 9194733 9194862 - SINE/Alu_FLAM_A chr1 9194955 9195262 + SINE/Alu_AluSz chr1 9195265 9195300 + Low_complexity_A-rich chr1 9195321 9195582 + SINE/Alu_AluJb chr1 9196991 9197115 + SINE/MIR_MIR3 chr1 9197674 9197712 + Simple_repeat_(CCTTCT)n chr1 9198919 9198958 + Simple_repeat_(CGGG)n chr1 9201744 9201768 + Simple_repeat_(CCCT)n chr1 9201769 9201821 + Simple_repeat_(CCT)n chr1 9201823 9201880 + Simple_repeat_(GCT)n chr1 9202907 9202939 + Simple_repeat_(AAAAAC)n chr1 9203539 9203566 + Simple_repeat_(GT)n chr1 9203932 9204133 - SINE/MIR_MIR3 chr1 9204166 9204250 + SINE/MIR_MIR3 chr1 9204402 9204828 - LTR/ERVL-MaLR_MLT1C2 chr1 9204910 9205210 - SINE/Alu_AluY chr1 9205296 9205599 - SINE/Alu_AluSp chr1 9205631 9205820 - SINE/MIR_MIR chr1 9206071 9206201 + SINE/Alu_FLAM_C chr1 9206787 9207083 - SINE/Alu_AluSp chr1 9207716 9207769 - LINE/L2_L2c chr1 9207871 9208046 - DNA/hAT-Tip100_MamTip2 chr1 9208237 9208529 - SINE/Alu_AluSz chr1 9208576 9208876 + SINE/Alu_AluSp chr1 9208888 9209082 + LINE/L2_L2a chr1 9209175 9209475 - SINE/Alu_AluSz6 chr1 9209974 9210006 + LINE/L2_L2b chr1 9210007 9210148 + SINE/Alu_AluJr chr1 9210149 9210450 + SINE/Alu_AluSp chr1 9210451 9210626 + SINE/Alu_AluJr chr1 9210627 9210740 + LINE/L2_L2b chr1 9210786 9211145 - LTR/ERVL-MaLR_MLT1B chr1 9211207 9211255 - LTR/ERVL-MaLR_MLT1B chr1 9211275 9211304 + Simple_repeat_(AGGA)n chr1 9211512 9211745 - SINE/MIR_MIRb chr1 9211749 9211989 + DNA/TcMar-Tigger_Tigger15a chr1 9212019 9212167 + DNA/hAT_MER53 chr1 9212168 9212470 + SINE/Alu_AluSz chr1 9212471 9212490 + DNA/hAT_MER53 chr1 9212518 9212584 - DNA/TcMar-Tigger_Tigger15a chr1 9212743 9213203 - LTR/ERVL-MaLR_MLT1J chr1 9213256 9213525 - SINE/Alu_AluJb chr1 9213830 9213892 - LINE/L2_L2a chr1 9214510 9214803 + SINE/Alu_AluJb chr1 9214804 9215111 + SINE/Alu_AluSg chr1 9215412 9215634 + LTR/ERV1_LTR78 chr1 9215635 9215928 + SINE/Alu_AluSz chr1 9215929 9216239 + SINE/Alu_AluSx chr1 9216240 9216524 + LTR/ERV1_LTR78 chr1 9216525 9216833 - SINE/Alu_AluSx chr1 9216834 9217011 + LTR/ERV1_LTR78 chr1 9217036 9217126 + DNA?_MER124 chr1 9217378 9217669 - SINE/Alu_AluSx chr1 9217686 9217954 - SINE/Alu_AluJb chr1 9218007 9218070 + DNA?_MER124 chr1 9218378 9218682 - SINE/Alu_AluY chr1 9218684 9218829 - SINE/Alu_AluSz chr1 9218842 9219305 - LINE/L1_L1MB4 chr1 9219306 9219596 - SINE/Alu_AluSx chr1 9219597 9220041 - LINE/L1_L1MB4 chr1 9220124 9220204 - LINE/L2_L2c chr1 9220210 9220343 + SINE/Alu_FLAM_C chr1 9220905 9221212 + SINE/Alu_AluJr chr1 9221286 9221580 + SINE/Alu_AluSx chr1 9222054 9222089 + Low_complexity_A-rich chr1 9222132 9222289 - SINE/Alu_AluSz chr1 9222290 9222583 - SINE/Alu_AluSc chr1 9222584 9222719 - SINE/Alu_AluSz chr1 9222722 9222784 + LINE/L2_L2a chr1 9222843 9223266 + LTR/ERVL_MER74A chr1 9223267 9223793 + Simple_repeat_(CTT)n chr1 9223796 9224105 - SINE/Alu_AluSc8 chr1 9224108 9224215 + LTR/ERVL_MER74A chr1 9224217 9224521 - SINE/Alu_AluSz chr1 9224628 9225200 + LINE/L2_L2a chr1 9225744 9225776 + Simple_repeat_(TCTCC)n chr1 9225974 9226026 - LINE/L2_L2a chr1 9226145 9226367 + LINE/L1_L1MB8 chr1 9226368 9226663 + SINE/Alu_AluSp chr1 9226664 9226895 + LINE/L1_L1MB8 chr1 9226896 9227209 + SINE/Alu_AluJb chr1 9227210 9227370 + LINE/L1_L1MB8 chr1 9227371 9227678 + SINE/Alu_AluSz chr1 9227679 9227822 + LINE/L1_L1MB8 chr1 9227823 9228120 + SINE/Alu_AluSx1 chr1 9228121 9228336 + LINE/L1_L1MB8 chr1 9228337 9228634 + SINE/Alu_AluSz6 chr1 9228635 9229017 + LINE/L1_L1MB8 chr1 9229023 9229317 + SINE/Alu_AluSx1 chr1 9229374 9229413 + LTR/ERVL-MaLR_MLT1E2 chr1 9229414 9229712 + SINE/Alu_AluSx1 chr1 9229722 9229997 + SINE/Alu_AluJo chr1 9229998 9230131 + LTR/ERVL-MaLR_MLT1E2 chr1 9230133 9230256 - SINE/Alu_AluSx3 chr1 9230257 9230510 - SINE/Alu_AluSx1 chr1 9230511 9230538 + LTR/ERVL-MaLR_MLT1E2 chr1 9230539 9230848 + SINE/Alu_AluY chr1 9230849 9231228 + LTR/ERVL-MaLR_MLT1E2 chr1 9231289 9231587 + SINE/Alu_AluJo chr1 9231745 9231857 + SINE/MIR_MIRc chr1 9232479 9232788 - SINE/Alu_AluSp chr1 9232789 9233093 - SINE/Alu_AluSx chr1 9233692 9233975 + SINE/Alu_AluJb chr1 9233976 9234003 + Simple_repeat_(AAGAA)n chr1 9234168 9234329 - SINE/MIR_MIR chr1 9234699 9234773 + Simple_repeat_(CCCCGC)n chr1 9235630 9235909 - SINE/Alu_AluJb chr1 9236025 9236288 - SINE/Alu_AluSx chr1 9236380 9236688 + SINE/Alu_AluSz chr1 9236691 9236874 - SINE/MIR_MIRb chr1 9237161 9237236 + SINE/MIR_MIR chr1 9237237 9237532 - SINE/Alu_AluSx1 chr1 9237533 9237708 + SINE/MIR_MIR chr1 9237749 9237790 + DNA/hAT-Tip100_MamTip2 chr1 9237841 9238779 - LINE/L2_L2a chr1 9238789 9239034 - DNA/hAT-Blackjack_MER63B chr1 9239035 9239325 - SINE/Alu_AluSz6 chr1 9239519 9239550 + Low_complexity_A-rich chr1 9239802 9239853 + LINE/Penelope_Penelope1_Vert chr1 9240467 9240641 - SINE/MIR_MIR3 chr1 9241394 9241699 - SINE/Alu_AluJb chr1 9241824 9241986 + SINE/MIR_MIR chr1 9242024 9242097 + DNA/hAT-Charlie_MER5A chr1 9242202 9242328 + DNA/hAT-Charlie_MER5A1 chr1 9243929 9244132 + SINE/MIR_MIR chr1 9244187 9244407 - SINE/MIR_MIR chr1 9245950 9246243 - SINE/Alu_AluSz chr1 9246561 9246647 + DNA/hAT-Charlie_Charlie18a chr1 9246642 9246844 + DNA/hAT-Charlie_Charlie18a chr1 9248087 9248112 + Simple_repeat_(CGCTG)n chr1 9248254 9248287 + Simple_repeat_(TC)n chr1 9248519 9248647 + SINE/Alu_FLAM_A chr1 9249392 9249510 + LINE/CR1_L3 chr1 9251437 9251473 + Simple_repeat_(TGT)n chr1 9251877 9251906 + Simple_repeat_(T)n chr1 9251974 9252093 + SINE/MIR_MIRc chr1 9252391 9252533 - SINE/Alu_FLAM_C chr1 9252571 9252696 - LINE/L1_L1MB7 chr1 9252946 9253059 + LINE/CR1_CR1_Mam chr1 9253225 9253395 + LINE/CR1_X7B_LINE chr1 9253469 9253632 + DNA/TcMar-Tc2_MER104 chr1 9253883 9254073 - SINE/MIR_MIR chr1 9254074 9254380 + SINE/Alu_AluSz chr1 9254686 9255254 - LINE/L1_L1MB8 chr1 9255268 9255298 + Simple_repeat_(TTTATTT)n chr1 9255300 9255581 - SINE/Alu_AluJb chr1 9255662 9255740 + DNA/hAT-Charlie_Charlie8 chr1 9255969 9256183 + SINE/MIR_MIR chr1 9256839 9256925 - LINE/L1_L1ME3E chr1 9256959 9257111 - LINE/L1_L1MEd chr1 9257130 9257436 - SINE/Alu_AluJb chr1 9257726 9257835 - LINE/L2_L2b chr1 9258984 9259287 - SINE/Alu_AluY chr1 9260813 9261424 + LINE/L2_L2c chr1 9261561 9261650 - SINE/MIR_MIR chr1 9262803 9262934 - SINE/MIR_MIRb chr1 9265078 9265367 - LINE/L2_L2d chr1 9265672 9265793 - SINE/Alu_AluJo chr1 9266825 9267119 - SINE/Alu_AluSx1 chr1 9267779 9267963 - DNA/hAT-Charlie_MER112 chr1 9267972 9268266 + SINE/Alu_AluJr chr1 9270948 9271233 - SINE/Alu_AluSx chr1 9272285 9272408 + LTR/ERVL-MaLR_MLT1K chr1 9272634 9272770 + DNA/hAT-Charlie_Charlie19a chr1 9273101 9273201 + LINE/L2_L2c chr1 9273907 9274254 - LINE/L1_HAL1b chr1 9274255 9274546 + SINE/Alu_AluSx1 chr1 9274547 9274702 - LINE/L1_HAL1b chr1 9274703 9274729 + Simple_repeat_(T)n chr1 9274737 9275014 - SINE/Alu_AluSz chr1 9275015 9275051 - LINE/L1_HAL1b chr1 9275748 9275875 - SINE/MIR_MIR1_Amn chr1 9276384 9276681 + SINE/Alu_AluSz chr1 9276725 9276768 + Simple_repeat_(AT)n chr1 9277081 9277133 - LTR/ERVL-MaLR_THE1B chr1 9277134 9277316 - SINE/Alu_AluSz chr1 9277317 9277616 - SINE/Alu_AluY chr1 9277617 9277752 - SINE/Alu_AluSz chr1 9277753 9278075 - LTR/ERVL-MaLR_THE1B chr1 9278160 9278340 - LTR/ERVL-MaLR_MLT1K chr1 9278365 9278552 + DNA/hAT_MER53 chr1 9278732 9278937 + DNA/hAT-Tip100_MamRep488 chr1 9279772 9279838 + Simple_repeat_(GA)n chr1 9279939 9280076 + DNA/hAT-Charlie_Charlie16a chr1 9280879 9281008 + SINE/MIR_MIRc chr1 9281393 9281685 - LINE/L2_L2b chr1 9281695 9281799 + SINE/MIR_MIR3 chr1 9282441 9282562 + Simple_repeat_(CCT)n chr1 9283011 9283290 + SINE/Alu_AluJr chr1 9283855 9284222 + LTR/ERVL-MaLR_MLT1A0 chr1 9284223 9284438 - LINE/L1_HAL1 chr1 9284439 9284642 + SINE/MIR_MIR chr1 9284689 9284988 - LINE/L1_HAL1 chr1 9284989 9285206 - SINE/Alu_AluSg chr1 9286177 9286304 + LTR/ERVL-MaLR_MLT1H chr1 9286477 9286685 - LINE/L2_L2 chr1 9286696 9287033 - LTR/ERVL-MaLR_MLT1C2 chr1 9287428 9287620 + SINE/MIR_MIR chr1 9287799 9288092 + SINE/Alu_AluSx1 chr1 9288127 9288201 - SINE/tRNA-RTE_MamSINE1 chr1 9288217 9288273 + LINE/L2_L2b chr1 9288593 9288767 + LINE/L2_L2b chr1 9289319 9289428 + SINE/Alu_AluJo chr1 9289736 9289857 - SINE/MIR_MIRb chr1 9290069 9290106 + Low_complexity_G-rich chr1 9290437 9290504 - SINE/5S-Deu-L2_AmnSINE1 chr1 9290938 9290949 + Simple_repeat_(ATT)n chr1 9290950 9291176 - SINE/Alu_AluSx chr1 9291177 9291484 - SINE/Alu_AluY chr1 9291485 9291571 - SINE/Alu_AluSx chr1 9291572 9291588 + Simple_repeat_(ATT)n chr1 9291593 9291721 - SINE/Alu_AluJo chr1 9292725 9292753 + Simple_repeat_(CGCGCCC)n chr1 9292797 9292821 + Simple_repeat_(GGGGC)n chr1 9292932 9292971 + Simple_repeat_(CAGGCGC)n chr1 9293043 9293087 + Simple_repeat_(GCG)n chr1 9293332 9293381 + Low_complexity_G-rich chr1 9293605 9293639 + Simple_repeat_(CGC)n chr1 9293836 9294382 + Simple_repeat_(GT)n chr1 9295087 9295143 + SINE/MIR_MIR3 chr1 9295192 9295242 + Simple_repeat_(GT)n chr1 9296199 9296397 - SINE/MIR_MIR chr1 9296429 9296522 - LINE/L1_L1MC5a chr1 9296526 9296639 + Simple_repeat_(CA)n chr1 9297468 9298369 + LTR/ERVL_ERVL-B4-int chr1 9298370 9298475 + Simple_repeat_(ATGA)n chr1 9298476 9299356 + LTR/ERVL_ERVL-B4-int chr1 9299357 9299468 + SINE/Alu_AluJr chr1 9299469 9299776 - SINE/Alu_AluSp chr1 9299777 9299982 + SINE/Alu_AluJr chr1 9299983 9301207 + LTR/ERVL_ERVL-B4-int chr1 9301208 9301512 + SINE/Alu_AluJr chr1 9301513 9302091 + LTR/ERVL_ERVL-B4-int chr1 9302116 9302225 + LTR/ERVL_ERVL-B4-int chr1 9302227 9302644 + LTR/ERVL_MLT2B5 chr1 9302649 9303809 + LTR/ERVL_ERVL-int chr1 9303811 9304444 + LTR/ERVL_MLT2B5 chr1 9304769 9304955 - SINE/Alu_AluJr chr1 9305397 9305517 + LINE/L2_L2b chr1 9305728 9305918 - LINE/L2_L2b chr1 9306484 9306674 - SINE/MIR_MIR chr1 9306917 9307227 - SINE/Alu_AluSx chr1 9307231 9307517 - LINE/L1_L1MB5 chr1 9308613 9308836 - SINE/MIR_MIRb chr1 9309262 9309301 + Simple_repeat_(GA)n chr1 9309302 9309334 + Simple_repeat_(GT)n chr1 9309338 9309532 - SINE/Alu_AluJr chr1 9309561 9309848 + SINE/Alu_AluSx chr1 9309849 9309876 + Simple_repeat_(TAGAG)n chr1 9310411 9310714 + SINE/Alu_AluJb chr1 9311161 9311178 + Simple_repeat_(T)n chr1 9311387 9311476 - LINE/L1_L1MC5 chr1 9312016 9312326 - SINE/Alu_AluJr chr1 9312407 9312535 - LINE/L1_L1MB8 chr1 9312741 9312765 + Simple_repeat_(TTA)n chr1 9312974 9313116 - SINE/MIR_MIR chr1 9313117 9313438 + SINE/Alu_AluSq chr1 9313439 9313503 - SINE/MIR_MIR chr1 9313727 9313821 + LINE/L2_L2b chr1 9313894 9314203 + SINE/Alu_AluSx1 chr1 9314804 9314929 - DNA/hAT-Charlie_MER102c chr1 9315363 9315514 - SINE/MIR_MIRb chr1 9315867 9315892 + Simple_repeat_(AAAAC)n chr1 9316379 9316425 + Simple_repeat_(TG)n chr1 9316439 9316564 + DNA/hAT_MamRep38 chr1 9316809 9316913 + SINE/MIR_MIRc chr1 9317176 9317329 - LINE/L2_L2d chr1 9317483 9317782 - SINE/Alu_AluSz chr1 9317838 9317856 + Simple_repeat_(T)n chr1 9318510 9318640 + SINE/MIR_MIRb chr1 9318710 9318863 - LINE/L2_L2b chr1 9318908 9319196 + SINE/Alu_AluSx chr1 9319389 9319444 + Simple_repeat_(CTCCTCC)n chr1 9320187 9320269 - LINE/L2_L2a chr1 9321487 9321524 - LINE/L2_L2a chr1 9321608 9321741 - DNA/TcMar-Tc2_MER104 chr1 9322183 9322390 - SINE/MIR_MIRb chr1 9325188 9325251 + Simple_repeat_(CCCACC)n chr1 9325568 9325992 - LINE/L2_L2a chr1 9327199 9327369 + DNA/hAT-Charlie_Charlie7 chr1 9327699 9327907 - DNA/hAT-Charlie_MER58A chr1 9328335 9328453 + DNA/hAT-Charlie_MER103C chr1 9328511 9328645 - SINE/MIR_MIR3 chr1 9328779 9328999 - DNA/hAT-Charlie_MER20 chr1 9329501 9329722 + SINE/Alu_AluSx chr1 9330186 9330493 + SINE/Alu_AluSx chr1 9330624 9330668 + Simple_repeat_(TTTCC)n chr1 9330710 9330797 - LINE/L1_L1MB3 chr1 9330799 9330942 - LINE/L1_L1M3 chr1 9331322 9331630 - SINE/Alu_AluSq chr1 9331782 9331952 - LINE/L1_L1M5 chr1 9332068 9332203 + SINE/Alu_FLAM_C chr1 9333241 9333320 - LINE/CR1_X7C_LINE chr1 9333326 9333630 - SINE/Alu_AluSx chr1 9334084 9334385 - SINE/Alu_AluSx1 chr1 9334386 9334520 - SINE/Alu_AluSz6 chr1 9334521 9335140 - LINE/L1_L1MB3 chr1 9335220 9335517 + SINE/Alu_AluSx1 chr1 9335527 9335827 + SINE/Alu_AluJb chr1 9335994 9336128 - SINE/MIR_MIRb chr1 9336220 9336524 - SINE/Alu_AluSp chr1 9337495 9337535 + Low_complexity_G-rich chr1 9337757 9337811 + Simple_repeat_(TCTCTG)n chr1 9340051 9340101 - DNA/hAT-Charlie_MER5A1 chr1 9340904 9341029 + SINE/MIR_MIR1_Amn chr1 9341375 9341489 + LINE/L2_L2a chr1 9341703 9342001 - SINE/Alu_AluSp chr1 9343075 9343731 - LINE/L1_L1MB5 chr1 9343783 9344087 - SINE/Alu_AluY chr1 9344188 9344361 - DNA/hAT-Charlie_MER5A chr1 9347036 9347168 + SINE/Alu_FLAM_C chr1 9347470 9347540 - LINE/L1_L1ME3G chr1 9347944 9348237 - SINE/Alu_AluSx1 chr1 9348938 9348979 + Simple_repeat_(TG)n chr1 9349924 9350147 + Simple_repeat_(AC)n chr1 9350574 9350627 + Simple_repeat_(GTGT)n chr1 9351081 9351242 + DNA/hAT-Charlie_MER58A chr1 9351984 9352045 + Simple_repeat_(CCCT)n chr1 9352399 9352571 + DNA/hAT-Charlie_MER58A chr1 9352637 9352703 + Simple_repeat_(CCCTCC)n chr1 9353638 9353935 + SINE/Alu_AluSq2 chr1 9354561 9354814 + SINE/Alu_AluSx1 chr1 9355598 9355650 + Simple_repeat_(AGGCTGC)n chr1 9356789 9357378 + Simple_repeat_(ATGG)n chr1 9358115 9358138 + Simple_repeat_(TCC)n chr1 9359422 9359721 + SINE/Alu_AluSg chr1 9359724 9360427 - LINE/L2_L2 chr1 9360428 9360853 - LTR/ERVL-MaLR_MLT1C2 chr1 9360854 9360894 - LINE/L2_L2 chr1 9361152 9361183 + Simple_repeat_(T)n chr1 9361184 9361302 - SINE/Alu_AluJo chr1 9362173 9362210 + Simple_repeat_(CCTC)n chr1 9363505 9363590 + SINE/MIR_MIR1_Amn chr1 9363704 9363999 - SINE/Alu_AluSx chr1 9364353 9364484 - SINE/MIR_MIR3 chr1 9364790 9364819 - SINE/Alu_AluSx chr1 9364820 9364860 + Simple_repeat_(TTG)n chr1 9364861 9365156 - SINE/Alu_AluSx chr1 9365159 9365416 - SINE/Alu_AluJo chr1 9365484 9365658 - LINE/L1_L1MB8 chr1 9366463 9366574 + SINE/MIR_MIR chr1 9366575 9366872 - SINE/Alu_AluSz chr1 9366873 9366910 + SINE/MIR_MIR chr1 9367024 9367272 + SINE/MIR_MIR chr1 9368573 9368606 + Simple_repeat_(CTTTTG)n chr1 9369036 9369061 + Simple_repeat_(AT)n chr1 9370309 9370451 + DNA/hAT-Charlie_MER5A chr1 9370545 9370685 + SINE/MIR_MIRb chr1 9370808 9371098 + SINE/Alu_AluSc chr1 9371972 9371988 + Simple_repeat_(T)n chr1 9373360 9373408 + SINE/MIR_MIRb chr1 9373487 9373779 - LINE/L1_L1MB5 chr1 9373822 9373978 + SINE/MIR_MIR chr1 9374226 9374386 - LTR/ERVL-MaLR_MLT1C2 chr1 9374387 9374715 + SINE/Alu_AluSz6 chr1 9374716 9375013 - LTR/ERVL-MaLR_MLT1C2 chr1 9375014 9375423 + LTR/ERV1_MER83 chr1 9377530 9377722 + SINE/MIR_MIRc chr1 9377753 9378052 - SINE/Alu_AluSz6 chr1 9378453 9378521 - LINE/L2_L2b chr1 9378522 9378837 + SINE/Alu_AluSz6 chr1 9378838 9379030 - LINE/L2_L2b chr1 9379082 9379115 + SINE/MIR_MIR chr1 9379403 9379710 + SINE/Alu_AluSz chr1 9379711 9379747 + Simple_repeat_(GGAA)n chr1 9379748 9379848 + Simple_repeat_(GAAA)n chr1 9379926 9379936 + Low_complexity_polypurine chr1 9379937 9380227 + SINE/Alu_AluSg4 chr1 9380228 9380396 + Low_complexity_GA-rich chr1 9380770 9380896 + SINE/MIR_MIR1_Amn chr1 9380936 9381107 - SINE/MIR_MIR chr1 9381303 9381365 + LINE/L2_L2a chr1 9381366 9381662 + SINE/Alu_AluSz chr1 9381663 9381679 + LINE/L2_L2a chr1 9381775 9382064 + SINE/Alu_AluSq2 chr1 9382456 9382558 - SINE/MIR_MIRb chr1 9382941 9383114 - SINE/MIR_MIRb chr1 9383495 9383540 + SINE/Alu_AluJo chr1 9383545 9383851 + SINE/Alu_AluJb chr1 9383913 9384098 - SINE/Alu_AluJb chr1 9384134 9384183 + LINE/L2_L2a chr1 9384441 9384470 + Simple_repeat_(CCCT)n chr1 9385251 9385561 - SINE/Alu_AluSz6 chr1 9386061 9386140 - DNA/hAT-Tip100_Zaphod2 chr1 9386164 9386247 + SINE/tRNA-Deu_AmnSINE2 chr1 9386752 9387050 - SINE/Alu_AluSx chr1 9387797 9388111 + SINE/Alu_AluSz6 chr1 9388260 9388455 - SINE/MIR_MIRb chr1 9388795 9388936 - SINE/MIR_MIRb chr1 9389077 9389139 + LTR/ERV1_MER4E1 chr1 9389140 9389292 + LTR/ERV1_MER4A1_ chr1 9389293 9389594 + SINE/Alu_AluY chr1 9389595 9390010 + LTR/ERV1_MER4A1_ chr1 9390011 9390144 + LTR/ERV1_MER4E1 chr1 9390145 9390411 + SINE/Alu_AluJb chr1 9390412 9390955 + LTR/ERV1_MER4E1 chr1 9391090 9391411 + LTR/ERV1_LTR8 chr1 9391412 9391698 + SINE/Alu_AluSx1 chr1 9391699 9392071 + LTR/ERV1_LTR8 chr1 9392246 9392574 + LTR/ERVL-MaLR_MLT1K chr1 9392686 9392831 + SINE/MIR_MIRb chr1 9392909 9393114 - LTR/ERVL_LTR33 chr1 9393138 9393309 + LTR/ERVL-MaLR_MLT1C2 chr1 9393310 9393603 + SINE/Alu_AluSz chr1 9393604 9393652 + LTR/ERVL-MaLR_MLT1C2 chr1 9394497 9395033 - LTR/ERVL-MaLR_MLT1K chr1 9395049 9395520 - LTR/ERVL-MaLR_MLT1D chr1 9395970 9396266 + SINE/Alu_AluSz chr1 9396283 9396376 + SINE/Alu_FLAM_C chr1 9396377 9396687 + SINE/Alu_AluY chr1 9396688 9396736 + SINE/Alu_FLAM_C chr1 9397194 9397250 + Simple_repeat_(CCTTT)n chr1 9397261 9397297 + Simple_repeat_(TC)n chr1 9397298 9397597 - SINE/Alu_AluJb chr1 9397598 9397619 + Simple_repeat_(CCTTT)n chr1 9398785 9399099 - SINE/Alu_AluSx1 chr1 9399103 9399411 - SINE/Alu_AluSz chr1 9400382 9400565 - LINE/L2_L2c chr1 9401292 9401607 + SINE/Alu_AluSz chr1 9401637 9401695 + LINE/L2_L2a chr1 9401686 9401749 + SINE/MIR_MIR chr1 9401772 9401863 + LINE/L2_L2b chr1 9402265 9402474 - DNA/TcMar-Tigger_Tigger19b chr1 9402539 9402836 + SINE/Alu_AluSx chr1 9402957 9403036 - LINE/L2_L2b chr1 9403112 9403168 + LINE/L1_L1MC4 chr1 9403169 9403355 - SINE/Alu_AluSz chr1 9403356 9403654 - SINE/Alu_AluSp chr1 9403655 9403789 - SINE/Alu_AluSz chr1 9403790 9404032 + LINE/L1_L1MC4 chr1 9404193 9404454 - LINE/L2_L2b chr1 9404975 9405049 - LINE/L2_L2a chr1 9405134 9405353 - SINE/MIR_MIRb chr1 9405656 9405695 + DNA/hAT_MER53 chr1 9405696 9405993 + SINE/Alu_AluSx3 chr1 9405994 9406041 + DNA/hAT_MER53 chr1 9405995 9406061 - DNA/hAT_MER53 chr1 9406330 9406360 + Simple_repeat_(CAC)n chr1 9406556 9406580 + Simple_repeat_(AC)n chr1 9406644 9406701 + LINE/L2_L2c chr1 9406821 9407113 + SINE/Alu_AluSx chr1 9407535 9407662 + SINE/Alu_FLAM_C chr1 9407692 9408001 + SINE/Alu_AluSz chr1 9408013 9408311 + SINE/Alu_AluSx4 chr1 9408593 9408911 - SINE/Alu_AluSg chr1 9408919 9409017 - DNA/hAT-Blackjack_MER63C chr1 9409024 9409340 + DNA/TcMar-Tigger_MER2 chr1 9409346 9409416 - SINE/MIR_MIRb chr1 9409417 9409658 + SINE/Alu_AluSx chr1 9409659 9409705 - SINE/MIR_MIRb chr1 9409920 9410040 - DNA/hAT-Blackjack_MER63C chr1 9410041 9410325 - SINE/Alu_AluSx chr1 9410326 9410747 - DNA/hAT-Blackjack_MER63C chr1 9410771 9410832 - SINE/MIR_MIRb chr1 9411350 9411661 - SINE/Alu_AluSp chr1 9411682 9411836 - SINE/Alu_AluSx3 chr1 9411841 9412205 + LTR/ERVL-MaLR_THE1B chr1 9412231 9412522 + SINE/Alu_AluSg4 chr1 9413016 9413056 - LINE/L2_L2b chr1 9414143 9414217 + LINE/L1_L1ME4a chr1 9414387 9414559 - SINE/Alu_AluSx chr1 9414628 9414940 - SINE/Alu_AluJb chr1 9415021 9415312 + SINE/Alu_AluSz chr1 9415330 9415462 - LINE/L1_L1ME4c chr1 9415463 9415774 - SINE/Alu_AluSx1 chr1 9415775 9415854 - LINE/L1_L1ME4c chr1 9415940 9415963 + Simple_repeat_(TGG)n chr1 9415965 9416270 - SINE/Alu_AluSx1 chr1 9416522 9416624 - LINE/L2_L2d2 chr1 9417071 9417170 + Simple_repeat_(CACA)n chr1 9417382 9417577 + SINE/MIR_MIRb chr1 9418494 9418804 - SINE/Alu_AluSp chr1 9419204 9419500 - SINE/Alu_AluSx chr1 9419578 9419755 + SINE/MIR_MIRb chr1 9420028 9420322 - SINE/Alu_AluSx chr1 9420633 9420916 + SINE/Alu_AluY chr1 9421111 9421240 - LINE/L1_L1MA10 chr1 9421286 9421593 - LTR/ERVL-MaLR_MLT1K chr1 9421620 9421818 + DNA/hAT-Blackjack_MER63A chr1 9422101 9422137 + Simple_repeat_(ACCCCCA)n chr1 9422967 9423314 + LTR/ERVL-MaLR_MLT1I chr1 9423545 9423747 - SINE/MIR_MIRc chr1 9423762 9424058 + SINE/Alu_AluSz6 chr1 9424082 9424142 - DNA/hAT-Charlie_MER5A chr1 9424254 9424327 + DNA/hAT-Tip100_MamTip2b chr1 9424537 9424582 + Low_complexity_G-rich chr1 9424640 9424657 + SINE/MIR_MIRc chr1 9424658 9424950 + SINE/Alu_AluSx chr1 9424951 9425159 + SINE/MIR_MIR chr1 9425101 9425347 - SINE/MIR_MIR chr1 9425804 9426008 - SINE/MIR_MIRb chr1 9426922 9427217 - SINE/Alu_AluSx chr1 9427481 9427663 - SINE/MIR_MIRb chr1 9427815 9427966 + SINE/Alu_AluSx3 chr1 9428045 9428340 - SINE/Alu_AluSz chr1 9428342 9428636 + DNA/TcMar-Tigger_Tigger15a chr1 9429368 9430665 + LINE/L2_L2a chr1 9430763 9431038 - SINE/Alu_AluSx1 chr1 9431039 9431300 + SINE/Alu_AluSx1 chr1 9431303 9431354 - SINE/Alu_FLAM_C chr1 9431361 9431435 + SINE/MIR_MIRb chr1 9431425 9431647 - LINE/L2_L2b chr1 9431678 9431920 - LTR/ERVL-MaLR_MLT1B chr1 9431921 9432207 - SINE/Alu_AluSx4 chr1 9432208 9432514 - SINE/Alu_AluSx chr1 9432515 9432685 - LTR/ERVL-MaLR_MLT1B chr1 9432691 9432879 - LINE/L2_L2b chr1 9432937 9433261 - DNA/hAT-Charlie_MER1B chr1 9433938 9434240 - SINE/Alu_AluJo chr1 9434241 9434418 - SINE/Alu_AluJr chr1 9434531 9434708 - LINE/CR1_L3 chr1 9434796 9435132 - LINE/L1_L1M5 chr1 9435136 9435410 - LINE/CR1_CR1-3_Croc chr1 9435478 9435557 + SINE/MIR_MIR3 chr1 9435583 9435899 - LINE/CR1_L3 chr1 9436117 9436252 + LINE/RTE-BovB_MamRTE1 chr1 9436564 9436873 + SINE/Alu_AluJb chr1 9436874 9436913 + Simple_repeat_(AT)n chr1 9436935 9437234 - SINE/Alu_AluSz chr1 9437240 9437530 + LTR/ERVL-MaLR_MLT1A1 chr1 9437545 9437698 - SINE/MIR_MIR chr1 9437708 9437778 - rRNA_5S chr1 9437779 9437868 - SINE/MIR_MIR chr1 9438255 9438340 - SINE/MIR_MIR1_Amn chr1 9440002 9440309 - SINE/Alu_AluJb chr1 9440380 9440741 - LTR/ERVL-MaLR_MSTD chr1 9440769 9441143 + LINE/L2_L2a chr1 9441153 9441200 - LINE/L2_L2b chr1 9442061 9442385 + SINE/Alu_AluJb chr1 9443141 9443251 + SINE/Alu_FLAM_A chr1 9443537 9443831 + SINE/Alu_AluSx1 chr1 9444508 9445011 - LINE/L1_L1MC1 chr1 9445107 9445200 - SINE/MIR_MIR chr1 9445262 9445571 + SINE/Alu_AluSz chr1 9445884 9445924 + Simple_repeat_(T)n chr1 9445932 9446209 - SINE/Alu_AluSx chr1 9446637 9446823 + LTR/ERVL-MaLR_MLT1L chr1 9446979 9447281 + SINE/Alu_AluSx chr1 9447466 9447776 - SINE/Alu_AluSq chr1 9447861 9448003 + LTR/ERV1_LTR78 chr1 9448004 9448312 + SINE/Alu_AluSz6 chr1 9448313 9448422 + LTR/ERV1_LTR78 chr1 9448627 9448679 + Simple_repeat_(GCAG)n chr1 9448737 9448834 + LTR/ERV1_LTR78 chr1 9449773 9449817 - LINE/L2_L2c chr1 9450296 9450384 + LINE/L2_L2b chr1 9450616 9450653 + Simple_repeat_(CCTGC)n chr1 9450638 9450676 + Simple_repeat_(CCCTGT)n chr1 9450751 9451051 + SINE/Alu_AluSx1 chr1 9451057 9451440 - LTR/ERVL-MaLR_THE1B chr1 9451463 9451837 - LTR/Gypsy?_LTR85a chr1 9452077 9452384 + SINE/Alu_AluJb chr1 9452610 9452641 + Simple_repeat_(AGCAC)n chr1 9452817 9453217 + LTR/ERV1_MER57B1 chr1 9454840 9454966 + SINE/Alu_AluSz6 chr1 9454983 9455101 - LINE/L1_HAL1 chr1 9455137 9455253 + SINE/Alu_AluSz chr1 9457378 9457515 - LINE/L1_L1MB7 chr1 9457516 9457804 + SINE/Alu_AluJb chr1 9457805 9457852 + Simple_repeat_(ACAAA)n chr1 9457853 9457928 - LINE/L1_L1MB8 chr1 9457929 9458011 - SINE/Alu_AluSz chr1 9458012 9458084 - SINE/Alu_Alu chr1 9458085 9458126 - LINE/L1_L1MB7 chr1 9458127 9458435 - SINE/Alu_AluJb chr1 9458436 9458650 - LINE/L1_L1MB7 chr1 9458651 9458951 - SINE/Alu_AluSx1 chr1 9458952 9459177 - LINE/L1_L1MB7 chr1 9459368 9459609 + SINE/Alu_AluJb chr1 9459701 9460184 - LTR/ERVL-MaLR_MLT1E2 chr1 9460631 9460932 + SINE/Alu_AluSz chr1 9460964 9461220 - LINE/L1_L1ME1 chr1 9461221 9461397 + LTR/ERVL-MaLR_MLT1B chr1 9461398 9461672 + SINE/Alu_AluJb chr1 9461673 9461939 + LTR/ERVL-MaLR_MLT1B chr1 9461940 9462259 - LINE/L1_L1ME1 chr1 9462260 9462558 - SINE/Alu_AluSp chr1 9462559 9462861 - LINE/L1_L1ME1 chr1 9462862 9463137 - SINE/Alu_AluJo chr1 9463138 9463284 - LINE/L1_L1ME1 chr1 9463285 9463583 + SINE/Alu_AluSg chr1 9463584 9463751 - LINE/L1_L1ME1 chr1 9463752 9464049 - SINE/Alu_AluY chr1 9464050 9464181 - LINE/L1_L1ME1 chr1 9464182 9464503 - SINE/Alu_AluSx1 chr1 9464504 9464761 - LINE/L1_L1ME1 chr1 9464762 9465063 - SINE/Alu_AluSx chr1 9465064 9465153 - LINE/L1_L1ME1 chr1 9465202 9465502 - SINE/Alu_AluSx chr1 9465509 9465570 - LINE/L1_L1MB7 chr1 9465590 9465636 - SINE/Alu_Alu chr1 9465775 9465944 + DNA/hAT-Charlie_MER102b chr1 9466181 9466287 + DNA/hAT-Blackjack_MER81 chr1 9466316 9466343 + Simple_repeat_(AATAA)n chr1 9466352 9466640 + SINE/Alu_AluSz6 chr1 9467602 9467656 + Low_complexity_A-rich chr1 9468639 9468729 + DNA/hAT-Charlie_MER5C1 chr1 9468730 9469039 + SINE/Alu_AluSx1 chr1 9469040 9469062 + DNA/hAT-Charlie_MER5C1 chr1 9469127 9469420 + SINE/Alu_AluJo chr1 9469421 9469723 + SINE/Alu_AluSz chr1 9469728 9470037 + SINE/Alu_AluSp chr1 9470039 9470284 + Low_complexity_GA-rich chr1 9470287 9470314 + Simple_repeat_(AGGC)n chr1 9470316 9470619 + SINE/Alu_AluSx chr1 9470900 9471201 + SINE/Alu_AluSx1 chr1 9471286 9471353 + LINE/L1_L1ME3A chr1 9471354 9471461 + LINE/L1_L1PA11 chr1 9471462 9471695 + LINE/L1_L1ME3A chr1 9472700 9472837 + SINE/MIR_MIRb chr1 9472823 9472874 - LINE/CR1_L3 chr1 9473349 9473392 + Simple_repeat_(CCTCCAG)n chr1 9473580 9473603 - LINE/L2_L2d chr1 9473645 9474051 - LTR/ERVL-MaLR_MLT1C2 chr1 9474080 9474227 - SINE/MIR_MIRb chr1 9474331 9474638 + SINE/Alu_AluJo chr1 9474810 9475105 + SINE/Alu_AluSx chr1 9475988 9476301 - SINE/Alu_AluSx1 chr1 9476303 9476331 - LINE/L1_L1MB5 chr1 9476332 9476644 + SINE/Alu_AluJb chr1 9476645 9476862 - LINE/L1_L1MB5 chr1 9476912 9476970 + LTR/ERVL-MaLR_MLT1K chr1 9477162 9477438 + LTR/ERVL-MaLR_MLT1K chr1 9478364 9478759 + LINE/L2_L2c chr1 9478802 9479091 + SINE/Alu_AluSq2 chr1 9479092 9479386 + SINE/Alu_AluSc chr1 9479718 9480024 - SINE/Alu_AluSz chr1 9480468 9480759 - SINE/Alu_AluSz chr1 9480762 9481058 - SINE/Alu_AluSx1 chr1 9481476 9481546 - LINE/L2_L2b chr1 9481719 9481817 - LTR/ERVL_LTR47A chr1 9481818 9482113 - SINE/Alu_AluSx1 chr1 9482114 9482627 - LTR/ERVL_LTR47A chr1 9482628 9482828 - SINE/MIR_MIR chr1 9483687 9483847 - DNA/hAT-Charlie_MER58B chr1 9483848 9484155 - SINE/Alu_AluSx1 chr1 9484156 9484205 - DNA/hAT-Charlie_MER58B chr1 9484206 9484504 - SINE/Alu_AluSx chr1 9484505 9484562 - DNA/hAT-Charlie_MER58B chr1 9484563 9484859 + SINE/Alu_AluSz chr1 9484860 9484936 - DNA/hAT-Charlie_MER58B chr1 9485086 9485378 + LINE/L2_L2c chr1 9485395 9485604 + LINE/L2_L2 chr1 9485793 9486099 + SINE/Alu_AluSx1 chr1 9487221 9487518 + SINE/Alu_AluSx1 chr1 9487527 9487583 - SINE/MIR_MIRb chr1 9487584 9487600 + Simple_repeat_(A)n chr1 9487619 9487790 - SINE/Alu_AluJb chr1 9487793 9487919 + SINE/Alu_AluSx3 chr1 9487923 9488054 - SINE/Alu_AluJb chr1 9488070 9488381 - SINE/Alu_AluSx3 chr1 9488391 9488800 - LINE/L2_L2 chr1 9488801 9489116 + SINE/Alu_AluSq chr1 9489117 9489242 - LINE/L2_L2 chr1 9489364 9489686 - LINE/L1_L1MB7 chr1 9489687 9489998 - SINE/Alu_AluSz chr1 9489999 9490044 - LINE/L1_L1MB7 chr1 9490045 9490341 + SINE/Alu_AluSp chr1 9490342 9490581 - LINE/L1_L1MB7 chr1 9490875 9491033 + SINE/Alu_FRAM chr1 9491034 9491081 + SINE/Alu_Alu chr1 9491421 9491490 - LINE/L1_L1MC5 chr1 9491491 9491789 + SINE/Alu_AluSx1 chr1 9491790 9491966 - LINE/L1_L1MC5 chr1 9492041 9492157 - SINE/Alu_AluJr chr1 9492163 9492292 - LINE/L1_L1MC5 chr1 9492513 9492664 + SINE/Alu_AluYc chr1 9492763 9493057 + SINE/Alu_AluSg chr1 9493080 9493169 - LTR/Gypsy?_LTR85b chr1 9493176 9493188 + Simple_repeat_(AAACAC)n chr1 9493189 9493448 + SINE/Alu_AluSz chr1 9493449 9493465 + Simple_repeat_(AAACAC)n chr1 9493789 9494019 + SINE/Alu_AluSz chr1 9494039 9494332 + SINE/Alu_AluSg chr1 9494519 9494813 + SINE/Alu_AluSz6 chr1 9495108 9495134 + Low_complexity_GA-rich chr1 9495641 9496093 - LINE/L1_L1MC1 chr1 9496102 9496308 - SINE/Alu_AluJo chr1 9496309 9496596 + SINE/Alu_AluSp chr1 9496597 9496728 - SINE/Alu_AluJo chr1 9496729 9497380 - LTR/ERVL_ERVL-int chr1 9497381 9497486 - SINE/Alu_AluJo chr1 9497489 9497784 - LINE/L1_L1MC chr1 9497785 9498072 - SINE/Alu_AluSg chr1 9498073 9498278 - LINE/L1_L1MC chr1 9498279 9498583 - SINE/Alu_AluSz6 chr1 9498584 9498630 - LINE/L1_L1MC chr1 9498631 9498930 - SINE/Alu_AluJr chr1 9498931 9499334 - LINE/L1_L1MC chr1 9499335 9499644 + SINE/Alu_AluSp chr1 9499645 9499816 - LINE/L1_L1MC chr1 9499817 9500117 - SINE/Alu_AluSx1 chr1 9500118 9500426 - LINE/L1_L1MC chr1 9500427 9500717 - SINE/Alu_AluSz chr1 9500718 9500834 - LINE/L1_L1MC chr1 9500929 9501120 + DNA/hAT-Tip100_MER91B chr1 9501406 9501627 + SINE/MIR_MIRb chr1 9501682 9501749 + DNA/hAT-Tip100_ORSL chr1 9501753 9502049 + SINE/Alu_AluSp chr1 9502091 9502290 + SINE/Alu_AluSx chr1 9502421 9502517 + DNA/hAT-Tip100_ORSL chr1 9502594 9502903 + SINE/Alu_AluJb chr1 9502905 9502982 + Low_complexity_A-rich chr1 9502983 9503051 + Simple_repeat_(GGAA)n chr1 9503052 9503066 + Low_complexity_A-rich chr1 9503321 9503344 + Simple_repeat_(A)n chr1 9503514 9503570 + LINE/CR1_L3 chr1 9503708 9503743 + Simple_repeat_(TTA)n chr1 9503744 9504051 - SINE/Alu_AluSp chr1 9504054 9504384 - SINE/Alu_AluSz chr1 9504385 9504399 + Simple_repeat_(TTA)n chr1 9504851 9505162 + SINE/Alu_AluSx chr1 9505445 9505593 + SINE/MIR_MIRb chr1 9505643 9505983 - LTR/ERVL-MaLR_MLT1C2 chr1 9505984 9506292 - SINE/Alu_AluSx chr1 9506293 9506398 - LTR/ERVL-MaLR_MLT1C2 chr1 9506704 9506999 + SINE/Alu_AluSx1 chr1 9507242 9507546 - SINE/Alu_AluSz chr1 9507823 9507892 - LINE/L1_L1MB8 chr1 9507893 9508185 + SINE/Alu_AluJb chr1 9508186 9508373 - LINE/L1_L1MB8 chr1 9508374 9508661 - SINE/Alu_AluSx1 chr1 9508662 9508728 - LINE/L1_L1MB8 chr1 9508729 9509037 - SINE/Alu_AluY chr1 9509038 9509174 - LINE/L1_L1MB8 chr1 9509186 9509732 + LTR/ERVL_MLT2A2 chr1 9509798 9510095 + SINE/Alu_AluSx chr1 9510245 9510490 + DNA/hAT-Tip100_Arthur1B chr1 9510491 9510651 + SINE/Alu_FRAM chr1 9510655 9510962 + SINE/Alu_AluJb chr1 9510963 9511042 + DNA/hAT-Tip100_Arthur1B chr1 9511132 9511360 + SINE/Alu_AluSx chr1 9511361 9511566 + LTR/ERV1_LTR9 chr1 9511739 9511867 + LTR/ERV1_LTR8 chr1 9511874 9512197 - SINE/Alu_AluY chr1 9512199 9512306 - SINE/Alu_AluSx3 chr1 9512307 9512896 + LTR/ERV1_LTR10C chr1 9512897 9513086 - SINE/Alu_AluSx3 chr1 9513094 9513510 + LTR/ERV1_LTR8 chr1 9513519 9513803 + SINE/Alu_AluSx1 chr1 9513806 9514107 + SINE/Alu_AluSg chr1 9514108 9514623 + LINE/L1_L1MC3 chr1 9514624 9514920 - SINE/Alu_AluSg chr1 9514921 9515182 + LINE/L1_L1MC3 chr1 9515188 9515336 + SINE/Alu_AluSz chr1 9515337 9515645 + SINE/Alu_AluSq2 chr1 9515646 9515814 + SINE/Alu_AluSz chr1 9515821 9516130 + SINE/Alu_AluSg chr1 9516131 9516216 + Low_complexity_GA-rich chr1 9516234 9516291 + LTR/ERV1_LTR60B chr1 9516336 9516519 + LTR/ERVL-MaLR_MLT1B chr1 9516765 9517072 - SINE/Alu_AluYa5 chr1 9517108 9517181 - LINE/L2_L2a chr1 9517233 9517446 - DNA/hAT-Charlie_MER58B chr1 9517447 9517743 - SINE/Alu_AluY chr1 9517963 9518241 + SINE/Alu_AluJr chr1 9518965 9519270 + SINE/Alu_AluSq2 chr1 9519372 9519662 + SINE/Alu_AluY chr1 9519884 9519940 - LINE/L2_L2c chr1 9519982 9520264 - SINE/Alu_AluSx chr1 9520553 9520801 + LTR/ERVL_LTR16C chr1 9520846 9520917 - LINE/L1_L1MEd chr1 9520918 9521116 - SINE/Alu_AluSx4 chr1 9521117 9521128 - LINE/L1_L1MEd chr1 9521129 9521444 - SINE/Alu_AluSp chr1 9521445 9521580 - LINE/L1_L1MEd chr1 9521691 9521734 + LINE/L1_L1MB3 chr1 9521735 9522043 + SINE/Alu_AluY chr1 9522044 9522258 + LINE/L1_L1MB3 chr1 9522259 9522557 + SINE/Alu_AluSx1 chr1 9522558 9522875 + LINE/L1_L1MB3 chr1 9522876 9523155 + SINE/Alu_AluSg chr1 9523156 9523460 + SINE/Alu_AluY chr1 9523516 9523560 + Simple_repeat_(TA)n chr1 9523562 9523873 - SINE/Alu_AluYk3 chr1 9523943 9524070 + SINE/Alu_AluJb chr1 9524071 9524281 + SINE/Alu_AluJb chr1 9524282 9524423 + SINE/Alu_AluJb chr1 9524424 9524699 - SINE/Alu_AluSc chr1 9524704 9524841 - SINE/Alu_AluSz chr1 9524941 9525011 - LTR/ERVL_MLT2B1 chr1 9525015 9525175 + SINE/Alu_AluSp chr1 9525177 9525534 - LTR/ERVL_MLT2B1 chr1 9525535 9525597 + Simple_repeat_(AC)n chr1 9525598 9525660 - LTR/ERVL_MLT2B1 chr1 9525665 9525716 + SINE/Alu_AluYi6 chr1 9525717 9525969 - LTR/ERV1_LTR2 chr1 9525970 9526257 - SINE/Alu_AluSp chr1 9526258 9526436 - LTR/ERV1_LTR2 chr1 9526437 9526679 + SINE/Alu_AluSz chr1 9526925 9527084 + LINE/L2_L2 chr1 9527085 9527193 + LTR/ERV1_MER4A1 chr1 9527194 9527348 + SINE/Alu_AluYk3 chr1 9527349 9527453 - LTR/ERVL_MLT2A1 chr1 9527465 9527742 + SINE/Alu_AluSp chr1 9527747 9528050 - LTR/ERV1_MER61A chr1 9528051 9528117 - LTR/ERVL_MLT2A2 chr1 9528121 9528291 + SINE/Alu_AluSx4 chr1 9528479 9528748 + SINE/Alu_AluSx1 chr1 9528862 9529008 + SINE/MIR_MIR chr1 9529482 9529579 + SINE/Alu_AluJo chr1 9529889 9530173 - SINE/Alu_AluSz chr1 9530216 9530437 - SINE/MIR_MIRb chr1 9530501 9530564 + SINE/MIR_MIR chr1 9530565 9530700 + SINE/Alu_AluSx1 chr1 9530701 9530997 + SINE/Alu_AluSp chr1 9530998 9531176 + SINE/Alu_AluSx1 chr1 9531177 9531251 + SINE/MIR_MIR chr1 9531277 9531359 + SINE/MIR_MIRb chr1 9531360 9531445 + DNA/TcMar-Tigger_MER46C chr1 9531504 9531825 - SINE/Alu_AluSx1 chr1 9531831 9531886 + Simple_repeat_(GT)n chr1 9531887 9532165 - SINE/Alu_AluSg chr1 9532192 9532492 - SINE/Alu_AluSp chr1 9532497 9532746 + SINE/Alu_AluSx chr1 9533588 9533796 - SINE/Alu_AluSz6 chr1 9533797 9534100 - SINE/Alu_AluY chr1 9534101 9534234 - SINE/Alu_AluSz6 chr1 9534265 9534334 - LINE/L1_L1ME1 chr1 9534335 9534626 + SINE/Alu_AluSq2 chr1 9534627 9534838 - LINE/L1_L1ME1 chr1 9534846 9535146 - SINE/Alu_AluJb chr1 9535151 9535223 - SINE/Alu_AluJb chr1 9535236 9535270 - snRNA_U2 chr1 9535316 9535627 + SINE/Alu_AluSz chr1 9535628 9535737 - SINE/Alu_AluJo chr1 9535749 9536035 - SINE/Alu_AluSq2 chr1 9536089 9536540 - LINE/L1_L1MD chr1 9536541 9536585 + Simple_repeat_(T)n chr1 9536591 9536626 - LTR/ERV1_MER4D1 chr1 9536627 9536783 - LINE/L1_L1MD chr1 9536900 9537213 + SINE/Alu_AluSq2 chr1 9537214 9537371 + SINE/Alu_AluSx chr1 9537372 9537679 + SINE/Alu_AluSg chr1 9537680 9537699 + SINE/Alu_AluSx chr1 9537715 9537860 - SINE/Alu_FLAM_C chr1 9538183 9538324 - LINE/L2_L2 chr1 9538326 9538461 - LINE/L2_L2a chr1 9538737 9539021 - SINE/Alu_AluSx chr1 9539663 9539701 + Simple_repeat_(CGGCGA)n chr1 9540987 9541147 - SINE/Alu_AluSx1 chr1 9541148 9541448 - SINE/Alu_AluYk3 chr1 9541449 9541589 - SINE/Alu_AluSx1 chr1 9541797 9542095 - SINE/Alu_AluY chr1 9542805 9543118 - SINE/Alu_AluSz6 chr1 9543132 9543443 - SINE/Alu_AluSq chr1 9543824 9544138 + SINE/Alu_AluSx chr1 9544265 9544566 - SINE/Alu_AluSx chr1 9545206 9545324 - SINE/Alu_AluSq2 chr1 9545365 9545403 + SINE/Alu_Alu chr1 9545406 9545700 - SINE/Alu_AluSg4 chr1 9545701 9545975 + SINE/Alu_AluSz chr1 9546065 9546174 - DNA/hAT-Charlie_Charlie30a chr1 9546175 9546483 - SINE/Alu_AluY chr1 9546484 9546569 - DNA/hAT-Charlie_Charlie30a chr1 9546693 9546747 - LTR/ERVL-MaLR_MLT1L chr1 9547173 9547469 + SINE/Alu_AluSq2 chr1 9547584 9547779 - DNA/hAT-Charlie_MER20 chr1 9547803 9547849 + Simple_repeat_(TTTT)n chr1 9547852 9548173 - SINE/Alu_AluJb chr1 9548214 9548299 - DNA/hAT-Charlie_Charlie7a chr1 9548300 9548611 + SINE/Alu_AluSx chr1 9548612 9548707 - DNA/hAT-Charlie_Charlie7a chr1 9549622 9549918 - SINE/Alu_AluSx1 chr1 9549922 9550011 + Simple_repeat_(AT)n chr1 9550012 9550199 - SINE/Alu_AluSx3 chr1 9550328 9550440 - SINE/tRNA-RTE_MamSINE1 chr1 9550661 9550684 + Simple_repeat_(TTAA)n chr1 9550685 9550977 - SINE/Alu_AluSz chr1 9551102 9551404 + SINE/Alu_AluSz chr1 9551594 9551695 - LINE/L2_L2c chr1 9551757 9551868 - LINE/L2_L2c chr1 9551943 9552125 + DNA/hAT-Charlie_MER113A chr1 9552225 9552531 - SINE/Alu_AluSz chr1 9552626 9552766 + LINE/L1_L1ME4a chr1 9552911 9553165 - SINE/Alu_AluSx chr1 9553212 9553531 - SINE/Alu_AluY chr1 9554169 9554349 - SINE/Alu_AluSx chr1 9554356 9554653 - SINE/Alu_AluSp chr1 9555111 9555417 - SINE/Alu_AluY chr1 9555674 9555982 - SINE/Alu_AluSx chr1 9556432 9556615 - DNA/TcMar-Tigger_Tigger4a chr1 9556664 9556734 + Simple_repeat_(TGTGTG)n chr1 9556949 9557231 - SINE/Alu_AluSp chr1 9557412 9557602 + SINE/Alu_AluJb chr1 9557805 9557916 + SINE/Alu_AluJb chr1 9557970 9558004 - LINE/L1_L1MA3 chr1 9558012 9558136 - SINE/MIR_MIRc chr1 9558289 9559382 + LINE/L2_L2a chr1 9559535 9559643 - LINE/RTE-X_L4_B_Mam chr1 9559733 9559792 + LINE/L2_L2 chr1 9559970 9560281 + SINE/Alu_AluSx chr1 9560327 9560572 + SINE/Alu_AluY chr1 9560948 9561242 - SINE/Alu_AluSc8 chr1 9561968 9562064 + SINE/Alu_AluSc chr1 9562077 9562366 + SINE/Alu_AluSx chr1 9562367 9562457 + SINE/Alu_AluSg4 chr1 9562573 9562870 + SINE/Alu_AluSp chr1 9562920 9563223 - SINE/Alu_AluY chr1 9563224 9563296 - LINE/L1_L1M4 chr1 9563711 9563958 - SINE/Alu_AluSz chr1 9564278 9564608 + LINE/L1_L1MB3 chr1 9564609 9564739 + SINE/Alu_AluSz6 chr1 9564740 9564766 + Simple_repeat_(AT)n chr1 9564767 9564952 + SINE/Alu_AluSz6 chr1 9564953 9565245 + LINE/L1_L1MB3 chr1 9565246 9565527 + SINE/Alu_AluY chr1 9565528 9565576 + LINE/L1_L1MB3 chr1 9565597 9565904 + SINE/Alu_AluY chr1 9566034 9566326 - SINE/Alu_AluJb chr1 9566565 9566871 + SINE/Alu_AluSp chr1 9566875 9567057 + SINE/Alu_AluSg4 chr1 9567622 9567772 + DNA/hAT-Charlie_MER5A1 chr1 9567874 9568066 - DNA/hAT-Charlie_MER3 chr1 9568160 9568473 + SINE/Alu_AluSx1 chr1 9568570 9568863 + SINE/Alu_AluY chr1 9568964 9569259 + SINE/Alu_AluSz chr1 9570667 9570699 + Simple_repeat_(ATAG)n chr1 9570703 9571012 - SINE/Alu_AluSz chr1 9571294 9571576 - SINE/Alu_AluSc8 chr1 9571624 9571940 - SINE/Alu_AluJb chr1 9572331 9572632 + SINE/Alu_AluY chr1 9572786 9572915 + SINE/Alu_FLAM_C chr1 9573869 9573954 - DNA/hAT-Charlie_Charlie4a chr1 9573981 9574087 - SINE/Alu_Alu chr1 9574099 9574142 + Simple_repeat_(T)n chr1 9574166 9574390 - SINE/Alu_AluJo chr1 9574421 9574624 - DNA/hAT-Charlie_Charlie4a chr1 9574837 9574928 - DNA/hAT-Charlie_Charlie4a chr1 9574932 9575241 + SINE/Alu_AluY chr1 9575336 9575640 + SINE/Alu_AluSg chr1 9577081 9577175 - LINE/L2_L2c chr1 9577176 9577510 + SINE/Alu_AluSz chr1 9577511 9577689 - LINE/L2_L2c chr1 9577729 9577923 + DNA/hAT-Charlie_MER20 chr1 9577928 9578217 - SINE/Alu_AluY chr1 9578219 9578337 - LINE/L2_L2c chr1 9578641 9578770 - SINE/Alu_AluJr4 chr1 9579108 9579841 - LTR/ERVL_MER21A chr1 9580418 9580562 - DNA/hAT-Charlie_MER102b chr1 9580576 9580865 + SINE/Alu_AluSc chr1 9580866 9580895 + Simple_repeat_(AAT)n chr1 9581469 9581747 + SINE/Alu_AluSx1 chr1 9581914 9582213 - SINE/Alu_AluSz6 chr1 9582943 9583230 + SINE/Alu_AluSx chr1 9583736 9584010 + SINE/Alu_AluY chr1 9584397 9584698 - SINE/Alu_AluSx chr1 9584710 9584994 - SINE/Alu_AluJr chr1 9585006 9585043 + Simple_repeat_(ATATTC)n chr1 9585313 9585506 - LINE/L1_L1MA1 chr1 9585507 9585788 + SINE/Alu_AluSq2 chr1 9585789 9585830 - LINE/L1_L1MA1 chr1 9585878 9586158 - SINE/Alu_AluSx1 chr1 9586161 9586412 + SINE/Alu_AluJr chr1 9587174 9587483 - SINE/Alu_AluSc chr1 9587494 9587796 - SINE/Alu_AluSx chr1 9587856 9587938 + Simple_repeat_(CTTT)n chr1 9587939 9588249 - SINE/Alu_AluYk2 chr1 9588259 9588335 - LINE/L1_L1MC chr1 9588585 9588670 + Simple_repeat_(GCC)n chr1 9589474 9589523 - SINE/MIR_MIR chr1 9589905 9590018 - SINE/MIR_MIR chr1 9590220 9590391 - SINE/MIR_MIR3 chr1 9591221 9591385 - SINE/MIR_MIRc chr1 9594044 9594200 - SINE/MIR_MIR chr1 9594477 9594571 - SINE/MIR_MIRb chr1 9594719 9594852 - SINE/MIR_MIRc chr1 9596142 9596340 - SINE/MIR_MIRb chr1 9596341 9596533 - DNA/hAT-Charlie_MER58A chr1 9598268 9598388 - SINE/MIR_MIRb chr1 9598737 9598765 + Simple_repeat_(TTTAT)n chr1 9598780 9598948 - SINE/Alu_AluSp chr1 9598949 9599239 - SINE/Alu_AluSx1 chr1 9599240 9599554 - SINE/Alu_AluSz chr1 9599613 9599897 - SINE/Alu_AluYc chr1 9600250 9600460 + SINE/MIR_MIR chr1 9600637 9600885 + SINE/Alu_AluSx1 chr1 9601219 9601256 + Simple_repeat_(GCCCCTG)n chr1 9601836 9601968 - SINE/MIR_MIR chr1 9602388 9602427 + Simple_repeat_(CCCAGC)n chr1 9603188 9603370 + SINE/MIR_MIRc chr1 9604972 9604995 + Simple_repeat_(TCTT)n chr1 9607966 9608255 + SINE/Alu_AluJr chr1 9608268 9608374 + SINE/MIR_MIR chr1 9609556 9609634 - SINE/Alu_Alu chr1 9609637 9609750 + SINE/MIR_MIRc chr1 9609970 9610133 + SINE/MIR_MIRb chr1 9611195 9611497 - SINE/Alu_AluSz chr1 9612098 9612279 - DNA/hAT-Charlie_MER3 chr1 9612558 9612708 + SINE/MIR_MIRb chr1 9615961 9616251 - SINE/Alu_AluJb chr1 9616304 9616435 + SINE/Alu_AluJb chr1 9616436 9616733 + SINE/Alu_AluY chr1 9616734 9616891 + SINE/Alu_AluJb chr1 9617165 9617492 + SINE/Alu_AluSp chr1 9617493 9617582 - LTR/ERV1_MER41A chr1 9618104 9618262 - LINE/L2_L2b chr1 9618316 9618510 - SINE/Alu_AluSx3 chr1 9618511 9618810 + SINE/Alu_AluSx chr1 9618811 9618859 - SINE/Alu_AluSx3 chr1 9619025 9619187 + LINE/L2_L2a chr1 9619473 9619616 + SINE/Alu_AluJr4 chr1 9619618 9619918 + SINE/Alu_AluSx chr1 9619919 9620245 + LTR/ERV1_MER41A chr1 9620320 9620617 - SINE/Alu_AluSg chr1 9620639 9620930 - SINE/Alu_AluSx chr1 9620931 9620985 - LTR/ERV1_LTR8A chr1 9621070 9621293 - LTR/ERV1_MER41A chr1 9621355 9621383 + LTR/ERV1_LTR8A chr1 9621384 9621682 + SINE/Alu_AluSx chr1 9621683 9622311 + LTR/ERV1_LTR8A chr1 9622327 9622522 - SINE/Alu_AluSg4 chr1 9622812 9623114 - SINE/Alu_AluY chr1 9623279 9623588 + SINE/Alu_AluYa5 chr1 9623590 9623768 - LTR/ERV1_MER34A chr1 9623924 9624228 + SINE/Alu_AluSx chr1 9624230 9624320 + Low_complexity_GA-rich chr1 9624412 9624630 + LINE/L2_L2 chr1 9624648 9624973 - SINE/Alu_AluY chr1 9625012 9625058 + Simple_repeat_(TTTTG)n chr1 9625070 9625370 - SINE/Alu_AluJr4 chr1 9625377 9625690 - SINE/Alu_AluSx chr1 9625735 9625766 + Simple_repeat_(CTTTC)n chr1 9626033 9626220 + LINE/L2_L2a chr1 9626623 9626924 + SINE/Alu_AluSx chr1 9627407 9627560 + SINE/MIR_MIRb chr1 9627578 9627648 - SINE/MIR_MIR chr1 9627972 9628038 + SINE/Alu_AluSx3 chr1 9628039 9628320 + SINE/Alu_AluSx chr1 9628399 9628499 - SINE/MIR_MIR chr1 9629569 9629676 + SINE/MIR_MIRb chr1 9630437 9630707 - LINE/L2_L2b chr1 9630708 9630740 + Simple_repeat_(GT)n chr1 9630741 9630928 - LINE/L2_L2b chr1 9631373 9631677 + SINE/Alu_AluSz chr1 9631960 9631989 + Simple_repeat_(CCTC)n chr1 9631990 9632001 - SINE/Alu_AluJb chr1 9632002 9632310 - SINE/Alu_AluSp chr1 9632311 9632489 - SINE/Alu_AluJr chr1 9632859 9633157 - SINE/Alu_AluY chr1 9633336 9633607 - SINE/Alu_AluSq2 chr1 9633970 9634138 - SINE/Alu_AluJb chr1 9634139 9634461 - SINE/Alu_AluSx chr1 9634462 9634598 - SINE/Alu_AluJb chr1 9634865 9635008 + LTR/ERVL-MaLR_MLT1C chr1 9635012 9635290 + SINE/Alu_AluSx chr1 9635344 9635696 - LTR/ERVL-MaLR_MLT1O chr1 9636116 9636199 - LINE/L1_L1ME4c chr1 9636200 9636492 - SINE/Alu_AluSz6 chr1 9636493 9636603 - LINE/L1_L1ME4c chr1 9636886 9637191 - SINE/Alu_AluY chr1 9637248 9637517 + SINE/Alu_AluJb chr1 9637533 9637694 - LTR/ERVL-MaLR_MLT1C2 chr1 9637694 9637833 + LTR/ERV1_MER34C2 chr1 9637850 9638107 + SINE/Alu_AluSx1 chr1 9638108 9638146 + Simple_repeat_(AAAT)n chr1 9638221 9638449 + LTR/ERV1_MER34C2 chr1 9638451 9638750 + SINE/Alu_AluY chr1 9638778 9639066 - SINE/Alu_AluSz6 chr1 9639068 9639119 - LTR/ERVL-MaLR_MLT1O chr1 9639720 9640041 - SINE/Alu_AluSx1 chr1 9640048 9640298 - LINE/L1_L1MC3 chr1 9640299 9640608 + SINE/Alu_AluSq2 chr1 9640609 9640708 - LINE/L1_L1MC3 chr1 9640806 9641242 + LINE/L2_L2a chr1 9641373 9641540 - SINE/MIR_MIR chr1 9641933 9642119 - DNA/hAT-Charlie_MER112 chr1 9642120 9642412 - SINE/Alu_AluSx chr1 9642413 9642435 - DNA/hAT-Charlie_MER112 chr1 9642436 9642734 + SINE/Alu_AluYk3 chr1 9642735 9642805 - DNA/hAT-Charlie_MER112 chr1 9642809 9643126 + SINE/Alu_AluJo chr1 9643127 9643452 + SINE/Alu_AluSx3 chr1 9643453 9643498 + Simple_repeat_(AGGG)n chr1 9643499 9643534 + Low_complexity_A-rich chr1 9643607 9643851 - SINE/MIR_MIR chr1 9644237 9644516 + SINE/Alu_AluSx1 chr1 9644517 9644557 + Simple_repeat_(AAAT)n chr1 9645001 9645319 - SINE/Alu_AluSx1 chr1 9645320 9645441 - LINE/RTE-X_L4_C_Mam chr1 9645448 9645604 - SINE/Alu_AluSq2 chr1 9645605 9645903 - SINE/Alu_AluSq2 chr1 9645904 9646071 - SINE/Alu_AluSq2 chr1 9646160 9646267 + SINE/MIR_MIRb chr1 9646268 9646622 - LTR/ERV1_MER34B chr1 9646672 9646961 + SINE/Alu_AluSx chr1 9646962 9647122 + SINE/Alu_AluSq2 chr1 9647132 9647414 + SINE/Alu_AluJb chr1 9647422 9647481 - LTR/ERV1_LTR54 chr1 9647482 9647521 + Simple_repeat_(ATT)n chr1 9647522 9647777 - SINE/Alu_AluJr chr1 9647788 9648118 - LTR/ERV1_MER57C1 chr1 9648119 9648241 - LTR/ERV1_MER34B chr1 9648387 9648478 - LINE/RTE-X_L4_C_Mam chr1 9648820 9649127 + SINE/Alu_AluSx chr1 9649231 9649532 - SINE/Alu_AluJr chr1 9649621 9649692 + SINE/MIR_MIRc chr1 9649918 9650141 - LTR/ERV1_MER34C2 chr1 9650142 9650447 + SINE/Alu_AluSg chr1 9650462 9650651 + SINE/Alu_AluSx chr1 9650665 9650838 - LTR/ERV1_MER34C2 chr1 9650839 9651144 - SINE/Alu_AluJb chr1 9651145 9651321 - LTR/ERV1_MER34C2 chr1 9651379 9651419 + Simple_repeat_(CCTGCT)n chr1 9653332 9653464 - SINE/MIR_MIRb chr1 9653925 9654090 + SINE/Alu_FRAM chr1 9654778 9655089 + SINE/Alu_AluSz chr1 9655666 9655685 + Simple_repeat_(CCTATT)n chr1 9655686 9655996 - SINE/Alu_AluSx chr1 9655997 9656010 + Simple_repeat_(CCTATT)n chr1 9656219 9656432 - DNA/TcMar-Tigger_Tigger4a chr1 9656434 9656490 - LTR/ERVL-MaLR_MLT1C2 chr1 9656496 9656652 + SINE/Alu_FAM chr1 9656661 9656963 + SINE/Alu_AluSx chr1 9656966 9657352 - LTR/ERVL-MaLR_MLT1C2 chr1 9658101 9658403 + SINE/Alu_AluJr chr1 9658451 9658521 + SINE/MIR_MIRb chr1 9658522 9658800 - SINE/Alu_AluSx chr1 9658801 9658955 + SINE/MIR_MIRb chr1 9659102 9659330 + LINE/L1_L1PA10 chr1 9659377 9659752 - LINE/L1_L1MB3 chr1 9659753 9659917 - SINE/Alu_AluSx chr1 9659918 9660211 - SINE/Alu_AluSp chr1 9660215 9660396 - LINE/L1_L1MB3 chr1 9660416 9660526 - LINE/L1_L1ME4c chr1 9660528 9660772 - LTR/ERVL_MER21-int chr1 9660924 9661234 - SINE/Alu_AluSz chr1 9661235 9661379 - SINE/Alu_AluSx chr1 9661387 9661578 - SINE/Alu_AluJo chr1 9661579 9661714 + SINE/Alu_AluSz chr1 9661715 9662007 + SINE/Alu_AluY chr1 9662008 9662167 + SINE/Alu_AluSz chr1 9662168 9662297 - SINE/Alu_AluJo chr1 9662683 9662987 - SINE/Alu_AluSx chr1 9662992 9663565 - LTR/ERVL_MER21-int chr1 9663580 9663990 - LINE/L1_L1PA5 chr1 9664038 9664348 - SINE/Alu_AluSx chr1 9664358 9664921 - LTR/ERVL_MER21-int chr1 9664922 9665223 + SINE/Alu_AluJo chr1 9665224 9665363 - LTR/ERVL_MER21-int chr1 9665390 9665446 - SINE/Alu_AluSx3 chr1 9665448 9665747 - LTR/ERVL_MER21-int chr1 9665748 9665920 - SINE/Alu_AluSx4 chr1 9665921 9666227 - SINE/Alu_AluSq2 chr1 9666228 9666531 - SINE/Alu_AluSx1 chr1 9666534 9666617 - LTR/ERV1_MER4D chr1 9666634 9666687 + SINE/Alu_AluJo chr1 9666691 9666863 - SINE/Alu_AluSx chr1 9666867 9667163 - SINE/Alu_AluSx chr1 9667177 9667312 + SINE/Alu_AluJr chr1 9667380 9667675 - SINE/Alu_AluSc8 chr1 9667725 9667902 - SINE/Alu_AluJb chr1 9667903 9667932 + Simple_repeat_(T)n chr1 9667933 9668050 - SINE/Alu_AluJb chr1 9668128 9668190 - LTR/ERV1_MER4B-int chr1 9668193 9668437 + SINE/MIR_MIR chr1 9668480 9668637 - LINE/L1_L1PB2 chr1 9669162 9669321 - SINE/Alu_FRAM chr1 9669323 9669374 - SINE/Alu_AluJo chr1 9669370 9669548 - LTR/ERV1_MER4D chr1 9669540 9669623 - SINE/Alu_AluJo chr1 9669893 9670172 + SINE/Alu_AluSq2 chr1 9670482 9670648 + SINE/MIR_MIRb chr1 9670757 9671057 - SINE/Alu_AluSx1 chr1 9671064 9671108 - SINE/Alu_FLAM_C chr1 9671109 9671398 - SINE/Alu_AluJb chr1 9671399 9671496 - SINE/Alu_FLAM_C chr1 9671639 9671745 - SINE/Alu_FLAM_C chr1 9672077 9672323 - DNA/hAT-Charlie_Charlie16a chr1 9672426 9672723 - SINE/Alu_AluJo chr1 9672949 9673038 - DNA/TcMar-Tigger_Tigger13a chr1 9673100 9673254 - SINE/Alu_AluJo chr1 9673255 9673560 - SINE/Alu_AluSz chr1 9673561 9673702 - SINE/Alu_AluJo chr1 9673704 9674098 - DNA/TcMar-Tigger_Tigger13a chr1 9674320 9674394 - LINE/L2_L2d2 chr1 9674398 9674750 + SINE/Alu_AluSx1 chr1 9674751 9675067 + SINE/Alu_AluSz6 chr1 9675083 9675103 - SINE/MIR_MIRc chr1 9675104 9675413 + SINE/Alu_AluYi6_4d chr1 9675414 9675569 - SINE/MIR_MIR chr1 9675754 9675931 - SINE/Alu_AluJb chr1 9675932 9676229 - SINE/Alu_AluSq2 chr1 9676230 9676361 - SINE/Alu_AluJr4 chr1 9676709 9676819 + SINE/MIR_MIRb chr1 9677364 9677665 + SINE/Alu_AluSz chr1 9677869 9678172 + SINE/Alu_AluSp chr1 9678182 9678474 + SINE/Alu_AluJb chr1 9678509 9678587 - DNA/hAT-Charlie_Charlie4z chr1 9678671 9678863 - LINE/CR1_X7A_LINE chr1 9679064 9679367 - SINE/Alu_AluSx chr1 9679694 9679723 + Simple_repeat_(CCT)n chr1 9679727 9679807 - LINE/L1_L1MB7 chr1 9679813 9680124 - SINE/Alu_AluSq2 chr1 9680151 9680295 + Retroposon/SVA_SVA_A chr1 9680299 9680330 - LINE/L1_L1MB7 chr1 9680331 9680459 + SINE/Alu_FLAM_C chr1 9680460 9680659 - LINE/L1_L1MB7 chr1 9680798 9681157 - LTR/ERVL-MaLR_MLT1F1 chr1 9681228 9681254 + Simple_repeat_(TAAT)n chr1 9681255 9681401 - SINE/Alu_FRAM chr1 9681414 9681709 - SINE/Alu_AluSx chr1 9682007 9682222 - SINE/Alu_AluJb chr1 9682232 9682537 - SINE/Alu_AluSq chr1 9682544 9682841 - SINE/Alu_AluSx chr1 9683038 9683055 + Simple_repeat_(T)n chr1 9683071 9683154 + SINE/Alu_Alu chr1 9683157 9683467 + SINE/Alu_AluY chr1 9683588 9683656 + SINE/MIR_MIRb chr1 9683664 9684195 - LTR/ERVL-MaLR_MLT1G chr1 9684255 9684565 + SINE/Alu_AluSx1 chr1 9684577 9684875 + SINE/Alu_AluJr chr1 9685294 9685350 + Simple_repeat_(GTTT)n chr1 9685359 9685657 - SINE/Alu_AluSx chr1 9685673 9685872 - SINE/Alu_AluJb chr1 9686258 9686498 - SINE/Alu_AluJr chr1 9686516 9686653 - SINE/Alu_FLAM_C chr1 9686753 9686858 + SINE/MIR_MIR3 chr1 9686862 9686931 - SINE/MIR_MIR chr1 9687551 9687594 + Low_complexity_G-rich chr1 9688488 9688557 + SINE/MIR_MIRb chr1 9688572 9688838 + SINE/Alu_AluJr chr1 9689762 9689806 + Simple_repeat_(GCCCC)n chr1 9691434 9691456 + Simple_repeat_(TTTC)n chr1 9692054 9692130 - SINE/MIR_MIR3 chr1 9692445 9692759 + SINE/Alu_AluSx3 chr1 9693207 9693528 - SINE/Alu_AluSz chr1 9693597 9693996 + LINE/L1_HAL1 chr1 9694024 9694250 + LINE/L1_HAL1 chr1 9694251 9694544 + SINE/Alu_AluSz chr1 9694545 9694688 + LINE/L1_HAL1 chr1 9694689 9694991 + SINE/Alu_AluJb chr1 9694992 9695561 + LINE/L1_HAL1 chr1 9695562 9695872 + SINE/Alu_AluSg chr1 9695873 9695975 + LINE/L1_HAL1 chr1 9695976 9696271 - SINE/Alu_AluJb chr1 9696316 9696431 + SINE/Alu_AluJo chr1 9696470 9696753 + SINE/Alu_AluJo chr1 9696813 9697084 + SINE/Alu_AluJo chr1 9697090 9697207 + SINE/Alu_AluJo chr1 9697412 9697747 + SINE/Alu_AluJr chr1 9697750 9698060 + SINE/Alu_AluSz6 chr1 9698134 9698441 - SINE/Alu_AluSp chr1 9698590 9698742 - LINE/L1_L1ME4c chr1 9698906 9699208 + SINE/Alu_AluJo chr1 9699272 9699596 + LINE/L2_L2b chr1 9699597 9699892 - SINE/Alu_AluSq2 chr1 9699893 9700102 + LINE/L2_L2b chr1 9700124 9700269 - SINE/Alu_AluSz6 chr1 9700665 9701248 + LINE/L2_L2a chr1 9701282 9701349 + LINE/L2_L2a chr1 9701391 9701689 + SINE/Alu_AluSx1 chr1 9701720 9701984 + LINE/CR1_L3 chr1 9701985 9702263 - SINE/Alu_AluSx chr1 9702264 9702500 + LINE/CR1_L3 chr1 9702501 9702820 - SINE/Alu_AluSp chr1 9702821 9702873 + LINE/CR1_L3 chr1 9702892 9703160 - LTR/ERVL-MaLR_MLT1K chr1 9703120 9703301 - LTR/ERVL-MaLR_MLT1K chr1 9703272 9703411 - LTR/ERVL-MaLR_MLT1K chr1 9703431 9703976 - LINE/L1_L1ME3A chr1 9704496 9704813 - SINE/Alu_AluJb chr1 9705068 9705188 + LINE/L1_L1ME3Cz chr1 9705189 9705529 + SINE/Alu_AluJb chr1 9705530 9705935 + LINE/L1_L1ME3Cz chr1 9705949 9706025 + DNA/hAT-Charlie_MER3 chr1 9706033 9706059 + DNA/hAT-Charlie_Charlie5 chr1 9706060 9706186 - SINE/Alu_FLAM_C chr1 9706187 9706222 + DNA/hAT-Charlie_Charlie5 chr1 9706223 9706513 + SINE/Alu_AluJo chr1 9706514 9706788 + DNA/hAT-Charlie_Charlie5 chr1 9706805 9707114 - SINE/Alu_AluJb chr1 9707180 9707313 + LINE/L1_L1ME3C chr1 9707199 9707319 + LINE/L1_L1M5 chr1 9707358 9707695 - LINE/L1_L1PA13 chr1 9707696 9707779 + LINE/L1_L1ME3C chr1 9707780 9708093 - SINE/Alu_AluY chr1 9708094 9708172 + LINE/L1_L1ME3C chr1 9708173 9708204 + Simple_repeat_(TTTG)n chr1 9708209 9708480 - SINE/Alu_AluSz6 chr1 9708481 9708566 + LINE/L1_L1ME3C chr1 9708567 9708855 + SINE/Alu_AluSx1 chr1 9708856 9708991 + LINE/L1_L1ME3C chr1 9709030 9709343 - SINE/Alu_AluSp chr1 9709425 9709714 + SINE/Alu_AluJr chr1 9709726 9710021 + SINE/Alu_AluSz chr1 9710632 9710721 + Simple_repeat_(CAGA)n chr1 9710779 9711084 - SINE/Alu_AluSp chr1 9711093 9711394 - SINE/Alu_AluSz6 chr1 9711581 9711797 - SINE/Alu_AluJb chr1 9711894 9712072 - SINE/Alu_AluYc chr1 9712122 9712200 - LINE/L1_L1MC5a chr1 9712270 9712580 - SINE/Alu_AluSz chr1 9712754 9712901 + SINE/Alu_AluJo chr1 9712902 9713193 + SINE/Alu_AluSq2 chr1 9713194 9713371 + SINE/Alu_AluJo chr1 9713597 9713770 - SINE/Alu_AluJb chr1 9713771 9713789 + Simple_repeat_(TAT)n chr1 9713838 9714151 - SINE/Alu_AluJr chr1 9714286 9714408 - DNA/hAT-Charlie_MER58A chr1 9714822 9714886 + DNA/hAT-Charlie_MER102a chr1 9714890 9715190 + SINE/Alu_AluSx chr1 9718362 9718492 - SINE/MIR_MIR3 chr1 9719316 9719347 + Simple_repeat_(GCTGG)n chr1 9719379 9719674 + SINE/Alu_AluSx3 chr1 9720373 9720521 + DNA/hAT-Charlie_MER20 chr1 9720666 9720689 + Simple_repeat_(GTGGG)n chr1 9720973 9721112 + Simple_repeat_(ACCCTG)n chr1 9723470 9723950 - LTR/ERVL-MaLR_MLT1H chr1 9725272 9725584 + SINE/Alu_AluSp chr1 9725597 9725904 + SINE/Alu_AluSg chr1 9725928 9726224 + SINE/Alu_AluSx chr1 9726227 9726513 + SINE/Alu_AluSc8 chr1 9727380 9727416 + Simple_repeat_(GCCTGGC)n chr1 9727807 9728104 - SINE/Alu_AluSx chr1 9728327 9728429 + DNA/hAT-Charlie_MER5A chr1 9729849 9729877 + Simple_repeat_(GGGGC)n chr1 9730573 9730604 + Simple_repeat_(CCT)n chr1 9730649 9730678 + Simple_repeat_(TTCCTC)n chr1 9731555 9731646 + SINE/MIR_MIRc chr1 9732074 9732115 + Simple_repeat_(AAAAAC)n chr1 9732183 9732390 - SINE/Alu_AluJb chr1 9732408 9732461 + DNA/hAT-Charlie_MER5C1 chr1 9732909 9733041 + SINE/Alu_FLAM_C chr1 9734293 9734589 + SINE/Alu_AluSx1 chr1 9735698 9735744 - SINE/tRNA_LFSINE_Vert chr1 9736793 9737093 + SINE/Alu_AluSx chr1 9738653 9738951 - SINE/Alu_AluSz chr1 9739079 9739554 + DNA/hAT-Charlie_Charlie4a chr1 9739563 9739692 + SINE/Alu_AluJo chr1 9739753 9739813 - SINE/Alu_FRAM chr1 9739815 9740115 - SINE/Alu_AluYk3 chr1 9740116 9740359 - SINE/Alu_AluSz chr1 9740389 9740538 + SINE/MIR_MIRb chr1 9741630 9741911 + SINE/Alu_AluSz6 chr1 9742082 9742491 + LINE/L1_L1MC5 chr1 9742710 9742928 + SINE/Alu_AluSx chr1 9743199 9743286 - DNA/hAT-Charlie_MER119 chr1 9743287 9743461 + SINE/Alu_AluSx4 chr1 9743550 9743856 - SINE/Alu_AluJo chr1 9744752 9745051 - SINE/Alu_AluSg chr1 9745143 9745252 + SINE/Alu_AluYc chr1 9745375 9745679 + SINE/Alu_AluJb chr1 9745725 9745762 - LINE/L1_L1ME4a chr1 9745763 9746067 - SINE/Alu_AluJb chr1 9746068 9746283 - LINE/L1_L1ME4a chr1 9746383 9746434 + SINE/MIR_MIRb chr1 9746435 9746727 + SINE/Alu_AluSx1 chr1 9746728 9746897 + SINE/MIR_MIRb chr1 9746898 9747203 + SINE/Alu_AluSz chr1 9747204 9747244 + SINE/MIR_MIRb chr1 9747493 9747739 - SINE/Alu_AluSx1 chr1 9747764 9748061 + SINE/Alu_AluY chr1 9748486 9748789 - SINE/Alu_AluSx chr1 9748913 9749203 - SINE/Alu_AluJb chr1 9750434 9750601 - SINE/Alu_AluSx3 chr1 9750607 9750735 + SINE/Alu_AluJr4 chr1 9750769 9751095 + SINE/Alu_AluSx1 chr1 9752584 9752891 + SINE/Alu_AluSx chr1 9752897 9752976 - SINE/MIR_MIRc chr1 9753032 9753372 - DNA/hAT-Charlie_MER1B chr1 9753494 9753806 - SINE/Alu_AluSp chr1 9753809 9754106 - SINE/Alu_AluJr chr1 9754256 9754279 - LINE/L2_L2d2 chr1 9754280 9754576 + SINE/Alu_AluSq4 chr1 9754577 9754884 + SINE/Alu_AluSp chr1 9754885 9754987 - LINE/L2_L2d2 chr1 9756785 9757076 - SINE/Alu_AluYk3 chr1 9757245 9757540 - SINE/Alu_AluY chr1 9757851 9757985 - LINE/L1_L1ME3F chr1 9757986 9758292 - SINE/Alu_AluSp chr1 9758293 9758315 - LINE/L1_L1ME3F chr1 9758316 9758613 - SINE/Alu_AluSx1 chr1 9758614 9758710 - LINE/L1_L1ME3F chr1 9758711 9758795 + SINE/MIR_MIR chr1 9758914 9759130 - DNA/hAT-Charlie_MER3 chr1 9759140 9759270 + DNA/hAT-Charlie_MER58B chr1 9759271 9759581 - SINE/Alu_AluY chr1 9759582 9759774 + DNA/hAT-Charlie_MER58B chr1 9760079 9760142 - SINE/Alu_AluJb chr1 9760601 9760828 - SINE/Alu_AluSz chr1 9760935 9761058 + LINE/L2_L2 chr1 9761214 9761505 + SINE/Alu_AluJb chr1 9761806 9761936 - LTR/ERVL-MaLR_MLT1N2 chr1 9762180 9762482 + SINE/Alu_AluSx1 chr1 9762961 9763261 - SINE/Alu_AluSz chr1 9763339 9763486 - DNA/hAT-Charlie_MER5A chr1 9763541 9763648 - DNA/hAT-Charlie_MER5A chr1 9763562 9763670 + DNA/hAT-Charlie_MER5A chr1 9763901 9764106 - LTR/ERVL-MaLR_MSTB1 chr1 9764107 9764142 + Simple_repeat_(AG)n chr1 9764143 9764358 - LTR/ERVL-MaLR_MSTB1 chr1 9764359 9764664 + SINE/Alu_AluSz chr1 9764665 9764709 - LTR/ERVL-MaLR_MSTB1 chr1 9764792 9764892 + LINE/L2_L2d2 chr1 9765109 9765399 + SINE/Alu_AluSp chr1 9765402 9765539 + SINE/Alu_AluSx4 chr1 9765576 9765594 + Simple_repeat_(A)n chr1 9765599 9765886 + SINE/Alu_AluSx1 chr1 9766377 9766692 + SINE/Alu_AluSz6 chr1 9766978 9767085 + SINE/MIR_MIRb chr1 9767085 9767181 - LINE/L2_L2a chr1 9767192 9767260 + SINE/MIR_MIRb chr1 9767261 9767560 + SINE/Alu_AluSx3 chr1 9767561 9767645 + SINE/MIR_MIRb chr1 9767917 9768151 - SINE/MIR_MIR chr1 9768885 9769190 + Low_complexity_GA-rich chr1 9769275 9769760 + LINE/L1_L1MB7 chr1 9769761 9770044 + SINE/Alu_AluYc chr1 9770045 9770082 + LINE/L1_L1MB7 chr1 9770339 9770376 + Low_complexity_A-rich chr1 9770495 9770711 + SINE/MIR_MIR chr1 9770712 9771022 + SINE/Alu_AluSx1 chr1 9771228 9771333 + SINE/Alu_AluSx chr1 9771334 9771626 + SINE/Alu_AluSg4 chr1 9771979 9772140 - SINE/Alu_AluY chr1 9772141 9772166 + Simple_repeat_(T)n chr1 9772167 9772281 - SINE/Alu_AluY chr1 9772305 9772469 - SINE/Alu_FRAM chr1 9772883 9772953 + DNA/hAT-Charlie_MER5A1 chr1 9773746 9774041 - SINE/Alu_AluJb chr1 9774051 9774114 - SINE/Alu_AluYa8 chr1 9774073 9774143 + Retroposon/SVA_SVA_C chr1 9774286 9774581 + SINE/Alu_AluSx chr1 9774586 9774665 + SINE/MIR_MIRb chr1 9774666 9775292 - LTR/ERV1_MER41B chr1 9775293 9775376 + SINE/MIR_MIRb chr1 9775977 9776266 - SINE/Alu_AluSx3 chr1 9776310 9776369 + LINE/L2_L2 chr1 9776374 9776531 + LINE/L1_L1MB2 chr1 9776664 9776788 - DNA/hAT-Charlie_Charlie1b chr1 9776789 9776906 + Simple_repeat_(TCTA)n chr1 9776907 9776937 - DNA/hAT-Charlie_Charlie1b chr1 9776938 9777237 + SINE/Alu_AluSz chr1 9777240 9777552 + SINE/Alu_AluSz chr1 9777553 9777889 - DNA/hAT-Charlie_Charlie1b chr1 9778055 9778212 + SINE/MIR_MIR chr1 9778224 9778269 + Simple_repeat_(AC)n chr1 9778292 9778365 + LINE/L2_L2a chr1 9778733 9779053 + SINE/Alu_AluSq10 chr1 9779291 9779598 + SINE/Alu_AluSq2 chr1 9779599 9779725 - SINE/Alu_AluJo chr1 9779852 9780154 - SINE/Alu_AluSx chr1 9780902 9781060 - DNA/TcMar-Tigger_Tigger4 chr1 9781441 9781741 - SINE/Alu_AluSp chr1 9782725 9783017 + SINE/Alu_AluSx chr1 9783176 9783268 + DNA/TcMar-Tigger_MER47C chr1 9783298 9783437 + SINE/Alu_AluJo chr1 9783438 9783708 + SINE/Alu_AluSx chr1 9783711 9784022 + SINE/Alu_AluSq2 chr1 9784026 9784358 + SINE/Alu_AluSx chr1 9784359 9784518 + SINE/Alu_AluJo chr1 9784994 9785306 - SINE/Alu_AluSz chr1 9785477 9785687 - DNA/hAT-Charlie_MER20 chr1 9785792 9785810 + Simple_repeat_(T)n chr1 9785894 9786201 + SINE/Alu_AluSz6 chr1 9786360 9786677 + SINE/Alu_AluSq2 chr1 9787237 9787312 - LINE/L2_L2a chr1 9787403 9787688 - SINE/Alu_AluY chr1 9787719 9787972 - LINE/L1_L1MB3 chr1 9787973 9788275 + SINE/Alu_AluJb chr1 9788276 9788288 - LINE/L1_L1MB3 chr1 9788289 9788576 + SINE/Alu_AluSx chr1 9788586 9788893 + SINE/Alu_AluYj4 chr1 9788894 9789385 - LINE/L1_L1MB3 chr1 9789413 9789697 - LINE/L1_L1MC5 chr1 9789698 9790004 + SINE/Alu_AluJb chr1 9790005 9790092 - LINE/L1_L1MC5 chr1 9790242 9790542 - LINE/L1_L1MC chr1 9790852 9791162 + SINE/Alu_AluY chr1 9791318 9791533 - SINE/Alu_AluJo chr1 9791541 9791833 - SINE/Alu_AluSx1 chr1 9791867 9792217 + SINE/Alu_AluSx1 chr1 9793016 9793312 - SINE/Alu_AluSx chr1 9793886 9794085 - DNA/hAT-Tip100_MamTip2b chr1 9794321 9794608 - SINE/Alu_AluSz chr1 9795022 9795042 + Simple_repeat_(A)n chr1 9795168 9795465 - SINE/Alu_AluSp chr1 9795490 9795613 + LINE/CR1_L3 chr1 9795672 9795977 + SINE/Alu_AluJr chr1 9795983 9796289 + SINE/Alu_AluSx1 chr1 9796310 9796394 + LINE/CR1_L3 chr1 9796395 9796704 + SINE/Alu_AluY chr1 9796705 9797086 + LINE/CR1_L3 chr1 9797208 9797297 + SINE/Alu_FLAM_C chr1 9797327 9797352 + LINE/CR1_L3 chr1 9797353 9797657 + SINE/Alu_AluJr chr1 9797658 9797754 + LINE/CR1_L3 chr1 9797784 9797859 + LINE/L1_L1MB5 chr1 9797860 9798105 + SINE/Alu_AluSz chr1 9798201 9798726 + LINE/L1_L1MB5 chr1 9798736 9798896 + LINE/L1_L1MCa chr1 9798897 9799194 + SINE/Alu_AluJr chr1 9799195 9799359 + LINE/L1_L1MCa chr1 9799361 9799660 + SINE/Alu_AluY chr1 9799662 9799963 + SINE/Alu_AluSc chr1 9799971 9800437 + LINE/L1_L1MCa chr1 9800438 9800759 + SINE/Alu_AluY chr1 9800772 9800962 + SINE/Alu_AluSx3 chr1 9800965 9801265 + SINE/Alu_AluSp chr1 9801266 9801559 + LINE/L1_L1MCa chr1 9801550 9801855 - SINE/Alu_AluSp chr1 9801856 9801961 + LINE/L1_L1MCa chr1 9801989 9802201 - SINE/MIR_MIR chr1 9802220 9802414 + LINE/CR1_L3 chr1 9802722 9802792 + SINE/MIR_MIR3 chr1 9802814 9803121 - SINE/Alu_AluJr chr1 9803351 9803520 - DNA/hAT-Charlie_Chap1a_Mam chr1 9803521 9803819 + SINE/Alu_AluSz chr1 9803820 9804033 - DNA/hAT-Charlie_Chap1a_Mam chr1 9804081 9804381 + SINE/Alu_AluY chr1 9804488 9804565 - DNA/hAT-Charlie_Chap1a_Mam chr1 9804566 9804842 + SINE/Alu_AluJb chr1 9804843 9805141 + SINE/Alu_AluSg chr1 9805142 9805306 - DNA/hAT-Charlie_Chap1a_Mam chr1 9805386 9805584 + DNA/hAT-Charlie_Charlie1a chr1 9805585 9805886 + SINE/Alu_AluSx chr1 9805887 9806007 + DNA/hAT-Charlie_Charlie1a chr1 9806008 9806322 + SINE/Alu_AluSq chr1 9806323 9806398 + DNA/hAT-Charlie_Charlie1a chr1 9806598 9806908 + SINE/Alu_AluY chr1 9807028 9807251 - DNA/hAT-Charlie_MER103C chr1 9808072 9808303 - DNA/TcMar-Tigger_MER8 chr1 9808359 9808483 + DNA/TcMar-Tigger_Tigger16a chr1 9809499 9809628 + SINE/Alu_AluSz chr1 9809629 9809916 + SINE/Alu_AluSg4 chr1 9809917 9810063 + SINE/Alu_AluSz chr1 9810064 9810191 + Low_complexity_GA-rich chr1 9810194 9810494 + SINE/Alu_AluSx chr1 9810505 9810777 + SINE/Alu_AluSx chr1 9810778 9810816 + Simple_repeat_(AAAT)n chr1 9811732 9811748 + Simple_repeat_(A)n chr1 9811856 9812157 + SINE/Alu_AluSc8 chr1 9812492 9812564 + DNA/hAT-Tip100_MER45A chr1 9812565 9812873 + SINE/Alu_AluSp chr1 9812883 9813181 + SINE/Alu_AluSx1 chr1 9813182 9813215 + DNA/hAT-Tip100_MER45A chr1 9813216 9813528 + SINE/Alu_AluSx chr1 9813529 9813608 + DNA/hAT-Tip100_MER45A chr1 9813818 9814122 + SINE/Alu_AluYk2 chr1 9814128 9814389 + SINE/Alu_AluJb chr1 9814629 9814918 + SINE/Alu_AluJr chr1 9815248 9815409 + LINE/CR1_L3 chr1 9815590 9815654 + LINE/L1_L1MB7 chr1 9815655 9815944 + SINE/Alu_AluSx1 chr1 9815945 9816541 + LINE/L1_L1MB7 chr1 9816578 9816643 - SINE/Alu_Alu chr1 9816653 9816946 - SINE/Alu_AluSp chr1 9817085 9817202 + SINE/Alu_AluJo chr1 9817330 9817627 - SINE/Alu_AluSp chr1 9818004 9818309 - SINE/Alu_AluJb chr1 9818318 9818626 - SINE/Alu_AluSc chr1 9818778 9819093 - SINE/Alu_AluY chr1 9819915 9819979 - LINE/L2_L2 chr1 9820042 9820173 - SINE/Alu_AluJo chr1 9820238 9820544 + SINE/Alu_AluSz chr1 9820663 9820926 - LINE/L2_L2a chr1 9821142 9821431 - SINE/Alu_AluSx1 chr1 9823796 9823840 + Low_complexity_G-rich chr1 9823863 9823881 + Simple_repeat_(GCC)n chr1 9824910 9825024 - SINE/MIR_MIRb chr1 9825152 9825248 - LINE/L2_L2d2 chr1 9825938 9826156 + SINE/MIR_MIRb chr1 9827101 9827145 - SINE/MIR_MIR chr1 9827146 9827447 + SINE/Alu_AluSx1 chr1 9827448 9827560 - SINE/MIR_MIR chr1 9827676 9827816 + SINE/MIR_MIRb chr1 9828239 9828301 - LINE/L1_L1MC5 chr1 9828302 9828446 + SINE/Alu_AluSx1 chr1 9828447 9828714 + SINE/Alu_AluSx1 chr1 9828715 9828858 + SINE/Alu_AluSx1 chr1 9828859 9828874 - LINE/L1_L1MC5 chr1 9828875 9829166 + SINE/Alu_AluY chr1 9829167 9829462 - LINE/L1_L1MC5 chr1 9829481 9829499 + Simple_repeat_(A)n chr1 9829500 9829651 - SINE/Alu_AluJr chr1 9829655 9829797 - LINE/L1_L1MC5 chr1 9829798 9830104 - SINE/Alu_AluSg chr1 9830105 9830134 - LINE/L1_L1MC5 chr1 9830137 9830284 + SINE/MIR_MIRb chr1 9830406 9830602 + SINE/Alu_AluSc5 chr1 9830606 9830894 - SINE/Alu_AluSp chr1 9831423 9831507 + SINE/MIR_MIRc chr1 9831572 9831688 + SINE/MIR_MIRb chr1 9831689 9831967 + SINE/Alu_AluSq2 chr1 9831968 9832088 + SINE/MIR_MIRb chr1 9832145 9832458 + SINE/Alu_AluYk3 chr1 9832464 9832776 + SINE/Alu_AluSp chr1 9832792 9833099 + SINE/Alu_AluSx1 chr1 9833157 9833318 - SINE/MIR_MIRc chr1 9833319 9833609 + SINE/Alu_AluJr chr1 9833610 9833677 - SINE/MIR_MIRc chr1 9833971 9834260 - SINE/Alu_AluSx1 chr1 9834272 9834419 - SINE/MIR_MIRb chr1 9834753 9834875 + SINE/MIR_MIRb chr1 9834876 9835180 + SINE/Alu_AluSz chr1 9835181 9835257 + SINE/MIR_MIRb chr1 9837192 9837370 + LTR/ERVL-MaLR_MLT1B chr1 9837371 9837672 + SINE/Alu_AluYk3 chr1 9837673 9837763 + LTR/ERVL-MaLR_MLT1B chr1 9837823 9838044 + Low_complexity_GA-rich chr1 9838052 9838356 + SINE/Alu_AluSg4 chr1 9838375 9838687 + SINE/Alu_AluY chr1 9838713 9838941 - SINE/Alu_AluJo chr1 9838944 9839057 + LTR/ERVL-MaLR_MLT1B chr1 9839413 9839509 - SINE/MIR_MIRc chr1 9839510 9839806 - SINE/Alu_AluJo chr1 9839807 9839927 - SINE/MIR_MIRc chr1 9839986 9840081 + SINE/Alu_AluSx chr1 9840653 9840852 - LINE/L2_L2a chr1 9840853 9841119 + SINE/Alu_AluSz chr1 9841120 9841365 - LINE/L2_L2a chr1 9841615 9841740 - LTR/ERVL-MaLR_MLT1I chr1 9841753 9841892 - LTR/ERVL-MaLR_MLT1I chr1 9844036 9844191 - DNA/hAT-Tip100_Zaphod3 chr1 9844201 9844356 - SINE/MIR_MIR chr1 9844531 9844558 + Simple_repeat_(TCC)n chr1 9845113 9845339 - SINE/MIR_MIR chr1 9845619 9845836 + SINE/Alu_AluSq2 chr1 9846466 9846511 + Simple_repeat_(AG)n chr1 9847349 9847573 + LINE/CR1_L3 chr1 9850576 9850627 + Low_complexity_G-rich chr1 9851007 9851068 - SINE/MIR_MIRc chr1 9851343 9851373 + Simple_repeat_(TTCTTT)n chr1 9851375 9851670 - SINE/Alu_AluSx3 chr1 9853809 9854092 + LINE/L1_L1M4 chr1 9854093 9854386 + SINE/Alu_AluSp chr1 9854387 9854682 + LINE/L1_L1M4 chr1 9854683 9854976 - SINE/Alu_AluSz chr1 9854977 9855725 + LINE/L1_L1M4 chr1 9855726 9855907 - SINE/Alu_AluJr chr1 9855908 9856177 + SINE/Alu_AluSz chr1 9856178 9856309 - SINE/Alu_AluJr chr1 9856310 9856510 + LINE/L1_L1M4 chr1 9856511 9856808 - SINE/Alu_AluSx1 chr1 9856809 9856841 + LINE/L1_L1M4 chr1 9856842 9857122 + SINE/Alu_AluSx1 chr1 9857123 9857151 + Simple_repeat_(AAAAC)n chr1 9857152 9857167 + SINE/Alu_AluSx1 chr1 9857168 9857191 + LINE/L1_L1M4 chr1 9857199 9857507 + SINE/Alu_AluYk3 chr1 9857508 9857829 + SINE/Alu_AluSx chr1 9857832 9857963 + LINE/L1_L1M4 chr1 9857964 9858781 + LINE/L2_L2b chr1 9858770 9858891 - SINE/MIR_MIRb chr1 9859093 9859173 + Simple_repeat_(CCCTGCC)n chr1 9860309 9860645 + SINE/Alu_AluSx chr1 9861780 9861918 + SINE/MIR_MIRc chr1 9862266 9862398 - SINE/Alu_FLAM_C chr1 9862399 9862525 - DNA/hAT-Charlie_MER5A chr1 9862526 9862547 + DNA/hAT-Charlie_MER5A1 chr1 9863690 9863785 - LINE/L2_L2b chr1 9864336 9864627 - SINE/Alu_AluY chr1 9864947 9865244 + SINE/Alu_AluSx chr1 9865319 9865633 + SINE/Alu_AluY chr1 9866327 9866411 - LINE/L2_L2b chr1 9866412 9866695 + SINE/Alu_AluSx1 chr1 9866696 9866959 - LINE/L2_L2b chr1 9867980 9868016 + Simple_repeat_(TGT)n chr1 9868625 9868934 - SINE/Alu_AluSx1 chr1 9868986 9869299 - SINE/Alu_AluJo chr1 9869364 9869388 + Simple_repeat_(GTTTT)n chr1 9869389 9869438 + Retroposon/SVA_SVA_A chr1 9869439 9869624 - SINE/Alu_AluSq10 chr1 9870469 9870568 + SINE/MIR_MIR chr1 9873656 9873743 + LINE/L2_L2b chr1 9873744 9874001 - SINE/Alu_AluJr chr1 9874002 9874285 + LINE/L2_L2b chr1 9874288 9874551 - SINE/Alu_AluJr chr1 9875274 9875347 + LINE/L2_L2b chr1 9876335 9876646 + SINE/Alu_AluSx1 chr1 9876846 9876890 + Simple_repeat_(AC)n chr1 9878917 9879207 + SINE/Alu_AluSx3 chr1 9879965 9880668 - LINE/L1_L1PA8 chr1 9880690 9880735 - SINE/MIR_MIRc chr1 9880789 9880934 + SINE/MIR_MIRc chr1 9880935 9881034 + DNA/hAT-Tip100_MER45A chr1 9881035 9881323 - SINE/Alu_AluJr chr1 9881330 9881355 + Simple_repeat_(T)n chr1 9881360 9881638 - SINE/Alu_AluSq2 chr1 9881639 9881710 + DNA/hAT-Tip100_MER45A chr1 9881711 9881771 + SINE/MIR_MIRc chr1 9882147 9882221 - LINE/L2_L2d2 chr1 9882237 9882380 - LINE/L2_L2c chr1 9882627 9882677 + Low_complexity_GA-rich chr1 9883968 9884052 + Low_complexity_GA-rich chr1 9884963 9885329 - LINE/L2_L2c chr1 9885307 9885437 + SINE/MIR_MIR chr1 9885449 9885661 + SINE/Alu_AluJb chr1 9885662 9885952 + SINE/Alu_AluSp chr1 9885953 9886011 + SINE/MIR_MIR chr1 9886270 9886459 - DNA/hAT-Charlie_MER20 chr1 9886489 9886686 - SINE/MIR_MIR1_Amn chr1 9887216 9887377 - SINE/MIR_MIRb chr1 9887435 9887726 + SINE/Alu_AluSc chr1 9887835 9888122 - SINE/Alu_AluSx1 chr1 9888151 9888276 + SINE/Alu_AluJb chr1 9888300 9888571 + SINE/Alu_AluSx chr1 9889266 9889431 + DNA/hAT-Charlie_MER20 chr1 9889456 9889714 + LINE/L2_L2b chr1 9889715 9889826 - DNA/hAT-Tip100_ORSL-2a chr1 9891664 9891781 + SINE/Alu_AluJr chr1 9891782 9892104 - SINE/Alu_AluSx chr1 9892105 9892142 + SINE/Alu_AluJr chr1 9892143 9892441 + SINE/Alu_AluY chr1 9892442 9892611 + SINE/Alu_AluJr chr1 9894072 9894347 - LINE/L1_L1MC4 chr1 9894348 9894691 - SINE/Alu_AluJb chr1 9894692 9894743 - SINE/Alu_AluJb chr1 9894744 9894905 - SINE/Alu_AluSz6 chr1 9894906 9895206 - SINE/Alu_AluY chr1 9895209 9895497 - SINE/Alu_AluSx1 chr1 9895501 9895796 - SINE/Alu_AluSx3 chr1 9895797 9895927 - SINE/Alu_AluSz6 chr1 9895928 9896315 - LINE/L1_L1MC4 chr1 9896418 9896552 + SINE/Alu_AluSx chr1 9896553 9896686 + SINE/Alu_AluSg chr1 9896687 9896992 + SINE/Alu_AluSx4 chr1 9896993 9897169 + SINE/Alu_AluSg chr1 9897173 9897482 + SINE/Alu_AluYm1 chr1 9897483 9897664 + SINE/Alu_AluSx chr1 9897668 9897832 + SINE/Alu_AluSg4 chr1 9897839 9898146 + SINE/Alu_AluSx4 chr1 9898244 9898548 + SINE/Alu_AluSc5 chr1 9898681 9898887 - DNA/TcMar-Tigger_MER2 chr1 9898987 9899068 - LINE/L1_L1PB1 chr1 9899069 9899134 - DNA/TcMar-Tigger_MER2 chr1 9899174 9899478 + SINE/Alu_AluSx chr1 9899482 9899763 + SINE/Alu_AluSc chr1 9899764 9899790 + Simple_repeat_(AAAG)n chr1 9899792 9900088 + SINE/Alu_AluSx chr1 9900122 9900381 + SINE/Alu_AluSc8 chr1 9902732 9902761 + LINE/L2_L2a chr1 9902762 9903063 - SINE/Alu_AluSc8 chr1 9903064 9903167 + LINE/L2_L2a chr1 9903177 9903201 + Simple_repeat_(GAGG)n chr1 9903633 9903766 - SINE/MIR_MIRc chr1 9903868 9904064 + SINE/MIR_MIR chr1 9905203 9905426 + LINE/L2_L2a chr1 9905427 9905729 - SINE/Alu_AluY chr1 9905730 9905904 + LINE/L2_L2a chr1 9905887 9906064 - LINE/L2_L2a chr1 9906027 9906272 - SINE/MIR_MIR chr1 9906276 9906581 + SINE/Alu_AluSp chr1 9906828 9906940 - LINE/L2_L2d2 chr1 9907050 9907115 + SINE/MIR_MIR3 chr1 9907169 9907471 - SINE/Alu_AluSx1 chr1 9907782 9908048 + LINE/L2_L2a chr1 9908078 9908356 - SINE/Alu_AluSg chr1 9908368 9908661 - SINE/Alu_AluYc3 chr1 9908695 9908886 + LINE/L2_L2a chr1 9910454 9910482 + Simple_repeat_(GGGCG)n chr1 9910656 9910693 + Simple_repeat_(GGCGGA)n chr1 9911435 9911722 - SINE/Alu_AluSz chr1 9911755 9912057 + SINE/Alu_AluSp chr1 9912063 9912649 + LINE/L2_L2c chr1 9912672 9912735 + LINE/L2_L2c chr1 9912973 9913267 - SINE/Alu_AluSx1 chr1 9913330 9913640 - SINE/Alu_AluSz chr1 9913744 9913907 - SINE/Alu_AluJr4 chr1 9913908 9914206 - SINE/Alu_AluSx1 chr1 9914207 9914325 - SINE/Alu_AluJr4 chr1 9915175 9915316 - LINE/RTE-BovB_MamRTE1 chr1 9915367 9915666 - SINE/Alu_AluSx3 chr1 9915695 9916003 + SINE/Alu_AluSx chr1 9916024 9916052 + Low_complexity_A-rich chr1 9916055 9916356 + SINE/Alu_AluSg chr1 9916413 9916429 + Simple_repeat_(T)n chr1 9916852 9916915 + Low_complexity_A-rich chr1 9917040 9917066 + Simple_repeat_(A)n chr1 9917298 9917608 - SINE/Alu_AluSx1 chr1 9917651 9917816 - SINE/Alu_AluJb chr1 9918178 9918781 - LINE/L2_L2c chr1 9918785 9918847 - LTR/ERVL-MaLR_MLT1H chr1 9918855 9918884 + Simple_repeat_(T)n chr1 9919075 9919357 + SINE/Alu_AluY chr1 9919358 9919392 + Simple_repeat_(AAAC)n chr1 9919564 9919725 - SINE/Alu_AluSg4 chr1 9919726 9919918 - SINE/Alu_AluSx chr1 9919919 9920220 - SINE/Alu_AluSx chr1 9920221 9920335 - SINE/Alu_AluSz6 chr1 9920735 9921060 - SINE/Alu_AluJb chr1 9921205 9921414 - SINE/Alu_AluSc8 chr1 9921415 9921690 - SINE/Alu_AluSc chr1 9921691 9921805 - SINE/Alu_AluSc8 chr1 9921806 9922107 - SINE/Alu_AluSx chr1 9923219 9923509 - SINE/Alu_AluSx1 chr1 9923519 9923625 - SINE/Alu_FLAM_A chr1 9923765 9924050 - SINE/Alu_AluSx chr1 9924055 9924185 - SINE/Alu_AluSx chr1 9924344 9924653 - SINE/Alu_AluSc8 chr1 9924654 9924706 - SINE/Alu_AluJb chr1 9925032 9925341 + SINE/Alu_AluSp chr1 9925600 9925879 - SINE/Alu_AluSg chr1 9925887 9926184 - SINE/Alu_AluY chr1 9926576 9926604 + Low_complexity_A-rich chr1 9926996 9927168 - SINE/MIR_MIRb chr1 9927313 9927599 - SINE/Alu_AluSx chr1 9927679 9927971 - SINE/Alu_AluSx3 chr1 9928030 9928321 + SINE/Alu_AluSp chr1 9928324 9928347 + Simple_repeat_(ACA)n chr1 9928348 9928596 + LINE/L1_L1MB5 chr1 9928597 9928893 + SINE/Alu_AluSq2 chr1 9928894 9929199 + LINE/L1_L1MB5 chr1 9929833 9929977 + SINE/MIR_MIR3 chr1 9930714 9931018 - SINE/Alu_AluSq2 chr1 9931086 9931249 - SINE/Alu_AluJr chr1 9931250 9931561 - SINE/Alu_AluY chr1 9931562 9931640 - SINE/Alu_AluJr chr1 9932053 9932361 + SINE/Alu_AluSq chr1 9932374 9932423 + SINE/Alu_FLAM_C chr1 9932424 9932708 + SINE/Alu_AluSq2 chr1 9932970 9933284 + SINE/Alu_AluYk3 chr1 9933285 9933314 + Simple_repeat_(TA)n chr1 9933472 9933623 - SINE/MIR_MIR chr1 9933624 9933933 + SINE/Alu_AluSz chr1 9933934 9933944 - SINE/MIR_MIR chr1 9933945 9934251 + SINE/Alu_AluSz chr1 9934252 9934332 - SINE/MIR_MIR chr1 9935048 9935142 + SINE/Alu_FLAM_C chr1 9935148 9935457 + SINE/Alu_AluSx chr1 9935849 9936144 + SINE/Alu_AluSx chr1 9936281 9936372 - SINE/MIR_MIRb chr1 9936686 9936820 - SINE/Alu_AluJr chr1 9936849 9936981 + SINE/Alu_AluYm1 chr1 9936982 9937112 + SINE/Alu_AluSx chr1 9937113 9937415 + SINE/Alu_AluY chr1 9937416 9937557 + SINE/Alu_AluSx chr1 9937562 9937871 + SINE/Alu_AluJr chr1 9938178 9938459 - SINE/Alu_AluJb chr1 9938565 9938657 - SINE/Alu_AluJb chr1 9939084 9939360 - SINE/Alu_AluSz chr1 9939361 9939651 - SINE/Alu_AluSx3 chr1 9939678 9939831 - LINE/L1_L1M5 chr1 9939837 9940145 + SINE/Alu_AluSz chr1 9940180 9940478 - SINE/Alu_AluY chr1 9940482 9940616 - SINE/Alu_AluY chr1 9940970 9941130 + LINE/L2_L2d chr1 9941190 9941494 - SINE/Alu_AluSx1 chr1 9941501 9941796 - SINE/Alu_AluSc chr1 9941914 9942215 - SINE/Alu_AluSx chr1 9942224 9942374 + LINE/L2_L2d chr1 9943973 9944273 + SINE/Alu_AluSx3 chr1 9944289 9944408 + SINE/Alu_FLAM_C chr1 9944547 9944670 - SINE/Alu_FRAM chr1 9944952 9945261 + SINE/Alu_AluSz chr1 9945420 9945700 + SINE/Alu_AluSx3 chr1 9945762 9946053 - SINE/Alu_AluJb chr1 9946678 9947000 + LTR/ERVL-MaLR_MLT1B chr1 9947001 9947176 + LINE/L2_L2 chr1 9947184 9947318 - SINE/Alu_FRAM chr1 9947319 9947562 + RNA_7SK chr1 9947553 9947597 - SINE/Alu_AluSx chr1 9947605 9947958 + LINE/L2_L2 chr1 9948293 9948577 + SINE/Alu_AluJb chr1 9948652 9948946 - SINE/Alu_AluSq2 chr1 9948988 9949291 + SINE/Alu_AluSx1 chr1 9949411 9949755 - SINE/Alu_AluSz chr1 9950091 9950389 - SINE/Alu_AluSx1 chr1 9950811 9951104 + SINE/Alu_AluSz chr1 9951456 9951774 - SINE/Alu_AluJb chr1 9952029 9952318 + SINE/Alu_AluY chr1 9952529 9952837 - SINE/Alu_AluSx chr1 9952987 9953276 - SINE/Alu_AluSc8 chr1 9953279 9953443 - SINE/Alu_AluSc chr1 9953444 9953740 - SINE/Alu_AluSx chr1 9953741 9953755 - SINE/Alu_AluSc chr1 9953798 9954105 - SINE/Alu_AluSx chr1 9954106 9954223 - DNA/TcMar-Tc2_Kanga1d chr1 9954224 9954508 - SINE/Alu_AluJr chr1 9954509 9954576 - DNA/TcMar-Tc2_Kanga1d chr1 9954651 9954943 + SINE/Alu_AluSc chr1 9955003 9955121 + SINE/Alu_AluSp chr1 9955122 9955430 + SINE/Alu_AluSq2 chr1 9955431 9955627 + SINE/Alu_AluSp chr1 9955675 9955808 + LINE/L2_L2c chr1 9956162 9956421 - SINE/Alu_AluSz chr1 9956422 9956722 - SINE/Alu_AluSx chr1 9956732 9957029 - SINE/Alu_AluSg chr1 9957030 9957328 - SINE/Alu_AluSp chr1 9957339 9957590 - SINE/Alu_AluSg chr1 9958428 9958737 - SINE/Alu_AluSg chr1 9958857 9958952 - SINE/MIR_MIRb chr1 9959014 9959089 + DNA/TcMar-Mariner_MARNA chr1 9959090 9959398 + SINE/Alu_AluSq2 chr1 9959401 9959706 + SINE/Alu_AluSg chr1 9959707 9959997 + DNA/TcMar-Mariner_MARNA chr1 9960136 9960313 + SINE/Alu_AluJr chr1 9960380 9960506 + SINE/Alu_AluJr chr1 9960557 9960626 - srpRNA_7SLRNA chr1 9961783 9962065 - SINE/Alu_AluSp chr1 9962202 9962496 + SINE/Alu_AluY chr1 9962501 9962675 - SINE/MIR_MIRb chr1 9962678 9962980 - SINE/Alu_AluY chr1 9963030 9963067 + LINE/L2_L2 chr1 9963169 9963230 + SINE/MIR_MIR3 chr1 9963409 9963706 - SINE/Alu_AluSg chr1 9963728 9963767 + LINE/L1_L1ME3G chr1 9963884 9963908 + Simple_repeat_(ATTTT)n chr1 9963909 9964191 - SINE/Alu_AluSz6 chr1 9964209 9964562 + SINE/Alu_AluJo chr1 9964667 9964949 + SINE/Alu_AluSc8 chr1 9965037 9965334 + SINE/Alu_AluSc chr1 9965391 9965702 - SINE/Alu_AluSz chr1 9965723 9965998 + SINE/Alu_AluJb chr1 9966017 9966312 + SINE/Alu_AluSz chr1 9966314 9966330 + Simple_repeat_(TATATA)n chr1 9966331 9966622 + SINE/Alu_AluSx1 chr1 9966623 9966654 + Simple_repeat_(TATATA)n chr1 9966728 9966932 + LINE/L1_HAL1 chr1 9966942 9967216 + SINE/Alu_AluSx1 chr1 9967352 9967433 - LINE/L1_HAL1 chr1 9967427 9967539 + LINE/L1_HAL1 chr1 9967687 9967993 + SINE/Alu_AluJb chr1 9968025 9968068 - SINE/MIR_MIR chr1 9968069 9968371 - SINE/Alu_AluY chr1 9968372 9968484 - SINE/MIR_MIR chr1 9968489 9968789 + SINE/Alu_AluY chr1 9968790 9968969 + SINE/Alu_AluSx chr1 9968973 9969087 - SINE/MIR_MIR chr1 9969089 9969159 + LINE/L1_HAL1 chr1 9969232 9969508 + LINE/L1_HAL1 chr1 9969509 9969765 + SINE/Alu_AluSz chr1 9969766 9970106 + LINE/L1_HAL1 chr1 9970113 9970406 + LINE/L1_HAL1 chr1 9970413 9970707 + SINE/Alu_AluY chr1 9970732 9970807 + SINE/MIR_MIRc chr1 9971159 9971290 - LTR/ERVL-MaLR_MLT1H chr1 9971317 9971611 - SINE/Alu_AluSc chr1 9971681 9971982 + SINE/Alu_AluJr chr1 9972230 9972509 + SINE/Alu_AluSc chr1 9972677 9972956 + SINE/Alu_AluJr chr1 9972971 9973086 + LINE/L2_L2 chr1 9973105 9973399 - SINE/Alu_AluSp chr1 9973426 9973732 + SINE/Alu_AluY chr1 9973743 9973858 + SINE/Alu_AluSx chr1 9973859 9974127 - SINE/Alu_AluSz chr1 9974138 9974211 - LINE/L1_L1ME4a chr1 9974218 9974390 - SINE/Alu_AluJb chr1 9974391 9974687 - SINE/Alu_AluY chr1 9974688 9974823 - SINE/Alu_AluJb chr1 9974862 9974885 + Simple_repeat_(GATG)n chr1 9975008 9975125 + LINE/L1_L1MB3 chr1 9975219 9975518 + SINE/Alu_AluSz chr1 9975834 9976004 + LTR/ERVL_LTR83 chr1 9976005 9976305 + SINE/Alu_AluSx chr1 9976306 9976615 + LTR/ERVL_LTR83 chr1 9976616 9976888 - SINE/Alu_AluSx1 chr1 9976896 9977206 - SINE/Alu_AluSq2 chr1 9977207 9977253 + LTR/ERVL_LTR83 chr1 9977254 9977390 - SINE/Alu_FLAM_C chr1 9977391 9977483 + LTR/ERVL_LTR83 chr1 9977484 9977584 - LTR/ERVL_LTR67B chr1 9977585 9977882 + SINE/Alu_AluSx chr1 9977883 9977897 - LTR/ERVL_LTR67B chr1 9978075 9978364 + SINE/Alu_AluSx chr1 9978390 9978501 - LTR/ERVL_LTR67B chr1 9978570 9978625 + LTR/ERVL_LTR83 chr1 9978955 9979062 + SINE/MIR_MIR chr1 9979186 9979484 + SINE/Alu_AluJr chr1 9979519 9979818 + SINE/Alu_AluY chr1 9979934 9980231 - SINE/Alu_AluSp chr1 9980277 9980350 + SINE/MIR_MIRb chr1 9980356 9980642 + SINE/Alu_AluSz chr1 9980654 9980954 - SINE/Alu_AluSc8 chr1 9981241 9981532 - SINE/Alu_AluYj4 chr1 9981539 9981670 - SINE/Alu_FLAM_C chr1 9981865 9982162 - SINE/Alu_AluSp chr1 9982828 9983119 + SINE/Alu_AluSc chr1 9983183 9983372 + SINE/Alu_AluSx3 chr1 9983373 9983669 + SINE/Alu_AluSx3 chr1 9984051 9984346 - SINE/Alu_AluSx1 chr1 9985246 9985308 - SINE/MIR_MIRb chr1 9985254 9985326 - LINE/L2_L2d chr1 9985341 9985400 - SINE/MIR_MIR chr1 9985642 9985673 + Simple_repeat_(TG)n chr1 9985674 9985948 - SINE/Alu_AluSg chr1 9986210 9986384 - LINE/L1_L1MC5a chr1 9986451 9986581 + SINE/Alu_FLAM_C chr1 9986601 9986892 + SINE/Alu_AluSz6 chr1 9986917 9987219 + SINE/Alu_AluSq chr1 9987234 9987359 + SINE/Alu_AluJr chr1 9987360 9987643 + SINE/Alu_AluY chr1 9987644 9987682 + Simple_repeat_(TAAAT)n chr1 9987683 9987836 + SINE/Alu_AluJr chr1 9987990 9988294 - SINE/Alu_AluSz chr1 9988419 9988723 + SINE/Alu_AluSx1 chr1 9988755 9988956 - SINE/Alu_AluSz6 chr1 9988985 9989182 + LTR/ERV1_LTR1D1 chr1 9989183 9989467 + SINE/Alu_AluSx1 chr1 9989468 9990121 + LTR/ERV1_LTR1D1 chr1 9990146 9990582 - LTR/ERVL-MaLR_MLT1L chr1 9991186 9991469 - SINE/Alu_AluSz chr1 9991590 9991800 - LINE/L2_L2a chr1 9991902 9992072 + SINE/Alu_AluSx chr1 9992088 9992380 - SINE/Alu_AluJb chr1 9992459 9992535 + DNA/hAT-Blackjack_MER81 chr1 9992630 9992936 + SINE/Alu_AluY chr1 9992953 9993196 - LINE/L2_L2a chr1 9993197 9993502 + SINE/Alu_AluSx chr1 9993503 9993900 - LINE/L2_L2a chr1 9993913 9994089 + SINE/MIR_MIR chr1 9994102 9994130 + Simple_repeat_(T)n chr1 9994140 9994412 - SINE/Alu_AluSx chr1 9994423 9994597 - SINE/Alu_AluSq2 chr1 9994598 9994911 - SINE/Alu_AluYm1 chr1 9994912 9995045 - SINE/Alu_AluSq2 chr1 9995062 9995115 - LINE/L2_L2a chr1 9995116 9995404 + SINE/Alu_AluJo chr1 9995413 9995723 + SINE/Alu_AluSx1 chr1 9995750 9996029 + SINE/Alu_AluSx3 chr1 9996031 9996337 + SINE/Alu_AluYm1 chr1 9996355 9996378 + Simple_repeat_(A)n chr1 9996420 9996528 + LTR/ERVL_LTR67B chr1 9996497 9996571 + LTR/ERVL-MaLR_MLT1J1 chr1 9997137 9997195 + Simple_repeat_(CCCGC)n chr1 9997336 9997359 + Simple_repeat_(GGC)n chr1 9997360 9997365 + Simple_repeat_(AGGCGG)n chr1 9998054 9998290 - SINE/Alu_AluSx chr1 9998293 9998395 - LTR/ERV1_LTR51 chr1 9998396 9998706 - SINE/Alu_AluY chr1 9998707 9998779 - LTR/ERV1_LTR51 chr1 9998793 9998910 - LTR/ERV1_MER39 chr1 9998950 9999265 + LTR/ERVL-MaLR_THE1A chr1 9999266 9999571 + SINE/Alu_AluSq2 chr1 9999572 9999617 + LTR/ERVL-MaLR_THE1A chr1 9999813 9999888 - SINE/Alu_Alu chr1 9999950 10000239 + SINE/Alu_AluSx3 chr1 10000251 10000566 + SINE/Alu_AluSx chr1 10000735 10001032 - SINE/Alu_AluSg4 chr1 10001318 10001563 - SINE/Alu_AluJr chr1 10001790 10001844 + Simple_repeat_(TATT)n chr1 10001845 10002127 - SINE/Alu_AluSx chr1 10002488 10002803 - SINE/Alu_AluSz chr1 10002898 10003145 + LTR/ERV1_MER66B chr1 10003146 10003456 + SINE/Alu_AluSq2 chr1 10003457 10003703 + LTR/ERV1_MER66B chr1 10003750 10004055 - SINE/Alu_AluSx1 chr1 10004066 10004369 - SINE/Alu_AluSp chr1 10004389 10004658 - SINE/Alu_AluSp chr1 10004793 10005008 + SINE/Alu_AluJb chr1 10005150 10005194 - SINE/Alu_AluSx3 chr1 10005202 10005500 - SINE/Alu_AluSz chr1 10005569 10005861 - SINE/Alu_AluSx chr1 10005869 10006073 + LINE/L2_L2a chr1 10006226 10006524 + SINE/Alu_AluJb chr1 10006546 10006718 + SINE/Alu_AluSx chr1 10006727 10006752 + Simple_repeat_(GT)n chr1 10006765 10006784 + Simple_repeat_(AG)n chr1 10006800 10006934 - DNA/hAT-Charlie_Charlie15a chr1 10006941 10007246 - SINE/Alu_AluSx chr1 10007252 10007360 - SINE/Alu_FLAM_C chr1 10007766 10008067 + SINE/Alu_AluSx1 chr1 10008329 10008443 + SINE/Alu_AluSz chr1 10008444 10008730 - SINE/Alu_AluSc chr1 10008731 10008796 + SINE/Alu_AluSz chr1 10008934 10009075 - SINE/MIR_MIRb chr1 10009096 10009129 - LTR/ERV1_LTR24C chr1 10009130 10009424 + SINE/Alu_AluSc5 chr1 10009425 10009518 - LTR/ERV1_LTR24C chr1 10009519 10009814 - SINE/Alu_AluSz chr1 10009861 10009901 + Simple_repeat_(TTTTC)n chr1 10009925 10010076 - LINE/L1_L1ME1 chr1 10010114 10010415 - SINE/Alu_AluSx chr1 10010418 10010583 - SINE/Alu_AluSx1 chr1 10010584 10010883 - SINE/Alu_AluSp chr1 10010884 10011018 - SINE/Alu_AluSx1 chr1 10011021 10011544 - LTR/ERV1_LTR24C chr1 10011788 10011920 + SINE/Alu_AluSx1 chr1 10011921 10012233 + SINE/Alu_AluSp chr1 10012234 10012404 + SINE/Alu_AluSx1 chr1 10012511 10012641 + SINE/Alu_FLAM_A chr1 10012651 10012965 + SINE/Alu_AluSq2 chr1 10013075 10013372 - SINE/Alu_AluSx1 chr1 10013401 10013526 + SINE/Alu_AluJr chr1 10013527 10013826 + SINE/Alu_AluSq2 chr1 10013827 10013986 + SINE/Alu_AluJr chr1 10013996 10014374 + LTR/ERVL_MER21B chr1 10014375 10014680 - SINE/Alu_AluSx chr1 10014681 10015065 + LTR/ERVL_MER21B chr1 10015066 10015159 + SINE/Alu_AluSz chr1 10015160 10015465 + SINE/Alu_AluSg chr1 10015466 10015676 + SINE/Alu_AluSz chr1 10016261 10016292 + Simple_repeat_(TTTCT)n chr1 10016293 10016575 - SINE/Alu_AluSx chr1 10016592 10016818 - LTR/ERV1_HUERS-P1-int chr1 10016819 10017121 - SINE/Alu_AluSc chr1 10017122 10017200 - LTR/ERV1_HUERS-P1-int chr1 10017201 10017494 - SINE/Alu_AluY chr1 10017495 10017954 - LTR/ERV1_HUERS-P1-int chr1 10017955 10018267 + SINE/Alu_AluSc chr1 10018268 10018366 - LTR/ERVL_MER21C chr1 10018386 10018570 + LTR/ERV1_MER51A chr1 10018583 10018688 - SINE/Alu_AluSx3 chr1 10018692 10018901 + LTR/ERV1_MER41B chr1 10018908 10019015 - SINE/Alu_AluSc chr1 10019016 10019246 + LTR/ERV1_MER51A chr1 10019247 10019546 - SINE/Alu_AluY chr1 10019547 10019599 + LTR/ERV1_MER51A chr1 10019640 10019904 - LTR/ERVL_MER21C chr1 10019912 10020000 + DNA/hAT-Tip100_Arthur1 chr1 10020011 10020290 + SINE/Alu_AluJb chr1 10020324 10020425 + SINE/Alu_AluJb chr1 10020556 10020633 + DNA/hAT-Tip100_Arthur1 chr1 10020648 10020821 + SINE/Alu_AluSq2 chr1 10020961 10020991 + Low_complexity_GA-rich chr1 10021027 10021305 - SINE/Alu_AluSz chr1 10021407 10021543 - LINE/L1_L1MB7 chr1 10021592 10021860 - SINE/Alu_AluSx1 chr1 10021915 10022176 + DNA/hAT-Charlie_Charlie1b chr1 10022239 10022299 - SINE/Alu_Alu chr1 10022360 10022651 - SINE/Alu_AluSg chr1 10022873 10023092 - SINE/Alu_AluSx1 chr1 10023093 10023381 - SINE/Alu_AluSc8 chr1 10023403 10023486 + SINE/Alu_AluJr chr1 10023529 10023574 - LTR/ERVL-MaLR_MLT1A0 chr1 10023595 10023887 - SINE/Alu_AluY chr1 10023914 10024220 - SINE/Alu_AluSp chr1 10024242 10024522 + SINE/Alu_AluJr chr1 10024523 10024715 - LINE/L2_L2 chr1 10024916 10025156 - SINE/Alu_AluSx chr1 10025238 10025256 + Simple_repeat_(T)n chr1 10025367 10025553 + LINE/CR1_X17_LINE chr1 10025565 10025865 + SINE/Alu_AluJr chr1 10025910 10026239 - SINE/Alu_AluSz6 chr1 10026240 10026267 - SINE/Alu_AluSx chr1 10026268 10026569 - SINE/Alu_AluYk2 chr1 10026570 10026685 - SINE/Alu_AluSx chr1 10026686 10026751 - SINE/Alu_AluSx chr1 10026752 10026951 - SINE/Alu_AluSz chr1 10027389 10027455 + LINE/L1_L1MB7 chr1 10027456 10027625 - SINE/Alu_AluJr chr1 10027626 10027919 - SINE/Alu_AluSp chr1 10027920 10028075 - SINE/Alu_AluJr chr1 10028076 10028267 + LINE/L1_L1MB7 chr1 10028344 10028381 + Simple_repeat_(CCCCTC)n chr1 10028383 10029766 + Retroposon/SVA_SVA_D chr1 10029825 10029859 + LINE/L1_L1MB7 chr1 10029860 10030171 + SINE/Alu_AluSz chr1 10030174 10030481 + SINE/Alu_AluSc chr1 10030482 10031098 + LINE/L1_L1MB7 chr1 10031099 10031376 - SINE/Alu_AluSg7 chr1 10031377 10031530 + LINE/L1_L1MB7 chr1 10031855 10031899 + Simple_repeat_(TA)n chr1 10031900 10032187 - SINE/Alu_AluY chr1 10032188 10032199 + Simple_repeat_(TA)n chr1 10032346 10032422 + LINE/Penelope_Penelope1_Vert chr1 10033001 10033043 + Low_complexity_G-rich chr1 10034992 10035296 - SINE/Alu_AluY chr1 10035390 10035705 - SINE/Alu_AluYk3 chr1 10035714 10035760 + Simple_repeat_(TTTTTTTATT)n chr1 10035761 10036043 - SINE/Alu_AluY chr1 10036083 10036099 + Simple_repeat_(T)n chr1 10036337 10036363 + Simple_repeat_(ATTAA)n chr1 10036504 10036600 - SINE/Alu_AluJr chr1 10037036 10037324 - SINE/Alu_AluSp chr1 10037567 10037855 - SINE/Alu_AluJb chr1 10037862 10037980 - SINE/tRNA-RTE_MamSINE1 chr1 10037999 10038296 + SINE/Alu_AluSp chr1 10038391 10038610 - SINE/MIR_MIRb chr1 10038815 10039080 + SINE/Alu_AluSg chr1 10039127 10039281 - LINE/L2_L2c chr1 10039426 10039738 - SINE/Alu_AluSz chr1 10040118 10040408 - SINE/Alu_AluSx3 chr1 10040410 10040529 - SINE/Alu_AluJo chr1 10040605 10040919 - SINE/Alu_AluSp chr1 10041141 10041268 - SINE/MIR_MIRb chr1 10041333 10041634 - SINE/Alu_AluSq2 chr1 10041692 10041978 - SINE/Alu_AluSz chr1 10041980 10042098 - SINE/Alu_AluSg4 chr1 10042303 10042366 - LINE/L2_L2a chr1 10042367 10042671 + SINE/Alu_AluSg chr1 10042672 10042997 - LINE/L2_L2a chr1 10043002 10043308 - SINE/Alu_AluSx chr1 10043340 10043421 + DNA/TcMar-Tc2_Kanga1c chr1 10043422 10043727 - SINE/Alu_AluYk2 chr1 10043728 10044031 + DNA/TcMar-Tc2_Kanga1c chr1 10044032 10044354 - SINE/Alu_AluSz6 chr1 10044355 10044397 + DNA/TcMar-Tc2_Kanga1c chr1 10044398 10044608 - DNA/hAT-Charlie_MER1A chr1 10044609 10044882 - SINE/Alu_AluSx3 chr1 10044883 10045262 - DNA/hAT-Charlie_MER1A chr1 10045263 10045564 + DNA/TcMar-Tc2_Kanga1c chr1 10045566 10045665 - LINE/L2_L2a chr1 10045636 10045867 - LINE/L2_L2a chr1 10045893 10046261 - LTR/ERVL-MaLR_MLT1J chr1 10046312 10046774 - LTR/ERVL-MaLR_MLT1J-int chr1 10046764 10047136 - LTR/ERVL-MaLR_MLT1J-int chr1 10047176 10047488 - LTR/ERVL-MaLR_MLT1J chr1 10047489 10047791 - SINE/Alu_AluYm1 chr1 10047792 10047853 - LTR/ERVL-MaLR_MLT1J chr1 10047869 10048148 - SINE/Alu_AluJr chr1 10048185 10048443 - LINE/L2_L2a chr1 10048445 10048744 - LINE/L2_L2-3_Crp chr1 10048745 10048791 - LINE/L2_L2a chr1 10049182 10049449 - LINE/L2_L2b chr1 10049464 10049593 + SINE/Alu_FLAM_A chr1 10049619 10049909 + SINE/Alu_AluJb chr1 10050083 10050371 - SINE/Alu_AluSx chr1 10050521 10050681 + LINE/L2_L2b chr1 10050725 10050865 - SINE/Alu_FLAM_C chr1 10051008 10051168 - SINE/Alu_FRAM chr1 10051173 10051341 + SINE/MIR_MIRb chr1 10052070 10052371 - SINE/Alu_AluJb chr1 10052559 10052714 + DNA/hAT-Tip100_MER45A chr1 10052761 10053052 - LTR/ERVL-MaLR_MLT1A0 chr1 10053060 10053138 - SINE/Alu_AluJr chr1 10053139 10053448 - SINE/Alu_AluY chr1 10053449 10053504 - SINE/Alu_AluJr chr1 10053520 10053664 - LINE/L1_L1PA10 chr1 10053688 10053982 - SINE/Alu_AluJb chr1 10054523 10054562 + Simple_repeat_(CTTTTT)n chr1 10054777 10055084 - SINE/Alu_AluSp chr1 10055262 10055271 + Simple_repeat_(CTTTT)n chr1 10055272 10055301 + Simple_repeat_(T)n chr1 10055307 10055563 - SINE/Alu_AluJr4 chr1 10055638 10055921 + SINE/Alu_AluSz chr1 10055922 10055946 + Simple_repeat_(AAAAC)n chr1 10056409 10056565 - LINE/L2_L2a chr1 10056584 10056673 - SINE/MIR_MIR chr1 10056844 10057111 - SINE/Alu_AluJb chr1 10057414 10057652 - SINE/Alu_AluJo chr1 10057655 10057758 + SINE/Alu_AluJb chr1 10058966 10059253 + SINE/Alu_AluYj4 chr1 10060156 10060506 - DNA/TcMar-Tigger_Tigger3a chr1 10060766 10061037 - SINE/Alu_AluJr4 chr1 10061259 10061550 - SINE/Alu_AluSx chr1 10061699 10061844 + LINE/RTE-X_L4_A_Mam chr1 10061874 10061902 + LINE/RTE-X_L4_A_Mam chr1 10061903 10062207 - SINE/Alu_AluSc chr1 10062208 10062280 + LINE/RTE-X_L4_A_Mam chr1 10062311 10062610 - SINE/Alu_AluSx chr1 10062675 10062982 + SINE/Alu_AluSp chr1 10063021 10063287 + SINE/Alu_AluSx chr1 10063308 10063546 + LINE/L2_L2 chr1 10063562 10063622 + LINE/L2_L2 chr1 10063623 10063929 + SINE/Alu_AluSq2 chr1 10063930 10064103 + LINE/L2_L2 chr1 10064425 10064538 - LTR/ERVL-MaLR_MLT1O chr1 10064583 10064743 + LINE/L2_L2 chr1 10064767 10065062 - SINE/Alu_AluSx1 chr1 10065256 10065370 - LINE/L2_L2a chr1 10065509 10065680 + LTR/ERVL-MaLR_MLT1I chr1 10066023 10066137 + Simple_repeat_(CCCT)n chr1 10066139 10066428 - SINE/Alu_AluJo chr1 10066625 10066920 + SINE/Alu_AluSx1 chr1 10067621 10067727 - SINE/Alu_AluJb chr1 10067728 10068027 - SINE/Alu_AluY chr1 10068030 10068328 - SINE/Alu_AluSx1 chr1 10068338 10068650 - SINE/Alu_AluSx chr1 10068651 10068701 - SINE/Alu_AluJb chr1 10069300 10069503 - LINE/L2_L2a chr1 10069501 10069534 - SINE/MIR_MIRc chr1 10069535 10069793 - SINE/Alu_AluSz chr1 10069794 10069938 - SINE/MIR_MIRc chr1 10069993 10070289 + SINE/Alu_AluJb chr1 10070376 10070636 - LINE/L1_L1ME3A chr1 10070685 10070722 + LINE/L2_L2a chr1 10070921 10071204 - SINE/Alu_AluSg chr1 10071300 10071588 + SINE/Alu_AluJr chr1 10071672 10071708 + Simple_repeat_(GTAT)n chr1 10071711 10071829 - SINE/Alu_FLAM_A chr1 10073226 10073276 + LINE/CR1_L3b chr1 10073283 10073439 + LINE/L2_L2a chr1 10073440 10073721 + SINE/Alu_AluSz chr1 10073722 10073746 + Simple_repeat_(AAC)n chr1 10073747 10073910 + LINE/L2_L2a chr1 10073921 10074098 - SINE/Alu_FAM chr1 10074104 10075072 + LINE/L2_L2a chr1 10075073 10075287 - DNA/hAT-Charlie_MER58A chr1 10075288 10075497 + LINE/L2_L2 chr1 10075770 10076061 + SINE/Alu_AluJb chr1 10076063 10076178 + LINE/CR1_L3 chr1 10076236 10076543 - SINE/Alu_AluSx1 chr1 10076742 10077044 - SINE/Alu_AluSq4 chr1 10077079 10077442 - LTR/ERVL-MaLR_MLT1C2 chr1 10077447 10077603 + LINE/L2_L2a chr1 10078015 10078307 - SINE/Alu_AluSx chr1 10078324 10078436 - LINE/L2_L2d2 chr1 10078440 10078788 - LINE/CR1_L3 chr1 10078836 10079101 - SINE/Alu_AluJr chr1 10079102 10079177 - DNA/hAT-Charlie_Charlie4a chr1 10079178 10079459 - SINE/Alu_AluSz chr1 10079460 10079608 - DNA/hAT-Charlie_Charlie4a chr1 10079723 10079876 - LINE/CR1_L3 chr1 10079870 10080008 - LINE/L2_L2a chr1 10080140 10080445 + SINE/Alu_AluSg4 chr1 10080456 10081043 + LINE/L1_L1MA7 chr1 10081078 10081358 - SINE/Alu_AluSp chr1 10081426 10081591 - SINE/Alu_AluJb chr1 10081592 10081862 - SINE/Alu_AluSq2 chr1 10081863 10082014 - SINE/Alu_AluJb chr1 10082175 10082217 - LINE/L2_L2a chr1 10082218 10082520 + SINE/Alu_AluSz6 chr1 10082521 10082632 - LINE/L2_L2a chr1 10082633 10083203 - LINE/L1_L1PA8 chr1 10083204 10083360 - LINE/L2_L2a chr1 10084193 10084288 - LINE/L2_L2d2 chr1 10084502 10084628 + LINE/L2_L2d chr1 10084687 10085003 - SINE/Alu_AluSx3 chr1 10085794 10085959 - SINE/Alu_AluSx1 chr1 10085960 10086270 - SINE/Alu_AluY chr1 10086271 10086375 - SINE/Alu_AluSx1 chr1 10086698 10086971 - SINE/Alu_AluSg chr1 10087104 10087292 - SINE/MIR_MIRc chr1 10087295 10087454 - LINE/L1_L1MD3 chr1 10087462 10088185 - LINE/L1_L1MC3 chr1 10088368 10088670 - SINE/Alu_AluSx1 chr1 10088674 10088978 - SINE/Alu_AluJo chr1 10089013 10089304 - SINE/Alu_AluSc chr1 10089398 10089517 + SINE/Alu_FRAM chr1 10089702 10089992 - SINE/Alu_AluSx3 chr1 10090128 10090419 - SINE/Alu_AluJo chr1 10090421 10090525 + SINE/MIR_MIR3 chr1 10090609 10090719 - SINE/Alu_AluJo chr1 10090794 10090832 + Simple_repeat_(TG)n chr1 10091443 10091607 - SINE/Alu_AluJr chr1 10091608 10091903 - SINE/Alu_AluSx1 chr1 10091904 10092038 - SINE/Alu_AluJr chr1 10092086 10092222 + SINE/Alu_AluJb chr1 10092223 10092520 - SINE/Alu_AluSx4 chr1 10092546 10092857 + SINE/Alu_AluSp chr1 10093032 10093098 + Simple_repeat_(TATAC)n chr1 10093692 10093994 - SINE/Alu_AluSz chr1 10094332 10094435 - LINE/L1_L1ME2z chr1 10094436 10094733 - SINE/Alu_AluSc8 chr1 10094734 10094795 - LINE/L1_L1ME2z chr1 10094796 10095077 - SINE/Alu_AluSx chr1 10095078 10095116 - LINE/L1_L1ME2z chr1 10095727 10095787 + DNA/hAT-Charlie_Charlie18a chr1 10095788 10096070 - SINE/Alu_AluSz chr1 10096071 10096219 + DNA/hAT-Charlie_Charlie18a chr1 10096452 10096734 + SINE/Alu_AluSx1 chr1 10096745 10096758 + LINE/L1_L1MEd chr1 10096759 10097054 + SINE/Alu_AluSz chr1 10097055 10097262 + LINE/L1_L1MEd chr1 10097529 10097832 + SINE/Alu_AluSz chr1 10097833 10097878 - LINE/L1_L1MA2 chr1 10097880 10098152 - SINE/Alu_AluSz chr1 10098187 10098400 + LINE/L1_L1MEd chr1 10098451 10098763 + LTR/ERVL-MaLR_MLT1I chr1 10098764 10098955 + LINE/L1_L1MEd chr1 10098956 10099253 + SINE/Alu_AluSz chr1 10099254 10099900 + LINE/L1_L1MEd chr1 10099981 10100288 - SINE/Alu_AluY chr1 10100326 10100507 - SINE/Alu_AluJo chr1 10100552 10100853 - SINE/Alu_AluSp chr1 10100896 10100987 + LINE/L1_L1MEd chr1 10101499 10101806 - SINE/Alu_AluY chr1 10101977 10102002 + Simple_repeat_(GT)n chr1 10102392 10102710 - SINE/Alu_AluSx chr1 10103400 10103621 - SINE/Alu_AluSc chr1 10103622 10103925 - SINE/Alu_AluSc chr1 10103926 10104059 - SINE/Alu_AluSc chr1 10104386 10104506 + LINE/L2_L2a chr1 10105213 10105435 - LINE/L1_L1MD3 chr1 10107276 10107348 + Simple_repeat_(TGA)n chr1 10107565 10107877 - SINE/Alu_AluSq2 chr1 10108147 10108175 + Simple_repeat_(GT)n chr1 10109657 10109915 - SINE/Alu_AluSx chr1 10109916 10109998 - SINE/Alu_AluSp chr1 10110402 10110520 + LINE/L1_L1PA14 chr1 10110851 10110995 + SINE/Alu_FRAM chr1 10111001 10111043 + Simple_repeat_(TCTCTCTGTC)n chr1 10111045 10111093 + Simple_repeat_(AC)n chr1 10111114 10111192 + LINE/L2_L2a chr1 10111200 10111272 + Simple_repeat_(AC)n chr1 10111278 10111452 + LINE/L2_L2a chr1 10111532 10111619 + LINE/L2_L2a chr1 10111675 10111978 + SINE/Alu_AluSz chr1 10112290 10112388 - DNA/TcMar-Tc2_Kanga11a chr1 10112389 10112665 - SINE/Alu_AluSx1 chr1 10112666 10113010 - DNA/TcMar-Tc2_Kanga11a chr1 10113011 10113414 - LTR/ERVL-MaLR_MSTB chr1 10113415 10113784 - DNA/TcMar-Tc2_Kanga11a chr1 10113785 10114094 + SINE/Alu_AluSc chr1 10114095 10114155 - DNA/TcMar-Tc2_Kanga11a chr1 10114582 10114872 + SINE/Alu_AluSz chr1 10115168 10115285 - SINE/Alu_AluSx chr1 10115297 10115571 - SINE/Alu_AluSz chr1 10115641 10115994 - DNA/TcMar-Tigger_Tigger3a chr1 10116144 10116442 - SINE/Alu_AluSz6 chr1 10116782 10116871 - SINE/MIR_MIR3 chr1 10117680 10117810 - LINE/L2_L2a chr1 10117797 10117860 - SINE/MIR_MIR chr1 10117918 10118132 - SINE/MIR_MIR chr1 10118169 10118217 + Simple_repeat_(TTCATT)n chr1 10118334 10118504 - SINE/Alu_AluSx1 chr1 10118505 10118802 - SINE/Alu_AluSp chr1 10118803 10118868 - SINE/Alu_AluSx1 chr1 10118869 10119180 - SINE/Alu_AluSc chr1 10119181 10119272 - SINE/Alu_AluSx1 chr1 10119518 10119540 + Simple_repeat_(CAGC)n chr1 10120239 10120549 + SINE/Alu_AluSx chr1 10120568 10120841 + SINE/Alu_AluSx chr1 10121083 10121383 + SINE/Alu_AluSx1 chr1 10121397 10121693 - SINE/Alu_AluJr4 chr1 10122294 10122366 - LINE/L2_L2d2 chr1 10123149 10123453 + SINE/Alu_AluSx1 chr1 10123663 10123682 + Simple_repeat_(TGTT)n chr1 10123717 10123761 - LINE/L1_L1MB5 chr1 10123762 10123788 + Simple_repeat_(TTGTT)n chr1 10123789 10124074 - SINE/Alu_AluSq2 chr1 10124075 10124143 - LINE/L1_L1MB5 chr1 10124176 10124487 - SINE/Alu_AluSz6 chr1 10124844 10125150 + SINE/Alu_AluSx3 chr1 10126019 10126301 + SINE/Alu_AluSg chr1 10126303 10126355 + Simple_repeat_(ATAG)n chr1 10127720 10127818 - LINE/L2_L2c chr1 10128470 10128623 - DNA/hAT-Charlie_MER5B chr1 10129058 10129240 + LINE/L1_L1ME4a chr1 10129649 10129951 - SINE/Alu_AluSx chr1 10130081 10130379 - SINE/Alu_AluSx chr1 10131195 10131496 + SINE/Alu_AluSx chr1 10131588 10131714 + SINE/Alu_AluSc chr1 10131715 10131972 + SINE/Alu_AluSq4 chr1 10131973 10132144 + SINE/Alu_AluSc chr1 10132520 10132928 - LINE/L2_L2a chr1 10133267 10133459 + SINE/MIR_MIR chr1 10133629 10133760 + SINE/Alu_AluJo chr1 10133766 10134077 + SINE/Alu_AluJr chr1 10134092 10134223 + SINE/MIR_MIRb chr1 10134229 10134538 + SINE/Alu_AluSq chr1 10134750 10134821 + SINE/Alu_AluSx chr1 10134820 10134942 + SINE/Alu_AluSx chr1 10135324 10135457 + SINE/Alu_AluSg chr1 10135458 10135751 + SINE/Alu_AluSg chr1 10136191 10136501 + SINE/Alu_AluJb chr1 10136638 10136920 + SINE/Alu_AluSq2 chr1 10136921 10136951 + Simple_repeat_(AAAT)n chr1 10137357 10137879 - LTR/ERVL_ERVL-int chr1 10137922 10138101 - SINE/Alu_AluSx chr1 10138102 10138396 - SINE/Alu_AluY chr1 10138397 10138533 - SINE/Alu_AluSx chr1 10138534 10139113 - LINE/L1_L1M5 chr1 10139114 10139414 + SINE/Alu_AluSx chr1 10139415 10139709 - LINE/L1_L1M5 chr1 10139710 10140020 - SINE/Alu_AluSp chr1 10140021 10140570 - LINE/L1_L1M5 chr1 10140592 10141108 - LTR/ERVL_ERVL-E-int chr1 10141143 10141576 + LTR/ERVL-MaLR_MLT1D chr1 10141586 10141777 - LTR/ERVL_ERVL-E-int chr1 10142298 10142390 - LTR/ERVL-MaLR_MLT1D chr1 10142391 10142678 + SINE/Alu_AluSx chr1 10142679 10142852 - LTR/ERVL-MaLR_MLT1D chr1 10142853 10143119 + SINE/Alu_AluSz chr1 10143123 10143409 + SINE/Alu_AluSx1 chr1 10143410 10143684 - LTR/ERVL-MaLR_MLT1D chr1 10143827 10143863 + Simple_repeat_(TCAGCC)n chr1 10143925 10144080 - LTR/ERVL_LTR67B chr1 10144112 10144238 - LINE/L2_L2a chr1 10144382 10144453 - DNA/TcMar-Tc2_MER104 chr1 10144454 10144761 + SINE/Alu_AluSx3 chr1 10144762 10144871 - DNA/TcMar-Tc2_MER104 chr1 10145316 10145627 + SINE/Alu_AluY chr1 10146171 10146478 + SINE/Alu_AluSx chr1 10147214 10147506 + SINE/Alu_AluSz6 chr1 10147824 10147863 + Simple_repeat_(ATT)n chr1 10147946 10148240 + SINE/Alu_AluY chr1 10148350 10148656 + SINE/Alu_AluSx1 chr1 10148658 10148967 + SINE/Alu_AluSz chr1 10149874 10149989 + SINE/Alu_AluJo chr1 10150158 10150212 + SINE/Alu_Alu chr1 10150580 10150877 + SINE/Alu_AluSc chr1 10150880 10151176 + SINE/Alu_AluY chr1 10151949 10151977 + DNA/hAT-Charlie_MER5A chr1 10151979 10152259 + SINE/Alu_AluSx3 chr1 10152260 10152296 + Simple_repeat_(AAT)n chr1 10152439 10152505 + LINE/L1_L1ME4a chr1 10152443 10152513 + LINE/L1_L1ME4c chr1 10152516 10152815 + SINE/Alu_AluSx1 chr1 10152817 10153217 - LTR/ERVL_LTR50 chr1 10153224 10153518 + SINE/Alu_AluJr chr1 10153539 10153667 + SINE/Alu_AluSp chr1 10153668 10153801 + SINE/Alu_AluSc chr1 10153802 10154093 + SINE/Alu_AluSp chr1 10154094 10154271 + SINE/Alu_AluSc chr1 10154272 10154372 + SINE/Alu_AluSp chr1 10154390 10154431 - LTR/ERVL_LTR50 chr1 10154435 10154554 + LINE/L1_L1ME4a chr1 10154556 10154856 + SINE/Alu_AluSq2 chr1 10154866 10154987 + LINE/L1_L1ME4a chr1 10155065 10155084 + Simple_repeat_(GA)n chr1 10155085 10155116 + Simple_repeat_(GT)n chr1 10155297 10155517 - SINE/MIR_MIR chr1 10155741 10156051 + SINE/Alu_AluSx1 chr1 10156230 10156517 - SINE/Alu_AluJb chr1 10156898 10157028 + SINE/Alu_AluJr chr1 10157029 10157320 - SINE/Alu_AluSz6 chr1 10157321 10157496 + SINE/Alu_AluJr chr1 10157502 10157800 + SINE/Alu_AluSc chr1 10158727 10159037 + SINE/Alu_AluSz chr1 10159391 10159709 + SINE/Alu_AluYk3 chr1 10160665 10160954 + SINE/Alu_AluJo chr1 10161983 10162165 - SINE/Alu_AluSq2 chr1 10162166 10162470 - SINE/Alu_AluSc8 chr1 10162471 10162605 - SINE/Alu_AluSq2 chr1 10162623 10162862 - LINE/L1_L1PA16 chr1 10163032 10163071 + Simple_repeat_(TTAT)n chr1 10163078 10163275 - SINE/Alu_AluJb chr1 10163276 10163373 + snRNA_U6 chr1 10163388 10163682 + SINE/Alu_AluSc chr1 10163724 10163761 + Simple_repeat_(TTA)n chr1 10163762 10164026 - SINE/Alu_AluSz6 chr1 10164027 10164040 + Simple_repeat_(TTA)n chr1 10164059 10164373 + SINE/Alu_AluSx1 chr1 10164374 10164513 - SINE/Alu_FRAM chr1 10164616 10164703 - DNA/TcMar-Tigger_Tigger19a chr1 10164723 10164821 + LINE/L2_L2a chr1 10164823 10165031 - DNA/hAT-Charlie_MER20 chr1 10165032 10165942 + LINE/L2_L2a chr1 10166240 10166405 + DNA/TcMar-Tigger_Tigger15a chr1 10166506 10166641 + SINE/Alu_AluSz chr1 10166642 10166941 + SINE/Alu_AluSx chr1 10166942 10166971 + Simple_repeat_(CA)n chr1 10166972 10166982 + SINE/Alu_AluSx chr1 10166983 10167140 + SINE/Alu_AluSz chr1 10167158 10167441 + SINE/Alu_AluSp chr1 10167442 10167474 + Simple_repeat_(AAT)n chr1 10167590 10167887 - SINE/Alu_AluSc chr1 10168612 10168919 + SINE/Alu_AluY chr1 10169440 10169506 + DNA/hAT-Tip100_Zaphod5a chr1 10169733 10170025 + SINE/Alu_AluSx chr1 10170060 10170108 + Simple_repeat_(CTGTGC)n chr1 10171465 10171606 + SINE/Alu_AluJb chr1 10171607 10171892 + SINE/Alu_AluSx3 chr1 10172328 10172525 - SINE/MIR_MIR chr1 10172805 10173105 + DNA/TcMar-Tigger_Tigger3a chr1 10173197 10173494 + SINE/Alu_AluY chr1 10173509 10173528 + Simple_repeat_(AC)n chr1 10174114 10174412 + SINE/Alu_AluSg4 chr1 10174419 10174722 + SINE/Alu_AluSq2 chr1 10174939 10174985 + Simple_repeat_(TTGGT)n chr1 10175371 10175650 + SINE/Alu_AluY chr1 10175651 10175677 + Simple_repeat_(AAAT)n chr1 10175678 10175700 + SINE/Alu_AluY chr1 10175825 10176773 - LINE/L1_L1MB4 chr1 10176774 10177086 - SINE/Alu_AluSc8 chr1 10177087 10177168 - LINE/L1_L1MB4 chr1 10177169 10177237 - LTR/ERVL-MaLR_MSTB chr1 10177238 10177494 + SINE/Alu_AluSz chr1 10177503 10177659 + SINE/Alu_AluJr chr1 10177660 10177690 + Simple_repeat_(TA)n chr1 10177852 10178020 - SINE/MIR_MIRb chr1 10180976 10181010 + Simple_repeat_(AC)n chr1 10181527 10181819 + SINE/Alu_AluSx3 chr1 10181820 10181883 + Simple_repeat_(TATA)n chr1 10181884 10182172 + SINE/Alu_AluSq10 chr1 10182173 10182199 + Simple_repeat_(TATA)n chr1 10182971 10183142 - SINE/Alu_AluSz chr1 10183143 10183169 + Simple_repeat_(T)n chr1 10183170 10183289 - SINE/Alu_AluSz chr1 10183385 10183442 - SINE/MIR_MIR chr1 10183443 10183739 - SINE/Alu_AluY chr1 10183740 10183780 - SINE/MIR_MIR chr1 10183917 10183981 - SINE/MIR_MIR3 chr1 10184099 10184236 + LINE/L2_L2a chr1 10184247 10184288 + LINE/L2_L2a chr1 10184289 10184583 + SINE/Alu_AluSx1 chr1 10184584 10184771 + LINE/L2_L2a chr1 10184775 10185088 + SINE/Alu_AluSx chr1 10185095 10185250 + SINE/Alu_AluJb chr1 10185254 10185568 + SINE/Alu_AluSq2 chr1 10185592 10185781 + SINE/Alu_FRAM chr1 10185787 10186264 + LINE/L2_L2a chr1 10186294 10186619 - LTR/ERVL-MaLR_MLT1G1 chr1 10186626 10186876 - LTR/ERVL-MaLR_MLT1G1-int chr1 10186877 10186951 + Simple_repeat_(CA)n chr1 10186959 10187263 - LTR/ERVL-MaLR_MLT1G1-int chr1 10187298 10187585 + SINE/Alu_AluSg7 chr1 10187588 10188317 + Simple_repeat_(TATATAT)n chr1 10188318 10188514 - LTR/ERVL-MaLR_MLT1G1-int chr1 10188524 10188567 - LTR/ERVL-MaLR_MLT1G1-int chr1 10188568 10188867 - SINE/Alu_AluSx chr1 10188868 10188954 - LTR/ERVL-MaLR_MLT1G1-int chr1 10188965 10189304 - LTR/ERVL-MaLR_MLT1G1-int chr1 10189318 10189362 - LTR/ERVL-MaLR_MLT1G1 chr1 10189363 10189658 + SINE/Alu_AluSx1 chr1 10189659 10189801 - LTR/ERVL-MaLR_MLT1G1 chr1 10189827 10190064 - LTR/ERVL-MaLR_MLT1G1 chr1 10190177 10190505 - SINE/Alu_AluJb chr1 10190516 10190560 + Simple_repeat_(TTG)n chr1 10190602 10190881 + SINE/Alu_AluSc chr1 10190882 10190913 + Simple_repeat_(AAT)n chr1 10190929 10190959 - LTR/ERVL-MaLR_MSTB chr1 10190960 10191191 + DNA/hAT-Charlie_MER30 chr1 10191192 10191573 - LTR/ERVL-MaLR_MSTB chr1 10191635 10191909 + SINE/Alu_AluSx chr1 10191915 10192211 + SINE/Alu_AluYk3 chr1 10192214 10192492 - LINE/L1_L1MEh chr1 10192655 10192831 - SINE/Alu_AluJb chr1 10192832 10193127 - SINE/Alu_AluSx chr1 10193128 10193264 - SINE/Alu_AluJb chr1 10193659 10193961 + SINE/Alu_AluSz chr1 10193969 10194066 - SINE/MIR_MIRb chr1 10194431 10194516 + Low_complexity_GA-rich chr1 10194803 10195112 + SINE/Alu_AluSz chr1 10195208 10195338 - SINE/Alu_FLAM_C chr1 10195344 10195599 - SINE/Alu_AluJo chr1 10195618 10195753 - SINE/Alu_AluJb chr1 10195754 10196067 - SINE/Alu_AluSc chr1 10196068 10196214 - SINE/Alu_AluJb chr1 10196389 10196683 + SINE/Alu_AluSq2 chr1 10197434 10197702 - SINE/Alu_AluSg chr1 10197706 10198012 - SINE/Alu_AluSq chr1 10198040 10198140 + SINE/MIR_MIR chr1 10198141 10198428 - LTR/ERVL-MaLR_MLT1D chr1 10198429 10198725 - SINE/Alu_AluSz chr1 10198727 10199026 - SINE/Alu_AluJo chr1 10199029 10199151 - SINE/Alu_AluJo chr1 10199152 10199326 - LTR/ERVL-MaLR_MLT1D chr1 10199377 10199501 - SINE/Alu_AluJb chr1 10199515 10199816 - SINE/Alu_AluY chr1 10199832 10199902 + SINE/MIR_MIR chr1 10199903 10200185 + SINE/Alu_AluSc8 chr1 10200186 10200221 + Simple_repeat_(AAT)n chr1 10200222 10200285 + SINE/MIR_MIR chr1 10200354 10200489 - SINE/MIR_MIR3 chr1 10200557 10200597 + Low_complexity_GA-rich chr1 10200669 10200879 - SINE/MIR_MIRb chr1 10200946 10201162 + SINE/Alu_AluSx chr1 10201787 10202046 + SINE/Alu_AluSc5 chr1 10202165 10202469 - SINE/Alu_AluY chr1 10202544 10202844 + SINE/Alu_AluSg chr1 10202937 10203102 - DNA/TcMar-Tigger_Tigger15a chr1 10203181 10203481 + SINE/Alu_AluSx chr1 10203491 10203802 + SINE/Alu_AluSx1 chr1 10204637 10204934 - SINE/Alu_AluSp chr1 10204960 10205150 - SINE/Alu_AluJb chr1 10205192 10205481 + SINE/Alu_AluSx3 chr1 10205540 10205669 + SINE/Alu_AluSx chr1 10205670 10205964 - SINE/Alu_AluSp chr1 10205965 10206155 + SINE/Alu_AluSz6 chr1 10206176 10206466 + SINE/Alu_AluSx1 chr1 10206467 10206498 + Simple_repeat_(TCAA)n chr1 10206521 10206616 + SINE/Alu_AluY chr1 10206703 10206950 - LINE/L1_L1ME3C chr1 10206953 10207233 - SINE/Alu_AluSz chr1 10207268 10207585 - SINE/Alu_AluJo chr1 10207633 10207901 + SINE/Alu_AluJb chr1 10207933 10207983 - LINE/L1_L1P4 chr1 10208334 10208654 - SINE/Alu_AluSz chr1 10208825 10208936 + LINE/RTE-X_L4_C_Mam chr1 10208937 10209232 + SINE/Alu_AluSx1 chr1 10209233 10209395 + LINE/RTE-X_L4_C_Mam chr1 10210638 10210713 + Simple_repeat_(CACCGCC)n chr1 10212605 10212811 + SINE/Alu_AluJr chr1 10212853 10212890 + Simple_repeat_(TG)n chr1 10212891 10212932 + Simple_repeat_(TA)n chr1 10213855 10214134 + SINE/Alu_AluJr4 chr1 10214280 10214587 - SINE/Alu_AluSg chr1 10214589 10214878 - SINE/Alu_AluSx4 chr1 10215130 10215173 + Simple_repeat_(TA)n chr1 10215174 10215475 - SINE/Alu_AluSp chr1 10215551 10215848 - SINE/Alu_AluJb chr1 10215987 10216153 - SINE/MIR_MIR chr1 10216918 10216952 + LINE/L2_L2a chr1 10216953 10217273 - SINE/Alu_AluSx chr1 10217274 10217333 + LINE/L2_L2a chr1 10217334 10217615 - SINE/Alu_AluSx1 chr1 10217616 10217989 + LINE/L2_L2a chr1 10218426 10218720 - SINE/Alu_AluSz chr1 10218845 10219179 - LINE/RTE-X_L4_C_Mam chr1 10219180 10219472 + SINE/Alu_AluSp chr1 10219475 10219775 + SINE/Alu_AluSp chr1 10219786 10219924 + SINE/Alu_AluJo chr1 10219925 10220221 + SINE/Alu_AluSp chr1 10220222 10220371 + SINE/Alu_AluJo chr1 10220372 10220441 + Simple_repeat_(ATAG)n chr1 10220442 10220497 - LINE/RTE-X_L4_C_Mam chr1 10220500 10220654 + SINE/Alu_AluJo chr1 10220656 10220962 - SINE/Alu_AluSq2 chr1 10220963 10221094 - LINE/RTE-X_L4_C_Mam chr1 10221095 10221397 - SINE/Alu_AluSz chr1 10221398 10221626 - LINE/RTE-X_L4_C_Mam chr1 10221810 10221833 + Simple_repeat_(TAAT)n chr1 10221835 10221991 - SINE/Alu_AluSz6 chr1 10222403 10222502 - LINE/L2_L2 chr1 10222972 10223254 + SINE/Alu_AluSg chr1 10223255 10223286 + Simple_repeat_(AAAAC)n chr1 10223525 10223843 - SINE/Alu_AluSc chr1 10223948 10224093 - SINE/Alu_AluJo chr1 10224116 10224415 - SINE/Alu_AluSc8 chr1 10224431 10224723 - SINE/Alu_AluJr chr1 10224841 10225176 - DNA/hAT-Charlie_MER1B chr1 10225204 10225483 + SINE/Alu_AluJb chr1 10226613 10226686 - LINE/L1_L1MC5a chr1 10226687 10226995 + SINE/Alu_AluJb chr1 10226996 10227032 - LINE/L1_L1MC5a chr1 10227033 10227332 - SINE/Alu_AluSc chr1 10227333 10227599 - LINE/L1_L1MC5a chr1 10227602 10227818 + SINE/Alu_AluSq2 chr1 10227819 10227903 - LINE/L1_L1MC5a chr1 10227904 10228217 + SINE/Alu_AluSx1 chr1 10228218 10228433 - LINE/L1_L1MC5a chr1 10229380 10229564 - LINE/L2_L2a chr1 10229576 10229895 + SINE/Alu_AluY chr1 10229911 10230154 + SINE/Alu_AluJr chr1 10230428 10230735 - SINE/Alu_AluSz chr1 10230932 10231234 + SINE/Alu_AluSq2 chr1 10231379 10231686 - SINE/Alu_AluSz chr1 10231974 10232096 + SINE/Alu_FLAM_A chr1 10233335 10233486 + SINE/Alu_AluJb chr1 10233649 10233711 + LINE/L1_L1MB8 chr1 10233712 10233941 - SINE/Alu_AluSx1 chr1 10234213 10234506 - SINE/Alu_AluSx1 chr1 10234510 10234821 - SINE/Alu_AluJb chr1 10234877 10235189 - SINE/Alu_AluSx1 chr1 10235254 10235552 - SINE/Alu_AluSz chr1 10235579 10235883 + SINE/Alu_AluSp chr1 10235991 10236273 + SINE/Alu_AluSx4 chr1 10236274 10236305 + Simple_repeat_(AAC)n chr1 10236657 10236723 + Simple_repeat_(CTGTTTT)n chr1 10237728 10238034 + SINE/Alu_AluJb chr1 10238099 10238401 + SINE/Alu_AluSq2 chr1 10238422 10238706 + SINE/Alu_AluSz chr1 10238707 10238738 + Simple_repeat_(AAC)n chr1 10238739 10238754 + SINE/Alu_AluSz chr1 10238932 10239201 - SINE/Alu_AluJb chr1 10239234 10239381 - LINE/L1_L1ME4a chr1 10239596 10239774 - SINE/Alu_AluSx4 chr1 10239775 10240064 - SINE/Alu_AluY chr1 10240065 10240201 - SINE/Alu_AluSx4 chr1 10240210 10240507 - SINE/Alu_AluSz chr1 10240728 10240863 - SINE/Alu_FLAM_C chr1 10240864 10240971 - LINE/L1_L1ME4a chr1 10240972 10241064 + SINE/MIR_MIRc chr1 10241130 10241403 - SINE/Alu_AluJb chr1 10242247 10242527 - DNA/TcMar-Tigger_Tigger3a chr1 10242528 10242814 - SINE/Alu_AluSg chr1 10242815 10242903 - DNA/TcMar-Tigger_Tigger3a chr1 10243135 10243424 + SINE/Alu_AluSz chr1 10243479 10243684 - SINE/MIR_MIR chr1 10243786 10244184 + DNA/TcMar-Tigger_X26_DNA chr1 10244302 10244611 - SINE/Alu_AluSp chr1 10244616 10244911 - SINE/Alu_AluY chr1 10245842 10246463 + LINE/L2_L2a chr1 10246464 10246762 + SINE/Alu_AluSg chr1 10246763 10246870 + LINE/L2_L2a chr1 10246871 10247164 - SINE/Alu_AluSz6 chr1 10247165 10247567 + LINE/L2_L2a chr1 10247622 10247703 - SINE/MIR_MIR chr1 10247774 10248090 - DNA/hAT-Charlie_MER1B chr1 10248189 10248499 - SINE/Alu_AluJb chr1 10248689 10248839 - LINE/L2_L2 chr1 10249620 10249918 + SINE/Alu_AluJr chr1 10249980 10250108 - SINE/Alu_AluJo chr1 10250342 10250513 - SINE/Alu_AluSg4 chr1 10250573 10250824 + SINE/Alu_AluJr chr1 10250881 10251188 + SINE/Alu_AluSx1 chr1 10251321 10251346 + Simple_repeat_(T)n chr1 10251453 10251746 + SINE/Alu_AluSp chr1 10252030 10252339 - SINE/Alu_AluSz chr1 10252387 10252424 + Simple_repeat_(TTG)n chr1 10252427 10252711 - SINE/Alu_AluJb chr1 10252722 10253024 - SINE/Alu_AluSx chr1 10253179 10253358 + SINE/MIR_MIR chr1 10253359 10253654 + SINE/Alu_AluJr chr1 10253655 10253716 + SINE/MIR_MIR chr1 10254351 10254410 - LINE/L2_L2b chr1 10254445 10254490 + Simple_repeat_(TACA)n chr1 10254595 10254900 + SINE/Alu_AluY chr1 10254944 10255247 - SINE/Alu_AluSz chr1 10255308 10255610 + SINE/Alu_AluSx1 chr1 10255671 10255733 - LINE/L2_L2b chr1 10255794 10255809 + Simple_repeat_(TTCA)n chr1 10255810 10256108 - SINE/Alu_AluJb chr1 10256354 10256398 + Simple_repeat_(CTCTTTC)n chr1 10256692 10256719 + Simple_repeat_(ATT)n chr1 10256720 10257010 - SINE/Alu_AluSz chr1 10257104 10257132 + Simple_repeat_(TTGT)n chr1 10257133 10257227 - SINE/Alu_AluSx3 chr1 10257281 10257492 + SINE/Alu_AluSg4 chr1 10257664 10257976 - SINE/Alu_AluSz chr1 10258295 10258329 + Simple_repeat_(TGTG)n chr1 10258745 10258859 - LINE/L2_L2c chr1 10259326 10259505 - SINE/Alu_AluJb chr1 10259506 10259799 - SINE/Alu_AluSx1 chr1 10259800 10259948 - SINE/Alu_AluJb chr1 10260010 10260588 - DNA/TcMar-Tigger_Tigger3b chr1 10260589 10260885 + SINE/Alu_AluSp chr1 10260886 10260952 - DNA/TcMar-Tigger_Tigger3b chr1 10260953 10261267 - SINE/Alu_AluSz6 chr1 10261271 10261541 - SINE/Alu_AluSc8 chr1 10261542 10261646 - DNA/TcMar-Tigger_Tigger3b chr1 10261638 10261746 - DNA/TcMar-Tigger_Tigger3b chr1 10262166 10262448 - SINE/Alu_AluJb chr1 10262516 10262745 - LINE/L1_L1ME4c chr1 10263020 10263297 + SINE/Alu_AluSz6 chr1 10263850 10264330 + LINE/L2_L2a chr1 10264670 10264960 - SINE/Alu_AluSx3 chr1 10264961 10265121 - SINE/Alu_AluJb chr1 10265131 10265175 - SINE/Alu_Alu chr1 10265201 10265251 + Simple_repeat_(TTTA)n chr1 10265255 10265535 - SINE/Alu_AluJb chr1 10265563 10265870 + SINE/Alu_AluJb chr1 10266034 10266505 - LTR?_MamRep605 chr1 10266669 10266722 - LTR?_MamRep605 chr1 10267008 10267326 - SINE/Alu_AluSz chr1 10268623 10268642 + Simple_repeat_(T)n chr1 10268772 10268999 + SINE/MIR_MIR chr1 10269233 10269529 + SINE/Alu_AluSx3 chr1 10269540 10269824 + SINE/Alu_AluSq2 chr1 10270061 10270642 - LINE/L1_L1ME1 chr1 10270643 10270955 + SINE/Alu_AluSx3 chr1 10270956 10271027 - LINE/L1_L1ME1 chr1 10271194 10271318 - SINE/Alu_FRAM chr1 10271620 10271796 + DNA/hAT-Charlie_MER58A chr1 10271806 10271960 - LINE/L2_L2d2 chr1 10273272 10273448 + SINE/Alu_AluSz chr1 10273710 10273739 + Simple_repeat_(A)n chr1 10273932 10274237 - SINE/Alu_AluSx1 chr1 10274805 10274893 + LINE/L1_L1MC5a chr1 10274936 10275249 + LINE/L1_L1MC5a chr1 10275696 10275898 + LINE/L2_L2 chr1 10275910 10276161 - SINE/Alu_AluSx chr1 10276799 10277098 + SINE/Alu_AluSz chr1 10277149 10277301 + SINE/Alu_FLAM_C chr1 10277331 10277628 - SINE/Alu_AluJr chr1 10277706 10277846 + SINE/MIR_MIRc chr1 10278252 10278277 + Simple_repeat_(TGTTT)n chr1 10279431 10279557 - SINE/MIR_MIR chr1 10279690 10280002 - SINE/Alu_AluJr chr1 10280226 10280533 - SINE/Alu_AluSc chr1 10281334 10281358 + Simple_repeat_(AAAG)n chr1 10282563 10282741 + LINE/CR1_L3 chr1 10282925 10283231 + SINE/Alu_AluY chr1 10284093 10284245 + SINE/Alu_FRAM chr1 10284268 10284404 + SINE/Alu_AluSx chr1 10284405 10284712 + SINE/Alu_AluSz chr1 10284713 10284883 + SINE/Alu_AluSx chr1 10286046 10286339 - SINE/Alu_AluSx chr1 10286697 10286872 - DNA/TcMar-Tigger_Tigger10 chr1 10287047 10287096 - LINE/CR1_X7D_LINE chr1 10287242 10287424 - SINE/Alu_AluSx chr1 10287525 10287843 - DNA/TcMar-Tigger_Tigger10 chr1 10288165 10288234 - SINE/Alu_AluJb chr1 10289388 10289601 + LINE/L2_L2a chr1 10289602 10289911 + SINE/Alu_AluSx chr1 10289912 10290052 + LINE/L2_L2a chr1 10290059 10290129 + LINE/L2_L2a chr1 10290130 10290156 + Simple_repeat_(AAATT)n chr1 10290157 10290296 - LINE/L1_L1PA8 chr1 10290477 10290642 - SINE/Alu_AluYc chr1 10290645 10290874 + SINE/Alu_AluSc chr1 10291429 10291723 + SINE/Alu_AluY chr1 10293394 10293690 - SINE/Alu_AluSc chr1 10294587 10294862 + SINE/Alu_AluSz chr1 10297149 10297165 + Simple_repeat_(T)n chr1 10298716 10298804 - SINE/MIR_MIRc chr1 10298874 10298935 + SINE/Alu_Alu chr1 10298966 10299070 + snRNA_U6 chr1 10299071 10299139 + Low_complexity_A-rich chr1 10299955 10300237 + SINE/Alu_AluSx1 chr1 10300238 10300268 + Simple_repeat_(AAT)n chr1 10301054 10301154 + SINE/Alu_FLAM_A chr1 10301160 10301185 + Simple_repeat_(CAAA)n chr1 10301348 10301383 - LTR/ERV1_MER41B chr1 10301384 10301685 + SINE/Alu_AluSx1 chr1 10301686 10301730 - LTR/ERV1_MER41B chr1 10306465 10306777 + srpRNA_7SLRNA chr1 10307300 10307609 - SINE/Alu_AluSx chr1 10309089 10309344 + DNA/hAT-Charlie_Charlie16 chr1 10309645 10310150 + LINE/L2_L2b chr1 10310183 10310366 - DNA/hAT-Charlie_MER5A chr1 10310378 10310599 - DNA/hAT-Charlie_MER58A chr1 10310679 10310868 - LINE/L2_L2c chr1 10310876 10311158 + LINE/L2_L2b chr1 10311213 10311512 - SINE/Alu_AluSx chr1 10311533 10311731 + LINE/L2_L2-3_Crp chr1 10311839 10312074 - SINE/MIR_MIR chr1 10312075 10312179 + LINE/L2_L2b chr1 10312196 10312933 + LINE/L2_L2b chr1 10312936 10312997 + SINE/MIR_MIR chr1 10313058 10313366 - SINE/Alu_AluSz6 chr1 10313367 10313448 + SINE/MIR_MIR chr1 10313549 10313849 - SINE/Alu_AluY chr1 10313888 10313910 + Simple_repeat_(ATT)n chr1 10313911 10314181 - SINE/Alu_AluSg chr1 10314182 10314197 + Simple_repeat_(ATT)n chr1 10314408 10314719 - SINE/Alu_AluSx1 chr1 10315063 10315171 - LINE/L1_L1MC4 chr1 10315172 10315475 - SINE/Alu_AluSx1 chr1 10315476 10315773 - LINE/L1_L1MC4 chr1 10315774 10316075 + SINE/Alu_AluSx chr1 10316087 10316248 + SINE/Alu_AluJb chr1 10316262 10316511 - LINE/L1_L1MC4 chr1 10316512 10316802 - SINE/Alu_AluJb chr1 10316803 10316897 - LINE/L1_L1MC4 chr1 10316903 10317020 - SINE/Alu_FAM chr1 10317025 10317069 + SINE/Alu_Alu chr1 10317076 10317149 - SINE/MIR_MIRc chr1 10317207 10317516 - LINE/L1_L1MC4 chr1 10317550 10317843 + SINE/Alu_AluSx3 chr1 10317847 10318084 - SINE/MIR_MIRc chr1 10318440 10318730 + SINE/Alu_AluSg chr1 10318862 10319054 - DNA/hAT-Charlie_MER106A chr1 10319104 10319400 - SINE/Alu_AluSx chr1 10320112 10320136 + Simple_repeat_(GAA)n chr1 10320712 10321000 - SINE/Alu_AluSp chr1 10321103 10321403 - SINE/Alu_AluSx chr1 10322009 10322053 + LINE/L2_L2a chr1 10322263 10322491 + SINE/MIR_MIR chr1 10322942 10323161 - SINE/Alu_AluJb chr1 10323349 10323620 + SINE/Alu_AluJb chr1 10324965 10325067 - LINE/L1_L1ME4a chr1 10325957 10325999 + Simple_repeat_(TGCTTT)n chr1 10327062 10327100 + SINE/Alu_Alu chr1 10327101 10327218 + SINE/Alu_AluSp chr1 10327219 10327519 + SINE/Alu_AluSq2 chr1 10327925 10328215 + SINE/Alu_AluJr chr1 10329453 10329747 + SINE/Alu_AluSg chr1 10330721 10330863 - LINE/L2_L2b chr1 10330979 10331107 + SINE/Alu_AluSx chr1 10332057 10332365 - SINE/Alu_AluSq10 chr1 10332502 10332814 - SINE/Alu_AluY chr1 10332817 10333111 - SINE/Alu_AluSx chr1 10333144 10333386 + SINE/Alu_AluSp chr1 10333410 10333695 + SINE/Alu_AluY chr1 10333873 10334169 + SINE/Alu_AluSc5 chr1 10334943 10335245 - SINE/Alu_AluJr chr1 10335257 10335551 - SINE/Alu_AluSx1 chr1 10335606 10335648 + Simple_repeat_(TTTG)n chr1 10335723 10335850 + SINE/Alu_FLAM_A chr1 10336155 10336446 - SINE/Alu_AluSg chr1 10339085 10339103 + Simple_repeat_(AG)n chr1 10339146 10339348 - LINE/L2_L2c chr1 10340412 10340564 - SINE/MIR_MIR chr1 10340565 10340791 + SINE/Alu_AluSz chr1 10340792 10340902 - SINE/MIR_MIR chr1 10341045 10341161 - LINE/L2_L2d2 chr1 10341689 10341968 + SINE/Alu_AluJb chr1 10343476 10343786 + SINE/Alu_AluSp chr1 10344714 10344878 - SINE/MIR_MIRc chr1 10344912 10345154 + SINE/Alu_AluSz chr1 10345252 10345418 - DNA/hAT-Charlie_MER5B chr1 10346079 10346215 - SINE/Alu_FLAM_C chr1 10346359 10346663 - SINE/Alu_AluSx chr1 10346801 10347006 - DNA/hAT-Charlie_MER20 chr1 10347251 10347300 - LINE/L2_L2d2 chr1 10348780 10348936 - SINE/Alu_FRAM chr1 10349157 10349456 + SINE/Alu_AluSz6 chr1 10349776 10350010 - DNA/hAT-Charlie_Charlie4a chr1 10350011 10350241 + SINE/Alu_AluSc chr1 10350242 10350277 - DNA/hAT-Charlie_Charlie4a chr1 10350278 10350565 + SINE/Alu_AluYj4 chr1 10350566 10350810 - DNA/hAT-Charlie_Charlie4a chr1 10350815 10351093 + SINE/Alu_AluJb chr1 10351170 10351199 + Simple_repeat_(T)n chr1 10351372 10351612 - SINE/MIR_MIR chr1 10351684 10352000 + SINE/Alu_AluJr chr1 10352285 10352477 - LINE/L2_L2 chr1 10354172 10354869 - DNA/TcMar-Tigger_Tigger13a chr1 10356086 10356374 + SINE/Alu_AluSg7 chr1 10356522 10356581 - SINE/Alu_FRAM chr1 10356620 10356930 + SINE/Alu_AluSc chr1 10356931 10357179 - SINE/Alu_AluJr chr1 10357209 10357426 + DNA/TcMar-Mariner_MARNA chr1 10357466 10357724 + SINE/Alu_AluJr chr1 10357727 10358029 + SINE/Alu_AluSq2 chr1 10358103 10358126 + Simple_repeat_(A)n chr1 10358127 10358158 + Low_complexity_A-rich chr1 10358573 10358701 + SINE/Alu_AluSx3 chr1 10358918 10358967 + Simple_repeat_(TGTCTT)n chr1 10359753 10360033 + SINE/Alu_AluSz chr1 10360034 10360076 + Simple_repeat_(AAAAT)n chr1 10360403 10360702 + SINE/Alu_AluSx1 chr1 10361350 10361493 + SINE/MIR_MIR chr1 10362198 10362508 + SINE/Alu_AluJb chr1 10362519 10362590 - LINE/L1_L1ME3E chr1 10362694 10362740 + Simple_repeat_(AATTTTT)n chr1 10362793 10363079 + SINE/Alu_AluJr chr1 10363410 10363714 + SINE/Alu_AluSx chr1 10364132 10364154 + Simple_repeat_(TG)n chr1 10364201 10364503 - SINE/Alu_AluSx1 chr1 10364725 10365024 + SINE/Alu_AluY chr1 10365714 10365814 - LINE/L2_L2c chr1 10365815 10365948 + SINE/Alu_AluJo chr1 10365949 10366256 + SINE/Alu_AluSx1 chr1 10366691 10366976 + SINE/Alu_AluSc chr1 10367100 10367378 - SINE/Alu_AluSx chr1 10367422 10367499 + SINE/Alu_AluSc chr1 10367539 10367706 - SINE/Alu_AluSx chr1 10367715 10368026 - SINE/Alu_AluSc chr1 10368262 10368415 - LTR/ERVL_MER21B chr1 10368965 10369315 + LINE/L2_L2a chr1 10369347 10369616 + SINE/Alu_AluJr chr1 10369651 10369957 + SINE/Alu_AluSp chr1 10369972 10370265 + SINE/Alu_AluSx chr1 10370280 10370574 + SINE/Alu_AluJb chr1 10370575 10370615 + Low_complexity_GA-rich chr1 10370644 10370688 + Simple_repeat_(AAT)n chr1 10370816 10370962 + SINE/Alu_FRAM chr1 10371332 10371440 + DNA/hAT-Charlie_MER20B chr1 10371663 10371959 + SINE/Alu_AluJr chr1 10371963 10372119 + LINE/L2_L2 chr1 10372124 10372250 - DNA/TcMar-Tc2_MER104 chr1 10372253 10372526 + SINE/Alu_AluJr chr1 10372527 10372663 + SINE/Alu_AluSx chr1 10372664 10372798 - SINE/Alu_AluY chr1 10372799 10373114 - SINE/Alu_AluSq4 chr1 10373115 10373257 - SINE/Alu_AluY chr1 10373258 10373533 - SINE/Alu_AluSx1 chr1 10373534 10373560 + SINE/Alu_AluSx chr1 10373785 10373969 - DNA/hAT-Charlie_MER5A chr1 10375382 10375684 - SINE/Alu_AluSz chr1 10375761 10376101 - SINE/Alu_AluSq chr1 10376835 10376873 + Simple_repeat_(AC)n chr1 10377657 10377950 + SINE/Alu_AluSg chr1 10380426 10380719 + SINE/Alu_AluSx chr1 10381372 10381405 + Simple_repeat_(TTCATT)n chr1 10381903 10382083 - SINE/Alu_AluSx chr1 10382084 10382119 + Simple_repeat_(TAT)n chr1 10382122 10382412 - SINE/Alu_AluSg chr1 10382418 10382714 - SINE/Alu_AluSq2 chr1 10382889 10383744 - LINE/L2_L2a chr1 10383745 10384023 + SINE/Alu_AluJo chr1 10384025 10384290 + SINE/Alu_AluSq chr1 10384291 10384690 - LINE/L2_L2a chr1 10384724 10384845 - LTR/ERVL_LTR79 chr1 10384846 10385155 - SINE/Alu_AluY chr1 10385156 10385194 - LTR/ERVL_LTR79 chr1 10385202 10385364 - DNA/hAT-Charlie_MER5A1 chr1 10385541 10385877 - LINE/L2_L2a chr1 10385984 10386287 + SINE/Alu_AluSx1 chr1 10386543 10386847 - SINE/Alu_AluY chr1 10387040 10387161 + SINE/Alu_FLAM_C chr1 10387162 10387458 + SINE/Alu_AluJr chr1 10387459 10387582 + SINE/Alu_AluJb chr1 10387606 10387636 + Simple_repeat_(TTCAG)n chr1 10388545 10388642 + SINE/MIR_MIRc chr1 10388655 10388762 - SINE/MIR_MIRc chr1 10388848 10389026 - SINE/Alu_AluSz6 chr1 10389034 10389305 - SINE/Alu_AluSq chr1 10389558 10389714 + LINE/L1_L1M4a1 chr1 10389720 10389848 + SINE/Alu_AluJr chr1 10389874 10390178 + SINE/Alu_AluSp chr1 10390190 10390488 - SINE/Alu_AluSp chr1 10390504 10390805 - SINE/Alu_AluY chr1 10390889 10391073 + LINE/L1_L1M4a1 chr1 10391074 10391380 + SINE/Alu_AluSx chr1 10391381 10391397 + LINE/L1_L1M4a1 chr1 10391398 10391701 + SINE/Alu_AluSq chr1 10391702 10391884 + LINE/L1_L1M4a1 chr1 10391887 10392196 + SINE/Alu_AluJr chr1 10392204 10392509 + SINE/Alu_AluSz chr1 10392514 10392637 + LINE/L1_L1M4a1 chr1 10392638 10392902 + SINE/Alu_AluJr4 chr1 10392903 10392989 + LINE/L1_L1M4a1 chr1 10392990 10393291 + SINE/Alu_AluSp chr1 10393292 10393407 + LINE/L1_L1M4a1 chr1 10393408 10393673 - SINE/Alu_AluSc8 chr1 10393674 10394022 + LINE/L1_L1M4a1 chr1 10394023 10394326 + SINE/Alu_AluSx chr1 10394327 10394798 + LINE/L1_L1M4a1 chr1 10394810 10395068 + SINE/Alu_AluSz chr1 10395071 10395233 - LINE/L1_L1M4 chr1 10395238 10395468 - SINE/Alu_AluSz6 chr1 10395470 10395617 - SINE/Alu_AluJb chr1 10395618 10395938 - SINE/Alu_AluJb chr1 10395991 10396263 + SINE/Alu_AluSx chr1 10396264 10396341 - SINE/Alu_AluJb chr1 10396357 10396575 - SINE/Alu_AluSz chr1 10396577 10396657 + LINE/L1_L1ME1 chr1 10396658 10396952 + SINE/Alu_AluSx3 chr1 10396953 10397428 + LINE/L1_L1ME1 chr1 10397475 10397636 + SINE/Alu_FRAM chr1 10397639 10397938 + SINE/Alu_AluSz chr1 10397939 10398235 + SINE/Alu_AluSg4 chr1 10398255 10398406 - DNA/hAT-Charlie_Charlie1b chr1 10400808 10400982 + SINE/Alu_AluJo chr1 10400999 10401219 + SINE/Alu_AluSx chr1 10401734 10402029 + SINE/Alu_AluYh3 chr1 10402031 10402065 + Simple_repeat_(TAA)n chr1 10402252 10402369 - SINE/Alu_AluJo chr1 10402453 10402528 + SINE/Alu_AluJr4 chr1 10402529 10402822 - SINE/Alu_AluY chr1 10402823 10402972 + SINE/Alu_AluJr4 chr1 10403310 10403610 + SINE/Alu_AluSc8 chr1 10404538 10404799 - SINE/Alu_AluSx3 chr1 10404927 10405055 + SINE/Alu_AluJo chr1 10405093 10405399 + SINE/Alu_AluSc chr1 10405402 10405426 + Simple_repeat_(AC)n chr1 10405475 10405539 - SINE/Alu_Alu chr1 10405558 10405851 + SINE/Alu_AluSx1 chr1 10405855 10406152 - SINE/Alu_AluY chr1 10406274 10406400 + SINE/Alu_FLAM_A chr1 10407031 10407146 + SINE/Alu_FLAM_A chr1 10407167 10407455 + SINE/Alu_AluJr chr1 10407672 10407973 + SINE/Alu_AluJb chr1 10408659 10408910 - SINE/Alu_AluJb chr1 10408962 10409144 + SINE/MIR_MIR3 chr1 10409466 10409618 + SINE/Alu_FRAM chr1 10409650 10409955 - SINE/Alu_AluSz chr1 10410169 10410457 + SINE/Alu_AluSx chr1 10410520 10410810 + LTR?_MamRep605 chr1 10410845 10410873 + Simple_repeat_(TATAAA)n chr1 10411767 10411857 - SINE/MIR_MIRb chr1 10411947 10412184 - LINE/L1_L1ME2z chr1 10412193 10412289 - DNA/TcMar-Tigger_Tigger13a chr1 10412310 10412569 - DNA/TcMar-Tigger_Tigger13a chr1 10412574 10412852 - LINE/L2_L2a chr1 10412864 10412988 - DNA/TcMar-Tigger_Tigger13a chr1 10413624 10413919 + SINE/Alu_AluSc8 chr1 10413927 10414066 + SINE/Alu_AluSx chr1 10414132 10414261 - LINE/L1_L1ME3Cz chr1 10414379 10414675 - SINE/Alu_AluSx chr1 10414811 10415105 + SINE/Alu_AluSg chr1 10416087 10416257 - SINE/Alu_FRAM chr1 10416523 10416951 - LTR/ERV1_LTR7 chr1 10417713 10418010 - SINE/Alu_AluSq2 chr1 10418105 10418396 + DNA/PiggyBac_MER75 chr1 10418495 10418790 + SINE/Alu_AluSc chr1 10418995 10419316 - SINE/Alu_AluSx3 chr1 10420276 10420370 + LINE/L1_L1ME3D chr1 10420377 10420413 + Simple_repeat_(T)n chr1 10420858 10421154 - SINE/Alu_AluY chr1 10421165 10421288 - SINE/Alu_FLAM_C chr1 10421606 10421811 + SINE/MIR_MIR chr1 10421814 10422108 + SINE/Alu_AluJr chr1 10422170 10422465 - SINE/Alu_AluJb chr1 10422725 10422743 + SINE/MIR_MIR chr1 10422744 10423042 - SINE/Alu_AluJr chr1 10423043 10423239 + SINE/MIR_MIR chr1 10423363 10423470 - LTR/ERVL_LTR84a chr1 10423471 10423763 - SINE/Alu_AluSx1 chr1 10423764 10423860 - LTR/ERVL_LTR84a chr1 10423947 10424517 - LTR/ERVL-MaLR_MLT1G3 chr1 10424518 10424602 - LTR/ERVL_LTR84a chr1 10424648 10424824 - SINE/MIR_MIR chr1 10424852 10424960 - LINE/L2_L2a chr1 10425038 10425332 - SINE/Alu_AluSx chr1 10425395 10425655 + LTR/ERVK_LTR5_Hs chr1 10425656 10426924 - LTR/ERV1_LTR12C chr1 10426925 10427626 + LTR/ERVK_LTR5_Hs chr1 10427627 10427899 - SINE/Alu_AluSp chr1 10427989 10428202 + SINE/MIR_MIR chr1 10428310 10428485 - SINE/Alu_AluJb chr1 10428521 10428811 - SINE/Alu_AluSz chr1 10428954 10429119 - SINE/MIR_MIRb chr1 10429509 10429570 + Simple_repeat_(ACAGAAT)n chr1 10429571 10429880 + SINE/Alu_AluJr chr1 10430229 10430268 - LINE/L2_L2a chr1 10431043 10431162 + SINE/MIR_MIRb chr1 10431361 10431573 - LINE/L1_L1MC4 chr1 10431574 10431871 + SINE/Alu_AluSx1 chr1 10431872 10432056 - LINE/L1_L1MC4 chr1 10432061 10432075 + LINE/L2_L2b chr1 10432076 10432384 - SINE/Alu_AluSq chr1 10432385 10432445 + LINE/L2_L2b chr1 10432507 10432597 - LINE/L2_L2a chr1 10432629 10432658 + Simple_repeat_(AGG)n chr1 10432749 10432854 + SINE/Alu_FAM chr1 10433420 10433542 + LINE/L2_L2b chr1 10435669 10435749 + Simple_repeat_(AT)n chr1 10435750 10435873 - SINE/Alu_FLAM_C chr1 10435982 10436277 - SINE/Alu_AluSz chr1 10436320 10436360 + Simple_repeat_(GTGT)n chr1 10436420 10436738 + SINE/Alu_AluSx chr1 10436902 10437044 - DNA/hAT-Charlie_Charlie16a chr1 10437053 10437141 + DNA/TcMar-Tigger_Tigger16b chr1 10437157 10437324 - SINE/Alu_FRAM chr1 10437384 10437413 + Simple_repeat_(T)n chr1 10437446 10437475 + Simple_repeat_(TTTTGTT)n chr1 10437476 10437756 - SINE/Alu_AluSx1 chr1 10437759 10438061 - SINE/Alu_AluSx chr1 10438133 10438444 - SINE/Alu_AluSx1 chr1 10439466 10439761 + SINE/Alu_AluSg7 chr1 10440988 10441115 + LINE/L2_L2a chr1 10441114 10441180 - SINE/MIR_MIRc chr1 10441186 10441310 - SINE/Alu_FLAM_C chr1 10441314 10441612 - SINE/Alu_AluJb chr1 10441621 10441924 - SINE/Alu_AluY chr1 10441957 10442086 + SINE/Alu_AluJr4 chr1 10442187 10442212 + Simple_repeat_(GTTT)n chr1 10442898 10443199 - SINE/Alu_AluSg7 chr1 10443206 10443394 - SINE/Alu_AluSc chr1 10443399 10443707 - SINE/Alu_AluSg chr1 10443716 10444017 - SINE/Alu_AluSg7 chr1 10444056 10444356 - SINE/Alu_AluSx chr1 10444600 10444793 - SINE/MIR_MIRb chr1 10445121 10445418 + SINE/Alu_AluJo chr1 10445420 10445557 + SINE/Alu_AluSx3 chr1 10445632 10445684 + SINE/MIR_MIRc chr1 10445699 10446002 - SINE/Alu_AluJb chr1 10446003 10446155 - SINE/Alu_FRAM chr1 10446160 10446465 - SINE/Alu_AluJb chr1 10446466 10446638 - SINE/Alu_AluJo chr1 10446639 10446929 - SINE/Alu_AluJb chr1 10446930 10447033 - SINE/Alu_AluJo chr1 10447311 10447602 + SINE/Alu_AluSz chr1 10447698 10448006 + SINE/Alu_AluY chr1 10448639 10448933 + SINE/Alu_AluSc8 chr1 10448944 10449237 + SINE/Alu_AluSp chr1 10449248 10449446 + SINE/Alu_AluSx1 chr1 10449463 10449568 + DNA/hAT-Charlie_MER5A1 chr1 10452214 10452372 - SINE/Alu_AluSx3 chr1 10452373 10452673 - SINE/Alu_AluSp chr1 10453045 10453352 + SINE/Alu_AluSz6 chr1 10453357 10453650 + SINE/Alu_AluSx chr1 10454167 10454471 - SINE/Alu_AluY chr1 10454532 10454570 - LTR/ERVL-MaLR_MSTB chr1 10454571 10454701 + SINE/Alu_AluSx3 chr1 10454707 10454856 - LTR/ERVL-MaLR_MSTB chr1 10454857 10455020 - SINE/Alu_AluSp chr1 10455050 10455360 - SINE/Alu_AluSp chr1 10455492 10455793 - SINE/Alu_AluY chr1 10455834 10456142 + SINE/Alu_AluSx chr1 10456255 10456395 + LINE/CR1_L3 chr1 10456479 10456507 + Simple_repeat_(A)n chr1 10457148 10457365 - SINE/Alu_AluJr chr1 10457390 10457700 + SINE/Alu_AluSx1 chr1 10457916 10458020 + SINE/Alu_AluJr chr1 10458021 10458404 + DNA/hAT-Charlie_MER20B chr1 10458535 10458836 - SINE/Alu_AluSx1 chr1 10458857 10459153 - SINE/Alu_AluSx3 chr1 10459730 10459747 + Simple_repeat_(A)n chr1 10459750 10459931 - SINE/Alu_AluJr chr1 10459963 10460261 + SINE/Alu_AluSx chr1 10460263 10460518 - SINE/Alu_AluSx1 chr1 10460531 10460827 - SINE/Alu_AluSc8 chr1 10460908 10461207 - SINE/Alu_AluY chr1 10461861 10462157 - SINE/Alu_AluYk3 chr1 10462168 10462430 - SINE/Alu_AluSq2 chr1 10463668 10463964 - SINE/Alu_AluSx chr1 10464088 10464391 - SINE/Alu_AluYj4 chr1 10464433 10464740 + SINE/Alu_AluJb chr1 10464751 10464969 + LINE/L2_L2a chr1 10464970 10465102 + SINE/Alu_AluJo chr1 10465103 10465261 - SINE/Alu_AluSc chr1 10465262 10465563 - SINE/Alu_AluSg4 chr1 10465564 10465689 - SINE/Alu_AluSc chr1 10465690 10465967 - SINE/Alu_AluJb chr1 10465968 10466149 + SINE/Alu_AluJo chr1 10466188 10466488 - SINE/Alu_AluSc8 chr1 10466489 10466613 - SINE/MIR_MIRb chr1 10466614 10466678 - DNA/hAT-Charlie_MER5A chr1 10466679 10466979 + SINE/Alu_AluSz chr1 10466980 10466995 - DNA/hAT-Charlie_MER5A chr1 10467507 10467810 - SINE/Alu_AluSx1 chr1 10468249 10468301 - SINE/MIR_MIR chr1 10468302 10468495 + DNA/hAT-Charlie_MER20 chr1 10468496 10468651 - SINE/MIR_MIR chr1 10468703 10468741 + LINE/L2_L2b chr1 10468742 10468988 - SINE/Alu_AluSx1 chr1 10468989 10469081 + LINE/L2_L2b chr1 10469488 10469521 - SINE/MIR_MIR chr1 10469522 10469818 - SINE/Alu_AluSg chr1 10469827 10470113 - SINE/Alu_AluSg7 chr1 10470114 10470313 - SINE/MIR_MIR chr1 10470415 10470724 - SINE/Alu_AluYk3 chr1 10470782 10471086 + SINE/Alu_AluYk3 chr1 10471676 10471871 - SINE/MIR_MIRb chr1 10473215 10473344 - SINE/Alu_FRAM chr1 10473376 10473685 + SINE/Alu_AluSx chr1 10473868 10474050 - SINE/Alu_AluJb chr1 10474130 10474307 + LINE/L2_L2d2 chr1 10474430 10474728 + SINE/Alu_AluSp chr1 10475135 10475175 + Simple_repeat_(GCCCC)n chr1 10475824 10475997 - SINE/MIR_MIRb chr1 10476503 10476789 - SINE/Alu_AluSx1 chr1 10477071 10477369 - SINE/Alu_AluY chr1 10477691 10477882 + SINE/MIR_MIR chr1 10477883 10478188 - SINE/Alu_AluSx chr1 10478189 10478234 + SINE/MIR_MIR chr1 10478264 10478433 - LINE/CR1_X7A_LINE chr1 10478601 10478746 - SINE/Alu_AluJb chr1 10478750 10479050 - SINE/Alu_AluY chr1 10479095 10479375 + SINE/Alu_AluJr chr1 10479404 10479443 - SINE/Alu_AluYi6 chr1 10479698 10479844 + SINE/MIR_MIRb chr1 10479851 10480036 + SINE/Alu_FAM chr1 10480201 10480386 - SINE/Alu_AluSx3 chr1 10480388 10480694 - SINE/Alu_AluSx1 chr1 10480695 10480827 - LINE/L1_L1MC4 chr1 10480828 10480860 + Simple_repeat_(TC)n chr1 10480861 10481118 - LINE/L1_L1MC4 chr1 10481120 10481427 - SINE/Alu_AluSx1 chr1 10481434 10481697 - SINE/Alu_AluJr chr1 10481699 10481823 - LINE/L1_L1ME3D chr1 10481824 10482124 - SINE/Alu_AluJb chr1 10482135 10482422 - SINE/Alu_AluSz chr1 10482423 10482437 - LINE/L1_L1ME3D chr1 10482438 10482731 - SINE/Alu_AluSc chr1 10482732 10482854 - LINE/L1_L1ME3D chr1 10482864 10483118 - LINE/L1_L1MC4 chr1 10483169 10483303 - SINE/Alu_AluJb chr1 10483316 10483614 - SINE/Alu_AluSc chr1 10483715 10483761 - LINE/L1_L1ME1 chr1 10483764 10484011 - LINE/L1_L1ME2 chr1 10484029 10484337 - SINE/Alu_AluSq chr1 10484338 10484472 - SINE/Alu_AluSx chr1 10484477 10484560 - LINE/L1_L1ME2 chr1 10484561 10485090 - LTR/ERVL-MaLR_MLT1F2 chr1 10485091 10485267 - LINE/L1_L1ME2 chr1 10485300 10485618 - SINE/Alu_AluJr4 chr1 10485619 10485758 - LINE/L1_L1ME2 chr1 10485759 10486057 - SINE/Alu_AluSx1 chr1 10486058 10486397 - LINE/L1_L1ME2 chr1 10486398 10486689 + SINE/Alu_AluJr4 chr1 10486690 10486986 - SINE/Alu_AluSg7 chr1 10486987 10487473 - LINE/L1_L1ME2 chr1 10487474 10487782 - SINE/Alu_AluSc chr1 10487786 10488080 - SINE/Alu_AluSz chr1 10488081 10488215 - LINE/L1_L1ME2 chr1 10488218 10488456 - SINE/Alu_AluSx1 chr1 10488462 10488498 - LINE/L1_L1ME2 chr1 10488499 10488802 - SINE/Alu_AluY chr1 10488803 10488985 - LINE/L1_L1ME2 chr1 10488986 10489280 - SINE/Alu_AluSp chr1 10489281 10490010 - LINE/L1_L1ME2 chr1 10490012 10490054 - LINE/L2_L2 chr1 10490053 10490178 - SINE/MIR_MIRb chr1 10490701 10490994 - SINE/Alu_AluJb chr1 10490995 10491013 + Simple_repeat_(T)n chr1 10491053 10491166 - SINE/Alu_AluSx chr1 10491239 10491288 + SINE/Alu_AluSx3 chr1 10491386 10491659 - SINE/Alu_AluJr chr1 10491675 10491982 - SINE/Alu_AluSz6 chr1 10492101 10492165 - LINE/L1_L1ME4b chr1 10492466 10492630 + SINE/MIR_MIR chr1 10492723 10493430 - LINE/L2_L2a chr1 10493431 10493701 - SINE/Alu_AluJo chr1 10493702 10493996 - LINE/L2_L2a chr1 10497405 10497751 - LINE/L2_L2d chr1 10497784 10497819 + Simple_repeat_(TTGTTT)n chr1 10497998 10498279 + SINE/Alu_AluJr chr1 10498281 10498319 + Simple_repeat_(AAAAC)n chr1 10499446 10499746 - SINE/Alu_AluSg chr1 10500122 10500402 + SINE/Alu_AluSx1 chr1 10500582 10500874 - SINE/Alu_AluSz6 chr1 10500975 10501172 - DNA/hAT-Charlie_Chap1a_Mam chr1 10501189 10501290 - DNA/hAT-Charlie_Chap1a_Mam chr1 10501291 10501316 + Simple_repeat_(TTTATT)n chr1 10501317 10501600 - SINE/Alu_AluYe5 chr1 10501601 10501632 - DNA/hAT-Charlie_Chap1a_Mam chr1 10501633 10501914 + SINE/Alu_AluJb chr1 10501930 10502008 - DNA/hAT-Charlie_Chap1a_Mam chr1 10502786 10503100 - SINE/Alu_AluJr chr1 10503177 10503310 + SINE/Alu_AluSz6 chr1 10503312 10503340 + Simple_repeat_(AAAGA)n chr1 10503724 10504033 - SINE/Alu_AluSz6 chr1 10504712 10504783 + Simple_repeat_(TTTC)n chr1 10504784 10504806 + Simple_repeat_(TTCCT)n chr1 10504813 10505089 - SINE/Alu_AluSz chr1 10505188 10505453 + SINE/Alu_AluJo chr1 10505554 10505675 - SINE/Alu_AluJo chr1 10505685 10505960 - SINE/Alu_AluSz6 chr1 10506272 10506575 - SINE/Alu_AluSp chr1 10506589 10507046 + DNA/TcMar-Tigger_Tigger13a chr1 10507102 10507253 - SINE/MIR_MIRb chr1 10508195 10508490 - SINE/Alu_AluY chr1 10508944 10509246 - SINE/Alu_AluY chr1 10510272 10510358 + LINE/L2_L2a chr1 10510664 10510722 + LINE/L2_L2c chr1 10511336 10511363 + Simple_repeat_(TCCC)n chr1 10511972 10512257 - SINE/Alu_AluY chr1 10512685 10512996 - SINE/Alu_AluSc8 chr1 10513593 10513696 - SINE/tRNA_LFSINE_Vert chr1 10514157 10514199 + Simple_repeat_(TG)n chr1 10519781 10520139 - SINE/Alu_AluJb chr1 10520143 10520457 - SINE/Alu_AluJr chr1 10521474 10521677 - SINE/MIR_MIR chr1 10522301 10522549 + LINE/CR1_L3 chr1 10522725 10523354 - DNA/TcMar-Tigger_Tigger15a chr1 10523485 10523595 - SINE/MIR_MIR3 chr1 10523826 10523847 + Simple_repeat_(A)n chr1 10524354 10524419 - DNA/TcMar-Tigger_Tigger19b chr1 10524678 10524707 + Simple_repeat_(TTGA)n chr1 10524708 10524989 - SINE/Alu_AluJr chr1 10525631 10525922 - SINE/Alu_AluSx4 chr1 10525926 10526218 - SINE/Alu_AluSc8 chr1 10526222 10526485 - SINE/Alu_AluSx chr1 10526591 10526837 - DNA/TcMar-Tigger_Tigger4a chr1 10526931 10527231 + SINE/Alu_AluSz chr1 10527238 10527537 + SINE/Alu_AluSc chr1 10527663 10527970 - SINE/Alu_AluSz6 chr1 10529829 10529857 + Simple_repeat_(AGTAA)n chr1 10531792 10531933 + SINE/tRNA-Deu_AmnSINE2 chr1 10535138 10535226 - LINE/RTE-BovB_MamRTE1 chr1 10535540 10535737 - LINE/L2_L2d chr1 10535777 10535811 + Simple_repeat_(GT)n chr1 10537989 10538297 - LINE/L2_L2a chr1 10539363 10539474 + DNA?_UCON14 chr1 10541178 10541276 + DNA/hAT-Blackjack_MER81 chr1 10542091 10542479 - LINE/L1_L1ME4b chr1 10542480 10542788 + SINE/Alu_AluSc8 chr1 10542789 10542879 - LINE/L1_L1ME4b chr1 10543137 10543433 - SINE/Alu_AluSg4 chr1 10543642 10543899 - SINE/Alu_AluJr chr1 10543920 10544068 - LINE/L2_L2 chr1 10544059 10544171 - SINE/MIR_MIRc chr1 10544192 10544238 + LINE/L2_L2a chr1 10544275 10544389 - SINE/MIR_MIRc chr1 10544515 10544754 - LINE/L1_L1ME1 chr1 10544755 10545062 - SINE/Alu_AluJb chr1 10545063 10545466 - LINE/L1_L1ME1 chr1 10545606 10545761 + SINE/MIR_MIR chr1 10545762 10546053 + SINE/Alu_AluJr chr1 10546054 10546139 + SINE/MIR_MIR chr1 10546212 10546283 - LTR/ERVL_LTR67B chr1 10546284 10546584 + SINE/Alu_AluSz chr1 10546585 10546884 + SINE/Alu_AluY chr1 10546885 10547178 - LTR/ERVL_LTR67B chr1 10547205 10547417 - LTR/ERVL_LTR67B chr1 10547458 10547760 - DNA/TcMar-Tigger_MER44A chr1 10547934 10548244 + SINE/Alu_AluSx chr1 10548879 10549033 + Unknown_UCON70 chr1 10549392 10549527 + DNA/Kolobok_UCON29 chr1 10549614 10549832 - DNA/TcMar-Tigger_Tigger4a chr1 10549926 10549963 + Simple_repeat_(ATCC)n chr1 10549966 10550329 - LINE/L2_L2b chr1 10550425 10550510 - LINE/L2_L2b chr1 10550591 10550674 - SINE/MIR_MIRc chr1 10551669 10551820 + DNA/TcMar-Tigger_Tigger19a chr1 10551821 10552102 + SINE/Alu_AluSg4 chr1 10552103 10552161 + DNA/TcMar-Tigger_Tigger19a chr1 10552162 10552451 + SINE/Alu_AluSg chr1 10552452 10552466 + DNA/TcMar-Tigger_Tigger19a chr1 10554008 10554324 + SINE/Alu_AluY chr1 10554712 10554932 - SINE/Alu_AluSz6 chr1 10555236 10555272 + Low_complexity_A-rich chr1 10555637 10555667 + Simple_repeat_(GT)n chr1 10556782 10556911 - SINE/MIR_MIRc chr1 10557127 10557303 - LINE/L1_L1MEg chr1 10557304 10557604 + SINE/Alu_AluSx1 chr1 10557605 10557715 - LINE/L1_L1MEg chr1 10557835 10557869 + Simple_repeat_(T)n chr1 10557910 10558121 - LINE/L1_L1MEg chr1 10558176 10558307 - SINE/Alu_AluSp chr1 10558454 10558764 + SINE/Alu_AluSz chr1 10560043 10560356 - SINE/Alu_AluSp chr1 10560364 10560425 - LINE/L1_L1MC4 chr1 10560426 10560670 - SINE/Alu_AluSx chr1 10560671 10560718 - LINE/L1_L1MC4 chr1 10560719 10561016 - SINE/Alu_AluSg chr1 10561017 10561936 - LINE/L1_L1MC4 chr1 10561965 10562202 - SINE/Alu_AluJr4 chr1 10562206 10562239 + Simple_repeat_(TTTTC)n chr1 10562266 10562856 - LINE/L1_L1MC4 chr1 10562900 10563215 - SINE/Alu_AluSx3 chr1 10563247 10563317 - LINE/L1_L1MEg chr1 10563318 10563638 + SINE/Alu_AluSp chr1 10563643 10563928 + SINE/Alu_AluYc chr1 10563929 10564430 - LINE/L1_L1MEg chr1 10564431 10564733 - SINE/Alu_AluJo chr1 10564734 10564891 - LINE/L1_L1MEg chr1 10564892 10565201 - SINE/Alu_AluSx4 chr1 10565202 10565488 - LINE/L1_L1MEg chr1 10565541 10565638 - LINE/L1_L1ME4b chr1 10565734 10565937 + SINE/Alu_AluJr chr1 10565953 10565989 + Simple_repeat_(ATTT)n chr1 10566051 10566208 - LINE/L1_L1MEg chr1 10566179 10566369 - LINE/L1_L1ME4b chr1 10566494 10566658 - SINE/Alu_AluSp chr1 10566659 10566694 + Simple_repeat_(TTTTG)n chr1 10566695 10566816 - SINE/Alu_AluSp chr1 10566860 10566944 - LINE/L1_L1ME4b chr1 10566945 10567260 - DNA/hAT-Charlie_MER58B chr1 10567261 10567482 - LINE/L1_L1ME4b chr1 10567506 10567794 - SINE/Alu_AluSz chr1 10567795 10567929 - SINE/Alu_AluJo chr1 10568010 10568605 - LINE/L1_L1ME4b chr1 10568647 10568730 + DNA/hAT-Charlie_Charlie13a chr1 10568731 10569039 - SINE/Alu_AluSz chr1 10569040 10569075 + DNA/hAT-Charlie_Charlie13a chr1 10569205 10570333 - LINE/L1_HAL1 chr1 10570334 10570630 - SINE/Alu_AluSc chr1 10570631 10570703 - LINE/L1_HAL1 chr1 10570849 10571156 - SINE/Alu_AluJb chr1 10571157 10571238 + DNA/TcMar-Tigger_MamRep137 chr1 10571348 10571526 + LINE/L1_L1MB3 chr1 10571527 10571830 + SINE/Alu_AluSx1 chr1 10571831 10571885 + LINE/L1_L1MB3 chr1 10571933 10572140 + DNA/hAT-Charlie_Charlie13a chr1 10572145 10572229 + LINE/L2_L2c chr1 10572307 10572503 + LINE/L1_L1ME3 chr1 10572504 10572534 - DNA/TcMar-Tigger_Tigger3a chr1 10572535 10572835 - SINE/Alu_AluY chr1 10572836 10573195 - DNA/TcMar-Tigger_Tigger3a chr1 10573196 10573314 + LINE/L1_L1ME3 chr1 10573315 10573425 + SINE/Alu_FLAM_C chr1 10573426 10573818 + LINE/L1_L1ME3 chr1 10573819 10574117 + SINE/Alu_AluSz chr1 10574118 10574478 + LINE/L1_L1ME3 chr1 10574636 10574821 + LINE/L1_L1MEi chr1 10574829 10575100 + SINE/Alu_AluJr chr1 10575101 10575583 + LINE/L1_L1MEi chr1 10575765 10576235 - LINE/L1_L1M5 chr1 10576303 10576454 - SINE/MIR_MIR chr1 10576476 10576759 - LINE/L1_L1M5 chr1 10576760 10577041 - SINE/Alu_AluSq2 chr1 10577042 10577116 - LINE/L1_L1M5 chr1 10577126 10577430 + SINE/Alu_AluSg chr1 10577439 10577530 + LINE/L1_L1MEb chr1 10577531 10577562 + Simple_repeat_(TA)n chr1 10577563 10577845 - SINE/Alu_AluY chr1 10577856 10578354 + LINE/L1_L1MEb chr1 10578448 10579421 + LINE/L1_L1M4a1 chr1 10579442 10579991 - LTR/ERV1_MER41A chr1 10579992 10580219 + LINE/L1_L1M4a1 chr1 10580229 10580520 - SINE/Alu_AluSx chr1 10580622 10580770 + LINE/L1_L1M4a1 chr1 10580839 10581014 - SINE/MIR_MIR chr1 10581099 10581144 - LTR/ERVL_MLT2F chr1 10581149 10581211 - LTR/ERVL_MLT2F chr1 10581306 10581602 - SINE/Alu_AluSg7 chr1 10581670 10581772 + Simple_repeat_(ATAATTT)n chr1 10581883 10581972 - LINE/L1_L1P4 chr1 10582289 10582389 - LINE/L2_L2a chr1 10582723 10583036 - SINE/Alu_AluSx1 chr1 10583044 10583204 + DNA/hAT-Charlie_Charlie15a chr1 10583225 10583520 - SINE/Alu_AluJb chr1 10583526 10584059 - LINE/L2_L2a chr1 10584138 10584395 - LINE/L2_L2-1_AMi chr1 10584395 10585070 + LINE/L1_L1ME3B chr1 10585075 10585127 - DNA/hAT-Charlie_Charlie1b chr1 10585128 10585308 + LINE/L1_L1MC4a chr1 10585326 10585637 + LINE/L1_L1ME3B chr1 10585638 10585912 + SINE/Alu_AluSx chr1 10585913 10586628 + LINE/L1_L1ME3B chr1 10586629 10586923 - SINE/Alu_AluY chr1 10586924 10587157 + LINE/L1_L1ME3B chr1 10587160 10587174 + LINE/L1_L1ME4b chr1 10587175 10587464 + SINE/Alu_AluSz6 chr1 10587465 10587542 + LINE/L1_L1ME4b chr1 10587538 10587895 + LINE/L1_L1ME4b chr1 10587896 10587920 + Simple_repeat_(T)n chr1 10587921 10587943 + LINE/L1_L1ME4b chr1 10587944 10588230 + SINE/Alu_AluSg7 chr1 10588231 10588340 - SINE/Alu_FLAM_A chr1 10588341 10588434 + LINE/L1_L1ME4b chr1 10588440 10588945 + DNA/TcMar-Tigger_Tigger7 chr1 10588928 10589037 + DNA/TcMar-Tigger_Tigger7 chr1 10589099 10589461 - DNA/TcMar-Tigger_MamRep137 chr1 10589465 10589561 - SINE/Alu_AluJo chr1 10589562 10589594 + Simple_repeat_(TTTG)n chr1 10589595 10589711 - SINE/Alu_AluJo chr1 10589713 10589853 - LINE/L1_L1M5 chr1 10590018 10590166 + SINE/MIR_MIRb chr1 10590455 10590495 + Simple_repeat_(TATCTA)n chr1 10590496 10590504 + Simple_repeat_(TTATACA)n chr1 10591480 10591609 - LINE/L1_L1ME4b chr1 10591636 10591677 + Simple_repeat_(TG)n chr1 10596571 10596704 + SINE/MIR_MIRc chr1 10600138 10600432 + SINE/Alu_AluSq chr1 10600435 10600743 + SINE/Alu_AluSz chr1 10600852 10600977 + SINE/Alu_AluSx3 chr1 10600980 10601284 + SINE/Alu_AluSq2 chr1 10603211 10603459 - LINE/L2_L2d chr1 10604022 10604150 - LINE/L2_L2c chr1 10604354 10604632 + SINE/Alu_AluJr chr1 10604714 10604842 - DNA/TcMar-Tigger_Tigger19a chr1 10604972 10605109 + LINE/L2_L2b chr1 10605435 10605592 - LINE/L2_L2c chr1 10605696 10605745 + Simple_repeat_(CT)n chr1 10606071 10606478 - LTR/ERVL-MaLR_MLT1J chr1 10606507 10606686 + LINE/L1_L1ME3F chr1 10606869 10607985 - LINE/L1_L1ME2 chr1 10607986 10608180 - SINE/Alu_AluJb chr1 10608199 10608380 - LINE/L1_L1ME2 chr1 10608381 10608686 + SINE/Alu_AluSx1 chr1 10608687 10608724 - LINE/L1_L1ME2 chr1 10608732 10608756 + Simple_repeat_(A)n chr1 10608759 10609313 - LINE/L1_L1MB8 chr1 10609334 10609605 - LINE/L1_L1ME1 chr1 10609606 10609913 + SINE/Alu_AluSx1 chr1 10609914 10610013 - LINE/L1_L1ME1 chr1 10610014 10610138 + Simple_repeat_(AT)n chr1 10610171 10610273 - LINE/L1_L1ME1 chr1 10610306 10610372 + Simple_repeat_(TA)n chr1 10610373 10610399 + Simple_repeat_(AC)n chr1 10610400 10610409 + Simple_repeat_(TA)n chr1 10610472 10610784 - SINE/Alu_AluSp chr1 10610789 10610872 - DNA/hAT-Charlie_MER58C chr1 10610877 10610923 + Simple_repeat_(TCTTCC)n chr1 10610940 10610965 - DNA/hAT-Charlie_MER58C chr1 10610966 10611283 + SINE/Alu_AluSz chr1 10611284 10611363 - DNA/hAT-Charlie_MER58C chr1 10611371 10612332 - LINE/L1_L1M4 chr1 10612710 10613201 - LINE/RTE-BovB_MamRTE1 chr1 10615150 10615789 + LINE/L1_L1ME4b chr1 10616698 10616741 + SINE/MIR_MIRc chr1 10616836 10617122 + srpRNA_7SLRNA chr1 10618756 10618966 + SINE/MIR_MIR chr1 10619316 10619613 - SINE/Alu_AluSx chr1 10619824 10620142 + SINE/Alu_AluY chr1 10620170 10620291 + SINE/Alu_AluSz chr1 10620292 10620332 + Simple_repeat_(AATAA)n chr1 10620333 10620506 + SINE/Alu_AluSz chr1 10620507 10620833 + SINE/Alu_AluSz chr1 10621190 10621210 + Simple_repeat_(A)n chr1 10621340 10621644 - SINE/Alu_AluSg chr1 10622537 10622665 - LINE/L1_L1ME4b chr1 10622717 10622858 - LINE/L1_L1ME4c chr1 10623930 10624021 - SINE/MIR_MIR3 chr1 10625029 10625148 + SINE/MIR_MIRb chr1 10626925 10627076 - LINE/L1_L1MC5 chr1 10627882 10627923 + DNA/hAT-Blackjack_MER81 chr1 10627924 10628230 - SINE/Alu_AluSx1 chr1 10628231 10628309 + DNA/hAT-Blackjack_MER81 chr1 10629740 10629875 + Low_complexity_GA-rich chr1 10630184 10630218 + Simple_repeat_(CCAGCC)n chr1 10635630 10635673 + Simple_repeat_(GTCCCTG)n chr1 10636201 10636236 + Low_complexity_GA-rich chr1 10636656 10636700 + Simple_repeat_(A)n chr1 10636841 10636946 + Simple_repeat_(TA)n chr1 10636997 10637032 + Low_complexity_A-rich chr1 10637105 10637131 + Simple_repeat_(TCT)n chr1 10639084 10639151 + Simple_repeat_(TCGTCG)n chr1 10639157 10639198 + Simple_repeat_(CCT)n chr1 10639213 10639319 + Simple_repeat_(GCG)n chr1 10640353 10640460 - SINE/5S-Deu-L2_AmnSINE1 chr1 10642172 10642206 + Low_complexity_GA-rich chr1 10642285 10642313 + Simple_repeat_(AGG)n chr1 10642338 10642402 + Low_complexity_A-rich chr1 10644394 10644495 + LINE/L2_L2a chr1 10645249 10645535 + SINE/Alu_AluY chr1 10647094 10647133 + Low_complexity_GA-rich chr1 10649860 10649891 + Simple_repeat_(AGGG)n chr1 10649961 10650019 + Low_complexity_G-rich chr1 10650355 10650400 + Low_complexity_A-rich chr1 10651305 10651354 + Simple_repeat_(GGGCGG)n chr1 10652355 10652459 + LINE/L2_L2b chr1 10652751 10652874 - DNA/hAT-Charlie_MER3 chr1 10653116 10653167 - SINE/MIR_MIR1_Amn chr1 10653688 10653731 + Low_complexity_G-rich chr1 10656231 10656410 + SINE/MIR_MIRb chr1 10656783 10656811 + Simple_repeat_(CCCAGC)n chr1 10657556 10657578 + Simple_repeat_(TAA)n chr1 10657653 10657689 + Simple_repeat_(GACAGA)n chr1 10657975 10657997 + Simple_repeat_(TC)n chr1 10657998 10658132 - SINE/Alu_FLAM_C chr1 10659174 10659209 + Low_complexity_G-rich chr1 10659375 10659424 + Simple_repeat_(TG)n chr1 10661369 10662760 + Simple_repeat_(AC)n chr1 10664203 10664257 + Low_complexity_G-rich chr1 10664754 10664827 + Simple_repeat_(CCAT)n chr1 10665630 10665845 + LINE/L2_L2b chr1 10665928 10666052 - SINE/MIR_MIR chr1 10666785 10666819 + Simple_repeat_(AC)n chr1 10666853 10666940 - LINE/L2_L2b chr1 10668922 10668968 + Simple_repeat_(GGCAGA)n chr1 10669138 10669260 - SINE/MIR_MIR chr1 10669318 10669361 + Simple_repeat_(CCTTCC)n chr1 10670207 10670328 - SINE/MIR_MIRb chr1 10670524 10670642 - LINE/L2_L2b chr1 10671072 10671101 + Simple_repeat_(TTTA)n chr1 10671665 10671710 + Simple_repeat_(GGCTGGA)n chr1 10672204 10672233 + Simple_repeat_(C)n chr1 10672454 10672460 + Simple_repeat_(CGCC)n chr1 10672461 10672499 + Simple_repeat_(CCGCCCT)n chr1 10672500 10672507 + Simple_repeat_(CGCC)n chr1 10676528 10676540 + Simple_repeat_(CTCTCCT)n chr1 10676541 10676577 + Simple_repeat_(CT)n chr1 10676578 10676584 + Simple_repeat_(CTCTCCT)n chr1 10680265 10680313 + Low_complexity_G-rich chr1 10680892 10681200 - SINE/Alu_AluSx1 chr1 10681235 10681270 + Simple_repeat_(ACCCTC)n chr1 10681394 10681700 + LINE/L2_L2b chr1 10684047 10684174 + SINE/MIR_MIRc chr1 10684176 10684207 + Low_complexity_G-rich chr1 10684577 10684672 - DNA/hAT-Blackjack_MER81 chr1 10688975 10689014 + Low_complexity_GA-rich chr1 10689845 10689961 - LINE/CR1_L3 chr1 10690984 10691022 + Simple_repeat_(TCCCCCC)n chr1 10691060 10691102 + Simple_repeat_(CCTC)n chr1 10692186 10692220 + Simple_repeat_(TGGA)n chr1 10693495 10693544 + Low_complexity_A-rich chr1 10694208 10694288 + Simple_repeat_(CGCCCGC)n chr1 10694462 10694527 + Simple_repeat_(GCGG)n chr1 10694517 10694561 + Simple_repeat_(CGGCCC)n chr1 10694585 10694622 + Simple_repeat_(CCG)n chr1 10694627 10694722 + Simple_repeat_(GCGCCC)n chr1 10695039 10695087 + Simple_repeat_(CCGCTGC)n chr1 10695550 10695604 + Simple_repeat_(C)n chr1 10699942 10700113 + Simple_repeat_(GAGACA)n chr1 10700081 10700117 + Simple_repeat_(AC)n chr1 10700604 10700723 + SINE/MIR_MIR chr1 10700724 10700937 + LINE/L1_L1MB7 chr1 10700938 10701040 + SINE/MIR_MIR chr1 10702946 10702986 + Simple_repeat_(GA)n chr1 10703485 10703612 - SINE/MIR_MIRc chr1 10703715 10703916 - DNA/hAT-Charlie_MER58A chr1 10706929 10706976 + Low_complexity_GA-rich chr1 10708058 10708179 - SINE/MIR_MIR3 chr1 10708761 10708840 + Low_complexity_GA-rich chr1 10708926 10709028 + Low_complexity_G-rich chr1 10709261 10709314 + Simple_repeat_(CTGCCCC)n chr1 10709591 10709669 + Low_complexity_GA-rich chr1 10710769 10710852 - LINE/L2_L2b chr1 10710853 10711152 + SINE/Alu_AluSx chr1 10711153 10711179 - LINE/L2_L2b chr1 10711168 10711303 + SINE/MIR_MIRb chr1 10711307 10711477 - LINE/L2_L2a chr1 10711415 10711494 + SINE/MIR_MIR chr1 10711537 10712061 + LINE/L1_L1MB5 chr1 10712064 10712353 + SINE/Alu_AluSq2 chr1 10712359 10712392 + Simple_repeat_(AAATA)n chr1 10713291 10713328 + Simple_repeat_(TTAT)n chr1 10714026 10714330 + SINE/Alu_AluSx chr1 10714348 10714467 - SINE/MIR_MIR chr1 10717382 10717407 + Simple_repeat_(T)n chr1 10717539 10717571 + Simple_repeat_(CCCTGC)n chr1 10718026 10718166 - SINE/MIR_MIRb chr1 10718767 10718858 - SINE/MIR_MIRb chr1 10718859 10719217 - SINE/Alu_AluSz chr1 10719218 10719359 - SINE/MIR_MIRb chr1 10720059 10720297 + SINE/MIR_MIRb chr1 10726001 10726101 - SINE/MIR_MIR chr1 10726104 10726144 + Simple_repeat_(ACT)n chr1 10726162 10726310 - SINE/MIR_MIRb chr1 10727147 10727193 + Low_complexity_GA-rich chr1 10728571 10728619 + Simple_repeat_(TTT)n chr1 10729091 10729130 + Low_complexity_GA-rich chr1 10729570 10729807 + DNA/hAT-Charlie_Charlie1a chr1 10729808 10730094 - SINE/Alu_AluSz chr1 10730095 10730108 + DNA/hAT-Charlie_Charlie1a chr1 10730109 10730333 - SINE/Alu_AluSx1 chr1 10730334 10730825 + DNA/hAT-Charlie_Charlie1a chr1 10730826 10731115 + SINE/Alu_AluSg chr1 10731116 10731301 + DNA/hAT-Charlie_Charlie1a chr1 10731302 10731613 + SINE/Alu_AluJb chr1 10731614 10732042 + DNA/hAT-Charlie_Charlie1a chr1 10732043 10732341 + SINE/Alu_AluSx chr1 10732342 10732440 + DNA/hAT-Charlie_Charlie1a chr1 10733444 10733715 - LINE/L2_L2b chr1 10733727 10733965 + SINE/MIR_MIR chr1 10734276 10734295 + Simple_repeat_(A)n chr1 10734651 10734701 + Low_complexity_GA-rich chr1 10736416 10736535 - DNA/hAT-Charlie_MER5A chr1 10736540 10736654 - SINE/MIR_MIR chr1 10736676 10736791 - LINE/L2_L2c chr1 10737076 10737151 - LINE/L2_L2c chr1 10737961 10738296 - LINE/L2_L2a chr1 10738919 10738944 + Simple_repeat_(T)n chr1 10740070 10740257 - SINE/MIR_MIRb chr1 10740363 10740430 + SINE/MIR_MIR3 chr1 10740671 10740986 + SINE/Alu_AluJr chr1 10741005 10741281 - SINE/Alu_AluSx chr1 10741781 10741813 + Simple_repeat_(TA)n chr1 10742702 10743004 + SINE/Alu_AluSz chr1 10743851 10743922 + Low_complexity_G-rich chr1 10745488 10745663 + DNA/hAT-Tip100_MER45A chr1 10745668 10745903 + SINE/MIR_MIRb chr1 10746036 10746177 - SINE/MIR_MIR1_Amn chr1 10747818 10748117 - SINE/Alu_AluY chr1 10748414 10748616 - SINE/MIR_MIRb chr1 10749254 10749546 - SINE/Alu_AluSc8 chr1 10750263 10750567 - SINE/Alu_AluJr chr1 10750569 10750617 + SINE/MIR_MIR chr1 10750618 10750936 + SINE/Alu_AluSx chr1 10750937 10751059 + SINE/MIR_MIR chr1 10751074 10751105 + Simple_repeat_(TAT)n chr1 10752690 10752753 - DNA/hAT_MER53 chr1 10752754 10753065 + SINE/Alu_AluSq2 chr1 10753066 10753200 - DNA/hAT_MER53 chr1 10753854 10754414 + LINE/L2_L2a chr1 10757269 10757505 + LINE/L2_L2c chr1 10757506 10757811 + SINE/Alu_AluSz chr1 10757812 10758046 + LINE/L2_L2c chr1 10757909 10758290 + LINE/L2_L2a chr1 10758321 10758631 - SINE/Alu_AluSz chr1 10760355 10760540 - SINE/MIR_MIR3 chr1 10763285 10763501 + SINE/MIR_MIRc chr1 10763533 10763781 + LINE/L2_L2c chr1 10763863 10764162 - SINE/Alu_AluSx chr1 10764311 10764380 - LINE/L2_L2b chr1 10764441 10764638 - SINE/MIR_MIRb chr1 10765385 10765413 + Low_complexity_A-rich chr1 10766181 10766228 + Simple_repeat_(CA)n chr1 10766256 10766474 + SINE/MIR_MIR chr1 10766729 10766927 - SINE/MIR_MIRb chr1 10768200 10768233 + Simple_repeat_(TTGT)n chr1 10768234 10768517 - SINE/Alu_AluSz chr1 10768860 10769160 + SINE/Alu_AluSz chr1 10769283 10769397 + DNA/hAT-Charlie_MER3 chr1 10769527 10769816 - SINE/Alu_AluSz chr1 10769817 10770030 - SINE/MIR_MIRb chr1 10770073 10770384 - SINE/Alu_AluJb chr1 10771314 10771335 + Simple_repeat_(TTA)n chr1 10771658 10771698 + Simple_repeat_(TCC)n chr1 10773021 10773050 + Simple_repeat_(AAGG)n chr1 10773375 10773445 + Low_complexity_G-rich chr1 10777053 10777202 + SINE/MIR_MIRc chr1 10779272 10779301 + Simple_repeat_(T)n chr1 10779466 10779500 + Simple_repeat_(CCCTCC)n chr1 10779564 10779805 - LINE/L1_L1ME4c chr1 10780719 10780945 + SINE/MIR_MIRb chr1 10781443 10781471 + Simple_repeat_(CCAGG)n chr1 10781749 10781923 - SINE/MIR_MIR chr1 10783247 10783288 + Simple_repeat_(GT)n chr1 10783591 10783893 + SINE/Alu_AluSz chr1 10783917 10784133 - SINE/MIR_MIRb chr1 10784334 10784508 + DNA/hAT-Charlie_MER3 chr1 10784555 10784865 - SINE/Alu_AluSz chr1 10785002 10785326 + Simple_repeat_(CTTC)n chr1 10785887 10786067 - SINE/MIR_MIRb chr1 10786560 10786695 - SINE/MIR_MIR chr1 10787040 10787259 - SINE/MIR_MIRc chr1 10789525 10789576 + Simple_repeat_(TC)n chr1 10791724 10791769 + Simple_repeat_(AG)n chr1 10792315 10792346 + Simple_repeat_(C)n chr1 10792367 10792393 + Low_complexity_G-rich chr1 10792675 10792710 + Simple_repeat_(CACCCC)n chr1 10792857 10792878 + Simple_repeat_(AATA)n chr1 10794135 10794199 + Simple_repeat_(GT)n chr1 10794882 10795001 + Simple_repeat_(CCGC)n chr1 10796378 10796435 + Simple_repeat_(CGCT)n chr1 10797252 10797353 - LINE/L2_L2a chr1 10797361 10797492 + SINE/Alu_FLAM_C chr1 10798591 10798664 - SINE/MIR_MIR3 chr1 10798718 10798762 + SINE/MIR_MIRb chr1 10798763 10798949 + SINE/MIR_MIR chr1 10799115 10799163 - LINE/L2_L2c chr1 10799724 10799915 + LINE/L2_L2b chr1 10799771 10800195 + LINE/L2_L2a chr1 10800625 10800722 + LINE/L2_L2b chr1 10800841 10801002 - SINE/MIR_MIR chr1 10801255 10801565 + SINE/Alu_AluSx chr1 10801571 10801613 + SINE/MIR_MIRb chr1 10801855 10802152 - SINE/Alu_AluSx chr1 10802345 10803012 - LINE/L1_L1ME4a chr1 10803615 10803652 + SINE/MIR_MIR3 chr1 10803946 10803998 + Simple_repeat_(GT)n chr1 10804725 10805008 + LINE/L2_L2c chr1 10805081 10805391 - SINE/Alu_AluSx1 chr1 10805408 10805621 + SINE/MIR_MIRb chr1 10805829 10805855 + Simple_repeat_(A)n chr1 10806086 10806379 + SINE/Alu_AluSx1 chr1 10806479 10806605 - SINE/MIR_MIR1_Amn chr1 10806868 10807368 + Simple_repeat_(ATCC)n chr1 10807369 10807467 - LINE/L2_L2a chr1 10807538 10807685 + SINE/MIR_MIR3 chr1 10808578 10808796 + SINE/MIR_MIR chr1 10808815 10808892 + SINE/MIR_MIR3 chr1 10809325 10809490 - SINE/MIR_MIRc chr1 10810643 10810819 + SINE/MIR_MIRb chr1 10811106 10811237 + LINE/L2_L2b chr1 10811518 10811708 + SINE/Alu_AluSz6 chr1 10811795 10811982 + SINE/MIR_MIRb chr1 10811985 10812305 - SINE/Alu_AluSp chr1 10812526 10812597 - SINE/MIR_MIRc chr1 10812534 10812678 - SINE/MIR_MIR3 chr1 10813218 10813345 - SINE/MIR_MIRb chr1 10813337 10813423 + LINE/L2_L2b chr1 10813467 10813709 - SINE/MIR_MIR chr1 10814467 10814481 - SINE/MIR_MIR3 chr1 10814482 10814729 + SINE/MIR_MIRb chr1 10814807 10814912 + DNA/hAT-Blackjack_MER81 chr1 10815502 10815584 - SINE/MIR_MIRb chr1 10816111 10816273 - SINE/MIR_MIRb chr1 10816310 10816511 + SINE/MIR_MIRb chr1 10817612 10817676 - LINE/L2_L2d chr1 10817806 10817893 + LINE/L2_L2d chr1 10817925 10818018 + Simple_repeat_(TTCC)n chr1 10818019 10818330 - SINE/Alu_AluSx1 chr1 10818359 10818541 - LINE/L2_L2d chr1 10819096 10819316 + SINE/MIR_MIRb chr1 10820005 10820111 + SINE/MIR_MIRb chr1 10820310 10820460 - SINE/MIR_MIR3 chr1 10820755 10820795 + Low_complexity_G-rich chr1 10820854 10821162 - SINE/Alu_AluSx1 chr1 10821219 10821350 - SINE/MIR_MIR chr1 10821355 10821544 + LINE/L2_L2b chr1 10821563 10821687 - LINE/L2_L2a chr1 10821734 10821822 + DNA/hAT-Charlie_Charlie16a chr1 10821823 10822134 - SINE/Alu_AluSx1 chr1 10822138 10822441 - SINE/Alu_AluJr chr1 10822442 10822513 + DNA/hAT-Charlie_Charlie16a chr1 10822690 10822882 + SINE/MIR_MIR3 chr1 10823068 10823152 + DNA/hAT?_UCON105 chr1 10823383 10823531 - SINE/MIR_MIR chr1 10823597 10823888 + SINE/Alu_AluSz chr1 10824633 10824722 + Low_complexity_A-rich chr1 10824813 10824855 + Simple_repeat_(GCCCCAC)n chr1 10825245 10825437 + SINE/MIR_MIR1_Amn chr1 10825448 10825615 - SINE/MIR_MIR chr1 10826227 10826524 + SINE/Alu_AluSz chr1 10826543 10826553 + Simple_repeat_(AAT)n chr1 10826554 10826844 - SINE/Alu_AluSx3 chr1 10826845 10826878 + Simple_repeat_(AAT)n chr1 10827048 10827200 - SINE/MIR_MIRb chr1 10827309 10827363 + Simple_repeat_(TGAA)n chr1 10827366 10827495 - SINE/MIR_MIRb chr1 10828271 10828313 + Simple_repeat_(CCCTC)n chr1 10828931 10828952 - SINE/MIR_MIRb chr1 10828953 10828977 + Simple_repeat_(GTTTT)n chr1 10828978 10829037 - SINE/MIR_MIRb chr1 10829038 10829346 + SINE/Alu_AluJb chr1 10829347 10829431 - SINE/MIR_MIRb chr1 10829590 10829813 + LINE/L1_L1ME4a chr1 10830463 10830553 + DNA/hAT-Tip100_MamTip2b chr1 10830708 10830949 + SINE/MIR_MIR chr1 10831067 10831105 + Low_complexity_GA-rich chr1 10831459 10831541 - SINE/MIR_MIRb chr1 10832060 10832371 - SINE/Alu_AluSx1 chr1 10832593 10832731 + DNA/TcMar-Tigger_X6a_DNA chr1 10833161 10833276 + LINE/L2_L2c chr1 10833383 10833686 + SINE/Alu_AluJb chr1 10833687 10833873 - LINE/L2_L2c chr1 10834372 10834460 - LINE/L2_L2b chr1 10835511 10835698 - SINE/MIR_MIR chr1 10836323 10836361 + SINE/MIR_MIRc chr1 10836364 10836513 - LINE/L2_L2a chr1 10836514 10836825 + SINE/Alu_AluSx1 chr1 10836826 10836991 - LINE/L2_L2a chr1 10837315 10837361 - SINE/MIR_MIR3 chr1 10837672 10837847 + SINE/Alu_AluJb chr1 10837899 10838089 + SINE/MIR_MIRc chr1 10838202 10838317 - SINE/MIR_MIRc chr1 10840601 10840614 + Simple_repeat_(TACATG)n chr1 10840615 10840925 + SINE/Alu_AluY chr1 10840926 10840976 + Simple_repeat_(TACATG)n chr1 10841676 10841960 - SINE/MIR_MIRb chr1 10842137 10842432 - SINE/Alu_AluSx1 chr1 10842434 10842918 + LINE/L2_L2a chr1 10843046 10843213 + LINE/L2_L2a chr1 10843214 10843257 + Simple_repeat_(TATT)n chr1 10843258 10843541 - SINE/Alu_AluJr chr1 10843542 10843612 + LINE/L2_L2a chr1 10843648 10844280 - LINE/L1_L1MEc chr1 10844298 10844583 - SINE/Alu_AluJb chr1 10844585 10844722 - LINE/L1_L1MC3 chr1 10844723 10844843 + SINE/Alu_AluJr4 chr1 10844850 10845005 - LINE/L1_L1MC3 chr1 10845006 10845208 - SINE/Alu_AluJr chr1 10845209 10845830 - LINE/L1_L1MC3 chr1 10845831 10846140 - SINE/Alu_AluJb chr1 10846147 10846434 - SINE/Alu_AluJb chr1 10846435 10846814 - LINE/L1_L1MC3 chr1 10846808 10847057 - LINE/L1_L1MEc chr1 10847067 10847274 - LINE/L1_L1MEc chr1 10847275 10847568 - SINE/Alu_AluSz chr1 10847569 10847668 - LINE/L1_L1MEc chr1 10847669 10847793 - SINE/Alu_FLAM_A chr1 10847794 10847866 - LINE/L1_L1MEc chr1 10847867 10847959 - LINE/L2_L2a chr1 10848012 10848306 + SINE/Alu_AluSx chr1 10848307 10848354 - DNA/hAT-Charlie_MER58D chr1 10848352 10848679 - DNA/hAT-Charlie_Cheshire chr1 10848681 10848726 - LINE/L2_L2a chr1 10848727 10849039 + SINE/Alu_AluSz chr1 10849040 10849213 - LINE/L2_L2a chr1 10849473 10849599 + SINE/MIR_MIRb chr1 10849934 10849991 + LINE/L1_L1MEc chr1 10850227 10850482 + LINE/L2_L2c chr1 10850493 10850619 - SINE/MIR_MIR3 chr1 10850654 10850692 + SINE/MIR_MIRc chr1 10850838 10850871 + SINE/MIR_MIRb chr1 10850909 10851187 - SINE/Alu_AluSz chr1 10852178 10852390 - LINE/L2_L2a chr1 10854093 10854357 + LTR/ERV1_MER83 chr1 10854358 10854428 - LTR/ERVL-MaLR_THE1D chr1 10854429 10854737 + SINE/Alu_AluSc8 chr1 10854738 10855033 - LTR/ERVL-MaLR_THE1D chr1 10855034 10855185 + LTR/ERV1_MER83 chr1 10855203 10855302 + Simple_repeat_(CAT)n chr1 10855533 10855828 + LINE/L2_L2c chr1 10857506 10857734 + Simple_repeat_(TC)n chr1 10858632 10858873 - LINE/L2_L2a chr1 10859002 10859124 - SINE/MIR_MIRb chr1 10859125 10859431 + SINE/Alu_AluSx1 chr1 10859437 10859734 + SINE/Alu_AluJb chr1 10859735 10859879 - SINE/MIR_MIRb chr1 10860274 10860353 + DNA/hAT-Charlie_UCON81 chr1 10861540 10861653 + SINE/MIR_MIR3 chr1 10862626 10862754 + SINE/MIR_MIRb chr1 10863309 10863335 + Simple_repeat_(GT)n chr1 10864296 10864322 + Simple_repeat_(TG)n chr1 10865282 10865331 + Simple_repeat_(CT)n chr1 10865779 10865820 + Simple_repeat_(GACA)n chr1 10865898 10866161 + Low_complexity_A-rich chr1 10865899 10866160 + Low_complexity_GA-rich chr1 10867071 10867145 + Simple_repeat_(CCCCGC)n chr1 10867222 10867259 + Low_complexity_G-rich chr1 10868287 10868454 - SINE/MIR_MIRb chr1 10868603 10868662 - DNA/hAT-Charlie_MER102a chr1 10868784 10868942 + SINE/MIR_MIRc chr1 10869022 10869253 - LTR/ERVL_LTR16C chr1 10869564 10869633 - LINE/L2_L2b chr1 10869973 10870098 - DNA/hAT-Charlie_MER5A chr1 10870577 10870701 + Simple_repeat_(TCAT)n chr1 10870702 10870844 - LINE/L2_L2a chr1 10870845 10870907 - LINE/L2_L2b chr1 10870920 10870948 + Simple_repeat_(ATTT)n chr1 10871226 10871537 - SINE/Alu_AluYm1 chr1 10871550 10871864 - SINE/Alu_AluSx chr1 10871997 10872236 - LINE/L2_L2b chr1 10872626 10872877 - SINE/MIR_MIRb chr1 10872882 10872918 + Low_complexity_G-rich chr1 10873559 10873723 - SINE/MIR_MIR3 chr1 10873727 10873749 - DNA/hAT-Charlie_Charlie4z chr1 10873750 10874053 + SINE/Alu_AluSz chr1 10874054 10874119 - DNA/hAT-Charlie_Charlie4z chr1 10874225 10874337 + SINE/MIR_MIRb chr1 10874586 10874632 - SINE/MIR_MIRc chr1 10875604 10875788 - SINE/Alu_AluJb chr1 10875789 10876088 + SINE/Alu_AluSp chr1 10876089 10876124 - SINE/Alu_AluJb chr1 10876144 10876633 - LINE/L1_L1MB5 chr1 10876808 10876848 - SINE/Alu_FLAM_A chr1 10876849 10877146 - SINE/Alu_AluSx3 chr1 10877523 10877583 - SINE/MIR_MIRc chr1 10877830 10878030 + SINE/MIR_MIRb chr1 10878922 10879048 - SINE/MIR_MIR3 chr1 10879066 10879365 + SINE/Alu_AluJb chr1 10879368 10879683 + SINE/Alu_AluSg chr1 10879750 10879828 + SINE/MIR_MIR chr1 10880094 10880182 + LINE/L2_L2b chr1 10880218 10880476 + LINE/L2_L2a chr1 10880477 10880772 - SINE/Alu_AluJb chr1 10880773 10880805 + LINE/L2_L2a chr1 10880806 10881101 - DNA/hAT-Charlie_MER33 chr1 10881102 10881324 + LINE/L2_L2a chr1 10881325 10881629 + SINE/Alu_AluSq2 chr1 10881630 10881839 + LINE/L2_L2a chr1 10881840 10882141 + SINE/Alu_AluSz6 chr1 10882142 10882222 + LINE/L2_L2a chr1 10882250 10882325 + LINE/L2_L2a chr1 10882343 10882489 + LINE/L2_L2a chr1 10882490 10882797 - SINE/Alu_AluSc5 chr1 10882798 10882912 + LINE/L2_L2a chr1 10882931 10883157 - LINE/L1_L1M5 chr1 10883158 10883447 - SINE/Alu_AluSx1 chr1 10883450 10883774 - SINE/Alu_AluJb chr1 10883775 10883923 - LINE/L1_L1M5 chr1 10883989 10884162 - SINE/Alu_AluJo chr1 10884355 10884634 + LINE/L1_L1ME3G chr1 10884635 10884944 - SINE/Alu_AluSx chr1 10884949 10885251 - SINE/Alu_AluJb chr1 10885252 10885606 + LINE/L1_L1ME3G chr1 10885612 10885924 + SINE/Alu_AluY chr1 10885925 10886297 + LINE/L1_L1MB8 chr1 10886298 10886598 + SINE/Alu_AluSz6 chr1 10886599 10887048 + LINE/L1_L1MB8 chr1 10887049 10887340 - SINE/Alu_AluJr chr1 10887341 10887413 + LINE/L1_L1MB8 chr1 10887529 10887716 + DNA/hAT-Tip100_MER96B chr1 10887717 10887858 + SINE/Alu_AluJb chr1 10887859 10888164 + SINE/Alu_AluSx3 chr1 10888165 10888291 + SINE/Alu_AluJb chr1 10888293 10888320 + Simple_repeat_(AAAT)n chr1 10888321 10888421 + DNA/hAT-Tip100_MER96B chr1 10888715 10888749 + Simple_repeat_(CGCCC)n chr1 10889111 10889330 + LINE/L2_L2b chr1 10889456 10889567 + SINE/MIR_MIRb chr1 10889822 10889895 + LINE/L2_L2a chr1 10889957 10890412 + LINE/L2_L2a chr1 10891065 10891152 - DNA/hAT-Tip100_Zaphod2 chr1 10891165 10891517 - LTR/ERVL-MaLR_MLT1I chr1 10891583 10891676 + LINE/L2_L2a chr1 10891677 10891988 + SINE/Alu_AluSq2 chr1 10891989 10892457 + LINE/L2_L2a chr1 10892306 10892597 + LINE/L2_L2a chr1 10892599 10892945 + Simple_repeat_(TTTC)n chr1 10892946 10892976 + Simple_repeat_(TCCC)n chr1 10892977 10893306 - SINE/Alu_AluSx chr1 10893307 10893461 + LINE/L2_L2a chr1 10893485 10893623 + SINE/MIR_MIR chr1 10893624 10893919 + SINE/Alu_AluSx1 chr1 10893920 10894020 + SINE/MIR_MIR chr1 10894030 10894161 + LINE/L1_L1ME4a chr1 10894162 10894469 - SINE/Alu_AluSp chr1 10894470 10894601 + LINE/L1_L1ME4a chr1 10894602 10894911 - SINE/Alu_AluJb chr1 10895194 10895228 + Simple_repeat_(TTATTTC)n chr1 10895409 10895720 + SINE/Alu_AluSq2 chr1 10895993 10896027 + Simple_repeat_(TCTTG)n chr1 10896028 10896335 - SINE/Alu_AluSz chr1 10896480 10896589 - LTR/ERVL_MER21B chr1 10897224 10897346 + LINE/L2_L2d2 chr1 10898020 10898047 + Simple_repeat_(GT)n chr1 10899293 10899591 + SINE/Alu_AluSz chr1 10899592 10899637 + Simple_repeat_(GGAA)n chr1 10899660 10899719 - SINE/MIR_MIRc chr1 10899775 10899797 + Simple_repeat_(GGA)n chr1 10899863 10900079 + SINE/MIR_MIRc chr1 10900921 10901094 - DNA/hAT?_MER121 chr1 10902149 10902217 - SINE/MIR_MIR chr1 10902978 10903026 - SINE/MIR_MIR chr1 10903102 10903412 - SINE/Alu_AluSz6 chr1 10903421 10903503 + LINE/L2_L2a chr1 10903856 10903960 - SINE/MIR_MIRb chr1 10904460 10904655 + SINE/MIR_MIRb chr1 10905357 10905383 + Low_complexity_G-rich chr1 10905883 10905901 + Simple_repeat_(T)n chr1 10908054 10908298 - SINE/MIR_MIRc chr1 10908341 10908982 + LINE/L2_L2a chr1 10909017 10909313 + SINE/Alu_AluSz chr1 10909762 10909952 + SINE/MIR_MIRb chr1 10910842 10911013 + SINE/MIR_MIR chr1 10911127 10911219 + Simple_repeat_(CA)n chr1 10911906 10912074 + SINE/MIR_MIRb chr1 10912075 10912376 + SINE/Alu_AluSx chr1 10912377 10912429 + SINE/MIR_MIRb chr1 10912465 10912677 - SINE/MIR_MIRc chr1 10912891 10912960 - SINE/MIR_MIR3 chr1 10913100 10913529 - LINE/L2_L2c chr1 10913530 10914108 - LTR/ERVL-MaLR_MLT1G1 chr1 10914880 10915017 + LINE/L2_L2b chr1 10915025 10915045 + LINE/L2_L2c chr1 10915046 10915366 + SINE/Alu_AluY chr1 10915367 10915431 + LINE/L2_L2c chr1 10915453 10915529 + LINE/L2_L2b chr1 10916732 10916838 + LINE/L2_L2c chr1 10916998 10917127 + SINE/MIR_MIR chr1 10917196 10917355 + LTR/ERV1?_LTR103b_Mam chr1 10917356 10917667 + SINE/Alu_AluJb chr1 10917668 10917791 + Low_complexity_GA-rich chr1 10917894 10917981 - SINE/MIR_MIRb chr1 10918077 10918295 - SINE/MIR_MIRb chr1 10918366 10918483 - DNA/hAT-Charlie_MER5B chr1 10918537 10918706 - DNA/hAT-Charlie_MER5A1 chr1 10918715 10918808 - DNA/hAT-Charlie_MER5B chr1 10918824 10918938 + DNA/hAT-Charlie_MER5B chr1 10918939 10919241 - SINE/Alu_AluY chr1 10919242 10919305 + DNA/hAT-Charlie_MER5B chr1 10919422 10919682 - SINE/Alu_AluSx1 chr1 10919707 10920013 - SINE/Alu_AluSp chr1 10920347 10920537 + SINE/MIR_MIRc chr1 10920637 10920994 - LINE/L1_L1ME4b chr1 10921302 10921376 - LTR/ERVL-MaLR_MLT1N2 chr1 10921377 10921668 + SINE/Alu_AluY chr1 10921751 10922120 + LINE/L1_L1MD chr1 10922121 10922434 + SINE/Alu_AluSp chr1 10922435 10922531 + LINE/L1_L1MD chr1 10922532 10922826 + SINE/Alu_AluSx chr1 10922827 10922969 + LINE/L1_L1MD chr1 10922964 10923452 - LINE/L1_L1MB5 chr1 10923453 10923741 - SINE/Alu_AluJb chr1 10923742 10923928 - LINE/L1_L1MB5 chr1 10923940 10924232 - SINE/Alu_AluJr chr1 10924242 10924353 - LINE/L1_L1MB5 chr1 10924354 10924747 + LTR/ERVL-MaLR_MLT1B chr1 10924748 10925157 - LINE/L1_L1MB5 chr1 10925158 10925201 - DNA/hAT-Blackjack_MER63D chr1 10925202 10925496 + SINE/Alu_AluSz chr1 10925497 10925655 - DNA/hAT-Blackjack_MER63D chr1 10925656 10925937 + SINE/Alu_AluJb chr1 10925938 10925957 - DNA/hAT-Blackjack_MER63D chr1 10925958 10926208 + SINE/Alu_AluSg chr1 10926209 10926260 - DNA/hAT-Blackjack_MER63D chr1 10926261 10926598 + LTR/ERV1_MER110A chr1 10926599 10926788 - SINE/Alu_AluSx1 chr1 10926789 10926958 + LTR/ERV1_MER110A chr1 10926959 10927412 - DNA/hAT-Blackjack_MER63D chr1 10927388 10927527 + SINE/Alu_FLAM_C chr1 10927531 10927566 + Simple_repeat_(AAT)n chr1 10927568 10927636 - DNA/hAT-Blackjack_MER63D chr1 10927637 10927944 - SINE/Alu_AluJb chr1 10927945 10927987 - DNA/hAT-Blackjack_MER63D chr1 10927988 10928286 - SINE/Alu_AluY chr1 10928287 10928404 - DNA/hAT-Blackjack_MER63D chr1 10928405 10928560 + LINE/L1_L1ME1 chr1 10928567 10928763 - LTR/ERVL-MaLR_MLT1N2 chr1 10928820 10928996 - LTR/ERVL-MaLR_MLT1N2 chr1 10929237 10929331 - LINE/L2_L2d2 chr1 10929341 10929633 + SINE/Alu_AluSz chr1 10929645 10929953 + SINE/Alu_AluSq chr1 10929954 10930244 + SINE/Alu_AluYm1 chr1 10930434 10930558 + SINE/Alu_AluJr chr1 10930559 10930855 - SINE/Alu_AluSc8 chr1 10930856 10931032 + SINE/Alu_AluJr chr1 10931053 10931193 + LINE/L1_L1MB3 chr1 10931194 10931384 + LTR/ERVL-MaLR_MSTB chr1 10931385 10931685 + SINE/Alu_AluSz6 chr1 10931692 10931998 + SINE/Alu_AluSz chr1 10931999 10932223 + LTR/ERVL-MaLR_MSTB chr1 10932224 10932421 + LINE/L1_L1MB3 chr1 10932422 10932548 + SINE/Alu_FLAM_C chr1 10932549 10932598 + LINE/L1_L1MB3 chr1 10932599 10932640 + Simple_repeat_(TA)n chr1 10932641 10932932 - SINE/Alu_AluY chr1 10932935 10933234 - SINE/Alu_AluSz chr1 10933235 10933245 + Simple_repeat_(TA)n chr1 10933252 10933293 + Simple_repeat_(AC)n chr1 10933313 10933512 + DNA/hAT-Charlie_MER30 chr1 10933677 10933760 + Simple_repeat_(TG)n chr1 10933996 10934249 + SINE/Alu_AluSz chr1 10934251 10934336 - LINE/L2_L2b chr1 10935300 10935334 + Simple_repeat_(ACCTCC)n chr1 10935766 10936031 - SINE/MIR_MIR chr1 10936108 10936180 + LINE/L2_L2b chr1 10936183 10936235 + Simple_repeat_(TTCA)n chr1 10936272 10936315 + SINE/MIR_MIRc chr1 10936318 10936617 + SINE/Alu_AluSx chr1 10936666 10936972 + SINE/Alu_AluSp chr1 10936973 10937240 - DNA/hAT-Charlie_MER33 chr1 10937340 10937376 + Simple_repeat_(GCCTT)n chr1 10937937 10937982 - LINE/L2_L2a chr1 10938045 10938347 + SINE/Alu_AluSz6 chr1 10939224 10939381 - SINE/MIR_MIRb chr1 10939664 10939853 + DNA/hAT-Charlie_MER113A chr1 10940353 10940384 + Simple_repeat_(GGA)n chr1 10940710 10940794 + SINE/MIR_MIR chr1 10940839 10940890 + SINE/MIR_MIRb chr1 10941030 10941179 + SINE/MIR_MIR3 chr1 10941361 10941402 + Simple_repeat_(CA)n chr1 10942646 10942856 + SINE/MIR_MIRc chr1 10942943 10943119 - DNA/hAT-Charlie_MER5A chr1 10943477 10943628 - SINE/MIR_MIR3 chr1 10943686 10943979 + SINE/Alu_AluSx chr1 10944623 10944786 - DNA/hAT_UCON73 chr1 10944959 10945215 + SINE/Alu_AluSg chr1 10945346 10945893 + LINE/L2_L2a chr1 10946478 10946498 + Simple_repeat_(TTAT)n chr1 10947023 10947268 - SINE/MIR_MIRb chr1 10948943 10949244 + SINE/Alu_AluSz chr1 10949860 10949941 - SINE/MIR_MIRb chr1 10950028 10950334 - SINE/Alu_AluSz chr1 10950533 10950735 - DNA/hAT-Charlie_MER20 chr1 10950742 10951020 + DNA/hAT-Charlie_Charlie8 chr1 10951152 10951282 + SINE/Alu_AluJo chr1 10951283 10951566 + SINE/Alu_AluSq2 chr1 10951567 10951746 + SINE/Alu_AluJo chr1 10951747 10951765 + Simple_repeat_(TA)n chr1 10951767 10951788 + Simple_repeat_(CA)n chr1 10952033 10952238 - LINE/L2_L2d2 chr1 10952460 10952522 + LINE/L2_L2d2 chr1 10952523 10952811 - SINE/Alu_AluSz chr1 10952812 10952947 + LINE/L2_L2d2 chr1 10953592 10953745 - SINE/MIR_MIRb chr1 10954174 10954482 + SINE/Alu_AluSc8 chr1 10954639 10954892 - SINE/Alu_AluJb chr1 10955561 10955870 + SINE/Alu_AluSq chr1 10956472 10956776 - SINE/Alu_AluY chr1 10956906 10956961 + Low_complexity_GA-rich chr1 10958053 10958384 + LINE/CR1_L3 chr1 10958421 10958546 - LINE/CR1_L3 chr1 10959228 10959325 - DNA/hAT-Blackjack_MER81 chr1 10960106 10960430 + SINE/Alu_AluSx1 chr1 10961563 10961660 + LINE/L2_L2b chr1 10961662 10961966 + SINE/Alu_AluSz chr1 10962015 10962076 + Simple_repeat_(CCT)n chr1 10962102 10962163 + DNA/hAT-Charlie_Charlie8 chr1 10962164 10962465 + SINE/Alu_AluSz chr1 10962466 10962552 + DNA/hAT-Charlie_Charlie8 chr1 10962644 10962832 + SINE/MIR_MIR chr1 10962838 10963067 + SINE/Alu_AluJr chr1 10963107 10963418 + SINE/Alu_AluSz chr1 10964539 10964636 - SINE/MIR_MIRc chr1 10964637 10964930 - SINE/Alu_AluSz6 chr1 10964931 10965038 - SINE/MIR_MIRc chr1 10965447 10965578 + SINE/Alu_AluJb chr1 10965580 10965846 + SINE/Alu_AluSz chr1 10965956 10966181 - LINE/L2_L2b chr1 10966205 10966231 + LINE/L2_L2a chr1 10966647 10966866 + LINE/L2_L2a chr1 10966867 10967173 + SINE/Alu_AluSz6 chr1 10967174 10967689 + LINE/L2_L2a chr1 10967690 10968000 + SINE/Alu_AluSx1 chr1 10968001 10968175 + SINE/Alu_AluSg4 chr1 10968199 10968298 + LINE/L1_L1ME4a chr1 10969587 10969932 - LINE/L2_L2a chr1 10970094 10970187 + Simple_repeat_(GAAG)n chr1 10970346 10970635 + SINE/Alu_AluSz chr1 10970643 10970778 - DNA/hAT-Charlie_MER5A1 chr1 10970864 10971165 + SINE/Alu_AluSz chr1 10971226 10971273 + Simple_repeat_(GCAGAGC)n chr1 10971279 10971429 + LINE/L2_L2b chr1 10971441 10971743 + LINE/L2_L2b chr1 10971762 10971863 + LINE/L2_L2b chr1 10971934 10972075 + LINE/L2_L2a chr1 10972259 10972419 + SINE/Alu_FAM chr1 10972421 10972466 + Simple_repeat_(AC)n chr1 10972566 10972681 + SINE/MIR_MIRb chr1 10972686 10972804 + LTR/ERVL_MER77 chr1 10972805 10972945 + SINE/Alu_FLAM_C chr1 10972946 10973090 + LTR/ERVL_MER77 chr1 10973091 10973396 + SINE/Alu_AluSx1 chr1 10973397 10973587 + LTR/ERVL_MER77 chr1 10973588 10974728 + LTR/ERV1_MER52C chr1 10974729 10974923 + LTR/ERVL_MER77 chr1 10974945 10974989 + SINE/MIR_MIRb chr1 10974990 10975125 + SINE/Alu_AluSx3 chr1 10975126 10975431 + SINE/Alu_AluSg chr1 10975432 10975615 + SINE/Alu_AluSx3 chr1 10975616 10975674 + SINE/MIR_MIRb chr1 10975632 10975682 + LINE/L2_L2b chr1 10975710 10975904 + SINE/MIR_MIR chr1 10976334 10976649 - SINE/Alu_AluSg7 chr1 10976701 10976747 + Low_complexity_A-rich chr1 10976874 10977154 + SINE/Alu_AluJb chr1 10977155 10977192 - SINE/Alu_Alu chr1 10977217 10977506 + SINE/Alu_AluSx chr1 10977509 10977835 - LTR/ERV1_LTR37A chr1 10977889 10978189 + SINE/Alu_AluSq2 chr1 10978194 10978348 - SINE/Alu_AluJb chr1 10978364 10978427 - LINE/L2_L2a chr1 10978428 10978722 + SINE/Alu_AluJr chr1 10978723 10978991 - LINE/L2_L2a chr1 10978992 10979291 - SINE/Alu_AluSz6 chr1 10979292 10979360 - LINE/L2_L2a chr1 10979423 10979765 + LINE/L1_L1ME3A chr1 10979766 10980067 + SINE/Alu_AluSp chr1 10980159 10980213 + Low_complexity_G-rich chr1 10980993 10981104 - DNA/hAT-Tip100_MamTip3 chr1 10981167 10981458 + SINE/Alu_AluJr chr1 10981467 10981571 + Simple_repeat_(AAGG)n chr1 10982466 10982762 + SINE/Alu_AluJr chr1 10982765 10983076 + SINE/Alu_AluSq2 chr1 10983091 10983387 + SINE/Alu_AluSg chr1 10983389 10983605 + LTR/ERVL-MaLR_MLT1B chr1 10983609 10983655 + SINE/MIR_MIR3 chr1 10983775 10983821 + SINE/Alu_Alu chr1 10983823 10983845 + Simple_repeat_(AG)n chr1 10983846 10984124 + SINE/Alu_AluSx chr1 10984126 10984358 - LTR/ERVL-MaLR_MLT1C2 chr1 10984355 10984418 - SINE/Alu_AluSc chr1 10984420 10984483 - SINE/Alu_FLAM_C chr1 10984484 10984597 - LTR/ERVL-MaLR_MLT1C2 chr1 10984599 10984807 - SINE/Alu_AluJb chr1 10984808 10985099 + SINE/Alu_AluSz chr1 10985100 10985250 - SINE/Alu_AluJb chr1 10985253 10985556 - SINE/Alu_AluSg7 chr1 10986256 10986559 - SINE/Alu_AluSc8 chr1 10987019 10987330 + SINE/Alu_AluSx1 chr1 10987331 10987533 + Simple_repeat_(GAAA)n chr1 10987547 10987844 + SINE/Alu_AluJo chr1 10987845 10988004 + SINE/Alu_AluJb chr1 10988005 10988055 + Simple_repeat_(GA)n chr1 10988056 10988112 + Simple_repeat_(AGGA)n chr1 10988287 10988584 + DNA/hAT-Charlie_Charlie4 chr1 10988613 10988841 + SINE/Alu_AluSx chr1 10989323 10989409 + LTR/ERVL_LTR84b chr1 10989415 10989458 - SINE/MIR_MIR chr1 10989483 10989798 + SINE/Alu_AluSq chr1 10989799 10989838 + Simple_repeat_(TTAT)n chr1 10989849 10990200 - LTR/ERVL-MaLR_THE1B chr1 10990460 10990761 + SINE/Alu_AluJb chr1 10991095 10991408 - SINE/Alu_AluSz chr1 10991825 10992130 + SINE/Alu_AluJo chr1 10992131 10992196 + Simple_repeat_(AAGG)n chr1 10992218 10992497 + SINE/Alu_AluSz6 chr1 10992500 10992562 + Low_complexity_GA-rich chr1 10992602 10992750 - SINE/MIR_MIR chr1 10992754 10992898 + LTR/ERVL_LTR84b chr1 10992934 10993034 + LINE/L1_L1M5 chr1 10993149 10993246 + SINE/MIR_MIRb chr1 10993273 10993356 + LINE/L2_L2a chr1 10993487 10993590 + SINE/Alu_AluSx1 chr1 10993591 10993896 + SINE/Alu_AluSx chr1 10993897 10994063 + SINE/Alu_AluSx1 chr1 10994066 10994404 + SINE/Alu_AluJb chr1 10994431 10994737 - SINE/Alu_AluSc5 chr1 10994738 10994770 - DNA/hAT-Charlie_MER58B chr1 10994783 10994861 - DNA/hAT-Charlie_MER58B chr1 10994862 10995164 - SINE/Alu_AluSz6 chr1 10995165 10995302 - DNA/hAT-Charlie_MER58B chr1 10995328 10995495 - SINE/Alu_AluJo chr1 10995496 10995805 - SINE/Alu_AluSx chr1 10995806 10995940 - SINE/Alu_AluJo chr1 10995980 10996140 - SINE/Alu_AluSx1 chr1 10996407 10996530 - DNA/hAT-Charlie_MER58B chr1 10996531 10996833 + SINE/Alu_AluSx chr1 10996834 10996959 - DNA/hAT-Charlie_MER58B chr1 10996960 10997255 + SINE/Alu_AluSx1 chr1 10997256 10997298 + Simple_repeat_(AT)n chr1 10997299 10997348 - DNA/hAT-Charlie_MER58B chr1 10997368 10997409 + LINE/L2_L2c chr1 10997480 10997558 + LINE/L1_L1ME5 chr1 10997559 10997867 + SINE/Alu_AluSz chr1 10997868 10997966 + LINE/L1_L1ME5 chr1 10998003 10998300 - SINE/Alu_AluJo chr1 10998301 10998472 + SINE/MIR_MIR chr1 10998497 10998544 + LTR/ERVL_LTR84a chr1 10998558 10998716 + SINE/MIR_MIR chr1 10998753 10998821 + LTR/ERVL_LTR84a chr1 10998921 10999229 + SINE/Alu_AluSx1 chr1 10999239 10999535 + LINE/L1_HAL1 chr1 10999536 10999846 - SINE/Alu_AluSp chr1 10999856 10999945 - scRNA_HY1 chr1 10999946 11000287 + LINE/L1_HAL1 chr1 11000288 11000582 + SINE/Alu_AluSz chr1 11000583 11000961 + LINE/L1_HAL1 chr1 11000962 11001238 + SINE/Alu_AluJb chr1 11001239 11001404 + LINE/L1_HAL1 chr1 11001499 11001805 + SINE/Alu_AluY chr1 11001865 11002164 + SINE/Alu_AluSz chr1 11002165 11002473 + SINE/Alu_AluSp chr1 11002489 11002546 + LINE/L1_L1MC5a chr1 11002547 11002826 + SINE/Alu_AluSg chr1 11002827 11002874 + Simple_repeat_(AAACA)n chr1 11002875 11003145 + LINE/L1_L1MC5a chr1 11003146 11003429 - SINE/Alu_AluJr chr1 11003430 11003606 + LINE/L1_L1MC5a chr1 11003607 11003904 + SINE/Alu_AluJr chr1 11003907 11004218 + SINE/Alu_AluSg4 chr1 11004219 11004425 + LINE/L1_L1MC5a chr1 11004426 11004726 - SINE/Alu_AluY chr1 11004727 11005121 + LINE/L1_L1MC5a chr1 11005122 11005434 + SINE/Alu_AluJb chr1 11005435 11005649 + LINE/L1_L1MC5a chr1 11005671 11005914 - SINE/Alu_AluSx3 chr1 11005923 11006232 - SINE/Alu_AluSc8 chr1 11006241 11006438 + LINE/L1_L1MC4 chr1 11006457 11006725 + SINE/Alu_AluY chr1 11006748 11007063 - SINE/Alu_AluJb chr1 11007104 11007298 + LINE/L1_L1MC4 chr1 11007299 11007590 - SINE/Alu_AluSx chr1 11007591 11007756 + LINE/L1_L1MC4 chr1 11007881 11008146 - SINE/Alu_AluJb chr1 11008156 11008441 - SINE/Alu_AluSp chr1 11008473 11008631 + SINE/Alu_AluJr4 chr1 11008634 11008672 + Simple_repeat_(ATAA)n chr1 11008674 11008967 + SINE/Alu_AluYc chr1 11009003 11009301 - SINE/Alu_AluSq2 chr1 11009440 11009733 + SINE/Alu_AluSz chr1 11009784 11009889 + SINE/MIR_MIRc chr1 11009855 11009924 + LINE/L2_L2d2 chr1 11010071 11010231 + SINE/MIR_MIRb chr1 11010235 11010531 + SINE/Alu_AluJr chr1 11010685 11010996 + SINE/Alu_AluYk4 chr1 11011022 11011271 + SINE/Alu_AluSx chr1 11011272 11011402 + Low_complexity_A-rich chr1 11011403 11011686 + SINE/Alu_AluSz6 chr1 11011715 11011731 + Simple_repeat_(TAT)n chr1 11011836 11011988 - SINE/Alu_AluSx3 chr1 11011991 11012127 + SINE/MIR_MIR chr1 11012279 11012317 + Simple_repeat_(GCCCC)n chr1 11013213 11013515 + LINE/L2_L2c chr1 11014657 11014962 + SINE/Alu_AluSq2 chr1 11014965 11015118 + SINE/Alu_AluSg4 chr1 11015204 11015491 + SINE/Alu_AluSx chr1 11015524 11015681 + SINE/Alu_AluSx chr1 11015734 11015923 - SINE/Alu_AluSg7 chr1 11015925 11016238 - SINE/Alu_AluSg4 chr1 11016266 11016454 - SINE/Alu_AluJr chr1 11017081 11017184 - SINE/Alu_AluJo chr1 11017199 11017500 - SINE/Alu_AluSx1 chr1 11017763 11017877 + SINE/Alu_FLAM_C chr1 11017878 11018181 - SINE/Alu_AluY chr1 11018186 11018488 - SINE/Alu_AluSz6 chr1 11019312 11019571 + DNA/TcMar-Tigger_Tigger3a chr1 11019572 11019865 - SINE/Alu_AluY chr1 11019867 11020159 - SINE/Alu_AluSq2 chr1 11020160 11020224 + DNA/TcMar-Tigger_Tigger3a chr1 11020627 11020923 + SINE/Alu_AluSc chr1 11021141 11021440 - SINE/Alu_AluSq2 chr1 11021444 11021574 - SINE/Alu_AluSx chr1 11021586 11021916 - SINE/Alu_AluJr chr1 11026140 11026217 - LINE/L1_L1M5 chr1 11027942 11028227 + SINE/Alu_AluSq chr1 11028698 11028981 - SINE/Alu_AluY chr1 11028984 11029299 - SINE/Alu_AluY chr1 11029328 11029417 + SINE/Alu_AluSc chr1 11029633 11029912 - SINE/Alu_AluSp chr1 11030952 11031110 + SINE/Alu_AluJr chr1 11031242 11031546 + SINE/Alu_AluY chr1 11031650 11031920 + SINE/Alu_AluJb chr1 11032041 11032214 + SINE/Alu_AluJr4 chr1 11032296 11032329 + LINE/L1_L1ME4a chr1 11032330 11032624 + SINE/Alu_AluSx1 chr1 11032627 11032931 + SINE/Alu_AluSz chr1 11032932 11033039 + LINE/L1_L1ME4a chr1 11033047 11033354 + SINE/Alu_AluSp chr1 11033363 11033667 + SINE/Alu_AluSx chr1 11033683 11033926 + SINE/Alu_AluSz chr1 11033927 11033966 + Simple_repeat_(AC)n chr1 11033990 11034262 + SINE/Alu_AluSx chr1 11034418 11034727 + SINE/Alu_AluSx1 chr1 11034745 11034779 + Simple_repeat_(CAG)n chr1 11035243 11035528 + SINE/Alu_AluSz chr1 11035529 11035557 + Simple_repeat_(ATC)n chr1 11035613 11035916 + SINE/Alu_AluJb chr1 11036224 11036540 + SINE/Alu_AluYb8 chr1 11036630 11036648 + Simple_repeat_(T)n chr1 11037045 11037388 - SINE/Alu_AluSx1 chr1 11038170 11038345 + SINE/MIR_MIRb chr1 11038762 11038864 + LINE/CR1_L3 chr1 11038998 11039243 + LINE/L2_L2b chr1 11039301 11040188 + Simple_repeat_(GGAT)n chr1 11040189 11040484 + SINE/Alu_AluSc chr1 11040485 11042702 + Simple_repeat_(GGAT)n chr1 11044574 11044658 + SINE/MIR_MIR1_Amn chr1 11045765 11045839 - SINE/MIR_MIRc chr1 11045863 11045981 - SINE/MIR_MIR chr1 11046012 11046312 - SINE/Alu_AluSz chr1 11047452 11047734 - SINE/Alu_AluSx chr1 11047785 11047819 - LINE/L1_HAL1 chr1 11047820 11048123 + SINE/Alu_AluSg chr1 11048124 11048232 - LINE/L1_HAL1 chr1 11048266 11048387 - SINE/Alu_AluJo chr1 11048389 11048608 - SINE/Alu_AluSp chr1 11048635 11048932 + SINE/Alu_AluJr chr1 11048943 11049151 + LINE/L1_L1MEj chr1 11049161 11049359 + SINE/Alu_AluSx chr1 11049361 11049637 - SINE/Alu_AluSg chr1 11049651 11049684 + Low_complexity_A-rich chr1 11049728 11050017 + SINE/Alu_AluY chr1 11050018 11050215 - LTR/ERV1_LTR8B chr1 11050275 11050471 + LINE/L1_L1MEj chr1 11050473 11050514 + SINE/Alu_Alu chr1 11050518 11050749 - SINE/Alu_AluSp chr1 11050754 11050831 + SINE/Alu_AluSz chr1 11050853 11051066 - SINE/Alu_AluSx1 chr1 11051107 11051198 - LTR/ERV1_LTR26C chr1 11051203 11051234 + Simple_repeat_(TTTTTC)n chr1 11051241 11051333 - SINE/Alu_AluSx chr1 11051334 11052013 - LTR/ERV1_LTR8 chr1 11052014 11052221 - SINE/Alu_AluSx chr1 11052254 11052399 - LTR/ERVL_MER21C chr1 11052442 11052532 - LTR/ERVL_MER21C chr1 11053377 11053472 + SINE/MIR_MIRb chr1 11054683 11054705 + Simple_repeat_(CAGA)n chr1 11055121 11055408 - SINE/Alu_AluSz chr1 11055418 11055713 - SINE/Alu_AluY chr1 11055960 11055987 + Simple_repeat_(CCCGC)n chr1 11056158 11056203 - SINE/MIR_MIRb chr1 11056875 11057156 - SINE/Alu_AluJr chr1 11057324 11057561 - SINE/Alu_AluYc chr1 11057593 11057775 + LINE/L2_L2b chr1 11057776 11058078 - SINE/Alu_AluSx chr1 11058079 11058205 + LINE/L2_L2b chr1 11058279 11058583 + SINE/Alu_AluSp chr1 11059591 11059630 + Simple_repeat_(CCCCAG)n chr1 11060049 11060084 + Low_complexity_G-rich chr1 11060218 11060270 + Low_complexity_G-rich chr1 11061175 11061477 + SINE/Alu_AluSc chr1 11061488 11061614 + SINE/Alu_FLAM_C chr1 11061766 11061876 + LINE/L2_L2c chr1 11062472 11062782 + SINE/Alu_AluSx chr1 11062792 11062837 + Simple_repeat_(AACAA)n chr1 11063528 11063673 - DNA/hAT-Charlie_Charlie18a chr1 11063674 11063993 + SINE/Alu_AluSx1 chr1 11063994 11064012 - DNA/hAT-Charlie_Charlie18a chr1 11064267 11064559 + SINE/Alu_AluSq2 chr1 11064572 11064881 + SINE/Alu_AluSq2 chr1 11064923 11065376 + LTR/ERVL-MaLR_MLT1J chr1 11065408 11065518 + LINE/L2_L2c chr1 11065625 11065755 + LINE/L1_L1M5 chr1 11065890 11066136 + SINE/Alu_AluJr chr1 11066172 11066480 - SINE/Alu_AluSc8 chr1 11067015 11067149 + SINE/Alu_AluSz6 chr1 11067150 11067452 + SINE/Alu_AluY chr1 11067453 11067610 + SINE/Alu_AluSz6 chr1 11069228 11069245 + Simple_repeat_(TG)n chr1 11069954 11070285 + SINE/Alu_AluJr chr1 11070308 11070545 + SINE/Alu_AluJr chr1 11071285 11071991 + LINE/L2_L2a chr1 11072365 11072580 - SINE/MIR_MIRb chr1 11073017 11073095 + LINE/L1_L1ME3 chr1 11073136 11073431 - SINE/Alu_AluY chr1 11073438 11073531 - DNA/hAT-Charlie_Charlie4z chr1 11073608 11073900 + SINE/Alu_AluSx1 chr1 11074414 11074706 - SINE/Alu_AluSz chr1 11074830 11075007 - SINE/MIR_MIR chr1 11075042 11075318 - SINE/Alu_AluSx chr1 11075440 11075640 - LINE/CR1_L3 chr1 11075655 11075883 + SINE/Alu_AluJb chr1 11075885 11076193 + SINE/Alu_AluSq chr1 11076202 11076359 + SINE/Alu_AluJb chr1 11076374 11076488 - LINE/CR1_L3 chr1 11076988 11077290 - SINE/Alu_AluSq2 chr1 11077724 11077848 + LINE/L2_L2c chr1 11078001 11078260 + SINE/Alu_AluJr chr1 11078261 11078555 - SINE/Alu_AluYa5 chr1 11078556 11078597 + SINE/Alu_AluJr chr1 11079053 11079357 + SINE/Alu_AluSx chr1 11079397 11079693 - SINE/Alu_AluSz chr1 11080564 11080605 + Simple_repeat_(AC)n chr1 11081495 11081700 + SINE/MIR_MIRb chr1 11081713 11081783 + Simple_repeat_(TTTATA)n chr1 11081801 11082104 + SINE/Alu_AluSp chr1 11082328 11082379 + Simple_repeat_(CA)n chr1 11082997 11083247 - SINE/Alu_AluJb chr1 11083284 11083585 + SINE/Alu_AluSp chr1 11083624 11086544 - LINE/L1_L1PA3 chr1 11086542 11087049 + LINE/L1_L1PA3 chr1 11088939 11089244 + SINE/Alu_AluJr chr1 11089328 11089633 + SINE/Alu_AluSp chr1 11089634 11089660 + Simple_repeat_(AAAG)n chr1 11089676 11089949 + SINE/Alu_AluJb chr1 11089997 11090297 - SINE/Alu_AluSx chr1 11090309 11090398 + SINE/MIR_MIRc chr1 11090747 11090885 - SINE/Alu_AluJb chr1 11091665 11091985 - SINE/Alu_AluSz chr1 11092108 11092216 + DNA/TcMar-Tigger_MER47A chr1 11092217 11092512 - SINE/Alu_AluSc chr1 11092516 11092818 - SINE/Alu_AluSz chr1 11092819 11093044 + DNA/TcMar-Tigger_MER47A chr1 11093207 11093582 + LINE/CR1_L3 chr1 11093785 11094094 + SINE/Alu_AluJr chr1 11094159 11094277 - SINE/Alu_AluJo chr1 11094296 11094600 - SINE/Alu_AluSq2 chr1 11094604 11094898 - SINE/Alu_AluSx chr1 11094940 11095232 + SINE/Alu_AluSx1 chr1 11095432 11095737 + SINE/Alu_AluY chr1 11095989 11096243 - SINE/Alu_AluSz6 chr1 11096244 11096449 - SINE/Alu_AluSz chr1 11096464 11096778 - SINE/Alu_AluJr chr1 11096819 11096931 + SINE/Alu_AluSx3 chr1 11096932 11097231 + SINE/Alu_AluSg chr1 11097232 11097403 + SINE/Alu_AluSx3 chr1 11097445 11097740 + SINE/Alu_AluY chr1 11098252 11098390 - LINE/L2_L2a chr1 11098410 11098600 + SINE/MIR_MIR chr1 11098621 11099204 + DNA/hAT-Charlie_Charlie29a chr1 11100092 11100144 - SINE/MIR_MIRc chr1 11100177 11100277 - SINE/Alu_FLAM_A chr1 11100310 11100466 + SINE/Alu_AluJb chr1 11100492 11100778 + SINE/Alu_AluSx chr1 11100811 11100858 - SINE/Alu_AluJo chr1 11100859 11101161 - SINE/Alu_AluSx3 chr1 11101164 11101446 - SINE/Alu_AluSz chr1 11101447 11101550 - SINE/Alu_AluJo chr1 11101555 11101915 + LINE/L2_L2c chr1 11101844 11101952 + LINE/L2_L2c chr1 11102220 11102412 - SINE/MIR_MIR chr1 11102417 11102718 - SINE/Alu_AluSg chr1 11102747 11103057 + SINE/Alu_AluJb chr1 11103067 11103316 - DNA/TcMar-Tigger_Tigger2 chr1 11103327 11103606 - SINE/Alu_AluJr4 chr1 11104013 11104165 - SINE/Alu_AluSg4 chr1 11104167 11104457 - SINE/Alu_AluSx chr1 11104487 11104594 + SINE/Alu_AluJo chr1 11104595 11105670 + LTR/ERVK_MER11C chr1 11105671 11105850 + SINE/Alu_AluJb chr1 11106038 11106112 - DNA/TcMar-Mariner_MADE2 chr1 11106141 11106483 + LINE/L2_L2c chr1 11108432 11108742 + SINE/Alu_AluSx chr1 11108763 11109002 + SINE/Alu_AluSq chr1 11109911 11110009 + SINE/Alu_FLAM_A chr1 11110407 11110693 - SINE/Alu_AluSp chr1 11110730 11110834 - SINE/Alu_FLAM_A chr1 11111127 11111182 + LINE/L2_L2d2 chr1 11111197 11111509 + SINE/Alu_AluSz chr1 11111958 11111987 + Simple_repeat_(AAAC)n chr1 11113563 11113597 + Simple_repeat_(T)n chr1 11113607 11113777 - SINE/Alu_AluJr4 chr1 11113778 11114155 + LTR/ERVL-MaLR_THE1C chr1 11114156 11114293 - SINE/Alu_AluJr4 chr1 11115049 11115117 + Low_complexity_A-rich chr1 11115552 11115777 + DNA/hAT-Charlie_MER58A chr1 11116316 11116609 - SINE/Alu_AluJo chr1 11116683 11116787 - SINE/Alu_AluJo chr1 11117175 11117460 - SINE/Alu_AluSx1 chr1 11117798 11118100 + SINE/Alu_AluSq chr1 11118229 11118534 - SINE/Alu_AluSg chr1 11118535 11118606 - SINE/Alu_FAM chr1 11118607 11118926 - SINE/Alu_AluJr chr1 11118927 11119025 - SINE/Alu_FAM chr1 11119038 11119144 + LINE/L1_L1MC chr1 11119149 11119284 + SINE/Alu_AluSz chr1 11119285 11119599 + SINE/Alu_AluY chr1 11119600 11119767 + SINE/Alu_AluSz chr1 11119768 11119816 + Low_complexity_A-rich chr1 11119889 11119947 - DNA/TcMar-Tigger_MER2B chr1 11119948 11120066 + SINE/Alu_FLAM_C chr1 11120067 11120077 - DNA/TcMar-Tigger_MER2B chr1 11120093 11120256 - SINE/Alu_AluJo chr1 11120257 11120517 + SINE/Alu_AluSz chr1 11120518 11120651 - SINE/Alu_AluJo chr1 11120667 11120898 - DNA/TcMar-Tigger_MER2 chr1 11120907 11120942 + Simple_repeat_(AAACC)n chr1 11121486 11121526 + Low_complexity_A-rich chr1 11122205 11122499 - SINE/Alu_AluSc chr1 11122503 11122805 - SINE/Alu_AluSx3 chr1 11123267 11123474 - SINE/Alu_AluJb chr1 11123475 11123766 - SINE/Alu_AluJr chr1 11123773 11124087 - SINE/Alu_AluSq2 chr1 11124088 11124229 - SINE/Alu_AluJb chr1 11124230 11124414 - SINE/Alu_AluJr chr1 11124667 11124810 - DNA/hAT-Charlie_Charlie32a chr1 11124914 11125300 + LTR/ERVL_LTR33 chr1 11125449 11125748 + SINE/Alu_AluSx chr1 11125760 11126066 + SINE/Alu_AluSq2 chr1 11126235 11126253 + Simple_repeat_(A)n chr1 11129867 11130012 + SINE/MIR_MIR chr1 11130153 11130197 + Low_complexity_GA-rich chr1 11131964 11132061 - LINE/L2_L2a chr1 11133583 11133779 - SINE/MIR_MIR chr1 11133783 11134072 + SINE/Alu_AluJr chr1 11135553 11135852 + SINE/Alu_AluSx chr1 11135853 11136160 + SINE/Alu_AluSq2 chr1 11136476 11136501 + Simple_repeat_(TTTAAT)n chr1 11136502 11136792 - SINE/Alu_AluJr chr1 11136793 11136808 + Simple_repeat_(TTTAAT)n chr1 11136845 11137140 - SINE/Alu_AluSz chr1 11137153 11137178 + Simple_repeat_(A)n chr1 11137848 11137933 - SINE/MIR_MIRc chr1 11138298 11138332 + Low_complexity_A-rich chr1 11139702 11139989 - SINE/Alu_AluSg chr1 11139992 11140123 - SINE/Alu_AluSz chr1 11140246 11140547 - DNA/hAT-Charlie_MER1B chr1 11140596 11140729 + SINE/MIR_MIR chr1 11140743 11140986 + DNA/TcMar-Tc1_EutTc1-N2 chr1 11141117 11141396 - SINE/Alu_AluJb chr1 11141397 11141668 - SINE/Alu_AluSz6 chr1 11141686 11141704 + Simple_repeat_(A)n chr1 11141705 11142002 + SINE/Alu_AluY chr1 11142003 11142034 + Simple_repeat_(AAAT)n chr1 11142035 11142045 + SINE/Alu_AluY chr1 11142233 11142291 + SINE/Alu_AluYh3 chr1 11142297 11142596 - SINE/Alu_AluSz chr1 11144186 11144352 - DNA/hAT-Charlie_MER5B chr1 11145380 11145667 - SINE/Alu_AluSx1 chr1 11145668 11145803 - SINE/Alu_AluSz chr1 11147961 11148083 + LINE/L1_L1MC5a chr1 11148110 11148335 + LTR/ERVL-MaLR_MLT1J chr1 11148349 11148445 + LTR/ERVL_MER21C chr1 11148370 11148464 + LTR/ERVL_MER21C chr1 11148620 11148909 + SINE/Alu_AluSg chr1 11148913 11148929 + Simple_repeat_(TG)n chr1 11148944 11149065 - SINE/Alu_AluJo chr1 11149265 11149366 + LTR/ERVL_MER21C chr1 11149391 11149844 + LTR/ERVL_MER21C chr1 11151488 11151670 - DNA/hAT-Charlie_MER5A chr1 11151673 11151782 + LINE/CR1_L3 chr1 11152350 11152413 + snRNA_U6 chr1 11153039 11153074 + SINE/MIR_MIRc chr1 11153043 11153115 + SINE/MIR_MIR3 chr1 11153171 11153237 + Low_complexity_GA-rich chr1 11153628 11153786 + DNA/hAT-Charlie_MER3 chr1 11153787 11154098 + SINE/Alu_AluSz chr1 11154099 11154142 + DNA/hAT-Charlie_MER3 chr1 11154169 11154199 + Simple_repeat_(AAC)n chr1 11154293 11154391 - SINE/Alu_AluJo chr1 11154407 11154458 + Low_complexity_A-rich chr1 11154646 11154863 + SINE/Alu_AluJr chr1 11154864 11155055 + SINE/Alu_AluJo chr1 11155175 11155332 + LINE/L1_L1ME3G chr1 11155341 11155629 + LTR/ERV1_MER39 chr1 11155635 11155894 + LINE/L2_L2b chr1 11155981 11156289 - SINE/Alu_AluSz chr1 11156462 11156750 + LINE/L2_L2c chr1 11159360 11159655 + SINE/Alu_AluSp chr1 11160076 11160112 + Simple_repeat_(ATTT)n chr1 11160114 11160398 - SINE/Alu_AluSp chr1 11160524 11160740 + SINE/MIR_MIR chr1 11160803 11164062 + LINE/L1_L1PA5 chr1 11164063 11164354 + SINE/Alu_AluY chr1 11164355 11167257 + LINE/L1_L1PA5 chr1 11167788 11168084 + SINE/Alu_AluY chr1 11168212 11168342 - SINE/Alu_FLAM_A chr1 11169243 11169396 - SINE/MIR_MIRc chr1 11169463 11169504 + SINE/MIR_MIRc chr1 11169505 11169613 - snRNA_U13_ chr1 11169614 11169713 + SINE/MIR_MIRc chr1 11169736 11169840 + SINE/MIR_MIRc chr1 11170434 11170562 + SINE/Alu_FLAM_C chr1 11170676 11170846 - SINE/Alu_AluSp chr1 11170957 11171219 + SINE/Alu_AluSg4 chr1 11171278 11171445 - SINE/Alu_FRAM chr1 11171753 11172047 + SINE/Alu_AluSq chr1 11172276 11172578 + SINE/Alu_AluY chr1 11172589 11172879 + SINE/Alu_AluSg chr1 11172994 11173293 - SINE/Alu_AluSx1 chr1 11173301 11173582 - SINE/Alu_AluSz chr1 11173910 11173949 + Low_complexity_GA-rich chr1 11173950 11174061 - SINE/MIR_MIRb chr1 11174185 11174282 + LINE/L2_L2c chr1 11175747 11176047 - SINE/Alu_AluSz chr1 11177276 11177573 + SINE/Alu_AluJr chr1 11177930 11178049 - LINE/L1_L1ME4c chr1 11179219 11179289 + DNA/TcMar-Tc2_MER104 chr1 11179915 11180204 - SINE/Alu_AluJr chr1 11180782 11181074 - SINE/Alu_AluSc chr1 11181378 11181551 + SINE/Alu_AluSg4 chr1 11181553 11181617 + SINE/MIR_MIR3 chr1 11181780 11181935 - LINE/L2_L2c chr1 11181936 11182233 + SINE/Alu_AluSp chr1 11182234 11182373 - LINE/L2_L2c chr1 11182408 11182497 - SINE/MIR_MIR chr1 11182498 11183299 - LINE/L1_L1ME3A chr1 11183300 11183431 - SINE/Alu_FLAM_A chr1 11183432 11184156 - LINE/L1_L1ME3A chr1 11184157 11184335 - SINE/MIR_MIR chr1 11184452 11184758 + SINE/Alu_AluJr4 chr1 11184893 11185103 + LINE/L2_L2c chr1 11185294 11185626 + SINE/Alu_AluJb chr1 11185800 11186105 + SINE/Alu_AluY chr1 11187255 11187608 - DNA/hAT-Charlie_MER1B chr1 11190179 11190255 - SINE/MIR_MIRc chr1 11191486 11191607 - DNA/hAT-Ac_OldhAT1 chr1 11191813 11191853 + Simple_repeat_(AAC)n chr1 11192474 11192774 + SINE/Alu_AluSx1 chr1 11193033 11193323 + SINE/Alu_AluSq chr1 11193830 11193924 + DNA/hAT-Tip100_MER91B chr1 11194607 11194640 + Simple_repeat_(CAAGGA)n chr1 11196574 11196878 + SINE/Alu_AluSc chr1 11196921 11197146 - LINE/RTE-X_L4_A_Mam chr1 11197296 11197374 - LINE/RTE-X_L4_A_Mam chr1 11197526 11197551 + Simple_repeat_(CATT)n chr1 11197553 11197842 - SINE/Alu_AluSz chr1 11199829 11199984 + DNA/hAT-Charlie_MER3 chr1 11200734 11201030 + SINE/Alu_AluY chr1 11201066 11201178 + SINE/Alu_FLAM_A chr1 11201576 11201722 - LINE/L1_L1ME4a chr1 11201723 11201775 + Simple_repeat_(TTATT)n chr1 11201818 11202105 - SINE/Alu_AluJr chr1 11202148 11202441 + SINE/Alu_AluSc8 chr1 11202474 11202615 + LINE/L1_L1PB4 chr1 11202639 11202927 + SINE/Alu_AluJr chr1 11202931 11203242 + SINE/Alu_AluSz chr1 11203256 11203393 + LINE/L1_L1MA4 chr1 11203404 11203685 + SINE/Alu_AluSz chr1 11203689 11204249 + LTR/ERVL-MaLR_MLT1G1 chr1 11204780 11204866 - LINE/L2_L2-2_Mam chr1 11205315 11205537 + SINE/MIR_MIR chr1 11205553 11205668 + SINE/MIR_MIRc chr1 11206336 11206591 + SINE/MIR_MIR chr1 11206747 11206932 + DNA/TcMar-Tigger_Tigger19a chr1 11207095 11207400 - SINE/Alu_AluJb chr1 11207408 11207794 - SINE/Alu_AluJr chr1 11208801 11208972 + DNA/hAT-Charlie_MER5A1 chr1 11209879 11210167 - SINE/Alu_AluSz chr1 11210178 11210434 - SINE/Alu_AluJo chr1 11210437 11210686 + DNA/hAT-Charlie_MER58B chr1 11211028 11211277 + LINE/L2_L2b chr1 11211279 11211382 - DNA/hAT-Charlie_MER5A chr1 11211383 11211665 - SINE/Alu_AluJr chr1 11211666 11211743 - DNA/hAT-Charlie_MER5A chr1 11211750 11212180 + LINE/L2_L2b chr1 11213666 11214053 + LINE/L2_L2c chr1 11216401 11216671 + SINE/Alu_AluJr chr1 11216775 11216918 + SINE/MIR_MIRb chr1 11217086 11217122 + Simple_repeat_(TAAT)n chr1 11217397 11217710 - SINE/Alu_AluY chr1 11217851 11217974 + LINE/CR1_X7C_LINE chr1 11218150 11218442 + SINE/Alu_AluSg chr1 11218926 11219229 + SINE/Alu_AluSx3 chr1 11219234 11219741 + LINE/L1_L1MB7 chr1 11219742 11220060 + SINE/Alu_AluSz6 chr1 11220061 11220108 + LINE/L1_L1MEd chr1 11220109 11220607 + LINE/L1_L1MB7 chr1 11220680 11220875 + LTR/ERVL-MaLR_MLT1D chr1 11220878 11220998 + LINE/L1_L1MC5a chr1 11220999 11221294 - SINE/Alu_AluSz chr1 11221295 11221490 + LINE/L1_L1MC5a chr1 11221499 11221692 + LINE/L1_L1MC5a chr1 11221693 11221750 + Simple_repeat_(AT)n chr1 11221929 11221962 + Simple_repeat_(TA)n chr1 11222029 11222182 + LINE/L1_L1MC5a chr1 11222201 11222496 - SINE/Alu_AluY chr1 11222520 11222631 - SINE/Alu_AluJo chr1 11222632 11222697 + LINE/L1_L1MC5a chr1 11222698 11222746 + LTR/ERV1_MER4E1 chr1 11222764 11223398 + LINE/L1_L1MC5a chr1 11223416 11223449 + LTR/ERVL_LTR82A chr1 11223470 11223610 + LINE/L1_L1MC5a chr1 11223621 11223757 + LINE/L1_L1MB7 chr1 11223758 11224038 + SINE/Alu_AluSq2 chr1 11224039 11224086 + Simple_repeat_(AAAAT)n chr1 11224087 11224271 + LINE/L1_L1MB7 chr1 11224091 11224860 + LINE/L1_L1M5 chr1 11224843 11225227 + LINE/L1_L1MB7 chr1 11225266 11225419 + LINE/L1_L1M5 chr1 11225420 11225721 - SINE/Alu_AluSx chr1 11225722 11225948 + LINE/L1_L1M5 chr1 11225963 11226253 + LINE/L1_L1M5 chr1 11226254 11226360 + snRNA_U6 chr1 11226361 11226507 + LINE/L1_L1M5 chr1 11226509 11226794 + SINE/Alu_AluJb chr1 11226913 11226940 + Simple_repeat_(C)n chr1 11226943 11227015 + LINE/L1_L1M5 chr1 11227016 11227326 + SINE/Alu_AluSx chr1 11227327 11227421 + LINE/L1_L1M5 chr1 11227433 11227648 + LINE/L1_L1MC5a chr1 11227675 11228102 + LINE/L1_L1MC4 chr1 11228199 11228488 - SINE/Alu_AluSx chr1 11229819 11229978 + SINE/Alu_AluJb chr1 11230009 11230092 - LINE/L1_L1MA6 chr1 11230141 11230426 + SINE/Alu_AluSz chr1 11231736 11232015 - SINE/Alu_AluJr chr1 11232287 11232400 - LINE/L2_L2a chr1 11232593 11232894 + SINE/Alu_AluSx chr1 11235331 11235497 + SINE/Alu_AluSx chr1 11235703 11235999 + SINE/Alu_AluSc chr1 11236008 11236096 + LINE/L2_L2b chr1 11236130 11236438 - SINE/Alu_AluJb chr1 11236510 11236810 - SINE/Alu_AluSx1 chr1 11236888 11236966 + DNA_X30_DNA chr1 11238764 11239073 - SINE/Alu_AluY chr1 11239624 11239932 + SINE/Alu_AluSz chr1 11240802 11240890 - SINE/MIR_MIRb chr1 11240917 11241040 - SINE/MIR_MIRb chr1 11241041 11241341 + SINE/Alu_AluY chr1 11241342 11241408 - SINE/MIR_MIRb chr1 11241779 11241958 + DNA/hAT-Tip100_MER91B chr1 11242082 11242217 + SINE/Alu_AluSx chr1 11242218 11242528 + SINE/Alu_AluSz chr1 11243410 11243711 + SINE/Alu_AluJr chr1 11243864 11244013 + SINE/Alu_AluJb chr1 11244014 11244316 + SINE/Alu_AluSp chr1 11244351 11244657 + SINE/Alu_AluSx chr1 11244663 11245004 + DNA/TcMar-Tigger_Tigger3a chr1 11245008 11245123 - DNA/TcMar_X5a_DNA chr1 11245617 11245917 + SINE/Alu_AluJr chr1 11246899 11247089 - SINE/MIR_MIRb chr1 11247124 11247195 - LINE/L2_L2d2 chr1 11248243 11248313 - SINE/5S-Deu-L2_AmnSINE1 chr1 11248530 11248831 + SINE/Alu_AluSq2 chr1 11248945 11249217 + SINE/Alu_AluSg chr1 11249408 11249440 + Simple_repeat_(TTTA)n chr1 11249441 11250365 - Retroposon/SVA_SVA_D chr1 11250366 11251323 + LINE/L2_L2a chr1 11251149 11251615 + LINE/L2_L2a chr1 11251659 11251791 - SINE/Alu_FLAM_C chr1 11251792 11251914 - SINE/MIR_MIRc chr1 11251967 11252029 - SINE/MIR_MIRb chr1 11252030 11252237 - DNA/TcMar-Tigger_MER2 chr1 11252259 11252293 + Simple_repeat_(T)n chr1 11252310 11252436 - SINE/Alu_AluJo chr1 11252442 11252501 - DNA/TcMar-Tigger_MER2 chr1 11252502 11252669 - SINE/MIR_MIRb chr1 11252746 11252882 + LINE/L2_L2a chr1 11252886 11253250 + LTR/ERVL_LTR16B2 chr1 11253266 11253790 + LINE/L2_L2a chr1 11254146 11254439 - SINE/Alu_AluSz chr1 11254445 11254601 - SINE/MIR_MIRc chr1 11254707 11254759 + DNA/TcMar-Tc2_Kanga1c chr1 11254769 11255049 - SINE/Alu_AluJo chr1 11255103 11255409 + SINE/Alu_AluSc chr1 11255415 11255473 + DNA/TcMar-Tc2_Kanga1c chr1 11255569 11255865 + SINE/Alu_AluSx chr1 11256418 11256533 + LINE/L2_L2a chr1 11256502 11256709 - SINE/MIR_MIRb chr1 11257254 11257591 + SINE/Alu_AluSx chr1 11257828 11258121 + SINE/Alu_AluSx1 chr1 11258232 11258361 + SINE/MIR_MIR3 chr1 11258765 11258888 - LINE/L2_L2d2 chr1 11259617 11259675 + SINE/MIR_MIR chr1 11259693 11259897 + SINE/Alu_AluSx chr1 11259903 11259931 + Simple_repeat_(AAAACA)n chr1 11259965 11260103 + SINE/MIR_MIR chr1 11260106 11260122 + Simple_repeat_(TAC)n chr1 11260238 11260260 - DNA/hAT-Charlie_MER33 chr1 11260261 11260567 + SINE/Alu_AluSq2 chr1 11260568 11260790 - DNA/hAT-Charlie_MER33 chr1 11260791 11261091 - SINE/Alu_AluSx chr1 11261092 11261159 - DNA/hAT-Charlie_MER33 chr1 11261221 11261487 + SINE/Alu_AluY chr1 11263024 11263178 + DNA/hAT-Tip100_ORSL-2b chr1 11263189 11263485 + SINE/Alu_AluSx chr1 11263495 11263535 + Simple_repeat_(AC)n chr1 11263556 11263678 + SINE/Alu_FLAM_C chr1 11263801 11264110 + SINE/Alu_AluSx3 chr1 11264162 11264462 + SINE/Alu_AluJr chr1 11264748 11264878 + SINE/Alu_FLAM_C chr1 11264934 11264960 + Simple_repeat_(GTT)n chr1 11264961 11264976 + Simple_repeat_(GTTATT)n chr1 11264991 11265249 + SINE/Alu_AluJb chr1 11265251 11265286 + Simple_repeat_(TA)n chr1 11265295 11265414 - SINE/Alu_AluSx3 chr1 11265501 11265804 - SINE/Alu_AluSx1 chr1 11266002 11266306 - SINE/Alu_AluSx chr1 11266396 11266519 - LTR/ERV1_LTR26 chr1 11266521 11267231 - LTR/ERV1_MER50 chr1 11267232 11267280 - LTR/ERV1_LTR26 chr1 11267281 11267590 - SINE/Alu_AluSg chr1 11267591 11267965 - LTR/ERV1_LTR26 chr1 11267966 11268118 - SINE/Alu_AluSz chr1 11268119 11268458 - SINE/Alu_AluSx chr1 11268459 11268594 - SINE/Alu_AluSz chr1 11268595 11268631 - LTR/ERV1_LTR26 chr1 11268633 11268943 - LINE/L1_L1ME4b chr1 11269085 11269312 - LINE/L1_L1PA6 chr1 11269343 11269387 - SINE/MIR_MIR chr1 11269468 11269583 + SINE/MIR_MIR chr1 11269584 11269868 + SINE/Alu_AluSx1 chr1 11269869 11269911 + SINE/MIR_MIR chr1 11269910 11270037 - SINE/MIR_MIRb chr1 11270188 11270231 + DNA/hAT-Tip100_MER45A chr1 11270232 11270550 - SINE/Alu_AluSp chr1 11270551 11270570 + DNA/hAT-Tip100_MER45A chr1 11270571 11270745 - SINE/Alu_AluJb chr1 11270746 11271053 - SINE/Alu_AluY chr1 11271054 11271179 - SINE/Alu_AluJb chr1 11271180 11271254 - SINE/Alu_FLAM_A chr1 11271255 11271542 + SINE/Alu_AluY chr1 11271543 11271587 - SINE/Alu_FLAM_A chr1 11271588 11271637 + DNA/hAT-Tip100_MER45B chr1 11271638 11271998 - DNA/hAT-Blackjack_MER63C chr1 11272007 11272182 - SINE/Alu_AluSx chr1 11272183 11272476 - SINE/Alu_AluSx1 chr1 11272481 11272699 - DNA/hAT-Blackjack_MER63C chr1 11272710 11272752 + LINE/L2_L2c chr1 11272907 11273190 + Simple_repeat_(CCGCC)n chr1 11274287 11274592 - SINE/Alu_AluSc chr1 11274603 11274907 - SINE/Alu_AluSx4 chr1 11274909 11275029 - DNA/hAT-Charlie_Charlie13b chr1 11275093 11275220 - LINE/L2_L2a chr1 11275221 11275338 - DNA/hAT-Blackjack_MER81 chr1 11275339 11275458 - LINE/L2_L2a chr1 11275459 11275747 + SINE/Alu_AluJr chr1 11275748 11276330 - LINE/L2_L2a chr1 11276382 11276679 + SINE/Alu_AluSz chr1 11276956 11277255 + SINE/Alu_AluSx chr1 11277294 11277573 - SINE/Alu_AluSx1 chr1 11277645 11277943 - SINE/Alu_AluSp chr1 11277948 11278257 - SINE/Alu_AluSz chr1 11278865 11279163 - SINE/Alu_AluSx3 chr1 11279289 11279414 - LINE/L2_L2a chr1 11279415 11279715 - SINE/Alu_AluSx1 chr1 11279716 11280040 - LINE/L2_L2a chr1 11280083 11280284 - LINE/L2_L2a chr1 11280361 11280755 + LINE/L2_L2-1_AMi chr1 11280763 11281490 + LINE/L2_L2a chr1 11281486 11281550 - LINE/L2_L2a chr1 11281556 11282307 - LINE/L1_L1ME2 chr1 11282559 11282865 - SINE/Alu_AluSp chr1 11282866 11283161 - SINE/Alu_AluSp chr1 11284016 11284455 - LINE/L2_L2c chr1 11284456 11284759 - SINE/Alu_AluSx chr1 11284760 11285032 - LINE/L2_L2c chr1 11286644 11286719 - LINE/Jockey_X12_LINE chr1 11287649 11287952 + SINE/Alu_AluY chr1 11288626 11288934 + SINE/Alu_AluJr chr1 11288986 11289140 + LINE/CR1_CR1-3_Croc chr1 11289147 11289195 + LINE/CR1_L3b chr1 11289196 11289499 + LINE/CR1_L3 chr1 11289500 11289523 + LINE/CR1_L3b chr1 11289556 11289757 - SINE/Alu_AluSx chr1 11289758 11290028 - SINE/Alu_AluSx3 chr1 11290029 11290164 - SINE/Alu_AluSx chr1 11290697 11290987 + SINE/Alu_AluSz chr1 11290990 11291195 + LINE/CR1_L3b chr1 11291248 11291303 + LINE/CR1_CR1-3_Croc chr1 11291326 11291629 + SINE/Alu_AluSz chr1 11291854 11291990 + LINE/CR1_L3 chr1 11291991 11292304 - SINE/Alu_AluSq2 chr1 11292305 11292511 + LINE/CR1_L3 chr1 11292669 11292717 + Simple_repeat_(AC)n chr1 11293371 11293537 + LINE/L2_L2d chr1 11293674 11293972 - SINE/Alu_AluSq2 chr1 11294016 11294059 + LINE/L2_L2a chr1 11294074 11294304 - SINE/MIR_MIRb chr1 11294333 11294445 - SINE/Alu_AluJr chr1 11295186 11295264 - LINE/L2_L2b chr1 11295774 11295880 - LINE/L2_L2c chr1 11296116 11296232 - LINE/L2_L2a chr1 11296267 11296506 + SINE/MIR_MIRb chr1 11296529 11296819 - SINE/Alu_AluJb chr1 11296887 11296981 + LINE/L2_L2d2 chr1 11296954 11297096 - LINE/L2_L2a chr1 11297114 11297477 + LTR/ERVL-MaLR_MLT1A0 chr1 11297950 11298253 - SINE/Alu_AluSp chr1 11298286 11298569 + SINE/Alu_AluSx chr1 11298570 11298608 + Simple_repeat_(AAAT)n chr1 11299633 11299661 + Simple_repeat_(CCTTC)n chr1 11299799 11300095 - SINE/Alu_AluSx chr1 11300096 11300133 - SINE/Alu_AluJo chr1 11300138 11300191 - LTR/ERV1_LTR37A chr1 11300212 11300505 - SINE/Alu_AluSx1 chr1 11300515 11300829 - SINE/Alu_AluJb chr1 11301040 11301353 - SINE/Alu_AluYm1 chr1 11301355 11301570 - SINE/MIR_MIRb chr1 11302317 11302342 + Simple_repeat_(CATC)n chr1 11302360 11302560 - SINE/Alu_AluJo chr1 11302668 11303022 + DNA/hAT-Tip100_MamTip2b chr1 11303023 11303324 - SINE/Alu_AluSz chr1 11303477 11303787 - SINE/Alu_AluJo chr1 11304086 11304264 - LTR/ERVL-MaLR_MLT1H chr1 11304346 11304647 - SINE/Alu_AluY chr1 11305080 11306612 - Retroposon/SVA_SVA_C chr1 11307154 11307317 + LINE/L1_L1ME3A chr1 11307371 11307654 - DNA/TcMar-Tigger_Tigger19a chr1 11307659 11307971 + SINE/Alu_AluSz chr1 11308206 11308645 + LTR/ERVL-MaLR_MLT1H chr1 11309560 11309928 + LTR/ERVL-MaLR_THE1C chr1 11309959 11310362 + LTR/ERVL-MaLR_MLT1F chr1 11310522 11310785 + LINE/L2_L2d2 chr1 11310923 11310944 + Simple_repeat_(TGAA)n chr1 11311193 11311492 - SINE/Alu_AluJr chr1 11311976 11312099 - SINE/MIR_MIRb chr1 11312788 11312959 - LTR/ERVL-MaLR_MLT1F chr1 11312993 11313052 - LTR/ERVL-MaLR_MLT1F chr1 11313053 11313357 - SINE/Alu_AluJr chr1 11313358 11313405 - LTR/ERVL-MaLR_MLT1F chr1 11313743 11313971 - SINE/MIR_MIR chr1 11314214 11314249 - LINE/L1_L1MB3 chr1 11314250 11314560 - SINE/Alu_AluSz chr1 11314561 11314656 - LINE/L1_L1MB3 chr1 11314709 11314730 + Simple_repeat_(CTCC)n chr1 11315346 11315377 + Simple_repeat_(CAG)n chr1 11315396 11315704 + SINE/Alu_AluSz chr1 11315751 11315777 + Simple_repeat_(TAA)n chr1 11315885 11315969 - SINE/MIR_MIR3 chr1 11316261 11316379 - SINE/MIR_MIRc chr1 11316772 11316805 + Simple_repeat_(AGCC)n chr1 11317012 11317062 - LINE/L2_L2b chr1 11317091 11317403 - SINE/Alu_AluJr chr1 11317421 11317725 - SINE/Alu_AluSx1 chr1 11317726 11317795 - SINE/MIR_MIRb chr1 11317857 11318061 - SINE/MIR_MIR chr1 11318216 11318328 - LTR/ERVL-MaLR_MLT1K chr1 11318329 11318617 - SINE/Alu_AluYk2 chr1 11318618 11318676 - LTR/ERVL-MaLR_MLT1K chr1 11318721 11318864 + DNA/hAT-Tip100_MER91A chr1 11319011 11319094 - LINE/L2_L2a chr1 11319096 11319164 - LTR/ERVL-MaLR_MLT1K chr1 11319197 11319387 - LTR/ERVL-MaLR_MLT1K chr1 11319802 11319999 - SINE/MIR_MIRb chr1 11320733 11320900 - LINE/L2_L2c chr1 11320901 11321228 + SINE/Alu_AluJr chr1 11321229 11321534 + SINE/Alu_AluSz chr1 11321535 11321765 - LINE/L2_L2c chr1 11321769 11321790 + Simple_repeat_(CT)n chr1 11322554 11322872 - SINE/Alu_AluY chr1 11323244 11323523 - SINE/Alu_AluSx chr1 11324185 11324270 + SINE/MIR_MIR3 chr1 11324274 11324396 + SINE/Alu_FRAM chr1 11324402 11324595 - LINE/L2_L2c chr1 11325492 11325803 + SINE/Alu_AluSz6 chr1 11326229 11326378 + SINE/MIR_MIR chr1 11326411 11326454 - SINE/MIR_MIRc chr1 11326478 11326838 - SINE/Alu_AluSz chr1 11326873 11326980 + SINE/MIR_MIR chr1 11327976 11328294 - SINE/Alu_AluJb chr1 11329459 11329592 + LINE/L1_L1MC2 chr1 11329774 11329819 + Low_complexity_G-rich chr1 11329820 11330118 - SINE/Alu_AluY chr1 11330119 11330130 + Low_complexity_G-rich chr1 11330387 11330419 + Simple_repeat_(GGCTGG)n chr1 11330653 11330709 + SINE/MIR_MIR3 chr1 11330737 11331029 + SINE/Alu_AluSx1 chr1 11331179 11331250 - SINE/MIR_MIRb chr1 11331208 11331432 + LINE/L2_L2a chr1 11331468 11331544 - SINE/MIR_MIRb chr1 11331936 11332131 + LINE/L2_L2a chr1 11332132 11332441 + SINE/Alu_AluJr chr1 11332442 11332506 + LINE/L2_L2a chr1 11332507 11332819 + SINE/Alu_AluJb chr1 11332822 11333121 + SINE/Alu_AluSz chr1 11333122 11333632 + LINE/L2_L2a chr1 11334030 11334338 + SINE/Alu_AluY chr1 11334600 11334648 + Low_complexity_GA-rich chr1 11335781 11335989 - SINE/MIR_MIRb chr1 11336326 11336609 + SINE/Alu_AluJr chr1 11336711 11336782 + SINE/MIR_MIR chr1 11337112 11337266 + Simple_repeat_(AT)n chr1 11337267 11337339 + Simple_repeat_(TACAC)n chr1 11337340 11337516 + Simple_repeat_(AT)n chr1 11338015 11338251 + Simple_repeat_(CTC)n chr1 11338512 11338696 + Simple_repeat_(GT)n chr1 11338978 11339276 + SINE/Alu_AluSx1 chr1 11340263 11340501 + LINE/CR1_Plat_L3 chr1 11340585 11340749 + SINE/MIR_MIRb chr1 11340761 11341053 + SINE/Alu_AluSx chr1 11341067 11341117 + SINE/MIR_MIRc chr1 11341128 11341338 - LINE/L2_L2a chr1 11341339 11341621 + SINE/Alu_AluJr chr1 11341622 11341844 - LINE/L2_L2a chr1 11342298 11342319 + Simple_repeat_(AC)n chr1 11342415 11342489 - DNA/hAT-Charlie_MER117 chr1 11342992 11343064 + Low_complexity_GA-rich chr1 11343231 11343296 + SINE/MIR_MIR1_Amn chr1 11343347 11343480 + LTR/ERVL_LTR79 chr1 11343646 11343947 - SINE/Alu_AluSx1 chr1 11344371 11344445 - LINE/L2_L2b chr1 11344503 11344567 - DNA/TcMar-Tigger_MER46C chr1 11344587 11344755 - DNA/TcMar-Tigger_MER46C chr1 11344778 11344955 - SINE/MIR_MIR chr1 11345023 11345346 - LINE/L2_L2b chr1 11345597 11345888 - SINE/Alu_AluSz6 chr1 11346776 11347039 - SINE/Alu_AluSx3 chr1 11347286 11347485 - SINE/MIR_MIRc chr1 11347493 11347711 + Simple_repeat_(CTCC)n chr1 11347712 11348021 - SINE/Alu_AluSz chr1 11348266 11348432 - LINE/L1_L1MD2 chr1 11348433 11348702 + SINE/Alu_AluJb chr1 11348704 11348738 + Simple_repeat_(TATG)n chr1 11348739 11349249 - LINE/L1_L1MD2 chr1 11349529 11349837 + SINE/Alu_AluSx chr1 11350939 11350959 + Simple_repeat_(CCCT)n chr1 11350963 11351301 - SINE/Alu_AluSz6 chr1 11351335 11351498 - LTR/ERVL-MaLR_MLT1I chr1 11351512 11351678 - LTR/ERVL-MaLR_MLT1I chr1 11351885 11352030 + SINE/MIR_MIR chr1 11352406 11352536 - LINE/L2_L2b chr1 11352545 11352626 + LTR/ERVL_LTR16A2 chr1 11352627 11352921 - SINE/Alu_AluSx chr1 11352922 11353326 + LTR/ERVL_LTR16A2 chr1 11353435 11353532 + LTR/ERVL-MaLR_MLT1L chr1 11353536 11353833 - SINE/Alu_AluSz chr1 11354015 11354130 + LTR/ERVL-MaLR_MLT1L chr1 11354139 11354432 - SINE/Alu_AluSx1 chr1 11354513 11354659 - LTR/ERVL_LTR105_Mam chr1 11354727 11355000 + LINE/RTE-X_L4_A_Mam chr1 11356504 11356528 + Simple_repeat_(CTTT)n chr1 11356529 11356849 - SINE/Alu_AluSz chr1 11356850 11356871 + Simple_repeat_(CTTT)n chr1 11356996 11357312 - SINE/Alu_AluSz chr1 11358091 11358150 - SINE/MIR_MIRb chr1 11358196 11358438 + SINE/MIR_MIR chr1 11358442 11358735 + SINE/Alu_AluSz chr1 11358773 11358992 - LINE/L2_L2c chr1 11359551 11359591 + Simple_repeat_(CCCACTC)n chr1 11359667 11359992 - LINE/L2_L2b chr1 11360176 11360727 - LTR/ERV1_MER41A chr1 11360954 11361033 + LINE/L2_L2d2 chr1 11361167 11361228 + SINE/MIR_MIRc chr1 11361406 11361705 - SINE/Alu_AluSq2 chr1 11361914 11361971 + Low_complexity_G-rich chr1 11362253 11362557 - SINE/Alu_AluSx chr1 11362706 11362753 + Simple_repeat_(TGAG)n chr1 11362962 11363011 + Low_complexity_GA-rich chr1 11363221 11363527 - SINE/Alu_AluSz chr1 11363607 11363917 - LTR/ERVL_LTR16E1 chr1 11363961 11364037 - LTR/ERVL_LTR16E1 chr1 11364148 11364236 + LINE/L2_L2b chr1 11364425 11364722 + SINE/Alu_AluSx chr1 11364724 11365106 + LTR/ERVL-MaLR_MSTA chr1 11365161 11365492 + SINE/Alu_AluSx chr1 11365494 11365592 + LTR/ERVL-MaLR_MLT1B chr1 11365884 11366081 + LTR/ERVL-MaLR_MLT1K chr1 11366501 11366595 - DNA/hAT-Charlie_MER33 chr1 11366596 11366884 - SINE/Alu_AluSz6 chr1 11366885 11367052 - DNA/hAT-Charlie_MER33 chr1 11367302 11367575 - LINE/L1_L1MD3 chr1 11367636 11367736 - LINE/L1_L1MA10 chr1 11367843 11368161 + SINE/Alu_AluSx chr1 11368257 11368328 + LTR/ERVL-MaLR_MLT1B chr1 11368352 11368623 + LTR/ERVL-MaLR_MLT1B chr1 11368626 11368715 + LTR/ERVL-MaLR_MLT1H2 chr1 11368857 11368938 + LTR/ERVL-MaLR_MLT1H2 chr1 11368962 11369259 + SINE/Alu_AluSp chr1 11369276 11369436 - SINE/MIR_MIR chr1 11369553 11369738 + LINE/L2_L2c chr1 11370494 11370528 + Simple_repeat_(TATT)n chr1 11370529 11370809 - SINE/Alu_AluY chr1 11370818 11370994 - LINE/L1_L1MB4 chr1 11371021 11371109 + SINE/MIR_MIR3 chr1 11371623 11371659 + Simple_repeat_(TTTCTT)n chr1 11371660 11371943 - SINE/Alu_AluSx chr1 11371946 11372249 - SINE/Alu_AluJb chr1 11372346 11372521 - SINE/Alu_AluSc5 chr1 11372735 11372828 - SINE/MIR_MIRc chr1 11373029 11373232 + SINE/MIR_MIRc chr1 11373491 11374455 + Simple_repeat_(TG)n chr1 11375005 11375305 - SINE/Alu_AluSg chr1 11375312 11375482 - SINE/MIR_MIR chr1 11376001 11376057 + DNA/hAT-Ac_OldhAT1 chr1 11376188 11376509 - SINE/Alu_AluSz chr1 11376552 11376667 + DNA/hAT-Charlie_MER103C chr1 11377240 11377512 + LTR/ERVL-MaLR_MLT1J2 chr1 11377538 11377835 - SINE/Alu_AluJr chr1 11377846 11378007 + SINE/MIR_MIRb chr1 11378008 11378141 + LTR/ERVL_LTR16 chr1 11378175 11378439 + LTR/ERVL_ERV3-16A3_I-int chr1 11378440 11378566 + SINE/Alu_AluJr chr1 11378613 11378719 + DNA/hAT-Charlie_MER20 chr1 11378720 11378856 + SINE/Alu_AluSx chr1 11378857 11379158 + SINE/Alu_AluSx1 chr1 11379159 11379382 + SINE/Alu_AluSx chr1 11379412 11380224 + LTR/ERVL_ERV3-16A3_I-int chr1 11380225 11380522 + SINE/Alu_AluJb chr1 11380523 11380936 + LTR/ERVL_ERV3-16A3_I-int chr1 11380937 11381226 - SINE/Alu_AluSp chr1 11381227 11381512 + LTR/ERVL_ERV3-16A3_I-int chr1 11381524 11381655 + SINE/Alu_AluY chr1 11381660 11381958 + SINE/Alu_AluY chr1 11381976 11382446 + LTR/ERVL_ERV3-16A3_I-int chr1 11382447 11382757 - SINE/Alu_AluSz chr1 11382758 11383024 + LTR/ERVL_HERV16-int chr1 11383039 11383218 + DNA/hAT-Charlie_MER20 chr1 11383273 11383561 - SINE/Alu_AluJr chr1 11383588 11384195 + LTR/ERVL_ERV3-16A3_I-int chr1 11384287 11385073 + LTR/ERVL_ERV3-16A3_I-int chr1 11385076 11385505 + LTR/ERVL_LTR16 chr1 11385560 11385945 - LTR/ERVL_MLT2B1 chr1 11385946 11386258 + SINE/Alu_AluSx1 chr1 11386259 11386348 - LTR/ERVL_MLT2B1 chr1 11386425 11386697 - SINE/Alu_AluSx chr1 11387074 11387239 - SINE/MIR_MIRb chr1 11387605 11387783 + SINE/MIR_MIRb chr1 11387991 11388044 + Simple_repeat_(CTCC)n chr1 11388325 11388401 + LINE/RTE-BovB_MamRTE1 chr1 11388493 11388792 - SINE/Alu_AluSx chr1 11388987 11389172 - LINE/L1_L1PB3 chr1 11389351 11389388 + Simple_repeat_(TC)n chr1 11389389 11389684 - SINE/Alu_AluSx chr1 11389693 11390002 - SINE/Alu_AluY chr1 11390047 11390354 + SINE/Alu_AluSz chr1 11390888 11391684 - LINE/L1_L1MC4 chr1 11391979 11392003 + Simple_repeat_(GT)n chr1 11392458 11392601 - SINE/MIR_MIR chr1 11392602 11392709 + SINE/MIR_MIRb chr1 11392849 11392929 + LTR/ERVL-MaLR_MLT1J chr1 11392930 11393243 - SINE/Alu_AluSx chr1 11393244 11393470 + LTR/ERVL-MaLR_MLT1J chr1 11394366 11394666 + SINE/Alu_AluJr chr1 11394717 11395036 - DNA/TcMar-Tigger_MER44A chr1 11395043 11395448 - LINE/L2_L2c chr1 11396372 11396417 + Low_complexity_G-rich chr1 11396487 11396809 + LINE/L2_L2a chr1 11396812 11397100 + SINE/Alu_AluSx1 chr1 11397846 11398163 + LINE/L2_L2 chr1 11398174 11398255 - LINE/L1_L1MB8 chr1 11398276 11398354 - DNA/TcMar-Mariner_MADE1 chr1 11398436 11398548 + SINE/MIR_MIR3 chr1 11398728 11399028 - SINE/Alu_AluSx1 chr1 11399059 11399296 + SINE/Alu_AluSz chr1 11399297 11399321 + Simple_repeat_(AAAC)n chr1 11399350 11399542 + SINE/MIR_MIRb chr1 11399551 11399831 - SINE/Alu_AluSx chr1 11400561 11400586 + Simple_repeat_(TG)n chr1 11400593 11401031 - LINE/L1_L1ME3 chr1 11401072 11401367 - SINE/Alu_AluSz6 chr1 11401403 11401903 - LINE/L1_L1MC1 chr1 11401904 11402214 - SINE/Alu_AluSx chr1 11402215 11402600 - LINE/L1_L1MC1 chr1 11402699 11403073 + LTR/ERVL-MaLR_MLT1H2 chr1 11403083 11403371 + SINE/Alu_AluSz chr1 11403425 11403459 - LINE/L1_L1MC4 chr1 11403460 11403768 - SINE/Alu_AluJo chr1 11403769 11404047 - LINE/L1_L1MC4 chr1 11404146 11404359 + SINE/MIR_MIR chr1 11404531 11404685 + Simple_repeat_(TTCT)n chr1 11404686 11404995 - SINE/Alu_AluSz chr1 11404996 11405125 + SINE/MIR_MIR3 chr1 11405136 11405445 - LINE/L1_L1ME3 chr1 11405446 11405756 - SINE/Alu_AluSp chr1 11405757 11405831 - LINE/L1_L1ME3 chr1 11405832 11406132 - SINE/Alu_AluJo chr1 11406133 11406729 - LINE/L1_L1ME3 chr1 11406730 11407043 - SINE/Alu_AluSz6 chr1 11407044 11407709 - LINE/L1_L1ME3 chr1 11407710 11408069 + LTR/ERVL-MaLR_THE1B chr1 11408070 11408328 - LINE/L1_L1ME3 chr1 11408376 11408489 - LINE/L2_L2c chr1 11408546 11409456 + DNA/TcMar-Tigger_Tigger1 chr1 11410130 11410346 + DNA/TcMar-Tigger_Tigger1 chr1 11410347 11410648 + SINE/Alu_AluSp chr1 11410649 11411903 + DNA/TcMar-Tigger_Tigger1 chr1 11411927 11412159 - SINE/MIR_MIR chr1 11412736 11412763 + Simple_repeat_(CA)n chr1 11412768 11412878 + LINE/L2_L2b chr1 11412913 11413252 - DNA/TcMar-Tigger_Tigger3a chr1 11413461 11413504 + Simple_repeat_(AC)n chr1 11414290 11414345 + Low_complexity_G-rich chr1 11414456 11414507 - DNA/hAT-Charlie_Charlie4z chr1 11414508 11414796 + SINE/Alu_AluSz chr1 11414797 11414830 - DNA/hAT-Charlie_Charlie4z chr1 11415430 11415614 - DNA/hAT-Charlie_MER3 chr1 11415696 11416160 - LTR/ERVL-MaLR_MLT1D chr1 11416179 11416236 - SINE/MIR_MIR3 chr1 11416240 11416574 + LTR/ERVL_LTR16 chr1 11416635 11417141 + LINE/L2_L2c chr1 11417648 11417732 + DNA/hAT-Charlie_Chap1a_Mam chr1 11417733 11418043 + SINE/Alu_AluSz chr1 11418044 11418277 + DNA/hAT-Charlie_Chap1a_Mam chr1 11418380 11418585 - SINE/MIR_MIR chr1 11418618 11418690 - LINE/L2_L2b chr1 11418804 11419218 + LINE/L2_L2a chr1 11419272 11420567 - LINE/L1_L1PA6 chr1 11420573 11420978 + LINE/L2_L2a chr1 11420979 11421284 - SINE/Alu_AluSx chr1 11421285 11421808 + LINE/L2_L2a chr1 11421809 11422101 + SINE/Alu_AluSz chr1 11422102 11422333 + LINE/L2_L2a chr1 11422525 11422643 - DNA/TcMar-Tc2_Kanga2_a chr1 11422699 11422823 - DNA/TcMar-Tc2_Kanga2_a chr1 11422873 11422930 - DNA/hAT-Blackjack_MER81 chr1 11422968 11423203 - DNA/TcMar-Tc2_Kanga2_a chr1 11423207 11423514 + LTR/ERV1_MER49 chr1 11423515 11423827 + SINE/Alu_AluSq2 chr1 11423828 11423853 + LTR/ERV1_MER49 chr1 11423854 11423878 + Simple_repeat_(T)n chr1 11423879 11424312 + LTR/ERV1_MER49 chr1 11424313 11424726 + LTR/ERVL-MaLR_MSTB chr1 11424727 11424850 + LTR/ERV1_MER49 chr1 11424926 11425244 + SINE/Alu_AluSz chr1 11425826 11426190 - LTR/ERVL-MaLR_THE1B chr1 11426347 11426757 + LTR/ERVL-MaLR_MSTA chr1 11426799 11426883 - SINE/MIR_MIRc chr1 11426922 11426953 + Simple_repeat_(ATCCCC)n chr1 11427152 11427207 + Simple_repeat_(GT)n chr1 11427439 11427522 - LINE/L2_L2b chr1 11427557 11427649 + LINE/L2_L2b chr1 11427650 11427691 + LINE/L2_L2d2 chr1 11427815 11427940 - SINE/MIR_MIR1_Amn chr1 11427931 11428206 - SINE/MIR_MIRb chr1 11428215 11428324 + DNA/hAT-Charlie_Charlie4z chr1 11428327 11428595 + SINE/Alu_AluSx3 chr1 11428687 11428730 + Simple_repeat_(GT)n chr1 11429640 11429726 + SINE/MIR_MIRb chr1 11429798 11430095 - SINE/Alu_AluSz chr1 11430104 11430259 + SINE/MIR_MIRb chr1 11430387 11430513 + LINE/L2_L2d2 chr1 11430487 11430683 + LINE/L2_L2a chr1 11430697 11430766 - LTR/ERVL-MaLR_MLT1C2 chr1 11430767 11430990 + LTR/ERV1_LTR43B chr1 11430991 11431300 - SINE/Alu_AluSx1 chr1 11431301 11431346 + LTR/ERV1_LTR43B chr1 11431347 11431661 - SINE/Alu_AluSx1 chr1 11431662 11431708 + LTR/ERV1_LTR43B chr1 11431709 11431783 + Simple_repeat_(TTTC)n chr1 11431784 11432074 - SINE/Alu_AluSx1 chr1 11432075 11432351 + LTR/ERV1_LTR43B chr1 11432352 11432642 - SINE/Alu_AluJb chr1 11432643 11432704 + LTR/ERV1_LTR43B chr1 11432705 11433058 - LTR/ERVL-MaLR_MLT1C2 chr1 11433082 11433351 + LINE/L2_L2a chr1 11433352 11433665 - SINE/Alu_AluJb chr1 11433666 11433814 + LINE/L2_L2a chr1 11433787 11433907 - LINE/L2_L2a chr1 11434079 11434175 + LINE/L2_L2a chr1 11434622 11434668 + Simple_repeat_(CTGCCAG)n chr1 11435762 11436064 - SINE/Alu_AluSx1 chr1 11436286 11436559 - DNA/hAT-Charlie_Charlie23a chr1 11436778 11437010 + DNA/hAT-Tip100_MamTip2b chr1 11437186 11437453 + LINE/L2_L2c chr1 11437286 11437454 + LINE/L2_L2a chr1 11437455 11437749 + SINE/Alu_AluSz6 chr1 11437750 11438058 + LINE/L2_L2a chr1 11438127 11438302 + DNA/hAT-Charlie_MER117 chr1 11438310 11438394 + LINE/L2_L2c chr1 11438486 11438706 - DNA/hAT-Charlie_Charlie15a chr1 11438708 11438824 - SINE/MIR_MIRc chr1 11440099 11440162 + Simple_repeat_(CTAACC)n chr1 11440422 11440627 - SINE/MIR_MIRb chr1 11440737 11440932 + SINE/MIR_MIR chr1 11441088 11441105 + Simple_repeat_(TG)n chr1 11441194 11441408 - LINE/L2_L2a chr1 11441427 11441484 + SINE/MIR_MIR chr1 11441715 11441938 + Low_complexity_GA-rich chr1 11442796 11443106 - LINE/L2_L2d chr1 11443158 11443249 + Simple_repeat_(ACCCA)n chr1 11443326 11443563 + SINE/MIR_MIRb chr1 11444521 11444572 + Simple_repeat_(TCTCTCCT)n chr1 11445408 11445809 + LTR/ERV1_HERVIP10FH-int chr1 11445973 11446084 + LTR/ERVL-MaLR_MLT1O chr1 11446303 11446519 - SINE/MIR_MIRb chr1 11447074 11447357 - DNA/hAT-Charlie_MER102c chr1 11447358 11447673 - SINE/Alu_AluSq2 chr1 11447674 11447740 - DNA/hAT-Charlie_MER102c chr1 11448053 11448105 + Low_complexity_G-rich chr1 11448758 11448979 + SINE/MIR_MIRb chr1 11449630 11449742 + LINE/L1_L1ME4b chr1 11449971 11450255 + LINE/L1_L1MB3 chr1 11450966 11451016 - DNA/hAT-Tip100_MER91B chr1 11451050 11451118 - SINE/MIR_MIR1_Amn chr1 11451326 11451636 + SINE/Alu_AluSx1 chr1 11451706 11451894 + SINE/MIR_MIRb chr1 11452938 11453027 + SINE/MIR_MIRb chr1 11453052 11453172 - SINE/MIR_MIRb chr1 11453256 11453302 + Simple_repeat_(CCAGCC)n chr1 11453324 11453447 + SINE/MIR_MIRc chr1 11453601 11453819 - DNA/TcMar-Tigger_Tigger19a chr1 11454543 11454756 - SINE/MIR_MIR3 chr1 11454797 11455280 - LTR/ERVL-MaLR_MLT1D chr1 11455325 11455497 - SINE/MIR_MIR chr1 11456770 11456952 - SINE/MIR_MIR chr1 11457150 11457191 - SINE/MIR_MIRc chr1 11457175 11457298 - SINE/MIR_MIR3 chr1 11457467 11457593 + LINE/L2_L2c chr1 11457728 11457920 + SINE/MIR_MIR chr1 11457923 11458015 + LTR/ERVL-MaLR_MLT1C chr1 11458016 11458274 + SINE/Alu_AluSz chr1 11458275 11458586 + LTR/ERVL-MaLR_MLT1C chr1 11459447 11459876 - LTR/ERVL-MaLR_MLT1C2 chr1 11459894 11460090 + LINE/L2_L2c chr1 11460091 11460391 + SINE/Alu_AluSx1 chr1 11460392 11460627 + LINE/L2_L2c chr1 11460622 11460839 - SINE/MIR_MIR chr1 11461019 11461100 + LINE/L2_L2c chr1 11461101 11461225 + SINE/MIR_MIRb chr1 11461284 11461411 + SINE/MIR_MIRb chr1 11462007 11462108 + SINE/MIR_MIRc chr1 11462248 11462316 + LINE/L2_L2b chr1 11462476 11462508 + LINE/L2_L2b chr1 11462876 11463161 - SINE/Alu_AluSx1 chr1 11463507 11463797 - LINE/L2_L2b chr1 11463800 11463897 + DNA/hAT-Charlie_MER117 chr1 11463921 11464031 - SINE/MIR_MIR3 chr1 11464039 11464145 - DNA/hAT-Charlie_MER5A chr1 11464146 11464436 - SINE/Alu_AluJr chr1 11464437 11464523 - DNA/hAT-Charlie_MER5A chr1 11464550 11464663 + SINE/MIR_MIRc chr1 11464664 11464827 + DNA/hAT-Charlie_MER5B chr1 11464855 11465165 + SINE/Alu_AluSp chr1 11465166 11465332 + Low_complexity_A-rich chr1 11465333 11465444 - DNA/hAT-Charlie_MER5A1 chr1 11465503 11465621 - SINE/Alu_FLAM_A chr1 11466054 11466097 - LINE/L2_L2d2 chr1 11466065 11466126 - LINE/L2_L2d2 chr1 11466117 11466246 + LINE/L2_L2c chr1 11467327 11467401 - LTR/ERVL-MaLR_MLT1K chr1 11467443 11467763 - LTR/ERVL-MaLR_MLT1K chr1 11468057 11468086 + LINE/RTE-BovB_MamRTE1 chr1 11468267 11468392 + LTR/ERVL-MaLR_MLT1H2 chr1 11468657 11468755 + SINE/MIR_MIR3 chr1 11468901 11469353 - LINE/L2_L2d2 chr1 11469680 11469793 + SINE/MIR_MIRb chr1 11469833 11470116 + SINE/Alu_AluSx1 chr1 11470117 11470146 + Simple_repeat_(AAT)n chr1 11470155 11470318 + SINE/Alu_FRAM chr1 11470327 11470575 - SINE/MIR_MIR chr1 11470794 11471010 - SINE/MIR_MIRb chr1 11471405 11471541 + SINE/MIR_MIRb chr1 11471618 11471709 + Low_complexity_GA-rich chr1 11471967 11472019 + SINE/MIR_MIR3 chr1 11472397 11472522 + SINE/MIR_MIRb chr1 11472523 11472721 + SINE/MIR_MIR chr1 11473295 11473344 + SINE/Alu_Alu chr1 11473384 11473425 - SINE/MIR_MIR chr1 11473431 11473676 + SINE/Alu_AluJb chr1 11473912 11474073 - Unknown_MamRep564 chr1 11474125 11474290 - SINE/MIR_MIR3 chr1 11474373 11474575 - SINE/MIR_MIRb chr1 11475316 11475421 + SINE/MIR_MIR3 chr1 11475641 11475786 - SINE/MIR_MIRc chr1 11476121 11476318 - SINE/MIR_MIRb chr1 11476486 11476679 + LINE/CR1_L3b chr1 11478171 11478255 + LINE/L2_L2c chr1 11480678 11480714 + Simple_repeat_(CA)n chr1 11481039 11481064 + Simple_repeat_(TC)n chr1 11481065 11481091 + Simple_repeat_(AC)n chr1 11481536 11481721 + SINE/MIR_MIR3 chr1 11481882 11481929 - SINE/MIR_MIR chr1 11481919 11482023 + LINE/L2_L2c chr1 11483275 11483373 + SINE/MIR_MIR chr1 11483326 11483550 - SINE/MIR_MIRb chr1 11483826 11483985 - SINE/MIR_MIR1_Amn chr1 11484141 11484308 - SINE/MIR_MIRb chr1 11484914 11484974 - LINE/L2_L2c chr1 11484975 11485056 - DNA/TcMar-Mariner_MADE1 chr1 11485057 11485091 - LINE/L2_L2c chr1 11485687 11485853 - SINE/MIR_MIR chr1 11486686 11486978 - SINE/Alu_AluJo chr1 11487027 11487076 + Simple_repeat_(AATG)n chr1 11488638 11488668 + Simple_repeat_(TG)n chr1 11488820 11488872 + SINE/MIR_MIRc chr1 11489552 11489654 + LINE/CR1_L3 chr1 11489621 11489803 - SINE/MIR_MIR3 chr1 11490100 11490138 + Low_complexity_G-rich chr1 11490256 11490568 - SINE/Alu_AluSq chr1 11490813 11490902 + SINE/MIR_MIRb chr1 11491248 11491322 + DNA/hAT-Charlie_MER5A chr1 11491323 11491637 + SINE/Alu_AluJo chr1 11491638 11491745 + DNA/hAT-Charlie_MER5A chr1 11491841 11492147 + SINE/Alu_AluYa5 chr1 11492180 11492213 - SINE/MIR_MIR1_Amn chr1 11493018 11493442 - LTR/ERVL_MLT2B4 chr1 11493443 11493736 + SINE/Alu_AluSz chr1 11493737 11493848 - LTR/ERVL_MLT2B4 chr1 11494049 11494286 + SINE/MIR_MIRc chr1 11494960 11495109 + LINE/RTE-BovB_MamRTE1 chr1 11495113 11495418 + SINE/Alu_AluSz chr1 11495543 11495671 + SINE/MIR_MIR3 chr1 11495720 11495928 - LINE/CR1_L3b chr1 11495963 11496160 - LINE/L1_L1ME4a chr1 11496366 11496398 + Low_complexity_GA-rich chr1 11496453 11496481 + Simple_repeat_(CCCCA)n chr1 11496810 11496968 - SINE/MIR_MIR3 chr1 11497221 11497393 - LINE/L1_L1PB1 chr1 11497394 11497687 - SINE/Alu_AluY chr1 11497688 11497840 - LINE/L1_L1PB1 chr1 11497899 11498013 - LINE/L2_L2a chr1 11498140 11498672 - LTR/ERVL-MaLR_MLT1G1 chr1 11498721 11498950 - SINE/MIR_MIR chr1 11499026 11499156 - LINE/L2_L2a chr1 11499686 11499692 + Simple_repeat_(TCTC)n chr1 11499693 11499766 + Simple_repeat_(CTCTCC)n chr1 11499767 11499801 + Simple_repeat_(TCTC)n chr1 11500051 11500096 + Simple_repeat_(CTGCC)n chr1 11500395 11500553 - SINE/MIR_MIRb chr1 11501035 11501057 + Simple_repeat_(GAG)n chr1 11502154 11502189 + Simple_repeat_(GGCCCA)n chr1 11503233 11503767 - LTR/ERVL_MLT2B3 chr1 11503772 11503930 + SINE/MIR_MIRc chr1 11504126 11504342 - SINE/MIR_MIR chr1 11504390 11504443 + LINE/L2_L2c chr1 11504485 11504886 + LTR/ERVL-MaLR_MSTB2 chr1 11504930 11505043 + SINE/MIR_MIRb chr1 11505069 11505203 + DNA/hAT-Charlie_MER5A1 chr1 11505206 11505343 + LINE/L2_L2c chr1 11505594 11506794 - LINE/L1_L1MEg chr1 11506795 11507073 - SINE/Alu_AluJr chr1 11507074 11507813 - LINE/L1_L1MEg chr1 11507837 11507897 + LINE/L2_L2b chr1 11507915 11507998 - DNA/TcMar-Tigger_Tigger19a chr1 11508016 11508137 - LINE/L1_L1MA10 chr1 11508141 11508434 + SINE/Alu_AluJr chr1 11508435 11508643 - LINE/L1_L1MEc chr1 11508644 11508896 - LINE/L1_L1MA1 chr1 11508897 11509828 - LINE/L1_L1MEc chr1 11509829 11510124 + SINE/Alu_AluY chr1 11510125 11510207 - LINE/L1_L1MD chr1 11510208 11510689 - LINE/L1_L1MEc chr1 11510690 11510800 + SINE/Alu_AluJr chr1 11510801 11511159 - LTR/ERVL-MaLR_THE1A chr1 11511160 11512751 - LTR/ERVL-MaLR_THE1A-int chr1 11512752 11513109 - LTR/ERVL-MaLR_THE1A chr1 11513110 11513306 + SINE/Alu_AluJr chr1 11513307 11513688 - LINE/L1_L1MEc chr1 11513722 11513923 - LINE/L1_L1MEc chr1 11514114 11514280 - DNA/hAT-Charlie_MER5B chr1 11514625 11514733 - SINE/MIR_MIR3 chr1 11515020 11515122 - LINE/L2_L2c chr1 11516383 11516568 - SINE/MIR_MIRc chr1 11516814 11516931 + SINE/MIR_MIRb chr1 11517148 11517236 - LINE/L2_L2a chr1 11517789 11518008 - SINE/MIR_MIR chr1 11518401 11518553 + DNA/hAT-Tip100_MER91B chr1 11518628 11518842 + SINE/MIR_MIRb chr1 11520119 11520341 + SINE/MIR_MIRb chr1 11520273 11520356 + LINE/RTE-BovB_MamRTE1 chr1 11520996 11521399 - LINE/L2_L2b chr1 11521836 11522118 - LINE/L2_L2b chr1 11523123 11523207 + SINE/MIR_MIRb chr1 11524489 11524518 + Simple_repeat_(CCAGCA)n chr1 11525476 11525675 + SINE/MIR_MIRb chr1 11525793 11526026 - SINE/Alu_AluJr chr1 11526131 11526442 + LINE/L2_L2b chr1 11526436 11526526 - SINE/MIR_MIR1_Amn chr1 11526945 11527234 - SINE/Alu_AluSx chr1 11527271 11527574 + SINE/Alu_AluSc8 chr1 11527663 11528089 + LINE/L2_L2a chr1 11528123 11528256 + LINE/L2_L2c chr1 11528430 11528600 - LINE/L2_L2a chr1 11529263 11529406 - SINE/MIR_MIR3 chr1 11529437 11529481 + Simple_repeat_(AGCCCC)n chr1 11531780 11531910 - SINE/MIR_MIRb chr1 11532302 11532388 + SINE/MIR_MIR3 chr1 11532643 11532695 - LINE/L1_L1MB3 chr1 11532696 11533005 - SINE/Alu_AluSx chr1 11533006 11533249 - LINE/L1_L1MB3 chr1 11533250 11533556 - SINE/Alu_AluSx chr1 11533557 11533636 - LINE/L1_L1MB3 chr1 11533840 11534199 - LINE/L2_L2b chr1 11534781 11534959 - SINE/MIR_MIRb chr1 11535827 11535933 + SINE/MIR_MIR3 chr1 11537006 11537134 - SINE/MIR_MIRb chr1 11538156 11538583 - LTR/ERVL-MaLR_MLT1C2 chr1 11538590 11538763 - SINE/MIR_MIR chr1 11539333 11539602 + SINE/Alu_AluSz6 chr1 11539604 11539639 + Simple_repeat_(TAA)n chr1 11539818 11539924 - SINE/MIR_MIRb chr1 11540580 11540734 - SINE/MIR_MIR1_Amn chr1 11540762 11541078 + LINE/L1_L1ME4b chr1 11541277 11541323 + Low_complexity_GA-rich chr1 11541573 11541792 + SINE/MIR_MIRb chr1 11542467 11542752 + LINE/L2_L2b chr1 11542749 11542806 + SINE/MIR_MIRc chr1 11543153 11543177 + Simple_repeat_(AGGG)n chr1 11543465 11543634 - SINE/MIR_MIRc chr1 11543778 11543827 + SINE/MIR_MIRc chr1 11543831 11544160 - LTR/ERVL-MaLR_MLT1H chr1 11544202 11544538 + SINE/Alu_AluSx1 chr1 11544541 11544659 + LTR/ERVL-MaLR_MLT1C chr1 11544662 11545071 + LTR/ERVL-MaLR_MLT1B chr1 11545072 11545387 + LTR/ERVL-MaLR_MLT1C chr1 11545388 11545578 - LTR/ERVL-MaLR_MLT1H chr1 11545584 11545679 + SINE/MIR_MIRb chr1 11547363 11547374 + Simple_repeat_(AGACAG)n chr1 11547375 11547532 + SINE/Alu_FRAM chr1 11547533 11547721 + Simple_repeat_(AGACAG)n chr1 11548086 11548401 - SINE/Alu_AluY chr1 11548404 11548696 - SINE/Alu_AluJr chr1 11549148 11549632 + LINE/L1_L1ME3 chr1 11549633 11549931 - SINE/Alu_AluY chr1 11549932 11549999 + LINE/L1_L1ME3 chr1 11550000 11550284 - SINE/Alu_AluSx chr1 11551349 11551744 - LINE/L2_L2a chr1 11551745 11552041 + SINE/Alu_AluJr chr1 11552042 11552192 - LINE/L2_L2a chr1 11552225 11552749 + LTR/ERVL_MLT2B4 chr1 11552753 11552840 - LINE/L2_L2a chr1 11553390 11553865 - LTR/ERV1_MER66B chr1 11553978 11554109 + SINE/MIR_MIR3 chr1 11554892 11555035 - SINE/MIR_MIRb chr1 11555397 11555695 - LTR/ERVL-MaLR_MLT1K chr1 11555749 11555817 + LTR/ERVL-MaLR_MLT1G3 chr1 11555818 11556334 + LTR/ERVL_MER70A chr1 11556335 11556498 + LTR/ERVL-MaLR_MLT1G3 chr1 11556515 11556674 + LTR/ERVL-MaLR_MLT1G3 chr1 11556677 11556709 + Simple_repeat_(GT)n chr1 11557400 11557603 + SINE/MIR_MIR chr1 11558294 11558711 - LTR/ERV1?_LTR103_Mam chr1 11559211 11559291 + LINE/L2_L2c chr1 11559335 11559383 + DNA/hAT-Tip100_MamTip2 chr1 11559393 11559501 - LINE/L2_L2b chr1 11559761 11559799 + Simple_repeat_(GCCCCA)n chr1 11559978 11560011 + Simple_repeat_(TGCACG)n chr1 11560012 11560045 + Simple_repeat_(CA)n chr1 11560670 11560778 + SINE/MIR_MIR3 chr1 11561721 11561844 + SINE/Alu_AluJo chr1 11561846 11561984 + Low_complexity_GA-rich chr1 11561986 11562156 + SINE/Alu_AluJo chr1 11562167 11562382 + LINE/L1_L1M4 chr1 11562383 11562684 + SINE/Alu_AluSx chr1 11562685 11562788 + LINE/L1_L1M4 chr1 11562810 11562980 + SINE/MIR_MIRb chr1 11563633 11563766 + DNA/TcMar-Tigger_X6a_DNA chr1 11563958 11564140 + LINE/L1_L1ME4a chr1 11564148 11564360 + SINE/Alu_AluJb chr1 11564361 11564839 + LTR/ERV1_MER66B chr1 11564840 11564910 + SINE/Alu_AluJb chr1 11564949 11565110 + LINE/L1_L1ME4a chr1 11565686 11566136 + LTR/ERVL-MaLR_MLT1C2 chr1 11566166 11566290 - SINE/MIR_MIR chr1 11566351 11566510 + SINE/MIR_MIRb chr1 11567381 11567531 - SINE/MIR_MIR chr1 11567619 11567822 + DNA/hAT-Charlie_MER58A chr1 11567965 11568267 - SINE/Alu_AluSc8 chr1 11568515 11568815 - SINE/Alu_AluSz chr1 11569184 11569349 + DNA/hAT-Tip100_MER91B chr1 11569351 11569501 - LINE/L2_L2b chr1 11570033 11570205 + SINE/MIR_MIR chr1 11570600 11570744 + LTR/Gypsy_MamGypLTR1a chr1 11571038 11571218 + LTR/ERVL-MaLR_MLT1K chr1 11571228 11571277 + DNA/hAT-Charlie_MER117 chr1 11571433 11571609 + SINE/MIR_MIR chr1 11571785 11571840 + LTR/ERVL_MLT2C2 chr1 11571841 11571904 + Simple_repeat_(ACACAT)n chr1 11571919 11571978 + DNA/MULE-MuDR_Ricksha_c chr1 11572719 11572929 - SINE/MIR_MIR chr1 11573099 11573158 - DNA/MULE-MuDR_Ricksha_c chr1 11573175 11573216 + Simple_repeat_(TGTATA)n chr1 11573223 11573652 - LTR/ERVL_MLT2C2 chr1 11574265 11574445 + LTR/ERVL-MaLR_MLT1B chr1 11574446 11574733 + SINE/Alu_AluJb chr1 11574734 11574883 + LTR/ERVL-MaLR_MLT1B chr1 11574884 11575209 - SINE/Alu_AluSz chr1 11575210 11575322 + LTR/ERVL-MaLR_MLT1B chr1 11575630 11575772 + SINE/MIR_MIR chr1 11575773 11576215 - LTR/ERVL_MLT2A1 chr1 11576216 11576317 + SINE/MIR_MIR chr1 11576521 11576772 + LINE/L2_L2a chr1 11576779 11576878 + SINE/Alu_AluJb chr1 11576879 11577161 + SINE/Alu_AluSq2 chr1 11577162 11577196 + Simple_repeat_(AAACA)n chr1 11577197 11577379 + SINE/Alu_AluJb chr1 11577400 11577442 + Simple_repeat_(CA)n chr1 11577589 11577930 + LINE/L2_L2a chr1 11578602 11578898 + SINE/Alu_AluSz chr1 11578958 11579157 + SINE/MIR_MIRb chr1 11579163 11579218 + Simple_repeat_(ATAC)n chr1 11579741 11579953 + SINE/MIR_MIR chr1 11580007 11580394 - LTR/ERVL-MaLR_THE1C chr1 11580502 11580771 + LTR/ERVL_LTR16C chr1 11580884 11581014 + SINE/Alu_FLAM_A chr1 11581544 11581980 + LTR/ERV1_MER66B chr1 11582220 11582298 + LINE/L1_L1ME4a chr1 11582299 11582594 - SINE/Alu_AluSx4 chr1 11582595 11583011 + LINE/L1_L1ME4a chr1 11583153 11583249 + LTR/ERVL_LTR67B chr1 11583285 11583606 + LTR/ERVL-MaLR_MLT1A chr1 11583833 11584086 + LTR/ERVL_LTR67B chr1 11584069 11584224 - LTR/Gypsy_MamGypLTR4 chr1 11584370 11584452 + SINE/Alu_AluY chr1 11584437 11584471 + SINE/Alu_FLAM_A chr1 11584689 11585005 + DNA/hAT-Charlie_MER33 chr1 11585130 11585203 + Simple_repeat_(TTTTTG)n chr1 11585739 11585784 + DNA/hAT-Charlie_MER5A chr1 11586203 11586427 - SINE/MIR_MIRb chr1 11587052 11587232 - SINE/MIR_MIR3 chr1 11587243 11587661 + LTR/ERVL-MaLR_MLT1F chr1 11587662 11588085 + LTR/ERVL-MaLR_MSTA chr1 11588086 11588213 + LTR/ERVL-MaLR_MLT1F chr1 11588388 11588702 - SINE/Alu_AluSp chr1 11588710 11588834 - LINE/L2_L2a chr1 11589096 11589370 + SINE/Alu_AluJr chr1 11590404 11590513 - LINE/L2_L2b chr1 11590548 11590885 + LTR/ERVL-MaLR_MLT1J2 chr1 11590915 11590953 + LTR/ERVL-MaLR_MLT1G1 chr1 11590954 11591264 + SINE/Alu_AluSz chr1 11591265 11591408 + LTR/ERVL-MaLR_MLT1G1 chr1 11591409 11591860 + LTR/ERVL_LTR47B2 chr1 11591861 11592180 + LTR/ERVL-MaLR_MLT1G1 chr1 11592244 11592494 - SINE/MIR_MIRb chr1 11592516 11592543 + Simple_repeat_(AAC)n chr1 11592602 11592644 + Simple_repeat_(AATTTTA)n chr1 11592690 11592893 + SINE/MIR_MIR chr1 11593002 11593076 + SINE/MIR_MIRc chr1 11593540 11593673 + SINE/MIR_MIRb chr1 11594839 11595186 + LINE/L2_L2a chr1 11595205 11595329 - LINE/L2_L2c chr1 11595379 11595755 + LINE/L2_L2b chr1 11595756 11596008 - SINE/Alu_AluJr chr1 11596016 11596354 - SINE/Alu_AluSx chr1 11596355 11596679 + LINE/L2_L2b chr1 11596986 11597296 - SINE/Alu_AluSz chr1 11597443 11597530 + SINE/Alu_FLAM_C chr1 11598269 11598513 + SINE/MIR_MIR chr1 11599204 11599359 - SINE/MIR_MIRc chr1 11599488 11599673 + SINE/MIR_MIR3 chr1 11599680 11599774 - SINE/MIR_MIR3 chr1 11600219 11600638 - DNA/hAT-Tip100_ORSL-2b chr1 11600640 11600685 + SINE/MIR_MIR1_Amn chr1 11601492 11601789 + SINE/Alu_AluJb chr1 11602093 11602348 - LINE/L2_L2b chr1 11602433 11602452 + Simple_repeat_(CAG)n chr1 11602581 11602631 - DNA/hAT-Tip100_MamTip2 chr1 11602634 11602735 + Simple_repeat_(CCAGCC)n chr1 11602949 11603032 - DNA/hAT-Tip100_MamTip2 chr1 11603049 11603181 - SINE/MIR_MIRb chr1 11603194 11603483 - DNA/hAT-Charlie_MER102a chr1 11603499 11603541 - LINE/L2_L2a chr1 11603542 11603603 + DNA/hAT-Blackjack_MER81 chr1 11603604 11603900 + SINE/Alu_AluSx3 chr1 11603901 11603936 + DNA/hAT-Blackjack_MER81 chr1 11603940 11604199 - LINE/L2_L2b chr1 11604212 11604408 + LINE/L1_L1MC5 chr1 11604411 11604556 - SINE/Alu_AluJo chr1 11604557 11604853 - SINE/Alu_AluSx chr1 11604854 11604987 - SINE/Alu_AluJo chr1 11605214 11605414 - DNA/hAT-Charlie_MER20 chr1 11605420 11605537 + LINE/L1_L1MC5 chr1 11605547 11605705 + SINE/Alu_AluSc chr1 11605706 11605933 + LINE/L1_L1MC5 chr1 11606094 11606299 + LINE/L1_L1MC5 chr1 11606376 11606647 - LINE/L2_L2b chr1 11606655 11606942 + SINE/Alu_AluJo chr1 11606949 11607319 + LINE/L1_L1MB7 chr1 11607320 11607614 + SINE/Alu_AluSx chr1 11607615 11607662 + LINE/L1_L1MB7 chr1 11607666 11607966 + SINE/Alu_AluSx1 chr1 11607969 11607990 + Simple_repeat_(T)n chr1 11608408 11608568 + LTR/ERVL-MaLR_MLT1I chr1 11608569 11609920 + LTR/ERV1_LTR12C chr1 11609921 11609955 + LTR/ERVL-MaLR_MLT1I chr1 11610556 11610624 + DNA/hAT-Tip100_MamTip2b chr1 11611148 11611335 + SINE/MIR_MIR chr1 11611420 11611640 + SINE/MIR_MIR chr1 11611650 11611973 - LTR/ERVL-MaLR_MLT1K chr1 11611997 11612104 - LTR/ERVL-MaLR_MLT1K chr1 11612485 11612789 + LTR/ERVL-MaLR_MLT1L chr1 11612892 11613043 - LTR/ERVL_MLT2D chr1 11613044 11613407 + LTR/ERVL-MaLR_THE1B chr1 11613408 11613701 - LTR/ERVL_MLT2D chr1 11613711 11613850 - SINE/MIR_MIR3 chr1 11614203 11614490 + DNA/hAT-Charlie_MER113 chr1 11614500 11614565 - LINE/CR1_L3b chr1 11615527 11615597 - SINE/MIR_MIRc chr1 11615598 11615899 + SINE/Alu_AluJb chr1 11615900 11616034 - SINE/MIR_MIRc chr1 11616029 11616074 - DNA/hAT-Ac_OldhAT1 chr1 11616054 11616208 - SINE/MIR_MIRb chr1 11616501 11616683 - SINE/MIR_MIR chr1 11616766 11616924 - SINE/MIR_MIR chr1 11617204 11617267 - SINE/MIR_MIR3 chr1 11617658 11617840 - DNA/hAT-Charlie_MER5B chr1 11617865 11618053 - SINE/MIR_MIR3 chr1 11618255 11618308 + Simple_repeat_(AC)n chr1 11618312 11618390 + LTR/ERVL-MaLR_MLT1K chr1 11618391 11618690 + SINE/Alu_AluSx chr1 11618691 11619028 + LTR/ERVL-MaLR_MLT1K chr1 11619178 11619252 + SINE/MIR_MIRc chr1 11619256 11619714 + LTR/ERVL_MER74A chr1 11619715 11620022 - SINE/Alu_AluSq2 chr1 11620023 11620080 + LTR/ERVL_MER74A chr1 11620159 11620316 - SINE/MIR_MIRc chr1 11621024 11621150 - SINE/MIR_MIR chr1 11621153 11621362 + DNA/hAT-Charlie_MER102c chr1 11621518 11621598 - SINE/MIR_MIR3 chr1 11621682 11621744 - SINE/MIR_MIRc chr1 11621982 11622172 - SINE/MIR_MIRc chr1 11622372 11622408 + DNA/hAT-Tip100_MER91C chr1 11623093 11623179 + SINE/MIR_MIR chr1 11623263 11623468 - LINE/L2_L2b chr1 11623469 11623755 - SINE/Alu_AluSq2 chr1 11623756 11623887 - LINE/L2_L2b chr1 11623918 11624201 - SINE/Alu_AluJb chr1 11624220 11624450 - DNA/hAT-Tip100_Arthur1B chr1 11624455 11624635 + SINE/Alu_AluJb chr1 11624636 11624698 + Simple_repeat_(TA)n chr1 11624701 11624804 - DNA/hAT-Tip100_Arthur1B chr1 11624805 11624985 + LTR/ERVL-MaLR_MLT1D chr1 11624986 11625284 + SINE/Alu_AluSz6 chr1 11625285 11625576 + LTR/ERVL-MaLR_MLT1D chr1 11625577 11625618 - DNA/hAT-Tip100_Arthur1B chr1 11625626 11625792 - DNA/hAT-Tip100_Arthur1B chr1 11626062 11626097 + Simple_repeat_(GGTGAG)n chr1 11626927 11627225 - SINE/Alu_AluSq2 chr1 11627410 11627500 - SINE/MIR_MIRc chr1 11627624 11627899 + SINE/Alu_AluSz6 chr1 11627927 11628042 - LINE/L2_L2b chr1 11628043 11628348 + SINE/Alu_AluY chr1 11628349 11628408 - LINE/L2_L2b chr1 11628870 11628977 - DNA/hAT-Blackjack_MER63B chr1 11628974 11629028 - DNA/hAT-Blackjack_MER63B chr1 11629025 11629127 - DNA/hAT-Blackjack_MER63B chr1 11629389 11629490 + DNA/TcMar-Tigger_Tigger19b chr1 11629532 11629642 + LINE/L2_L2b chr1 11630466 11630777 - SINE/Alu_AluJb chr1 11630793 11630842 + LINE/L2_L2a chr1 11631189 11631274 - DNA/hAT-Tip100_MamTip2 chr1 11631864 11632108 + Low_complexity_GA-rich chr1 11632109 11632376 + SINE/Alu_AluSx chr1 11632377 11632414 + Simple_repeat_(AAACA)n chr1 11632415 11632445 + Low_complexity_GA-rich chr1 11632446 11632524 + Simple_repeat_(GGAA)n chr1 11632533 11632761 - SINE/MIR_MIRb chr1 11633088 11633226 + SINE/MIR_MIR chr1 11633227 11633509 + SINE/Alu_AluSz chr1 11633550 11633846 - SINE/Alu_AluSz chr1 11633864 11633975 + SINE/MIR_MIR chr1 11633941 11634293 - LINE/L2_L2c chr1 11635125 11635250 + SINE/MIR_MIRb chr1 11635251 11635504 + SINE/Alu_AluYc chr1 11635505 11635578 + SINE/MIR_MIRb chr1 11635643 11635949 - SINE/Alu_AluY chr1 11636530 11636639 - SINE/MIR_MIRc chr1 11636787 11636935 + SINE/Alu_AluSx3 chr1 11637845 11638093 + LTR/ERVL-MaLR_MLT1K chr1 11638098 11638246 - SINE/MIR_MIR chr1 11638324 11638378 + SINE/MIR_MIR3 chr1 11638913 11638951 + Simple_repeat_(T)n chr1 11639262 11639485 + LINE/L2_L2b chr1 11639424 11639640 - SINE/MIR_MIR chr1 11639606 11639724 + LINE/L2_L2b chr1 11639729 11640032 + SINE/Alu_AluSz chr1 11640247 11640443 + SINE/MIR_MIRb chr1 11640506 11640542 + Simple_repeat_(A)n chr1 11640795 11641001 + SINE/MIR_MIRb chr1 11641235 11641534 - SINE/Alu_AluSz chr1 11642729 11643023 + SINE/Alu_AluSc chr1 11643333 11643453 + SINE/Alu_AluSx3 chr1 11643565 11643743 + SINE/MIR_MIRb chr1 11643767 11643803 + Simple_repeat_(CAT)n chr1 11643987 11644185 + LINE/L2_L2a chr1 11644186 11644485 + SINE/Alu_AluSz chr1 11644486 11644556 + LINE/L2_L2a chr1 11644601 11644915 + SINE/Alu_AluSx1 chr1 11644916 11644979 + SINE/MIR_MIR chr1 11644981 11645025 + Simple_repeat_(T)n chr1 11645050 11645193 - SINE/Alu_AluSx chr1 11645194 11645328 + SINE/MIR_MIR chr1 11645310 11645452 + LINE/L2_L2a chr1 11645463 11645747 + SINE/Alu_AluJr chr1 11645877 11645951 - LINE/CR1_L3 chr1 11645958 11646049 + SINE/MIR_MIR chr1 11646331 11646374 + Simple_repeat_(GGCTCCT)n chr1 11647214 11647305 + LINE/L2_L2b chr1 11647707 11647751 + Simple_repeat_(TCCCCA)n chr1 11648382 11648540 - LINE/L2_L2a chr1 11648973 11649005 + Simple_repeat_(CAGCC)n chr1 11649233 11649271 + Low_complexity_GA-rich chr1 11649340 11649422 - SINE/MIR_MIR chr1 11649527 11649569 + Simple_repeat_(AT)n chr1 11650733 11650803 + Simple_repeat_(CCT)n chr1 11651178 11651393 + LINE/L2_L2b chr1 11651414 11651652 - SINE/MIR_MIR chr1 11651705 11651997 - SINE/Alu_AluSx chr1 11652002 11652126 - SINE/Alu_FLAM_C chr1 11652139 11652236 - SINE/MIR_MIRb chr1 11652377 11652518 + SINE/MIR_MIRc chr1 11652870 11652919 + LINE/L2_L2b chr1 11652907 11653071 - SINE/MIR_MIR chr1 11654463 11654515 + Simple_repeat_(CCCCG)n chr1 11654820 11654873 + Simple_repeat_(GCGGG)n chr1 11655504 11655604 + SINE/MIR_MIRc chr1 11655670 11655778 - SINE/MIR_MIRb chr1 11656154 11656289 - SINE/MIR_MIR chr1 11656310 11656464 - SINE/Alu_AluSx chr1 11656465 11656762 - SINE/Alu_AluSx1 chr1 11656781 11656902 - SINE/MIR_MIRc chr1 11657168 11657305 + DNA/hAT-Charlie_MER58C chr1 11657306 11657446 - SINE/MIR_MIRc chr1 11657458 11657763 + SINE/Alu_AluSx chr1 11657764 11657854 + DNA/hAT-Charlie_MER58C chr1 11657857 11658026 - SINE/MIR_MIRc chr1 11658920 11659078 + SINE/MIR_MIRb chr1 11659106 11659407 + SINE/Alu_AluSp chr1 11659483 11659505 + Simple_repeat_(TTTG)n chr1 11659522 11659795 - SINE/Alu_AluJr chr1 11659799 11660004 - DNA/hAT-Charlie_MER58A chr1 11660004 11660154 + DNA/hAT-Charlie_MER3 chr1 11660155 11660458 - SINE/Alu_AluSx chr1 11660459 11660516 + DNA/hAT-Charlie_MER3 chr1 11660546 11660730 - SINE/MIR_MIRc chr1 11660781 11661082 - SINE/Alu_AluJo chr1 11662250 11662312 - DNA/hAT-Ac_OldhAT1 chr1 11662649 11662684 + SINE/MIR_MIR chr1 11662686 11662944 + LTR/ERVL-MaLR_THE1D chr1 11662945 11663256 - SINE/Alu_AluY chr1 11663257 11663370 + LTR/ERVL-MaLR_THE1D chr1 11663535 11663588 - DNA/hAT-Tip100_MER91A chr1 11663667 11663947 - SINE/Alu_AluSz chr1 11665044 11665215 - SINE/Alu_AluJb chr1 11665216 11665554 - SINE/Alu_AluY chr1 11665555 11665858 - SINE/Alu_AluSx4 chr1 11665859 11665905 - SINE/Alu_AluJb chr1 11666029 11666115 - SINE/Alu_AluJb chr1 11666194 11666365 - SINE/Alu_AluJr chr1 11666373 11666681 - SINE/Alu_AluSx3 chr1 11666866 11667173 + SINE/Alu_AluSq2 chr1 11667538 11667578 - SINE/MIR_MIR chr1 11667687 11667810 - DNA/TcMar-Tigger_X1_DNA chr1 11667811 11668110 + SINE/Alu_AluSc8 chr1 11668111 11668174 - DNA/TcMar-Tigger_X1_DNA chr1 11668402 11668564 - SINE/Alu_AluJb chr1 11668972 11669188 + LINE/L2_L2b chr1 11669189 11669482 + SINE/Alu_AluSp chr1 11669483 11669609 + LINE/L2_L2b chr1 11669738 11669762 + Simple_repeat_(AC)n chr1 11669763 11669931 - SINE/Alu_AluSx1 chr1 11669932 11670228 + SINE/Alu_AluY chr1 11670646 11670755 - SINE/Alu_AluJo chr1 11672194 11672265 - SINE/MIR_MIRc chr1 11672292 11672589 - SINE/Alu_AluSx1 chr1 11672696 11672756 - LINE/L2_L2a chr1 11674023 11674333 + SINE/Alu_AluY chr1 11676288 11676439 + DNA/hAT-Charlie_MER5B chr1 11676440 11676527 - SINE/MIR_MIR3 chr1 11676579 11676684 + SINE/MIR_MIR3 chr1 11677399 11677485 + SINE/MIR_MIRc chr1 11677778 11678073 + SINE/Alu_AluSx4 chr1 11678133 11678431 + SINE/Alu_AluSx chr1 11678465 11678589 + DNA/hAT-Charlie_Charlie4z chr1 11678596 11678679 + SINE/MIR_MIR3 chr1 11678707 11678895 - DNA/TcMar-Tigger_MER47A chr1 11678896 11679191 + SINE/Alu_AluSx chr1 11679192 11679277 - DNA/TcMar-Tigger_MER47A chr1 11679322 11679385 - LINE/L2_L2a chr1 11679534 11679826 - SINE/Alu_AluSg chr1 11679986 11680295 - SINE/Alu_AluSq2 chr1 11681725 11681960 - SINE/MIR_MIR chr1 11682031 11682226 - SINE/MIR_MIR chr1 11682235 11682328 - SINE/MIR_MIR3 chr1 11682554 11682722 + DNA/hAT-Charlie_MER5A chr1 11683198 11683244 + Simple_repeat_(TCAGCCC)n chr1 11683427 11683678 + LINE/L1_L1PB2 chr1 11683700 11683987 + SINE/Alu_AluSp chr1 11684095 11684168 + SINE/MIR_MIR3 chr1 11684850 11684916 + LINE/L2_L2b chr1 11684917 11685230 - SINE/Alu_AluJr chr1 11685231 11685271 + LINE/L2_L2b chr1 11685499 11685675 + SINE/MIR_MIR chr1 11685676 11685973 + SINE/Alu_AluJb chr1 11685974 11686017 + SINE/MIR_MIR chr1 11686441 11686702 - SINE/Alu_AluSz chr1 11686808 11686824 + Simple_repeat_(A)n chr1 11686825 11687055 - LINE/L1_L1MB7 chr1 11687056 11687232 - SINE/Alu_AluJr chr1 11687233 11687537 - SINE/Alu_AluSx1 chr1 11687538 11687675 - SINE/Alu_AluJr chr1 11687676 11687884 - LINE/L1_L1MB7 chr1 11687906 11687936 - SINE/MIR_MIR3 chr1 11688155 11688277 + LINE/L2_L2a chr1 11688278 11688578 + SINE/Alu_AluSc8 chr1 11688579 11688683 + LINE/L2_L2a chr1 11688684 11689013 + LTR/ERV1_MER41A chr1 11689014 11689315 + SINE/Alu_AluSx chr1 11689316 11689609 + SINE/Alu_AluSx chr1 11689610 11689849 + LTR/ERV1_MER41A chr1 11689850 11690291 + LINE/L2_L2a chr1 11690489 11690578 - SINE/MIR_MIRb chr1 11690924 11690961 + Simple_repeat_(GT)n chr1 11691133 11691165 + Simple_repeat_(GCCCC)n chr1 11692789 11692904 + SINE/MIR_MIR chr1 11693138 11693164 + Simple_repeat_(CT)n chr1 11694159 11694248 + LINE/L2_L2c chr1 11694418 11694497 - SINE/MIR_MIR1_Amn chr1 11694496 11694577 + SINE/MIR_MIR chr1 11694863 11694899 + Simple_repeat_(GCCCCAT)n chr1 11695129 11695296 + SINE/MIR_MIR3 chr1 11695297 11695612 + SINE/Alu_AluSz chr1 11695613 11695687 + SINE/MIR_MIRb chr1 11696307 11696606 + SINE/Alu_AluSq2 chr1 11696607 11696887 + SINE/Alu_AluSc chr1 11696888 11697062 + SINE/Alu_AluSx chr1 11697093 11697155 - SINE/MIR_MIR chr1 11697255 11697339 + LINE/L2_L2c chr1 11697387 11697439 + LINE/L2_L2d chr1 11698573 11698869 + SINE/Alu_AluJb chr1 11699218 11699307 - DNA/hAT-Charlie_Charlie15b chr1 11699484 11699602 - SINE/Alu_FLAM_C chr1 11699647 11699926 + SINE/Alu_AluSx chr1 11699927 11699958 + Simple_repeat_(AATA)n chr1 11699959 11700255 + SINE/Alu_AluSg chr1 11700256 11700471 - DNA/hAT-Charlie_Charlie15b chr1 11700458 11700632 - DNA/hAT-Charlie_Charlie15b chr1 11701270 11701453 + SINE/MIR_MIRc chr1 11702057 11702705 + Simple_repeat_(CA)n chr1 11702745 11703042 - SINE/Alu_AluSx chr1 11703097 11703621 - LINE/L2_L2c chr1 11705616 11705776 + SINE/MIR_MIR chr1 11705957 11706173 - DNA/hAT-Charlie_MER20 chr1 11706932 11707218 + SINE/Alu_AluYc chr1 11707236 11707297 - LINE/CR1_Plat_L3 chr1 11707354 11707427 - SINE/MIR_MIRc chr1 11707364 11707554 + SINE/MIR_MIR chr1 11708233 11708327 + SINE/MIR_MIR3 chr1 11708307 11708329 + SINE/MIR_MIRb chr1 11708330 11708584 + SINE/Alu_AluSz chr1 11708585 11708789 + SINE/MIR_MIRb chr1 11708906 11709087 + LINE/L2_L2c chr1 11709487 11709612 - SINE/MIR_MIR3 chr1 11709891 11709904 + LINE/L1_L1MC4 chr1 11709905 11710205 + SINE/Alu_AluSg chr1 11710210 11710509 + SINE/Alu_AluJb chr1 11710510 11710632 + LINE/L1_L1MC4 chr1 11710633 11710935 + SINE/Alu_AluY chr1 11710964 11711005 + Simple_repeat_(ATAA)n chr1 11711006 11711140 + SINE/Alu_AluSx chr1 11711169 11711238 - DNA/hAT-Charlie_Charlie4z chr1 11712618 11712915 + SINE/Alu_AluSx chr1 11712920 11713211 + SINE/Alu_AluSp chr1 11713231 11713349 - SINE/MIR_MIRb chr1 11713557 11713606 - LTR/ERVL_ERV3-16A3_LTR chr1 11713607 11713758 + SINE/Alu_AluJb chr1 11713759 11714051 + SINE/Alu_AluSg7 chr1 11714052 11714222 + SINE/Alu_AluJb chr1 11714223 11714532 - LTR/ERVL_ERV3-16A3_LTR chr1 11714727 11714801 + SINE/MIR_MIR3 chr1 11715712 11715859 - SINE/MIR_MIRb chr1 11715876 11716168 - SINE/Alu_AluSz chr1 11716171 11716476 - LINE/L1_L1MC5 chr1 11716562 11716619 + SINE/Alu_AluJb chr1 11716682 11716791 - SINE/Alu_AluJb chr1 11716815 11717002 - LINE/L2_L2a chr1 11717003 11717297 + SINE/Alu_AluSz chr1 11717298 11717392 - LINE/L2_L2a chr1 11717393 11717678 + SINE/Alu_AluSg chr1 11717679 11717700 - LINE/L2_L2a chr1 11717703 11718017 + SINE/Alu_AluJb chr1 11718022 11718334 + SINE/Alu_AluSz chr1 11718462 11718763 - SINE/Alu_AluJr chr1 11718766 11718884 - LINE/L1_L1MC2 chr1 11718885 11719203 - SINE/Alu_AluSx1 chr1 11719204 11719419 - LINE/L1_L1MC2 chr1 11720083 11720244 - SINE/MIR_MIRb chr1 11720248 11720310 - DNA/hAT-Charlie_MER5A1 chr1 11720311 11720613 + SINE/Alu_AluSp chr1 11720614 11720688 - DNA/hAT-Charlie_MER5A1 chr1 11720703 11720777 - SINE/MIR_MIRb chr1 11721703 11721838 + SINE/Alu_AluSx chr1 11721840 11722128 + SINE/Alu_AluSx1 chr1 11722487 11722608 - LINE/CR1_Plat_L3 chr1 11722613 11722814 - DNA/hAT-Charlie_MER58B chr1 11722815 11723126 - SINE/Alu_AluSx chr1 11723127 11723253 - DNA/hAT-Charlie_MER58B chr1 11723269 11723411 - DNA/hAT-Charlie_MER5B chr1 11723425 11723586 + SINE/MIR_MIR chr1 11723587 11723892 - SINE/Alu_AluSq chr1 11723893 11723962 + SINE/MIR_MIR chr1 11724055 11724149 - DNA/hAT-Charlie_MER103C chr1 11725057 11725118 + DNA/hAT-Charlie_MER5A1 chr1 11725120 11725343 + SINE/Alu_AluJr chr1 11725344 11725468 + DNA/hAT-Charlie_MER5A chr1 11726089 11726393 - SINE/Alu_AluSz chr1 11726898 11727021 + DNA/hAT-Charlie_MER5A chr1 11726933 11727023 - DNA/hAT-Charlie_MER5A chr1 11727167 11727352 - DNA/hAT-Charlie_MER58A chr1 11727584 11727627 - LTR/ERVL_MLT2C1 chr1 11727628 11727664 + Simple_repeat_(TATA)n chr1 11727669 11727691 + Simple_repeat_(TG)n chr1 11727699 11727764 + DNA/TcMar-Tigger_Tigger3a chr1 11727765 11728097 - SINE/Alu_AluJr chr1 11728098 11728424 + DNA/TcMar-Tigger_Tigger3a chr1 11728425 11728747 - LTR/ERVL_MLT2C1 chr1 11728966 11729017 + Low_complexity_G-rich chr1 11729344 11729501 - SINE/MIR_MIRb chr1 11730176 11730416 + Simple_repeat_(GCACAT)n chr1 11730960 11731261 + SINE/Alu_AluSg chr1 11731262 11731433 + SINE/Alu_AluJr chr1 11731434 11731722 - DNA/TcMar-Tigger_MER47A chr1 11732109 11732287 + SINE/MIR_MIR chr1 11732288 11732602 - SINE/Alu_AluSx chr1 11732603 11732644 + SINE/MIR_MIR chr1 11732645 11732829 + LTR/ERV1_MER39 chr1 11732830 11733123 + SINE/Alu_AluJb chr1 11733124 11733550 + LTR/ERV1_MER39 chr1 11733551 11733570 + SINE/MIR_MIR chr1 11733610 11733909 - SINE/Alu_AluSz chr1 11734028 11734332 + SINE/Alu_AluSx chr1 11734387 11734434 + Simple_repeat_(TAA)n chr1 11735000 11735296 - SINE/Alu_AluSx chr1 11735362 11735702 + LTR/ERV1_MER31B chr1 11735710 11735805 - SINE/MIR_MIR chr1 11737503 11737675 - SINE/MIR_MIRb chr1 11737925 11738105 - DNA/hAT_MER53 chr1 11738553 11738785 - SINE/MIR_MIRb chr1 11739051 11739214 + SINE/MIR_MIRb chr1 11739238 11739268 + SINE/MIR_MIR chr1 11739269 11739568 + SINE/Alu_AluSz chr1 11739569 11739668 + SINE/MIR_MIRc chr1 11741054 11741203 + LINE/L2_L2 chr1 11741551 11741594 + SINE/MIR_MIR1_Amn chr1 11741584 11741806 + SINE/MIR_MIR chr1 11742141 11742359 + SINE/MIR_MIRb chr1 11742395 11742518 + Simple_repeat_(TCTT)n chr1 11742519 11742829 - SINE/Alu_AluJr chr1 11743239 11743545 - SINE/Alu_AluSx1 chr1 11743555 11743865 - SINE/Alu_AluSp chr1 11743995 11744293 + SINE/Alu_AluJo chr1 11744658 11744860 - LTR/ERV1_MER41A chr1 11744861 11745152 - SINE/Alu_AluSx chr1 11745153 11745541 - LTR/ERV1_MER41A chr1 11745896 11746094 + SINE/MIR_MIRb chr1 11746277 11746362 - DNA/hAT-Charlie_MER5B chr1 11746564 11746809 + SINE/MIR_MIR chr1 11747051 11747312 - LINE/L2_L2 chr1 11747999 11748134 + LINE/CR1_L3 chr1 11749229 11749267 + Simple_repeat_(CT)n chr1 11749433 11749539 - SINE/MIR_MIRc chr1 11749725 11749929 + SINE/MIR_MIR chr1 11751007 11751071 + SINE/MIR_MIR chr1 11751869 11752165 + LINE/L1_L1ME3C chr1 11752796 11752975 - LINE/L2_L2 chr1 11752976 11753267 - SINE/Alu_AluSg chr1 11753268 11753293 + Simple_repeat_(GTTTT)n chr1 11753294 11753563 - SINE/Alu_AluSq2 chr1 11753564 11753827 - LINE/L2_L2 chr1 11754016 11754371 - LTR/ERVL-MaLR_THE1B chr1 11754596 11754797 + SINE/MIR_MIR chr1 11754947 11755012 - SINE/MIR_MIRc chr1 11755202 11755326 + SINE/MIR_MIR chr1 11755370 11755457 - LINE/RTE-BovB_MamRTE1 chr1 11755605 11755893 + SINE/Alu_AluSx chr1 11756140 11756322 + LINE/L1_L1MEd chr1 11756323 11756627 + SINE/Alu_AluSg chr1 11756628 11756794 + LINE/L1_L1MEd chr1 11756795 11757058 + SINE/Alu_AluJb chr1 11757059 11757599 + LINE/L1_L1MEd chr1 11757600 11757888 + SINE/Alu_AluSx1 chr1 11757894 11757937 + Simple_repeat_(GT)n chr1 11757938 11758051 + LINE/L1_L1MEd chr1 11758052 11758349 + SINE/Alu_AluY chr1 11758350 11758662 + LINE/L1_L1MEd chr1 11758677 11758982 + SINE/Alu_AluSq chr1 11758985 11759295 + SINE/Alu_AluSp chr1 11759386 11759515 - SINE/MIR_MIRc chr1 11759516 11759830 - SINE/Alu_AluSz chr1 11759831 11759912 - SINE/MIR_MIRc chr1 11759913 11760211 - SINE/Alu_AluJo chr1 11760212 11760259 - SINE/MIR_MIRc chr1 11760486 11760812 - SINE/Alu_AluSz chr1 11760813 11761137 - SINE/Alu_AluSg chr1 11761138 11761157 + Simple_repeat_(TTTTC)n chr1 11762555 11762748 + SINE/MIR_MIRb chr1 11762734 11763012 - LINE/L2_L2a chr1 11763013 11763122 - LINE/L2_L2b chr1 11763135 11763157 - DNA/hAT-Charlie_MER58A chr1 11763158 11763459 + SINE/Alu_AluSx chr1 11763460 11763606 - DNA/hAT-Charlie_MER58A chr1 11763636 11764108 - LINE/L2_L2b chr1 11764773 11765081 + SINE/Alu_AluSx1 chr1 11765131 11765259 + SINE/MIR_MIR chr1 11765444 11765658 + LINE/L2_L2 chr1 11767541 11767829 + SINE/Alu_AluJb chr1 11768139 11768176 + Simple_repeat_(CAGCTGG)n chr1 11768394 11768654 + SINE/MIR_MIR chr1 11768797 11768819 + Simple_repeat_(TTCA)n chr1 11768838 11768924 + SINE/MIR_MIRb chr1 11769219 11769303 + LINE/L1_L1ME4a chr1 11769304 11769577 + SINE/Alu_AluJr chr1 11769578 11769674 + LINE/L1_L1ME4a chr1 11769675 11769959 - SINE/Alu_AluY chr1 11770275 11770362 + LINE/L1_L1MB2 chr1 11770444 11770761 - SINE/Alu_AluSz chr1 11770811 11770972 - SINE/Alu_AluSz6 chr1 11770973 11771001 + Simple_repeat_(TG)n chr1 11771002 11771020 - SINE/Alu_AluSz6 chr1 11771021 11771050 + Simple_repeat_(GT)n chr1 11771051 11771069 - SINE/Alu_AluSz6 chr1 11771070 11771096 + Simple_repeat_(T)n chr1 11771097 11771218 - SINE/Alu_AluSz6 chr1 11771757 11771901 - SINE/MIR_MIRb chr1 11772030 11772045 + Simple_repeat_(TCCTCCC)n chr1 11772046 11772076 + Simple_repeat_(CT)n chr1 11772276 11772566 - SINE/Alu_AluSz chr1 11772875 11772895 - LINE/L1_L1MC5 chr1 11772896 11773192 - SINE/Alu_AluSx chr1 11773193 11773425 - LINE/L1_L1MC5 chr1 11773426 11773737 + SINE/Alu_AluSz chr1 11773738 11773921 - LINE/L1_L1MC5 chr1 11773922 11774203 - SINE/Alu_AluJr chr1 11774204 11774216 - LINE/L1_L1MC5 chr1 11774217 11774346 - SINE/Alu_AluJo chr1 11774347 11774480 - LINE/L1_L1MC5 chr1 11774559 11774950 - LINE/L2_L2 chr1 11774972 11775002 - SINE/MIR_MIR3 chr1 11775003 11775306 + SINE/Alu_AluSp chr1 11775307 11775433 - SINE/MIR_MIR3 chr1 11775976 11776286 + SINE/Alu_AluSq2 chr1 11776656 11776705 + Low_complexity_G-rich chr1 11776766 11776815 + LINE/L2_L2b chr1 11776937 11777056 + LINE/L2_L2b chr1 11777106 11777197 - SINE/MIR_MIR3 chr1 11777369 11777663 + SINE/Alu_AluSx chr1 11777907 11777984 + SINE/MIR_MIR3 chr1 11778143 11778315 + SINE/Alu_AluSx chr1 11778332 11778493 + DNA/hAT-Charlie_MER5B chr1 11779320 11779532 + SINE/MIR_MIR chr1 11779546 11779657 + SINE/MIR_MIR chr1 11780075 11780245 + LTR/ERVL-MaLR_MLT1F2 chr1 11780475 11780632 + SINE/MIR_MIR chr1 11780998 11781297 - SINE/Alu_AluSx1 chr1 11781303 11781456 - LTR/ERVL-MaLR_MLT1F2 chr1 11781476 11781628 - DNA/hAT-Charlie_MER102b chr1 11781629 11781931 + SINE/Alu_AluY chr1 11781932 11782034 - DNA/hAT-Charlie_MER102b chr1 11782640 11782946 + SINE/Alu_AluSx1 chr1 11783239 11783546 - SINE/Alu_AluSp chr1 11783847 11784154 - SINE/Alu_AluSq2 chr1 11785723 11786032 - SINE/Alu_AluSz chr1 11786038 11786142 - LINE/L1_L1ME4a chr1 11786166 11786308 + DNA/TcMar-Tigger_Tigger15a chr1 11786427 11786743 - SINE/Alu_AluJr chr1 11787992 11788032 + Simple_repeat_(AGCCCAG)n chr1 11788460 11788531 + DNA/hAT-Charlie_MER5A chr1 11790901 11790945 + Simple_repeat_(AC)n chr1 11791919 11792238 + LINE/L2_L2c chr1 11792816 11792915 - SINE/MIR_MIRc chr1 11792979 11793128 - SINE/MIR_MIRb chr1 11793142 11793446 - SINE/Alu_AluSg chr1 11793451 11793730 - SINE/Alu_AluJb chr1 11795543 11795652 - SINE/Alu_AluJr chr1 11795742 11795923 + DNA/hAT-Charlie_MER5B chr1 11796704 11796916 + LINE/CR1_L3 chr1 11796917 11797225 + SINE/Alu_AluSq2 chr1 11797226 11797278 + LINE/CR1_L3 chr1 11797293 11797612 - SINE/Alu_AluJr chr1 11797752 11797837 + LINE/CR1_L3 chr1 11798281 11798395 - SINE/Alu_AluJo chr1 11798612 11798728 - DNA/hAT-Charlie_Charlie16a chr1 11798729 11799041 - SINE/Alu_AluSg chr1 11799042 11799183 - DNA/hAT-Charlie_Charlie16a chr1 11799310 11799623 + LTR/ERVL-MaLR_MLT1O chr1 11799631 11799929 - SINE/Alu_AluSx chr1 11800411 11800483 + LINE/L2_L2c chr1 11801498 11801631 + SINE/MIR_MIR chr1 11801645 11801947 + SINE/Alu_AluSx1 chr1 11801949 11801975 + Simple_repeat_(CAA)n chr1 11802391 11802668 - DNA/TcMar-Tc1_X33a_DNA chr1 11804080 11804182 + SINE/Alu_FLAM_A chr1 11804183 11804478 + SINE/Alu_AluSq2 chr1 11804698 11804787 - DNA/hAT-Charlie_Charlie4z chr1 11806284 11806316 + Simple_repeat_(CTG)n chr1 11808038 11808201 - LINE/L1_L1MC5 chr1 11808214 11808270 + Simple_repeat_(TG)n chr1 11808271 11808393 - SINE/Alu_AluJo chr1 11808394 11808790 - LINE/L1_L1MC5 chr1 11808843 11809158 - SINE/Alu_AluSq2 chr1 11809428 11809446 + Simple_repeat_(ATATTC)n chr1 11809447 11809757 - SINE/Alu_AluSx chr1 11809758 11809811 + Simple_repeat_(ATATTC)n chr1 11810772 11810929 + SINE/Alu_FLAM_C chr1 11810930 11811218 + SINE/Alu_AluJb chr1 11811261 11811384 - LINE/L1_L1MB8 chr1 11811385 11811680 - SINE/Alu_AluSp chr1 11811681 11811823 - LINE/L1_L1MB8 chr1 11811995 11812124 + SINE/Alu_FRAM chr1 11812130 11812630 - LTR/ERVL_MER21B chr1 11812665 11812711 + Simple_repeat_(TG)n chr1 11812714 11813290 + LTR/ERV1_LTR48B chr1 11813393 11813690 - SINE/Alu_AluSz chr1 11814246 11814547 - SINE/Alu_AluSz chr1 11814751 11815050 + SINE/Alu_AluY chr1 11815327 11815391 - DNA/hAT-Charlie_MER5A1 chr1 11815392 11815694 - SINE/Alu_AluSx chr1 11815695 11815772 - DNA/hAT-Charlie_MER5A1 chr1 11816033 11816159 + DNA/TcMar-Tigger_Tigger1 chr1 11816156 11816371 + DNA/TcMar-Tigger_Tigger1 chr1 11817086 11817237 - SINE/Alu_AluJb chr1 11817599 11817979 - DNA/TcMar-Tigger_Tigger22N1 chr1 11817980 11818114 + SINE/Alu_FLAM_C chr1 11818115 11818265 - DNA/TcMar-Tigger_Tigger22N1 chr1 11818266 11818489 + DNA/TcMar-Tigger_Tigger4a chr1 11818490 11818715 - DNA/TcMar-Tigger_Tigger22N1 chr1 11818790 11818913 + SINE/Alu_FLAM_A chr1 11819027 11819186 + DNA/hAT-Charlie_MER30 chr1 11819691 11820173 + LINE/L1_L1ME3E chr1 11820196 11820295 + SINE/Alu_FRAM chr1 11820296 11820484 + LINE/L1_L1ME3E chr1 11820485 11820785 + SINE/Alu_AluY chr1 11820793 11821103 + SINE/Alu_AluSq2 chr1 11821104 11821275 + LINE/L1_L1ME3E chr1 11821276 11821566 + SINE/Alu_AluSz chr1 11821567 11821691 + LINE/L1_L1ME3E chr1 11821723 11822245 + LINE/L1_L1ME3E chr1 11822260 11822542 - SINE/Alu_AluJb chr1 11823016 11823178 + LINE/L1_L1PA16 chr1 11823209 11823498 + SINE/Alu_AluSp chr1 11823500 11823599 + DNA/hAT-Charlie_MER5A chr1 11824080 11824283 - DNA/hAT-Charlie_MER58D chr1 11824981 11825117 - SINE/MIR_MIR chr1 11825621 11825932 - SINE/Alu_AluSx chr1 11826069 11826134 - LINE/L1_L1PB2 chr1 11827431 11827705 - SINE/Alu_AluSx chr1 11830289 11830629 + DNA/TcMar-Tigger_MER2 chr1 11830725 11830764 + Simple_repeat_(TA)n chr1 11830765 11830822 + Simple_repeat_(AC)n chr1 11830830 11831130 - SINE/Alu_AluSx1 chr1 11831131 11831145 + Simple_repeat_(TA)n chr1 11831146 11831177 + Simple_repeat_(TGTTTT)n chr1 11831182 11831453 - SINE/Alu_AluSg chr1 11831454 11831575 + DNA/hAT-Charlie_MER58C chr1 11831644 11831686 + DNA/hAT-Charlie_MER103C chr1 11831687 11831966 - SINE/Alu_AluJr chr1 11831967 11832003 + DNA/hAT-Charlie_MER103C chr1 11832036 11832185 - SINE/MIR_MIRb chr1 11832197 11832345 - SINE/MIR_MIR chr1 11834037 11834128 + Simple_repeat_(TGTGTG)n chr1 11835075 11835188 - DNA/hAT-Charlie_MER58B chr1 11835261 11835574 - SINE/Alu_AluJr chr1 11835626 11835853 - LINE/L2_L2c chr1 11837643 11838039 - LTR/ERVL-MaLR_MLT1C2 chr1 11838040 11838228 - SINE/MIR_MIRb chr1 11839250 11839561 - SINE/Alu_AluJb chr1 11843165 11843467 - SINE/Alu_AluY chr1 11844473 11844820 - LINE/L2_L2c chr1 11845091 11845384 - SINE/Alu_AluJb chr1 11846319 11846619 - SINE/Alu_AluY chr1 11846620 11846913 - SINE/Alu_AluSc chr1 11848929 11849018 + SINE/MIR_MIRc chr1 11849019 11849318 + SINE/Alu_AluSz6 chr1 11849319 11849418 + SINE/MIR_MIRc chr1 11849450 11849666 - SINE/MIR_MIRb chr1 11850603 11850689 - LTR/ERVL_LTR16C chr1 11850741 11851023 + SINE/Alu_AluJb chr1 11851024 11851152 + SINE/Alu_FLAM_C chr1 11851172 11851669 - LTR/ERVL-MaLR_MLT1F2 chr1 11851710 11851906 + SINE/Alu_AluSz chr1 11851949 11852186 - LTR/ERVL_LTR16C chr1 11852187 11852488 - SINE/Alu_AluJr chr1 11852489 11852517 - LTR/ERVL_LTR16C chr1 11852518 11852834 + SINE/Alu_AluJr chr1 11852835 11853011 - LTR/ERVL_LTR16C chr1 11853341 11853617 + SINE/Alu_AluSc8 chr1 11853618 11853649 + Simple_repeat_(AAAAC)n chr1 11853908 11854218 + SINE/Alu_AluSz chr1 11854231 11854547 + SINE/Alu_AluJb chr1 11855104 11855253 + SINE/MIR_MIR chr1 11855254 11855555 + SINE/Alu_AluSz chr1 11855556 11855632 + SINE/MIR_MIR chr1 11855727 11856048 - SINE/Alu_AluJb chr1 11856070 11856367 - SINE/Alu_AluSq2 chr1 11856522 11856709 + SINE/MIR_MIRb chr1 11857102 11857193 - DNA/hAT-Tip100_MamTip2b chr1 11857529 11857571 + Low_complexity_A-rich chr1 11857785 11857845 + SINE/MIR_MIRc chr1 11858855 11858892 + Simple_repeat_(GCT)n chr1 11859875 11860129 + SINE/Alu_AluYc chr1 11860133 11860183 + Simple_repeat_(GAAA)n chr1 11860550 11860715 - SINE/MIR_MIR chr1 11860959 11860992 + Simple_repeat_(TG)n chr1 11861286 11861397 - LINE/L2_L2d chr1 11861561 11861595 - LINE/L2_L2c chr1 11861595 11861673 + SINE/MIR_MIR chr1 11861674 11862185 + LTR/ERVL-MaLR_MLT1D chr1 11862186 11862249 + SINE/MIR_MIR chr1 11862362 11862525 + SINE/MIR_MIR chr1 11862538 11862843 - LINE/L2_L2c chr1 11863132 11863439 - SINE/Alu_AluSx chr1 11863585 11863887 - SINE/Alu_AluJb chr1 11864053 11864092 + Simple_repeat_(GT)n chr1 11864231 11864553 + SINE/Alu_AluJr chr1 11864635 11864689 + Simple_repeat_(CTCC)n chr1 11864787 11864951 + LTR/ERVL-MaLR_MLT1K chr1 11865256 11865535 - SINE/Alu_AluSz6 chr1 11865641 11865854 - LTR/ERVL-MaLR_MLT1B chr1 11865855 11866157 - SINE/Alu_AluSp chr1 11866158 11866363 - LTR/ERVL-MaLR_MLT1B chr1 11866364 11866651 - SINE/Alu_AluJb chr1 11866652 11866687 - LTR/ERVL-MaLR_MLT1B chr1 11866764 11867161 - LTR/ERVL-MaLR_MLT1F chr1 11867162 11867477 + SINE/Alu_AluSp chr1 11867478 11867488 - LTR/ERVL-MaLR_MLT1F chr1 11867489 11867747 + SINE/Alu_AluJo chr1 11867748 11867814 - LTR/ERVL-MaLR_MLT1F chr1 11868112 11868426 - SINE/Alu_AluJb chr1 11868678 11868747 - SINE/MIR_MIR chr1 11868777 11869095 + LTR/ERVL_LTR33 chr1 11869096 11869406 - SINE/Alu_AluJb chr1 11869407 11869519 + LTR/ERVL_LTR33 chr1 11869524 11869566 + Simple_repeat_(ATCACC)n chr1 11869976 11870016 + Simple_repeat_(GGCTCTAGAT)n chr1 11870186 11870361 - LINE/L2_L2a chr1 11870362 11870634 + SINE/Alu_AluSx1 chr1 11870635 11870789 - LINE/L2_L2a chr1 11870790 11871079 + SINE/Alu_AluJb chr1 11871080 11871113 - LINE/L2_L2a chr1 11871114 11871237 + SINE/Alu_FLAM_C chr1 11871238 11871472 - LINE/L2_L2a chr1 11871473 11871781 - SINE/Alu_AluSg chr1 11871782 11871824 - LINE/L2_L2a chr1 11871893 11872145 + LTR/ERVL_LTR50 chr1 11872192 11872490 - SINE/Alu_AluJr chr1 11872539 11872989 - LTR/ERVL-MaLR_MLT1C2 chr1 11873185 11873366 - SINE/Alu_AluJb chr1 11873367 11873400 + Simple_repeat_(T)n chr1 11873401 11873519 - SINE/Alu_AluJb chr1 11873586 11873698 - LINE/L1_L1MC4 chr1 11874648 11875081 + LTR/ERVL-MaLR_MLT1C2 chr1 11875262 11875651 + LTR/ERVL-MaLR_MLT1G1 chr1 11875652 11875951 + SINE/Alu_AluSx1 chr1 11875952 11876061 + LTR/ERVL-MaLR_MLT1G1 chr1 11876111 11876215 - LINE/L2_L2a chr1 11876700 11876984 + SINE/Alu_AluSz chr1 11876986 11877018 + Simple_repeat_(AC)n chr1 11877021 11877030 + Simple_repeat_(TATG)n chr1 11877035 11877071 + Simple_repeat_(TG)n chr1 11877072 11877077 + Simple_repeat_(TATGTG)n chr1 11877083 11877117 + Simple_repeat_(TG)n chr1 11877397 11877418 + Simple_repeat_(AAAC)n chr1 11877434 11877738 - SINE/Alu_AluJr chr1 11879024 11879055 + Simple_repeat_(GCTGGT)n chr1 11880551 11880583 + Simple_repeat_(GCCCC)n chr1 11880760 11880786 + Low_complexity_A-rich chr1 11880870 11880914 + Simple_repeat_(TCCC)n chr1 11881106 11881213 - SINE/MIR_MIR chr1 11881342 11881375 + Simple_repeat_(CCCCT)n chr1 11881590 11881636 - LINE/L2_L2c chr1 11881663 11881858 - LINE/L2_L2c chr1 11881859 11882365 + LTR/ERVL-MaLR_MLT1D chr1 11882366 11882380 - LINE/L2_L2c chr1 11882622 11882725 + SINE/Alu_AluJb chr1 11882816 11882854 + Simple_repeat_(CTCCT)n chr1 11884109 11884421 - SINE/Alu_AluY chr1 11884494 11884803 + SINE/Alu_AluSx3 chr1 11884824 11884863 + Simple_repeat_(AT)n chr1 11884864 11885002 - LINE/L1_L1ME4b chr1 11885310 11885449 - SINE/Alu_FLAM_C chr1 11886642 11886944 + SINE/Alu_AluSz chr1 11887934 11888010 - SINE/MIR_MIR chr1 11888053 11888365 - SINE/Alu_AluSx chr1 11888538 11888587 - SINE/MIR_MIR3 chr1 11889449 11889754 + SINE/Alu_AluSz chr1 11889756 11889783 + Simple_repeat_(AAAG)n chr1 11889786 11889859 - DNA/hAT-Charlie_MER102a chr1 11889866 11890411 + LTR/ERVK_LTR5B chr1 11890412 11890698 + SINE/Alu_AluY chr1 11890699 11891158 + LTR/ERVK_LTR5B chr1 11891159 11891410 - DNA/hAT-Charlie_Charlie8 chr1 11891470 11891627 - SINE/MIR_MIRc chr1 11891628 11891934 + SINE/Alu_AluJr chr1 11891935 11891973 - SINE/MIR_MIRc chr1 11892000 11892115 + LTR/ERVL_HERVL66-int chr1 11892811 11893106 - SINE/Alu_AluSx1 chr1 11893129 11893260 - SINE/Alu_FLAM_A chr1 11893670 11893833 - SINE/MIR_MIRb chr1 11894343 11894798 + Simple_repeat_(GGAT)n chr1 11895161 11895257 - SINE/MIR_MIRb chr1 11895406 11895494 + SINE/MIR_MIR chr1 11895567 11895623 + Simple_repeat_(AT)n chr1 11895624 11895635 + Low_complexity_A-rich chr1 11895639 11895771 + SINE/Alu_FLAM_C chr1 11895778 11896085 + SINE/Alu_AluSg chr1 11896113 11896407 + SINE/Alu_AluSx chr1 11896472 11896524 - DNA/hAT-Charlie_MER102a chr1 11896553 11896686 + SINE/Alu_FLAM_A chr1 11896698 11897007 + SINE/Alu_AluY chr1 11897118 11897480 + LINE/L1_L1MC5a chr1 11897470 11897648 + LINE/L1_L1MEf chr1 11897660 11897721 + scRNA_HY3 chr1 11897747 11898057 + SINE/Alu_AluY chr1 11898066 11898123 + DNA/TcMar-Tigger_Tigger19a chr1 11899756 11899788 + Simple_repeat_(TC)n chr1 11899837 11900131 + SINE/Alu_AluSc8 chr1 11900137 11900290 + SINE/Alu_AluSx chr1 11900298 11900397 - LINE/L1_L1M5 chr1 11900398 11900685 + SINE/Alu_AluSz6 chr1 11900686 11901006 - LINE/L1_L1M5 chr1 11901007 11901138 - SINE/Alu_FLAM_C chr1 11901139 11901185 - LINE/L1_L1M5 chr1 11901297 11901593 + SINE/Alu_AluJb chr1 11901594 11901871 + SINE/Alu_AluSx chr1 11901873 11902181 + SINE/Alu_AluSg chr1 11902205 11902369 + LTR/ERV1_LTR24B chr1 11902370 11902632 + LTR/ERV1_ERV24B_Prim-int chr1 11902634 11902933 + SINE/Alu_AluSz chr1 11902940 11903250 + SINE/Alu_AluSx1 chr1 11903283 11903311 + Simple_repeat_(AC)n chr1 11903343 11903469 + SINE/Alu_AluJb chr1 11903486 11903610 + LINE/L1_L1MC5a chr1 11903614 11903798 + SINE/Alu_AluJb chr1 11903805 11903885 + LINE/L1_L1M5 chr1 11903886 11904181 + SINE/Alu_AluSz6 chr1 11904182 11904352 + LINE/L1_L1M5 chr1 11904353 11904654 + SINE/Alu_AluSc5 chr1 11904707 11904837 + SINE/Alu_AluJr4 chr1 11904881 11904916 + Simple_repeat_(AAAAATT)n chr1 11904961 11905346 - LINE/L1_L1MB5 chr1 11905352 11905649 - SINE/Alu_AluSx chr1 11905785 11905809 + Simple_repeat_(TC)n chr1 11905867 11906212 - LTR/ERVL-MaLR_MLT1A0 chr1 11906458 11906761 - SINE/Alu_AluSx chr1 11906850 11906966 - LINE/L2_L2 chr1 11907093 11907163 - SINE/Alu_FLAM_C chr1 11907415 11907514 - DNA/hAT-Charlie_MER5A chr1 11907530 11907654 - SINE/Alu_FLAM_C chr1 11907662 11907683 + Simple_repeat_(T)n chr1 11907734 11907776 - DNA/TcMar-Mariner_HSMAR1 chr1 11908152 11908271 + snRNA_U5 chr1 11908526 11908796 + DNA/hAT-Charlie_MER1B chr1 11908990 11909143 - LTR/ERVL-MaLR_MLT1J chr1 11909187 11909506 + SINE/Alu_AluSx chr1 11909549 11909596 + Simple_repeat_(GCCCCGCCGT)n chr1 11909807 11909926 - snRNA_U5 chr1 11910252 11910299 - LINE/L1_L1MA3 chr1 11910311 11910605 - SINE/Alu_AluSx chr1 11910769 11911307 - LTR/ERVL_MLT2B3 chr1 11911310 11911555 - SINE/Alu_AluSx1 chr1 11911642 11911774 + SINE/MIR_MIR1_Amn chr1 11911916 11912193 + SINE/Alu_AluJr chr1 11912251 11912412 + LINE/L2_L2b chr1 11912413 11913376 - LTR/ERVK_LTR13A chr1 11913377 11913441 + LINE/L2_L2b chr1 11913695 11913851 + SINE/MIR_MIRb chr1 11913866 11914342 + LTR/ERVL-MaLR_MLT1K chr1 11914345 11914657 - SINE/Alu_AluJr chr1 11914702 11914989 + SINE/Alu_AluJb chr1 11915074 11915403 - LTR/ERVL-MaLR_MLT1A0 chr1 11915404 11915702 + SINE/Alu_AluJb chr1 11915703 11915744 - LTR/ERVL-MaLR_MLT1A0 chr1 11915838 11915864 - LTR/ERVL_LTR16E2 chr1 11915865 11915931 - LTR/ERVL-MaLR_MSTB2 chr1 11915932 11916110 - SINE/Alu_AluSc5 chr1 11916111 11916420 - SINE/Alu_AluY chr1 11916421 11916553 - SINE/Alu_AluSc5 chr1 11916554 11916894 - LTR/ERVL-MaLR_MSTB2 chr1 11916895 11917069 - LTR/ERVL_LTR16E2 chr1 11917160 11917475 + SINE/Alu_AluSx chr1 11917655 11917953 + SINE/Alu_AluY chr1 11918191 11918381 + SINE/MIR_MIRc chr1 11918424 11918716 - SINE/Alu_AluSp chr1 11921018 11921106 + Low_complexity_GA-rich chr1 11921517 11921539 + Simple_repeat_(TTATTT)n chr1 11921540 11921849 - SINE/Alu_AluSx chr1 11921853 11922147 - SINE/Alu_AluJr chr1 11922148 11922184 + Simple_repeat_(TTATTT)n chr1 11922188 11922353 - SINE/Alu_FRAM chr1 11923879 11924030 - SINE/Alu_AluSx3 chr1 11924035 11924083 + LINE/L2_L2a chr1 11924183 11924252 + SINE/Alu_Alu chr1 11924253 11924389 - SINE/Alu_AluSx chr1 11924412 11924572 + LINE/L2_L2a chr1 11924955 11925131 - SINE/MIR_MIRb chr1 11926101 11926132 + Simple_repeat_(CCGC)n chr1 11926270 11926368 + Simple_repeat_(CGCC)n chr1 11926473 11926538 + Low_complexity_G-rich chr1 11926728 11926751 + Simple_repeat_(AC)n chr1 11926783 11927092 + SINE/Alu_AluSg chr1 11927148 11927210 + Low_complexity_GA-rich chr1 11927219 11927354 + SINE/Alu_AluSx1 chr1 11927355 11927665 + SINE/Alu_AluY chr1 11927666 11927857 + SINE/Alu_AluSx1 chr1 11928323 11928370 + Simple_repeat_(TTTTTATTT)n chr1 11928371 11928642 - SINE/Alu_AluSx chr1 11928645 11928945 - SINE/Alu_AluSq chr1 11928949 11929256 - SINE/Alu_AluJb chr1 11929270 11929357 - LINE/L1_L1PA17 chr1 11929358 11929652 - SINE/Alu_AluSp chr1 11929660 11929721 + SINE/Alu_Alu chr1 11929726 11929762 + Simple_repeat_(ATT)n chr1 11930250 11930566 - SINE/Alu_AluJr chr1 11930644 11930795 + SINE/MIR_MIRb chr1 11930821 11931122 + SINE/Alu_AluJb chr1 11931294 11931406 + SINE/MIR_MIR3 chr1 11931477 11931614 - SINE/Alu_AluJo chr1 11931670 11931958 - SINE/Alu_AluSp chr1 11931997 11932025 + Low_complexity_A-rich chr1 11932028 11932176 - LINE/L1_L1ME4a chr1 11932177 11932473 + SINE/Alu_AluSg7 chr1 11932474 11932531 - LINE/L1_L1ME4a chr1 11932581 11932891 - SINE/Alu_AluJb chr1 11933099 11933403 - SINE/Alu_AluSg chr1 11933589 11933883 + SINE/Alu_AluSp chr1 11933884 11934060 + SINE/Alu_AluJb chr1 11934297 11934401 - SINE/MIR_MIR chr1 11934632 11934685 + Simple_repeat_(GGCGG)n chr1 11934781 11934824 + Simple_repeat_(TGC)n chr1 11934934 11935085 + SINE/MIR_MIR3 chr1 11935112 11935329 - SINE/MIR_MIR chr1 11935512 11935625 - SINE/Alu_AluSx chr1 11935626 11935922 - SINE/Alu_AluSg chr1 11935923 11936128 - SINE/Alu_AluSx chr1 11936132 11936250 - SINE/MIR_MIR1_Amn chr1 11936509 11936822 - SINE/Alu_AluSz6 chr1 11936835 11937147 - SINE/Alu_AluSp chr1 11937230 11937375 + SINE/MIR_MIRb chr1 11937923 11937939 + Simple_repeat_(T)n chr1 11937943 11938250 - SINE/Alu_AluSz chr1 11938258 11938387 - SINE/MIR_MIRb chr1 11939347 11939383 + Low_complexity_G-rich chr1 11939608 11939901 - SINE/Alu_AluSx1 chr1 11939928 11940062 - SINE/Alu_AluJo chr1 11940077 11940318 - SINE/Alu_AluJr chr1 11940448 11940586 - SINE/MIR_MIR chr1 11940984 11941184 + SINE/MIR_MIR chr1 11941233 11941305 - SINE/Alu_Alu chr1 11941340 11941467 - SINE/Alu_AluSx chr1 11941468 11941494 + Simple_repeat_(ATT)n chr1 11941495 11941781 - SINE/Alu_AluSq2 chr1 11941782 11941985 - SINE/Alu_AluSx chr1 11941986 11942292 - SINE/Alu_AluSz chr1 11942734 11942894 + DNA/hAT-Charlie_MER5B chr1 11942980 11943286 - SINE/Alu_AluSg7 chr1 11943287 11943596 - SINE/Alu_AluSq2 chr1 11943783 11943950 + SINE/MIR_MIR chr1 11943951 11944244 + SINE/Alu_AluSx1 chr1 11944245 11944290 + SINE/MIR_MIR chr1 11944423 11944454 + Simple_repeat_(AC)n chr1 11945138 11945458 + SINE/Alu_AluJr chr1 11945697 11946008 - SINE/Alu_AluSz chr1 11946022 11946236 - SINE/MIR_MIR chr1 11946427 11946602 + DNA/hAT-Charlie_MER117 chr1 11946760 11946987 - LTR/ERVL-MaLR_MSTB chr1 11946988 11947266 + SINE/Alu_AluSz chr1 11947267 11947291 - LTR/ERVL-MaLR_MSTB chr1 11947292 11947582 + SINE/Alu_AluSx chr1 11947583 11947760 - LTR/ERVL-MaLR_MSTB chr1 11947774 11947828 + SINE/MIR_MIR1_Amn chr1 11948459 11948758 + SINE/Alu_AluSx chr1 11948844 11948950 - LINE/L2_L2a chr1 11948951 11949338 - LTR/ERVL-MaLR_MLT1K chr1 11949416 11949719 - SINE/Alu_AluSx chr1 11950590 11950799 + LINE/L2_L2 chr1 11950800 11951093 - SINE/Alu_AluSz chr1 11951094 11951269 + LINE/L2_L2 chr1 11951284 11951534 + SINE/Alu_AluSz chr1 11951541 11951646 + Simple_repeat_(AT)n chr1 11951648 11951941 + SINE/Alu_AluY chr1 11952194 11952280 + SINE/MIR_MIR3 chr1 11952832 11953060 - LTR/ERVL-MaLR_MLT1A0 chr1 11953061 11953354 - SINE/Alu_AluSx chr1 11953373 11953510 + SINE/MIR_MIR chr1 11953511 11953802 + SINE/Alu_AluSq2 chr1 11953803 11953869 + SINE/MIR_MIR chr1 11953870 11954164 - SINE/Alu_AluJr chr1 11954165 11954176 + SINE/MIR_MIR chr1 11954188 11954497 + SINE/Alu_AluSq2 chr1 11955361 11955459 + SINE/MIR_MIR3 chr1 11955474 11955573 - DNA/hAT-Charlie_Charlie4z chr1 11955617 11955925 - SINE/Alu_AluSx chr1 11955932 11956060 - SINE/Alu_AluJr4 chr1 11956124 11956403 + SINE/Alu_AluSx chr1 11956661 11956876 - SINE/MIR_MIRb chr1 11957536 11957631 + DNA/hAT-Charlie_MER5A chr1 11958802 11958923 + SINE/MIR_MIR chr1 11958924 11959235 + SINE/Alu_AluSq2 chr1 11959236 11959346 + SINE/MIR_MIR chr1 11959436 11959609 - SINE/Alu_AluSx3 chr1 11959610 11959914 - SINE/Alu_AluY chr1 11959922 11960269 - SINE/Alu_AluSg4 chr1 11960298 11960378 + DNA/hAT-Charlie_MER5B chr1 11960454 11960528 - LINE/L2_L2c chr1 11960601 11960644 + Simple_repeat_(TCCCCA)n chr1 11960864 11960961 + SINE/MIR_MIRc chr1 11961102 11961411 + SINE/Alu_AluJr chr1 11961452 11961607 - DNA/hAT-Charlie_MER58B chr1 11961608 11961786 - SINE/Alu_AluJr chr1 11961787 11962086 - SINE/Alu_AluSz chr1 11962087 11962227 - SINE/Alu_AluJr chr1 11962228 11962264 - DNA/hAT-Charlie_MER58B chr1 11962265 11962575 - SINE/Alu_AluY chr1 11962602 11962758 + SINE/Alu_AluJb chr1 11962759 11963070 + SINE/Alu_AluSx chr1 11963071 11963084 + SINE/Alu_AluJb chr1 11963089 11963162 - DNA/hAT-Charlie_MER58B chr1 11963174 11963256 - LINE/L1_L1M5 chr1 11963698 11963890 + Simple_repeat_(CCT)n chr1 11964410 11964464 + LINE/CR1_L3b chr1 11965279 11965469 - LINE/L2_L2b chr1 11965769 11965953 + SINE/MIR_MIRc chr1 11966022 11966051 + Simple_repeat_(CA)n chr1 11967598 11967658 + Simple_repeat_(TA)n chr1 11967662 11967714 - SINE/Alu_AluJr4 chr1 11967724 11967739 - SINE/Alu_AluSx chr1 11967740 11967765 + Simple_repeat_(TTAA)n chr1 11967766 11967929 - SINE/Alu_AluSz6 chr1 11967956 11968253 - SINE/Alu_AluSx chr1 11968263 11968291 + Simple_repeat_(TTTTA)n chr1 11968507 11968814 - SINE/Alu_AluSx1 chr1 11968840 11969017 - SINE/Alu_AluSg4 chr1 11969021 11969288 - SINE/Alu_AluSq2 chr1 11969565 11969825 - LTR/ERVL-MaLR_MLT1E1 chr1 11969826 11969959 + SINE/Alu_AluJb chr1 11969960 11970278 + SINE/Alu_AluSx chr1 11970279 11970441 + SINE/Alu_AluJb chr1 11970442 11970645 - LTR/ERVL-MaLR_MLT1E1 chr1 11971482 11971667 - SINE/Alu_AluSx1 chr1 11972166 11972250 + Simple_repeat_(TTCC)n chr1 11972251 11972551 - SINE/Alu_AluSq2 chr1 11972552 11972674 + Simple_repeat_(TTCC)n chr1 11972677 11972739 - LINE/L2_L2a chr1 11973218 11973516 + SINE/Alu_AluSz6 chr1 11973581 11973797 - SINE/Alu_AluJb chr1 11973798 11973847 - SINE/MIR_MIRb chr1 11973848 11974018 + DNA/hAT-Charlie_MER20 chr1 11974019 11974196 - SINE/MIR_MIRb chr1 11974202 11974482 - SINE/Alu_AluSx chr1 11975562 11975594 + Simple_repeat_(GT)n chr1 11975619 11975925 - SINE/Alu_AluSx1 chr1 11975977 11976152 - SINE/Alu_AluSx chr1 11976180 11976478 + SINE/Alu_AluSx chr1 11976538 11976645 - LINE/L1_L1ME4b chr1 11976673 11976973 - SINE/Alu_AluSx1 chr1 11977060 11977361 + SINE/Alu_AluSx1 chr1 11977370 11977682 + SINE/Alu_AluSz6 chr1 11978093 11978399 + SINE/Alu_AluSx chr1 11978810 11978959 - DNA/hAT-Charlie_MER33 chr1 11978983 11979257 + SINE/Alu_AluJr chr1 11979262 11979528 - SINE/Alu_AluSg chr1 11979584 11979654 - DNA/hAT-Charlie_MER33 chr1 11980864 11981022 + LINE/L2_L2c chr1 11981223 11981469 + SINE/Alu_AluSx chr1 11982875 11983044 + DNA/hAT-Charlie_MER103C chr1 11983045 11983370 - SINE/Alu_AluY chr1 11983371 11983471 + DNA/hAT-Charlie_MER103C chr1 11983779 11983900 - LINE/L2_L2c chr1 11984496 11985279 + LTR/ERVL_MER21A chr1 11985289 11985418 - SINE/MIR_MIRb chr1 11985456 11985482 + Simple_repeat_(T)n chr1 11985496 11985767 - SINE/Alu_AluSx3 chr1 11986581 11986876 - SINE/Alu_AluSg chr1 11987028 11987342 + SINE/Alu_AluSz chr1 11987348 11987647 + SINE/Alu_AluSz chr1 11987652 11987976 + SINE/Alu_AluJr chr1 11988085 11988109 + Simple_repeat_(TG)n chr1 11988110 11988401 - SINE/Alu_AluSg7 chr1 11988406 11988538 - SINE/Alu_FLAM_C chr1 11988553 11988695 - SINE/Alu_FRAM chr1 11988703 11989001 - SINE/Alu_AluSx chr1 11989550 11989718 - SINE/MIR_MIRb chr1 11990076 11990250 + DNA/hAT-Charlie_Charlie15a chr1 11990402 11990490 - LINE/L2_L2c chr1 11991262 11991454 - LINE/L2_L2c chr1 11991643 11991952 + SINE/Alu_AluYa5 chr1 11991955 11992033 + SINE/MIR_MIR chr1 11992053 11992133 + SINE/MIR_MIR chr1 11992160 11992392 - SINE/MIR_MIR chr1 11992813 11992979 - SINE/Alu_AluJr chr1 11992981 11993245 - LINE/L1_L1MB2 chr1 11993251 11993454 - LTR/ERV1_MER34B-int chr1 11993455 11993761 + SINE/Alu_AluYe5 chr1 11993762 11993861 - LTR/ERV1_MER34B-int chr1 11994307 11994618 + SINE/Alu_AluY chr1 11994966 11995297 + SINE/Alu_AluJr chr1 11995351 11995656 + SINE/Alu_AluSx chr1 11995989 11996068 - LINE/L2_L2b chr1 11996756 11997060 + SINE/Alu_AluSx1 chr1 11997878 11998181 - SINE/Alu_AluSg chr1 11998267 11998541 + SINE/Alu_AluSx3 chr1 11998550 11998575 + Simple_repeat_(AAAAC)n chr1 11999517 11999657 - SINE/Alu_AluSz6 chr1 11999726 11999807 + SINE/Alu_FLAM_C chr1 11999813 12000120 + SINE/Alu_AluYk2 chr1 12000135 12000194 + SINE/MIR_MIRb chr1 12000195 12000486 - SINE/Alu_AluSq2 chr1 12000487 12000606 + SINE/MIR_MIRb chr1 12000661 12000724 - SINE/MIR_MIR3 chr1 12000862 12001029 + LINE/L1_L1ME4b chr1 12001040 12001312 - SINE/Alu_AluSx1 chr1 12002413 12002705 + SINE/Alu_AluJr4 chr1 12002813 12003384 - LINE/L1_L1ME3Cz chr1 12003385 12003680 + SINE/Alu_AluSp chr1 12003681 12003732 - LINE/L1_L1ME3Cz chr1 12005084 12005382 - SINE/Alu_AluSz6 chr1 12006104 12006185 - DNA/hAT-Charlie_MER113A chr1 12006715 12006755 + Low_complexity_G-rich chr1 12007669 12007766 - SINE/MIR_MIR chr1 12007767 12009247 - Retroposon/SVA_SVA_C chr1 12009248 12009381 - SINE/MIR_MIRb chr1 12009872 12010170 + SINE/Alu_AluSq2 chr1 12010197 12010257 - LINE/L2_L2c chr1 12010330 12010498 - SINE/MIR_MIRb chr1 12010547 12010595 - SINE/MIR_MIR3 chr1 12010631 12010822 + SINE/MIR_MIRb chr1 12011150 12011238 - SINE/MIR_MIR3 chr1 12011243 12011440 + SINE/MIR_MIRb chr1 12012370 12012460 + Simple_repeat_(TGGATGG)n chr1 12012944 12012989 + SINE/tRNA_LFSINE_Vert chr1 12013800 12014093 - SINE/Alu_AluSg4 chr1 12014094 12014113 + Simple_repeat_(T)n chr1 12014204 12014465 + LINE/L2_L2 chr1 12015075 12015173 - LINE/L2_L2a chr1 12015178 12015476 + SINE/Alu_AluSg chr1 12015512 12015634 + LTR/ERV1_LTR51 chr1 12015635 12015921 - SINE/Alu_AluJb chr1 12015978 12016277 + SINE/Alu_AluJb chr1 12016279 12016393 - SINE/Alu_AluJo chr1 12016331 12016413 + Retroposon/SVA_SVA_E chr1 12016395 12016474 + LTR/ERV1_LTR51 chr1 12016470 12016690 + LTR/ERV1_LTR51 chr1 12016713 12016898 - LINE/L1_L1M5 chr1 12016913 12017214 - SINE/Alu_AluSx1 chr1 12017396 12017709 - SINE/Alu_AluSp chr1 12017710 12017844 - SINE/Alu_AluSx chr1 12017849 12017999 - LINE/L1_L1M5 chr1 12018067 12018155 - SINE/Alu_AluSz chr1 12018162 12018277 - LINE/L1_L1M5 chr1 12018278 12018483 - SINE/Alu_AluJr chr1 12018487 12018688 - SINE/Alu_AluSg4 chr1 12018809 12019144 + SINE/Alu_AluSx3 chr1 12020133 12020262 + SINE/MIR_MIR chr1 12020427 12020481 - SINE/MIR_MIRb chr1 12020501 12020677 - SINE/Alu_AluSx1 chr1 12020678 12020974 - SINE/Alu_AluSx chr1 12020975 12021095 - SINE/Alu_AluSx1 chr1 12021113 12021423 + SINE/Alu_AluY chr1 12021451 12021628 - SINE/MIR_MIR chr1 12021902 12022043 - SINE/MIR_MIR3 chr1 12022479 12022609 + SINE/MIR_MIRb chr1 12023058 12023359 - SINE/Alu_AluSz chr1 12023370 12023381 - SINE/Alu_AluYc chr1 12023382 12023419 + Simple_repeat_(TTAT)n chr1 12023420 12023676 - SINE/Alu_AluYc chr1 12023677 12023702 + Simple_repeat_(T)n chr1 12023714 12024017 + SINE/Alu_AluSg chr1 12024026 12024124 - scRNA_HY1 chr1 12024393 12024422 - LINE/L1_L1MB3 chr1 12024423 12024710 - SINE/Alu_AluSq2 chr1 12024711 12025047 - LINE/L1_L1MB3 chr1 12025048 12025358 - SINE/Alu_AluSz6 chr1 12025359 12025522 - LINE/L1_L1MB3 chr1 12025523 12025708 + LINE/L2_L2 chr1 12025712 12025974 - SINE/Alu_AluSx1 chr1 12026023 12026316 + SINE/Alu_AluSx1 chr1 12026388 12026433 + LINE/L2_L2a chr1 12026445 12026618 + LINE/L2_L2a chr1 12026677 12026848 - SINE/MIR_MIR chr1 12026853 12026995 + LINE/L2_L2a chr1 12026996 12027309 - SINE/Alu_AluSz chr1 12027310 12027869 + LINE/L2_L2a chr1 12027871 12027929 - DNA/TcMar-Tigger_MER47C chr1 12027930 12028224 + SINE/Alu_AluSg chr1 12028225 12028260 - DNA/TcMar-Tigger_MER47C chr1 12028380 12028823 + LINE/L2_L2a chr1 12029317 12029348 + Simple_repeat_(CCTGC)n chr1 12030367 12030538 - SINE/MIR_MIRb chr1 12030557 12030648 - SINE/Alu_AluSq2 chr1 12030650 12030788 + SINE/Alu_AluSx3 chr1 12030902 12031032 + SINE/MIR_MIRb chr1 12032742 12032821 - LINE/L1_L1M3 chr1 12032822 12033121 - SINE/Alu_AluJb chr1 12033122 12033382 - LINE/L1_L1M3 chr1 12033418 12033524 - SINE/Alu_AluJo chr1 12033525 12033593 - LINE/L1_L1M3 chr1 12033710 12034003 + SINE/Alu_AluSz chr1 12034017 12034320 + SINE/Alu_AluSz6 chr1 12034467 12034564 - LTR/ERVL-MaLR_MLT1J-int chr1 12034651 12034769 - LTR/ERVL-MaLR_MLT1J-int chr1 12034782 12035074 - SINE/Alu_AluSz6 chr1 12035355 12035611 + LINE/L2_L2b chr1 12036054 12036122 + srpRNA_7SLRNA chr1 12036125 12036423 + SINE/Alu_AluY chr1 12036424 12036731 + SINE/Alu_AluSz chr1 12036734 12037010 - srpRNA_7SLRNA chr1 12037056 12037360 + SINE/Alu_AluSc chr1 12037441 12037664 - LINE/L2_L2a chr1 12037675 12038072 + LINE/L2_L2b chr1 12038702 12038825 + SINE/MIR_MIR chr1 12039560 12039619 + LINE/CR1_L3 chr1 12039764 12039804 - LTR/ERV1_LTR64 chr1 12039805 12040117 + SINE/Alu_AluJb chr1 12040119 12040417 + SINE/Alu_AluSp chr1 12040418 12040874 - LTR/ERV1_LTR64 chr1 12040903 12041037 + SINE/MIR_MIRc chr1 12041040 12041353 + SINE/Alu_AluJo chr1 12041387 12041490 + SINE/MIR_MIRb chr1 12042102 12042197 + LTR/ERVL-MaLR_MLT1C chr1 12042198 12042222 + Simple_repeat_(T)n chr1 12042246 12042301 - LTR/ERVL-MaLR_MLT1B chr1 12042302 12042606 - SINE/Alu_AluSx3 chr1 12042656 12042701 + Simple_repeat_(TCCCTC)n chr1 12042702 12043767 + Retroposon/SVA_SVA_D chr1 12043768 12043786 + Simple_repeat_(A)n chr1 12043802 12043909 + LTR/ERVL-MaLR_MLT1C chr1 12043910 12044213 + SINE/Alu_AluSx chr1 12044214 12044454 + LTR/ERVL-MaLR_MLT1C chr1 12044455 12044753 + SINE/Alu_AluSz chr1 12044898 12045280 + LTR/ERVL-MaLR_MLT1B chr1 12045691 12045998 - SINE/Alu_AluSx chr1 12046044 12046350 - SINE/Alu_AluSc8 chr1 12046354 12046577 - SINE/MIR_MIRb chr1 12047247 12047306 + Simple_repeat_(AC)n chr1 12048103 12048419 + SINE/Alu_AluJb chr1 12048427 12048456 + Low_complexity_A-rich chr1 12048721 12048821 + LTR/ERVL-MaLR_MLT1B chr1 12048822 12049131 - SINE/Alu_AluSg chr1 12049132 12049384 + LTR/ERVL-MaLR_MLT1B chr1 12049385 12049692 + SINE/Alu_AluSx chr1 12049693 12049757 + LTR/ERVL-MaLR_MLT1B chr1 12049822 12049919 + LTR/ERVL_LTR40a chr1 12050157 12050453 - SINE/Alu_AluJb chr1 12050454 12050538 + LTR/ERVL_LTR75B chr1 12050546 12050842 - SINE/Alu_AluSx1 chr1 12050873 12051160 - SINE/Alu_AluSx1 chr1 12051167 12051266 - LINE/L2_L2a chr1 12051473 12051686 - SINE/MIR_MIRb chr1 12051709 12051860 - SINE/Alu_AluSx3 chr1 12052267 12052550 + LINE/L2_L2d chr1 12052534 12052910 + LINE/L2_L2b chr1 12052908 12052987 + LINE/L2_L2b chr1 12053003 12053253 - LTR_MamRep1527 chr1 12053254 12053421 - SINE/Alu_FRAM chr1 12053422 12053645 - LTR_MamRep1527 chr1 12053857 12053938 + Simple_repeat_(TG)n chr1 12054141 12054194 + Low_complexity_G-rich chr1 12054878 12055120 - LINE/L2_L2c chr1 12055145 12055282 - LINE/L1_L1MA10 chr1 12055285 12055418 - LINE/L2_L2c chr1 12055521 12055685 - DNA/hAT-Tip100_MamTip2b chr1 12056073 12056156 + SINE/MIR_MIRb chr1 12056160 12056451 - SINE/Alu_AluJo chr1 12056743 12056820 + SINE/MIR_MIR3 chr1 12057007 12057173 - SINE/MIR_MIRb chr1 12057268 12057408 - SINE/MIR_MIR3 chr1 12057502 12057593 + DNA/hAT-Tip100_MamTip2b chr1 12057756 12057883 - SINE/MIR_MIRc chr1 12057885 12058343 + LTR/ERVL-MaLR_MLT1F chr1 12058346 12058388 - SINE/MIR_MIR3 chr1 12058432 12058674 + SINE/MIR_MIRb chr1 12059038 12059347 + SINE/Alu_AluSz chr1 12060412 12060541 + SINE/MIR_MIR chr1 12060691 12061250 - LINE/L1_L1ME4c chr1 12061298 12061411 - SINE/Alu_AluJo chr1 12061432 12061518 + SINE/MIR_MIR1_Amn chr1 12062170 12062232 + Simple_repeat_(ATCC)n chr1 12062205 12062245 + Simple_repeat_(CCAC)n chr1 12062253 12062900 + Simple_repeat_(CATC)n chr1 12064123 12064180 + Low_complexity_G-rich chr1 12064989 12065066 - SINE/Alu_FRAM chr1 12065079 12065382 - SINE/Alu_AluSp chr1 12065402 12066160 - LINE/L1_L1ME3G chr1 12066355 12066652 - SINE/Alu_AluSx1 chr1 12066657 12066948 - SINE/Alu_AluSx1 chr1 12067407 12067689 + SINE/Alu_AluSx1 chr1 12067699 12067874 - DNA/hAT-Tip100_MamTip2b chr1 12068336 12068422 - SINE/MIR_MIR3 chr1 12068662 12068694 + Simple_repeat_(CATG)n chr1 12068744 12068862 - LINE/CR1_L3 chr1 12068863 12069170 - SINE/Alu_AluSx3 chr1 12069171 12069479 - SINE/Alu_AluSq2 chr1 12069480 12069649 - LINE/CR1_L3 chr1 12069653 12070224 - LINE/L2_L2b chr1 12070243 12070503 - SINE/Alu_AluY chr1 12070505 12070622 - SINE/Alu_AluJo chr1 12070625 12070894 - LINE/L2_L2b chr1 12071072 12071174 - LTR/ERVL-MaLR_MLT1D chr1 12071175 12071452 + SINE/Alu_AluSz6 chr1 12071453 12071609 - LTR/ERVL-MaLR_MLT1D chr1 12071629 12071857 - SINE/Alu_AluSx1 chr1 12071859 12071986 - SINE/Alu_AluSz chr1 12071995 12072255 + SINE/MIR_MIR chr1 12073006 12073289 + SINE/Alu_AluJb chr1 12073427 12073581 + Simple_repeat_(CCTTTC)n chr1 12073582 12073895 - SINE/Alu_AluSx1 chr1 12074274 12074579 - SINE/Alu_AluSz chr1 12074605 12074753 - DNA/hAT-Charlie_MER103C chr1 12074782 12074974 - SINE/Alu_AluJr chr1 12074975 12075251 + SINE/Alu_AluSz chr1 12075252 12075385 - SINE/Alu_AluJr chr1 12075463 12075654 - LTR/ERVL-MaLR_MLT1D chr1 12075725 12076087 + LTR/ERV1_LTR19A chr1 12076088 12076399 - SINE/Alu_AluSp chr1 12076400 12076477 + LTR/ERV1_LTR19A chr1 12076957 12077223 - SINE/Alu_AluJb chr1 12077260 12077613 + LTR/ERVL-MaLR_MLT1E2 chr1 12077617 12077814 - LINE/L2_L2b chr1 12077883 12077984 - snRNA_U6 chr1 12078282 12078584 + SINE/Alu_AluSz chr1 12078656 12078772 + SINE/MIR_MIR chr1 12078863 12078994 - DNA/hAT-Charlie_MER5A1 chr1 12079935 12080248 - SINE/Alu_AluSx chr1 12080558 12080859 - SINE/Alu_AluSq2 chr1 12080931 12081075 + SINE/MIR_MIRb chr1 12082264 12082579 + SINE/Alu_AluJo chr1 12082623 12083406 + LINE/L1_L1ME3 chr1 12083407 12083706 + SINE/Alu_AluSz chr1 12083707 12084082 + LINE/L1_L1ME3 chr1 12084125 12084342 - LINE/L2_L2 chr1 12084799 12085067 - SINE/MIR_MIRb chr1 12085098 12085409 - SINE/Alu_AluSp chr1 12085415 12085551 - SINE/Alu_AluJo chr1 12086203 12086432 + DNA/hAT-Tip100_MamTip1 chr1 12086443 12086550 + Simple_repeat_(CCAC)n chr1 12086551 12086595 + Simple_repeat_(CCAT)n chr1 12086596 12086979 - LTR/ERVL-MaLR_MLT1B chr1 12086980 12087046 + Simple_repeat_(CCAT)n chr1 12087075 12087138 + Simple_repeat_(ATCC)n chr1 12087156 12087467 - SINE/Alu_AluSz chr1 12088135 12088246 - LINE/CR1_L3 chr1 12088258 12088487 - SINE/MIR_MIR chr1 12088864 12088902 + Simple_repeat_(CTCCCC)n chr1 12089062 12089115 + SINE/Alu_AluYh9 chr1 12089225 12089334 + LINE/L2_L2a chr1 12089798 12089921 + Simple_repeat_(CATC)n chr1 12089922 12089962 + Simple_repeat_(CCAT)n chr1 12089963 12090646 + Simple_repeat_(CATC)n chr1 12090793 12090823 - LTR/ERVL_LTR16C chr1 12090824 12091214 + LTR/ERVL-MaLR_MLT1B chr1 12091215 12091514 - LTR/ERVL_LTR16C chr1 12091683 12091738 - LTR/ERVL-MaLR_MLT1B chr1 12091739 12092073 - DNA/hAT-Charlie_MER1B chr1 12092074 12092188 - LTR/ERVL-MaLR_MLT1B chr1 12092189 12092469 - SINE/Alu_AluJr chr1 12092470 12092495 - LTR/ERVL-MaLR_MLT1B chr1 12092496 12092807 - SINE/Alu_AluY chr1 12092808 12093104 - SINE/Alu_AluSx chr1 12093105 12093284 - LTR/ERVL-MaLR_MLT1B chr1 12093545 12093788 - SINE/Alu_AluSz chr1 12093789 12094082 + SINE/Alu_AluSc8 chr1 12094083 12094139 - SINE/Alu_Alu chr1 12094256 12094559 - LINE/L2_L2b chr1 12094614 12094924 - SINE/Alu_AluSz chr1 12094969 12095287 + DNA/hAT-Charlie_Charlie4a chr1 12095288 12095587 - SINE/Alu_AluSq2 chr1 12095588 12095747 + DNA/hAT-Charlie_Charlie4a chr1 12095750 12096245 - LTR/ERVL-MaLR_MLT1K chr1 12096308 12096555 - DNA/hAT-Charlie_MER30 chr1 12096628 12096750 - SINE/MIR_MIR chr1 12096790 12096807 + Simple_repeat_(CA)n chr1 12096899 12096980 + SINE/MIR_MIR1_Amn chr1 12097402 12097638 - SINE/MIR_MIR chr1 12097765 12098195 - LINE/L1_L1M5 chr1 12098305 12099143 - LINE/L1_HAL1b chr1 12099144 12099444 - SINE/Alu_AluSz chr1 12099451 12099592 - SINE/Alu_AluJb chr1 12099620 12099886 - DNA/TcMar-Tigger_Tigger3b chr1 12099887 12100200 + SINE/Alu_AluSx chr1 12100201 12100674 - DNA/TcMar-Tigger_Tigger3b chr1 12100675 12100975 + SINE/Alu_AluSz chr1 12100976 12101143 - DNA/TcMar-Tigger_Tigger3b chr1 12101144 12101447 + SINE/Alu_AluJr chr1 12101448 12101674 - DNA/TcMar-Tigger_Tigger3b chr1 12101675 12101973 - SINE/Alu_AluSx chr1 12101974 12102049 - DNA/TcMar-Tigger_Tigger3b chr1 12102520 12102825 - SINE/Alu_AluSx chr1 12102826 12103056 - LINE/L1_L1M4 chr1 12103057 12103081 - LINE/L1_L1MC2 chr1 12103082 12103367 + SINE/Alu_AluSx chr1 12103368 12103477 - LINE/L1_L1MC2 chr1 12103478 12103792 - SINE/Alu_AluJb chr1 12103793 12104182 - LINE/L1_L1MC2 chr1 12104926 12105030 + SINE/MIR_MIRb chr1 12105528 12105574 + Simple_repeat_(CT)n chr1 12105575 12105598 + Simple_repeat_(AC)n chr1 12105656 12105963 + SINE/Alu_AluSq2 chr1 12105966 12106145 - LINE/L1_L1MDa chr1 12106172 12106604 + LINE/L2_L2b chr1 12106722 12106876 + SINE/MIR_MIR3 chr1 12107052 12107112 + DNA/hAT-Charlie_MER33 chr1 12107113 12107425 + SINE/Alu_AluSx1 chr1 12107426 12107671 + DNA/hAT-Charlie_MER33 chr1 12107811 12107968 - SINE/MIR_MIRb chr1 12108075 12108376 - SINE/Alu_AluSx3 chr1 12108434 12108536 - SINE/MIR_MIRb chr1 12108539 12108840 + SINE/Alu_AluSx1 chr1 12108935 12109453 - LTR/ERVL-MaLR_MLT1K chr1 12109822 12109914 - LINE/L2_L2d chr1 12110596 12110900 - SINE/Alu_AluSq chr1 12110904 12111072 - LINE/L1_L1ME4c chr1 12111171 12111481 - SINE/Alu_AluSx1 chr1 12112203 12112362 - SINE/MIR_MIR chr1 12112387 12112695 - SINE/Alu_AluJb chr1 12113001 12113318 - LTR/ERVL-MaLR_MLT1G3 chr1 12113341 12113473 - SINE/Alu_AluSz chr1 12113485 12113768 + Low_complexity_GA-rich chr1 12113770 12113996 - LTR/ERVL-MaLR_MLT1G3 chr1 12114077 12114326 + SINE/MIR_MIR chr1 12114695 12114723 + Simple_repeat_(T)n chr1 12114728 12115006 - SINE/Alu_AluSg chr1 12115014 12115153 - LINE/L1_L1MC5 chr1 12115163 12115267 - LINE/L1_L1MA10 chr1 12115348 12115418 - LINE/L1_L1M5 chr1 12115986 12116280 - SINE/Alu_AluSp chr1 12116413 12116441 + Simple_repeat_(T)n chr1 12116519 12116813 + SINE/Alu_AluSx1 chr1 12117075 12117382 - SINE/Alu_AluSx1 chr1 12117465 12117687 + LINE/L1_HAL1ME chr1 12117694 12118079 - LINE/L1_L1PB4 chr1 12118119 12118410 - SINE/Alu_AluSx chr1 12118429 12118673 + LINE/L1_HAL1ME chr1 12118675 12118854 + LTR/ERVL_LTR41B chr1 12118855 12119163 - SINE/Alu_AluSg chr1 12119164 12119194 + LTR/ERVL_LTR41B chr1 12119187 12119327 + LTR/ERVL_LTR41B chr1 12119327 12119394 + LTR/ERVL_LTR41B chr1 12119458 12119588 - LINE/L1_L1M5 chr1 12119592 12119776 - SINE/Alu_AluJr chr1 12119777 12119887 - LINE/L1_L1M5 chr1 12120031 12120500 + LINE/L1_HAL1 chr1 12120641 12121057 - LTR/ERVL?_Eutr10 chr1 12121537 12121580 + SINE/MIR_MIR1_Amn chr1 12121712 12122185 + LINE/L1_L1ME1 chr1 12122186 12122330 - SINE/Alu_FLAM_C chr1 12122331 12122364 + LINE/L1_L1ME1 chr1 12122365 12122674 + SINE/Alu_AluSx1 chr1 12122675 12122701 + LINE/L1_L1ME1 chr1 12122708 12122833 + DNA/hAT-Charlie_MER102b chr1 12122836 12123128 - SINE/Alu_AluSz chr1 12123506 12123581 - LINE/L2_L2d chr1 12124011 12124307 - SINE/Alu_AluSx1 chr1 12124415 12124496 + SINE/MIR_MIRb chr1 12124542 12124829 + SINE/Alu_AluSg chr1 12124830 12124887 + Simple_repeat_(AAACA)n chr1 12124986 12125143 + DNA/hAT-Charlie_MER5A1 chr1 12125281 12125482 + SINE/MIR_MIR chr1 12126556 12126644 + Simple_repeat_(CCTCTC)n chr1 12126704 12126869 + SINE/MIR_MIRb chr1 12127312 12127415 - SINE/MIR_MIRc chr1 12127842 12127867 + Simple_repeat_(AAGA)n chr1 12128150 12128208 - LINE/L2_L2b chr1 12128447 12128608 - SINE/MIR_MIRb chr1 12128637 12128822 - DNA/hAT-Charlie_MER20 chr1 12128831 12129132 - SINE/Alu_AluSg chr1 12129274 12129894 - LINE/L1_L1MC4 chr1 12129895 12130210 - SINE/Alu_AluSz chr1 12130211 12130487 - LINE/L1_L1MC4 chr1 12131168 12131477 + SINE/Alu_AluSx chr1 12131504 12131792 - SINE/Alu_AluJb chr1 12131802 12132091 - SINE/Alu_AluSg chr1 12132097 12132555 - LINE/L1_L1MC3 chr1 12132556 12132862 + SINE/Alu_AluSx chr1 12132863 12132881 - LINE/L1_L1MC3 chr1 12132887 12133018 - SINE/MIR_MIRb chr1 12133045 12133232 - LINE/L1_L1M5 chr1 12133293 12133341 + Simple_repeat_(TTTT)n chr1 12133360 12133457 + DNA/TcMar-Mariner_MARNA chr1 12133460 12133762 + SINE/Alu_AluSc chr1 12133771 12134053 + SINE/Alu_AluSx chr1 12135035 12135337 + SINE/Alu_AluSx3 chr1 12136367 12136667 + SINE/Alu_AluSx chr1 12136871 12136895 + Simple_repeat_(T)n chr1 12136898 12137102 - Retroposon/SVA_SVA_B chr1 12137287 12137332 + Simple_repeat_(ATA)n chr1 12137541 12137579 + Simple_repeat_(T)n chr1 12137789 12138027 + SINE/MIR_MIR chr1 12138586 12138663 + SINE/MIR_MIR chr1 12138751 12138990 - SINE/MIR_MIRb chr1 12139072 12139608 + LINE/L2_L2b chr1 12139750 12139936 - SINE/Alu_AluSz chr1 12141613 12141719 - DNA/hAT-Blackjack_MER81 chr1 12141801 12141937 + SINE/MIR_MIRb chr1 12144248 12144498 - LINE/CR1_L3 chr1 12144499 12144803 + SINE/Alu_AluSq2 chr1 12144804 12144884 - LINE/CR1_L3 chr1 12145160 12145222 + Simple_repeat_(ACACCC)n chr1 12146177 12146316 + SINE/MIR_MIRb chr1 12146732 12147090 - LINE/L2_L2c chr1 12147625 12147716 + SINE/MIR_MIRc chr1 12147719 12148021 + SINE/Alu_AluSz chr1 12148132 12148326 - SINE/MIR_MIRc chr1 12148391 12148531 - SINE/MIR_MIRc chr1 12148646 12148943 - SINE/Alu_AluJo chr1 12149088 12149168 - SINE/MIR_MIR3 chr1 12149407 12149576 + DNA/hAT-Tip100_MER45A chr1 12149724 12150146 - LTR/ERVL-MaLR_MLT1G1 chr1 12150174 12150327 - SINE/MIR_MIRb chr1 12150361 12150412 - SINE/MIR_MIRb chr1 12150433 12150491 - SINE/MIR_MIRb chr1 12150778 12150894 - LINE/L2_L2b chr1 12150895 12151114 - SINE/MIR_MIR chr1 12151619 12151649 + Simple_repeat_(ATTC)n chr1 12151653 12151894 - LINE/L2_L2b chr1 12151894 12151964 + SINE/MIR_MIR chr1 12151973 12152036 - LINE/L2_L2c chr1 12152596 12152822 - SINE/MIR_MIRb chr1 12152989 12153017 + LINE/L2_L2b chr1 12153768 12153885 - SINE/MIR_MIR3 chr1 12153994 12154303 + SINE/Alu_AluSz chr1 12154420 12154437 + Simple_repeat_(ATTT)n chr1 12154438 12154757 - SINE/Alu_AluY chr1 12154970 12155073 - LINE/L2_L2d2 chr1 12155751 12155936 + LINE/L1_L1ME3Cz chr1 12155937 12156235 + SINE/Alu_AluSc chr1 12156236 12156596 + LINE/L1_L1ME3Cz chr1 12156603 12156914 + SINE/Alu_AluJb chr1 12156964 12157512 + LINE/L1_L1ME3Cz chr1 12157366 12157677 + LINE/L1_L1M5 chr1 12157894 12157977 + LINE/L1_L1ME5 chr1 12158002 12158021 + Simple_repeat_(A)n chr1 12158528 12159054 + LINE/L1_L1ME3Cz chr1 12159245 12159544 + SINE/Alu_AluSg chr1 12159549 12159708 + SINE/Alu_AluSx chr1 12159727 12160116 - LINE/L2_L2a chr1 12160254 12160385 + SINE/Alu_AluSz chr1 12160424 12160734 + SINE/Alu_AluSx1 chr1 12160735 12161024 + SINE/Alu_AluJb chr1 12161269 12161306 - SINE/MIR_MIR3 chr1 12161307 12161490 - DNA/hAT-Charlie_MER117 chr1 12161491 12161605 - SINE/MIR_MIR3 chr1 12162379 12162482 - DNA/hAT-Charlie_MER5A1 chr1 12162424 12162502 + DNA/hAT-Charlie_MER5A chr1 12162503 12162562 + LINE/L2_L2b chr1 12162958 12162982 + Simple_repeat_(CATT)n chr1 12163301 12163651 - LINE/L2_L2c chr1 12163756 12163977 - DNA/hAT-Charlie_MER20 chr1 12164509 12164608 + LINE/L2_L2a chr1 12165209 12165340 - LINE/L2_L2c chr1 12166253 12166286 - SINE/MIR_MIR chr1 12166293 12166585 + SINE/Alu_AluJr chr1 12166722 12166765 + Simple_repeat_(GCAGG)n chr1 12168056 12168094 - SINE/MIR_MIRc chr1 12168698 12168892 + LINE/L2_L2c chr1 12169044 12169292 + LINE/L2_L2c chr1 12169298 12169528 - SINE/MIR_MIRb chr1 12169721 12169749 + Simple_repeat_(TCAT)n chr1 12170185 12170302 - LINE/L2_L2b chr1 12170665 12170975 - SINE/Alu_AluSz chr1 12170991 12171313 - SINE/Alu_AluSz chr1 12171336 12171642 + LINE/L2_L2a chr1 12171643 12171838 - DNA/hAT-Charlie_MER20 chr1 12171839 12171885 + LINE/L2_L2a chr1 12172241 12172427 + SINE/MIR_MIRb chr1 12173060 12173199 + SINE/MIR_MIRb chr1 12174125 12174267 + Simple_repeat_(CTTCTC)n chr1 12174268 12174546 - SINE/Alu_AluSz chr1 12175179 12175338 + SINE/MIR_MIR1_Amn chr1 12175837 12175932 - LINE/L2_L2d2 chr1 12175933 12176246 + SINE/Alu_AluSz chr1 12176247 12176362 - LINE/L2_L2d2 chr1 12176393 12176631 - SINE/MIR_MIR chr1 12176659 12176796 - LINE/L2_L2d2 chr1 12177005 12177306 - SINE/Alu_AluSx3 chr1 12177410 12177442 + Simple_repeat_(GGCTG)n chr1 12177676 12177815 + SINE/Alu_AluSx3 chr1 12177816 12178054 - SINE/MIR_MIRb chr1 12178314 12178436 - SINE/MIR_MIR chr1 12179050 12179126 + LINE/L2_L2c chr1 12179566 12179690 + SINE/MIR_MIR3 chr1 12179752 12179919 + SINE/MIR_MIRb chr1 12180148 12180282 + SINE/Alu_AluJr4 chr1 12181419 12181521 - SINE/MIR_MIRc chr1 12181948 12182024 - SINE/MIR_MIR3 chr1 12182141 12182290 + SINE/MIR_MIRb chr1 12182315 12182575 + LINE/L2_L2a chr1 12182596 12182748 + SINE/MIR_MIRb chr1 12182703 12182761 + LINE/L2_L2d2 chr1 12183283 12183393 + SINE/MIR_MIRb chr1 12183447 12183473 + Simple_repeat_(TC)n chr1 12183474 12183505 + Simple_repeat_(TATC)n chr1 12183506 12183853 + Simple_repeat_(TCTA)n chr1 12183854 12183883 + Simple_repeat_(TATC)n chr1 12185939 12186096 - LINE/L2_L2d2 chr1 12186438 12186520 - DNA/hAT_MamRep1894 chr1 12186537 12186642 - SINE/MIR_MIRc chr1 12187137 12187452 - SINE/Alu_AluSz chr1 12187559 12187620 - LTR/ERV1_LTR27B chr1 12187704 12187906 - LTR/ERVL_LTR16A1 chr1 12187905 12188002 - LTR/ERVL_LTR16A1 chr1 12188016 12188061 + SINE/MIR_MIR chr1 12188228 12188424 - LINE/L2_L2c chr1 12188988 12189159 + LINE/L2_L2c chr1 12189186 12189264 - LINE/L2_L2c chr1 12189261 12189469 + LINE/L2_L2c chr1 12189570 12190089 - LINE/L2_L2a chr1 12190217 12190480 + SINE/Alu_AluSx1 chr1 12190662 12190707 - DNA/hAT-Tip100_MamTip2b chr1 12191555 12191602 + Simple_repeat_(GCGGAGAGTA)n chr1 12192084 12192159 - LINE/L2_L2c chr1 12192277 12192307 + Simple_repeat_(TG)n chr1 12193116 12193169 + Simple_repeat_(TCTT)n chr1 12193291 12193596 + SINE/Alu_AluJr chr1 12193752 12193810 + DNA/hAT-Charlie_MER102b chr1 12194058 12194351 - LINE/L2_L2b chr1 12195037 12195223 + DNA/hAT-Charlie_Charlie15a chr1 12195276 12195678 - LTR/ERVL-MaLR_MLT1J chr1 12195636 12195720 - LTR/ERVL_LTR67B chr1 12195755 12195820 - SINE/MIR_MIRc chr1 12195901 12195995 + SINE/Alu_FLAM_C chr1 12196019 12196329 + SINE/Alu_AluSz chr1 12196342 12196501 - DNA/hAT-Charlie_MER20 chr1 12196566 12196702 - SINE/MIR_MIRc chr1 12196943 12197247 - SINE/Alu_AluSz chr1 12197334 12197457 - DNA/hAT-Blackjack_MER63A chr1 12197480 12197697 - SINE/MIR_MIRb chr1 12197844 12198141 + SINE/Alu_AluY chr1 12198210 12198459 - SINE/MIR_MIRb chr1 12198660 12198684 + Simple_repeat_(GCTG)n chr1 12198687 12198992 - SINE/Alu_AluYf1 chr1 12199403 12199490 + SINE/MIR_MIRc chr1 12200080 12200271 + SINE/MIR_MIRb chr1 12200407 12200624 - DNA/hAT-Charlie_MER58B chr1 12200625 12200920 - SINE/Alu_AluSp chr1 12200921 12200974 - DNA/hAT-Charlie_MER58B chr1 12200975 12201290 + SINE/Alu_AluJo chr1 12201291 12201313 - DNA/hAT-Charlie_MER58B chr1 12201349 12201598 - SINE/MIR_MIR chr1 12202010 12202053 + Simple_repeat_(AGCAGC)n chr1 12202225 12202610 + LINE/L2_L2c chr1 12202645 12202872 - SINE/MIR_MIR chr1 12203139 12203302 + SINE/MIR_MIRb chr1 12203433 12203489 + Simple_repeat_(CCTG)n chr1 12203607 12203666 + LINE/L2_L2c chr1 12203726 12204029 - SINE/Alu_AluSx chr1 12204505 12204534 + Simple_repeat_(CAG)n chr1 12204688 12204789 - DNA/hAT-Charlie_MER102a chr1 12204791 12204816 + Simple_repeat_(CAC)n chr1 12204839 12204892 - LINE/L2_L2b chr1 12204893 12205028 + SINE/Alu_FLAM_C chr1 12205029 12205617 - LINE/L2_L2b chr1 12205618 12205927 - SINE/Alu_AluSx1 chr1 12205928 12206102 - LINE/L2_L2b chr1 12206106 12206388 + SINE/Alu_AluSz chr1 12206389 12206462 - SINE/Alu_AluJo chr1 12206476 12206649 - SINE/MIR_MIRc chr1 12207356 12207390 + Simple_repeat_(GTTT)n chr1 12207628 12207924 + SINE/Alu_AluSx1 chr1 12208417 12208453 + Simple_repeat_(TG)n chr1 12208812 12208960 + SINE/Alu_FRAM chr1 12209600 12209768 - LINE/L2_L2b chr1 12209769 12209987 + SINE/Alu_AluSx3 chr1 12210044 12210214 + LINE/L2_L2 chr1 12210224 12210265 - DNA/hAT-Charlie_MER58A chr1 12210266 12210541 - SINE/Alu_AluJb chr1 12210542 12210647 - DNA/hAT-Charlie_MER58A chr1 12210676 12210741 + LINE/L2_L2 chr1 12210742 12210978 + LINE/L2_L2 chr1 12210979 12211247 + SINE/Alu_AluSz chr1 12211248 12211305 + LINE/L2_L2 chr1 12211314 12211669 - LTR/ERVL-MaLR_MLT1D chr1 12211787 12212091 - SINE/Alu_AluSx chr1 12212126 12212276 - SINE/Alu_FRAM chr1 12212281 12212639 + LINE/L2_L2 chr1 12212641 12212980 + LINE/L1_L1MB3 chr1 12212981 12213283 + SINE/Alu_AluSx chr1 12213284 12213567 + LINE/L1_L1MB3 chr1 12213568 12213858 + SINE/Alu_AluJr chr1 12213859 12213956 + LINE/L1_L1MB3 chr1 12214006 12214260 + SINE/Alu_AluSq2 chr1 12214378 12214729 + LINE/L2_L2-3_Crp chr1 12215320 12215469 + LTR/ERVL_LTR33A_ chr1 12215470 12216101 + LTR/ERV1_LTR1C1 chr1 12216102 12216235 + LTR/ERVL_LTR33A_ chr1 12216407 12216493 + SINE/MIR_MIR chr1 12216870 12216905 + Simple_repeat_(TTATCA)n chr1 12216941 12217110 + SINE/Alu_AluJb chr1 12217111 12217411 - SINE/Alu_AluY chr1 12217412 12217437 + SINE/Alu_AluJb chr1 12217448 12217677 + SINE/MIR_MIR chr1 12217691 12217952 - LINE/L2_L2a chr1 12218008 12218301 - SINE/Alu_AluSx1 chr1 12218393 12218518 + SINE/Alu_AluJr chr1 12218534 12218597 + SINE/Alu_AluJb chr1 12218633 12218710 - SINE/MIR_MIRc chr1 12218914 12219036 + SINE/MIR_MIR3 chr1 12219065 12219374 - SINE/Alu_AluSc8 chr1 12219406 12219454 - DNA/hAT-Tip100_MamTip2 chr1 12219455 12219772 + DNA/hAT-Charlie_MER33 chr1 12219773 12219830 - DNA/hAT-Tip100_MamTip2 chr1 12219817 12220019 + LINE/L2_L2a chr1 12220035 12220173 + SINE/Alu_AluJr chr1 12220174 12220462 + SINE/Alu_AluSz6 chr1 12220463 12220598 + SINE/Alu_AluJr chr1 12220622 12220677 + LINE/L2_L2a chr1 12220698 12220725 + Simple_repeat_(T)n chr1 12221242 12221406 - DNA/hAT-Charlie_Charlie8 chr1 12221621 12221646 - SINE/Alu_FLAM_C chr1 12221647 12222011 - LTR/ERVL-MaLR_THE1B chr1 12222012 12223093 - LTR/ERVL-MaLR_THE1B-int chr1 12223094 12223408 - SINE/Alu_AluY chr1 12223409 12223835 - LTR/ERVL-MaLR_THE1B-int chr1 12223836 12223908 - LTR/ERVL-MaLR_THE1B chr1 12223909 12224208 + SINE/Alu_AluSc5 chr1 12224209 12224505 - LTR/ERVL-MaLR_THE1B chr1 12224506 12224635 - SINE/Alu_FLAM_C chr1 12224636 12224940 - LTR/ERVL_MER21C chr1 12224941 12225242 - SINE/Alu_AluSg chr1 12225243 12225349 - LTR/ERVL_MER21C chr1 12225350 12225646 - SINE/Alu_AluY chr1 12225647 12225956 - LTR/ERVL_MER21C chr1 12225963 12226280 + SINE/Alu_AluSz chr1 12226281 12226407 + LINE/L1_L1MA7 chr1 12226408 12226705 + SINE/Alu_AluYk4 chr1 12226706 12226835 + LINE/L1_L1MA7 chr1 12226855 12227121 + SINE/Alu_AluSz6 chr1 12227126 12227153 + Simple_repeat_(CAAA)n chr1 12227207 12227559 - LINE/L1_L1ME1 chr1 12227616 12227899 - SINE/Alu_AluJr chr1 12227910 12228209 - SINE/Alu_AluSx1 chr1 12228216 12228252 - DNA/hAT-Charlie_MER5A chr1 12228605 12228787 - LINE/L2_L2a chr1 12228798 12229090 - SINE/Alu_AluSx chr1 12229193 12229472 + DNA/hAT-Charlie_MER33 chr1 12229476 12229734 - LINE/L2_L2a chr1 12231707 12232005 + SINE/Alu_AluSx1 chr1 12232638 12232662 + Simple_repeat_(T)n chr1 12233003 12233305 - SINE/Alu_AluSx1 chr1 12233364 12233527 - DNA/hAT-Charlie_MER20 chr1 12233767 12234076 + SINE/Alu_AluSq2 chr1 12234689 12234875 + SINE/MIR_MIRb chr1 12235297 12235598 + SINE/Alu_AluSq4 chr1 12236383 12236682 - SINE/Alu_AluJb chr1 12236716 12236763 - LINE/L2_L2d chr1 12236755 12236991 + SINE/MIR_MIR chr1 12237052 12237162 + Simple_repeat_(TGTA)n chr1 12237261 12237483 + SINE/Alu_AluJb chr1 12237537 12237839 + SINE/Alu_AluJr chr1 12237874 12238169 + SINE/Alu_AluSz6 chr1 12238240 12238293 + Simple_repeat_(AT)n chr1 12238294 12238444 + SINE/Alu_FRAM chr1 12238480 12238625 + SINE/MIR_MIRb chr1 12238630 12238929 - SINE/Alu_AluJo chr1 12238948 12239133 - LINE/L2_L2d chr1 12239134 12239438 - SINE/Alu_AluJb chr1 12239439 12239545 - LINE/L2_L2d chr1 12240313 12240618 + SINE/Alu_AluSq2 chr1 12240925 12241242 - SINE/Alu_AluJo chr1 12241481 12241563 + LINE/L2_L2d2 chr1 12241848 12241972 - DNA/hAT-Charlie_MER5A chr1 12241996 12242149 + DNA/hAT-Charlie_MER58C chr1 12242724 12242899 + DNA/hAT-Charlie_MER5A chr1 12242943 12243254 - SINE/Alu_AluSx chr1 12243258 12243454 - SINE/Alu_AluJb chr1 12243510 12243608 + SINE/Alu_AluJr chr1 12244934 12245045 + SINE/MIR_MIR3 chr1 12245426 12245577 - LINE/L1_L1MB7 chr1 12245578 12245741 + SINE/Alu_FRAM chr1 12245742 12245977 - LINE/L1_L1MB7 chr1 12245978 12246332 - LTR/ERV1_MER67B chr1 12246333 12246563 + DNA/TcMar-Tigger_Tigger4a chr1 12246564 12246848 - LTR/ERV1_MER67B chr1 12246849 12247143 - LINE/L1_L1MB7 chr1 12247158 12247429 + SINE/Alu_AluSz chr1 12247479 12247533 + Low_complexity_A-rich chr1 12247534 12247699 - LINE/L1_L1MB7 chr1 12247704 12247878 - SINE/Alu_AluSx chr1 12247879 12248188 - SINE/Alu_AluSx1 chr1 12248189 12248312 - LINE/L1_L1MB7 chr1 12248313 12248325 - SINE/Alu_AluJo chr1 12248326 12248361 + Simple_repeat_(TTA)n chr1 12248362 12248644 - SINE/Alu_AluJo chr1 12248645 12248672 - LINE/L1_L1MB7 chr1 12248947 12248997 + Simple_repeat_(ATAAC)n chr1 12249544 12249587 - SINE/MIR_MIR chr1 12249585 12249733 + LINE/L2_L2b chr1 12249761 12250225 - LTR/ERVL_MER21C chr1 12250217 12250472 - LTR/ERVL_MER21C chr1 12250577 12250662 - SINE/MIR_MIR chr1 12250776 12250933 + DNA/hAT-Charlie_MER58A chr1 12250958 12251018 + DNA/hAT-Charlie_MER58B chr1 12251043 12251103 + DNA/hAT-Charlie_MER58B chr1 12251128 12251222 + DNA/hAT-Charlie_MER58B chr1 12251476 12251584 - DNA/hAT-Charlie_Charlie17a chr1 12251839 12252250 - LTR/ERVL-MaLR_MLT1C2 chr1 12252415 12252482 + DNA/TcMar-Tigger_MER2B chr1 12252490 12252800 + SINE/Alu_AluSx chr1 12252817 12252871 + Low_complexity_A-rich chr1 12252877 12253175 + SINE/Alu_AluSp chr1 12253180 12253377 + DNA/TcMar-Tigger_MER2 chr1 12253491 12253667 + LINE/L2_L2b chr1 12253991 12254164 - LINE/L1_L1ME5 chr1 12254217 12254340 - SINE/Alu_AluJo chr1 12254514 12254827 - SINE/Alu_AluSx1 chr1 12254944 12255244 - SINE/Alu_AluSx chr1 12255279 12255611 + LINE/L1_L1ME4b chr1 12255612 12255918 + SINE/Alu_AluSc5 chr1 12256053 12256181 + SINE/Alu_FLAM_C chr1 12256691 12256710 + Simple_repeat_(T)n chr1 12257644 12257768 - DNA/TcMar-Tigger_Tigger19a chr1 12259032 12259295 - SINE/Alu_AluJb chr1 12259314 12259603 - SINE/Alu_AluSz chr1 12260016 12260324 - SINE/Alu_AluY chr1 12261448 12261629 - DNA/hAT-Charlie_MER3 chr1 12262136 12262339 - DNA/TcMar-Tigger_Tigger17a chr1 12262340 12262698 + DNA/TcMar-Tigger_MER47B chr1 12262699 12263002 - SINE/Alu_AluSz chr1 12263003 12263065 + DNA/TcMar-Tigger_MER47B chr1 12263066 12263508 - DNA/TcMar-Tigger_Tigger17a chr1 12263851 12263868 + Simple_repeat_(TAGA)n chr1 12263869 12265876 + DNA/TcMar-Tigger_Tigger1 chr1 12265877 12266192 + SINE/Alu_AluJr4 chr1 12266193 12266728 + DNA/TcMar-Tigger_Tigger1 chr1 12266729 12266739 + Simple_repeat_(TAGA)n chr1 12267971 12268246 - SINE/Alu_AluSz6 chr1 12268250 12268270 + Simple_repeat_(T)n chr1 12269866 12270166 + SINE/Alu_AluJo chr1 12270175 12270479 + SINE/Alu_AluSz chr1 12270718 12270895 - SINE/Alu_AluJb chr1 12271388 12271688 + SINE/Alu_AluSx1 chr1 12271910 12272190 + SINE/Alu_AluJo chr1 12272200 12272295 + LINE/RTE-BovB_MamRTE1 chr1 12272393 12272430 + Simple_repeat_(GT)n chr1 12273395 12273903 - LINE/L1_L1MB7 chr1 12273944 12274041 - LINE/L1_L1MB7 chr1 12274042 12274339 - SINE/Alu_AluSx1 chr1 12274343 12274410 - SINE/Alu_AluSx chr1 12274411 12274611 - SINE/Alu_AluSz chr1 12274700 12274800 - LINE/L2_L2b chr1 12274801 12274936 + SINE/Alu_AluSx chr1 12274937 12275247 + SINE/Alu_AluSx1 chr1 12275248 12275425 + SINE/Alu_AluSx chr1 12275426 12275446 - LINE/L2_L2b chr1 12278262 12278572 - SINE/Alu_AluSq2 chr1 12279679 12279726 + Simple_repeat_(AT)n chr1 12280501 12280788 - SINE/Alu_AluSz chr1 12280942 12281872 - LINE/L1_L1MC5a chr1 12281890 12282186 - SINE/Alu_AluJb chr1 12283990 12284081 + SINE/MIR_MIRb chr1 12284168 12284404 + SINE/MIR_MIR chr1 12285127 12285203 - LINE/L1_L1MC5a chr1 12285256 12285583 - SINE/Alu_AluSz chr1 12285590 12285621 - LTR/ERVL_LTR82A chr1 12285622 12285785 - LINE/L1_L1MC5a chr1 12285922 12286073 + Simple_repeat_(TTCCT)n chr1 12286076 12286377 - SINE/Alu_AluSz chr1 12286393 12286769 - LINE/L1_L1MC5a chr1 12286852 12287159 - SINE/Alu_AluSx1 chr1 12287335 12287378 + Simple_repeat_(TTCCT)n chr1 12287390 12287479 - LINE/L2_L2c chr1 12288460 12288513 - DNA/hAT-Charlie_MER5A chr1 12288628 12288758 - DNA/hAT-Blackjack_MER94 chr1 12288841 12289141 - SINE/Alu_AluSz chr1 12289249 12289393 - SINE/Alu_FLAM_C chr1 12289672 12289953 + SINE/Alu_AluJb chr1 12290442 12290737 + SINE/Alu_AluY chr1 12291352 12291624 + SINE/Alu_AluJr chr1 12291677 12291836 + SINE/MIR_MIR3 chr1 12291977 12292286 + SINE/Alu_AluSq2 chr1 12292332 12292350 + Simple_repeat_(T)n chr1 12292352 12292435 - DNA/hAT-Charlie_MER5B chr1 12292436 12292738 - SINE/Alu_AluSx3 chr1 12292739 12292792 - DNA/hAT-Charlie_MER5B chr1 12294038 12294532 - DNA/hAT-Charlie_MER119 chr1 12294533 12294820 + SINE/Alu_AluSc chr1 12294821 12294877 - DNA/hAT-Charlie_MER119 chr1 12294941 12295245 + SINE/Alu_AluJb chr1 12295370 12295508 - LINE/L1_L1ME4b chr1 12295675 12296390 - LINE/L1_L1ME3F chr1 12296522 12296783 - LINE/L1_L1ME4b chr1 12297600 12297684 - SINE/MIR_MIRc chr1 12297688 12297855 - LINE/L2_L2a chr1 12298113 12298247 - SINE/MIR_MIR chr1 12298362 12298656 + SINE/Alu_AluSz6 chr1 12298698 12299107 - LINE/L1_L1M5 chr1 12299903 12300046 - LINE/L1_L1PA16 chr1 12300053 12300119 + LTR/ERVL_MER21C chr1 12300120 12300207 - LINE/L1_L1PA16 chr1 12300208 12300507 - SINE/Alu_AluSx1 chr1 12300508 12300610 - LINE/L1_L1PA16 chr1 12300736 12300818 - SINE/MIR_MIR3 chr1 12301078 12301142 - SINE/MIR_MIRb chr1 12301154 12301898 - LTR/ERVL_MER21C chr1 12301899 12302248 - DNA/TcMar-Tigger_Tigger3a chr1 12302833 12302851 + Simple_repeat_(T)n chr1 12303095 12303132 + Simple_repeat_(TTTGCA)n chr1 12305277 12305589 - SINE/Alu_AluJb chr1 12305889 12305956 - SINE/MIR_MIRb chr1 12305957 12306230 - SINE/Alu_AluY chr1 12306231 12306348 - SINE/MIR_MIRb chr1 12306486 12306636 + DNA/hAT-Tip100_MamTip2 chr1 12306642 12306698 + DNA/hAT-Charlie_Charlie15b chr1 12306699 12306994 - DNA/hAT-Charlie_MER1B chr1 12306995 12307080 + DNA/hAT-Charlie_Charlie15a chr1 12307330 12307494 - LINE/L2_L2 chr1 12307495 12307790 - SINE/Alu_AluSx1 chr1 12307791 12308024 - LINE/L2_L2 chr1 12308649 12308944 - SINE/Alu_AluSx chr1 12309046 12309191 - LINE/L2_L2c chr1 12309204 12309463 - SINE/Alu_AluSc chr1 12309482 12309791 + SINE/Alu_AluSg chr1 12310938 12311215 - SINE/Alu_AluJb chr1 12312001 12312089 + DNA/hAT-Charlie_MER102b chr1 12312099 12312256 - LTR/ERVL-MaLR_MLT1I chr1 12312476 12312742 + SINE/Alu_AluJr chr1 12312768 12312910 + DNA/hAT-Charlie_MER102c chr1 12312987 12313175 - SINE/MIR_MIRc chr1 12313300 12313482 - SINE/Alu_AluSx3 chr1 12314800 12315023 + SINE/MIR_MIRb chr1 12315457 12315676 - SINE/MIR_MIRb chr1 12315819 12316114 - SINE/Alu_AluSx4 chr1 12317039 12317114 - LINE/CR1_L3 chr1 12317284 12317411 - SINE/MIR_MIRb chr1 12317419 12317598 - DNA/hAT-Charlie_MER20 chr1 12317599 12317765 + SINE/Alu_FRAM chr1 12317766 12317808 - DNA/hAT-Charlie_MER20 chr1 12318697 12318827 + DNA/TcMar-Tigger_X6b_DNA chr1 12321103 12321132 + Simple_repeat_(CT)n chr1 12321146 12321256 - LINE/L1_L1MC5 chr1 12321401 12321616 - LINE/L1_L1ME4c chr1 12322055 12322314 - SINE/Alu_AluY chr1 12323080 12323364 - SINE/Alu_AluJr chr1 12323945 12324188 - SINE/Alu_AluSz chr1 12324201 12324225 + SINE/MIR_MIR1_Amn chr1 12324226 12324512 + SINE/Alu_AluSx chr1 12324513 12324737 + SINE/MIR_MIR chr1 12324778 12325016 - SINE/MIR_MIRb chr1 12325081 12325216 - SINE/Alu_FRAM chr1 12325258 12325557 - SINE/Alu_AluSg4 chr1 12325713 12325835 - LINE/L1_L1M5 chr1 12325973 12326015 + Simple_repeat_(T)n chr1 12326322 12326634 - SINE/Alu_AluJr chr1 12326647 12326942 - SINE/Alu_AluSc8 chr1 12327041 12327244 - SINE/MIR_MIR chr1 12327296 12327324 + Simple_repeat_(TCCTC)n chr1 12327461 12327496 + Simple_repeat_(C)n chr1 12328362 12328492 - SINE/Alu_FLAM_C chr1 12328546 12328837 - SINE/Alu_AluSp chr1 12329204 12329512 - SINE/Alu_AluSc chr1 12329559 12329717 - DNA/hAT-Tip100_MER96B chr1 12330133 12330422 + SINE/Alu_AluSx1 chr1 12330568 12330867 - SINE/Alu_AluSx1 chr1 12331449 12331738 + SINE/Alu_AluY chr1 12331998 12332771 + LINE/L1_L1ME3D chr1 12334485 12334787 + SINE/Alu_AluJr chr1 12335113 12335366 - SINE/Alu_AluSz6 chr1 12335367 12335477 - LINE/L1_L1M5 chr1 12335515 12335639 + LINE/L2_L2c chr1 12337014 12337092 + Simple_repeat_(CTTT)n chr1 12339200 12339391 - SINE/MIR_MIRb chr1 12343144 12343188 + Simple_repeat_(ATTTT)n chr1 12343189 12343469 - SINE/Alu_AluSz chr1 12343542 12343674 - SINE/Alu_FLAM_C chr1 12343851 12343935 + DNA/hAT-Charlie_UCON34 chr1 12346271 12346300 + Simple_repeat_(A)n chr1 12347173 12347469 - SINE/Alu_AluSc8 chr1 12348986 12349026 + LINE/L2_L2a chr1 12349990 12350065 - LINE/L1_L1MC4 chr1 12350060 12351289 + LINE/L1_L1ME2 chr1 12351290 12351589 - SINE/Alu_AluSq2 chr1 12351593 12351891 - SINE/Alu_AluSx1 chr1 12351892 12352008 + LINE/L1_L1ME2 chr1 12352009 12352304 + SINE/Alu_AluSz chr1 12352305 12352682 + LINE/L1_L1ME2 chr1 12352683 12352995 + SINE/Alu_AluSp chr1 12352996 12353251 + LINE/L1_L1ME2 chr1 12353252 12353556 + SINE/Alu_AluY chr1 12353557 12353836 + LINE/L1_L1ME2 chr1 12354382 12354547 + SINE/Alu_AluJr chr1 12354730 12354867 - SINE/MIR_MIRb chr1 12354882 12355307 - LTR/ERV1_MER57A1 chr1 12355371 12355577 + LINE/L1_L1MA6 chr1 12355582 12355618 - SINE/Alu_Alu chr1 12356412 12356449 - LINE/CR1_L3b chr1 12356550 12356694 + LINE/CR1_X17_LINE chr1 12357472 12357675 - SINE/MIR_MIRc chr1 12357728 12358019 + SINE/Alu_AluSg chr1 12358946 12359028 - SINE/MIR_MIR1_Amn chr1 12359358 12359560 + DNA/hAT-Charlie_MER3 chr1 12360262 12360321 + LINE/CR1_Plat_L3 chr1 12361004 12361221 + SINE/MIR_MIR chr1 12361223 12361265 + Simple_repeat_(TTATTA)n chr1 12361357 12361694 - SINE/Alu_AluYa5 chr1 12361855 12362162 - SINE/Alu_AluSz chr1 12362922 12363032 + LINE/L2_L2b chr1 12363673 12363980 + SINE/Alu_AluSc chr1 12364059 12364870 - LINE/L1_L1MB2 chr1 12364872 12365423 - LINE/L1_L1MB2 chr1 12365424 12365720 + SINE/Alu_AluSg chr1 12365721 12365746 - LINE/L1_L1MB2 chr1 12365902 12366208 - SINE/Alu_AluJr4 chr1 12366319 12366476 + DNA/hAT-Charlie_Charlie15a chr1 12366693 12366819 - LINE/L1_L1MC4 chr1 12366827 12366890 - LTR/ERVL_LTR82A chr1 12366897 12367547 - LINE/L1_L1MC4 chr1 12367554 12367589 + Simple_repeat_(ATTT)n chr1 12367593 12367905 - SINE/Alu_AluY chr1 12367940 12368070 - SINE/Alu_AluJo chr1 12368073 12368175 - LINE/L1_L1MC4 chr1 12368176 12368217 + Simple_repeat_(CATA)n chr1 12368220 12368293 + LINE/L1_L1MC5a chr1 12369216 12369254 + Simple_repeat_(TG)n chr1 12370677 12370700 + Simple_repeat_(TGTT)n chr1 12371059 12371387 - LINE/L1_L1MB5 chr1 12371388 12371692 - SINE/Alu_AluSp chr1 12371693 12372046 - LINE/L1_L1MB5 chr1 12372047 12372362 - SINE/Alu_AluJb chr1 12372365 12372389 + Simple_repeat_(GTTTT)n chr1 12372390 12372764 - LINE/L1_L1MB5 chr1 12372801 12372819 + Simple_repeat_(T)n chr1 12373096 12373122 + Simple_repeat_(T)n chr1 12373137 12373420 - SINE/Alu_AluSz6 chr1 12373700 12373735 + Simple_repeat_(TA)n chr1 12373883 12373954 - SINE/MIR_MIR3 chr1 12374392 12374446 - LINE/L1_L1MC4 chr1 12374401 12374456 - LINE/L1_L1MC4 chr1 12374465 12374742 - LINE/L1_L1MC4 chr1 12374743 12375037 - SINE/Alu_AluSx1 chr1 12375038 12375531 - LINE/L1_L1MC4 chr1 12376242 12376415 + SINE/MIR_MIRb chr1 12376444 12376662 - SINE/MIR_MIR chr1 12376864 12377046 + SINE/MIR_MIR chr1 12377047 12377359 - SINE/Alu_AluSq2 chr1 12377360 12377427 + SINE/MIR_MIR chr1 12377538 12377844 + SINE/Alu_AluSp chr1 12378785 12378865 - LINE/L1_HAL1ME chr1 12378993 12379065 + SINE/MIR_MIR chr1 12379772 12380068 - SINE/Alu_AluY chr1 12381476 12381885 + LTR/ERV1_MER57A1 chr1 12381893 12382091 + Simple_repeat_(TCTT)n chr1 12382092 12382399 - SINE/Alu_AluSq2 chr1 12383267 12383326 + SINE/MIR_MIRb chr1 12383503 12383677 - LINE/L2_L2b chr1 12383700 12383879 - LINE/L2_L2a chr1 12383925 12384512 - LINE/L2_L2a chr1 12384617 12384640 + Simple_repeat_(TTTA)n chr1 12384646 12384915 - SINE/Alu_AluJr chr1 12385795 12385838 - LINE/L2_L2d2 chr1 12386558 12386661 + LINE/CR1_L3b chr1 12386676 12387560 + LINE/L1_L1MEc chr1 12387565 12387744 + LINE/L1_L1MEc chr1 12387741 12388292 + LINE/L1_L1MEc chr1 12388293 12388608 + SINE/Alu_AluSz chr1 12388609 12388944 + LINE/L1_L1MEc chr1 12388681 12388948 + LINE/L1_L1M4 chr1 12388951 12389357 - LTR/ERVL_MLT2A1 chr1 12389383 12391577 - LTR/ERVL_HERVL-int chr1 12391580 12391700 - SINE/Alu_AluSz chr1 12391850 12392161 - SINE/Alu_AluSz6 chr1 12392210 12392488 + SINE/Alu_AluSq2 chr1 12392546 12392563 + Simple_repeat_(A)n chr1 12392564 12395953 - LTR/ERVL_HERVL-int chr1 12395954 12395984 - DNA/MULE-MuDR_Ricksha_c chr1 12395985 12395996 + LINE/L1_L1M4 chr1 12395997 12396029 + Simple_repeat_(AT)n chr1 12396030 12396122 + LINE/L1_L1MEc chr1 12396117 12396436 - LINE/L1_L1M4 chr1 12396875 12396915 + Simple_repeat_(TTTAC)n chr1 12396924 12397233 - SINE/Alu_AluSx1 chr1 12397531 12398157 - LINE/L2_L2a chr1 12398251 12398356 + SINE/MIR_MIRc chr1 12398358 12398401 - SINE/Alu_AluYd8 chr1 12398408 12398478 + SINE/MIR_MIRb chr1 12398847 12399018 + SINE/MIR_MIRb chr1 12399068 12399114 + SINE/MIR_MIRb chr1 12399199 12399494 - SINE/Alu_AluSg chr1 12399504 12399537 - LINE/L1_L1MA8 chr1 12399538 12399832 + SINE/Alu_AluSp chr1 12399833 12399970 - LINE/L1_L1MA8 chr1 12400672 12400944 + SINE/Alu_AluJb chr1 12400963 12401004 + Simple_repeat_(CA)n chr1 12404026 12404082 + Simple_repeat_(TG)n chr1 12404609 12404680 - SINE/MIR_MIRb chr1 12404827 12404905 - LINE/CR1_L3 chr1 12404914 12404988 - SINE/MIR_MIRc chr1 12405064 12405404 - DNA/hAT-Charlie_Charlie8 chr1 12406576 12406677 - SINE/MIR_MIRc chr1 12407990 12408108 + SINE/MIR_MIRc chr1 12408318 12408341 + Simple_repeat_(AG)n chr1 12408387 12408663 - SINE/Alu_AluJb chr1 12408911 12409013 - LINE/L1_L1M chr1 12409085 12409106 + Simple_repeat_(GT)n chr1 12409189 12409251 - snRNA_U3 chr1 12409452 12409601 + LINE/L1_L1M5 chr1 12409813 12410062 + LINE/L1_L1ME3A chr1 12410065 12410616 + LINE/L1_L1ME3A chr1 12410670 12411325 + LINE/L1_L1ME3A chr1 12411353 12411474 + SINE/Alu_AluJo chr1 12411476 12411571 + Low_complexity_GA-rich chr1 12411584 12412083 - LTR/ERVL-MaLR_MLT1H chr1 12412085 12413108 + LINE/L1_L1ME3A chr1 12413164 12413211 + Simple_repeat_(TGTT)n chr1 12413233 12413458 + SINE/Alu_AluJr chr1 12413490 12413771 - SINE/Alu_AluJr chr1 12413804 12414001 + LINE/L1_L1ME3A chr1 12414002 12414280 + SINE/Alu_AluJb chr1 12414281 12414799 + LINE/L1_L1ME3A chr1 12415897 12416189 + SINE/Alu_AluJb chr1 12417032 12417096 + LINE/L2_L2 chr1 12417896 12418205 - SINE/Alu_AluSx1 chr1 12418593 12418688 + SINE/MIR_MIRb chr1 12419378 12419782 - LTR/ERVL-MaLR_MSTA chr1 12419783 12420054 - DNA/TcMar-Tigger_Tigger3a chr1 12420490 12420646 + DNA/hAT-Charlie_Charlie4z chr1 12421834 12422128 - SINE/Alu_AluSz chr1 12422164 12422241 + DNA_Eutr11 chr1 12422304 12422566 + DNA_Eutr11 chr1 12422664 12422857 - SINE/MIR_MIRb chr1 12423132 12423284 - SINE/MIR_MIRc chr1 12423380 12423425 + Low_complexity_A-rich chr1 12425276 12425577 + SINE/Alu_AluSx chr1 12425686 12425711 + Simple_repeat_(AGGA)n chr1 12425717 12425775 - SINE/MIR_MIRc chr1 12426736 12427040 + SINE/Alu_AluSq2 chr1 12427126 12427223 - SINE/MIR_MIRc chr1 12427224 12427281 + Simple_repeat_(TAT)n chr1 12427282 12427404 - SINE/MIR_MIRc chr1 12427422 12427762 + DNA/TcMar-Tigger_MER2 chr1 12429291 12429591 - SINE/Alu_AluSq2 chr1 12430199 12430421 - SINE/MIR_MIRc chr1 12430457 12430670 - SINE/MIR_MIRc chr1 12431303 12431460 - SINE/MIR_MIR3 chr1 12431566 12431716 + LINE/L2_L2c chr1 12432113 12432414 + SINE/Alu_AluSp chr1 12432610 12432893 - SINE/Alu_AluJb chr1 12433259 12433297 + Simple_repeat_(A)n chr1 12433918 12433950 + Simple_repeat_(GA)n chr1 12433951 12433970 + Simple_repeat_(TG)n chr1 12433970 12433987 + Simple_repeat_(GGA)n chr1 12435212 12435477 + SINE/Alu_AluJb chr1 12435481 12435729 - SINE/MIR_MIR chr1 12436051 12436179 - LINE/L2_L2b chr1 12436661 12436967 - SINE/Alu_AluSp chr1 12438162 12438501 + LTR/ERVL-MaLR_MLT1A0 chr1 12438867 12439587 + LINE/L2_L2a chr1 12439588 12439627 - LTR/ERVL-MaLR_MLT1A chr1 12439628 12439733 + LINE/L2_L2a chr1 12439771 12439939 - SINE/MIR_MIRb chr1 12440594 12440905 + SINE/Alu_AluSz6 chr1 12440927 12441226 - SINE/Alu_AluSq2 chr1 12441903 12442086 - LINE/L1_L1MEg chr1 12442177 12442402 - DNA/hAT-Charlie_MER33 chr1 12442586 12442888 - SINE/Alu_AluJr chr1 12442953 12443478 - LINE/L1_L1ME3D chr1 12443551 12443838 - LINE/L1_L1MEg chr1 12443839 12444139 - SINE/Alu_AluSx1 chr1 12444140 12444683 - LINE/L1_L1MEg chr1 12444751 12444898 - LINE/L1_L1MEg chr1 12446136 12446440 + SINE/Alu_AluSg chr1 12447346 12447464 + SINE/MIR_MIR chr1 12448097 12448231 - SINE/MIR_MIRc chr1 12449772 12449842 - SINE/MIR_MIRb chr1 12449843 12450132 + SINE/Alu_AluSz6 chr1 12450133 12450253 - SINE/MIR_MIRb chr1 12450437 12450815 + DNA/TcMar-Tigger_MER47B chr1 12451747 12451847 - SINE/MIR_MIRb chr1 12451852 12452054 - SINE/MIR_MIRb chr1 12453027 12453071 - LINE/CR1_X17_LINE chr1 12453679 12453819 + LINE/L2_L2d chr1 12453840 12453906 - SINE/MIR_MIR3 chr1 12454120 12454294 - SINE/MIR_MIRb chr1 12454448 12454501 + DNA/hAT-Charlie_UCON34 chr1 12455207 12455251 + LINE/L2_L2b chr1 12455607 12455636 + Simple_repeat_(ATATTT)n chr1 12455637 12455897 + SINE/Alu_AluSz chr1 12455898 12455926 + Simple_repeat_(ATATTT)n chr1 12456222 12456355 + SINE/Alu_AluSg chr1 12456356 12456665 + SINE/Alu_AluSz chr1 12457254 12457353 + LINE/L2_L2d chr1 12458422 12458580 + SINE/Alu_FRAM chr1 12459122 12459436 + DNA/TcMar-Tigger_MER2B chr1 12459470 12459776 - SINE/Alu_AluSc chr1 12459949 12459969 + Simple_repeat_(T)n chr1 12460594 12460688 + Simple_repeat_(AT)n chr1 12462030 12462424 - LINE/L2_L2c chr1 12462425 12462636 - SINE/MIR_MIRc chr1 12463058 12463090 + Low_complexity_A-rich chr1 12463470 12463750 + SINE/Alu_AluJb chr1 12463757 12463996 + SINE/MIR_MIRc chr1 12465855 12466148 + SINE/Alu_AluSc chr1 12466436 12466594 - DNA/hAT-Charlie_Charlie16a chr1 12467171 12467469 - SINE/Alu_AluSg chr1 12468751 12469058 + SINE/Alu_AluJb chr1 12469195 12469473 + DNA/hAT-Charlie_Charlie1b chr1 12470626 12470811 + DNA/TcMar-Tigger_MamRep434 chr1 12471020 12471316 + SINE/Alu_AluSx chr1 12471385 12471586 - LINE/L1_HAL1 chr1 12471611 12471862 - LINE/L1_HAL1 chr1 12472117 12472302 - LINE/L1_HAL1 chr1 12472327 12472698 - LINE/L1_HAL1 chr1 12472704 12472798 + DNA/TcMar-Tigger_MamRep434 chr1 12473415 12473647 + SINE/MIR_MIRb chr1 12475168 12475360 + SINE/MIR_MIR3 chr1 12475889 12475916 + Simple_repeat_(A)n chr1 12477359 12477450 - SINE/MIR_MIR chr1 12479469 12479678 - SINE/MIR_MIR chr1 12482524 12482643 - SINE/MIR_MIRb chr1 12482721 12482783 + Simple_repeat_(TA)n chr1 12482785 12483478 - LINE/L1_L1ME4a chr1 12483719 12484021 + SINE/Alu_AluSg chr1 12484514 12484655 + SINE/MIR_MIR chr1 12484976 12485003 + Simple_repeat_(GT)n chr1 12486849 12486942 - LINE/L2_L2a chr1 12487328 12487634 + SINE/Alu_AluSx3 chr1 12487715 12487858 + DNA/hAT-Charlie_Charlie15a chr1 12488258 12488484 - SINE/MIR_MIRb chr1 12488599 12488669 + SINE/Alu_AluJo chr1 12488670 12488692 + Simple_repeat_(A)n chr1 12489738 12489970 - SINE/MIR_MIR chr1 12489971 12490146 + LINE/L2_L2b chr1 12491207 12491343 + DNA_X30_DNA chr1 12492249 12492271 + Simple_repeat_(ATTT)n chr1 12492404 12492492 - DNA/hAT-Charlie_MER3 chr1 12492500 12493022 + LINE/L1_L1ME3F chr1 12493184 12493201 + Simple_repeat_(A)n chr1 12493207 12493501 + SINE/Alu_AluY chr1 12494945 12495081 - LINE/L2_L2a chr1 12495096 12495149 - LINE/L2_L2a chr1 12495150 12495457 - SINE/Alu_AluY chr1 12495458 12495658 - LINE/L2_L2a chr1 12496857 12496903 + Simple_repeat_(CCAGGT)n chr1 12497121 12497324 - SINE/MIR_MIRb chr1 12497836 12498037 + DNA/hAT-Charlie_Charlie22a chr1 12498111 12498241 + DNA/hAT-Charlie_Charlie22a chr1 12498618 12498954 - LTR/ERVL-MaLR_MLT1A chr1 12499188 12499296 + LINE/L2_L2a chr1 12500643 12500709 - LINE/L1_L1ME3Cz chr1 12500722 12500820 - SINE/Alu_AluJr4 chr1 12500823 12501058 + SINE/Alu_AluJb chr1 12501630 12502031 - LINE/L2_L2c chr1 12502178 12502432 - LINE/L2_L2c chr1 12503350 12503483 - SINE/Alu_FLAM_C chr1 12504144 12504183 + Low_complexity_A-rich chr1 12504538 12504636 - SINE/MIR_MIRc chr1 12506208 12506391 + LINE/CR1_L3 chr1 12507274 12507408 + SINE/MIR_MIR1_Amn chr1 12507725 12507797 - SINE/MIR_MIR chr1 12508388 12508423 + DNA/hAT-Charlie_MER5B chr1 12508424 12508707 + SINE/Alu_AluSc8 chr1 12508708 12508728 + DNA/hAT-Charlie_MER5B chr1 12510514 12510555 + Simple_repeat_(TG)n chr1 12512335 12512450 + SINE/Alu_AluY chr1 12512553 12512745 + SINE/Alu_AluY chr1 12512749 12513000 - LINE/L2_L2-3_Crp chr1 12513019 12513419 - LINE/L2_L2 chr1 12513818 12513863 + DNA/TcMar_X4b_DNA chr1 12514113 12514363 + SINE/Alu_AluJo chr1 12514511 12514563 - LINE/L1_L1ME3G chr1 12514564 12514744 - LINE/L1_L1ME3F chr1 12514923 12515032 - LINE/L1_L1ME3G chr1 12515029 12515317 - LINE/L1_L1ME3G chr1 12515318 12515617 - SINE/Alu_AluJr chr1 12515618 12515776 - LINE/L1_L1ME3G chr1 12515777 12516065 - SINE/Alu_AluY chr1 12516070 12516372 - SINE/Alu_AluY chr1 12516373 12516652 - SINE/Alu_AluJr chr1 12516653 12516665 - LINE/L1_L1ME3G chr1 12516666 12516967 - SINE/Alu_AluSx1 chr1 12516968 12517592 - LINE/L1_L1ME3G chr1 12517593 12517648 + Simple_repeat_(TATC)n chr1 12517650 12517928 - SINE/Alu_AluSz chr1 12517929 12518022 - LINE/L1_L1ME3G chr1 12518023 12518315 + SINE/Alu_AluJb chr1 12518316 12518370 - LINE/L1_L1ME3G chr1 12518372 12518668 + SINE/Alu_AluSx4 chr1 12518724 12518746 + Simple_repeat_(AGAC)n chr1 12519567 12519669 - LINE/L2_L2b chr1 12519730 12520148 - LINE/L2_L2b chr1 12520149 12520448 + SINE/Alu_AluJo chr1 12520449 12520513 - LINE/L2_L2b chr1 12520518 12520695 - LINE/L2_L2b chr1 12520696 12520991 + SINE/Alu_AluYk2 chr1 12520992 12521177 - LINE/L2_L2b chr1 12521205 12521745 + DNA/hAT-Tip100_Zaphod3 chr1 12521746 12522056 - SINE/Alu_AluSz chr1 12522057 12522096 + DNA/hAT-Tip100_Zaphod3 chr1 12522097 12522394 + SINE/Alu_AluSx chr1 12522395 12522668 + DNA/hAT-Tip100_Zaphod3 chr1 12522669 12522976 + SINE/Alu_AluSx1 chr1 12522977 12523121 + DNA/hAT-Tip100_MER97c chr1 12523150 12523443 - SINE/Alu_AluSp chr1 12523858 12524028 - LINE/L1_HAL1 chr1 12524060 12524388 + LINE/L1_L1ME2z chr1 12524387 12524502 + LINE/L1_L1ME2z chr1 12524668 12524966 + SINE/Alu_AluSc8 chr1 12525098 12525295 - SINE/Alu_AluSx chr1 12525966 12526089 - LTR/ERVL_LTR33C chr1 12526202 12526312 - LTR/ERVL_MLT2E chr1 12526352 12526594 + SINE/Alu_AluSz6 chr1 12526595 12526626 - DNA/hAT-Charlie_MER33 chr1 12526627 12526925 + SINE/Alu_AluJo chr1 12526928 12527210 + SINE/Alu_AluSg chr1 12527211 12527252 + Simple_repeat_(AAAT)n chr1 12527260 12527524 - DNA/hAT-Charlie_MER33 chr1 12528737 12529051 + SINE/Alu_AluSg chr1 12529592 12529693 - LINE/L1_L1ME4b chr1 12529751 12529890 + LINE/L2_L2 chr1 12529923 12530238 - SINE/Alu_AluSz chr1 12530278 12530344 - LINE/L1_L1ME4b chr1 12530350 12530431 - scRNA_HY3 chr1 12530436 12530900 - LINE/L1_L1ME4b chr1 12530901 12531209 - SINE/Alu_AluSz chr1 12531210 12531268 - LINE/L1_L1ME4b chr1 12531291 12531312 + Simple_repeat_(A)n chr1 12531561 12531810 + LINE/L2_L2 chr1 12531811 12532122 - SINE/Alu_AluSx chr1 12532123 12532193 + LINE/L2_L2 chr1 12532194 12532339 - LINE/L1_L1ME4b chr1 12532358 12532905 - LTR/ERVL_MLT2A2 chr1 12532906 12533186 - SINE/Alu_AluJb chr1 12533187 12533644 - LINE/L1_L1ME4b chr1 12533645 12533830 - LINE/L1_L1MC1 chr1 12533831 12534109 - LINE/L1_L1ME4b chr1 12534110 12534418 - SINE/Alu_AluSx chr1 12534419 12534509 - LINE/L1_L1ME4b chr1 12534510 12534812 - SINE/Alu_AluYm1 chr1 12534813 12534848 - LINE/L1_L1ME4b chr1 12534849 12534888 + Simple_repeat_(GT)n chr1 12534891 12535171 - SINE/Alu_AluJb chr1 12535172 12535825 - LINE/L1_L1ME4b chr1 12535826 12535936 + SINE/Alu_AluJr chr1 12535938 12536750 - LINE/L1_L1ME4b chr1 12536754 12536914 + LINE/L2_L2b chr1 12536915 12537215 - SINE/Alu_AluSc8 chr1 12537216 12537798 + LINE/L2_L2b chr1 12537799 12537937 - LTR/ERVL_LTR33 chr1 12537976 12538242 - LTR/ERVL_LTR33 chr1 12538341 12538502 + LTR/ERVL-MaLR_MLT1J2 chr1 12538519 12538634 + SINE/MIR_MIR chr1 12538635 12538647 + Simple_repeat_(ATT)n chr1 12538648 12538932 - SINE/Alu_AluSz6 chr1 12538933 12538968 + Simple_repeat_(ATT)n chr1 12539052 12539089 + Simple_repeat_(GT)n chr1 12539196 12539352 + LTR/ERVL-MaLR_MLT1H1 chr1 12539435 12539619 + LTR/ERVL-MaLR_MLT1H1 chr1 12539620 12539921 - SINE/Alu_AluJr chr1 12539922 12539985 + LTR/ERVL-MaLR_MLT1H1 chr1 12540773 12540940 + SINE/MIR_MIRb chr1 12541152 12541268 - SINE/MIR_MIRb chr1 12542060 12542441 + LINE/L2_L2b chr1 12542475 12542585 + LINE/L2_L2b chr1 12543218 12543523 + SINE/Alu_AluJr chr1 12543527 12543575 + Simple_repeat_(AAAG)n chr1 12544140 12544220 + LINE/L1_L1ME4c chr1 12544221 12544496 - SINE/Alu_AluJr chr1 12544497 12544928 + LINE/L1_L1ME4c chr1 12544965 12544994 + Simple_repeat_(TTTTC)n chr1 12545278 12545333 - LINE/L2_L2a chr1 12545436 12545743 + SINE/Alu_AluSp chr1 12545754 12545927 - SINE/Alu_AluJb chr1 12545928 12545958 + Simple_repeat_(TTAT)n chr1 12545959 12546237 - SINE/Alu_AluSc5 chr1 12546238 12546341 - SINE/Alu_AluJb chr1 12546383 12546665 + SINE/Alu_AluSx chr1 12546851 12546992 + LINE/RTE-BovB_MamRTE1 chr1 12547763 12547905 + LINE/L2_L2a chr1 12548658 12548689 + Simple_repeat_(GAGG)n chr1 12550198 12550430 - SINE/Alu_AluJr chr1 12550692 12550787 - SINE/Alu_FLAM_C chr1 12550801 12551217 - LINE/L1_L1ME3 chr1 12551264 12551564 + SINE/Alu_AluSx1 chr1 12551690 12551941 - LINE/L2_L2 chr1 12552903 12553029 + LINE/L2_L2c chr1 12553315 12553362 + LINE/L2_L2c chr1 12553939 12554471 + LTR/ERVL-MaLR_MLT1K chr1 12555063 12555356 - SINE/Alu_AluJb chr1 12555357 12555424 + LINE/L2_L2d chr1 12555849 12556052 + SINE/MIR_MIR chr1 12556086 12556327 - SINE/MIR_MIRb chr1 12556855 12557027 + SINE/MIR_MIRb chr1 12557029 12557066 + DNA/hAT-Charlie_MER20 chr1 12557067 12557571 + DNA/hAT-Charlie_MER1A chr1 12557572 12557754 + DNA/hAT-Charlie_MER20 chr1 12557906 12558217 + SINE/Alu_AluSp chr1 12558219 12558244 + Simple_repeat_(AAG)n chr1 12558255 12558370 - LINE/L2_L2a chr1 12558390 12558842 + LINE/L1_L1MB8 chr1 12558868 12559001 + SINE/Alu_FLAM_C chr1 12559438 12559734 - SINE/Alu_AluSx chr1 12559747 12559930 - SINE/MIR_MIRc chr1 12559967 12560259 - SINE/Alu_AluY chr1 12560611 12560842 - SINE/Alu_AluSz6 chr1 12560843 12561147 - SINE/Alu_AluSc8 chr1 12561148 12561242 - SINE/Alu_AluSz6 chr1 12561422 12561582 - SINE/MIR_MIRc chr1 12561932 12562107 + LTR/ERVL-MaLR_MSTA chr1 12562108 12562397 + SINE/Alu_AluSg chr1 12562398 12562604 + LTR/ERVL-MaLR_MSTA chr1 12562609 12564223 + LTR/ERVL-MaLR_MSTA-int chr1 12564226 12564611 + LTR/ERVL-MaLR_MSTA chr1 12565678 12565917 + SINE/Alu_AluJr chr1 12566139 12566428 + LTR/ERVL-MaLR_MLT1I chr1 12566596 12566917 + SINE/Alu_AluJo chr1 12567212 12567278 - LTR/ERV1_LTR65 chr1 12568644 12568931 + SINE/Alu_AluJb chr1 12568941 12569215 + SINE/Alu_AluSq2 chr1 12569232 12569262 + Simple_repeat_(CA)n chr1 12569383 12569547 - LINE/L1_L1MA2 chr1 12569548 12569865 - SINE/Alu_AluSx chr1 12569866 12569970 - LINE/L1_L1MA2 chr1 12570339 12570415 + LINE/L2_L2b chr1 12570448 12570515 - LINE/L2_L2d2 chr1 12571398 12571455 + LINE/L2_L2d2 chr1 12571456 12571526 + DNA/hAT-Charlie_Charlie1b chr1 12571527 12571833 - SINE/Alu_AluSp chr1 12571834 12572159 + DNA/hAT-Charlie_Charlie1b chr1 12572160 12572469 - SINE/Alu_AluY chr1 12572470 12572596 + DNA/hAT-Charlie_Charlie1b chr1 12572597 12572616 + LINE/L2_L2d2 chr1 12572624 12572742 - DNA/hAT-Charlie_MER117 chr1 12573184 12573549 + LINE/L2_L2b chr1 12573708 12573730 + Simple_repeat_(AATG)n chr1 12573826 12573862 - SINE/MIR_MIRc chr1 12574165 12574471 - SINE/Alu_AluJb chr1 12574860 12574999 + SINE/MIR_MIRb chr1 12575045 12575346 + SINE/Alu_AluSx1 chr1 12575606 12575697 - SINE/MIR_MIR chr1 12575698 12576005 - SINE/Alu_AluSx1 chr1 12576006 12576124 - SINE/MIR_MIR chr1 12576281 12576582 + SINE/Alu_AluY chr1 12576822 12576894 - SINE/MIR_MIRb chr1 12576897 12576981 - SINE/Alu_FLAM_C chr1 12577252 12577458 - SINE/MIR_MIRb chr1 12577543 12577852 + SINE/Alu_AluSx chr1 12577853 12578148 - LINE/L1_L1ME2 chr1 12578318 12578618 - SINE/Alu_AluJb chr1 12579474 12579772 - SINE/Alu_AluSx1 chr1 12579951 12580117 + SINE/MIR_MIRb chr1 12580828 12581126 - SINE/Alu_AluJr chr1 12581512 12581662 + LINE/CR1_X7C_LINE chr1 12582006 12582228 - DNA/hAT-Charlie_Charlie15b chr1 12582732 12583981 + Simple_repeat_(ATCC)n chr1 12584555 12584577 + Simple_repeat_(TGAA)n chr1 12584837 12585438 + Simple_repeat_(TG)n chr1 12586176 12586298 - SINE/MIR_MIRb chr1 12586484 12586633 - LINE/L2_L2b chr1 12586661 12586822 - SINE/MIR_MIRb chr1 12587058 12587137 + LINE/L1_L1M5 chr1 12587142 12587433 - SINE/Alu_AluJb chr1 12587597 12587719 + SINE/Alu_AluJb chr1 12587724 12587752 + Simple_repeat_(AAAAC)n chr1 12588153 12588229 + LINE/L2_L2b chr1 12588268 12588508 - SINE/MIR_MIR chr1 12588529 12588648 + LINE/L2_L2b chr1 12589255 12589335 + SINE/MIR_MIR chr1 12589377 12589590 - SINE/Alu_AluSx chr1 12589597 12589637 - SINE/Alu_Alu chr1 12589702 12589818 + SINE/Alu_AluJr chr1 12589841 12589868 + Simple_repeat_(ATT)n chr1 12590152 12590277 + DNA/hAT-Charlie_Charlie15a chr1 12590315 12590609 - SINE/Alu_AluSp chr1 12591174 12591403 - LINE/CR1_L3b chr1 12591677 12591754 - SINE/MIR_MIR chr1 12592241 12592705 + LTR/ERVL-MaLR_MLT1C2 chr1 12592873 12593304 + LINE/L2_L2 chr1 12593376 12593503 - SINE/Alu_AluJr chr1 12594280 12594728 - LINE/L2_L2a chr1 12594750 12594827 - LINE/L2_L2a chr1 12594900 12594983 - LINE/L2_L2a chr1 12595391 12595442 + Low_complexity_G-rich chr1 12596653 12596807 + SINE/MIR_MIRb chr1 12597096 12597376 - SINE/Alu_AluSg4 chr1 12597663 12597686 + Simple_repeat_(TGAA)n chr1 12597800 12597927 - DNA/hAT-Tip100_MER45A chr1 12598192 12598403 + SINE/Alu_AluJb chr1 12598460 12598754 + DNA/TcMar-Tigger_MER46C chr1 12599787 12599990 + SINE/MIR_MIR chr1 12600051 12600170 - SINE/MIR_MIR chr1 12600200 12600216 + Simple_repeat_(TG)n chr1 12600217 12600251 + Simple_repeat_(AC)n chr1 12601149 12601571 + LTR/ERVL-MaLR_MLT1K chr1 12601703 12601932 + SINE/MIR_MIR chr1 12601958 12602068 - LINE/L2_L2b chr1 12602104 12602164 - SINE/tRNA-RTE_MamSINE1 chr1 12602705 12602787 - SINE/MIR_MIR3 chr1 12603371 12603568 - DNA/hAT-Charlie_Charlie15a chr1 12604008 12604158 + DNA/hAT-Charlie_Charlie4z chr1 12604654 12604745 + SINE/MIR_MIRb chr1 12604769 12605097 + SINE/Alu_AluSc chr1 12605551 12605858 + DNA/TcMar-Mariner_HSMAR2 chr1 12605859 12606161 + SINE/Alu_AluSc chr1 12606162 12606470 + DNA/TcMar-Mariner_HSMAR2 chr1 12606471 12606503 + Simple_repeat_(ATTTT)n chr1 12606504 12606793 - SINE/Alu_AluSp chr1 12606794 12607288 + DNA/TcMar-Mariner_HSMAR2 chr1 12607289 12607667 + LTR/ERVL-MaLR_THE1D chr1 12607668 12607840 + DNA/TcMar-Mariner_HSMAR2 chr1 12607841 12607876 + Simple_repeat_(GT)n chr1 12607897 12608176 - SINE/Alu_AluSz chr1 12608177 12608235 + DNA/TcMar-Mariner_HSMAR2 chr1 12608557 12608594 + Simple_repeat_(CCTGCC)n chr1 12608614 12608700 - LINE/CR1_L3 chr1 12608728 12609064 - LINE/CR1_L3 chr1 12609182 12609256 + LINE/L2_L2a chr1 12609474 12609631 - LINE/CR1_L3 chr1 12609784 12610068 + LINE/L2_L2a chr1 12610069 12610376 - SINE/Alu_AluSz chr1 12610377 12610544 + LINE/L2_L2a chr1 12611304 12611463 + LINE/CR1_X7C_LINE chr1 12611690 12611796 - SINE/MIR_MIR3 chr1 12611810 12611928 + SINE/Alu_AluJo chr1 12611935 12611961 + Simple_repeat_(AAAT)n chr1 12612301 12612332 + Simple_repeat_(CTTCT)n chr1 12612450 12612607 + LINE/L2_L2c chr1 12612639 12612780 + LTR/ERVL-MaLR_MLT1C2 chr1 12612781 12613073 + SINE/Alu_AluSp chr1 12613074 12613357 + LTR/ERVL-MaLR_MLT1C2 chr1 12613763 12614070 - SINE/Alu_AluSz chr1 12614703 12614730 + Simple_repeat_(T)n chr1 12617515 12617546 + Simple_repeat_(A)n chr1 12617789 12617815 + Simple_repeat_(A)n chr1 12617860 12617878 + Simple_repeat_(A)n chr1 12618036 12618085 + Simple_repeat_(CGCT)n chr1 12619303 12619402 - DNA/hAT-Charlie_MER5A chr1 12620156 12620248 - SINE/MIR_MIR3 chr1 12620403 12620515 - SINE/MIR_MIRb chr1 12621094 12621135 + DNA/hAT-Charlie_MER5A1 chr1 12621136 12621434 + SINE/Alu_AluSx chr1 12621435 12621538 + DNA/hAT-Charlie_MER5A1 chr1 12621563 12621745 - LINE/L2_L2 chr1 12621842 12621869 + Simple_repeat_(CCCCT)n chr1 12622888 12623011 + SINE/MIR_MIRb chr1 12623125 12623159 + Low_complexity_G-rich chr1 12623277 12623555 - DNA/hAT-Charlie_MER58B chr1 12623647 12623958 + SINE/Alu_AluJb chr1 12624010 12624319 - SINE/Alu_AluJb chr1 12624397 12624425 + Simple_repeat_(AAAAC)n chr1 12624461 12624592 - SINE/MIR_MIRb chr1 12624771 12624811 + Simple_repeat_(GTTAGG)n chr1 12624852 12624936 + DNA/TcMar-Tigger_Tigger8 chr1 12624937 12625234 - SINE/Alu_AluSx1 chr1 12625235 12625354 + DNA/TcMar-Tigger_Tigger8 chr1 12625366 12625553 - SINE/MIR_MIR chr1 12625668 12625969 + SINE/Alu_AluSz chr1 12625980 12626287 + SINE/Alu_AluSz chr1 12626310 12626611 + SINE/Alu_AluJb chr1 12626629 12626710 + DNA/TcMar-Tigger_Tigger8 chr1 12626831 12627055 - SINE/MIR_MIRb chr1 12627119 12627248 + LINE/L1_L1ME4a chr1 12627647 12627963 - SINE/Alu_AluY chr1 12628166 12628421 + SINE/Alu_AluSx1 chr1 12628427 12628599 + LTR/ERV1_LTR8A chr1 12628602 12628684 - SINE/MIR_MIR chr1 12628725 12628823 + DNA/hAT-Tip100_MER45A chr1 12628824 12629123 - SINE/Alu_AluSg chr1 12629124 12629196 + DNA/hAT-Tip100_MER45A chr1 12629351 12629389 + LINE/L2_L2b chr1 12629865 12629949 + LINE/L2_L2c chr1 12630131 12630212 + LINE/L2_L2a chr1 12630213 12632001 + LTR/ERV1_MER52A chr1 12632002 12632063 + LINE/L2_L2a chr1 12632070 12632494 + LINE/L1_L1MB8 chr1 12632495 12632806 - SINE/Alu_AluJb chr1 12632807 12632882 + LINE/L1_L1MB8 chr1 12633306 12633617 - SINE/Alu_AluY chr1 12633649 12633841 - LINE/L1_L1MA3 chr1 12633842 12633971 - SINE/Alu_FLAM_C chr1 12633972 12634076 - LINE/L1_L1MA3 chr1 12634127 12634417 + LTR/ERVL-MaLR_MLT1C2 chr1 12634418 12634514 + LTR/ERV1_LTR9D chr1 12634515 12634792 - SINE/Alu_AluSp chr1 12634793 12635313 + LTR/ERV1_LTR9D chr1 12635314 12635465 + LTR/ERVL-MaLR_MLT1C2 chr1 12635467 12635494 + SINE/MIR_MIRb chr1 12635495 12635648 + SINE/MIR_MIR chr1 12635825 12636123 - SINE/Alu_AluSc chr1 12636170 12636368 + SINE/MIR_MIR chr1 12636835 12636876 + Low_complexity_G-rich chr1 12637291 12637415 - LTR/ERV1_MER57A1 chr1 12637425 12637667 - SINE/Alu_AluSz chr1 12637690 12637809 - SINE/Alu_FLAM_C chr1 12637771 12638114 - LTR/ERV1_MER57A1 chr1 12638124 12638355 + SINE/MIR_MIR chr1 12638525 12638576 + SINE/MIR_MIRb chr1 12638665 12638731 - DNA/hAT-Charlie_Charlie19a chr1 12638817 12638828 + Simple_repeat_(TTCC)n chr1 12638829 12638960 + Simple_repeat_(CTTT)n chr1 12638961 12638982 + Simple_repeat_(TTCC)n chr1 12638989 12639291 - SINE/Alu_AluJb chr1 12639335 12639891 + DNA/MULE-MuDR_Ricksha_c chr1 12639896 12640217 - LINE/L1_L1M4b chr1 12640216 12640317 - LINE/L1_L1M4b chr1 12640343 12640757 - LTR/ERV1_LTR10B2 chr1 12640849 12641529 - LINE/L1_L1M4b chr1 12641530 12641692 - SINE/Alu_AluJb chr1 12641693 12641979 - SINE/Alu_AluYm1 chr1 12641980 12642114 - SINE/Alu_AluJb chr1 12642115 12642413 - LINE/L1_L1M4b chr1 12642532 12642843 + SINE/Alu_AluY chr1 12643009 12643055 + Low_complexity_A-rich chr1 12643094 12643445 + LTR/ERVL_LTR41 chr1 12643449 12643758 - SINE/Alu_AluSz chr1 12644036 12644075 + SINE/Alu_FRAM chr1 12644779 12645362 + Simple_repeat_(CTTC)n chr1 12645365 12645453 - LINE/L2_L2c chr1 12645535 12645842 - SINE/Alu_AluJo chr1 12645847 12645940 + LINE/L2_L2b chr1 12646192 12646336 + SINE/MIR_MIR chr1 12646991 12647090 + SINE/MIR_MIRb chr1 12647091 12647398 - SINE/Alu_AluJr chr1 12647399 12647505 + SINE/MIR_MIRb chr1 12647643 12647957 - SINE/Alu_AluJb chr1 12647966 12648267 - SINE/Alu_AluSc8 chr1 12648347 12648408 + Simple_repeat_(TCCT)n chr1 12648409 12648702 - SINE/Alu_AluSz chr1 12649109 12649168 + SINE/Alu_AluSx3 chr1 12649675 12649895 - SINE/MIR_MIRb chr1 12649921 12650116 + SINE/MIR_MIR chr1 12650319 12650414 - DNA/hAT-Charlie_MER117 chr1 12650528 12650830 - SINE/Alu_AluSc chr1 12651611 12651828 + LINE/L2_L2b chr1 12651829 12652122 - SINE/Alu_AluYc chr1 12652123 12652428 + LINE/L2_L2b chr1 12652437 12653005 - LTR/ERV1_MER41E chr1 12653009 12653247 - LTR/ERV1_MER66-int chr1 12653248 12653297 - DNA/TcMar-Mariner_MADE1 chr1 12653305 12653572 - LTR/ERV1_MER66-int chr1 12653578 12654380 - LTR/ERV1_MER52-int chr1 12654608 12655079 + LTR/ERVK_LTR22B1 chr1 12655136 12655168 + Simple_repeat_(AT)n chr1 12655190 12655582 - LTR/ERV1_MER52-int chr1 12655729 12655865 - LTR/ERV1_MER52-int chr1 12655867 12656134 - LTR/ERV1_HERV4_I-int chr1 12656859 12657165 + SINE/Alu_AluJb chr1 12657427 12657987 - LTR/ERV1_MER41E chr1 12658001 12658203 + Simple_repeat_(CTTT)n chr1 12658206 12658258 + Simple_repeat_(CCCT)n chr1 12658259 12658558 - SINE/Alu_AluSz6 chr1 12658559 12658605 - LTR/ERV1_MER41E chr1 12658648 12658744 + Simple_repeat_(CTGCC)n chr1 12658774 12658949 + LINE/L2_L2b chr1 12658965 12659065 + Simple_repeat_(TGAA)n chr1 12659215 12659427 - LINE/L2_L2b chr1 12659428 12659655 - DNA/hAT-Tip100_MER96B chr1 12659656 12659820 - SINE/Alu_AluJo chr1 12659821 12660117 - SINE/Alu_AluJb chr1 12660118 12660247 - SINE/Alu_AluJo chr1 12660248 12660416 - DNA/hAT-Tip100_MER96B chr1 12660417 12660500 - LINE/L2_L2b chr1 12660545 12660651 + DNA/hAT-Charlie_MER5B chr1 12660652 12660929 - SINE/Alu_AluSx4 chr1 12660930 12660994 + DNA/hAT-Charlie_MER5B chr1 12661139 12661287 - DNA/hAT-Charlie_MER5B chr1 12661947 12662457 + LINE/L1_L1ME3F chr1 12662744 12662787 + SINE/MIR_MIRc chr1 12662828 12663043 + DNA/hAT-Tip100_MamTip2b chr1 12663238 12663591 - LTR/ERVL-MaLR_MLT1A0 chr1 12664048 12664212 + DNA/hAT-Charlie_Charlie15a chr1 12664357 12664657 - SINE/Alu_AluSx1 chr1 12665063 12665186 - SINE/Alu_AluJb chr1 12665214 12665524 - SINE/Alu_AluY chr1 12665773 12665886 + SINE/MIR_MIRb chr1 12667517 12667808 - SINE/Alu_AluSg chr1 12667891 12667956 + DNA/hAT-Tip100_Zaphod4a chr1 12667986 12668211 - LTR/Gypsy_MamGypLTR4 chr1 12668213 12668785 - LTR/ERVL-MaLR_MLT1E1A chr1 12668909 12669039 - LTR/Gypsy_MamGypLTR4 chr1 12669040 12669087 - LTR/ERVL_LTR41C chr1 12669213 12670382 + LTR/ERVL_ERVL-E-int chr1 12670425 12670513 + LTR/ERVL_MER68-int chr1 12670514 12670839 - SINE/Alu_AluJb chr1 12670840 12670898 + LTR/ERVL_MER68-int chr1 12671131 12671247 + LTR/ERVL_MER68-int chr1 12671261 12671447 + LTR/ERVL_HERVL40-int chr1 12671452 12671630 + LTR/ERVL_LTR40b chr1 12671632 12672387 - LINE/L1_L1PA8 chr1 12672370 12673241 + LINE/L1_L1PA8 chr1 12673259 12673523 + LTR/ERVL_LTR40b chr1 12673549 12673612 + LTR/ERVL_LTR40b chr1 12673615 12673656 + LTR/ERVL_LTR41 chr1 12673657 12673958 - SINE/Alu_AluYm1 chr1 12673959 12674270 + LTR/ERVL_LTR41 chr1 12674318 12674354 + LTR/ERVL_LTR41 chr1 12674390 12675590 - LTR/ERVL_ERVL-E-int chr1 12675606 12675895 - SINE/Alu_AluJo chr1 12675938 12676020 - LTR/ERVL_ERVL-E-int chr1 12676021 12676465 - LTR/ERVL_MLT2A1 chr1 12676466 12676557 - LTR/ERVL_ERVL-E-int chr1 12676558 12676871 - SINE/Alu_AluSq chr1 12676872 12677130 - LTR/ERVL_ERVL-E-int chr1 12677137 12677434 + SINE/Alu_AluJb chr1 12677497 12677768 - LTR/ERVL-MaLR_MLT1F2 chr1 12677809 12677940 - LTR/ERVL_MER70C chr1 12677941 12678322 + LINE/L1_L1PA15 chr1 12678323 12678669 - LTR/ERVL_MER70C chr1 12678670 12678849 - LTR/ERVL-MaLR_MLT1F2 chr1 12678850 12679080 - LINE/L1_L1M3 chr1 12679090 12679242 - SINE/Alu_AluSz chr1 12679243 12679269 + Simple_repeat_(T)n chr1 12679270 12679389 - SINE/Alu_AluSz chr1 12679391 12679448 + LINE/L2_L2 chr1 12679455 12679716 + SINE/Alu_AluSx1 chr1 12680010 12680583 - LTR/ERV1_MER34A chr1 12680651 12680727 - SINE/MIR_MIR chr1 12680956 12681044 + SINE/MIR_MIRb chr1 12681221 12681548 - SINE/Alu_AluJb chr1 12681732 12683058 + LINE/L1_L1PA4 chr1 12683098 12683119 + Simple_repeat_(A)n chr1 12683238 12683688 - LINE/L2_L2a chr1 12683821 12683947 - SINE/Alu_FLAM_C chr1 12684193 12684368 - SINE/MIR_MIRb chr1 12684483 12684515 + LINE/L2_L2c chr1 12684768 12685185 - LTR/ERVL-MaLR_MSTB chr1 12685722 12685825 + DNA/TcMar-Tigger_MER46C chr1 12685858 12685892 + Simple_repeat_(TATT)n chr1 12685893 12686012 - SINE/Alu_FLAM_C chr1 12686013 12686030 + Simple_repeat_(TATT)n chr1 12686062 12686192 + DNA/TcMar-Tigger_MER46C chr1 12686193 12686498 - SINE/Alu_AluSq2 chr1 12686543 12686795 + LINE/L1_L1MD2 chr1 12686796 12687095 + SINE/Alu_AluSg chr1 12687096 12687583 + LINE/L1_L1MD2 chr1 12687584 12687629 + Simple_repeat_(AC)n chr1 12687630 12687795 + LINE/L1_L1MD2 chr1 12687995 12688139 + LINE/L1_L1ME4c chr1 12688214 12688499 - SINE/Alu_AluJb chr1 12688850 12689161 - SINE/Alu_AluSz chr1 12689422 12689502 - LINE/L2_L2a chr1 12689512 12689819 + SINE/Alu_AluSg chr1 12689821 12689863 + Low_complexity_A-rich chr1 12690107 12690751 + LINE/L1_L1ME5 chr1 12690759 12691051 - SINE/Alu_AluY chr1 12691185 12691306 - SINE/MIR_MIR1_Amn chr1 12691706 12691795 - LINE/L2_L2a chr1 12692163 12692789 - LTR/ERVL_LTR102_Mam chr1 12692796 12692905 - SINE/Alu_AluJb chr1 12693335 12693458 + SINE/MIR_MIR chr1 12693459 12693493 - LTR/ERVL?_LTR89B chr1 12693494 12693793 + LTR/ERVL-MaLR_MSTA chr1 12693794 12694097 - SINE/Alu_AluSc8 chr1 12694098 12694139 + LTR/ERVL-MaLR_MSTA chr1 12694140 12694198 - LTR/ERVL?_LTR89B chr1 12694386 12694540 - LINE/L2_L2a chr1 12694598 12694905 + SINE/Alu_AluSq2 chr1 12694906 12695122 - LINE/L2_L2a chr1 12695123 12695620 - LINE/L1_L1MA2 chr1 12695621 12695784 - LINE/L2_L2a chr1 12696166 12696463 - SINE/Alu_AluSx3 chr1 12696654 12696872 + DNA/TcMar-Tigger_MER44C chr1 12696865 12697128 - LTR/ERVL-MaLR_MSTD chr1 12697173 12697920 - LINE/L1_L1ME3A chr1 12697921 12698231 - SINE/Alu_AluJb chr1 12698232 12698355 - LINE/L1_L1ME3A chr1 12698420 12698920 - LINE/L1_L1ME3A chr1 12698910 12699117 - LINE/L1_L1ME1 chr1 12699126 12699593 - LINE/L1_L1ME3A chr1 12700002 12700079 - DNA/hAT-Charlie_Charlie10 chr1 12700083 12700420 + DNA/hAT-Charlie_Charlie4a chr1 12700446 12700678 - SINE/Alu_AluJb chr1 12700695 12701120 + LINE/L1_L1PA15 chr1 12701451 12701765 + LTR/ERVL-MaLR_MLT1D chr1 12701766 12702060 - SINE/Alu_AluSz6 chr1 12702061 12702171 + LTR/ERVL-MaLR_MLT1D chr1 12702172 12702485 + SINE/Alu_AluSz6 chr1 12702589 12702734 + LTR/ERVL_LTR33 chr1 12702778 12702903 - DNA/hAT-Tip100_Arthur1B chr1 12703297 12703454 + SINE/MIR_MIR chr1 12703460 12703527 - SINE/MIR_MIRc chr1 12703944 12704434 - LTR/ERV1_LTR10F chr1 12704435 12705235 - LTR/ERV1_HERVIP10F-int chr1 12705236 12706103 + LTR/ERVK_MER11D chr1 12706104 12707851 - LTR/ERV1_HERVIP10F-int chr1 12707852 12708145 + SINE/Alu_AluY chr1 12708146 12713280 - LTR/ERV1_HERVIP10F-int chr1 12713282 12713437 - LTR/ERV1_HERVH-int chr1 12713417 12713470 - LTR/ERV1_HERVH-int chr1 12713470 12713728 + LTR/ERV1_HERVH-int chr1 12713729 12714137 + LTR/ERV1_LTR7 chr1 12714138 12714416 + SINE/Alu_AluSx chr1 12714448 12714642 + SINE/Alu_AluSx3 chr1 12714735 12714864 - SINE/MIR_MIR chr1 12714876 12715079 - DNA/hAT-Charlie_MER20 chr1 12715296 12715486 - LINE/L2_L2a chr1 12715492 12715561 + Low_complexity_G-rich chr1 12715562 12715759 - LINE/L2_L2a chr1 12716668 12716777 - LINE/L1_L1MB3 chr1 12717355 12717487 + SINE/MIR_MIRb chr1 12717864 12718240 + DNA/hAT-Charlie_Charlie4a chr1 12718241 12718375 + SINE/Alu_FLAM_C chr1 12718505 12718799 - SINE/Alu_AluSq2 chr1 12718804 12719065 - LINE/L1_L1MC5 chr1 12720151 12720316 - DNA/hAT-Charlie_Charlie22a chr1 12720366 12720640 - SINE/MIR_MIR chr1 12721091 12721120 + Low_complexity_GA-rich chr1 12721121 12721421 + SINE/Alu_AluJb chr1 12721422 12721471 + Low_complexity_GA-rich chr1 12721631 12721758 - DNA/hAT-Charlie_MER20B chr1 12722023 12722050 + Simple_repeat_(TGAGA)n chr1 12722051 12722349 + SINE/Alu_AluSz chr1 12722350 12722383 + Simple_repeat_(TGAGA)n chr1 12722691 12722760 + DNA/hAT-Charlie_MER102a chr1 12722849 12722995 - DNA/TcMar-Tigger_MER6 chr1 12723047 12723168 - SINE/Alu_AluJo chr1 12723171 12723482 - DNA/TcMar-Tigger_MER6 chr1 12723483 12723777 - SINE/Alu_AluSx3 chr1 12723778 12723788 - DNA/TcMar-Tigger_MER6 chr1 12723789 12724081 - SINE/Alu_AluSp chr1 12724082 12724486 - DNA/TcMar-Tigger_MER6 chr1 12724487 12724784 - SINE/Alu_AluJb chr1 12726845 12727307 - LTR/ERVL-MaLR_MLT1K chr1 12728180 12728285 - SINE/MIR_MIR chr1 12728286 12730557 - DNA/TcMar-Tigger_Tigger1 chr1 12730559 12730678 + SINE/Alu_FLAM_A chr1 12730739 12734274 - LINE/L1_L1PA7 chr1 12734246 12734529 - LINE/L1_L1ME3G chr1 12735112 12735196 + SINE/MIR_MIRb chr1 12735451 12735672 - SINE/MIR_MIRb chr1 12735771 12735924 + LINE/L2_L2c chr1 12735973 12736334 + LINE/L1_L1MB4 chr1 12736335 12736368 + Simple_repeat_(AC)n chr1 12736369 12736443 + LINE/L1_L1MB4 chr1 12736444 12736893 - LTR/ERV1_MER57A1 chr1 12736894 12737849 + LINE/L1_L1MB4 chr1 12737905 12738595 + LINE/L1_L1MB4 chr1 12738596 12738791 + LINE/L1_L1PA17 chr1 12738792 12739094 + SINE/Alu_AluJb chr1 12739095 12739480 + LINE/L1_L1PA17 chr1 12739481 12739734 + LINE/L1_L1MB4 chr1 12739735 12739841 + snRNA_U13 chr1 12739842 12740311 + LINE/L1_L1MB4 chr1 12740309 12740461 + LINE/L1_L1ME3B chr1 12740462 12740538 + DNA/hAT-Charlie_Charlie1a chr1 12740539 12740849 - SINE/Alu_AluSx3 chr1 12740850 12740899 + DNA/hAT-Charlie_Charlie1a chr1 12740900 12741201 - SINE/Alu_AluSq2 chr1 12741202 12741632 + DNA/hAT-Charlie_Charlie1a chr1 12741635 12741767 - SINE/Alu_AluSx3 chr1 12741782 12741982 + DNA/hAT-Charlie_Charlie1a chr1 12741983 12742277 - SINE/Alu_AluJb chr1 12742278 12742390 + DNA/hAT-Charlie_Charlie1a chr1 12742382 12742494 + DNA/hAT-Charlie_Charlie1a chr1 12742495 12742812 - SINE/Alu_AluJr chr1 12742813 12742855 + DNA/hAT-Charlie_Charlie1a chr1 12742857 12743049 - DNA/hAT-Blackjack_MER63A chr1 12743051 12743139 + DNA/hAT-Charlie_Charlie1b chr1 12743184 12743292 + LTR/ERV1_LTR51 chr1 12743293 12743585 - SINE/Alu_AluJb chr1 12743586 12743861 + LTR/ERV1_LTR51 chr1 12743870 12743917 + LTR/ERV1_LTR51 chr1 12743979 12744108 + LINE/L1_L1ME3B chr1 12744110 12744348 + LTR/ERV1_LTR51 chr1 12744395 12744783 + LINE/L1_L1ME3B chr1 12744808 12745102 + SINE/Alu_AluY chr1 12745446 12745529 - LINE/L1_L1MC5a chr1 12745568 12745677 + LINE/L2_L2b chr1 12745748 12745994 + SINE/MIR_MIR chr1 12747065 12747368 - SINE/Alu_AluSz chr1 12747875 12749333 + LINE/L1_L1MB3 chr1 12749334 12749639 + SINE/Alu_AluJb chr1 12749640 12751344 + LINE/L1_L1MB3 chr1 12751345 12751656 + SINE/Alu_AluSx chr1 12751657 12752302 + LINE/L1_L1MB3 chr1 12752303 12752582 + SINE/Alu_AluSz chr1 12752583 12753782 + LINE/L1_L1MB3 chr1 12753783 12753899 + SINE/Alu_AluJr chr1 12753900 12755035 + LINE/L1_L1MB3 chr1 12755089 12755120 + Low_complexity_A-rich chr1 12755125 12755415 + SINE/Alu_AluSc chr1 12756126 12756231 + DNA/TcMar-Tigger_Tigger19a chr1 12757315 12757369 + Simple_repeat_(TTCTT)n chr1 12757370 12757685 - SINE/Alu_AluSz6 chr1 12757868 12758286 + LTR/ERVL_MER74B chr1 12759009 12759055 - LINE/L2_L2-2_Mam chr1 12759830 12760164 - LINE/L2_L2a chr1 12761195 12761299 - SINE/MIR_MIR3 chr1 12761526 12761834 - SINE/Alu_AluSx4 chr1 12762252 12762299 + LINE/L2_L2b chr1 12762309 12762336 + Simple_repeat_(TGAA)n chr1 12762465 12762512 + LINE/L2_L2b chr1 12762522 12762549 + Simple_repeat_(TGAA)n chr1 12762824 12762904 - DNA/hAT-Charlie_MER30 chr1 12762905 12763209 + SINE/Alu_AluSx chr1 12763210 12763336 - DNA/hAT-Charlie_MER30 chr1 12763583 12764079 - LTR/ERVL-MaLR_MLT1D chr1 12764480 12764669 - DNA/hAT-Charlie_MER5A chr1 12764715 12764922 - LINE/L1_L1MA4 chr1 12764923 12765238 + SINE/Alu_AluY chr1 12765239 12766462 - LINE/L1_L1MA4 chr1 12766463 12766765 - SINE/Alu_AluSc chr1 12766766 12768126 - LINE/L1_L1MA4 chr1 12768436 12768457 + Simple_repeat_(GTAT)n chr1 12769272 12769605 + DNA/TcMar-Tigger_Tigger6a chr1 12769661 12769752 + LINE/L2_L2a chr1 12771483 12771598 + SINE/MIR_MIR chr1 12772144 12772175 + Simple_repeat_(T)n chr1 12772210 12772438 - SINE/Alu_AluJb chr1 12775414 12775467 + Low_complexity_GA-rich chr1 12777782 12777824 + Simple_repeat_(GAGAAT)n chr1 12778779 12778909 + SINE/MIR_MIRb chr1 12779328 12779901 + LTR/ERV1_PRIMA4-int chr1 12780115 12784947 - LTR/ERVK_HERVK-int chr1 12784950 12785935 - LTR/ERVK_LTR5B chr1 12786075 12786097 + Simple_repeat_(ATATTA)n chr1 12786098 12786387 - SINE/Alu_AluSc chr1 12786388 12786412 + Simple_repeat_(ATATTA)n chr1 12786917 12787135 - SINE/Alu_AluSx1 chr1 12788002 12788029 + Simple_repeat_(CA)n chr1 12788872 12788970 + SINE/MIR_MIRc chr1 12789133 12789328 - SINE/Alu_AluJo chr1 12789331 12789341 + Simple_repeat_(CTTG)n chr1 12789342 12789409 + Simple_repeat_(TTCC)n chr1 12789411 12789462 + Simple_repeat_(TCTT)n chr1 12789463 12789488 + Simple_repeat_(CTTG)n chr1 12789489 12789782 - SINE/Alu_AluSc8 chr1 12790363 12790644 + SINE/Alu_AluSz chr1 12792030 12792320 - SINE/Alu_AluSq2 chr1 12792395 12792562 - SINE/Alu_AluSx chr1 12792582 12792820 - SINE/Alu_AluSz chr1 12793773 12793832 + Simple_repeat_(AACAGAG)n chr1 12796145 12796261 - SINE/Alu_AluJo chr1 12796644 12796931 - SINE/Alu_AluSz6 chr1 12797378 12797470 + LTR/ERV1_MER52D chr1 12797516 12797559 + LINE/CR1_L3 chr1 12797641 12799270 + LTR/ERV1_MER52D chr1 12799274 12799604 + LINE/L1_L1M1 chr1 12799679 12800601 + LINE/L1_L1PB chr1 12800736 12801034 - LTR/ERV1_MER41G chr1 12801035 12801070 + Simple_repeat_(TTAT)n chr1 12801072 12801351 - SINE/Alu_AluSp chr1 12801352 12801667 - LTR/ERV1_MER41G chr1 12801698 12801723 + Simple_repeat_(TCCCC)n chr1 12801727 12801964 + Simple_repeat_(CTTT)n chr1 12801966 12805488 - LINE/L1_L1PA7 chr1 12805489 12805761 - LTR/ERV1_MER41G chr1 12805808 12806054 - LINE/L1_L1MA7 chr1 12806447 12806744 + LINE/L2_L2 chr1 12808070 12808103 + Simple_repeat_(CTAATA)n chr1 12808359 12808632 + DNA/TcMar-Tigger_Tigger6a chr1 12808633 12808926 - SINE/Alu_AluSx1 chr1 12808927 12809062 + DNA/TcMar-Tigger_Tigger6a chr1 12809071 12809367 + SINE/Alu_AluJb chr1 12809592 12809721 - SINE/Alu_AluSp chr1 12812813 12812996 - SINE/Alu_AluSx chr1 12812998 12816083 - LINE/L1_L1PA4 chr1 12816084 12816355 - LINE/L1_L1HS chr1 12816356 12816606 - LINE/L1_L1P1 chr1 12816607 12816749 - LINE/L1_L1PA4 chr1 12816750 12819764 - LINE/L1_L1PA4 chr1 12819770 12819905 - SINE/Alu_AluSx chr1 12820089 12820155 - LINE/L2_L2b chr1 12820208 12820859 + LINE/L1_L1MC1 chr1 12820861 12821178 + SINE/Alu_AluSx chr1 12821184 12821481 + SINE/Alu_AluJb chr1 12821651 12821933 - SINE/Alu_AluSx3 chr1 12822056 12822312 - SINE/Alu_AluSg chr1 12822329 12822625 - SINE/Alu_AluSp chr1 12822966 12823092 - SINE/MIR_MIRb chr1 12823833 12824130 + SINE/Alu_AluJr chr1 12824131 12824431 + SINE/Alu_AluSq2 chr1 12825692 12825994 + SINE/Alu_AluSz6 chr1 12826433 12826462 + Simple_repeat_(CACTT)n chr1 12826480 12826772 + SINE/Alu_AluSx chr1 12826815 12826925 - SINE/Alu_FLAM_C chr1 12827986 12828265 - SINE/Alu_AluSx1 chr1 12829241 12829423 + Simple_repeat_(TTTC)n chr1 12829424 12829708 - SINE/Alu_AluJr chr1 12829718 12829864 + SINE/Alu_AluSx3 chr1 12830289 12830442 - SINE/Alu_AluSz6 chr1 12830518 12830705 - SINE/Alu_AluJb chr1 12830706 12831014 + SINE/Alu_AluSz chr1 12831015 12831135 - SINE/Alu_AluJb chr1 12831713 12832008 + SINE/Alu_AluSx1 chr1 12832378 12832947 + LTR/ERV1_LTR26 chr1 12833081 12833210 + LTR/ERV1_MER65A chr1 12833431 12833724 + LTR/ERV1_LTR44 chr1 12833813 12833864 + SINE/Alu_Alu chr1 12833866 12834175 + SINE/Alu_AluSc chr1 12834542 12834848 - SINE/Alu_AluSc8 chr1 12834904 12834964 + Simple_repeat_(TTTA)n chr1 12834967 12835259 - SINE/Alu_AluSq2 chr1 12835804 12836100 + SINE/Alu_AluSp chr1 12836104 12836131 + Simple_repeat_(AGAAA)n chr1 12836307 12836527 + LINE/L1_L1MA4 chr1 12836528 12836630 - SINE/Alu_AluSz chr1 12836631 12837183 + LINE/L1_L1MA4 chr1 12837184 12837396 + SINE/Alu_AluSx chr1 12837712 12838019 + SINE/Alu_AluSz chr1 12838207 12838475 + SINE/Alu_AluYa5 chr1 12839437 12839723 - SINE/Alu_AluSg chr1 12841683 12841825 - SINE/Alu_AluJr chr1 12841951 12842247 + SINE/Alu_AluSx chr1 12842682 12842825 - SINE/Alu_AluSz6 chr1 12842913 12843130 - SINE/Alu_AluJb chr1 12843131 12843416 + SINE/Alu_AluSz chr1 12843417 12843533 - SINE/Alu_AluJb chr1 12844109 12844408 + SINE/Alu_AluSx1 chr1 12844778 12845355 + LTR/ERV1_LTR26 chr1 12845491 12845703 + LTR/ERV1_MER65A chr1 12845850 12846143 + LTR/ERV1_LTR44 chr1 12846200 12846277 + LTR/ERV1_LTR44 chr1 12846278 12847056 + LINE/L1_L1PA3 chr1 12847057 12847103 + Low_complexity_A-rich chr1 12849048 12849335 + SINE/Alu_AluSx1 chr1 12849704 12850277 + LTR/ERV1_LTR26 chr1 12850413 12850627 + LTR/ERV1_MER65A chr1 12850930 12851191 + SINE/Alu_AluSp chr1 12851855 12851921 - LTR/ERV1_LTR45B chr1 12851922 12852216 + SINE/Alu_AluSg chr1 12852217 12852233 - LTR/ERV1_LTR45B chr1 12852234 12852540 + SINE/Alu_AluSg chr1 12852541 12852619 - LTR/ERV1_LTR45B chr1 12852803 12852862 - LTR/ERV1_MER57A-int chr1 12852967 12853240 + SINE/Alu_AluSz6 chr1 12853585 12853629 + Simple_repeat_(TATATT)n chr1 12853673 12853731 - DNA/hAT-Blackjack_MER94 chr1 12853736 12854048 - SINE/Alu_AluSx1 chr1 12854182 12854344 - SINE/MIR_MIR chr1 12854345 12854555 - SINE/Alu_AluY chr1 12854925 12855234 - SINE/Alu_AluSp chr1 12855583 12855779 - SINE/Alu_AluJo chr1 12856040 12856315 + SINE/Alu_AluSz chr1 12857707 12857997 - SINE/Alu_AluSq2 chr1 12858075 12858247 - SINE/Alu_AluSx chr1 12858262 12858502 - SINE/Alu_AluSz chr1 12861928 12862018 - SINE/Alu_AluYc chr1 12862403 12862689 - SINE/Alu_AluSz6 chr1 12863132 12863224 + LTR/ERV1_MER52D chr1 12863362 12865024 + LTR/ERV1_MER52D chr1 12865028 12865356 + LINE/L1_L1M1 chr1 12865431 12866364 + LINE/L1_L1PB chr1 12866498 12866791 - LTR/ERV1_MER41G chr1 12866792 12866833 + Simple_repeat_(ATTT)n chr1 12866835 12867119 - SINE/Alu_AluSp chr1 12867120 12867435 - LTR/ERV1_MER41G chr1 12867441 12867467 + Simple_repeat_(TCCCT)n chr1 12867469 12867496 + Simple_repeat_(CCTCC)n chr1 12867498 12867877 + Simple_repeat_(TCTT)n chr1 12867879 12871181 - LINE/L1_L1PA7 chr1 12871182 12871324 - LINE/L1_L1PA4 chr1 12871325 12874333 - LINE/L1_L1PA7 chr1 12874339 12874479 - SINE/Alu_AluSz chr1 12874659 12874725 - LINE/L2_L2b chr1 12874778 12875443 + LINE/L1_L1MC1 chr1 12875445 12875763 + SINE/Alu_AluSx chr1 12875769 12876058 + SINE/Alu_AluJb chr1 12876060 12876091 + Simple_repeat_(AAATA)n chr1 12876261 12876543 - SINE/Alu_AluSx3 chr1 12876665 12876921 - SINE/Alu_AluSg chr1 12876938 12877235 - SINE/Alu_AluSp chr1 12877576 12877702 - SINE/MIR_MIRb chr1 12878443 12878732 + SINE/Alu_AluJr chr1 12878733 12879033 + SINE/Alu_AluSp chr1 12880294 12880597 + SINE/Alu_AluSx1 chr1 12881037 12881066 + Simple_repeat_(CACTT)n chr1 12881084 12881377 + SINE/Alu_AluSx chr1 12881399 12881529 - SINE/Alu_FLAM_C chr1 12881470 12881540 + Retroposon/SVA_SVA_B chr1 12882591 12882870 - SINE/Alu_AluSx1 chr1 12883846 12884028 + Simple_repeat_(TTTC)n chr1 12884029 12884315 - SINE/Alu_AluJr chr1 12884433 12884692 + SINE/Alu_AluSx chr1 12885154 12885284 - SINE/Alu_AluSz6 chr1 12885360 12885504 - SINE/Alu_AluJb chr1 12885505 12885801 + SINE/Alu_AluSz chr1 12885802 12885922 - SINE/Alu_AluJb chr1 12886504 12886791 + SINE/Alu_AluSx1 chr1 12887162 12887731 + LTR/ERV1_LTR26 chr1 12887865 12887994 + LTR/ERV1_MER65A chr1 12888076 12888213 - LTR/ERV1_MER34-int chr1 12888215 12888507 + LTR/ERV1_LTR44 chr1 12888596 12888646 + SINE/Alu_Alu chr1 12888648 12888956 + SINE/Alu_AluSc chr1 12889002 12889107 + Unknown_UCON80_AMi chr1 12889321 12889627 - SINE/Alu_AluSc8 chr1 12889683 12889743 + Simple_repeat_(TTTA)n chr1 12889746 12890036 - SINE/Alu_AluSq2 chr1 12890581 12890894 + SINE/Alu_AluSp chr1 12891082 12891302 + LINE/L1_L1MA4 chr1 12891303 12891404 - SINE/Alu_AluSz chr1 12891406 12891957 + LINE/L1_L1MA4 chr1 12891958 12892175 + SINE/Alu_AluSx1 chr1 12892487 12892771 + SINE/Alu_AluSz chr1 12892772 12892833 + Simple_repeat_(AAAAT)n chr1 12893928 12894212 - SINE/Alu_AluSx3 chr1 12894481 12894530 + LINE/L2_L2a chr1 12896169 12896310 - SINE/Alu_AluJr chr1 12896441 12896699 + SINE/Alu_AluSx chr1 12896720 12896738 + Simple_repeat_(AG)n chr1 12897170 12897306 - SINE/Alu_AluSz6 chr1 12897387 12897573 - SINE/Alu_AluJb chr1 12897574 12897854 + SINE/Alu_AluSx1 chr1 12897855 12897992 - SINE/Alu_AluJb chr1 12898555 12898568 + Simple_repeat_(ATTC)n chr1 12898569 12898858 + SINE/Alu_AluSx chr1 12898859 12898871 + Simple_repeat_(ATTC)n chr1 12899231 12899806 + LTR/ERV1_LTR26 chr1 12899945 12900152 + LTR/ERV1_MER65A chr1 12900162 12900299 - LTR/ERV1_MER34-int chr1 12900301 12900846 + LTR/ERV1_LTR44 chr1 12901584 12901706 - LTR/ERV1_MER34-int chr1 12901900 12902191 + SINE/Alu_AluSx4 chr1 12902210 12902342 + SINE/Alu_AluSx4 chr1 12902350 12903649 + Retroposon/SVA_SVA_D chr1 12903650 12903676 + Simple_repeat_(ATAA)n chr1 12903677 12903695 + Retroposon/SVA_SVA_D chr1 12903696 12903858 + SINE/Alu_AluSx chr1 12903859 12903871 + Retroposon/SVA_SVA_D chr1 12903874 12903944 - LTR/ERV1_LTR45 chr1 12904307 12904600 + SINE/Alu_AluSz6 chr1 12904699 12904900 - LTR/ERV1_MER57A-int chr1 12905135 12907619 + LINE/L1_L1PA4 chr1 12907620 12907697 - LTR/ERV1_LTR12C chr1 12907698 12910276 + LINE/L1_L1PA4 chr1 12910284 12910527 + LINE/L1_L1PA4 chr1 12910610 12910668 - DNA/hAT-Blackjack_MER94 chr1 12910674 12910731 - SINE/Alu_AluSc chr1 12910983 12911153 + SINE/MIR_MIR chr1 12912510 12912871 + DNA/TcMar-Tigger_Tigger6a chr1 12914795 12914975 + SINE/MIR_MIRb chr1 12915466 12915479 + Simple_repeat_(TCCC)n chr1 12915480 12915542 + Simple_repeat_(TCTT)n chr1 12915543 12915674 + Simple_repeat_(TCCC)n chr1 12919609 12919702 - SINE/MIR_MIRb chr1 12921558 12921687 + SINE/MIR_MIRb chr1 12922255 12922548 + SINE/Alu_AluY chr1 12922554 12922660 + snRNA_U6 chr1 12922661 12923323 + LINE/L1_L1PA7 chr1 12923473 12924122 - LINE/L1_L1MC1 chr1 12924125 12924254 + LINE/L2_L2c chr1 12924546 12925058 - LTR/ERV1_MER101-int chr1 12925063 12925414 - LTR/ERV1_MER49 chr1 12925416 12925511 + LTR/ERV1_MER4E1 chr1 12925512 12925797 + SINE/Alu_AluSp chr1 12925801 12925850 + Simple_repeat_(ATAA)n chr1 12925851 12925877 + LTR/ERV1_MER4E1 chr1 12928397 12928513 - SINE/Alu_AluJo chr1 12928918 12928947 + Simple_repeat_(A)n chr1 12928955 12929208 - SINE/Alu_AluSz6 chr1 12929626 12929715 + LTR/ERV1_MER52D chr1 12929888 12931058 + LTR/ERV1_MER52D chr1 12931059 12931338 + LINE/L1_L1M1 chr1 12931339 12931499 + SINE/Alu_AluJb chr1 12931597 12931675 + LINE/L2_L2a chr1 12931702 12931829 - SINE/Alu_AluSx chr1 12934955 12935125 - SINE/Alu_AluSx chr1 12935126 12935350 - LINE/L1_L1PA4 chr1 12935351 12935397 + LINE/L1_L1PA4 chr1 12935398 12935532 - SINE/Alu_AluSx chr1 12935706 12935916 - SINE/Alu_AluSx chr1 12936036 12936310 - SINE/Alu_AluSz chr1 12936320 12936371 - SINE/Alu_Alu chr1 12936374 12936410 + Simple_repeat_(ATAAT)n chr1 12936712 12936839 - SINE/MIR_MIRb chr1 12937586 12937872 + SINE/Alu_AluJo chr1 12937873 12938173 + SINE/Alu_AluSp chr1 12939425 12939728 + SINE/Alu_AluSx1 chr1 12940168 12940197 + Simple_repeat_(CACTT)n chr1 12940215 12940507 + SINE/Alu_AluSx chr1 12940550 12940660 - SINE/Alu_AluJo chr1 12940600 12940670 + Retroposon/SVA_SVA_B chr1 12941721 12942000 - SINE/Alu_AluSx1 chr1 12942976 12943186 + Simple_repeat_(TTTC)n chr1 12943187 12943473 - SINE/Alu_AluJr chr1 12943591 12943850 + SINE/Alu_AluSx chr1 12943946 12943995 + Low_complexity_A-rich chr1 12944290 12944442 - SINE/Alu_AluSx3 chr1 12944518 12944705 - SINE/Alu_AluJb chr1 12944706 12945011 + SINE/Alu_AluSz chr1 12945012 12945132 - SINE/Alu_AluJb chr1 12945706 12946010 + SINE/Alu_AluSx chr1 12946466 12946602 - SINE/Alu_AluSz6 chr1 12946685 12946870 - SINE/Alu_AluJb chr1 12946871 12947177 + SINE/Alu_AluSz chr1 12947178 12947298 - SINE/Alu_AluJb chr1 12947874 12948169 + SINE/Alu_AluSx1 chr1 12948540 12949109 + LTR/ERV1_LTR26 chr1 12949243 12949445 + LTR/ERV1_MER65A chr1 12949453 12949590 - LTR/ERV1_MER34-int chr1 12949592 12949881 + LTR/ERV1_LTR44 chr1 12949974 12950023 + SINE/Alu_Alu chr1 12950024 12950335 + SINE/Alu_AluSc chr1 12950393 12950499 + Unknown_UCON80_AMi chr1 12950712 12951020 - SINE/Alu_AluSc8 chr1 12951075 12951132 + Simple_repeat_(ATTT)n chr1 12951135 12951425 - SINE/Alu_AluSq2 chr1 12952449 12952585 + SINE/Alu_AluSz chr1 12952586 12952858 + SINE/Alu_AluY chr1 12952859 12953016 + SINE/Alu_AluSz chr1 13005745 13005859 - SINE/Alu_AluJo chr1 13006271 13006582 - SINE/Alu_AluSp chr1 13006590 13006823 - SINE/Alu_AluSz chr1 13007634 13008022 + LINE/L1_L1M3f chr1 13008020 13008110 - LINE/L1_L1PA15 chr1 13008111 13008406 - SINE/Alu_AluSz chr1 13008407 13008813 - LINE/L1_L1PA15 chr1 13008822 13008945 - SINE/Alu_FLAM_C chr1 13008959 13009252 + SINE/Alu_AluSc chr1 13009267 13009396 + SINE/Alu_FLAM_C chr1 13009389 13009659 - LTR/ERV1_MER49 chr1 13009660 13009819 - SINE/Alu_AluSx chr1 13009820 13010122 - SINE/Alu_AluY chr1 13010123 13010261 - SINE/Alu_AluSx chr1 13010262 13010590 - LTR/ERV1_MER49 chr1 13010591 13010890 + SINE/Alu_AluSx3 chr1 13010891 13011206 - LTR/ERV1_MER49 chr1 13011265 13011394 - SINE/MIR_MIRb chr1 13012043 13012432 - LTR/ERVL_LTR57 chr1 13012447 13013444 - LTR/ERVK_LTR5B chr1 13013447 13013533 - Retroposon/SVA_SVA_F chr1 13013534 13020996 - LTR/ERVK_HERVK-int chr1 13020998 13021988 - LTR/ERVK_LTR5B chr1 13022119 13022153 + Simple_repeat_(TTTATA)n chr1 13022154 13022457 - SINE/Alu_AluSc chr1 13022458 13022467 + Simple_repeat_(TTTATA)n chr1 13022986 13023204 - SINE/Alu_AluSx1 chr1 13023616 13023690 + Simple_repeat_(GAGGCA)n chr1 13024069 13024087 + Simple_repeat_(CA)n chr1 13025188 13025383 - SINE/Alu_AluJo chr1 13025385 13025632 + Simple_repeat_(TCTT)n chr1 13025633 13025926 - SINE/Alu_AluSc8 chr1 13025927 13025941 + Simple_repeat_(TCTT)n chr1 13026507 13026799 + SINE/Alu_AluSz chr1 13028166 13028450 - SINE/Alu_AluSq2 chr1 13028529 13028697 - SINE/Alu_AluSx chr1 13028717 13028956 - SINE/Alu_AluSz chr1 13032279 13032397 - SINE/Alu_AluJo chr1 13032782 13033068 - SINE/Alu_AluSz6 chr1 13033510 13033602 + LTR/ERV1_MER52D chr1 13033648 13033691 + LINE/CR1_L3 chr1 13033777 13035401 + LTR/ERV1_MER52D chr1 13035405 13035733 + LINE/L1_L1M1 chr1 13035808 13036744 + LINE/L1_L1PB chr1 13036753 13036841 - DNA/hAT-Charlie_MER102c chr1 13036878 13037171 - LTR/ERV1_MER41G chr1 13037172 13037213 + Simple_repeat_(ATTT)n chr1 13037215 13037499 - SINE/Alu_AluSp chr1 13037500 13037815 - LTR/ERV1_MER41G chr1 13037821 13037847 + Simple_repeat_(TCCCT)n chr1 13037849 13037876 + Simple_repeat_(CCTCC)n chr1 13037875 13038115 + Simple_repeat_(CTTT)n chr1 13038116 13041430 - LINE/L1_L1PA7 chr1 13041431 13041573 - LINE/L1_L1PA4 chr1 13041574 13044582 - LINE/L1_L1PA7 chr1 13044588 13044728 - SINE/Alu_AluSz chr1 13044895 13044974 - LINE/L2_L2b chr1 13045027 13045692 + LINE/L1_L1MC1 chr1 13045694 13046011 + SINE/Alu_AluSx chr1 13046017 13046306 + SINE/Alu_AluJb chr1 13046308 13046349 + Simple_repeat_(AAATA)n chr1 13046519 13046801 - SINE/Alu_AluSx3 chr1 13046923 13047179 - SINE/Alu_AluSg chr1 13047191 13047493 - SINE/Alu_AluSp chr1 13047834 13047960 - SINE/MIR_MIRb chr1 13048708 13048996 + SINE/Alu_AluJr chr1 13048997 13049297 + SINE/Alu_AluSp chr1 13050563 13050861 + SINE/Alu_AluSx1 chr1 13051346 13051641 + SINE/Alu_AluSx chr1 13051684 13051794 - SINE/Alu_FLAM_C chr1 13051734 13051804 + Retroposon/SVA_SVA_B chr1 13052005 13052070 + LINE/L2_L2c chr1 13052855 13053135 - SINE/Alu_AluSx1 chr1 13054111 13054293 + Simple_repeat_(TTTC)n chr1 13054294 13054580 - SINE/Alu_AluJr chr1 13054698 13054957 + SINE/Alu_AluSx chr1 13055396 13055555 - SINE/Alu_AluSz6 chr1 13055631 13055775 - SINE/Alu_AluJb chr1 13055776 13056070 + SINE/Alu_AluSz chr1 13056071 13056191 - SINE/Alu_AluJb chr1 13056771 13057066 + SINE/Alu_AluSx1 chr1 13057435 13058003 + LTR/ERV1_LTR26 chr1 13058092 13058379 + LTR/ERV1_LTR44 chr1 13058418 13058446 + Low_complexity_A-rich chr1 13058468 13058519 + SINE/Alu_Alu chr1 13058521 13058832 + SINE/Alu_AluSc chr1 13058882 13058987 + Unknown_UCON80_AMi chr1 13059201 13059507 - SINE/Alu_AluSc8 chr1 13059563 13059623 + Simple_repeat_(TTTA)n chr1 13059626 13059916 - SINE/Alu_AluSq2 chr1 13060271 13060302 + Simple_repeat_(TGT)n chr1 13060303 13060577 - SINE/Alu_AluSx1 chr1 13062887 13063025 + SINE/Alu_AluJb chr1 13063026 13063305 - SINE/Alu_AluSx1 chr1 13063306 13063481 + SINE/Alu_AluJb chr1 13063727 13064013 + SINE/Alu_AluSx1 chr1 13064435 13064583 + LTR/ERV1_MER57A-int chr1 13064678 13064971 - SINE/Alu_AluSg chr1 13064982 13065056 + LTR/ERV1_LTR45B chr1 13065057 13065205 + LTR/ERV1_MER57A-int chr1 13065229 13065324 + LTR/ERV1_MER34-int chr1 13066130 13066672 - LTR/ERV1_LTR44 chr1 13066674 13066811 + LTR/ERV1_MER34-int chr1 13066820 13067028 - LTR/ERV1_MER65A chr1 13067162 13067730 - LTR/ERV1_LTR26 chr1 13068102 13068401 - SINE/Alu_AluSx1 chr1 13068972 13069095 + SINE/Alu_AluJb chr1 13069096 13069397 - SINE/Alu_AluSz chr1 13069398 13069586 + SINE/Alu_AluJb chr1 13069667 13069802 + SINE/Alu_AluSz6 chr1 13070252 13070554 - SINE/Alu_AluSx chr1 13071128 13071248 + SINE/Alu_AluJb chr1 13071249 13071556 - SINE/Alu_AluSz chr1 13071557 13071737 + SINE/Alu_AluJb chr1 13071814 13071950 + SINE/Alu_AluSz6 chr1 13072396 13072668 - SINE/Alu_AluSx chr1 13072790 13073067 + SINE/Alu_AluJb chr1 13073068 13073216 + Low_complexity_GA-rich chr1 13074219 13074503 + SINE/Alu_AluSx1 chr1 13075564 13075695 + SINE/Alu_FLAM_C chr1 13075717 13076012 - SINE/Alu_AluSx chr1 13076499 13076802 - SINE/Alu_AluSx1 chr1 13078063 13078363 - SINE/Alu_AluSp chr1 13078364 13078655 - SINE/Alu_AluJr chr1 13079396 13079522 + SINE/MIR_MIRb chr1 13079863 13080168 + SINE/Alu_AluSp chr1 13080180 13080436 + SINE/Alu_AluSg chr1 13080558 13080840 + SINE/Alu_AluSx3 chr1 13081010 13081036 + Simple_repeat_(TTTAT)n chr1 13081038 13081327 - SINE/Alu_AluJb chr1 13081333 13081651 - SINE/Alu_AluSx chr1 13081653 13082351 - LINE/L1_L1MC1 chr1 13082374 13082461 + LINE/L2_L2b chr1 13082619 13082925 + SINE/Alu_AluSz chr1 13082926 13082986 + Low_complexity_A-rich chr1 13084016 13084243 - SINE/Alu_AluJb chr1 13084331 13084636 - SINE/Alu_AluSx chr1 13084708 13084851 + LTR/ERV1_LTR61 chr1 13084852 13084880 + Simple_repeat_(AAAC)n chr1 13084881 13085294 + LTR/ERV1_LTR61 chr1 13085295 13086998 + LTR/ERV1_HERV3-int chr1 13086999 13087106 + LTR/ERVK_LTR5B chr1 13087109 13089246 + LTR/ERV1_HERV3-int chr1 13089247 13089563 - SINE/Alu_AluY chr1 13089564 13089947 + LTR/ERV1_HERV3-int chr1 13089948 13090172 - SINE/Alu_AluSx4 chr1 13090173 13090289 - DNA/TcMar-Tigger_Tigger6 chr1 13090552 13090656 + Simple_repeat_(TATTAG)n chr1 13091585 13091609 + Simple_repeat_(AT)n chr1 13091623 13093037 - Satellite/centr_GSATII chr1 13093036 13093326 - LTR/ERV1_MER52A chr1 13093308 13093520 - LTR/ERV1_MER52A chr1 13093634 13093750 - LTR/ERV1_MER52A chr1 13094206 13094500 + SINE/Alu_AluSz6 chr1 13094913 13095029 + SINE/Alu_AluJr chr1 13098481 13098707 + SINE/Alu_AluSz chr1 13098708 13098740 + Simple_repeat_(AAT)n chr1 13098759 13098921 + SINE/Alu_AluSx chr1 13098978 13099048 + SINE/Alu_FLAM_C chr1 13101013 13101316 + SINE/Alu_AluJb chr1 13101317 13101347 + Simple_repeat_(GGGA)n chr1 13101349 13101537 + Simple_repeat_(AGAA)n chr1 13102294 13102363 + LTR/ERV1_MER41G chr1 13102385 13102987 + LTR/ERV1_HERVE-int chr1 13103065 13103100 + Simple_repeat_(ATTGAT)n chr1 13103197 13103261 - SINE/Alu_AluSz chr1 13103415 13103716 - SINE/Alu_AluSx chr1 13103782 13104091 + SINE/Alu_AluSp chr1 13104282 13104349 + LINE/L2_L2c chr1 13105989 13106131 - SINE/Alu_AluJr chr1 13106257 13106561 + SINE/Alu_AluSx chr1 13106562 13106581 + Simple_repeat_(GA)n chr1 13107008 13107151 - SINE/Alu_AluSz6 chr1 13107233 13107451 - SINE/Alu_AluJr chr1 13107452 13107737 + SINE/Alu_AluSz chr1 13107738 13107858 - SINE/Alu_AluJr chr1 13108432 13108741 + SINE/Alu_AluSx1 chr1 13109110 13109687 + LTR/ERV1_LTR26 chr1 13109823 13110033 + LTR/ERV1_MER65A chr1 13110180 13110724 + LTR/ERV1_LTR44 chr1 13111528 13111623 - LTR/ERV1_MER34-int chr1 13111652 13111792 - LTR/ERV1_MER57A-int chr1 13111793 13111867 - LTR/ERV1_LTR45B chr1 13111876 13112160 + SINE/Alu_AluSg chr1 13112263 13112424 - LTR/ERV1_MER57A-int chr1 13112526 13112568 + Simple_repeat_(TATATC)n chr1 13112773 13112831 - DNA/hAT-Blackjack_MER94 chr1 13112841 13113128 - SINE/Alu_AluSx1 chr1 13113370 13113520 - SINE/Alu_AluJb chr1 13113521 13114378 + LTR/ERVK_LTR5B chr1 13114420 13114700 + SINE/Alu_AluSx1 chr1 13114717 13114838 - SINE/Alu_AluJb chr1 13117146 13117431 + SINE/Alu_AluSx1 chr1 13117810 13118379 + LTR/ERV1_LTR26 chr1 13118515 13118699 + LTR/ERV1_MER65A chr1 13118877 13119416 + LTR/ERV1_LTR44 chr1 13119684 13119719 + Simple_repeat_(GTCTAT)n chr1 13120225 13120319 - LTR/ERV1_MER34-int chr1 13120422 13120489 - LTR/ERV1_MER57A-int chr1 13120557 13120846 + SINE/Alu_AluSg chr1 13120944 13121114 - LTR/ERV1_MER57A-int chr1 13121343 13121415 + Simple_repeat_(AT)n chr1 13121505 13121800 - SINE/Alu_AluSx1 chr1 13121913 13122096 - SINE/MIR_MIR chr1 13122097 13122312 - SINE/Alu_AluY chr1 13122412 13122494 - LINE/L1_L1M5 chr1 13122683 13122992 - SINE/Alu_AluSp chr1 13123058 13123359 + SINE/Alu_AluSx chr1 13123513 13123577 + SINE/Alu_AluSz chr1 13123787 13124389 - LTR/ERV1_HERVE-int chr1 13124411 13124480 - LTR/ERV1_MER41G chr1 13125237 13125402 + Simple_repeat_(TTTC)n chr1 13125439 13125469 + Simple_repeat_(CCCT)n chr1 13125470 13125773 - SINE/Alu_AluJb chr1 13127738 13127808 - SINE/Alu_FLAM_C chr1 13127865 13128027 - SINE/Alu_AluSx chr1 13128046 13128081 + Simple_repeat_(ATT)n chr1 13128082 13128308 - SINE/Alu_AluSz chr1 13129362 13129415 + Simple_repeat_(AGAGAACAG)n chr1 13131760 13131876 - SINE/Alu_AluJr chr1 13132289 13132583 - SINE/Alu_AluSz6 chr1 13133039 13133155 + LTR/ERV1_MER52A chr1 13133269 13133481 + LTR/ERV1_MER52A chr1 13133463 13133753 + LTR/ERV1_MER52A chr1 13133752 13135166 + Satellite/centr_GSATII chr1 13135180 13135204 + Simple_repeat_(TA)n chr1 13136133 13136237 + Simple_repeat_(CTAATA)n chr1 13136500 13136616 + DNA/TcMar-Tigger_Tigger6 chr1 13136617 13136841 + SINE/Alu_AluSx4 chr1 13136842 13137225 - LTR/ERV1_HERV3-int chr1 13137226 13137542 + SINE/Alu_AluY chr1 13137543 13139680 - LTR/ERV1_HERV3-int chr1 13139683 13139790 - LTR/ERVK_LTR5B chr1 13139791 13141494 - LTR/ERV1_HERV3-int chr1 13141495 13141908 - LTR/ERV1_LTR61 chr1 13141909 13141937 + Simple_repeat_(TTGT)n chr1 13141938 13142081 - LTR/ERV1_LTR61 chr1 13142131 13142152 + Simple_repeat_(ATAGAAT)n chr1 13142153 13142458 + SINE/Alu_AluSx chr1 13142459 13142473 + Simple_repeat_(ATAGAAT)n chr1 13142546 13142773 + SINE/Alu_AluJb chr1 13143803 13143863 + Simple_repeat_(CTTT)n chr1 13143864 13144170 - SINE/Alu_AluSz chr1 13144328 13144415 - LINE/L2_L2b chr1 13144438 13145136 + LINE/L1_L1MC1 chr1 13145138 13145456 + SINE/Alu_AluSx chr1 13145462 13145751 + SINE/Alu_AluJb chr1 13145753 13145779 + Simple_repeat_(AAATA)n chr1 13145949 13146231 - SINE/Alu_AluSx3 chr1 13146353 13146609 - SINE/Alu_AluSg chr1 13146621 13146926 - SINE/Alu_AluSp chr1 13147267 13147393 - SINE/MIR_MIRb chr1 13148134 13148425 + SINE/Alu_AluJr chr1 13148426 13148726 + SINE/Alu_AluSp chr1 13149987 13150290 + SINE/Alu_AluSx1 chr1 13150777 13151072 + SINE/Alu_AluSx chr1 13151094 13151225 - SINE/Alu_FLAM_C chr1 13152286 13152570 - SINE/Alu_AluSx1 chr1 13153547 13153721 + Simple_repeat_(TTTC)n chr1 13153722 13153999 - SINE/Alu_AluJb chr1 13154121 13154393 + SINE/Alu_AluSx chr1 13154839 13154975 - SINE/Alu_AluSz6 chr1 13155052 13155235 - SINE/Alu_AluJb chr1 13155236 13155543 + SINE/Alu_AluSz chr1 13155544 13155664 - SINE/Alu_AluJb chr1 13156237 13156540 + SINE/Alu_AluSx chr1 13156990 13157125 - SINE/Alu_AluSz6 chr1 13157206 13157393 - SINE/Alu_AluJb chr1 13157394 13157695 + SINE/Alu_AluSz chr1 13157696 13157819 - SINE/Alu_AluJb chr1 13158391 13158690 + SINE/Alu_AluSx1 chr1 13159062 13159630 + LTR/ERV1_LTR26 chr1 13159764 13159972 + LTR/ERV1_MER65A chr1 13159981 13160118 - LTR/ERV1_MER34-int chr1 13160120 13160662 + LTR/ERV1_LTR44 chr1 13161468 13161563 - LTR/ERV1_MER34-int chr1 13161587 13161735 - LTR/ERV1_MER57A-int chr1 13161736 13161810 - LTR/ERV1_LTR45B chr1 13161821 13162114 + SINE/Alu_AluSg chr1 13162209 13162357 - LTR/ERV1_MER57A-int chr1 13162507 13162552 + Simple_repeat_(TCCATT)n chr1 13162779 13163065 - SINE/Alu_AluSx1 chr1 13163311 13163486 - SINE/Alu_AluJb chr1 13163487 13163766 + SINE/Alu_AluSx1 chr1 13163767 13163905 - SINE/Alu_AluJb chr1 13166215 13166489 + SINE/Alu_AluSx1 chr1 13166490 13166521 + Simple_repeat_(CAA)n chr1 13166876 13167166 + SINE/Alu_AluSq2 chr1 13167169 13167229 + Simple_repeat_(AATA)n chr1 13167285 13167591 + SINE/Alu_AluSc8 chr1 13167805 13167910 - Unknown_UCON80_AMi chr1 13167960 13168271 - SINE/Alu_AluSc chr1 13168272 13168323 - SINE/Alu_Alu chr1 13168345 13168373 + Simple_repeat_(TTATT)n chr1 13168412 13168699 - LTR/ERV1_LTR44 chr1 13168788 13169356 - LTR/ERV1_LTR26 chr1 13169716 13169727 + Simple_repeat_(ATGA)n chr1 13169728 13170023 - SINE/Alu_AluSx1 chr1 13170024 13170041 + Simple_repeat_(ATGA)n chr1 13170601 13170721 + SINE/Alu_AluJb chr1 13170722 13171025 - SINE/Alu_AluSz chr1 13171026 13171211 + SINE/Alu_AluJb chr1 13171294 13171430 + SINE/Alu_AluSz6 chr1 13171866 13171899 + Simple_repeat_(T)n chr1 13171915 13172174 - SINE/Alu_AluSx chr1 13172755 13172875 + SINE/Alu_AluJb chr1 13172876 13173170 - SINE/Alu_AluSz chr1 13173171 13173309 + SINE/Alu_AluJb chr1 13173385 13173544 + SINE/Alu_AluSz6 chr1 13173983 13174242 - SINE/Alu_AluSx chr1 13174360 13174646 + SINE/Alu_AluJr chr1 13174647 13174829 + Low_complexity_GA-rich chr1 13175805 13176085 + SINE/Alu_AluSx1 chr1 13177146 13177256 + SINE/Alu_FLAM_C chr1 13177299 13177594 - SINE/Alu_AluSx chr1 13178079 13178377 - SINE/Alu_AluSx1 chr1 13179643 13179943 - SINE/Alu_AluSp chr1 13179944 13180233 - SINE/Alu_AluJr chr1 13180981 13181107 + SINE/MIR_MIRb chr1 13181448 13181746 + SINE/Alu_AluSp chr1 13181763 13182019 + SINE/Alu_AluSg chr1 13182141 13182423 + SINE/Alu_AluSx3 chr1 13182593 13182624 + Simple_repeat_(TTTAT)n chr1 13182626 13182915 - SINE/Alu_AluJb chr1 13182921 13183241 - SINE/Alu_AluSx chr1 13183243 13183908 - LINE/L1_L1MC1 chr1 13183961 13184040 + LINE/L2_L2b chr1 13184207 13184347 + SINE/Alu_AluSz chr1 13184353 13187361 + LINE/L1_L1PA7 chr1 13187362 13187504 + LINE/L1_L1PA4 chr1 13187505 13190819 + LINE/L1_L1PA7 chr1 13190820 13191072 + Simple_repeat_(AGAA)n chr1 13191071 13191098 + Simple_repeat_(AGGGG)n chr1 13191100 13191126 + Simple_repeat_(GAAGG)n chr1 13191132 13191447 + LTR/ERV1_MER41G chr1 13191448 13191732 + SINE/Alu_AluSp chr1 13191734 13191776 + Simple_repeat_(ATAA)n chr1 13191777 13192073 + LTR/ERV1_MER41G chr1 13192110 13192198 + DNA/hAT-Charlie_MER102c chr1 13192207 13193143 - LINE/L1_L1PB chr1 13193218 13193546 - LINE/L1_L1M1 chr1 13193550 13195175 - LTR/ERV1_MER52D chr1 13195261 13195304 - LINE/CR1_L3 chr1 13195350 13195442 - LTR/ERV1_MER52D chr1 13195885 13196171 + SINE/Alu_AluSz6 chr1 13196556 13196674 + SINE/Alu_AluJo chr1 13199008 13199047 + Simple_repeat_(TCCCTTT)n chr1 13199997 13200236 + SINE/Alu_AluSz chr1 13200256 13200424 + SINE/Alu_AluSx chr1 13200503 13200787 + SINE/Alu_AluSq2 chr1 13202154 13202446 - SINE/Alu_AluSz chr1 13203012 13203026 + Simple_repeat_(AAAG)n chr1 13203027 13203320 + SINE/Alu_AluSc8 chr1 13203321 13203575 + Simple_repeat_(AAAG)n chr1 13203577 13203772 + SINE/Alu_AluJo chr1 13204873 13204891 + Simple_repeat_(GT)n chr1 13205270 13205344 + Simple_repeat_(CCTCTG)n chr1 13205756 13205974 + SINE/Alu_AluSx1 chr1 13206477 13206492 + Low_complexity_A-rich chr1 13206493 13206502 + Simple_repeat_(ATATAA)n chr1 13206503 13206806 + SINE/Alu_AluSc chr1 13206807 13206841 + Simple_repeat_(ATATAA)n chr1 13206972 13207962 + LTR/ERVK_LTR5B chr1 13207964 13215426 + LTR/ERVK_HERVK-int chr1 13215427 13215513 + Retroposon/SVA_SVA_F chr1 13215516 13216513 + LTR/ERVK_LTR5B chr1 13216528 13216917 + LTR/ERVL_LTR57 chr1 13217566 13217695 + SINE/MIR_MIRb chr1 13217754 13218069 + LTR/ERV1_MER49 chr1 13218070 13218368 - SINE/Alu_AluSx3 chr1 13218369 13218697 + LTR/ERV1_MER49 chr1 13218698 13218836 + SINE/Alu_AluSx chr1 13218837 13219138 + SINE/Alu_AluY chr1 13219139 13219298 + SINE/Alu_AluSx chr1 13219299 13219569 + LTR/ERV1_MER49 chr1 13219562 13219691 - SINE/Alu_FLAM_C chr1 13219706 13219998 - SINE/Alu_AluSc chr1 13220012 13220135 + SINE/Alu_FLAM_C chr1 13220144 13220550 + LINE/L1_L1PA15 chr1 13220551 13220846 + SINE/Alu_AluSz chr1 13220847 13220937 + LINE/L1_L1PA15 chr1 13220935 13221323 - LINE/L1_L1M3f chr1 13222134 13222367 + SINE/Alu_AluSx chr1 13222375 13222685 + SINE/Alu_AluSp chr1 13223097 13223211 + SINE/Alu_AluJo chr1 13224737 13224810 + LINE/L2_L2b chr1 13226469 13226777 + SINE/Alu_AluSx1 chr1 13226916 13227223 + SINE/Alu_AluJb chr1 13227309 13227443 + SINE/Alu_AluJr chr1 13227444 13227754 + SINE/Alu_AluSx chr1 13227755 13227939 + SINE/Alu_AluJr chr1 13227988 13228284 + SINE/Alu_AluSg chr1 13228597 13228892 - SINE/Alu_AluSp chr1 13229434 13229733 + SINE/Alu_AluSc chr1 13229749 13230056 + SINE/Alu_AluY chr1 13230083 13230377 + SINE/Alu_AluSx4 chr1 13230386 13230552 + SINE/Alu_AluSx4 chr1 13230869 13231077 + LTR/ERV1_MER65A chr1 13231237 13231554 + SINE/Alu_AluSx chr1 13231555 13232009 + LTR/ERV1_LTR44 chr1 13232052 13232323 + SINE/Alu_AluSz6 chr1 13232325 13232364 + Simple_repeat_(ATAA)n chr1 13232365 13232665 + SINE/Alu_AluSp chr1 13232808 13233119 + SINE/Alu_AluSx3 chr1 13233201 13233517 - SINE/Alu_AluSx1 chr1 13233579 13233614 + Simple_repeat_(TGTTT)n chr1 13233615 13233920 - SINE/Alu_AluSx1 chr1 13233921 13233957 + Simple_repeat_(TGTTT)n chr1 13233958 13234104 - SINE/Alu_FRAM chr1 13234274 13234594 + SINE/Alu_AluJb chr1 13234678 13234959 + SINE/Alu_AluSz chr1 13234960 13235257 - SINE/Alu_AluYh3 chr1 13235273 13235290 + Simple_repeat_(A)n chr1 13235314 13235617 + SINE/Alu_AluY chr1 13235661 13235969 - SINE/Alu_AluSz chr1 13236114 13236396 - LTR/ERV1_LTR1C3 chr1 13236397 13236717 + SINE/Alu_AluSx1 chr1 13236718 13237046 - LTR/ERV1_LTR1C3 chr1 13237058 13237254 + SINE/Alu_AluSc chr1 13237256 13237922 + LTR/ERV1_MER4B-int chr1 13237924 13238177 + LTR/ERV1_MER72 chr1 13238160 13238209 + LTR/ERV1_MER72 chr1 13238210 13238512 - SINE/Alu_AluSx chr1 13238513 13238556 + LTR/ERV1_MER72 chr1 13238657 13238760 - LTR/ERV1_MER4E1 chr1 13238761 13239063 + SINE/Alu_AluY chr1 13239064 13239184 - LTR/ERV1_MER4E1 chr1 13239185 13239485 + SINE/Alu_AluSz chr1 13239486 13239497 - LTR/ERV1_MER4E1 chr1 13239498 13239796 + SINE/Alu_AluSx chr1 13239797 13239839 - LTR/ERV1_MER4E1 chr1 13239840 13239924 + Simple_repeat_(AAGAA)n chr1 13239925 13240496 - LTR/ERV1_MER4E1 chr1 13240665 13240970 + SINE/Alu_AluSx chr1 13240972 13241123 + LINE/L1_L1M5 chr1 13241864 13242167 - SINE/Alu_AluSx3 chr1 13242214 13242394 - SINE/Alu_AluSx chr1 13242430 13242456 + Simple_repeat_(T)n chr1 13242500 13242971 - LTR/ERV1_LTR7Y chr1 13242972 13243210 - SINE/Alu_AluSg chr1 13243215 13243348 - SINE/Alu_AluSx chr1 13243360 13243597 - SINE/Alu_AluSx1 chr1 13243598 13243661 - SINE/Alu_AluSx3 chr1 13243664 13243824 + SINE/Alu_AluSx chr1 13243892 13244186 + SINE/Alu_AluSx chr1 13244275 13244563 - SINE/Alu_AluSx chr1 13244648 13244771 - SINE/Alu_AluJo chr1 13244855 13244899 + Simple_repeat_(TTA)n chr1 13244901 13245194 - SINE/Alu_AluJb chr1 13245205 13245506 - SINE/Alu_AluSx3 chr1 13247177 13247244 - LINE/L2_L2b chr1 13248776 13248933 - SINE/Alu_AluSz chr1 13248934 13249206 - SINE/Alu_AluY chr1 13249207 13249343 - SINE/Alu_AluSz chr1 13250367 13250669 + SINE/Alu_AluSq2 chr1 13250676 13250717 + Simple_repeat_(ATAA)n chr1 13250772 13251080 + SINE/Alu_AluSc8 chr1 13251293 13251399 - Unknown_UCON80_AMi chr1 13251457 13251768 - SINE/Alu_AluSc chr1 13251769 13251818 - SINE/Alu_Alu chr1 13251911 13252200 - LTR/ERV1_LTR44 chr1 13252202 13252339 + LTR/ERV1_MER34-int chr1 13252347 13252549 - LTR/ERV1_MER65A chr1 13252683 13253252 - LTR/ERV1_LTR26 chr1 13253623 13253918 - SINE/Alu_AluSx1 chr1 13254494 13254614 + SINE/Alu_AluJb chr1 13254615 13254921 - SINE/Alu_AluSz chr1 13254922 13255107 + SINE/Alu_AluJb chr1 13255190 13255326 + SINE/Alu_AluSz6 chr1 13255765 13255806 + Simple_repeat_(T)n chr1 13255807 13256081 - SINE/Alu_AluSx chr1 13256655 13256775 + SINE/Alu_AluJb chr1 13256776 13257081 - SINE/Alu_AluSz chr1 13257082 13257269 + SINE/Alu_AluJb chr1 13257345 13257497 + SINE/Alu_AluSx3 chr1 13257792 13257841 + Simple_repeat_(AATATTT)n chr1 13257923 13258196 - SINE/Alu_AluSx chr1 13258314 13258600 + SINE/Alu_AluJr chr1 13258601 13258811 + Low_complexity_GA-rich chr1 13259787 13260066 + SINE/Alu_AluSx1 chr1 13261127 13261258 + SINE/Alu_FLAM_C chr1 13261280 13261572 - SINE/Alu_AluSx chr1 13261590 13261619 + Simple_repeat_(GAAGT)n chr1 13262056 13262361 - SINE/Alu_AluSx1 chr1 13263613 13263913 - SINE/Alu_AluSp chr1 13263914 13264200 - SINE/Alu_AluJo chr1 13264947 13265082 + SINE/MIR_MIRb chr1 13265376 13265412 + Simple_repeat_(ATTAT)n chr1 13265415 13265466 + SINE/Alu_AluYj4 chr1 13265476 13265750 + SINE/Alu_AluSz chr1 13265870 13266080 + SINE/Alu_AluSx3 chr1 13266254 13266388 + SINE/Alu_AluSx chr1 13266389 13266435 - LINE/L1_L1PA4 chr1 13266436 13266660 + LINE/L1_L1PA4 chr1 13266661 13266836 + SINE/Alu_AluSx chr1 13269961 13270088 + SINE/Alu_AluSq2 chr1 13270115 13270193 - LINE/L2_L2a chr1 13270291 13270451 - SINE/Alu_AluJb chr1 13270452 13270731 - LINE/L1_L1M1 chr1 13270732 13271902 - LTR/ERV1_MER52D chr1 13272075 13272164 - LTR/ERV1_MER52D chr1 13272582 13272842 + SINE/Alu_AluSz6 chr1 13272843 13272871 + Simple_repeat_(T)n chr1 13272872 13272893 + SINE/Alu_AluSz6 chr1 13273276 13273395 + SINE/Alu_FLAM_C chr1 13275912 13275938 - LTR/ERV1_MER4E1 chr1 13275939 13275984 + Simple_repeat_(TTTA)n chr1 13275988 13276273 - SINE/Alu_AluSp chr1 13276274 13276365 - LTR/ERV1_MER4E1 chr1 13276364 13276722 + LTR/ERV1_MER49 chr1 13276843 13276906 + LTR/ERV1_MER101-int chr1 13277026 13277239 + LTR/ERV1_MER101-int chr1 13277531 13277660 - LINE/L2_L2c chr1 13277663 13278312 + LINE/L1_L1MC1 chr1 13278462 13279124 - LINE/L1_L1PA7 chr1 13279125 13279231 - snRNA_U6 chr1 13279237 13279530 - SINE/Alu_AluY chr1 13280097 13280226 - SINE/MIR_MIRb chr1 13282082 13282175 + SINE/MIR_MIRb chr1 13286111 13286213 + Low_complexity_GA-rich chr1 13286214 13286298 + Simple_repeat_(GAAA)n chr1 13286787 13286967 - SINE/MIR_MIRb chr1 13288891 13289252 - DNA/TcMar-Tigger_Tigger6a chr1 13290627 13290797 - SINE/MIR_MIR chr1 13291049 13291107 + SINE/Alu_AluSc chr1 13291113 13291171 + DNA/hAT-Blackjack_MER94 chr1 13291254 13291497 - LINE/L1_L1PA4 chr1 13291505 13294083 - LINE/L1_L1PA4 chr1 13294084 13294161 + LTR/ERV1_LTR12C chr1 13294162 13296657 - LINE/L1_L1PA4 chr1 13296892 13297093 + LTR/ERV1_MER57A-int chr1 13297192 13297484 - SINE/Alu_AluSz6 chr1 13297847 13297917 + LTR/ERV1_LTR45 chr1 13297920 13298116 - SINE/Alu_AluSx3 chr1 13298117 13299441 - Retroposon/SVA_SVA_D chr1 13299442 13299593 - SINE/Alu_AluSx3 chr1 13299612 13299903 - SINE/Alu_AluSx4 chr1 13300097 13300219 + LTR/ERV1_MER34-int chr1 13300957 13301501 - LTR/ERV1_LTR44 chr1 13301650 13301857 - LTR/ERV1_MER65A chr1 13301996 13302571 - LTR/ERV1_LTR26 chr1 13302933 13302943 + Simple_repeat_(GAAT)n chr1 13302944 13303233 - SINE/Alu_AluSx chr1 13303234 13303247 + Simple_repeat_(GAAT)n chr1 13303810 13303947 + SINE/Alu_AluJb chr1 13303948 13304228 - SINE/Alu_AluSx1 chr1 13304229 13304415 + SINE/Alu_AluJb chr1 13304496 13304632 + SINE/Alu_AluSz6 chr1 13305064 13305082 + Simple_repeat_(TC)n chr1 13305103 13305361 - SINE/Alu_AluSx chr1 13305492 13305633 + SINE/Alu_AluJr chr1 13307272 13307321 - LINE/L2_L2a chr1 13307590 13307875 + SINE/Alu_AluSx3 chr1 13308970 13309031 + Simple_repeat_(ATTTT)n chr1 13309032 13309316 - SINE/Alu_AluSz chr1 13309628 13309845 - SINE/Alu_AluSx1 chr1 13309846 13310398 - LINE/L1_L1MA4 chr1 13310400 13310501 + SINE/Alu_AluSz chr1 13310502 13310721 - LINE/L1_L1MA4 chr1 13310909 13311224 - SINE/Alu_AluSp chr1 13311769 13312059 + SINE/Alu_AluSq2 chr1 13312062 13312122 + Simple_repeat_(AATA)n chr1 13312178 13312484 + SINE/Alu_AluSc8 chr1 13312698 13312803 - Unknown_UCON80_AMi chr1 13312849 13313157 - SINE/Alu_AluSc chr1 13313159 13313209 - SINE/Alu_Alu chr1 13313298 13313590 - LTR/ERV1_LTR44 chr1 13313592 13313729 + LTR/ERV1_MER34-int chr1 13313811 13313940 - LTR/ERV1_MER65A chr1 13314074 13314643 - LTR/ERV1_LTR26 chr1 13315014 13315301 - SINE/Alu_AluSx1 chr1 13315882 13316002 + SINE/Alu_AluJb chr1 13316003 13316297 - SINE/Alu_AluSz chr1 13316298 13316442 + SINE/Alu_AluJb chr1 13316518 13316671 + SINE/Alu_AluSz6 chr1 13317110 13317369 - SINE/Alu_AluSx chr1 13317487 13317773 + SINE/Alu_AluJr chr1 13317788 13317937 + Low_complexity_GA-rich chr1 13318932 13319216 + SINE/Alu_AluSx1 chr1 13320270 13320387 + SINE/Alu_FLAM_C chr1 13320430 13320723 - SINE/Alu_AluSx chr1 13321210 13321514 - SINE/Alu_AluSx1 chr1 13322775 13323074 - SINE/Alu_AluSp chr1 13323075 13323365 - SINE/Alu_AluJr chr1 13324106 13324232 + SINE/MIR_MIRb chr1 13324573 13324876 + SINE/Alu_AluSp chr1 13324888 13325144 + SINE/Alu_AluSg chr1 13325266 13325548 + SINE/Alu_AluSx3 chr1 13325718 13326061 - SINE/Alu_AluJb chr1 13326067 13326388 - SINE/Alu_AluSx chr1 13326390 13327055 - LINE/L1_L1MC1 chr1 13327108 13327187 + LINE/L2_L2b chr1 13327358 13327493 + SINE/Alu_AluSz chr1 13327613 13328921 + Retroposon/SVA_SVA_E chr1 13328939 13330093 + Retroposon/SVA_SVA_E chr1 13330113 13333125 + LINE/L1_L1PA7 chr1 13333126 13333268 + LINE/L1_L1PA4 chr1 13333269 13336570 + LINE/L1_L1PA7 chr1 13336572 13336752 + Simple_repeat_(GAAA)n chr1 13336767 13336823 + Low_complexity_G-rich chr1 13336828 13337143 + LTR/ERV1_MER41G chr1 13337144 13337428 + SINE/Alu_AluSp chr1 13337430 13337481 + Simple_repeat_(ATAA)n chr1 13337482 13337777 + LTR/ERV1_MER41G chr1 13337814 13337902 + DNA/hAT-Charlie_MER102c chr1 13337911 13338847 - LINE/L1_L1PB chr1 13338923 13339250 - LINE/L1_L1M1 chr1 13339254 13340879 - LTR/ERV1_MER52D chr1 13340965 13341008 - LINE/CR1_L3 chr1 13341054 13341146 - LTR/ERV1_MER52D chr1 13341589 13341875 + SINE/Alu_AluSz6 chr1 13342261 13342379 + SINE/Alu_AluJo chr1 13343325 13343349 + Simple_repeat_(T)n chr1 13344715 13344758 + Simple_repeat_(TGTTCCC)n chr1 13345722 13345961 + SINE/Alu_AluSz chr1 13345981 13346149 + SINE/Alu_AluSx chr1 13346227 13346512 + SINE/Alu_AluSq2 chr1 13347879 13348186 - SINE/Alu_AluSz chr1 13348752 13348766 + Simple_repeat_(AAAG)n chr1 13348767 13349060 + SINE/Alu_AluSc8 chr1 13349061 13349338 + Simple_repeat_(AAAG)n chr1 13349340 13349535 + SINE/Alu_AluJr chr1 13349698 13349797 - SINE/MIR_MIRc chr1 13350637 13350655 + Simple_repeat_(GT)n chr1 13351034 13351108 + Simple_repeat_(CCTCTG)n chr1 13351502 13351555 + SINE/Alu_Alu chr1 13351556 13351737 + SINE/Alu_AluSg4 chr1 13352240 13352266 + Simple_repeat_(AACATAT)n chr1 13352267 13352570 + SINE/Alu_AluSc chr1 13352571 13352609 + Simple_repeat_(AACATAT)n chr1 13352736 13353726 + LTR/ERVK_LTR5B chr1 13353727 13354460 + LTR/ERVK_HERVK-int chr1 13354553 13361170 + LTR/ERVK_HERVK-int chr1 13361171 13361257 + Retroposon/SVA_SVA_F chr1 13361260 13362257 + LTR/ERVK_LTR5B chr1 13362272 13362661 + LTR/ERVL_LTR57 chr1 13363310 13363439 + SINE/MIR_MIRb chr1 13363498 13363813 + LTR/ERV1_MER49 chr1 13363814 13364109 - SINE/Alu_AluSg chr1 13364110 13364438 + LTR/ERV1_MER49 chr1 13364439 13364577 + SINE/Alu_AluSx chr1 13364578 13364873 + SINE/Alu_AluY chr1 13364874 13365033 + SINE/Alu_AluSx chr1 13365034 13365279 + LTR/ERV1_MER49 chr1 13365296 13365426 - SINE/Alu_FLAM_C chr1 13365441 13365736 - SINE/Alu_AluSc chr1 13365750 13365873 + SINE/Alu_FLAM_C chr1 13365882 13366288 + LINE/L1_L1PA15 chr1 13366289 13366578 + SINE/Alu_AluSz chr1 13366579 13366667 + LINE/L1_L1PA15 chr1 13366665 13367053 - LINE/L1_L1M3f chr1 13367867 13368100 + SINE/Alu_AluSz chr1 13368113 13368422 + SINE/Alu_AluSp chr1 13368832 13368946 + SINE/Alu_AluJo chr1 13370549 13370604 + LINE/L2_L2c chr1 13371953 13371998 + Unknown_UCON76 chr1 13372160 13372454 + SINE/Alu_AluYe5 chr1 13372570 13372877 + SINE/Alu_AluSx1 chr1 13373013 13373320 + SINE/Alu_AluJb chr1 13373406 13373540 + SINE/Alu_AluJr chr1 13373541 13373676 + SINE/Alu_AluSx1 chr1 13373677 13373978 + SINE/Alu_AluY chr1 13373979 13374153 + SINE/Alu_AluSx1 chr1 13374154 13374330 + SINE/Alu_AluJr chr1 13374380 13374683 + SINE/Alu_AluSg chr1 13374997 13375157 - SINE/Alu_AluSp chr1 13375717 13376016 + SINE/Alu_AluSc chr1 13376032 13376337 + SINE/Alu_AluY chr1 13376369 13376664 + SINE/Alu_AluSx4 chr1 13376673 13376839 + SINE/Alu_AluSx4 chr1 13377157 13377365 + LTR/ERV1_MER65A chr1 13377525 13377837 + SINE/Alu_AluSx chr1 13377838 13378291 + LTR/ERV1_LTR44 chr1 13378334 13378627 + SINE/Alu_AluSz6 chr1 13378631 13378865 + SINE/Alu_AluSp chr1 13378988 13379122 - SINE/Alu_FRAM chr1 13379292 13379611 + SINE/Alu_AluJb chr1 13379694 13379987 + SINE/Alu_AluSz chr1 13380048 13380354 - SINE/Alu_AluSz chr1 13380499 13380781 - LTR/ERV1_LTR1C3 chr1 13380782 13381108 + SINE/Alu_AluSx1 chr1 13381109 13381437 - LTR/ERV1_LTR1C3 chr1 13381449 13381655 + SINE/Alu_AluSc chr1 13381657 13382322 + LTR/ERV1_MER4B-int chr1 13382337 13382637 + SINE/Alu_AluSg7 chr1 13382638 13382885 + LTR/ERV1_MER72 chr1 13382867 13382916 + LTR/ERV1_MER72 chr1 13382917 13383212 - SINE/Alu_AluSx chr1 13383213 13383256 + LTR/ERV1_MER72 chr1 13383357 13383558 - LTR/ERV1_MER4E1 chr1 13383559 13383868 + SINE/Alu_AluSz chr1 13383869 13383879 - LTR/ERV1_MER4E1 chr1 13383880 13384195 + SINE/Alu_AluSx chr1 13384196 13384776 - LTR/ERV1_MER4E1 chr1 13384945 13385250 + SINE/Alu_AluSx chr1 13385251 13385427 + LINE/L1_L1M5 chr1 13386145 13386163 + Simple_repeat_(AT)n chr1 13386164 13386474 - SINE/Alu_AluSx3 chr1 13386524 13386738 - SINE/Alu_AluSx1 chr1 13386739 13387043 - SINE/Alu_AluSg chr1 13387044 13387181 - SINE/Alu_AluSx1 chr1 13387193 13387428 - SINE/Alu_AluSx1 chr1 13387429 13387492 - SINE/Alu_AluSx3 chr1 13387495 13387655 + SINE/Alu_AluJb chr1 13387722 13388014 + SINE/Alu_AluSx chr1 13388110 13388392 - SINE/Alu_AluSx4 chr1 13388476 13388600 - SINE/Alu_FLAM_C chr1 13388699 13388993 - SINE/Alu_AluJb chr1 13389006 13389301 - SINE/Alu_AluSx3 chr1 13390975 13391042 - LINE/L2_L2b chr1 13392584 13392745 - SINE/Alu_AluSx chr1 13392757 13393110 - SINE/Alu_AluSz chr1 13394128 13394155 + Retroposon/SVA_SVA_B chr1 13394156 13394275 - SINE/Alu_FLAM_C chr1 13394277 13395679 - Retroposon/SVA_SVA_D chr1 13395680 13395706 + Simple_repeat_(GAGAGG)n chr1 13395731 13395776 - LINE/L1_L1MA4 chr1 13395777 13396083 - SINE/Alu_AluSx1 chr1 13396084 13396317 - LINE/L1_L1MA4 chr1 13396318 13396640 - SINE/Alu_AluY chr1 13396641 13396971 - LINE/L1_L1MA4 chr1 13396972 13397284 + SINE/Alu_AluSp chr1 13397285 13397500 - LINE/L1_L1MA4 chr1 13397508 13397640 - SINE/Alu_FLAM_C chr1 13397837 13398143 - SINE/Alu_AluJr chr1 13398360 13398491 + SINE/Alu_AluSz chr1 13398492 13398793 + SINE/Alu_AluSg chr1 13399132 13399440 - SINE/Alu_AluSp chr1 13400003 13400286 + SINE/Alu_AluSq2 chr1 13400287 13400334 + Simple_repeat_(AAAT)n chr1 13400335 13400357 + SINE/Alu_AluSq2 chr1 13400414 13400705 + SINE/Alu_AluSc8 chr1 13401026 13401293 - SINE/Alu_AluSc chr1 13401294 13401593 - SINE/Alu_AluSg chr1 13401681 13401824 + LTR/ERV1_ERV24B_Prim-int chr1 13401852 13402145 - SINE/Alu_AluSg4 chr1 13402157 13402191 + Simple_repeat_(TTG)n chr1 13402192 13402477 - SINE/Alu_AluSx chr1 13402694 13402939 + LINE/L2_L2 chr1 13402943 13403236 + SINE/Alu_AluJb chr1 13403304 13403513 - LINE/L1_L1ME3Cz chr1 13403516 13403805 + SINE/Alu_AluSz chr1 13403809 13403884 + LINE/L1_L1M5 chr1 13403885 13404197 - SINE/Alu_AluSx1 chr1 13404198 13404346 + LINE/L1_L1M5 chr1 13404347 13404644 - SINE/Alu_AluSx chr1 13404645 13404956 + LINE/L1_L1M5 chr1 13404995 13405225 + SINE/MIR_MIR chr1 13405307 13405337 + DNA/hAT-Blackjack_MER94 chr1 13405338 13405647 + SINE/Alu_AluSx1 chr1 13405648 13405703 + DNA/hAT-Blackjack_MER94 chr1 13405902 13405953 + Simple_repeat_(GATATA)n chr1 13406240 13406314 - LINE/RTE-BovB_MamRTE1 chr1 13406355 13406639 - SINE/Alu_AluSz6 chr1 13407082 13407390 - SINE/Alu_AluSp chr1 13407495 13408036 - LTR/ERV1_LTR44 chr1 13408183 13408404 - LTR/ERV1_MER65A chr1 13408540 13409117 - LTR/ERV1_LTR26 chr1 13409318 13409363 - SINE/Alu_Alu chr1 13409364 13409550 - SINE/Alu_AluSg chr1 13409734 13410043 - SINE/Alu_AluSx1 chr1 13410045 13410174 - SINE/Alu_AluSz chr1 13410741 13410861 + SINE/Alu_AluJb chr1 13410862 13411158 - SINE/Alu_AluSx1 chr1 13411159 13411327 + SINE/Alu_AluJb chr1 13411560 13411758 - LTR/ERV1_MER65-int chr1 13412022 13412081 + Simple_repeat_(ATTT)n chr1 13412131 13412317 - SINE/Alu_AluSz chr1 13412781 13412917 + SINE/Alu_AluSz6 chr1 13413355 13413664 - SINE/Alu_AluSz chr1 13414032 13414358 - SINE/Alu_AluSx3 chr1 13414469 13414764 - SINE/Alu_AluSx chr1 13414818 13415018 - SINE/Alu_AluSx chr1 13415083 13415381 - SINE/Alu_AluSz chr1 13415500 13415801 + SINE/Alu_AluJb chr1 13415802 13415826 + Simple_repeat_(AGGG)n chr1 13415832 13415896 + Low_complexity_A-rich chr1 13416890 13416993 + SINE/Alu_AluSx chr1 13416994 13417381 - LTR/ERV1_LTR12F chr1 13417382 13417614 + SINE/Alu_AluSx1 chr1 13417735 13417910 - SINE/Alu_AluY chr1 13417911 13417953 + Simple_repeat_(TG)n chr1 13417954 13418073 - SINE/Alu_AluY chr1 13419006 13419136 + SINE/Alu_FLAM_C chr1 13419158 13419448 - SINE/Alu_AluSq2 chr1 13419463 13419723 - SINE/Alu_AluY chr1 13420208 13420508 - SINE/Alu_AluSx1 chr1 13421333 13421413 - SINE/Alu_AluSx3 chr1 13421473 13421795 + SINE/Alu_AluSc chr1 13422049 13422172 + SINE/MIR_MIRb chr1 13422222 13422524 - SINE/Alu_AluSc chr1 13422948 13422984 + SINE/Alu_Alu chr1 13422987 13423282 + SINE/Alu_AluSp chr1 13423434 13423735 - SINE/Alu_AluJb chr1 13423743 13424063 - SINE/Alu_AluSx chr1 13424088 13424398 - SINE/Alu_AluSq2 chr1 13424405 13424868 - LINE/L1_L1MC1 chr1 13424878 13425184 - SINE/Alu_AluSg7 chr1 13425185 13425397 - LINE/L1_L1MC1 chr1 13425406 13425722 - SINE/Alu_AluSx chr1 13425773 13425852 + LINE/L2_L2b chr1 13425883 13426187 + SINE/Alu_AluSp chr1 13426297 13426603 + SINE/Alu_AluSz chr1 13426815 13427117 - SINE/Alu_AluSc chr1 13427151 13427325 - SINE/Alu_AluSx chr1 13427328 13427462 - LTR/ERV1_MER57A-int chr1 13427463 13427772 + SINE/Alu_AluSc chr1 13427799 13427915 + SINE/Alu_AluSx3 chr1 13428058 13428084 + Simple_repeat_(A)n chr1 13428182 13428426 + SINE/Alu_AluSz chr1 13428493 13428667 - LINE/L1_L1MEd chr1 13428756 13428782 + Simple_repeat_(TTTTGT)n chr1 13428783 13429061 - SINE/Alu_AluY chr1 13429164 13429609 + LTR/ERV1_HERV9NC-int chr1 13429624 13430387 + LTR/ERV1_HERV9NC-int chr1 13430389 13430769 + LTR/ERV1_LTR12E chr1 13430767 13430931 - LTR/ERV1_LTR12D chr1 13430963 13431303 - LTR/ERVL_LTR16A1 chr1 13431538 13431672 + SINE/Alu_AluSq2 chr1 13431673 13431969 + SINE/Alu_AluSg7 chr1 13431970 13432133 + SINE/Alu_AluSq2 chr1 13432826 13432900 - SINE/Alu_AluSx chr1 13432901 13433520 - LTR/ERV1_LTR12C chr1 13433521 13433565 + Simple_repeat_(ACCTTATA)n chr1 13433566 13434402 - LTR/ERV1_LTR12C chr1 13434403 13434513 - SINE/Alu_AluSx chr1 13434542 13435106 + LTR/ERV1_MER51A chr1 13435222 13435238 + Simple_repeat_(T)n chr1 13435822 13435984 + LINE/L2_L2b chr1 13436108 13436637 - LTR/ERVL-MaLR_MLT1G1 chr1 13436656 13436956 + SINE/Alu_AluSx chr1 13436968 13437286 + SINE/Alu_AluSx chr1 13437458 13437825 - LTR/ERV1_LOR1b chr1 13437826 13438128 + SINE/Alu_AluSp chr1 13438129 13438211 - LTR/ERV1_LOR1b chr1 13438244 13438297 + DNA/hAT-Tip100_Zaphod2 chr1 13438589 13438658 + DNA/hAT-Charlie_MER58C chr1 13438659 13438901 + LINE/L1_L1MA9 chr1 13438906 13439095 + SINE/Alu_AluJo chr1 13439101 13440398 + LTR/ERV1_LTR12C chr1 13440408 13440650 + SINE/Alu_AluSz6 chr1 13440651 13440812 + SINE/Alu_AluJb chr1 13440857 13441047 + LINE/L1_L1MA10 chr1 13441053 13441165 + DNA/hAT-Charlie_MER58B chr1 13441574 13441881 + SINE/Alu_AluY chr1 13441924 13441942 + Simple_repeat_(TTG)n chr1 13441945 13442229 - SINE/Alu_AluSz chr1 13442507 13442541 + Simple_repeat_(ATT)n chr1 13442631 13442942 + SINE/Alu_AluSz chr1 13443004 13443061 + LINE/CR1_L3 chr1 13443062 13443298 + SINE/Alu_AluSz chr1 13443299 13443337 + Simple_repeat_(AAT)n chr1 13443338 13443385 + LINE/CR1_L3 chr1 13444020 13444165 + SINE/MIR_MIRc chr1 13444166 13444458 + SINE/Alu_AluY chr1 13444459 13444508 + SINE/MIR_MIRc chr1 13444539 13444569 - DNA/TcMar-Tigger_Tigger17a chr1 13444570 13444873 - SINE/Alu_AluY chr1 13444874 13445130 - DNA/TcMar-Tigger_Tigger17a chr1 13445131 13445260 - SINE/Alu_FLAM_C chr1 13445261 13445345 - DNA/TcMar-Tigger_Tigger17a chr1 13445361 13445668 - SINE/Alu_AluSg chr1 13445973 13446045 - LINE/L2_L2a chr1 13446046 13446342 + SINE/Alu_AluSz6 chr1 13446343 13446651 + SINE/Alu_AluSx chr1 13446652 13446730 - LINE/L2_L2a chr1 13446756 13446833 + SINE/Alu_AluSz6 chr1 13446835 13446917 + SINE/Alu_Alu chr1 13446977 13447286 - SINE/Alu_AluSp chr1 13447299 13447602 - SINE/Alu_AluSz chr1 13447608 13447775 + LTR_LTR106_Mam chr1 13447776 13447907 + SINE/Alu_AluSp chr1 13447908 13448202 + SINE/Alu_AluY chr1 13448203 13448369 + SINE/Alu_AluSp chr1 13448370 13448620 + LTR_LTR106_Mam chr1 13448740 13449044 - SINE/Alu_AluSx1 chr1 13449045 13449353 - SINE/Alu_AluSx chr1 13449430 13449460 + Simple_repeat_(GAAT)n chr1 13449656 13450103 + LINE/L1_L1PA4 chr1 13450104 13450131 + Simple_repeat_(ATA)n chr1 13450665 13450975 - SINE/Alu_AluSp chr1 13451019 13451173 + SINE/MIR_MIR chr1 13451218 13451383 - SINE/Alu_AluJb chr1 13451439 13451509 - SINE/MIR_MIRc chr1 13451762 13451834 - DNA/hAT-Ac_OldhAT1 chr1 13452023 13452072 + Low_complexity_A-rich chr1 13452160 13452393 - SINE/Alu_AluYk3 chr1 13452396 13452701 - SINE/Alu_AluSq2 chr1 13452713 13453012 - SINE/Alu_AluYm1 chr1 13453215 13453501 + SINE/Alu_AluSz chr1 13453503 13453654 + SINE/MIR_MIRb chr1 13453839 13454000 + DNA/hAT-Charlie_MER30 chr1 13454001 13454251 - SINE/Alu_AluSz chr1 13454252 13454330 + DNA/hAT-Charlie_MER30 chr1 13454405 13454605 + LTR/ERVL-MaLR_MLT1J chr1 13454620 13454651 + Simple_repeat_(ATT)n chr1 13454660 13454932 - SINE/Alu_AluSx1 chr1 13454940 13455246 + LTR/ERVL-MaLR_MLT1J chr1 13455281 13455588 - SINE/Alu_AluSz chr1 13455590 13455673 + SINE/MIR_MIR1_Amn chr1 13456310 13456494 - SINE/Alu_AluSx chr1 13456534 13456821 + SINE/Alu_AluJb chr1 13457424 13457484 - SINE/MIR_MIR3 chr1 13457492 13457784 + LINE/L1_L1ME1 chr1 13457785 13457839 + Simple_repeat_(GATA)n chr1 13457840 13457893 + LINE/L1_L1ME1 chr1 13457899 13458194 + SINE/Alu_AluJo chr1 13458195 13458497 + SINE/Alu_AluSp chr1 13458499 13458527 + Simple_repeat_(CAA)n chr1 13458672 13459154 - LTR/ERVL-MaLR_MLT1E1A chr1 13459158 13459240 + SINE/Alu_AluYc chr1 13459436 13459693 + DNA/TcMar-Tigger_MER44A chr1 13459694 13460015 - SINE/Alu_AluSx1 chr1 13460016 13460086 + DNA/TcMar-Tigger_MER44A chr1 13461017 13461307 + LINE/L1_L1MB7 chr1 13461308 13461604 + SINE/Alu_AluSz chr1 13461605 13461737 + LINE/L1_L1MB7 chr1 13461745 13461885 + SINE/Alu_AluJb chr1 13461939 13462275 + LINE/L1_L1MB7 chr1 13462317 13462342 + Simple_repeat_(TGGT)n chr1 13462343 13462626 - SINE/Alu_AluJb chr1 13463002 13463058 + LINE/L2_L2c chr1 13463059 13463628 - LTR/ERV1_MER67B chr1 13463629 13463858 + LINE/L2_L2c chr1 13464043 13464171 - SINE/Alu_AluSx3 chr1 13464172 13464414 - SINE/Alu_AluSx1 chr1 13465295 13465400 + Simple_repeat_(CCAT)n chr1 13465401 13465598 - LINE/L2_L2a chr1 13465614 13465696 - LTR/ERVL_MLT2B3 chr1 13465697 13465877 + Simple_repeat_(AGAT)n chr1 13465882 13466131 - LTR/ERVL_MLT2B3 chr1 13466132 13466426 + SINE/Alu_AluY chr1 13466427 13466677 - LTR/ERVL_MLT2B3 chr1 13466703 13466907 - LINE/L2_L2a chr1 13467283 13467395 - LINE/L2_L2a chr1 13467932 13468227 + SINE/Alu_AluSx chr1 13468434 13468555 + LTR/Gypsy?_LTR88a chr1 13468894 13468973 - SINE/MIR_MIR chr1 13470041 13470346 - SINE/Alu_AluSx chr1 13470348 13470826 - DNA/hAT-Tip100_MER115 chr1 13471117 13471379 + SINE/MIR_MIR chr1 13471390 13471531 + SINE/MIR_MIRb chr1 13471582 13471641 - LINE/L2_L2a chr1 13471642 13471949 - SINE/Alu_AluSg chr1 13471950 13472157 - LINE/L2_L2a chr1 13472587 13472899 - SINE/Alu_AluY chr1 13473532 13473574 + LINE/L2_L2a chr1 13473575 13473989 + LTR/ERVL_MLT2B4 chr1 13473990 13474225 + LINE/L2_L2a chr1 13474564 13474589 + Simple_repeat_(T)n chr1 13474934 13475305 - LTR/ERVL-MaLR_MLT1A chr1 13476215 13476333 + DNA/hAT-Tip100_MamTip1 chr1 13476334 13476628 - SINE/Alu_AluSx chr1 13476629 13476730 + DNA/hAT-Tip100_MamTip1 chr1 13476885 13477116 + SINE/Alu_AluSx3 chr1 13477199 13477254 - LINE/L2_L2a chr1 13477255 13477555 - LINE/L2_L2a chr1 13477556 13477832 + SINE/Alu_AluJr4 chr1 13477833 13477924 - LINE/L2_L2a chr1 13477963 13478101 + LTR/ERVL-MaLR_MLT1J2 chr1 13478234 13478488 - SINE/MIR_MIR chr1 13479901 13480064 - DNA/hAT-Charlie_MER20 chr1 13480065 13480358 + SINE/Alu_AluSp chr1 13480359 13480426 - DNA/hAT-Charlie_MER20 chr1 13480479 13480515 - SINE/Alu_FLAM_C chr1 13480516 13480863 + LTR/ERVL-MaLR_THE1B chr1 13480865 13480957 - SINE/Alu_AluJr chr1 13480968 13481122 + LTR/ERVL-MaLR_MLT1A chr1 13481143 13481301 - SINE/Alu_AluSx3 chr1 13481303 13481357 - SINE/Alu_FLAM_C chr1 13481380 13481694 - SINE/Alu_AluY chr1 13481698 13481762 + LTR/ERVL-MaLR_MLT1B chr1 13481763 13481826 + Simple_repeat_(CT)n chr1 13481827 13482012 + LTR/ERVL-MaLR_MLT1B chr1 13482138 13482202 + Low_complexity_GA-rich chr1 13482295 13482601 + SINE/Alu_AluSx chr1 13482805 13483058 - SINE/MIR_MIR chr1 13483134 13483448 - SINE/Alu_AluSx chr1 13483712 13483818 + SINE/MIR_MIRb chr1 13483884 13483916 - LTR/ERVL_MLT2B4 chr1 13483918 13483971 + Simple_repeat_(GAGGG)n chr1 13484019 13484421 + LINE/L2_L2a chr1 13484453 13484588 - LTR/ERVL_LTR79 chr1 13484729 13484974 - SINE/MIR_MIR chr1 13484996 13485294 + SINE/Alu_AluSp chr1 13485296 13485589 + SINE/Alu_AluYk2 chr1 13485763 13485936 - LTR/ERVL-MaLR_MLT1C2 chr1 13485937 13486166 - SINE/Alu_AluSx1 chr1 13486167 13486463 - LTR/ERVL-MaLR_MLT1C2 chr1 13486589 13486885 - SINE/Alu_AluSz chr1 13487098 13487227 + LTR/ERVL-MaLR_MLT1N2 chr1 13488023 13488063 + Simple_repeat_(TG)n chr1 13488120 13488266 - LINE/L1_L1ME4a chr1 13488267 13488568 - SINE/Alu_AluSg chr1 13488569 13488808 - LINE/L1_L1ME4a chr1 13488923 13489086 + LINE/L1_L1M5 chr1 13489378 13489562 - SINE/MIR_MIRc chr1 13490148 13490458 - SINE/Alu_AluSz chr1 13490532 13490594 + Simple_repeat_(CACCTC)n chr1 13491081 13491256 + DNA/hAT-Charlie_MER20 chr1 13491327 13491546 - SINE/Alu_AluJb chr1 13491581 13491653 + LINE/L1_L1MB3 chr1 13491666 13491975 - SINE/Alu_AluSx chr1 13491978 13492360 - LINE/L1_L1MB3 chr1 13492419 13492718 + SINE/Alu_AluSx1 chr1 13493470 13493782 + LTR/ERVL-MaLR_MLT1D chr1 13493783 13494075 + SINE/Alu_AluSg chr1 13494076 13494180 + LTR/ERVL-MaLR_MLT1D chr1 13494184 13494250 - SINE/MIR_MIR3 chr1 13494399 13494418 + Simple_repeat_(T)n chr1 13494431 13494638 - SINE/MIR_MIRb chr1 13494659 13494832 + SINE/MIR_MIR3 chr1 13495647 13495943 - LINE/CR1_L3 chr1 13496019 13496133 + DNA/hAT-Tip100_MamTip2 chr1 13496183 13496470 - SINE/Alu_AluJo chr1 13496638 13497221 - LINE/L2_L2a chr1 13497499 13497580 + SINE/MIR_MIR chr1 13497608 13497693 + DNA/hAT-Charlie_MER33 chr1 13497694 13497986 + SINE/Alu_AluSp chr1 13497987 13498119 + DNA/hAT-Charlie_MER33 chr1 13498230 13498258 + Simple_repeat_(AAAAC)n chr1 13498350 13498652 + SINE/Alu_AluSg chr1 13498752 13499094 - LTR/ERVL-MaLR_MLT1C chr1 13499095 13499398 - SINE/Alu_AluSx1 chr1 13499399 13499502 - LTR/ERVL-MaLR_MLT1C chr1 13499553 13499738 - LTR/ERVL_LTR33 chr1 13499851 13499968 + LTR/ERV1_MER4A1_ chr1 13499969 13500269 + SINE/Alu_AluSx chr1 13500270 13500777 + LTR/ERV1_MER4A1_ chr1 13500835 13501151 + LINE/L1_L1MC2 chr1 13501152 13501235 + Simple_repeat_(TA)n chr1 13501240 13501402 + LINE/L1_L1MC2 chr1 13501403 13501582 - SINE/Alu_AluSx chr1 13501587 13501926 - SINE/Alu_AluJo chr1 13501930 13502240 - SINE/Alu_AluYj4 chr1 13502852 13502958 + DNA/hAT-Charlie_Charlie32a chr1 13503248 13503568 - SINE/Alu_AluSx chr1 13503689 13503796 - SINE/MIR_MIR3 chr1 13504198 13504305 - SINE/MIR_MIRc chr1 13504439 13504715 + SINE/Alu_AluSz6 chr1 13504726 13504786 + Simple_repeat_(GGGGA)n chr1 13504785 13504904 + Low_complexity_A-rich chr1 13505593 13505998 - LINE/L2_L2a chr1 13506446 13506758 - SINE/Alu_AluSx chr1 13507422 13507549 + LINE/L1_L1ME4a chr1 13507550 13507856 + SINE/Alu_AluSx1 chr1 13507857 13507954 + LINE/L1_L1ME4a chr1 13507955 13508252 + SINE/Alu_AluSx1 chr1 13508253 13508691 + LINE/L1_L1ME4a chr1 13509716 13509841 - SINE/MIR_MIR3 chr1 13509871 13510049 - DNA/hAT-Charlie_MER5A chr1 13510262 13510374 + SINE/MIR_MIR3 chr1 13510408 13510618 - SINE/MIR_MIR chr1 13510678 13510912 - SINE/MIR_MIRb chr1 13511202 13511392 - SINE/MIR_MIRc chr1 13511389 13511533 - SINE/MIR_MIR3 chr1 13512915 13512939 + Simple_repeat_(CTCC)n chr1 13514446 13514580 + SINE/MIR_MIRb chr1 13515682 13515787 + SINE/MIR_MIR1_Amn chr1 13515955 13516106 + DNA/hAT-Charlie_Charlie19a chr1 13516110 13516220 + SINE/MIR_MIRc chr1 13516485 13516582 - SINE/MIR_MIR1_Amn chr1 13516989 13517294 + LTR/ERVL?_Eutr10 chr1 13517489 13517556 + LTR/ERVL?_Eutr10 chr1 13517609 13517781 + SINE/MIR_MIRb chr1 13518055 13518204 + SINE/tRNA-RTE_MamSINE1 chr1 13518387 13518586 + LINE/L2_L2b chr1 13518587 13519094 + LTR/ERVL-MaLR_MLT1F2 chr1 13519095 13519555 + LINE/L2_L2a chr1 13519556 13519726 - SINE/Alu_AluJb chr1 13519727 13519756 + Simple_repeat_(T)n chr1 13519757 13519879 - SINE/Alu_AluJb chr1 13519880 13519914 + LINE/L2_L2a chr1 13520593 13520807 + SINE/MIR_MIRb chr1 13520850 13521090 - LINE/L2_L2d2 chr1 13521181 13521504 + LINE/L2_L2b chr1 13522757 13523062 + SINE/Alu_AluSc8 chr1 13523070 13523261 - SINE/MIR_MIR chr1 13523258 13523393 - LINE/L2_L2a chr1 13523450 13523760 - SINE/Alu_AluSz chr1 13524103 13524155 - LTR/ERVL-MaLR_MLT1L chr1 13524476 13524594 - SINE/MIR_MIRc chr1 13524601 13524614 - LINE/L2_L2c chr1 13524615 13524913 + SINE/Alu_AluSx4 chr1 13524914 13525266 - LINE/L2_L2c chr1 13525333 13525389 - SINE/MIR_MIR1_Amn chr1 13525792 13526089 - SINE/Alu_AluY chr1 13526160 13526608 + LINE/L1_L1ME1 chr1 13526616 13526879 + SINE/Alu_AluSz chr1 13526880 13527087 - SINE/Alu_AluSc chr1 13527117 13527407 + SINE/Alu_AluJb chr1 13527411 13527705 + SINE/Alu_AluY chr1 13527711 13527806 + LINE/L1_L1ME4a chr1 13528104 13528135 + Simple_repeat_(GGACA)n chr1 13528216 13528249 + Low_complexity_GA-rich chr1 13528462 13528651 + LINE/L2_L2c chr1 13528661 13528698 + Simple_repeat_(TGT)n chr1 13528699 13528973 - SINE/Alu_AluSx chr1 13529036 13529182 - LTR/ERV1?_LTR103_Mam chr1 13529183 13529223 + Simple_repeat_(TC)n chr1 13529321 13529403 - LTR/ERV1?_LTR103_Mam chr1 13529404 13529710 + SINE/Alu_AluSp chr1 13529711 13529756 - LTR/ERV1?_LTR103_Mam chr1 13530031 13530276 + LTR/ERVL_LTR79 chr1 13530277 13530580 + SINE/Alu_AluY chr1 13530581 13530673 + LTR/ERVL_LTR79 chr1 13530862 13531166 + SINE/Alu_AluY chr1 13531279 13531411 + SINE/MIR_MIRc chr1 13531516 13531645 - SINE/Alu_FLAM_C chr1 13531723 13531856 - LINE/L1_L1MB7 chr1 13532046 13532331 + SINE/Alu_AluSg chr1 13532491 13532801 - SINE/Alu_AluSq2 chr1 13532906 13533470 - DNA/hAT-Blackjack_MER63D chr1 13533471 13533782 - SINE/Alu_AluSz chr1 13533783 13533970 - DNA/hAT-Blackjack_MER63D chr1 13533971 13534032 + Simple_repeat_(CTATT)n chr1 13534033 13534142 - DNA/hAT-Blackjack_MER63D chr1 13534170 13534476 - SINE/Alu_AluSx chr1 13534483 13534787 - LINE/L1_L1MB5 chr1 13534788 13535092 - SINE/Alu_AluJo chr1 13535093 13535232 - LINE/L1_L1MB5 chr1 13535233 13535375 - DNA/hAT-Blackjack_MER63D chr1 13535401 13535566 + SINE/MIR_MIR3 chr1 13535738 13535946 + LTR/ERV1_MER31A chr1 13536155 13536194 - LINE/L2_L2d chr1 13536164 13536216 - LINE/L2_L2b chr1 13536258 13536544 - SINE/Alu_AluSx chr1 13536570 13536611 - LTR/ERVL_MER21C chr1 13537400 13537497 - DNA/TcMar-Tigger_Tigger23a chr1 13537514 13537671 - DNA/TcMar-Tigger_Tigger23a chr1 13537708 13537912 - SINE/MIR_MIRb chr1 13538310 13538620 - SINE/Alu_AluSp chr1 13538625 13538756 - SINE/Alu_AluSp chr1 13538797 13539230 + LTR/ERVL-MaLR_MLT1F2 chr1 13539231 13539538 - SINE/Alu_AluSq2 chr1 13539539 13539640 + LTR/ERVL-MaLR_MLT1F2 chr1 13539864 13539921 - DNA/hAT-Tip100_MER91B chr1 13540144 13540383 + SINE/MIR_MIR chr1 13540393 13540503 - SINE/Alu_AluJr chr1 13541346 13541661 + SINE/Alu_AluJr chr1 13542092 13542310 - LINE/L2_L2b chr1 13542312 13542407 - DNA/hAT-Tip100_MER45A chr1 13542422 13542661 - LINE/L2_L2b chr1 13542662 13542797 - SINE/Alu_AluJr chr1 13542807 13543090 - SINE/Alu_AluSq2 chr1 13543091 13543380 - SINE/Alu_AluSc chr1 13543381 13543476 - LINE/L2_L2b chr1 13543937 13543972 + Low_complexity_A-rich chr1 13544844 13545145 - SINE/Alu_AluSp chr1 13545337 13545498 - SINE/MIR_MIRb chr1 13545578 13545611 - LTR/ERVL_LTR79 chr1 13545612 13545915 - SINE/Alu_AluSc8 chr1 13545916 13545966 - LTR/ERVL_LTR79 chr1 13545967 13546452 - LTR/ERVL-MaLR_MLT1F chr1 13546453 13546511 - LTR/ERVL_LTR79 chr1 13546512 13546787 + SINE/Alu_AluY chr1 13546788 13546963 - LTR/ERVL_LTR79 chr1 13547196 13547267 + LINE/L2_L2c chr1 13547297 13547528 - SINE/MIR_MIR chr1 13547845 13548127 + SINE/Alu_AluSp chr1 13548609 13548909 + SINE/Alu_AluSg chr1 13549123 13549483 - LTR/ERVL-MaLR_MSTD chr1 13549888 13550184 - SINE/Alu_AluJo chr1 13550186 13550281 + Simple_repeat_(AT)n chr1 13550315 13550575 - DNA/hAT-Charlie_MER113 chr1 13550891 13551415 + LINE/L1_L1MEd chr1 13551453 13551503 + SINE/MIR_MIR chr1 13551504 13551981 + LINE/L1_L1MEd chr1 13551982 13552249 + SINE/Alu_AluJb chr1 13552250 13552277 + LINE/L1_L1MEd chr1 13552278 13552529 + SINE/Alu_AluSx3 chr1 13552530 13552573 + LINE/L1_L1MEd chr1 13552600 13552676 + LINE/L1_L1MEd chr1 13552677 13552977 + SINE/Alu_AluYk3 chr1 13552978 13553639 + LINE/L1_L1MEd chr1 13553642 13553739 + SINE/MIR_MIR chr1 13553954 13554061 - DNA/hAT-Charlie_Charlie2b chr1 13554062 13554358 + SINE/Alu_AluSx3 chr1 13554359 13554480 - DNA/hAT-Charlie_Charlie2b chr1 13554547 13554839 - SINE/Alu_AluSx3 chr1 13554845 13554908 - DNA/hAT-Charlie_Charlie2b chr1 13555297 13555609 + SINE/Alu_AluSp chr1 13556201 13556628 - LINE/RTE-BovB_MamRTE1 chr1 13557270 13557578 + SINE/Alu_AluSp chr1 13557595 13557811 + SINE/MIR_MIRc chr1 13557964 13557999 - DNA/hAT-Charlie_Charlie2b chr1 13558000 13558295 - SINE/Alu_AluSp chr1 13558296 13558818 - DNA/hAT-Charlie_Charlie2b chr1 13558821 13558899 - DNA/TcMar-Tigger_Tigger7 chr1 13558900 13558966 + DNA/TcMar-Tigger_MER2 chr1 13558967 13559258 + SINE/Alu_AluSz chr1 13559259 13559529 + DNA/TcMar-Tigger_MER2 chr1 13559530 13559650 - DNA/TcMar-Tigger_MER44B chr1 13559651 13560150 + LTR/ERV1_LTR10F chr1 13560151 13560497 + LTR/ERV1_HERVIP10FH-int chr1 13560504 13560581 + LTR/ERV1_HERVIP10FH-int chr1 13560841 13561228 + LTR/ERV1_LTR7B chr1 13561229 13561656 + LTR/ERV1_HERVH-int chr1 13561715 13564107 + LTR/ERV1_HERVH-int chr1 13564108 13564912 + LTR/ERV1_HERVH-int chr1 13564902 13566541 + LTR/ERV1_HERVH-int chr1 13566535 13567107 + LTR/ERV1_HERVH-int chr1 13567108 13567494 + LTR/ERV1_LTR7B chr1 13567680 13568045 + LTR/ERV1_HERVIP10FH-int chr1 13568060 13568521 + LTR/ERV1_HERVIP10FH-int chr1 13568522 13568810 + SINE/Alu_AluY chr1 13568811 13572722 + LTR/ERV1_HERVIP10FH-int chr1 13572723 13573195 + LTR/ERV1_LTR10F chr1 13573196 13573542 - DNA/TcMar-Tigger_MER44B chr1 13573543 13573837 - SINE/Alu_AluJb chr1 13573838 13573920 - DNA/TcMar-Tigger_MER44B chr1 13573924 13574097 - DNA/hAT-Charlie_Charlie2a chr1 13574098 13574130 + Simple_repeat_(TTTTCTT)n chr1 13574133 13574410 - SINE/Alu_AluSp chr1 13574425 13574545 - SINE/Alu_AluJr chr1 13574568 13574902 - SINE/Alu_AluY chr1 13574916 13575057 - DNA/hAT-Charlie_Charlie2a chr1 13575063 13575354 - SINE/Alu_AluJb chr1 13575441 13575859 - DNA/hAT-Charlie_Charlie2b chr1 13575860 13576163 - SINE/Alu_AluSx chr1 13576164 13576310 - DNA/hAT-Charlie_Charlie2b chr1 13576343 13576554 + LINE/L2_L2a chr1 13578314 13578598 + SINE/Alu_AluSc chr1 13579107 13579143 + Low_complexity_GA-rich chr1 13579326 13579431 + DNA/hAT-Charlie_Charlie2a chr1 13579433 13579559 - SINE/Alu_AluJr chr1 13579567 13579870 - SINE/Alu_AluSp chr1 13579871 13580016 + DNA/hAT-Charlie_Charlie2a chr1 13580084 13580525 - LTR/ERV1_LTR51 chr1 13580630 13580663 + Simple_repeat_(TAT)n chr1 13580670 13580942 - SINE/Alu_AluJr chr1 13581348 13581507 + DNA/hAT-Charlie_MER5A chr1 13582517 13582598 + DNA/hAT-Charlie_MER5A1 chr1 13582699 13582769 - DNA/hAT-Charlie_MER5A chr1 13584939 13585109 + LINE/L2_L2a chr1 13586695 13586760 - SINE/MIR_MIRb chr1 13586764 13586783 + Simple_repeat_(A)n chr1 13586786 13587070 + SINE/Alu_AluSx4 chr1 13587472 13587491 + Simple_repeat_(GGA)n chr1 13588128 13588154 + Simple_repeat_(CA)n chr1 13588571 13588686 + Simple_repeat_(TA)n chr1 13588690 13588964 - SINE/Alu_AluJb chr1 13588987 13589073 - LINE/L2_L2d chr1 13589312 13589454 + LTR?_MamRep605b chr1 13589474 13589710 - SINE/MIR_MIR chr1 13589815 13589843 + Simple_repeat_(TTGA)n chr1 13589844 13590127 - SINE/Alu_AluSz chr1 13590352 13590686 - LINE/L2_L2d chr1 13590942 13591229 - SINE/Alu_AluSg chr1 13591347 13591697 - LINE/L1_L1MB7 chr1 13591706 13591854 - SINE/Alu_FRAM chr1 13591860 13592543 - LINE/L1_L1MB7 chr1 13592544 13592849 - SINE/Alu_AluSp chr1 13592850 13593059 - LINE/L1_L1MB7 chr1 13594240 13594363 - LINE/L2_L2d2 chr1 13594545 13594703 + SINE/MIR_MIRb chr1 13594706 13595024 + SINE/Alu_AluY chr1 13595916 13596217 + SINE/Alu_AluSx1 chr1 13596250 13596301 - LINE/L2_L2b chr1 13596639 13596677 - rRNA_5S chr1 13597724 13597981 + SINE/Alu_AluJr chr1 13598012 13598316 - SINE/Alu_AluSz6 chr1 13598793 13598935 - LINE/L2_L2a chr1 13599012 13599311 - SINE/Alu_AluSc chr1 13599374 13599658 - SINE/Alu_AluSx1 chr1 13599676 13599828 + LINE/L2_L2a chr1 13600372 13600664 - SINE/Alu_AluSq2 chr1 13600686 13600932 - LTR/ERV1_MER31B chr1 13601363 13601656 - SINE/Alu_AluSz6 chr1 13602074 13602339 + SINE/Alu_AluY chr1 13602596 13602867 - SINE/Alu_AluSx chr1 13602897 13603018 + SINE/Alu_AluJo chr1 13603019 13603065 + Simple_repeat_(TA)n chr1 13603071 13603110 + Low_complexity_GA-rich chr1 13603135 13603402 + SINE/Alu_AluSq2 chr1 13603592 13603886 - SINE/Alu_AluSg4 chr1 13603922 13603965 + LINE/L2_X15_LINE chr1 13604144 13604190 + Simple_repeat_(TGTG)n chr1 13605629 13605920 - SINE/Alu_AluSc chr1 13606010 13606302 - SINE/Alu_AluSq chr1 13606521 13606710 + SINE/Alu_AluJr chr1 13607364 13607409 + Simple_repeat_(TA)n chr1 13607463 13607624 - DNA/TcMar-Tigger_MER6B chr1 13607686 13607823 - DNA/hAT-Charlie_Charlie10 chr1 13607824 13608123 + SINE/Alu_AluSp chr1 13608124 13608199 - DNA/hAT-Charlie_Charlie10 chr1 13608399 13608581 + LINE/L2_L2a chr1 13608613 13608903 - LTR/ERV1_LTR10B chr1 13609000 13609202 + DNA/hAT-Charlie_MER58A chr1 13609205 13609235 + Simple_repeat_(AGTCT)n chr1 13609236 13609427 + LINE/L2_L2a chr1 13609429 13609784 - LINE/L1_L1MB2 chr1 13609785 13610092 + SINE/Alu_AluSp chr1 13610093 13610235 - LINE/L1_L1MB2 chr1 13610941 13611248 + SINE/Alu_AluY chr1 13611274 13611753 + LINE/L1_L1MB3 chr1 13613061 13613121 - LTR/ERVL-MaLR_MLT1J chr1 13613317 13613503 + SINE/MIR_MIR1_Amn chr1 13613871 13613891 + Simple_repeat_(T)n chr1 13614504 13614802 + SINE/Alu_AluJo chr1 13615278 13615585 - SINE/Alu_AluSc chr1 13617474 13617670 - SINE/Alu_AluSc chr1 13618238 13618534 - SINE/Alu_AluSx4 chr1 13618954 13619057 + SINE/Alu_FLAM_A chr1 13619060 13619144 + SINE/MIR_MIRc chr1 13620586 13621250 - LINE/L2_L2a chr1 13621256 13621443 - LINE/L2_L2-1_AMi chr1 13621446 13621662 - LINE/L2_L2a chr1 13621760 13622053 + SINE/Alu_AluSc8 chr1 13622054 13622078 + Simple_repeat_(TAA)n chr1 13622086 13622312 + LTR?_MamRep605 chr1 13622404 13622753 + LINE/L2_L2a chr1 13622753 13622833 + LINE/L2_L2a chr1 13623066 13623199 - SINE/Alu_FLAM_A chr1 13623211 13623284 - rRNA_5S chr1 13623335 13623442 + SINE/MIR_MIRb chr1 13623648 13623890 + LTR/ERVL_LTR33 chr1 13623891 13624185 - SINE/Alu_AluSz chr1 13624186 13624464 + LTR/ERVL_LTR33 chr1 13624565 13624780 - LTR/ERVL-MaLR_MLT1M chr1 13625040 13625195 - SINE/MIR_MIRc chr1 13625258 13625288 + Simple_repeat_(TTTTCTT)n chr1 13625289 13625570 - SINE/Alu_AluSq chr1 13625666 13625964 - SINE/Alu_AluY chr1 13625975 13626490 - LINE/L1_L1ME3Cz chr1 13626607 13626769 + SINE/Alu_FRAM chr1 13626834 13627081 + SINE/Alu_AluSz6 chr1 13627584 13627644 + Simple_repeat_(TTTC)n chr1 13627645 13627940 - SINE/Alu_AluSz6 chr1 13627941 13627952 + Simple_repeat_(TTTC)n chr1 13628023 13628172 - LINE/L1_L1MA4 chr1 13628215 13628654 + LTR/ERVL-MaLR_MLT1C2 chr1 13629071 13629128 - LINE/L2_L2a chr1 13629152 13629431 + SINE/Alu_AluSx1 chr1 13629671 13629732 - DNA/hAT-Tip100_MamTip2 chr1 13629808 13629916 + SINE/MIR_MIRc chr1 13629962 13630113 - SINE/MIR_MIRb chr1 13630213 13630284 - DNA/hAT-Tip100_MamTip2 chr1 13630300 13630606 + SINE/Alu_AluY chr1 13630615 13630890 + SINE/Alu_AluSg chr1 13631539 13631693 + SINE/Alu_FRAM chr1 13631734 13631929 - SINE/Alu_AluJb chr1 13632002 13632084 - DNA/TcMar-Mariner_MADE2 chr1 13632109 13633220 - LTR/ERVK_MER11C chr1 13633263 13633345 - DNA/hAT-Charlie_Charlie7 chr1 13633346 13633653 + SINE/Alu_AluJr chr1 13633654 13633712 - DNA/hAT-Charlie_Charlie7 chr1 13634058 13634433 + LTR/ERVL-MaLR_MLT1C2 chr1 13634434 13634740 - SINE/Alu_AluY chr1 13634741 13634817 + LTR/ERVL-MaLR_MLT1C2 chr1 13635553 13635597 + Low_complexity_G-rich chr1 13636640 13636934 - SINE/Alu_AluSx chr1 13637044 13637192 - DNA/hAT-Charlie_Charlie10 chr1 13637229 13637282 + DNA/hAT-Charlie_Charlie10 chr1 13637309 13637498 - DNA/hAT-Charlie_Charlie10 chr1 13637499 13637795 - SINE/Alu_AluSq2 chr1 13637796 13637863 - DNA/hAT-Charlie_Charlie10 chr1 13637910 13637947 - LINE/L2_L2a chr1 13637948 13638366 - LTR/ERVL-MaLR_MSTA chr1 13638367 13638932 - LINE/L2_L2a chr1 13638946 13638964 + Simple_repeat_(A)n chr1 13639005 13639512 + LTR/ERVL-MaLR_MLT1F chr1 13639980 13640195 - LINE/L2_L2a chr1 13640196 13640488 - LTR/ERVL-MaLR_MLT1A1 chr1 13640489 13640797 + SINE/Alu_AluY chr1 13640798 13640940 - LTR/ERVL-MaLR_MLT1A1 chr1 13640941 13641301 - LINE/L2_L2a chr1 13642263 13642633 + LTR/ERVL-MaLR_MLT1L chr1 13642634 13642861 - LTR/ERVL-MaLR_MSTB1 chr1 13642862 13643514 - DNA/TcMar-Mariner_HSMAR1 chr1 13643515 13643817 - SINE/Alu_AluY chr1 13643818 13644478 - DNA/TcMar-Mariner_HSMAR1 chr1 13644479 13644667 - LTR/ERVL-MaLR_MSTB1 chr1 13644927 13645116 + SINE/MIR_MIRb chr1 13645136 13645431 - SINE/Alu_AluY chr1 13645433 13645809 - DNA/hAT-Charlie_Charlie1a chr1 13646123 13646421 + SINE/Alu_AluSc chr1 13647236 13647549 - SINE/Alu_AluSx1 chr1 13647578 13647875 + SINE/Alu_AluSc chr1 13647876 13648122 - LINE/L1_L1MC5a chr1 13648123 13648457 - LINE/L1_L1PREC2 chr1 13648458 13649023 - LINE/L1_L1MC5a chr1 13649024 13649320 - SINE/Alu_AluY chr1 13649321 13649996 - LINE/L1_L1MC5a chr1 13649997 13650296 - SINE/Alu_AluSq2 chr1 13650297 13650962 - LINE/L1_L1MC5a chr1 13650963 13651271 - SINE/Alu_AluSx chr1 13651272 13651866 - LINE/L1_L1MC5a chr1 13651908 13651944 + Simple_repeat_(TTTGTCT)n chr1 13652560 13652863 - SINE/Alu_AluSg chr1 13653439 13653540 - SINE/tRNA-RTE_MamSINE1 chr1 13653653 13654079 + LINE/L1_L1M5 chr1 13654089 13654199 + SINE/Alu_AluJo chr1 13654200 13654496 - SINE/Alu_AluSp chr1 13654499 13654806 - SINE/Alu_AluSc8 chr1 13654821 13654947 + SINE/Alu_AluJb chr1 13654948 13654974 + Low_complexity_GA-rich chr1 13654975 13655548 + LINE/L1_L1M5 chr1 13655640 13655704 + Low_complexity_A-rich chr1 13655735 13656023 - SINE/Alu_AluSc chr1 13656078 13656103 + Simple_repeat_(TATT)n chr1 13656789 13656811 + Simple_repeat_(AAAC)n chr1 13657371 13659077 + Retroposon/SVA_SVA_D chr1 13659166 13659223 + Low_complexity_A-rich chr1 13659235 13659577 + LTR/ERVK_LTR14A chr1 13659956 13659985 + Simple_repeat_(TA)n chr1 13659986 13660013 + Simple_repeat_(CA)n chr1 13660741 13661036 + SINE/Alu_AluY chr1 13661236 13661559 + LTR/ERVL-MaLR_THE1D chr1 13661890 13661968 - LINE/L2_L2c chr1 13661969 13662406 + LTR/ERV1_MER57A1 chr1 13662407 13662459 - LINE/L2_L2c chr1 13662491 13662710 - SINE/MIR_MIR chr1 13662755 13662874 + SINE/Alu_FLAM_C chr1 13662929 13663328 + LINE/L1_L1ME3F chr1 13663331 13663395 + DNA/hAT-Charlie_MER58A chr1 13663396 13663691 - SINE/Alu_AluSg chr1 13663692 13663860 + DNA/hAT-Charlie_MER58A chr1 13663903 13663963 + SINE/MIR_MIR1_Amn chr1 13664001 13664156 + LINE/L1_L1MB8 chr1 13664175 13664404 - DNA/hAT-Charlie_MER20 chr1 13664674 13664787 + SINE/5S-Deu-L2_AmnSINE1 chr1 13665209 13665505 - SINE/Alu_AluSx1 chr1 13665592 13665693 + DNA/hAT-Blackjack_MER81 chr1 13666139 13666177 - SINE/MIR_MIRb chr1 13666840 13666952 - LINE/L1_L1PA11 chr1 13667108 13667150 + Simple_repeat_(T)n chr1 13667273 13667582 + SINE/Alu_AluSz chr1 13667997 13668007 + Simple_repeat_(CAGGCT)n chr1 13668008 13668316 - SINE/Alu_AluSg chr1 13668317 13668327 + Simple_repeat_(CAGGCT)n chr1 13668328 13668631 - SINE/Alu_AluY chr1 13668632 13668642 + Simple_repeat_(CAGGCT)n chr1 13668688 13668819 - SINE/Alu_FLAM_A chr1 13669195 13669318 + Simple_repeat_(ATATTTG)n chr1 13669357 13669429 - DNA/hAT-Charlie_Charlie25 chr1 13669478 13670179 - LINE/L1_L1PA8 chr1 13670200 13670267 - DNA/hAT-Charlie_Charlie25 chr1 13670274 13670582 - LTR/ERVL-MaLR_MLT1K chr1 13670583 13670896 + SINE/Alu_AluSp chr1 13670897 13671113 - LTR/ERVL-MaLR_MLT1K chr1 13671114 13671408 + SINE/Alu_AluSx chr1 13671409 13671454 - LTR/ERVL-MaLR_MLT1K chr1 13671457 13671813 - DNA/hAT-Charlie_Charlie25 chr1 13671826 13671958 - SINE/Alu_AluY chr1 13671959 13672265 - SINE/Alu_AluSx1 chr1 13672290 13672505 - DNA/hAT-Charlie_Charlie25 chr1 13672509 13672562 - LINE/L1_L1MEh chr1 13672563 13672872 - SINE/Alu_AluSx chr1 13672873 13673089 - LINE/L1_L1MEh chr1 13673109 13673240 + SINE/Alu_AluJb chr1 13673241 13673541 + SINE/Alu_AluYb8 chr1 13673542 13673722 + SINE/Alu_AluJb chr1 13673935 13674086 - SINE/Alu_FLAM_C chr1 13674230 13674638 - LINE/L1_L1MEh chr1 13674666 13674775 + SINE/Alu_AluSp chr1 13674777 13674822 + Simple_repeat_(AAAAT)n chr1 13674827 13674899 - LINE/L1_L1MEh chr1 13674996 13675076 - DNA/hAT-Charlie_Charlie25 chr1 13675085 13676015 - LINE/L1_L1ME1 chr1 13676016 13676302 - SINE/Alu_AluJb chr1 13676303 13676476 - LINE/L1_L1ME1 chr1 13676477 13676707 - SINE/Alu_AluSp chr1 13676708 13676742 - LINE/L1_L1ME1 chr1 13676743 13677152 - LTR/ERVL-MaLR_MSTB chr1 13677153 13677345 - LINE/L1_L1ME1 chr1 13677346 13677465 - SINE/Alu_FLAM_A chr1 13677480 13678041 - LINE/L1_L1MA9 chr1 13678039 13678138 - LINE/L1_L1ME1 chr1 13678153 13678198 + DNA/hAT-Charlie_Charlie5 chr1 13678192 13678282 + DNA/hAT-Charlie_MER33 chr1 13678582 13678943 - LTR/ERVL-MaLR_THE1B chr1 13678980 13679144 - LTR/ERVL-MaLR_MLT1L chr1 13680056 13680347 - SINE/Alu_AluSc8 chr1 13680394 13680674 + SINE/Alu_AluJb chr1 13680856 13681152 - SINE/Alu_AluY chr1 13681202 13681270 - LINE/L2_L2d2 chr1 13681301 13681517 - SINE/MIR_MIRb chr1 13681529 13681878 - LTR/ERVL-MaLR_THE1C chr1 13681919 13682005 - LINE/L2_L2d2 chr1 13682690 13683031 + DNA/TcMar-Tigger_MER47A chr1 13683400 13683493 + LINE/L2_L2b chr1 13683916 13683968 + Simple_repeat_(TACATT)n chr1 13684092 13684261 + SINE/Alu_AluSx1 chr1 13684262 13684338 + SINE/Alu_AluSq2 chr1 13684653 13684956 - SINE/Alu_AluSx chr1 13684986 13685376 + LTR/ERVL_MLT2D chr1 13685401 13685538 + DNA/TcMar-Tigger_Tigger5b chr1 13685723 13685842 + LINE/RTE-X_L4_A_Mam chr1 13686321 13686414 + Simple_repeat_(TACATTT)n chr1 13686415 13686455 + Simple_repeat_(TA)n chr1 13686841 13686955 - SINE/MIR_MIR3 chr1 13688010 13688713 + LINE/L1_L1MB3 chr1 13688714 13688844 + SINE/Alu_FLAM_A chr1 13688845 13689332 + LINE/L1_L1MB3 chr1 13689333 13689623 + SINE/Alu_AluJb chr1 13689624 13689770 + LINE/L1_L1MB3 chr1 13689872 13689891 + DNA/hAT-Charlie_MER106B chr1 13689892 13690246 - LTR/ERVL-MaLR_THE1B chr1 13690247 13690384 + DNA/hAT-Charlie_MER106B chr1 13690502 13690802 + SINE/Alu_AluY chr1 13690803 13690953 - DNA/hAT-Charlie_MER20 chr1 13690954 13691252 - SINE/Alu_AluSg4 chr1 13691253 13691291 - DNA/hAT-Charlie_MER20 chr1 13691738 13691864 - LINE/L2_L2d chr1 13691865 13692153 + SINE/Alu_AluJb chr1 13692154 13692259 - LINE/L2_L2d chr1 13692272 13692382 + LTR/ERVL-MaLR_MLT1C chr1 13692383 13692515 + SINE/Alu_FLAM_C chr1 13692516 13692794 + LTR/ERVL-MaLR_MLT1C chr1 13692795 13693416 - DNA/hAT-Charlie_MER1A chr1 13693417 13693477 + LTR/ERVL-MaLR_MLT1C chr1 13694145 13694180 + Simple_repeat_(T)n chr1 13694246 13694557 + SINE/Alu_AluJb chr1 13694578 13694877 - SINE/Alu_AluY chr1 13694998 13695055 + LINE/L2_L2d chr1 13695113 13695289 + SINE/MIR_MIRb chr1 13695948 13695986 + Simple_repeat_(TGCG)n chr1 13696199 13696491 - SINE/Alu_AluSz chr1 13696494 13696629 + SINE/MIR_MIRb chr1 13696883 13696983 - LINE/L2_L2b chr1 13697624 13698098 + LTR/ERVL-MaLR_MLT1D chr1 13698640 13698941 - SINE/Alu_AluY chr1 13698993 13699288 - SINE/Alu_AluYf1 chr1 13699383 13699460 - LTR?_MamRep605b chr1 13699470 13699598 + LINE/L2_L2 chr1 13700858 13700897 + Simple_repeat_(GT)n chr1 13701626 13701701 - SINE/MIR_MIR3 chr1 13701788 13701870 + SINE/MIR_MIR chr1 13701984 13702354 - LINE/L1_L1ME4a chr1 13703195 13703376 - DNA/hAT-Charlie_MER20 chr1 13705702 13706005 + SINE/Alu_AluSg chr1 13710805 13711100 + SINE/Alu_AluY chr1 13711440 13711559 + DNA/hAT-Charlie_MER20B chr1 13712019 13712239 + SINE/Alu_AluJr chr1 13712431 13712545 + LINE/L2_L2a chr1 13712551 13712787 + SINE/Alu_AluSc chr1 13712792 13712815 + Simple_repeat_(TCCG)n chr1 13712816 13712886 + LINE/L2_L2a chr1 13712897 13713313 + LINE/L2_L2a chr1 13713874 13714168 - SINE/Alu_AluY chr1 13715984 13716460 + LINE/L1_L1PA2 chr1 13718576 13718608 + Simple_repeat_(TTCT)n chr1 13718934 13719366 - LINE/L2_L2a chr1 13719406 13719524 - LINE/L2_L2a chr1 13720390 13720545 - LINE/L2_L2 chr1 13720623 13720762 - SINE/MIR_MIR chr1 13720864 13721204 - LINE/L1_L1ME1 chr1 13721825 13722030 + SINE/MIR_MIR chr1 13722086 13722196 + LINE/L2_L2a chr1 13722224 13722480 - LINE/L2_L2d chr1 13723038 13723401 + LINE/L2_L2a chr1 13724498 13724757 - SINE/Alu_AluSc chr1 13725086 13725189 - DNA/hAT-Charlie_Charlie1a chr1 13725214 13725504 - SINE/Alu_AluSz chr1 13725509 13725627 - LINE/L1_L1MC4a chr1 13725633 13725834 - DNA/hAT-Charlie_Charlie1 chr1 13725860 13725949 + SINE/MIR_MIR chr1 13725960 13726188 + SINE/MIR_MIR chr1 13726194 13726270 + SINE/MIR_MIRb chr1 13727271 13727549 - SINE/Alu_AluSx chr1 13728203 13728378 - LINE/L2_L2b chr1 13729016 13729212 + SINE/MIR_MIRb chr1 13731314 13731353 + Simple_repeat_(AC)n chr1 13731360 13731610 + LINE/L2_L2c chr1 13732018 13732102 - SINE/MIR_MIR3 chr1 13733552 13733681 + SINE/MIR_MIR chr1 13735547 13735779 - LINE/L1_L1MC1 chr1 13735797 13735919 - LINE/L1_HAL1b chr1 13736007 13736101 - LINE/L1_L1ME4a chr1 13736102 13736420 - SINE/Alu_AluJb chr1 13736421 13736781 - LINE/L1_L1ME4a chr1 13737202 13737494 + DNA/hAT-Charlie_MER58B chr1 13737803 13737865 - LINE/L2_L2a chr1 13738229 13738320 - DNA/hAT-Charlie_Charlie4z chr1 13739036 13739347 - SINE/Alu_AluY chr1 13739550 13739917 - LINE/L1_L1M5 chr1 13740431 13740468 + LINE/L2_L2a chr1 13740469 13740544 + SINE/MIR_MIR chr1 13740587 13740756 + SINE/MIR_MIRb chr1 13741721 13741765 + Simple_repeat_(TG)n chr1 13742202 13742499 - SINE/Alu_AluJb chr1 13743118 13743426 + SINE/Alu_AluY chr1 13744193 13744454 + SINE/MIR_MIR chr1 13744467 13744697 + SINE/MIR_MIR chr1 13745052 13745425 - LINE/L2_L2a chr1 13745426 13745722 - SINE/Alu_AluSx4 chr1 13745723 13745815 - LINE/L2_L2a chr1 13746502 13746853 - SINE/Alu_AluSz chr1 13747770 13747793 + Simple_repeat_(A)n chr1 13747796 13748498 - LINE/L1_L1MC4 chr1 13749182 13749232 + Simple_repeat_(CCGG)n chr1 13749236 13749322 + Simple_repeat_(CGC)n chr1 13749494 13749577 + Simple_repeat_(GCCC)n chr1 13750370 13750405 + Simple_repeat_(TTTGTTTT)n chr1 13751238 13751275 + Simple_repeat_(TTTCATA)n chr1 13751927 13751981 + Simple_repeat_(CCCCT)n chr1 13751984 13752099 - LINE/L2_L2b chr1 13752125 13752184 + LINE/L2_L2a chr1 13752781 13752944 + SINE/MIR_MIRc chr1 13753207 13753432 + DNA/TcMar-Tigger_Tigger15a chr1 13753567 13753809 - LINE/L2_L2d chr1 13753856 13754099 - LINE/L2_L2a chr1 13754399 13754490 - SINE/MIR_MIRc chr1 13755418 13755472 + DNA?_UCON58 chr1 13755803 13755916 - SINE/Alu_AluJo chr1 13755970 13756282 + SINE/Alu_AluY chr1 13756804 13756938 - SINE/MIR_MIRb chr1 13757779 13757800 + Simple_repeat_(T)n chr1 13758163 13758465 + SINE/Alu_AluSx1 chr1 13759163 13759179 + Simple_repeat_(T)n chr1 13760570 13760764 + SINE/MIR_MIRb chr1 13761587 13761709 + DNA/hAT-Charlie_Charlie4z chr1 13762106 13762205 - SINE/MIR_MIRb chr1 13765465 13765764 - SINE/Alu_AluSg chr1 13767304 13767605 - SINE/Alu_AluJr chr1 13767695 13767997 + SINE/Alu_AluJo chr1 13768075 13768382 - SINE/Alu_AluY chr1 13768383 13768683 - SINE/Alu_AluSq2 chr1 13768684 13768781 + SINE/MIR_MIR3 chr1 13769851 13770066 + DNA/hAT-Charlie_MER20 chr1 13771450 13771748 + SINE/Alu_AluY chr1 13771855 13771896 + Simple_repeat_(ATTTGT)n chr1 13772420 13772559 - LINE/CR1_CR1-3_Croc chr1 13773009 13773066 + Simple_repeat_(ATGA)n chr1 13773430 13773725 + SINE/Alu_AluJr chr1 13773803 13773956 + LINE/L1_L1ME3 chr1 13774695 13774989 + SINE/Alu_AluY chr1 13775972 13776269 + LINE/L2_L2c chr1 13776427 13776628 + LINE/L2_L2a chr1 13776647 13776742 + LINE/L2_L2-1_AMi chr1 13776744 13777722 + LINE/L2_L2a chr1 13778134 13778234 + LINE/L2_L2a chr1 13778593 13778682 + Low_complexity_GA-rich chr1 13780949 13781032 + Simple_repeat_(TCTTCC)n chr1 13782895 13782929 + Simple_repeat_(T)n chr1 13785832 13786135 - SINE/Alu_AluY chr1 13787048 13787076 + Simple_repeat_(T)n chr1 13788464 13788528 - DNA/TcMar-Mariner_MADE2 chr1 13788879 13788980 + SINE/MIR_MIR3 chr1 13789358 13789468 - LINE/L2_L2a chr1 13790067 13790194 - DNA/hAT-Charlie_MER5B chr1 13790918 13791075 - SINE/MIR_MIRb chr1 13792798 13792976 - SINE/MIR_MIR chr1 13793723 13793921 - DNA/hAT-Charlie_MER30 chr1 13794214 13794498 + LINE/L2_L2a chr1 13794502 13794631 + SINE/MIR_MIRb chr1 13795095 13795132 + Simple_repeat_(TTTCAA)n chr1 13795265 13795435 - SINE/MIR_MIR chr1 13795896 13796032 + DNA/hAT-Charlie_MER117 chr1 13796057 13796145 - DNA/TcMar-Tigger_Tigger15a chr1 13796249 13796444 - DNA/TcMar-Tigger_Tigger15a chr1 13796474 13796758 + SINE/Alu_AluJr chr1 13797754 13797826 + DNA/hAT-Charlie_MER5A chr1 13799162 13799460 + SINE/Alu_AluSq2 chr1 13799657 13800059 + DNA/TcMar-Tigger_Tigger13a chr1 13800064 13800090 + Simple_repeat_(CATT)n chr1 13800111 13800673 - LINE/L2_L2a chr1 13801376 13801464 - DNA/hAT-Charlie_MER103C chr1 13801498 13801717 + SINE/MIR_MIR chr1 13803297 13803431 - LINE/L2_L2b chr1 13803566 13803662 + SINE/MIR_MIR3 chr1 13803722 13803756 + Simple_repeat_(CCAGG)n chr1 13804015 13804175 + SINE/MIR_MIRb chr1 13804503 13804641 - DNA/hAT-Charlie_MER5A1 chr1 13804669 13804802 + DNA/hAT-Charlie_MER5B chr1 13804949 13805196 + SINE/MIR_MIRb chr1 13805227 13805355 - SINE/MIR_MIRb chr1 13805945 13806790 + LINE/L2_L2b chr1 13806850 13807000 + LINE/L2_L2b chr1 13807304 13807672 - LINE/L2_L2a chr1 13807683 13807711 + Simple_repeat_(TGAA)n chr1 13807962 13808018 - DNA/hAT-Ac_OldhAT1 chr1 13808128 13808209 - DNA/TcMar-Tigger_Tigger15a chr1 13808210 13808483 + SINE/Alu_AluY chr1 13808484 13808527 - DNA/TcMar-Tigger_Tigger15a chr1 13808682 13808699 - LINE/L2_L2b chr1 13808700 13808807 - LINE/L2_L2a chr1 13808808 13808826 - LINE/L2_L2b chr1 13808924 13809077 - DNA/hAT-Charlie_MER5B chr1 13809191 13809712 - LINE/L2_L2b chr1 13809803 13810162 - LTR/ERVL-MaLR_MLT1A chr1 13810290 13810482 - DNA/TcMar-Tigger_Tigger15a chr1 13810483 13810792 - SINE/Alu_AluY chr1 13810903 13811197 + SINE/Alu_AluSx1 chr1 13811605 13811706 - LINE/L2_L2c chr1 13811892 13811929 + Low_complexity_G-rich chr1 13812109 13812323 - LINE/L2_L2c chr1 13812542 13812680 - LINE/L2_L2c chr1 13812877 13813018 - SINE/MIR_MIRb chr1 13813153 13813300 - SINE/MIR_MIRb chr1 13813522 13813647 + SINE/MIR_MIRc chr1 13814520 13814710 - DNA/hAT-Charlie_MER20 chr1 13814925 13815068 + SINE/MIR_MIR chr1 13815098 13815411 - SINE/Alu_AluSx1 chr1 13815428 13815671 - LINE/L2_L2b chr1 13816257 13816353 - LTR/ERVL-MaLR_MLT1I chr1 13816730 13816955 + SINE/MIR_MIR chr1 13817001 13817073 - DNA/hAT-Tip100_MamTip1b chr1 13817074 13817317 + DNA/TcMar-Tigger_Tigger4a chr1 13817318 13817508 - DNA/hAT-Tip100_MamTip1b chr1 13817719 13817947 + SINE/Alu_AluYk3 chr1 13818368 13818683 - SINE/Alu_AluSc chr1 13818928 13819039 - SINE/MIR_MIR1_Amn chr1 13819591 13819970 - LTR/ERVL-MaLR_MLT1A chr1 13819988 13820129 - SINE/MIR_MIR3 chr1 13820516 13820720 - LTR/ERV1_LTR78B chr1 13820732 13821011 - LTR/ERV1_LTR78B chr1 13821117 13821285 + SINE/MIR_MIR chr1 13821307 13821359 + Simple_repeat_(TAATAG)n chr1 13821432 13821479 + Simple_repeat_(ATTT)n chr1 13821480 13821766 - SINE/Alu_AluSx1 chr1 13821769 13821823 - LINE/L2_L2c chr1 13821875 13822053 + SINE/MIR_MIRc chr1 13822063 13822206 + LINE/L1_L1M5 chr1 13822215 13822386 + LINE/L1_L1ME3A chr1 13822387 13822691 - SINE/Alu_AluY chr1 13822692 13823065 + LINE/L1_L1ME3A chr1 13823267 13823297 + Simple_repeat_(GTGC)n chr1 13825319 13825432 - LINE/L1_L1MB8 chr1 13825433 13825729 - SINE/Alu_AluY chr1 13825730 13826341 - LINE/L1_L1MB8 chr1 13826603 13827080 - LTR/ERVL_MLT2B1 chr1 13827139 13827392 - LINE/L2_L2c chr1 13827517 13827729 - SINE/MIR_MIRc chr1 13827729 13827980 - SINE/MIR_MIRb chr1 13828135 13828283 + SINE/MIR_MIRb chr1 13828311 13828452 + DNA/hAT-Charlie_MER5A chr1 13829235 13829274 + Simple_repeat_(GTGAG)n chr1 13829672 13829762 + DNA/hAT-Tip100_MER96 chr1 13829808 13830266 + LINE/L2_L2b chr1 13830534 13830714 - DNA/hAT_MER53 chr1 13831556 13831855 - SINE/Alu_AluSq2 chr1 13831911 13832065 + SINE/MIR_MIR chr1 13832357 13832444 - SINE/MIR_MIR chr1 13833834 13833891 + LINE/CR1_L3 chr1 13833915 13834224 - SINE/Alu_AluSx1 chr1 13834378 13834421 + Simple_repeat_(TGCTGGA)n chr1 13834537 13835007 - LTR/ERV1_MER4A1 chr1 13835829 13836143 - LTR/ERVL-MaLR_MLT1B chr1 13836319 13836480 - LTR/ERVL-MaLR_MLT1I chr1 13836483 13836702 - LINE/L1_L1ME3B chr1 13836695 13836757 - LINE/L1_L1MD2 chr1 13836794 13837094 - LINE/L1_L1MD1 chr1 13837095 13837410 - SINE/Alu_AluJb chr1 13837411 13838565 - LINE/L1_L1MD1 chr1 13838569 13838586 - SINE/Alu_AluSx1 chr1 13838587 13838695 + Simple_repeat_(TTCC)n chr1 13838696 13838984 - SINE/Alu_AluSx1 chr1 13839011 13839021 - LINE/L1_L1PA15 chr1 13839022 13839332 + SINE/Alu_AluY chr1 13839333 13839971 - LINE/L1_L1PA15 chr1 13840113 13840306 - SINE/MIR_MIR chr1 13840541 13840951 + LINE/L1_L1PA3 chr1 13840952 13840991 + Simple_repeat_(ATA)n chr1 13840992 13841010 + LINE/L1_L1PA3 chr1 13841016 13842872 - LINE/L1_L1PA14 chr1 13842902 13843186 + LTR/ERVL-MaLR_THE1D chr1 13843187 13843225 + LINE/L1_L1PREC2 chr1 13843226 13843294 - LTR/ERVL-MaLR_THE1C chr1 13843295 13844339 - LINE/L1_L1PA14 chr1 13844340 13844811 - LTR/ERVK_LTR22B1 chr1 13844824 13845069 - LINE/L1_L1PA14 chr1 13845082 13845517 - LINE/L1_L1MD1 chr1 13845518 13845812 - SINE/Alu_AluSx3 chr1 13845813 13845929 - LINE/L1_L1MD1 chr1 13845930 13845962 + Simple_repeat_(CCTT)n chr1 13845963 13847468 - LINE/L1_L1MD1 chr1 13847469 13847845 + LTR/ERVL-MaLR_THE1D chr1 13847846 13849381 + LTR/ERVL-MaLR_THE1D-int chr1 13849382 13849436 + LTR/ERVL-MaLR_THE1D chr1 13849437 13849792 + LTR/ERVL-MaLR_THE1B chr1 13849793 13851075 + LTR/ERVL-MaLR_THE1B-int chr1 13851076 13851249 - DNA/TcMar-Tigger_MER6C chr1 13851254 13851689 + DNA/hAT-Charlie_Charlie7 chr1 13851719 13852029 + LTR/ERVL-MaLR_THE1B-int chr1 13852030 13852387 + LTR/ERVL-MaLR_THE1B chr1 13852388 13852715 + LTR/ERVL-MaLR_THE1D chr1 13852717 13852901 - LINE/L1_L1MD1 chr1 13852917 13853422 - LINE/L1_L1MD1 chr1 13853423 13853938 - LTR/ERVL-MaLR_MLT1D chr1 13853939 13854233 - LINE/L1_L1MD1 chr1 13854226 13854387 - LINE/L1_HAL1M8 chr1 13854430 13854707 - LINE/L1_L1PA14 chr1 13854708 13855067 + LTR/ERVL-MaLR_THE1D chr1 13855068 13856679 + LTR/ERVL-MaLR_THE1D-int chr1 13856680 13857024 + LTR/ERVL-MaLR_THE1D chr1 13857025 13858815 - LINE/L1_L1PA14 chr1 13858842 13859081 - LTR/ERVL-MaLR_MLT1I chr1 13859167 13859404 + SINE/MIR_MIRb chr1 13859505 13859670 - LINE/L1_HAL1 chr1 13859793 13859890 + DNA/TcMar-Tigger_MER2B chr1 13859938 13860889 - LINE/L1_HAL1 chr1 13860890 13860914 + Simple_repeat_(T)n chr1 13860915 13860965 - LINE/L1_HAL1 chr1 13860966 13861265 - SINE/Alu_AluSq2 chr1 13861266 13861346 - LINE/L1_HAL1 chr1 13861430 13861626 - SINE/MIR_MIR chr1 13861642 13862376 - LINE/L1_HAL1 chr1 13862377 13862667 + SINE/Alu_AluSx1 chr1 13862668 13862730 - LINE/L1_HAL1 chr1 13862735 13863025 - SINE/Alu_AluJr chr1 13863694 13863795 + LTR/ERVL-MaLR_MLT1N2 chr1 13863924 13864058 - LTR/ERVL-MaLR_MLT1N2 chr1 13864201 13864398 - SINE/Alu_AluYc chr1 13864900 13864980 + SINE/tRNA_LFSINE_Vert chr1 13865117 13865245 + DNA/hAT-Charlie_Charlie4z chr1 13865294 13865399 + LINE/L1_L1MB8 chr1 13865410 13865716 + SINE/Alu_AluSc chr1 13866890 13867362 - LTR/ERVL-MaLR_MLT1C2 chr1 13867371 13868165 + LINE/L2_L2b chr1 13868761 13868989 - LTR/ERVL_LTR16A chr1 13869858 13869916 - SINE/MIR_MIR chr1 13869917 13869993 + Simple_repeat_(TCTT)n chr1 13869999 13870857 - LINE/L1_L1PA13 chr1 13871263 13871496 - LINE/L1_L1PA14 chr1 13871499 13871753 + DNA/hAT-Charlie_MER58B chr1 13872176 13872274 - LINE/L2_L2c chr1 13872308 13872364 - LTR/ERVL-MaLR_MLT1N2 chr1 13872698 13872970 - LINE/L2_L2c chr1 13874030 13874142 + SINE/MIR_MIR3 chr1 13874146 13874515 - LTR/ERVL-MaLR_MLT1B chr1 13874732 13874790 + LTR/ERVL-MaLR_MLT1L chr1 13874802 13874880 - DNA/hAT-Tip100_ORSL chr1 13874918 13874995 - DNA/hAT-Tip100_ORSL chr1 13875122 13875232 + LTR/ERVL-MaLR_MLT1L chr1 13875382 13875607 - LTR/ERVL-MaLR_MLT1C chr1 13875608 13875915 + SINE/Alu_AluYk3 chr1 13875916 13876161 - LTR/ERVL-MaLR_MLT1C chr1 13876201 13876379 - SINE/Alu_AluJr chr1 13876537 13876704 - SINE/Alu_AluJb chr1 13876705 13877015 - SINE/Alu_AluSq2 chr1 13877016 13877151 - SINE/Alu_AluJb chr1 13877232 13877300 - LINE/L2_L2b chr1 13877328 13877615 - LTR/ERVL-MaLR_MLT1D chr1 13877777 13877947 + DNA/hAT-Charlie_MER5A chr1 13877940 13878084 - DNA/hAT-Tip100_MER96B chr1 13878156 13878317 - LINE/L2_L2c chr1 13878352 13878397 - LINE/L2_L2c chr1 13878782 13878899 + DNA/hAT-Blackjack_MER94 chr1 13879618 13879923 + SINE/Alu_AluYf1 chr1 13879946 13880493 - LINE/L1_L1ME3 chr1 13880494 13880809 - SINE/Alu_AluJb chr1 13880810 13881115 - LINE/L1_L1ME3 chr1 13881116 13881410 + SINE/Alu_AluSx1 chr1 13881411 13882380 - LINE/L1_L1ME3 chr1 13882744 13883591 - LINE/L1_L1ME3 chr1 13883615 13884157 - LTR/ERVL_MLT2B3 chr1 13884508 13884702 - LINE/L1_L1ME3 chr1 13884819 13884940 + Simple_repeat_(TCCT)n chr1 13884941 13885243 - SINE/Alu_AluJb chr1 13885370 13886144 - LINE/L1_L1MC4 chr1 13886145 13886186 + Simple_repeat_(ATCC)n chr1 13886187 13886735 - LINE/L1_L1MC4 chr1 13887019 13887094 - LINE/L1_L1ME3 chr1 13887357 13888005 + LINE/L1_L1MB7 chr1 13888181 13888279 + SINE/MIR_MIRc chr1 13888634 13889063 - LTR/ERVK_LTR22A chr1 13889228 13889318 + LINE/L2_L2d2 chr1 13889679 13889836 - DNA/hAT-Charlie_MER102a chr1 13889896 13889993 + LINE/L1_L1M5 chr1 13889994 13890360 - LTR/ERVL-MaLR_MLT1A0 chr1 13890361 13891087 + LINE/L1_L1M5 chr1 13891106 13891203 + DNA/hAT-Blackjack_MER94 chr1 13891232 13891554 - SINE/Alu_AluSx1 chr1 13891555 13891708 + LINE/L1_L1M5 chr1 13892999 13893036 + Simple_repeat_(GCGCCGC)n chr1 13893836 13893860 + Simple_repeat_(TGTC)n chr1 13894358 13894439 + SINE/tRNA-RTE_MamSINE1 chr1 13894639 13894818 + SINE/MIR_MIR3 chr1 13894878 13895088 + SINE/MIR_MIR chr1 13895992 13896117 - SINE/Alu_AluJb chr1 13896138 13896330 - DNA/hAT-Charlie_MER58A chr1 13896331 13896590 - SINE/Alu_AluJr chr1 13897067 13897296 + DNA/hAT-Charlie_MER58A chr1 13897674 13897725 - SINE/MIR_MIR chr1 13898210 13898258 - LINE/CR1_L3 chr1 13898750 13898784 + Simple_repeat_(TGTC)n chr1 13899204 13899421 - SINE/MIR_MIRb chr1 13899542 13899643 + LINE/L2_L2c chr1 13899645 13899909 - SINE/Alu_AluSx chr1 13899989 13900141 - DNA_X30_DNA chr1 13900649 13900872 + SINE/MIR_MIR chr1 13901043 13901074 + Simple_repeat_(A)n chr1 13901191 13901295 + DNA/hAT-Blackjack_MER94 chr1 13901484 13901645 - DNA/hAT-Charlie_MER102a chr1 13902522 13902833 + LINE/L1_L1MA4 chr1 13904529 13905337 - LINE/L1_L1MEd chr1 13905357 13905796 - LINE/L1_L1MEd chr1 13905797 13906215 - LTR/ERVL-MaLR_MLT1D chr1 13906216 13906275 - LINE/L1_L1MEd chr1 13907483 13907520 + Simple_repeat_(GT)n chr1 13907521 13907532 + Low_complexity_G-rich chr1 13910035 13910226 + LTR/ERVL-MaLR_MSTA chr1 13910227 13910517 + SINE/Alu_AluSc5 chr1 13910518 13910754 + LTR/ERVL-MaLR_MSTA chr1 13910813 13911041 - LTR/ERVL-MaLR_MSTA chr1 13911042 13911347 - SINE/Alu_AluSx1 chr1 13911348 13911536 - LTR/ERVL-MaLR_MSTA chr1 13911892 13912148 + SINE/MIR_MIRb chr1 13912611 13912895 - SINE/Alu_AluSx chr1 13913111 13913216 - LINE/L2_L2b chr1 13913730 13913824 - DNA/hAT-Charlie_MER5A chr1 13913900 13914066 + DNA/hAT-Charlie_MER5A chr1 13914283 13914412 + SINE/MIR_MIR chr1 13914471 13914531 + LTR/ERVL-MaLR_MLT1E1 chr1 13914600 13914661 + LTR/ERVL-MaLR_MLT1E1 chr1 13914661 13914693 + DNA/TcMar-Tigger_Tigger1 chr1 13914696 13914766 + SINE/Alu_AluJo chr1 13914799 13915074 + LTR/ERVL-MaLR_MLT1E1 chr1 13915149 13915181 + Simple_repeat_(ACAC)n chr1 13915263 13915501 + DNA/hAT-Tip100_ORSL chr1 13916319 13916481 + LINE/L2_L2d chr1 13916499 13917039 - LINE/L2_L2c chr1 13917070 13917094 + Simple_repeat_(GT)n chr1 13917170 13917513 + DNA/TcMar-Tigger_Tigger1 chr1 13917514 13917827 + SINE/Alu_AluJb chr1 13917828 13918827 + DNA/TcMar-Tigger_Tigger1 chr1 13918828 13918922 - SINE/Alu_AluJb chr1 13918923 13919042 + DNA/hAT-Charlie_Charlie18a chr1 13919083 13919605 - LINE/L1_L1ME3A chr1 13919601 13920014 - LINE/L1_L1ME3A chr1 13920015 13920263 + SINE/Alu_AluSx4 chr1 13920264 13920456 - LINE/L1_L1ME3A chr1 13920500 13920939 - LTR/ERV1_LTR19A chr1 13920990 13921059 - SINE/MIR_MIRb chr1 13921070 13921118 - DNA/TcMar-Tigger_Tigger18a chr1 13921122 13921274 - LINE/L1_L1ME3A chr1 13921275 13921496 - DNA/hAT-Charlie_MER58A chr1 13921497 13921514 - LINE/L1_L1ME3A chr1 13921633 13921930 - SINE/Alu_AluY chr1 13923040 13923291 - DNA/TcMar-Tigger_Tigger2 chr1 13923292 13923592 + SINE/Alu_AluY chr1 13923593 13923792 - DNA/TcMar-Tigger_Tigger2 chr1 13923778 13923844 - DNA/TcMar-Tigger_Tigger2b_Pri chr1 13923891 13924072 - LINE/L2_L2 chr1 13924325 13924415 + LINE/L1_L1ME3Cz chr1 13924423 13924461 - LINE/L1_L1MB7 chr1 13924462 13924931 + LTR/ERV1_MER4A1 chr1 13924932 13925286 - LINE/L1_L1MB7 chr1 13925417 13926019 - LINE/L2_L2a chr1 13926262 13926287 + Simple_repeat_(GGCT)n chr1 13927171 13927212 + LINE/CR1_L3 chr1 13927594 13928016 - LINE/L2_L2a chr1 13928287 13928331 + Simple_repeat_(GAT)n chr1 13928438 13928482 - SINE/MIR_MIRc chr1 13929048 13929206 - SINE/Alu_FRAM chr1 13929792 13930045 - SINE/MIR_MIR chr1 13930531 13930771 - LINE/L2_L2b chr1 13931448 13931498 + Simple_repeat_(GT)n chr1 13931847 13932157 - SINE/Alu_AluSz chr1 13932219 13932258 - SINE/MIR_MIR chr1 13932259 13932559 - SINE/Alu_AluYa5 chr1 13932560 13932749 - SINE/MIR_MIR chr1 13932917 13933185 - LINE/L1_L1MD3 chr1 13933211 13933492 + SINE/Alu_AluSp chr1 13933546 13933668 - LINE/L2_L2b chr1 13933698 13933904 - SINE/MIR_MIRb chr1 13933912 13933934 + Simple_repeat_(AATG)n chr1 13934102 13934157 + Simple_repeat_(GAATA)n chr1 13934654 13934705 + LINE/L2_L2c chr1 13934789 13934856 + LINE/L2_L2c chr1 13934955 13935240 + SINE/Alu_AluSq2 chr1 13935241 13935270 + Simple_repeat_(TAA)n chr1 13935285 13935475 - SINE/MIR_MIR chr1 13935713 13935860 + SINE/MIR_MIRb chr1 13935863 13935901 + Simple_repeat_(TG)n chr1 13935910 13936042 + Simple_repeat_(AT)n chr1 13936061 13936096 - LINE/L1_L1PB4 chr1 13936097 13936401 - SINE/Alu_AluSp chr1 13936402 13936890 - LINE/L1_L1PB4 chr1 13936891 13937036 - LINE/L1_L1P4 chr1 13937037 13937343 - SINE/Alu_AluSx3 chr1 13937344 13938560 - LINE/L1_L1P4 chr1 13938563 13938716 + LTR/ERVL-MaLR_MST-int chr1 13938682 13939656 - LTR/ERVL-MaLR_MSTA-int chr1 13939657 13940072 - LTR/ERVL-MaLR_MSTA chr1 13940073 13940529 - LINE/L1_L1P4 chr1 13940544 13940709 + LINE/L1_L1MEg chr1 13940913 13941218 + SINE/Alu_AluSx chr1 13941327 13941363 - LINE/L2_L2c chr1 13941749 13941783 + Simple_repeat_(ACC)n chr1 13941784 13942258 + LINE/L1_L1MEg chr1 13942259 13942572 + SINE/Alu_AluYb9 chr1 13942573 13943324 + LINE/L1_L1MEg chr1 13943325 13943463 + SINE/Alu_AluJr4 chr1 13943466 13943723 + LINE/L1_L1MEg chr1 13943753 13944543 + LINE/L1_L1MB8 chr1 13944541 13945156 + LINE/L1_L1MEg chr1 13945192 13945490 + SINE/Alu_AluSx chr1 13945680 13945711 + Simple_repeat_(TG)n chr1 13945712 13945736 + Simple_repeat_(AG)n chr1 13946047 13946213 - LINE/CR1_L3b chr1 13946295 13946429 + SINE/MIR_MIR chr1 13946838 13947070 - LTR/ERVL-MaLR_MLT1D chr1 13947071 13947516 - LTR/ERVL-MaLR_THE1D chr1 13947517 13947604 - LTR/ERVL-MaLR_MLT1D chr1 13947605 13948015 - LTR/ERVL-MaLR_MLT1B chr1 13948016 13948121 - LTR/ERVL-MaLR_MLT1D chr1 13948217 13948569 + LTR/ERVL-MaLR_THE1C chr1 13948860 13948968 - SINE/MIR_MIRb chr1 13949035 13949102 - SINE/MIR_MIRc chr1 13949585 13949813 - SINE/MIR_MIR chr1 13950258 13950501 + LINE/L2_L2a chr1 13950695 13950850 - LTR/ERVL_LTR33 chr1 13951225 13951414 + SINE/MIR_MIR chr1 13951417 13951539 + DNA/hAT-Blackjack_MER94 chr1 13951624 13951671 + Simple_repeat_(ATA)n chr1 13952335 13952379 + Simple_repeat_(ATA)n chr1 13952795 13952938 + SINE/MIR_MIR3 chr1 13952941 13953070 + LINE/CR1_L3 chr1 13953276 13953383 - LTR/ERVL?_LTR89B chr1 13953413 13953740 - LINE/L1_HAL1ME chr1 13953828 13953953 + DNA/MULE-MuDR_Ricksha_c chr1 13953936 13954121 + DNA/MULE-MuDR_Ricksha_0 chr1 13954125 13954228 + SINE/Alu_AluSx3 chr1 13954229 13954269 + Simple_repeat_(CCAA)n chr1 13954272 13955530 + DNA/MULE-MuDR_Ricksha chr1 13955431 13955590 - DNA/MULE-MuDR_Ricksha_a chr1 13956293 13956323 + Simple_repeat_(T)n chr1 13956324 13956508 - LINE/CR1_CR1_Mam chr1 13957359 13957383 + Simple_repeat_(AGCTA)n chr1 13957470 13957893 - LTR/ERVL-MaLR_MSTB1 chr1 13958057 13958288 - LINE/L2_L2b chr1 13958289 13958596 + SINE/Alu_AluY chr1 13958597 13958874 - LINE/L2_L2b chr1 13959141 13959360 + LTR/ERVL-MaLR_MLT1A0 chr1 13959556 13959890 + LINE/L2_L2b chr1 13959977 13960034 + LINE/L1_L1MC5a chr1 13960040 13960107 - LINE/L2_L2b chr1 13960152 13960370 - SINE/MIR_MIR chr1 13960963 13961067 - LINE/L2_L2a chr1 13961245 13961287 + Low_complexity_A-rich chr1 13961287 13961325 + Simple_repeat_(ACAC)n chr1 13961331 13961456 - LINE/L2_L2a chr1 13961925 13962031 + LINE/L2_L2c chr1 13962116 13962296 - LINE/L2_L2a chr1 13962475 13962558 - SINE/MIR_MIRb chr1 13962559 13962857 - SINE/Alu_AluSx chr1 13962858 13962902 - SINE/MIR_MIRb chr1 13963146 13963196 + Low_complexity_G-rich chr1 13963620 13963689 + LINE/L2_L2d chr1 13963760 13963804 + Simple_repeat_(TG)n chr1 13963819 13964065 + SINE/MIR_MIRb chr1 13964111 13964361 - LINE/L2_L2a chr1 13964362 13964842 - LTR/ERVL-MaLR_MLT1H chr1 13964843 13965282 - LINE/L2_L2a chr1 13965360 13965438 + SINE/MIR_MIRb chr1 13965539 13965621 + LINE/L2_L2d chr1 13965994 13966067 - LTR/ERVL_LTR80A chr1 13966111 13966219 + Simple_repeat_(CTCC)n chr1 13966466 13966597 - SINE/MIR_MIRb chr1 13966620 13966945 + DNA/hAT-Charlie_MER33 chr1 13967016 13967086 + LINE/L2_L2d chr1 13967118 13967231 - LINE/L1_L1MC4a chr1 13967479 13967679 + SINE/MIR_MIRb chr1 13968483 13969007 + LINE/L2_L2a chr1 13969047 13969174 - LINE/L2_L2a chr1 13969175 13969549 + LTR/ERVL-MaLR_MLT1C chr1 13969550 13969720 + LINE/L2_L2a chr1 13969893 13970063 - SINE/MIR_MIRb chr1 13970290 13970506 - SINE/MIR_MIR chr1 13970968 13971451 - LTR/ERVL-MaLR_MLT1H chr1 13971595 13971625 + Simple_repeat_(GTT)n chr1 13971653 13972067 + LTR/ERVL-MaLR_MSTA chr1 13972262 13972367 - SINE/Alu_AluYk3 chr1 13972707 13972848 + SINE/MIR_MIR3 chr1 13973020 13973128 + Simple_repeat_(CTTCTT)n chr1 13973147 13973254 - SINE/MIR_MIR chr1 13973262 13973485 + LTR/ERVL_LTR33 chr1 13973497 13973735 + LTR/ERVL_LTR33 chr1 13973736 13973769 - SINE/MIR_MIRc chr1 13973759 13973808 + SINE/MIR_MIR3 chr1 13973810 13973856 + LINE/L2_L2b chr1 13973879 13974359 - LTR/ERVL_MER68 chr1 13974360 13974836 + LTR/ERVL-MaLR_MLT1C2 chr1 13974837 13974922 - LTR/ERVL_MER68 chr1 13975014 13975177 - LINE/L2_L2a chr1 13975572 13975642 + LINE/L2_L2b chr1 13975805 13975829 + Simple_repeat_(TGAA)n chr1 13976870 13977117 + DNA/Kolobok_UCON29 chr1 13977238 13977405 + SINE/MIR_MIRb chr1 13977732 13977841 + LINE/L1_L1MEg chr1 13977846 13978151 + SINE/Alu_AluYj4 chr1 13978279 13978828 + LINE/L1_L1MEg chr1 13978829 13979117 + SINE/Alu_AluJb chr1 13979118 13979646 + LINE/L1_L1MEg chr1 13979647 13979954 + SINE/Alu_AluY chr1 13979955 13980475 + LINE/L1_L1MEg chr1 13980282 13980723 + LINE/L1_L1M5 chr1 13980768 13981010 - LTR/ERVL-MaLR_MLT1G chr1 13981012 13981082 + DNA/hAT-Charlie_MER106B chr1 13981090 13981119 + Simple_repeat_(TA)n chr1 13981244 13981391 + Simple_repeat_(AATTATT)n chr1 13981395 13981540 - LTR/ERVL-MaLR_MLT1G chr1 13981638 13981882 + DNA/hAT-Charlie_Charlie31a chr1 13981914 13981979 - SINE/MIR_MIR3 chr1 13982242 13983727 - LTR/ERV1_LTR12C chr1 13984007 13984309 - SINE/Alu_AluY chr1 13984404 13984649 + LINE/L1_L1MEg chr1 13984691 13984771 + LINE/L2_L2b chr1 13984806 13984920 - LINE/L1_L1MB8 chr1 13985054 13985099 - LTR/ERVL_MLT2B2 chr1 13985125 13985152 + Simple_repeat_(TCTA)n chr1 13985203 13985307 - LINE/L1_L1PA3 chr1 13985308 13985617 + LINE/L1_L1PA3 chr1 13985614 13986171 - LINE/L1_L1MB7 chr1 13986609 13986723 - LTR/ERVL-MaLR_MLT1J2 chr1 13987160 13987436 - LINE/L1_L1PA8 chr1 13987491 13987803 - LTR/ERVL-MaLR_MSTD chr1 13987804 13988160 - LTR/ERVL-MaLR_MSTD chr1 13988161 13988188 - LTR/ERVL-MaLR_MSTD chr1 13988318 13988718 - LTR/ERVL-MaLR_MLT1J2 chr1 13988775 13988879 - DNA/hAT-Charlie_Charlie4z chr1 13988916 13989273 - LTR/ERVL-MaLR_MSTD chr1 13989309 13989449 - LINE/L2_L2d2 chr1 13989578 13989737 - LINE/L2_L2a chr1 13990022 13990094 + DNA/hAT-Tip100_Arthur1 chr1 13990096 13990934 + LINE/L1_L1ME2z chr1 13990937 13991011 - LTR/ERVL-MaLR_MLT1H2 chr1 13991098 13991273 - DNA/hAT-Blackjack_MER63B chr1 13991274 13991449 + LINE/L1_L1PA7 chr1 13991450 13991685 - DNA/hAT-Blackjack_MER63B chr1 13991810 13991962 + LINE/L2_L2c chr1 13991982 13992016 + Simple_repeat_(ACC)n chr1 13992029 13992288 + LINE/L2_L2c chr1 13993030 13993289 + LTR/ERVL-MaLR_MLT1I chr1 13993544 13993792 - LINE/L2_L2a chr1 13994401 13994495 - SINE/MIR_MIRc chr1 13994504 13994871 - LINE/L2_L2b chr1 13994872 13994899 + Simple_repeat_(AAAC)n chr1 13994900 13994987 - LINE/L2_L2b chr1 13995051 13995663 + LINE/L1_L1ME3A chr1 13995672 13995829 + LTR/ERVL_MLT2A1 chr1 13995900 13999067 + LINE/L1_L1PA8A chr1 13999068 13999377 + SINE/Alu_AluSx3 chr1 13999378 14000782 + LINE/L1_L1PA8A chr1 14000783 14001081 - SINE/Alu_AluYa5 chr1 14001082 14002267 + LINE/L1_L1PA8A chr1 14002268 14002615 + LTR/ERVL-MaLR_THE1A chr1 14002616 14003343 + LINE/L1_L1PA8A chr1 14003348 14004554 + LINE/L1_L1ME3A chr1 14004552 14004710 + LINE/L1_L1ME3A chr1 14004711 14005128 + LTR/ERVL-MaLR_MSTC chr1 14005129 14005162 + LINE/L1_L1ME3A chr1 14005216 14005586 - LINE/L2_L2b chr1 14006036 14006203 - LINE/L1_L1M3 chr1 14006208 14006454 - LTR/ERVL-MaLR_MSTB chr1 14006456 14007407 - LINE/L1_L1M3 chr1 14007850 14008020 + SINE/MIR_MIR3 chr1 14008487 14008642 - LINE/L2_L2b chr1 14008681 14008711 + Simple_repeat_(GAGGAT)n chr1 14008726 14008785 - SINE/MIR_MIR chr1 14008786 14009519 - LINE/L1_L1MB3 chr1 14009520 14009680 - SINE/MIR_MIR chr1 14010017 14010246 - DNA/hAT-Charlie_MER113A chr1 14011192 14011408 + LINE/L2_L2b chr1 14011502 14011582 - LINE/CR1_L3 chr1 14011610 14011867 + SINE/MIR_MIR chr1 14011912 14012109 + DNA/hAT-Charlie_MER20B chr1 14012120 14012371 + DNA/hAT-Charlie_MER20B chr1 14012533 14012836 + SINE/Alu_AluSz6 chr1 14012920 14012982 + DNA/hAT-Charlie_MER20B chr1 14013041 14013230 - LINE/CR1_CR1-3_Croc chr1 14014498 14014673 + DNA/hAT-Charlie_MER58B chr1 14015460 14015645 - SINE/Alu_AluSq2 chr1 14015646 14015757 + SINE/Alu_AluSx chr1 14015772 14015935 - SINE/MIR_MIRb chr1 14016321 14016440 - SINE/MIR_MIRc chr1 14016410 14016445 - SINE/MIR_MIR1_Amn chr1 14017509 14017624 - SINE/MIR_MIRb chr1 14017945 14018095 - SINE/MIR_MIR chr1 14018484 14019031 + LTR/ERVL_MLT2B3 chr1 14019279 14019375 - LINE/L2_L2c chr1 14019803 14020320 - DNA/hAT-Charlie_MER1A chr1 14021054 14021283 - DNA/hAT-Charlie_MER20 chr1 14021292 14021444 + DNA/hAT-Charlie_MER5A1 chr1 14021469 14021624 - DNA/hAT-Charlie_MER5A chr1 14021954 14021972 + Simple_repeat_(T)n chr1 14022486 14022552 + Simple_repeat_(A)n chr1 14022815 14023000 + SINE/MIR_MIRb chr1 14023033 14023351 + SINE/Alu_AluJo chr1 14023352 14023570 - SINE/MIR_MIR chr1 14024162 14024349 + LINE/L1_L1ME4c chr1 14024772 14024928 - DNA/hAT-Charlie_MER5B chr1 14025376 14025436 + DNA/hAT-Charlie_MER5A1 chr1 14025502 14025585 + DNA/hAT-Charlie_MER5A1 chr1 14025594 14025791 + DNA/hAT-Charlie_MER3 chr1 14026124 14026251 - SINE/MIR_MIRb chr1 14026205 14026311 + LINE/L2_L2a chr1 14026302 14026442 - SINE/MIR_MIR chr1 14026627 14026741 - DNA/hAT-Charlie_MER5A1 chr1 14026742 14027056 - LINE/L2_L2b chr1 14028755 14030285 - LINE/L1_L1PA2 chr1 14030286 14030589 + LINE/L1_L1PA2 chr1 14030636 14030677 + Simple_repeat_(AC)n chr1 14031284 14031337 - Unknown_UCON35 chr1 14032196 14032452 - SINE/MIR_MIR chr1 14032488 14032647 + LINE/L2_L2 chr1 14032801 14032893 - SINE/MIR_MIRb chr1 14033614 14033658 + LINE/L2_L2 chr1 14035214 14035451 - LTR/ERVL_LTR86B1 chr1 14035452 14035774 - SINE/Alu_AluSx chr1 14035775 14035974 - LTR/ERVL_LTR86B1 chr1 14035993 14036204 - LINE/L2_L2c chr1 14036541 14036660 + SINE/Alu_FRAM chr1 14036706 14036793 - LINE/L1_L1ME4a chr1 14036794 14037076 - SINE/Alu_AluSg chr1 14037077 14037288 - LINE/L1_L1ME4a chr1 14038000 14038070 + Simple_repeat_(CACC)n chr1 14038073 14038526 - LINE/L2_L2a chr1 14038753 14038879 + SINE/MIR_MIRb chr1 14038884 14039188 + SINE/Alu_AluSc5 chr1 14039325 14039432 + SINE/MIR_MIRc chr1 14039440 14039712 + Unknown_UCON41 chr1 14040036 14040091 + Simple_repeat_(TG)n chr1 14040091 14040117 + Simple_repeat_(GA)n chr1 14040480 14040823 + SINE/Alu_AluSx chr1 14040960 14042234 - LINE/L1_HAL1 chr1 14042240 14042572 - LINE/L1_HAL1 chr1 14042573 14042724 - LINE/L1_L1M7 chr1 14042913 14043655 - LINE/L1_L1MB3 chr1 14043680 14043865 - SINE/MIR_MIRb chr1 14043866 14044115 - LTR/ERVL-MaLR_MLT1F chr1 14044385 14044594 + SINE/MIR_MIR chr1 14044740 14045270 + LTR/ERVL-MaLR_MLT1H1 chr1 14045353 14045430 + SINE/MIR_MIR chr1 14045469 14045531 + SINE/MIR_MIR chr1 14045663 14045851 + SINE/MIR_MIRb chr1 14047044 14047127 + Simple_repeat_(TCCA)n chr1 14047589 14047919 + SINE/Alu_AluSz chr1 14048033 14048208 + SINE/MIR_MIR chr1 14048393 14048704 - SINE/Alu_AluSx1 chr1 14048757 14048990 - LINE/L1_L1PA4 chr1 14048991 14049475 + LINE/L1_L1PA4 chr1 14049716 14050006 - LINE/L2_L2b chr1 14050337 14050692 - LTR/ERVL-MaLR_THE1D chr1 14050996 14051394 - LTR/ERV1_MER31B chr1 14051572 14051845 + SINE/Alu_AluJb chr1 14051846 14051897 + Simple_repeat_(CCAACAA)n chr1 14052748 14052866 - LINE/L2_L2a chr1 14052924 14053502 + LTR/ERVL-MaLR_MLT1F2 chr1 14053604 14053722 - DNA/hAT-Tip100_ORSL chr1 14053727 14054080 - DNA/TcMar-Tigger_Tigger2 chr1 14054065 14054228 - DNA/TcMar-Tigger_Tigger2b_Pri chr1 14054275 14054380 - DNA/hAT-Tip100_ORSL chr1 14054749 14054861 - LINE/L2_L2c chr1 14054925 14055081 - DNA/hAT-Charlie_MER20 chr1 14055135 14055309 + SINE/MIR_MIR chr1 14056153 14056623 + LTR/ERVL-MaLR_MLT1D chr1 14056627 14056767 - SINE/MIR_MIRb chr1 14056895 14057062 + SINE/Alu_AluJb chr1 14057126 14057431 - SINE/Alu_AluSg chr1 14057491 14057546 + SINE/tRNA-RTE_MamSINE1 chr1 14057604 14057644 + LINE/L2_L2a chr1 14057779 14057891 + LINE/L2_L2a chr1 14057898 14058024 + SINE/MIR_MIRc chr1 14058187 14058312 - LTR/Gypsy_MamGypLTR1c chr1 14058494 14058586 - LINE/L2_L2c chr1 14058745 14058961 - LINE/L2_L2c chr1 14059111 14059690 - LTR/ERVL_MLT2B3 chr1 14060085 14060386 + SINE/Alu_AluY chr1 14060757 14060865 - DNA/hAT-Blackjack_MER94 chr1 14060918 14061022 + SINE/MIR_MIRc chr1 14061283 14061469 - LINE/L2_L2b chr1 14061630 14061764 + LINE/L2_L2b chr1 14062574 14062868 - LINE/L2_L2a chr1 14063299 14063467 - DNA/hAT-Charlie_MER113 chr1 14063586 14063629 - LTR/ERVL_LTR82A chr1 14063662 14063948 + LTR/ERVL-MaLR_THE1B chr1 14063949 14064158 - SINE/Alu_AluSg7 chr1 14064447 14064531 + DNA/hAT-Tip100_Zaphod chr1 14065044 14065126 + DNA/hAT-Tip100_Zaphod chr1 14065837 14066400 - LINE/L1_L1PB3 chr1 14066678 14066911 - LTR/ERVL_LTR82B chr1 14066954 14067265 + LINE/L2_L2a chr1 14067491 14067604 + LINE/L2_L2c chr1 14067675 14068124 - LTR/ERVL-MaLR_MLT1K chr1 14068413 14068559 + DNA/hAT-Charlie_MER5B chr1 14068795 14069096 - SINE/Alu_AluSx4 chr1 14069889 14070199 + SINE/Alu_AluSc chr1 14070888 14071034 - SINE/MIR_MIRb chr1 14071696 14071780 + LTR/Gypsy?_LTR88b chr1 14071996 14072127 + SINE/MIR_MIR chr1 14072136 14072177 + Simple_repeat_(ATG)n chr1 14072439 14072584 + SINE/MIR_MIRb chr1 14072626 14072824 + SINE/MIR_MIRb chr1 14073406 14073954 - LINE/L1_L1PA7 chr1 14074054 14074684 - DNA/hAT-Tip100_MER115 chr1 14074759 14074933 + SINE/MIR_MIR3 chr1 14075744 14075839 - DNA/hAT-Charlie_MER20 chr1 14075840 14075999 + LTR/ERVL-MaLR_MLT1C chr1 14076000 14076289 + SINE/Alu_AluSc8 chr1 14076290 14076585 + LTR/ERVL-MaLR_MLT1C chr1 14076586 14076672 - DNA/hAT-Charlie_MER20 chr1 14076676 14076804 + DNA/hAT-Charlie_MER58B chr1 14077646 14078078 + LTR/ERVL-MaLR_MLT1C2 chr1 14078316 14078372 + LINE/L2_L2d2 chr1 14078368 14078441 + SINE/MIR_MIRc chr1 14078467 14078687 - LTR/ERVL-MaLR_MLT1A0 chr1 14078688 14078985 - SINE/Alu_AluSx chr1 14078986 14079010 - LTR/ERVL-MaLR_MLT1A0 chr1 14079011 14079248 - LTR/ERVL-MaLR_MSTB chr1 14079249 14079766 - DNA/PiggyBac_MER75 chr1 14079767 14079962 - LTR/ERVL-MaLR_MSTB chr1 14079963 14080097 - LTR/ERVL-MaLR_MSTB chr1 14080098 14080591 - LTR/ERV1_MER34A1 chr1 14080593 14080670 + SINE/MIR_MIRb chr1 14081443 14081649 + DNA/hAT-Charlie_MER58B chr1 14081741 14082074 - SINE/Alu_AluJb chr1 14082914 14083092 + SINE/Alu_AluYk3 chr1 14083351 14083567 + SINE/MIR_MIRb chr1 14083873 14083999 - LINE/L2_L2a chr1 14084009 14084343 - LINE/L2_L2a chr1 14084714 14084887 + LINE/L1_L1PA4 chr1 14084888 14084981 + Simple_repeat_(TG)n chr1 14085262 14086200 - LINE/L1_L1ME1 chr1 14086201 14086465 - LINE/L1_L1PA10 chr1 14086466 14088172 - LINE/L1_L1ME1 chr1 14088176 14089012 - LINE/L1_L1ME1 chr1 14089013 14089138 - LINE/L1_L1MC1 chr1 14089139 14090832 - LINE/L1_L1ME1 chr1 14090833 14091133 + SINE/Alu_AluSg chr1 14091134 14091540 - LINE/L1_L1ME1 chr1 14091667 14091738 - DNA/hAT-Charlie_Charlie4z chr1 14092083 14092390 - SINE/Alu_AluY chr1 14092414 14092472 + Simple_repeat_(AT)n chr1 14092472 14092491 + Simple_repeat_(AC)n chr1 14092536 14092597 + Simple_repeat_(TATGTACATA)n chr1 14092621 14092650 + Simple_repeat_(AC)n chr1 14093667 14094225 - LTR/ERV1_MER39 chr1 14094250 14094692 + LINE/L1_L1ME3G chr1 14094718 14094981 - DNA/TcMar-Tigger_MamRep137 chr1 14095055 14095227 - DNA/hAT-Charlie_MER20 chr1 14095228 14095371 - DNA/TcMar-Tigger_MamRep137 chr1 14095399 14095951 - LTR/ERVL-MaLR_MLT1G chr1 14095960 14095998 + LINE/L1_L1ME3G chr1 14095999 14096070 - DNA/TcMar-Mariner_MADE2 chr1 14096071 14096196 + LINE/L1_L1ME3G chr1 14096581 14096622 - SINE/MIR_MIR chr1 14096650 14096739 - LINE/L2_L2d2 chr1 14096905 14097025 - DNA/hAT-Charlie_MER5A chr1 14097162 14097239 - LINE/L2_L2d2 chr1 14097270 14097326 - LINE/L2_L2d2 chr1 14097381 14097484 + LTR/ERVL-MaLR_MLT1J chr1 14097601 14097637 + Simple_repeat_(TG)n chr1 14097971 14098129 - SINE/MIR_MIRb chr1 14099300 14099913 + LTR/ERV1_LTR36 chr1 14099914 14099979 + Simple_repeat_(TCCC)n chr1 14100075 14100273 + LTR/ERVL_LTR33 chr1 14100333 14100770 - LTR/ERVL_MER68 chr1 14100771 14101123 + LTR/ERVL-MaLR_THE1B chr1 14101124 14101243 - LTR/ERVL_MER68 chr1 14101259 14101486 - SINE/MIR_MIR chr1 14101543 14101648 + LINE/L1_L1ME4a chr1 14101674 14101902 - DNA/TcMar-Tigger_MamRep434 chr1 14102715 14102914 - LTR/ERVL-MaLR_MLT1B chr1 14102915 14103220 - SINE/Alu_AluSz chr1 14103221 14103404 - LTR/ERVL-MaLR_MLT1B chr1 14103405 14103886 - LTR/ERVL-MaLR_MLT1D chr1 14105089 14105132 - LINE/L2_L2a chr1 14105274 14105423 - LINE/L2_L2a chr1 14106038 14106073 + Simple_repeat_(AATAAC)n chr1 14106931 14107099 + Simple_repeat_(TCCC)n chr1 14107360 14107395 + Simple_repeat_(GTTT)n chr1 14107867 14107992 + SINE/MIR_MIR3 chr1 14108044 14108467 + LINE/L2_L2b chr1 14108777 14108900 - DNA/hAT-Ac_OldhAT1 chr1 14110272 14110425 - DNA/hAT-Tip100_ORSL-2a chr1 14110663 14110993 + DNA/hAT-Charlie_MER33 chr1 14111100 14111201 - LINE/L2_L2a chr1 14111733 14112037 - SINE/Alu_AluSx4 chr1 14112234 14112325 + LINE/L2_L2a chr1 14112540 14112648 + LTR/ERVL-MaLR_MLT1E1 chr1 14112633 14112754 + LTR/ERVL-MaLR_MLT1E1 chr1 14113562 14113705 - SINE/MIR_MIRb chr1 14113705 14113843 + SINE/MIR_MIRb chr1 14114581 14114612 + Simple_repeat_(CCTT)n chr1 14114765 14115015 - SINE/MIR_MIR chr1 14115025 14115305 - LTR/ERVL-MaLR_MLT1A chr1 14115306 14115636 + LTR/ERVL-MaLR_THE1B chr1 14115637 14117208 + LTR/ERVL-MaLR_THE1B-int chr1 14117209 14117544 + LTR/ERVL-MaLR_THE1B chr1 14117545 14117613 - LTR/ERVL-MaLR_MLT1A chr1 14117627 14117860 + SINE/MIR_MIRb chr1 14118683 14118821 - SINE/MIR_MIR3 chr1 14119024 14119061 + Simple_repeat_(AAAACA)n chr1 14119854 14120012 - SINE/MIR_MIR chr1 14120064 14120485 - LTR/ERVL-MaLR_MSTA chr1 14120709 14121100 + LTR/ERVL-MaLR_MLT1K chr1 14121414 14121545 - SINE/MIR_MIR chr1 14121565 14121601 - LINE/L2_L2d chr1 14121572 14122308 - LINE/L2_L2a chr1 14122449 14122572 - SINE/MIR_MIRb chr1 14123539 14123588 + Low_complexity_GA-rich chr1 14124234 14124335 - snRNA_U6 chr1 14124761 14124784 + Simple_repeat_(TTCA)n chr1 14125102 14125275 - DNA/hAT-Charlie_MER20 chr1 14125310 14125749 + LTR/ERVL-MaLR_MLT1C2 chr1 14125886 14126101 - LINE/L2_L2 chr1 14126389 14126492 - SINE/MIR_MIR3 chr1 14126552 14126770 - SINE/MIR_MIRc chr1 14126824 14127123 + SINE/Alu_AluSx1 chr1 14128349 14128749 - LTR/ERVL-MaLR_MLT1B chr1 14129021 14129134 + LINE/L2_L2a chr1 14129756 14129799 + Simple_repeat_(GA)n chr1 14130932 14131309 - LTR/ERVL-MaLR_MSTA chr1 14131528 14131622 - LINE/CR1_L3 chr1 14131627 14131835 - LTR/ERVL-MaLR_MLT1K chr1 14131997 14132420 - LTR/ERVL_LTR16E1 chr1 14132454 14132649 - LTR/ERVL-MaLR_MLT1K chr1 14132654 14132814 - LINE/CR1_L3 chr1 14133043 14133086 + DNA/TcMar-Tigger_Tigger14a chr1 14133087 14133377 + SINE/Alu_AluSc chr1 14133378 14133440 + Simple_repeat_(AAAG)n chr1 14133441 14133480 + DNA/TcMar-Tigger_Tigger14a chr1 14133519 14133628 + LTR/ERVL-MaLR_MLT1K chr1 14134366 14134522 - SINE/MIR_MIRb chr1 14134970 14135003 + Simple_repeat_(CA)n chr1 14135047 14135124 - DNA/TcMar-Mariner_MADE1 chr1 14135174 14135266 + LINE/L2_L2b chr1 14135649 14135938 + RC?/Helitron?_Helitron2Na_Mam chr1 14136286 14136438 + SINE/MIR_MIRb chr1 14137471 14137696 + SINE/MIR_MIR chr1 14137702 14137871 - SINE/Alu_AluSp chr1 14137969 14138066 + SINE/MIR_MIRb chr1 14138083 14138108 + Simple_repeat_(GT)n chr1 14138124 14138651 - LTR/ERVL-MaLR_MLT1L chr1 14139196 14139344 - DNA/hAT-Tip100_MamTip2b chr1 14139780 14139846 + SINE/MIR_MIR3 chr1 14139928 14139991 + Simple_repeat_(TG)n chr1 14140151 14140376 + SINE/MIR_MIRb chr1 14140386 14140411 + Simple_repeat_(TAA)n chr1 14140412 14140431 + Simple_repeat_(TAATTA)n chr1 14141237 14141265 + Simple_repeat_(A)n chr1 14142060 14142107 + Simple_repeat_(TTCC)n chr1 14142109 14142125 + Simple_repeat_(T)n chr1 14143239 14143716 - LINE/L1_HAL1 chr1 14143717 14144017 - SINE/Alu_AluJb chr1 14144018 14144398 - LINE/L1_HAL1 chr1 14144464 14144588 - SINE/Alu_AluJo chr1 14144590 14145008 - LTR/ERVL-MaLR_MSTB1 chr1 14145161 14145452 + SINE/Alu_AluJr chr1 14145454 14145485 + Simple_repeat_(AC)n chr1 14145587 14145869 - SINE/Alu_AluSx chr1 14146023 14146153 - LINE/L1_HAL1 chr1 14146154 14146252 - LINE/L1_HAL1ME chr1 14146253 14146566 + SINE/Alu_AluSq2 chr1 14146567 14146863 - LINE/L1_HAL1ME chr1 14146876 14147308 + DNA/TcMar-Tigger_Tigger2a chr1 14147310 14147511 + SINE/MIR_MIR chr1 14147512 14147817 + SINE/Alu_AluSc chr1 14147818 14147891 + SINE/MIR_MIR chr1 14147940 14148212 + SINE/Alu_AluJb chr1 14148327 14148828 - LINE/L1_HAL1 chr1 14149034 14149091 + Simple_repeat_(CCTT)n chr1 14149092 14149406 - SINE/Alu_AluSc8 chr1 14150023 14150181 - SINE/MIR_MIRc chr1 14150851 14150960 - LINE/L1_L1ME4a chr1 14150963 14150981 + Simple_repeat_(CA)n chr1 14151495 14151625 - LINE/L2_L2d2 chr1 14152033 14153493 - LINE/L1_L1MA2 chr1 14153494 14153801 + SINE/Alu_AluSp chr1 14153802 14154953 - LINE/L1_L1MA2 chr1 14154954 14154996 + Simple_repeat_(CTTC)n chr1 14154997 14155636 - LINE/L1_L1MA2 chr1 14155637 14155662 + Simple_repeat_(TTC)n chr1 14155663 14155936 - SINE/Alu_AluSz6 chr1 14155937 14157705 - LINE/L1_L1MA2 chr1 14157706 14158074 - LTR/ERVL-MaLR_THE1D chr1 14158075 14160688 - LINE/L1_L1MA2 chr1 14161189 14161720 - LTR/ERVL-MaLR_MLT1F chr1 14161713 14162532 - LTR/ERVL-MaLR_MLT1F-int chr1 14162533 14162843 - SINE/Alu_AluY chr1 14162844 14163089 - LTR/ERVL-MaLR_MLT1F-int chr1 14163087 14163780 - LTR/ERV1_MER50 chr1 14163781 14164099 - LTR/ERVL-MaLR_MLT1F-int chr1 14164100 14164454 - LTR/ERVL-MaLR_MLT1F chr1 14164455 14164481 + Simple_repeat_(TG)n chr1 14164482 14164524 - LTR/ERVL-MaLR_MLT1F chr1 14164528 14164838 - SINE/Alu_AluSp chr1 14165057 14165213 - Unknown_MamRep564 chr1 14166049 14166333 + SINE/Alu_AluSx chr1 14166363 14166561 + SINE/MIR_MIRb chr1 14167207 14167330 - DNA/hAT-Charlie_Charlie16 chr1 14167441 14167743 + SINE/Alu_AluSx1 chr1 14168664 14168738 + LTR_EUTREP8 chr1 14168755 14168931 + SINE/MIR_MIRb chr1 14168962 14169088 + LTR_EUTREP8 chr1 14169089 14169347 - LTR/ERVL-MaLR_MLT1O chr1 14169739 14169958 - SINE/MIR_MIR chr1 14170519 14170728 - LINE/L1_L1MB5 chr1 14170729 14171011 - SINE/Alu_AluSg4 chr1 14171012 14171444 - LINE/L1_L1MB5 chr1 14172332 14172358 + Simple_repeat_(GAGTG)n chr1 14172411 14172519 - LINE/L2_L2b chr1 14172557 14172714 - LTR/ERVL-MaLR_MLT1H1 chr1 14172805 14173172 - LTR/ERVL-MaLR_MLT1H1 chr1 14173243 14173521 - LTR/ERVL-MaLR_MLT1K chr1 14173530 14173642 + LTR/ERVL-MaLR_MLT1K chr1 14173674 14173968 - LTR/ERVL-MaLR_MLT1K chr1 14174155 14174675 - LTR/ERVL?_LTR55 chr1 14174769 14174866 - SINE/MIR_MIRb chr1 14174874 14175001 - LTR/ERVL-MaLR_MLT1I chr1 14175100 14175402 - SINE/Alu_AluY chr1 14176023 14176300 - LTR/Gypsy_MamGypLTR2c chr1 14176652 14176758 - LTR/Gypsy_MamGypLTR2c chr1 14176761 14176884 - LINE/L1_L1MEc chr1 14176885 14177166 + SINE/Alu_AluSx4 chr1 14177167 14177930 - LINE/L1_L1MEc chr1 14177931 14178292 + LTR/ERVL-MaLR_THE1C chr1 14178293 14179510 - LINE/L1_L1MEc chr1 14179695 14179911 - LINE/L2_L2a chr1 14180047 14180264 + LINE/L2_L2b chr1 14182931 14183029 + LINE/L2_L2d2 chr1 14184172 14184192 + Simple_repeat_(TTTTC)n chr1 14184193 14184225 + Simple_repeat_(CTTTTT)n chr1 14184391 14184449 + Simple_repeat_(CTTT)n chr1 14186329 14186423 - LTR/ERVL-MaLR_MLT1N2 chr1 14186987 14187103 - SINE/MIR_MIRc chr1 14187595 14187783 + DNA/hAT-Charlie_MER20 chr1 14187923 14188065 - LINE/L2_L2a chr1 14188176 14188194 + Simple_repeat_(GA)n chr1 14188195 14188237 + Simple_repeat_(GT)n chr1 14188732 14188911 + DNA/TcMar-Tigger_Tigger19a chr1 14189395 14189800 - LTR/ERVL-MaLR_MLT1H2 chr1 14190280 14190447 - SINE/MIR_MIRb chr1 14190451 14190776 + LTR/ERVL-MaLR_MLT1H chr1 14190876 14190919 + LTR/ERVL-MaLR_MLT1H chr1 14190966 14191053 - LTR/ERVL-MaLR_MLT1H2 chr1 14191089 14191593 - LINE/L2_L2a chr1 14191621 14191831 - LTR/ERVL-MaLR_MLT1N2 chr1 14191990 14192106 - LTR/ERVL_LTR67B chr1 14192196 14192275 + LTR/ERVL-MaLR_MLT1K chr1 14192420 14192739 - DNA/hAT-Charlie_Charlie15b chr1 14192792 14192941 + LTR/ERVL-MaLR_MLT1C chr1 14192942 14193293 + LTR/ERVL-MaLR_THE1D chr1 14193294 14193618 + LTR/ERVL-MaLR_MLT1C chr1 14193662 14193679 + Simple_repeat_(T)n chr1 14194020 14194143 - DNA/hAT-Ac_OldhAT1 chr1 14195512 14195543 + Simple_repeat_(CA)n chr1 14195544 14195726 + LINE/L1_HAL1ME chr1 14195745 14197283 - LINE/L2_L2a chr1 14197333 14197406 + LINE/L1_L1MEi chr1 14197407 14197717 + SINE/Alu_AluSx chr1 14197718 14198016 + LINE/L1_L1MEi chr1 14198020 14198058 + Simple_repeat_(TAG)n chr1 14198059 14198137 + LINE/L1_L1MEi chr1 14198318 14198622 + SINE/Alu_AluSz6 chr1 14198725 14198958 - SINE/MIR_MIR chr1 14199244 14199583 + LINE/L2_L2b chr1 14199628 14199743 + LINE/CR1_L3b chr1 14200116 14200294 + LINE/L1_L1ME3G chr1 14200287 14200384 + LINE/L1_L1ME4a chr1 14201226 14201272 + Simple_repeat_(CTTTCCT)n chr1 14201675 14201967 - SINE/Alu_AluSp chr1 14201975 14202001 + Simple_repeat_(TTTTA)n chr1 14202009 14202218 - SINE/MIR_MIR chr1 14202484 14202650 + SINE/MIR_MIR3 chr1 14202775 14203022 + SINE/Alu_AluSq2 chr1 14203253 14203288 + LINE/L2_L2d2 chr1 14204891 14205292 - LTR/ERVL-MaLR_MLT1J2 chr1 14205615 14205906 + SINE/Alu_AluSc chr1 14205909 14206028 + LINE/L1_L1PA4 chr1 14206740 14206947 - LINE/L1_L1ME4a chr1 14206953 14207263 - SINE/Alu_AluSq chr1 14207450 14207703 + DNA/hAT-Tip100_MER96B chr1 14208394 14208422 + Simple_repeat_(TCAT)n chr1 14208424 14208570 - LINE/L2_L2a chr1 14208622 14208858 - SINE/MIR_MIRb chr1 14209485 14209668 - DNA?_MER124 chr1 14210075 14210439 + LTR/ERVL-MaLR_THE1B chr1 14210565 14211087 - DNA/hAT-Charlie_Charlie1b chr1 14211211 14211523 - SINE/Alu_AluY chr1 14211606 14211800 + DNA/hAT-Blackjack_MER63A chr1 14211826 14211863 + Simple_repeat_(TTTTG)n chr1 14212924 14213068 + DNA/TcMar-Tigger_X6b_DNA chr1 14213199 14213298 - LINE/L1_L1ME5 chr1 14213381 14213625 - LINE/L2_L2a chr1 14213467 14214211 - LINE/L2_L2 chr1 14214221 14214432 + LINE/L2_L2a chr1 14214643 14214791 - LINE/L2_L2a chr1 14214816 14214942 - LINE/L2_L2d2 chr1 14215659 14215800 - SINE/MIR_MIRb chr1 14215994 14216624 + LINE/L1_HAL1M8 chr1 14216625 14216918 + SINE/Alu_AluSq2 chr1 14216919 14216965 + LINE/L1_HAL1M8 chr1 14216971 14217290 + LTR/ERVL-MaLR_MSTD chr1 14217308 14217520 + LINE/L1_L1M8 chr1 14217529 14218107 + LINE/L1_L1M8 chr1 14218110 14219167 + LINE/L1_L1MC5 chr1 14219188 14219450 + LINE/L1_L1ME3B chr1 14219614 14219787 - LINE/L2_L2d2 chr1 14220195 14220411 + LINE/L2_L2 chr1 14220794 14220910 + DNA_X34_DNA chr1 14220974 14221071 - LINE/L2_L2c chr1 14221288 14221344 - LINE/L2_L2d2 chr1 14222906 14222946 + Simple_repeat_(ATC)n chr1 14222947 14223218 - LTR/ERVL_LTR41B chr1 14223779 14223941 - LTR/ERVL-MaLR_MLT1O chr1 14224386 14224569 + LINE/CR1_L3 chr1 14224784 14224988 - DNA/hAT-Charlie_MER20B chr1 14224990 14227061 + LINE/L1_L1PA15 chr1 14227062 14227213 - DNA/hAT-Charlie_MER20B chr1 14227214 14227737 - LTR/ERVL-MaLR_MLT1G chr1 14227738 14228006 - DNA/hAT-Charlie_MER20B chr1 14228152 14228208 + Simple_repeat_(ATG)n chr1 14228209 14228215 + Simple_repeat_(TGATAA)n chr1 14228235 14228397 - SINE/MIR_MIRb chr1 14229976 14230161 + DNA/hAT-Charlie_MER3 chr1 14230310 14230549 - SINE/MIR_MIRb chr1 14230924 14231080 + SINE/MIR_MIRb chr1 14231301 14231328 + Simple_repeat_(ACAA)n chr1 14231499 14231645 + SINE/MIR_MIRc chr1 14231737 14232153 - LTR/ERVL-MaLR_MLT1J1 chr1 14232154 14232241 - DNA/hAT-Charlie_Charlie19a chr1 14233074 14233130 + SINE/MIR_MIR chr1 14233131 14233433 - SINE/Alu_AluJo chr1 14233434 14233540 + SINE/MIR_MIR chr1 14233741 14233826 - LINE/L2_L2d2 chr1 14233827 14234558 + LINE/L1_L1PA7 chr1 14234789 14234923 + LINE/L1_L1ME4c chr1 14234923 14235641 + LINE/L1_L1MB7 chr1 14235611 14235788 + LINE/L1_L1ME4c chr1 14236052 14236443 - LTR/Gypsy_LTR104_Mam chr1 14236636 14236933 + SINE/Alu_AluJb chr1 14237663 14237761 - LINE/L2_L2c chr1 14237911 14238007 - LINE/L1_L1M5 chr1 14238034 14238157 - LINE/L1_L1MB8 chr1 14238158 14238240 + SINE/MIR_MIRb chr1 14239452 14239758 - SINE/Alu_AluSp chr1 14239793 14239993 + DNA/hAT-Charlie_MER20 chr1 14240106 14240161 + SINE/MIR_MIRb chr1 14240893 14241095 + SINE/MIR_MIRb chr1 14241225 14241302 - SINE/MIR_MIRc chr1 14241563 14241735 - SINE/MIR_MIRb chr1 14242486 14242581 - LINE/L2_L2a chr1 14243582 14243660 + LINE/L1_L1MB1 chr1 14244111 14244133 + Simple_repeat_(AAAC)n chr1 14244147 14244324 + LINE/L2_L2a chr1 14244431 14244942 - LINE/L2_L2a chr1 14244955 14245222 - SINE/Alu_AluSx1 chr1 14245362 14245798 - LINE/L1_L1ME4a chr1 14245801 14246338 + LINE/L1_L1PA10 chr1 14246491 14246623 - LINE/L1_L1PA8A chr1 14246515 14246864 - LINE/L1_L1ME3E chr1 14246862 14247090 - LINE/L1_L1M5 chr1 14247468 14247594 - LINE/L1_L1PA8A chr1 14248194 14248332 + SINE/MIR_MIR3 chr1 14248332 14248666 - LINE/L2_L2c chr1 14248695 14249242 + LTR/ERVL-MaLR_MLT1G chr1 14249407 14249783 - LTR/ERVL-MaLR_MLT1K chr1 14251644 14251948 - SINE/Alu_AluJb chr1 14252431 14252596 + LINE/L2_L2b chr1 14252726 14253009 - LTR/ERVL-MaLR_MLT1J2 chr1 14253025 14253123 + LINE/L1_L1M5 chr1 14253166 14253240 - LTR/ERVL-MaLR_MLT1J2 chr1 14253447 14253523 - DNA/hAT-Tip100_MamTip2b chr1 14254595 14254656 - DNA/TcMar-Tigger_MamRep137 chr1 14254661 14254840 - LTR/ERVL-MaLR_MSTA chr1 14254841 14255149 + SINE/Alu_AluSg chr1 14255150 14255392 - LTR/ERVL-MaLR_MSTA chr1 14255679 14255905 - SINE/MIR_MIRb chr1 14256395 14256528 + Low_complexity_GA-rich chr1 14256591 14256772 + SINE/MIR_MIRb chr1 14257028 14257120 - LINE/CR1_L3b chr1 14257126 14257710 - LINE/L1_L1P1 chr1 14257711 14257955 + LINE/L1_L1HS chr1 14257960 14257995 + Simple_repeat_(A)n chr1 14258196 14258221 + Simple_repeat_(TTCT)n chr1 14258222 14258529 - SINE/Alu_AluYk2 chr1 14258705 14258905 + SINE/MIR_MIR chr1 14260131 14260521 - LINE/L2_L2a chr1 14260715 14260946 - SINE/MIR_MIR chr1 14261132 14261327 + SINE/MIR_MIRb chr1 14261924 14262133 + DNA/hAT-Charlie_MER113A chr1 14262134 14262262 + SINE/Alu_AluJo chr1 14262263 14262317 + DNA/hAT-Charlie_MER113A chr1 14264010 14264364 - LTR/ERVL-MaLR_MLT1E2 chr1 14264365 14264749 + LTR/ERVL-MaLR_MSTD chr1 14264750 14264965 - LTR/ERVL-MaLR_MLT1E2 chr1 14265011 14265193 - DNA/TcMar-Tigger_MamRep434 chr1 14266064 14266115 + Simple_repeat_(ATGG)n chr1 14266405 14266886 - DNA/TcMar-Tigger_MER6A chr1 14266887 14267080 - LINE/L1_L1PA4 chr1 14267081 14267146 - DNA/TcMar-Tigger_MER6A chr1 14267188 14267267 + DNA/TcMar-Tigger_MER2 chr1 14267268 14267485 - DNA/hAT-Charlie_MER30 chr1 14267486 14267623 + DNA/TcMar-Tigger_MER2 chr1 14267701 14267981 + SINE/Alu_AluSz chr1 14268190 14268268 + SINE/MIR_MIR chr1 14268472 14268495 + Simple_repeat_(A)n chr1 14269091 14269111 + Simple_repeat_(TC)n chr1 14269120 14269346 + DNA/hAT-Charlie_MER106A chr1 14269896 14270027 - LTR/ERVL-MaLR_MSTB2 chr1 14270033 14270164 - LTR/ERVL-MaLR_MSTB2 chr1 14270167 14270233 - LTR/ERVL-MaLR_MLT1K chr1 14270420 14270580 + SINE/MIR_MIR chr1 14270979 14271316 - LTR/ERVL-MaLR_MLT1B chr1 14271398 14271920 - LTR/ERVL-MaLR_MLT1M chr1 14271948 14272024 + SINE/MIR_MIRb chr1 14272109 14272358 - LTR/ERVL-MaLR_MLT1H chr1 14272377 14272439 + LTR/ERV1?_LTR103_Mam chr1 14272577 14272886 + SINE/Alu_AluSx3 chr1 14272941 14273042 - SINE/MIR_MIR3 chr1 14274461 14274635 - DNA/hAT-Charlie_MER5A chr1 14274639 14274790 + SINE/MIR_MIRb chr1 14274837 14274860 + Simple_repeat_(T)n chr1 14274998 14275246 - LINE/L1_L1ME3D chr1 14275247 14275414 + DNA/hAT-Charlie_MER5A chr1 14275445 14275491 + Simple_repeat_(TG)n chr1 14275492 14275802 - LINE/L1_L1ME3D chr1 14275806 14275869 - DNA/hAT-Charlie_Charlie17 chr1 14275970 14276104 + LTR/ERVL_LTR67B chr1 14276161 14276202 + Simple_repeat_(GT)n chr1 14276206 14276257 - LINE/L1_L1ME3D chr1 14276258 14276293 + Simple_repeat_(TCA)n chr1 14276294 14276380 - LINE/L1_L1ME3D chr1 14276381 14276829 - LTR/ERVL-MaLR_MLT1C chr1 14276830 14277077 - LINE/L1_L1ME3D chr1 14277079 14277345 - LINE/L1_L1MC5 chr1 14277384 14277691 + SINE/Alu_AluSc chr1 14277699 14277851 - LINE/L1_L1MC5 chr1 14278033 14278190 + SINE/Alu_AluSq chr1 14278220 14278291 - LINE/L1_L1MC5 chr1 14278292 14278589 - SINE/Alu_AluSx1 chr1 14278590 14279007 - LINE/L1_L1MC5 chr1 14279008 14279158 - DNA/hAT-Charlie_Charlie2b chr1 14279205 14279331 - LTR/ERVL_LTR79 chr1 14279575 14279693 + SINE/MIR_MIRb chr1 14279692 14279798 - LINE/L2_L2a chr1 14279953 14280080 + LTR/ERVL-MaLR_MLT1C chr1 14280081 14280398 + SINE/Alu_AluSc8 chr1 14280399 14280740 + LTR/ERVL-MaLR_MLT1C chr1 14280774 14281014 - SINE/MIR_MIR chr1 14281624 14281687 - LINE/RTE-BovB_MamRTE1 chr1 14281689 14281789 - LINE/L2_L2c chr1 14281861 14281953 - DNA/hAT-Blackjack_MER81 chr1 14283115 14283141 + Simple_repeat_(TTTC)n chr1 14283396 14284305 + LINE/L1_L1MB7 chr1 14284323 14284734 - LINE/L2_L2c chr1 14284759 14284812 - SINE/MIR_MIRc chr1 14284914 14285047 - LTR_MamRep1527 chr1 14285055 14285276 + SINE/MIR_MIR chr1 14285293 14285618 - LTR_MamRep1527 chr1 14285627 14285797 + DNA/hAT-Tip100_MER45A chr1 14286066 14286411 - LTR/ERVL-MaLR_MLT1B chr1 14286462 14286572 + SINE/MIR_MIRb chr1 14287291 14287359 + LTR/ERVL-MaLR_MLT1J chr1 14287381 14291820 - LINE/L1_L1P2 chr1 14291830 14292048 + LTR/ERVL-MaLR_THE1C chr1 14292049 14292119 - DNA/TcMar-Tigger_MER6B chr1 14292122 14292170 + Simple_repeat_(GTAT)n chr1 14293019 14293217 - SINE/MIR_MIRb chr1 14293383 14293438 + Simple_repeat_(TTC)n chr1 14293979 14294198 - SINE/MIR_MIR chr1 14294415 14294595 - SINE/MIR_MIRb chr1 14294959 14295127 + SINE/MIR_MIRb chr1 14295707 14295768 + LINE/L2_L2b chr1 14295944 14296146 + SINE/MIR_MIR chr1 14296332 14296446 + SINE/MIR_MIR chr1 14296626 14296909 - SINE/Alu_AluSc8 chr1 14296973 14297012 + Simple_repeat_(TG)n chr1 14297067 14297197 - LTR/ERVL-MaLR_MLT1H2 chr1 14297947 14298232 + SINE/Alu_AluJb chr1 14298814 14298912 + DNA/hAT-Charlie_Chap1a_Mam chr1 14299056 14299215 - LINE/L2_L2a chr1 14299221 14299529 + SINE/Alu_AluSx chr1 14299743 14299871 - LTR/ERVL-MaLR_MLT1H2 chr1 14300041 14300295 - LINE/L2_L2 chr1 14300296 14300535 - SINE/Alu_AluJb chr1 14300536 14300755 - LINE/L2_L2 chr1 14300786 14300874 + DNA/hAT-Charlie_Chap1a_Mam chr1 14301788 14302099 + LINE/L2_L2a chr1 14302614 14302720 + DNA/hAT-Charlie_Charlie24 chr1 14303150 14303269 + DNA/hAT-Charlie_MER5A chr1 14303270 14303540 - SINE/Alu_AluSc8 chr1 14303541 14303602 + DNA/hAT-Charlie_MER5A chr1 14303671 14303813 + LINE/L2_L2d chr1 14305301 14305451 + DNA/hAT-Charlie_Charlie4z chr1 14305658 14305682 + Simple_repeat_(T)n chr1 14305725 14305810 - LINE/L2_L2d chr1 14307393 14307638 - SINE/MIR_MIR chr1 14307755 14308544 + LINE/L1_L1MC4 chr1 14308557 14308781 + SINE/MIR_MIR chr1 14309396 14309562 - LINE/L2_L2d chr1 14309563 14309870 - SINE/Alu_AluJb chr1 14309871 14310308 - LINE/L2_L2d chr1 14310594 14310770 + LINE/L2_X15_LINE chr1 14311175 14311402 + SINE/MIR_MIRb chr1 14311477 14311541 - LINE/L2_L2d chr1 14311705 14311776 + Simple_repeat_(TGGA)n chr1 14312127 14312250 + SINE/MIR_MIRb chr1 14312336 14312566 - SINE/MIR_MIRb chr1 14312609 14313058 + LTR/ERVL-MaLR_MLT1F2 chr1 14313059 14313335 - LINE/L1_L1ME3A chr1 14313563 14313649 + SINE/MIR_MIR3 chr1 14314389 14314529 + LINE/L2_L2a chr1 14314394 14314558 + LINE/L2_L2b chr1 14314651 14315152 - LINE/L1_L1MB7 chr1 14315171 14315305 + LINE/L2_L2a chr1 14315354 14316266 - LINE/L1_L1ME1 chr1 14316148 14316292 - LINE/L1_L1PREC2 chr1 14316270 14317688 - LINE/L1_L1ME1 chr1 14317689 14317887 + DNA/TcMar-Tigger_MER6B chr1 14317888 14319010 - LINE/L1_L1ME1 chr1 14319011 14319065 + Simple_repeat_(TTTA)n chr1 14319066 14319858 - LINE/L1_L1ME1 chr1 14320069 14320104 - SINE/MIR_MIRc chr1 14320333 14320366 + Simple_repeat_(ATTT)n chr1 14320831 14320934 + SINE/MIR_MIR3 chr1 14322134 14322233 - Retroposon/SVA_SVA_A chr1 14322149 14322274 + SINE/Alu_FLAM_A chr1 14322589 14322827 - LTR/ERVL-MaLR_MLT1I chr1 14322828 14323206 - LTR/ERVL-MaLR_THE1D chr1 14323207 14323371 - LTR/ERVL-MaLR_MLT1I chr1 14323384 14323521 - LTR/ERVL-MaLR_MLT1I-int chr1 14323536 14323709 - LTR/ERVL-MaLR_MLT1I-int chr1 14323758 14324301 - LTR/ERVL-MaLR_MLT1I-int chr1 14324303 14324685 - LTR/ERVL-MaLR_MLT1I chr1 14324718 14324786 - DNA/hAT-Charlie_Charlie19a chr1 14324868 14324959 - DNA/hAT-Charlie_Charlie19a chr1 14325171 14325190 + Simple_repeat_(CT)n chr1 14325479 14325643 + LINE/L1_L1M4 chr1 14325657 14325810 + LINE/L1_L1M4 chr1 14325811 14326105 + LINE/L2_L2c chr1 14326108 14326202 + LINE/L2_L2-1_AMi chr1 14326232 14326780 + LINE/L2_L2c chr1 14326630 14327098 + LINE/L2_L2a chr1 14327103 14327309 + DNA/hAT-Charlie_Chap1a_Mam chr1 14327577 14327656 + LINE/L2_L2c chr1 14328422 14328715 + SINE/Alu_AluY chr1 14328749 14328771 + Simple_repeat_(A)n chr1 14328967 14329263 - LTR/ERVL-MaLR_MLT1K chr1 14329270 14329458 - LTR/ERVL-MaLR_MLT1K chr1 14329589 14329635 - SINE/MIR_MIRb chr1 14330757 14330902 + LINE/L1_L1ME3G chr1 14330985 14331333 + LTR/ERVL-MaLR_MLT1J chr1 14331546 14331666 - LINE/L1_L1ME5 chr1 14331687 14331993 - LINE/L1_L1ME3F chr1 14331994 14332165 - DNA/hAT-Charlie_MER58C chr1 14332166 14332399 - LINE/L1_L1ME3F chr1 14332658 14332770 + SINE/MIR_MIRb chr1 14332873 14333385 - LINE/L1_L1PA10 chr1 14333487 14333882 - LINE/L2_L2a chr1 14333897 14334009 - LINE/L2_L2a chr1 14334011 14334077 - LINE/L2_L2 chr1 14334085 14334390 + SINE/Alu_AluSx chr1 14334398 14334507 - LINE/L2_L2 chr1 14334554 14334654 - LINE/L2_L2 chr1 14334655 14334834 - LTR/ERVL-MaLR_MLT1F chr1 14334836 14334986 + Simple_repeat_(ACAGAG)n chr1 14334987 14335199 + LTR/ERVL-MaLR_MSTB chr1 14335200 14335502 - SINE/Alu_AluSx chr1 14335503 14335731 + LTR/ERVL-MaLR_MSTB chr1 14335732 14335831 + Simple_repeat_(ACAGAG)n chr1 14335923 14336166 - LINE/L2_L2 chr1 14336167 14336980 - LINE/L1_L1MB5 chr1 14337098 14337181 + SINE/MIR_MIRb chr1 14337614 14337912 + SINE/Alu_AluSx chr1 14337994 14338191 + LTR/ERVL-MaLR_MLT1L chr1 14338192 14338505 + SINE/Alu_AluSz chr1 14338506 14338891 + LTR/ERVL-MaLR_MLT1L chr1 14338896 14339010 - RNA_7SK chr1 14339063 14339128 + Simple_repeat_(AG)n chr1 14339403 14339839 - LTR/ERVL_MLT2B4 chr1 14340165 14340287 + LINE/L2_L2d2 chr1 14340416 14340538 + SINE/MIR_MIR chr1 14340541 14340881 + LTR/ERV1_LTR37A chr1 14340882 14341192 - SINE/Alu_AluSx chr1 14341193 14341228 + LTR/ERV1_LTR37A chr1 14341256 14341292 + SINE/MIR_MIR1_Amn chr1 14341328 14341828 + LINE/L2_L2a chr1 14341829 14342443 - LINE/L1_L1PA13 chr1 14342444 14342567 + LINE/L2_L2a chr1 14342575 14342637 + SINE/MIR_MIRb chr1 14342862 14343176 + SINE/Alu_AluSq2 chr1 14344161 14344180 + Simple_repeat_(T)n chr1 14344217 14344476 + DNA/hAT-Charlie_MER33 chr1 14344484 14344738 - LINE/L2_L2a chr1 14344779 14344879 - LTR/ERVL-MaLR_MLT1K chr1 14344915 14345217 - SINE/Alu_AluSx1 chr1 14345260 14345384 - DNA/hAT-Charlie_MER5A chr1 14345396 14345657 - DNA/hAT-Charlie_MER33 chr1 14345760 14345810 - DNA/hAT-Charlie_MER5A chr1 14345816 14346016 + LINE/L1_HAL1 chr1 14346823 14346913 - LINE/L2_L2a chr1 14347065 14347160 + LTR/ERVL-MaLR_MLT1K chr1 14347205 14347314 - DNA/hAT-Tip100_MER91C chr1 14347531 14347606 + LTR/ERVL-MaLR_MLT1K chr1 14347673 14347718 + Simple_repeat_(TATATAT)n chr1 14347769 14347864 + LINE/L1_L1ME4a chr1 14347866 14348051 - SINE/Alu_AluSx3 chr1 14348052 14348351 - SINE/Alu_AluSz chr1 14348801 14348961 + LINE/CR1_L3b chr1 14348977 14349311 - LINE/L2_L2d chr1 14349362 14349663 + DNA/hAT-Charlie_Charlie8 chr1 14349855 14350152 + SINE/Alu_AluY chr1 14350238 14350410 + SINE/MIR_MIRb chr1 14350462 14350684 + DNA/hAT-Charlie_MER20 chr1 14351284 14351584 + SINE/Alu_AluSc chr1 14351690 14352122 - DNA/hAT-Tip100_ORSL-2b chr1 14352774 14353225 + LINE/L1_L1ME2 chr1 14353226 14353518 - SINE/Alu_AluY chr1 14353519 14354645 + LINE/L1_L1ME2 chr1 14354646 14354688 + Simple_repeat_(AC)n chr1 14354689 14355334 + LINE/L1_L1ME2 chr1 14355401 14355444 + Simple_repeat_(TATT)n chr1 14355445 14361840 - LINE/L1_L1PA7 chr1 14362049 14362578 - LTR/ERVL-MaLR_MLT1E chr1 14362627 14362851 - SINE/MIR_MIRb chr1 14362889 14363007 - SINE/MIR_MIR1_Amn chr1 14363619 14363809 + DNA/hAT-Charlie_MER58C chr1 14363815 14363883 + DNA/hAT-Charlie_MER103C chr1 14363968 14363984 + Simple_repeat_(T)n chr1 14364213 14364464 + SINE/MIR_MIR chr1 14364470 14364514 + SINE/MIR_MIR1_Amn chr1 14364626 14364909 - LTR/ERVL-MaLR_MLT1C2 chr1 14364910 14365042 - SINE/Alu_FLAM_C chr1 14365050 14365342 - SINE/Alu_AluY chr1 14365343 14365554 - LTR/ERVL-MaLR_MLT1C2 chr1 14365557 14365765 - DNA/hAT-Charlie_MER20 chr1 14366448 14366950 - LTR/ERV1_LTR1D1 chr1 14366951 14367243 + SINE/Alu_AluSg chr1 14367244 14367600 - LTR/ERV1_LTR1D1 chr1 14370689 14370984 - SINE/Alu_AluJo chr1 14371080 14371265 - DNA/hAT-Charlie_MER5A chr1 14371689 14371817 - LINE/L2_L2c chr1 14371855 14372061 - DNA/TcMar-Tigger_X6b_DNA chr1 14372317 14372390 + DNA/TcMar-Tigger_Tigger14a chr1 14372396 14372602 - DNA/hAT-Charlie_MER20 chr1 14372764 14373059 - LINE/L2_L2c chr1 14373169 14373198 - LTR/ERVL_MLT2B1 chr1 14373199 14373226 + Simple_repeat_(AT)n chr1 14373227 14373652 - LTR/ERVL_MLT2B1 chr1 14373971 14374826 - LTR/ERVL_LTR82A chr1 14375128 14375237 - DNA/hAT-Charlie_Charlie23a chr1 14375290 14375597 - DNA/TcMar-Tigger_MamRep137 chr1 14375605 14375911 + SINE/Alu_AluY chr1 14376254 14376645 + LTR/ERVL-MaLR_MSTD chr1 14376692 14376741 + SINE/MIR_MIR chr1 14377776 14377980 - DNA/hAT-Charlie_MER20 chr1 14378056 14378135 - LINE/L2_L2a chr1 14378325 14380997 + LINE/L1_L1MA3 chr1 14381005 14381106 - LINE/L1_L1M1 chr1 14381137 14381287 + LINE/L1_L1MA3 chr1 14381317 14382322 + LINE/L1_L1MA3 chr1 14382319 14388341 - LINE/L1_L1PA3 chr1 14388343 14390012 + LINE/L1_L1MA3 chr1 14390133 14390161 + Low_complexity_A-rich chr1 14390179 14390621 - LINE/L2_L2a chr1 14390622 14390947 - LTR/ERVL-MaLR_MLT1A1 chr1 14390948 14391197 - LINE/L2_L2a chr1 14391200 14391597 + LINE/L2_L2 chr1 14392560 14392741 - DNA/hAT-Charlie_MER20 chr1 14393740 14393862 + SINE/Alu_AluSx3 chr1 14394513 14394612 - SINE/MIR_MIRc chr1 14394722 14394955 - SINE/MIR_MIRb chr1 14394971 14395056 + LINE/L2_L2b chr1 14395070 14395216 + Low_complexity_GA-rich chr1 14395437 14395728 - LTR/ERVL_LTR33 chr1 14395891 14396184 + SINE/Alu_AluSg chr1 14397089 14397192 + LTR/ERVL-MaLR_MLT1H chr1 14397193 14397526 - LTR/ERVL-MaLR_MLT1A chr1 14397540 14397575 + LTR/ERVL-MaLR_MLT1H chr1 14397576 14397882 + SINE/Alu_AluSq2 chr1 14397883 14397941 + LTR/ERVL-MaLR_MLT1H chr1 14398026 14398215 + LTR/ERVL-MaLR_MLT1H chr1 14398276 14398504 + SINE/MIR_MIR chr1 14399031 14399316 - SINE/Alu_AluJr chr1 14399319 14399540 - DNA/hAT-Tip100_MamTip2b chr1 14399854 14399982 + LINE/L2_L2d2 chr1 14400341 14400397 - SINE/MIR_MIR3 chr1 14400465 14400551 - SINE/MIR_MIR chr1 14401381 14401533 + LINE/L1_L1ME3Cz chr1 14401534 14401674 + SINE/Alu_AluJb chr1 14401719 14402129 + LINE/L1_L1ME3Cz chr1 14402411 14402832 + LINE/L1_L1ME3Cz chr1 14402833 14403125 - SINE/Alu_AluSz6 chr1 14403126 14403798 + LINE/L1_L1ME3Cz chr1 14403799 14404469 - LTR/ERV1_LTR9A1 chr1 14404470 14404726 + LINE/L1_L1ME3Cz chr1 14405463 14405564 + SINE/MIR_MIRc chr1 14405573 14405606 - LTR/ERVL_MLT2B4 chr1 14405607 14405647 + Simple_repeat_(TG)n chr1 14405648 14405974 - LTR/ERVL_MLT2B4 chr1 14405975 14406243 + LINE/L1_L1MA1 chr1 14406244 14406293 - LTR/ERVL_MLT2B4 chr1 14406294 14406700 + LINE/L1_L1PB1 chr1 14406701 14406832 - LTR/ERVL_MLT2B4 chr1 14406839 14406980 + SINE/MIR_MIR chr1 14407010 14407107 - LINE/L1_L1ME3F chr1 14407143 14407333 - LINE/L1_L1ME3F chr1 14407521 14407726 + SINE/MIR_MIRb chr1 14408420 14408771 - LTR/ERVL-MaLR_MLT1A chr1 14408773 14409108 - LINE/L2_L2a chr1 14409122 14409245 + LINE/L1_L1MEd chr1 14409332 14410105 + LINE/L1_L1MEd chr1 14410106 14410401 - SINE/Alu_AluSz6 chr1 14410402 14410500 + LINE/L1_L1MEd chr1 14410501 14411014 + LTR/ERVL-MaLR_MLT1D chr1 14411015 14411303 + LINE/L1_L1MEd chr1 14411304 14411595 - SINE/Alu_AluSz chr1 14411596 14411920 + LINE/L1_L1MEd chr1 14411938 14412095 - LINE/L1_L1MEd chr1 14412107 14412331 + LINE/L1_L1MEd chr1 14412306 14412924 + LINE/L1_L1ME3Cz chr1 14413168 14413308 + LINE/L1_L1ME3Cz chr1 14413309 14413618 + SINE/Alu_AluY chr1 14413619 14414018 + LINE/L1_L1ME3Cz chr1 14414072 14414613 + LINE/L1_L1ME3Cz chr1 14414614 14414643 + Simple_repeat_(GT)n chr1 14414644 14414742 + LINE/L1_L1ME3Cz chr1 14414743 14415037 + SINE/Alu_AluSx chr1 14415038 14415227 + LINE/L1_L1ME3Cz chr1 14415259 14415398 - LINE/L2_L2 chr1 14415588 14415858 + LTR/ERVL_LTR16C chr1 14416455 14416749 + SINE/Alu_AluSx1 chr1 14416797 14416920 + SINE/Alu_FLAM_C chr1 14416933 14417009 + Simple_repeat_(TA)n chr1 14417012 14417041 + Simple_repeat_(AC)n chr1 14417043 14417207 + SINE/Alu_AluSc chr1 14417715 14418044 + SINE/Alu_AluY chr1 14418200 14418423 + SINE/MIR_MIRb chr1 14418815 14418996 + LTR?_MamRep605b chr1 14419156 14419349 - SINE/MIR_MIRb chr1 14419528 14421085 - LTR/ERV1_LTR12C chr1 14421641 14421782 + DNA/hAT-Charlie_MER103C chr1 14422043 14422105 - DNA/hAT-Charlie_MER103C chr1 14423042 14423176 - DNA/hAT-Charlie_Charlie15a chr1 14423184 14423350 + SINE/MIR_MIR3 chr1 14423575 14423617 - SINE/MIR_MIRb chr1 14423681 14424113 + LTR/ERVL-MaLR_MLT1K chr1 14424120 14424229 + SINE/MIR_MIRb chr1 14424327 14424458 - SINE/MIR_MIRb chr1 14424507 14424602 + DNA/hAT-Tip100_MER91C chr1 14425546 14425611 + SINE/MIR_MIRb chr1 14425699 14425913 - SINE/MIR_MIRb chr1 14426002 14426520 + LINE/L2_L2a chr1 14426923 14427187 - LINE/L2_L2b chr1 14427243 14427422 + SINE/MIR_MIR chr1 14427514 14427534 + Simple_repeat_(AC)n chr1 14427536 14427630 + Simple_repeat_(TA)n chr1 14428545 14428903 - LTR/ERVL-MaLR_THE1C chr1 14428904 14428995 + Simple_repeat_(TA)n chr1 14429376 14429482 + SINE/MIR_MIRb chr1 14429749 14429783 + Simple_repeat_(TCA)n chr1 14429790 14429949 - SINE/MIR_MIR3 chr1 14429950 14430424 + LINE/L2_L2a chr1 14430762 14430885 - LINE/L2_L2d2 chr1 14431055 14431112 + Simple_repeat_(AATG)n chr1 14431197 14431397 - SINE/MIR_MIRb chr1 14431450 14432018 + LTR/ERVL_MLT2B3 chr1 14432201 14432339 + Unknown_MamRep564 chr1 14432556 14432946 - LINE/L1_L1PA8 chr1 14432957 14433005 + Simple_repeat_(TATATG)n chr1 14433105 14433410 - LINE/L1_L1ME2 chr1 14433425 14433575 + LTR/ERVL-MaLR_MLT1A chr1 14433576 14433874 + SINE/Alu_AluSx chr1 14433875 14434105 + LTR/ERVL-MaLR_MLT1A chr1 14434114 14434253 - LINE/L1_L1ME2 chr1 14435349 14435644 + LTR/ERVL_LTR16C chr1 14435687 14435752 + SINE/MIR_MIR chr1 14436018 14436247 + SINE/Alu_AluSq2 chr1 14436261 14436320 - SINE/MIR_MIRc chr1 14436327 14436345 + Simple_repeat_(ATG)n chr1 14436354 14436390 + Simple_repeat_(GTG)n chr1 14436392 14436461 - SINE/MIR_MIRb chr1 14436462 14436738 + SINE/Alu_AluSx1 chr1 14436739 14436922 - SINE/MIR_MIRb chr1 14437614 14437649 + Simple_repeat_(CTCCCT)n chr1 14437878 14437894 + Simple_repeat_(AGG)n chr1 14438096 14438112 + Simple_repeat_(A)n chr1 14438228 14438734 - LINE/L2_L2a chr1 14438914 14439525 + LINE/L2_L2c chr1 14439526 14439730 + DNA/hAT-Charlie_MER3 chr1 14439731 14439903 + LINE/L2_L2c chr1 14439745 14440193 + LINE/L2_L2a chr1 14440439 14440592 + LINE/L2_L2c chr1 14440606 14440784 - DNA/hAT-Tip100_MER45A chr1 14441022 14441163 + LINE/L2_L2b chr1 14441263 14441282 + Simple_repeat_(AT)n chr1 14441361 14441396 + Simple_repeat_(GCA)n chr1 14441397 14441526 + DNA/hAT-Charlie_MER5A chr1 14441891 14441957 + SINE/MIR_MIRc chr1 14441965 14442046 + DNA/hAT-Tip100_MER91A chr1 14442128 14442221 + SINE/MIR_MIRc chr1 14442654 14442728 + DNA/hAT-Tip100_MARE4 chr1 14443178 14443203 + Simple_repeat_(GTTTT)n chr1 14444307 14444612 - SINE/Alu_AluSp chr1 14444878 14445030 + LTR/ERVL-MaLR_MLT1C chr1 14445031 14445325 - SINE/Alu_AluSx chr1 14445326 14445616 + LTR/ERVL-MaLR_MLT1C chr1 14445656 14445848 - LTR/ERVL_LTR16C chr1 14445915 14446212 + SINE/Alu_AluJr chr1 14446912 14447131 - LINE/L1_L1PA16 chr1 14447209 14447256 + Simple_repeat_(ATT)n chr1 14447257 14447538 - SINE/Alu_AluY chr1 14447745 14447922 - SINE/MIR_MIRb chr1 14448178 14448296 - SINE/MIR_MIRc chr1 14448889 14448983 - SINE/MIR_MIR3 chr1 14449067 14449299 - LTR/ERVL-MaLR_MLT1K chr1 14450309 14450607 + SINE/Alu_AluSx1 chr1 14450797 14451219 + LTR/ERVL-MaLR_MSTA chr1 14451503 14451536 - LTR/ERVL_MLT2B1 chr1 14451578 14451911 - LTR/ERVL_MLT2B1 chr1 14451910 14451931 + LTR/ERVL_MLT2B4 chr1 14451932 14452239 - SINE/Alu_AluSx chr1 14452240 14452275 + LTR/ERVL_MLT2B4 chr1 14452536 14452670 + DNA/TcMar-Tigger_Tigger12c chr1 14452726 14452818 + DNA/TcMar-Tigger_Tigger12A chr1 14452841 14452905 - DNA/hAT-Charlie_MER102a chr1 14452950 14452970 + Simple_repeat_(TTG)n chr1 14452971 14453254 - SINE/Alu_AluY chr1 14453556 14453850 + SINE/Alu_AluSz chr1 14454386 14454488 + LINE/L2_L2d chr1 14454783 14455136 - LTR/ERVL-MaLR_MLT1F1 chr1 14455280 14455381 + LINE/L2_L2d chr1 14455413 14455457 - LTR/ERVL-MaLR_MLT1F1 chr1 14456141 14456236 - LTR/ERVL-MaLR_MLT1F2 chr1 14456278 14456419 + LINE/L2_L2b chr1 14456453 14456798 - LINE/L1_L1ME5 chr1 14456799 14457102 + SINE/Alu_AluSz chr1 14457103 14457512 - LINE/L1_L1ME5 chr1 14457594 14457812 - LINE/L1_L1ME5 chr1 14457813 14458118 - SINE/Alu_AluY chr1 14458119 14458529 - LINE/L1_L1ME5 chr1 14458666 14458844 - LTR/ERVL_HERVL74-int chr1 14458856 14458883 + LINE/L2_L2a chr1 14459249 14459282 + Simple_repeat_(GT)n chr1 14459477 14459656 + DNA/hAT-Charlie_MER117 chr1 14459820 14459847 + Simple_repeat_(AGAA)n chr1 14459887 14459917 + Simple_repeat_(TTCT)n chr1 14460788 14460994 - SINE/MIR_MIRc chr1 14461225 14461585 + LTR/ERVL-MaLR_THE1B chr1 14461949 14462013 + LTR/ERVL-MaLR_THE1B chr1 14462521 14462556 - LTR/ERV1_MER66B chr1 14462557 14462924 - LTR/ERVL-MaLR_THE1B chr1 14462925 14463395 - LTR/ERV1_MER66C chr1 14463493 14463556 + SINE/MIR_MIR chr1 14463629 14463735 - Unknown_UCON41 chr1 14464046 14464203 + SINE/MIR_MIR3 chr1 14464204 14464517 + DNA/hAT-Charlie_MER102a chr1 14464529 14464820 - SINE/Alu_AluSz chr1 14464909 14465118 - LTR/ERVL-MaLR_MLT1H1 chr1 14465119 14465417 + SINE/Alu_AluYj4 chr1 14465418 14465747 - LTR/ERVL-MaLR_MLT1H1 chr1 14465832 14466166 + DNA/TcMar-Tigger_Tigger3a chr1 14466328 14466343 + Low_complexity_A-rich chr1 14466344 14466685 + LINE/L1_L1PA10 chr1 14466687 14466977 + SINE/Alu_AluY chr1 14467016 14467680 + LINE/L1_L1M4 chr1 14467717 14468126 - LTR/ERVL-MaLR_MLT1K chr1 14468413 14468548 - SINE/MIR_MIRb chr1 14469430 14469456 + Simple_repeat_(TTCCA)n chr1 14470768 14471235 + LTR/ERVL-MaLR_MLT1F chr1 14471494 14471580 - LINE/L2_L2 chr1 14471908 14472271 - LTR/ERVL-MaLR_MLT1A0 chr1 14472276 14472630 - LTR/ERVL-MaLR_MLT1I chr1 14472816 14472903 - LINE/L2_L2d2 chr1 14473166 14473332 - DNA/hAT_MER53 chr1 14473347 14473670 + SINE/Alu_AluSx4 chr1 14473724 14474164 + LINE/L2_L2a chr1 14474350 14474868 - LINE/L2_L2a chr1 14474881 14475178 + Simple_repeat_(ATGG)n chr1 14475245 14475435 + SINE/MIR_MIRb chr1 14475989 14476112 - SINE/MIR_MIRb chr1 14476759 14477009 + LINE/L2_L2b chr1 14477045 14477404 + LTR/ERVL-MaLR_THE1B chr1 14477426 14477480 + Simple_repeat_(CT)n chr1 14477687 14478155 + LTR/ERVL_LTR16D1 chr1 14478185 14478223 + Simple_repeat_(GATG)n chr1 14478225 14478343 + Simple_repeat_(GAAG)n chr1 14478361 14478431 + Simple_repeat_(GATG)n chr1 14478817 14479320 - LTR/ERVL_LTR16E1 chr1 14479515 14479793 + LINE/L2_L2b chr1 14480305 14480418 - LINE/L2_L2c chr1 14480514 14480825 + Simple_repeat_(TA)n chr1 14480902 14480975 + Simple_repeat_(AT)n chr1 14481127 14481359 + SINE/MIR_MIR chr1 14482395 14482612 + SINE/MIR_MIR chr1 14482836 14483039 + SINE/MIR_MIR chr1 14483497 14483687 - LTR/ERV1_MER4D chr1 14483688 14484234 - LINE/L1_L1M2 chr1 14484235 14484476 + LINE/L1_L1MA8 chr1 14484546 14485088 - LTR/ERVL-MaLR_MLT1H chr1 14485352 14485618 + LTR/ERV1_MER66B chr1 14485619 14485911 + SINE/Alu_AluSg chr1 14485912 14486136 + LTR/ERV1_MER66B chr1 14487674 14488003 - LINE/L2_L2 chr1 14488028 14488095 + LINE/L2_L2a chr1 14488194 14488409 - DNA/hAT-Tip100_MamTip3 chr1 14488411 14488632 + SINE/MIR_MIR chr1 14488662 14488750 - DNA/hAT-Tip100_MamTip3 chr1 14488888 14489007 + LINE/L2_L2d2 chr1 14489136 14489339 - LINE/L1_L1ME4a chr1 14489356 14489400 + Simple_repeat_(TA)n chr1 14489448 14489736 + SINE/Alu_AluSz chr1 14489737 14489777 + Simple_repeat_(AAT)n chr1 14489880 14490168 - LINE/L1_L1ME4a chr1 14490473 14490686 - SINE/Alu_AluSc chr1 14490896 14491292 - LINE/L1_L1ME4a chr1 14491303 14491508 - LINE/L1_L1PA7 chr1 14491528 14491751 - LINE/L1_L1MC chr1 14491811 14491842 + Simple_repeat_(TGAATT)n chr1 14492385 14492521 - LINE/L2_L2a chr1 14492646 14492892 + Simple_repeat_(CA)n chr1 14492895 14493318 + LINE/L2_L2a chr1 14493381 14493511 - LTR/ERVL_LTR79 chr1 14493559 14493656 - LINE/L1_L1M6 chr1 14493708 14494098 - LINE/L1_L1M6 chr1 14494103 14494302 - LINE/L2_L2c chr1 14494460 14494726 - LINE/L1_L1M6 chr1 14495956 14495983 + Simple_repeat_(TCTT)n chr1 14496840 14496858 + Simple_repeat_(A)n chr1 14497336 14497363 + Simple_repeat_(A)n chr1 14497822 14497848 + Low_complexity_GA-rich chr1 14498270 14498409 + LTR/ERVL_LTR33 chr1 14498410 14498720 + SINE/Alu_AluSx1 chr1 14498721 14499073 + LTR/ERVL_LTR33 chr1 14499897 14500160 + DNA/hAT-Tip100_ORSL chr1 14500182 14500361 - DNA/hAT-Charlie_MER103C chr1 14500458 14501066 + LINE/L1_L1MB7 chr1 14501048 14501080 + LINE/L1_L1MB7 chr1 14501081 14501131 + Simple_repeat_(AAAT)n chr1 14501132 14501765 + LINE/L1_L1MB7 chr1 14501772 14502146 + LINE/L1_L1MB7 chr1 14502312 14502422 - LINE/L2_L2b chr1 14502777 14502982 - SINE/MIR_MIRb chr1 14502997 14503074 - DNA/hAT-Tip100_MamTip2 chr1 14503124 14503201 + SINE/MIR_MIR chr1 14503202 14503492 + SINE/Alu_AluY chr1 14503493 14503572 + SINE/MIR_MIR chr1 14503624 14503765 - SINE/MIR_MIR chr1 14503939 14504101 + DNA/hAT-Tip100_MER96 chr1 14504862 14505380 - LTR/ERVL-MaLR_MLT1O chr1 14505483 14505691 + LINE/L1_L1MB3 chr1 14505692 14505983 + SINE/Alu_AluSx3 chr1 14505984 14506106 + LINE/L1_L1MB3 chr1 14506178 14506460 + LINE/L2_L2c chr1 14506464 14506704 + SINE/MIR_MIR chr1 14507803 14507992 + SINE/Alu_AluYc chr1 14508313 14508403 - LTR/ERVL-MaLR_MLT1G3 chr1 14509173 14509417 - SINE/MIR_MIRb chr1 14509606 14510078 - LINE/L2_L2a chr1 14510079 14510102 - DNA/hAT_MER53 chr1 14510103 14510394 + SINE/Alu_AluSp chr1 14510395 14510564 - DNA/hAT_MER53 chr1 14510565 14510886 - LINE/L2_L2a chr1 14512273 14512509 - DNA/hAT-Charlie_MER20 chr1 14512574 14512656 - SINE/MIR_MIR3 chr1 14514039 14514330 + SINE/Alu_AluY chr1 14514333 14514640 + Simple_repeat_(TA)n chr1 14514696 14514949 + SINE/MIR_MIRb chr1 14515182 14515339 - SINE/MIR_MIR chr1 14515521 14515654 + LINE/L2_L2b chr1 14516227 14516332 - LTR/ERVL-MaLR_MLT1K chr1 14516365 14516550 + LTR/ERVL-MaLR_MLT1K chr1 14516643 14516732 + LINE/L2_L2d chr1 14516935 14517504 + LTR/ERVL-MaLR_MLT1K chr1 14518019 14518119 + SINE/MIR_MIR3 chr1 14518179 14518476 - SINE/Alu_AluSx chr1 14518632 14518851 + SINE/MIR_MIRb chr1 14522786 14523410 - LTR/Gypsy_MamGypLTR1a chr1 14523992 14524036 + Simple_repeat_(GTTTT)n chr1 14524039 14524317 - SINE/Alu_AluSz chr1 14524361 14524541 + SINE/MIR_MIR3 chr1 14525121 14525176 + Simple_repeat_(TATG)n chr1 14525339 14525650 - DNA/hAT-Charlie_MER58B chr1 14525658 14525778 - DNA/TcMar-Tigger_Tigger16b chr1 14525845 14525952 - SINE/MIR_MIRb chr1 14526850 14526982 - DNA/hAT-Tip100_MamTip2 chr1 14527033 14527200 - LTR/ERVL-MaLR_MLT1G chr1 14527201 14527269 - LTR/ERVL-MaLR_MSTB1 chr1 14527270 14527565 + SINE/Alu_AluSg chr1 14527566 14527935 - LTR/ERVL-MaLR_MSTB1 chr1 14527936 14527998 - LTR/ERVL-MaLR_MLT1G chr1 14527999 14528072 + Simple_repeat_(CTAT)n chr1 14528073 14528134 - LTR/ERVL-MaLR_MLT1G chr1 14528141 14528365 + SINE/Alu_AluSx chr1 14528375 14528510 - LINE/CR1_L3 chr1 14528511 14528771 + SINE/Alu_AluJb chr1 14528772 14528894 - LINE/CR1_L3 chr1 14529022 14529313 + SINE/Alu_AluSq2 chr1 14529317 14529650 + LTR/Gypsy_LTR81C chr1 14529988 14530419 + LTR/Gypsy_LTR81C chr1 14530421 14530802 + LTR/ERVL-MaLR_MSTB chr1 14530803 14531074 + SINE/Alu_AluJb chr1 14531075 14531148 + LTR/ERVL-MaLR_MSTB chr1 14531581 14531700 + SINE/MIR_MIRc chr1 14532456 14532486 + Simple_repeat_(TTA)n chr1 14532487 14533058 - LINE/L1_L1PA4 chr1 14534363 14534669 + SINE/Alu_AluSx1 chr1 14535385 14535693 + SINE/Alu_AluSc5 chr1 14536134 14536298 + DNA/hAT-Charlie_Chap1a_Mam chr1 14536600 14536894 + SINE/Alu_AluSx chr1 14536981 14537098 + LINE/L2_L2b chr1 14537270 14537483 + DNA/hAT-Charlie_MER3 chr1 14537524 14537719 + SINE/MIR_MIRc chr1 14537876 14537990 - LTR/ERVL-MaLR_MLT1O chr1 14538364 14538688 - LTR/ERVL-MaLR_MLT1A0 chr1 14538722 14538820 + DNA/TcMar-Tigger_Tigger18a chr1 14538821 14538977 + DNA/hAT_MamRep38 chr1 14538953 14539072 + DNA/hAT_MamRep38 chr1 14539144 14539324 + DNA/TcMar-Tigger_Tigger18a chr1 14539328 14539565 + LINE/L1_L1MD3 chr1 14539567 14539882 + DNA/TcMar-Tigger_Tigger18a chr1 14540103 14540253 + LTR/ERV1?_LTR103b_Mam chr1 14540333 14540413 - SINE/MIR_MIRc chr1 14540475 14540600 - DNA/hAT-Charlie_Charlie8 chr1 14542184 14542456 + LINE/L1_L1PA4 chr1 14542547 14542762 - LINE/L1_L1PA16 chr1 14543068 14543134 + SINE/MIR_MIRc chr1 14543152 14543225 + LTR/ERVL_LTR82B chr1 14543237 14543358 + LTR/ERVL_LTR82B chr1 14544321 14544369 + Simple_repeat_(T)n chr1 14544413 14544521 + LINE/CR1_CR1_Mam chr1 14544541 14544724 + LINE/CR1_X7B_LINE chr1 14544753 14544869 - SINE/Alu_AluJo chr1 14545366 14545602 + SINE/MIR_MIR chr1 14545606 14546019 - LTR/ERVL-MaLR_MLT1I chr1 14546905 14547054 - SINE/MIR_MIR3 chr1 14547357 14547489 + SINE/MIR_MIR3 chr1 14547604 14547861 - SINE/Alu_AluY chr1 14547939 14548173 - DNA/hAT-Charlie_MER30 chr1 14548199 14548498 - SINE/Alu_AluSx1 chr1 14548504 14548867 - LINE/L1_L1ME3E chr1 14548879 14549204 - LINE/L1_L1ME3E chr1 14549480 14550010 - LINE/L1_L1ME3E chr1 14550120 14550508 - LINE/L2_L2a chr1 14550691 14550739 + Simple_repeat_(CT)n chr1 14550740 14550772 + Simple_repeat_(CCCTCCC)n chr1 14550773 14550778 + Simple_repeat_(CCCTCC)n chr1 14551957 14552015 + Simple_repeat_(TCTT)n chr1 14552827 14552928 - DNA/hAT-Charlie_MER102c chr1 14553021 14553055 - SINE/MIR_MIRc chr1 14553061 14553092 - DNA/hAT-Charlie_Charlie8 chr1 14553093 14553364 + SINE/Alu_AluSz6 chr1 14553365 14553425 - DNA/hAT-Charlie_Charlie8 chr1 14553512 14553608 - SINE/MIR_MIRb chr1 14553799 14553959 - DNA/hAT-Charlie_MER5B chr1 14554009 14554240 + SINE/MIR_MIR chr1 14555978 14556089 + DNA/hAT-Blackjack_MER63B chr1 14556104 14556407 - SINE/Alu_AluY chr1 14556712 14556833 + SINE/MIR_MIR3 chr1 14557489 14557550 + Simple_repeat_(ATAA)n chr1 14557584 14557619 - LTR/ERVL_MLT2B4 chr1 14557620 14557673 + Simple_repeat_(GT)n chr1 14557674 14557795 - LTR/ERVL_MLT2B4 chr1 14557815 14557886 + LINE/L2_L2c chr1 14558798 14558879 - LTR/ERVL-MaLR_MLT1M chr1 14558917 14558956 + Simple_repeat_(TCTTTCT)n chr1 14559453 14559563 + SINE/MIR_MIRb chr1 14559765 14559886 + SINE/MIR_MIR3 chr1 14559901 14560203 + DNA/hAT-Charlie_Charlie18a chr1 14560283 14560344 + SINE/MIR_MIRc chr1 14560345 14560594 + SINE/Alu_AluSz chr1 14560595 14560695 + SINE/MIR_MIRb chr1 14560696 14560744 + Simple_repeat_(TGA)n chr1 14560729 14560808 + Low_complexity_GA-rich chr1 14561140 14561204 - LTR/ERVL-MaLR_MLT1M chr1 14561442 14561509 - LTR/ERVL-MaLR_MLT1M chr1 14561621 14561924 + SINE/Alu_AluSx1 chr1 14562116 14562219 + Unknown_UCON71_Crp chr1 14562705 14562755 - LINE/L2_L2b chr1 14563261 14563378 + DNA/hAT-Charlie_MER5A chr1 14563554 14563838 + LINE/L2_L2c chr1 14563875 14564096 - SINE/Alu_AluSx1 chr1 14564210 14564278 + LINE/L2_L2c chr1 14564345 14564530 + SINE/MIR_MIR chr1 14564531 14564849 + SINE/Alu_AluSg4 chr1 14564850 14564906 + SINE/MIR_MIR chr1 14565094 14565178 - LINE/L1_L1ME3C chr1 14565204 14567590 - DNA/TcMar-Tigger_Tigger1 chr1 14567874 14568342 + LTR/ERVL-MaLR_MLT1E1 chr1 14568343 14568694 - LTR/ERVL-MaLR_THE1B chr1 14568695 14568838 + LTR/ERVL-MaLR_MLT1E1 chr1 14568877 14568967 - LINE/L2_L2a chr1 14568967 14569029 + LINE/L2_L2c chr1 14569321 14569621 - SINE/Alu_AluY chr1 14569833 14570133 + SINE/Alu_AluSg4 chr1 14570242 14570874 - LINE/L1_L1MB7 chr1 14572881 14572992 - LINE/L2_L2d chr1 14573369 14573672 + SINE/Alu_AluSx3 chr1 14574326 14574677 + LTR/ERVL-MaLR_THE1B chr1 14574682 14575223 - LINE/L2_L2b chr1 14575224 14575585 - LTR/ERVL-MaLR_THE1B chr1 14575586 14575671 - LINE/L2_L2b chr1 14575805 14575924 + LINE/L2_L2b chr1 14576048 14576277 + SINE/MIR_MIRb chr1 14576281 14576505 + Simple_repeat_(GATG)n chr1 14577747 14577958 - SINE/MIR_MIRc chr1 14577903 14578142 + SINE/MIR_MIR chr1 14578836 14578914 - SINE/MIR_MIRc chr1 14579097 14579326 + SINE/MIR_MIRb chr1 14579346 14579654 + SINE/Alu_AluY chr1 14580194 14580475 + SINE/Alu_AluSp chr1 14580476 14580518 + Simple_repeat_(AAAT)n chr1 14580653 14580757 - SINE/MIR_MIR chr1 14580710 14582114 + LINE/L2_L2a chr1 14582130 14582274 - SINE/MIR_MIRc chr1 14582354 14582469 + SINE/MIR_MIR3 chr1 14582887 14583001 + LINE/L2_L2c chr1 14583009 14583264 - SINE/MIR_MIR chr1 14583402 14583759 - LTR/ERVL_LTR16A chr1 14583766 14583948 - SINE/MIR_MIRb chr1 14584049 14584556 + LTR/ERVL-MaLR_MLT1K chr1 14584561 14584649 + LTR/ERVL-MaLR_MLT1B chr1 14584650 14584942 - SINE/Alu_AluSp chr1 14584943 14585252 + LTR/ERVL-MaLR_MLT1B chr1 14585294 14585414 - LTR/ERVL-MaLR_MLT1H chr1 14585522 14585672 - LTR/ERVL-MaLR_MLT1H chr1 14586333 14586373 - LINE/L2_L2b chr1 14586429 14586512 + SINE/MIR_MIR3 chr1 14586513 14586821 - SINE/Alu_AluJo chr1 14586970 14587063 + LINE/CR1_L3 chr1 14587095 14587153 - LTR/ERVL-MaLR_MSTB chr1 14587154 14587461 + SINE/Alu_AluSx chr1 14587462 14587833 - LTR/ERVL-MaLR_MSTB chr1 14587973 14588145 + SINE/MIR_MIR chr1 14588539 14588644 + DNA/hAT-Tip100_MER91A chr1 14590164 14590298 - DNA_MER135 chr1 14590460 14590663 + SINE/MIR_MIRb chr1 14590693 14590720 + Simple_repeat_(CATCAC)n chr1 14590724 14590964 - SINE/MIR_MIR chr1 14591419 14591450 + Simple_repeat_(AC)n chr1 14592409 14592544 - DNA/hAT-Charlie_MER5A chr1 14593054 14593193 + SINE/MIR_MIRc chr1 14593296 14593405 - LINE/L2_L2b chr1 14593692 14593814 - LINE/L2_L2b chr1 14593940 14594075 + SINE/MIR_MIR3 chr1 14594104 14594242 + DNA/hAT-Blackjack_MER94 chr1 14594900 14595157 + SINE/Alu_AluSg7 chr1 14595261 14595324 + LINE/L1_L1M4 chr1 14595396 14595710 + SINE/Alu_AluSz chr1 14595998 14596027 + Simple_repeat_(ATTTG)n chr1 14596119 14596287 - LINE/L2_L2c chr1 14596309 14596337 + Simple_repeat_(CA)n chr1 14596438 14597032 - LINE/L1_L1MB5 chr1 14597156 14597327 + SINE/tRNA-RTE_MamSINE1 chr1 14597398 14597580 + SINE/MIR_MIRc chr1 14598733 14598793 + Simple_repeat_(CTC)n chr1 14598768 14598807 + Simple_repeat_(CCG)n chr1 14599119 14599192 + Simple_repeat_(GCG)n chr1 14600667 14600688 + Simple_repeat_(A)n chr1 14601138 14601256 + LINE/L1_L1ME4a chr1 14605355 14605795 - DNA/TcMar-Tigger_Tigger2a chr1 14605801 14605936 - SINE/MIR_MIRc chr1 14605937 14606128 - SINE/MIR_MIR chr1 14606141 14606215 - SINE/MIR_MIRb chr1 14606711 14606928 - DNA/hAT-Charlie_MER20 chr1 14606959 14607089 + LINE/L2_L2a chr1 14607171 14607431 - SINE/MIR_MIR chr1 14607869 14608119 - SINE/MIR_MIRb chr1 14608217 14608257 + Simple_repeat_(TCTT)n chr1 14609707 14609868 - SINE/MIR_MIRc chr1 14610104 14610192 - DNA/TcMar-Tigger_X6a_DNA chr1 14610193 14610226 + Simple_repeat_(TGTT)n chr1 14610227 14610522 - SINE/Alu_AluY chr1 14610523 14610606 - DNA/TcMar-Tigger_X6a_DNA chr1 14611451 14611705 + SINE/Alu_AluJo chr1 14611833 14612013 - DNA/hAT-Charlie_MER58A chr1 14612052 14612125 + LINE/L2_L2c chr1 14612298 14612332 - tRNA_tRNA-Thr-ACA chr1 14612401 14612450 + SINE/MIR_MIRc chr1 14612554 14612587 + SINE/MIR_MIR3 chr1 14612561 14612629 + SINE/MIR_MIRb chr1 14612630 14612829 - DNA/hAT-Charlie_MER20 chr1 14612830 14612865 + SINE/MIR_MIRb chr1 14613514 14613776 - DNA/hAT-Charlie_Charlie23a chr1 14614819 14614983 + SINE/MIR_MIR1_Amn chr1 14614848 14615067 + SINE/MIR_MIR chr1 14615145 14615206 - LINE/L2_L2b chr1 14615258 14615320 + SINE/MIR_MIR3 chr1 14615343 14615640 + SINE/Alu_AluSz6 chr1 14615796 14615866 + SINE/MIR_MIR3 chr1 14616102 14616278 - SINE/MIR_MIR3 chr1 14617191 14617399 - DNA/TcMar-Tigger_Tigger1a_Art chr1 14617394 14617559 - DNA/TcMar-Tigger_Tigger1a_Art chr1 14617560 14617669 + SINE/MIR_MIR chr1 14618222 14618375 + SINE/MIR_MIR3 chr1 14618508 14618736 - SINE/MIR_MIR chr1 14618755 14619111 - LINE/L2_L2c chr1 14619203 14619498 - SINE/Alu_AluJr chr1 14619518 14619983 - LINE/L1_L1MB5 chr1 14620268 14620404 + SINE/MIR_MIR chr1 14620659 14620886 + SINE/MIR_MIR chr1 14621576 14621919 + LTR/ERVL-MaLR_THE1B chr1 14622409 14622705 + SINE/Alu_AluY chr1 14622807 14622924 + SINE/MIR_MIRb chr1 14622941 14623632 + LINE/L1_L1PA15 chr1 14623635 14624140 - LINE/L1_L1M4 chr1 14624141 14624448 + SINE/Alu_AluSc8 chr1 14624449 14624590 - LINE/L1_L1M4 chr1 14624682 14624786 - LINE/L2_L2c chr1 14624896 14625109 - DNA/hAT-Charlie_Charlie32a chr1 14626414 14626513 - SINE/MIR_MIRb chr1 14626994 14627508 - LINE/L2_L2a chr1 14627506 14627669 + LINE/L2_L2b chr1 14627979 14628028 + Simple_repeat_(CT)n chr1 14628652 14628729 - SINE/MIR_MIRb chr1 14628840 14629146 - SINE/Alu_AluSx1 chr1 14629963 14630077 - DNA/hAT-Blackjack_MER63A chr1 14630405 14630522 + SINE/Alu_AluJo chr1 14630547 14630678 + SINE/MIR_MIRc chr1 14630720 14630913 + SINE/MIR_MIRb chr1 14631010 14631106 + LTR/ERVL-MaLR_MLT1K chr1 14632322 14632388 - DNA/TcMar-Tigger_X6a_DNA chr1 14632570 14632586 + Simple_repeat_(T)n chr1 14632917 14633216 - SINE/Alu_AluSx1 chr1 14633290 14633662 + LTR/ERVL-MaLR_MLT1D chr1 14633684 14633815 - DNA/TcMar-Tigger_Tigger14a chr1 14633868 14633885 + Simple_repeat_(CT)n chr1 14634932 14635141 + SINE/MIR_MIR chr1 14635282 14635405 - DNA/hAT-Charlie_MER20 chr1 14635770 14635882 + DNA/hAT-Charlie_MER20 chr1 14636088 14636293 + SINE/MIR_MIR3 chr1 14636530 14636653 - SINE/MIR_MIR chr1 14637290 14637548 - SINE/MIR_MIRb chr1 14637570 14637699 - DNA/hAT-Charlie_MER5B chr1 14637803 14638254 - LTR/ERVL-MaLR_MLT1C2 chr1 14638291 14638598 + SINE/Alu_AluSx chr1 14638739 14639132 + LINE/L2_L2 chr1 14640687 14640918 + LINE/L2_L2c chr1 14640920 14641280 - LTR/ERVL-MaLR_MLT1K chr1 14641343 14641732 - LTR/ERVL-MaLR_MLT1J chr1 14641766 14642117 + LINE/L1_L1ME1 chr1 14642118 14642421 + SINE/Alu_AluSc8 chr1 14642422 14642449 + LINE/L1_L1ME1 chr1 14642450 14642828 + LINE/L1_L1MA5A chr1 14642829 14643167 + LINE/L1_L1ME1 chr1 14643163 14644002 - LINE/L1_L1PA14 chr1 14644003 14644308 - SINE/Alu_AluSq chr1 14644309 14644351 - LINE/L1_L1PA14 chr1 14644352 14644660 - SINE/Alu_AluY chr1 14644661 14645919 - LINE/L1_L1PA14 chr1 14645920 14646242 + LINE/L1_L1ME1 chr1 14646265 14646432 + SINE/Alu_AluJb chr1 14646540 14646754 - DNA/hAT-Charlie_MER20 chr1 14647553 14647818 - DNA/hAT-Charlie_MER102a chr1 14648216 14648273 + LINE/L1_L1ME3G chr1 14649306 14649449 - SINE/MIR_MIR chr1 14649917 14650032 - SINE/MIR_MIRb chr1 14650033 14650058 + Simple_repeat_(T)n chr1 14650059 14650129 - SINE/MIR_MIRb chr1 14650273 14650569 + SINE/Alu_AluJo chr1 14650615 14650881 - DNA/hAT-Charlie_Charlie23a chr1 14652815 14652968 + DNA/hAT-Charlie_MER20 chr1 14653016 14653082 - LINE/L2_L2c chr1 14653307 14653667 - DNA/hAT-Charlie_MER102b chr1 14653668 14653747 + SINE/MIR_MIR chr1 14653693 14653759 + LINE/L2_L2a chr1 14653833 14654003 - DNA/hAT-Charlie_MER20 chr1 14654004 14654308 + SINE/Alu_AluSq2 chr1 14654309 14654331 - DNA/hAT-Charlie_MER20 chr1 14654986 14655149 - DNA/hAT-Charlie_MER5A chr1 14655794 14656237 - LTR/ERVL_LTR32 chr1 14656265 14656373 + DNA/hAT-Blackjack_MER81 chr1 14656448 14656496 - SINE/MIR_MIRb chr1 14656497 14656881 - LTR/ERVL-MaLR_MSTA chr1 14656882 14657041 - SINE/MIR_MIRb chr1 14657078 14657381 - SINE/Alu_AluSp chr1 14657384 14657523 - DNA/hAT-Charlie_MER5A chr1 14657670 14657814 + LINE/L2_L2b chr1 14657839 14657877 + Simple_repeat_(TGTT)n chr1 14657878 14657910 + Simple_repeat_(TTTTG)n chr1 14658210 14658358 + SINE/MIR_MIR chr1 14658387 14658531 + SINE/MIR_MIRb chr1 14658546 14658694 - DNA/hAT-Charlie_Charlie4z chr1 14658969 14659255 + SINE/Alu_AluSz6 chr1 14659882 14659915 + Low_complexity_A-rich chr1 14660277 14660367 - SINE/MIR_MIRb chr1 14660406 14660518 - DNA/hAT-Tip100_MER96B chr1 14660542 14660564 + Simple_repeat_(T)n chr1 14660565 14660662 - SINE/MIR_MIRb chr1 14661036 14661184 - SINE/MIR_MIRb chr1 14661390 14661591 - DNA/hAT-Charlie_MER58A chr1 14661612 14661925 + SINE/Alu_AluSz6 chr1 14661926 14662103 + SINE/MIR_MIRb chr1 14662651 14662732 + Simple_repeat_(GT)n chr1 14662794 14662863 - SINE/MIR_MIRb chr1 14662874 14662981 + Simple_repeat_(TA)n chr1 14662982 14663256 - SINE/Alu_AluJr chr1 14663295 14663455 - SINE/MIR_MIRb chr1 14663576 14664150 + LINE/L1_L1MB3 chr1 14664151 14664436 + SINE/Alu_AluSz6 chr1 14664437 14664463 + LINE/L1_L1MB3 chr1 14664518 14664641 + DNA/hAT-Charlie_MER5B chr1 14664642 14664953 - SINE/Alu_AluY chr1 14664954 14665019 + DNA/hAT-Charlie_MER5B chr1 14665628 14665757 + SINE/MIR_MIR chr1 14665933 14665956 + Simple_repeat_(A)n chr1 14666474 14666739 - LTR/ERVL-MaLR_MLT1C2 chr1 14666740 14666995 - SINE/Alu_AluSc chr1 14666996 14667171 - LTR/ERVL-MaLR_MLT1C2 chr1 14667807 14667833 + Low_complexity_A-rich chr1 14668399 14668455 - SINE/MIR_MIR3 chr1 14669288 14669420 + DNA/hAT-Charlie_MER20 chr1 14669463 14669772 + SINE/Alu_AluJr chr1 14669923 14669978 - LINE/L2_L2d chr1 14670088 14670153 + Simple_repeat_(TCCCT)n chr1 14670297 14670488 - DNA/hAT-Charlie_MER5A chr1 14670490 14670670 - DNA/hAT-Charlie_MER5A chr1 14670706 14670844 - DNA/hAT-Charlie_MER5A1 chr1 14670852 14671061 + DNA/hAT-Charlie_MER20 chr1 14671103 14671158 - LINE/L2_L2d chr1 14671192 14671380 + LINE/L2_L2a chr1 14672612 14673102 - LTR/ERVL-MaLR_MLT1N2 chr1 14673105 14673272 - LTR/ERVL_LTR84b chr1 14673514 14673587 - SINE/MIR_MIR3 chr1 14673871 14674027 + DNA/hAT-Charlie_MER5A chr1 14674033 14674190 - SINE/MIR_MIR3 chr1 14674778 14675005 - SINE/Alu_AluJr chr1 14675549 14675587 + Simple_repeat_(ATCTCCC)n chr1 14676332 14676522 + SINE/MIR_MIRc chr1 14676525 14676971 + LINE/L1_L1MB7 chr1 14677064 14677261 - SINE/MIR_MIRb chr1 14678009 14678063 + SINE/MIR_MIR3 chr1 14678064 14678620 + LTR/ERVL_MLT2B3 chr1 14678621 14678718 + SINE/MIR_MIR3 chr1 14678739 14678820 - LINE/L2_L2a chr1 14678866 14678998 - LINE/L2_L2a chr1 14679076 14679633 - LTR/ERVL_MER74B chr1 14679635 14679821 - SINE/MIR_MIR chr1 14680017 14680095 + DNA/hAT-Charlie_MER5A chr1 14680138 14680458 - LINE/L1_L1ME2 chr1 14680454 14680813 - LINE/L1_L1PA8 chr1 14680814 14681237 - LTR/ERVL-MaLR_MSTB chr1 14681238 14681399 - LINE/L1_L1PA8 chr1 14681544 14681593 - LINE/L1_L1MB3 chr1 14681594 14681658 + Simple_repeat_(TA)n chr1 14681665 14681985 - SINE/Alu_AluY chr1 14681986 14682286 - LINE/L1_L1MB3 chr1 14682287 14682587 - SINE/Alu_AluJb chr1 14682588 14682649 - LINE/L1_L1MB3 chr1 14683378 14683480 + LINE/L2_L2b chr1 14683609 14683721 + LINE/L2_L2b chr1 14683833 14683922 + LINE/L2_L2b chr1 14684249 14684414 + SINE/MIR_MIRc chr1 14684443 14684958 - LTR/ERVL-MaLR_MLT1D chr1 14684979 14685051 + DNA/TcMar-Tigger_Tigger13a chr1 14685052 14685515 - LTR/ERVL_LTR16C chr1 14685516 14685662 + DNA/TcMar-Tigger_Tigger13a chr1 14685652 14685860 + DNA/TcMar-Tigger_Tigger13a chr1 14685880 14686063 - SINE/MIR_MIRb chr1 14686067 14686362 - SINE/Alu_AluSc chr1 14687237 14687564 - LINE/L2_L2b chr1 14688225 14688351 + SINE/MIR_MIR3 chr1 14688462 14688516 - LINE/L2_L2d2 chr1 14688912 14689240 + SINE/Alu_AluSz6 chr1 14691361 14691388 + Simple_repeat_(ATAT)n chr1 14691484 14691791 - SINE/Alu_AluSz chr1 14692425 14692541 - DNA/TcMar-Tigger_Tigger2b_Pri chr1 14692650 14692942 + SINE/Alu_AluSz6 chr1 14694478 14694598 - LINE/L2_L2c chr1 14694622 14694826 + SINE/MIR_MIR chr1 14694895 14694994 - LINE/L2_L2c chr1 14695511 14695803 - SINE/Alu_AluSg chr1 14696157 14696380 - LINE/L2_L2b chr1 14696626 14696812 - DNA/hAT-Charlie_MER5A chr1 14697009 14697359 + SINE/Alu_AluJo chr1 14698058 14698105 - LINE/CR1_L3 chr1 14698531 14698576 + LINE/L2_L2d chr1 14699399 14699713 - LINE/L2_L2a chr1 14699714 14699922 - DNA/hAT-Blackjack_MER63A chr1 14699923 14700127 - LINE/L2_L2a chr1 14700250 14700484 + SINE/Alu_AluSg chr1 14701125 14701430 - SINE/Alu_AluJr chr1 14701512 14701812 + SINE/Alu_AluJb chr1 14702256 14702326 + DNA/hAT-Charlie_Charlie4z chr1 14702327 14702366 + Simple_repeat_(TG)n chr1 14702367 14702454 + DNA/hAT-Charlie_Charlie4z chr1 14702941 14703122 - SINE/MIR_MIR chr1 14703249 14703625 + LTR/ERVL-MaLR_THE1C chr1 14703626 14703909 + SINE/Alu_AluJr chr1 14703952 14704030 - LTR/ERVL_LTR79 chr1 14704082 14704232 - LTR/ERVL_LTR79 chr1 14704756 14704905 + LTR/ERVL_LTR33 chr1 14704997 14705302 - LTR/ERVL-MaLR_MLT1B chr1 14705303 14705861 + LINE/L1_L1MA5A chr1 14705862 14706403 + DNA/hAT-Charlie_MER1A chr1 14706404 14706628 + LINE/L1_L1MA5A chr1 14706629 14706743 - LTR/ERVL-MaLR_MLT1B chr1 14706771 14706817 + LTR/ERVL_LTR33 chr1 14707070 14707192 + DNA/hAT-Charlie_Charlie31a chr1 14707718 14707766 + DNA/hAT?_MER121 chr1 14707835 14707950 - LINE/L2_L2a chr1 14709062 14709249 - SINE/MIR_MIR chr1 14709822 14709998 - SINE/MIR_MIRc chr1 14710078 14710208 - LINE/L2_L2a chr1 14710208 14710897 + LINE/L2_L2a chr1 14710902 14711192 + SINE/Alu_AluJr chr1 14711226 14711357 + SINE/Alu_FLAM_A chr1 14711462 14711953 + LTR/ERVL-MaLR_MLT1F2 chr1 14711954 14712338 - LINE/L2_L2a chr1 14713131 14713333 - LTR/ERVL-MaLR_MLT1H2 chr1 14713461 14713657 + DNA/hAT-Charlie_MER20 chr1 14713659 14713983 + SINE/Alu_AluJo chr1 14714004 14714062 - SINE/MIR_MIRb chr1 14714768 14715094 - SINE/Alu_AluJb chr1 14715095 14715186 - SINE/Alu_AluJo chr1 14715759 14715810 + DNA/hAT-Tip100_MamTip2b chr1 14716138 14716195 + Simple_repeat_(TG)n chr1 14716332 14716635 + LINE/L2_L2d2 chr1 14716921 14717129 + LINE/L2_L2d2 chr1 14717239 14717357 - LINE/L2_L2a chr1 14717360 14717695 - LTR/ERVL-MaLR_MLT1C2 chr1 14718331 14718607 - LINE/CR1_CR1-1_Amn chr1 14719045 14719216 + LINE/L2_L2c chr1 14719268 14719557 - LINE/L2_L2a chr1 14719558 14719859 + SINE/Alu_AluSx chr1 14719860 14720020 - LINE/L2_L2a chr1 14720026 14720143 - SINE/MIR_MIRb chr1 14720402 14720707 - LINE/CR1_X1_LINE chr1 14720723 14720865 - SINE/Alu_AluSx3 chr1 14721124 14721498 + LTR/ERVL-MaLR_THE1C chr1 14721872 14722179 + SINE/Alu_AluSz chr1 14722640 14722827 - LINE/L1_L1MC5 chr1 14722845 14723152 + SINE/Alu_AluJb chr1 14723819 14723921 - LINE/L2_L2a chr1 14723923 14724088 + LINE/L1_HAL1b chr1 14725223 14725557 + LINE/L2_L2a chr1 14725555 14725743 - SINE/MIR_MIR1_Amn chr1 14725773 14725897 - DNA/hAT-Charlie_MER5A1 chr1 14726707 14726876 + DNA/hAT-Charlie_MER5A chr1 14726913 14727029 - SINE/MIR_MIR chr1 14727326 14727450 - DNA/hAT-Charlie_MER1A chr1 14727451 14727757 - SINE/Alu_AluSp chr1 14727758 14728003 - DNA/hAT-Charlie_MER1A chr1 14728004 14728317 + SINE/Alu_AluSq chr1 14728318 14728464 - DNA/hAT-Charlie_MER1A chr1 14728519 14728636 - DNA/hAT-Charlie_MER5B chr1 14728743 14728890 + SINE/MIR_MIR chr1 14729836 14730075 + DNA/hAT-Charlie_MER58B chr1 14730090 14730381 - SINE/Alu_AluSg chr1 14730449 14730648 - SINE/MIR_MIR chr1 14730805 14730868 - SINE/MIR_MIR3 chr1 14731538 14731711 - SINE/MIR_MIR chr1 14731737 14731945 + SINE/MIR_MIR chr1 14731959 14732356 + DNA/hAT-Charlie_MER119 chr1 14732357 14732666 + SINE/Alu_AluSz chr1 14732667 14732867 + DNA/hAT-Charlie_MER119 chr1 14734136 14734189 + SINE/MIR_MIR chr1 14734190 14734296 - DNA/hAT-Charlie_MER5A1 chr1 14734297 14734610 - SINE/Alu_AluSp chr1 14734611 14734688 - DNA/hAT-Charlie_MER5A1 chr1 14734689 14734767 + SINE/MIR_MIR chr1 14735060 14735359 - SINE/Alu_AluYk4 chr1 14736156 14736223 - DNA/hAT-Charlie_MER5A chr1 14736255 14736300 + Simple_repeat_(GT)n chr1 14736304 14736465 - SINE/Alu_AluSp chr1 14736466 14736490 + Simple_repeat_(T)n chr1 14736491 14736610 - SINE/Alu_AluSp chr1 14736618 14736687 - DNA/hAT-Charlie_MER5A chr1 14736854 14737296 - LINE/L2_L2a chr1 14737361 14737453 + LTR/ERVL-MaLR_MLT1O chr1 14737505 14738061 - LTR/ERVL-MaLR_MLT1E1A chr1 14738724 14738890 - SINE/MIR_MIRb chr1 14738891 14739195 + SINE/Alu_AluJr chr1 14739196 14739207 - SINE/MIR_MIRb chr1 14741204 14741308 + LINE/L2_L2b chr1 14742154 14742272 + LINE/L2_L2a chr1 14742280 14742398 + LINE/L2_L2b chr1 14742390 14742433 - LINE/L2_L2a chr1 14742480 14742507 + Simple_repeat_(TGAA)n chr1 14743153 14743443 + SINE/Alu_AluSz chr1 14744226 14744379 - SINE/MIR_MIRb chr1 14744395 14744436 + Simple_repeat_(T)n chr1 14744447 14744742 + SINE/Alu_AluJr chr1 14744744 14744945 - LINE/L1_L1ME3A chr1 14745249 14745284 + Low_complexity_A-rich chr1 14746492 14746625 - LINE/L2_L2d chr1 14746648 14746670 + Simple_repeat_(AGGG)n chr1 14746841 14747263 - LINE/L1_L1MB4 chr1 14747264 14747579 - SINE/Alu_AluSz chr1 14747580 14747899 - LINE/L1_L1MB4 chr1 14748372 14748451 + SINE/MIR_MIRc chr1 14748484 14748621 + SINE/MIR_MIR chr1 14748658 14748803 + SINE/MIR_MIR chr1 14748975 14749082 + SINE/MIR_MIR3 chr1 14749100 14749277 - DNA/hAT-Charlie_MER117 chr1 14749768 14749927 + SINE/MIR_MIRc chr1 14749929 14750212 + LINE/L1_L1MD2 chr1 14751163 14751330 - LINE/L1_L1ME4a chr1 14751408 14751666 - SINE/MIR_MIR chr1 14751715 14751878 - DNA/hAT-Charlie_MER117 chr1 14751879 14752279 - LTR/ERVL-MaLR_MSTB1 chr1 14752280 14752316 - DNA/hAT-Charlie_MER117 chr1 14752320 14752400 - SINE/MIR_MIR3 chr1 14752401 14752771 - LTR/ERVL-MaLR_MLT1A1 chr1 14752772 14752836 - SINE/MIR_MIRb chr1 14753161 14753199 - LINE/L2_L2b chr1 14753200 14753233 + Simple_repeat_(TTATTT)n chr1 14753234 14753297 - LINE/L1_L1PB1 chr1 14753444 14753563 + SINE/Alu_FLAM_C chr1 14753578 14753608 + Simple_repeat_(TAAAA)n chr1 14753819 14753931 - LTR/ERVL-MaLR_MLT1A1 chr1 14754351 14754645 + SINE/Alu_AluSx1 chr1 14754755 14754950 + SINE/MIR_MIRb chr1 14754960 14755272 + SINE/Alu_AluJb chr1 14757363 14757610 + SINE/MIR_MIRb chr1 14757890 14757924 + Low_complexity_G-rich chr1 14758078 14758122 + Simple_repeat_(TTTG)n chr1 14758129 14758185 + Simple_repeat_(TCCT)n chr1 14758186 14758455 - SINE/Alu_AluJr chr1 14758459 14758604 - SINE/Alu_AluJo chr1 14758609 14758690 + LTR/ERVL-MaLR_MLT1H2 chr1 14758907 14758980 + Low_complexity_GA-rich chr1 14759688 14759799 + SINE/MIR_MIRb chr1 14759801 14760282 + LTR/ERVL_LTR16A2 chr1 14760331 14760394 - SINE/MIR_MIRb chr1 14760525 14760671 + LINE/L2_L2b chr1 14760672 14760803 + SINE/Alu_AluJo chr1 14760804 14760822 + LINE/L2_L2b chr1 14760823 14761045 - DNA/hAT-Charlie_MER58A chr1 14761046 14761526 + LINE/L2_L2b chr1 14761891 14761907 + Simple_repeat_(T)n chr1 14762444 14762752 + SINE/Alu_AluYk2 chr1 14762859 14762880 + Simple_repeat_(AAT)n chr1 14763740 14764047 - SINE/Alu_AluSz chr1 14764209 14764635 + LINE/L2_L2a chr1 14764804 14765018 - SINE/MIR_MIRb chr1 14765080 14765293 - DNA/hAT-Charlie_MER20 chr1 14765498 14765883 - LTR/ERVL_LTR16B chr1 14765980 14766276 - LTR/ERVL_LTR16C chr1 14766599 14766694 - LINE/L2_L2c chr1 14766749 14766905 + SINE/MIR_MIRb chr1 14768285 14768438 - SINE/MIR_MIRb chr1 14768706 14768937 - DNA/hAT-Charlie_MER102b chr1 14769358 14769653 - SINE/Alu_AluSq2 chr1 14769735 14770517 - LINE/L2_L2b chr1 14771543 14771849 + SINE/Alu_AluSx1 chr1 14772200 14772479 + SINE/Alu_AluJr chr1 14772753 14772835 - LINE/L2_L2a chr1 14773315 14773355 + Simple_repeat_(ACCTCCC)n chr1 14773528 14773618 + SINE/MIR_MIRb chr1 14774167 14774332 + SINE/MIR_MIRb chr1 14774469 14774784 - SINE/Alu_AluJr chr1 14774904 14775053 - LINE/L2_L2b chr1 14775090 14775206 - SINE/MIR_MIRc chr1 14775295 14775494 - SINE/MIR_MIRb chr1 14776000 14776301 - SINE/Alu_AluSx chr1 14776563 14776646 - SINE/MIR_MIRb chr1 14776647 14776955 + SINE/Alu_AluJo chr1 14776956 14776983 - SINE/MIR_MIRb chr1 14776984 14777019 + Simple_repeat_(TTTGT)n chr1 14777020 14777295 - SINE/Alu_AluY chr1 14777296 14777417 - SINE/MIR_MIRb chr1 14777445 14777701 + LINE/L2_L2a chr1 14777711 14778169 - LTR/ERV1_MER41B chr1 14778170 14778576 - LTR/ERV1_MER72 chr1 14778736 14779250 - LTR/ERVL-MaLR_MLT1H1 chr1 14779960 14780068 - SINE/MIR_MIRb chr1 14780140 14780385 + LINE/L2_L2b chr1 14780462 14780612 + SINE/MIR_MIR chr1 14781041 14781346 + SINE/Alu_AluY chr1 14781690 14781779 + SINE/Alu_AluJo chr1 14781788 14781839 + SINE/MIR_MIRb chr1 14781935 14782119 + SINE/MIR_MIR chr1 14782265 14782577 + SINE/Alu_AluSx chr1 14783493 14783928 - LINE/L2_L2c chr1 14783983 14784378 - LTR/ERVL_MLT2C1 chr1 14784477 14784766 + SINE/Alu_AluSz chr1 14784970 14784987 + Simple_repeat_(T)n chr1 14785099 14785225 - LTR/ERVL-MaLR_MLT1O chr1 14785303 14785430 - LTR/ERVL-MaLR_MLT1O chr1 14785632 14785739 + SINE/MIR_MIR chr1 14785859 14786111 + SINE/MIR_MIRb chr1 14786726 14786882 + DNA/TcMar-Tigger_MER127 chr1 14787083 14787181 + SINE/MIR_MIRc chr1 14787236 14787442 - SINE/MIR_MIR chr1 14787396 14787607 + SINE/MIR_MIR chr1 14788011 14788183 - DNA/hAT-Tip100_MER45A chr1 14788809 14789087 + DNA/hAT-Tip100_MamTip2b chr1 14789380 14789635 + LINE/L2_L2d2 chr1 14789761 14789796 + Simple_repeat_(A)n chr1 14789797 14789850 + LINE/L2_L2a chr1 14789990 14790310 - SINE/Alu_AluSp chr1 14790440 14790565 - LINE/L1_L1MB7 chr1 14790721 14790990 - SINE/Alu_AluSz chr1 14792159 14792263 - LINE/L2_L2b chr1 14792266 14792414 + LINE/L1_L1MB5 chr1 14792489 14792695 - LINE/L2_L2b chr1 14792696 14792991 + SINE/Alu_AluSc chr1 14792992 14793047 - LINE/L2_L2b chr1 14793147 14793240 - LINE/L2_L2b chr1 14793402 14793656 + SINE/MIR_MIR chr1 14794433 14794480 + SINE/MIR_MIRb chr1 14794481 14794675 - DNA/hAT-Blackjack_MER63A chr1 14794676 14794698 + SINE/MIR_MIRb chr1 14794708 14795261 + LTR/ERVL_MLT2A2 chr1 14795654 14795901 + LINE/L2_L2c chr1 14795706 14796014 + LINE/L2_L2a chr1 14796402 14796507 + LINE/L2_L2c chr1 14797051 14797360 - SINE/Alu_AluSx chr1 14797669 14797898 + SINE/MIR_MIRb chr1 14797922 14798142 - SINE/MIR_MIR chr1 14798390 14798416 - SINE/Alu_AluY chr1 14798417 14798733 - SINE/Alu_AluYk3 chr1 14798749 14799077 - SINE/Alu_AluJb chr1 14799128 14799194 + LTR/ERVL-MaLR_MLT1I chr1 14800206 14800419 + LINE/L1_L1MB7 chr1 14800420 14800714 + SINE/Alu_AluJb chr1 14800715 14801054 + LINE/L1_L1MB7 chr1 14801673 14801983 - SINE/Alu_AluY chr1 14802124 14802423 + SINE/Alu_AluSz6 chr1 14802426 14802627 + DNA/hAT-Charlie_MER20 chr1 14802861 14803088 + SINE/MIR_MIRc chr1 14803113 14803335 - SINE/MIR_MIRb chr1 14803970 14804008 + Simple_repeat_(CTGTCC)n chr1 14804191 14804320 - LTR/ERVL-MaLR_MLT1K chr1 14804471 14804582 - LTR/ERVL-MaLR_MLT1K chr1 14804989 14805098 + SINE/MIR_MIRb chr1 14805155 14805456 - LTR/ERVL-MaLR_MLT1J chr1 14805477 14805762 + SINE/Alu_AluSx chr1 14805763 14805806 + Simple_repeat_(AAT)n chr1 14806328 14806450 + Unknown_UCON65 chr1 14806804 14806837 - SINE/MIR_MIR chr1 14806882 14807432 + LTR/ERVL_LTR67B chr1 14807493 14807782 + SINE/Alu_AluJb chr1 14807824 14808081 - LTR/ERVL_LTR79 chr1 14808287 14808597 - SINE/Alu_AluSz chr1 14808708 14809148 - LTR/ERVL-MaLR_MLT1L chr1 14809270 14809429 - DNA/TcMar-Tigger_Tigger15a chr1 14809936 14810112 - DNA/hAT-Charlie_MER20 chr1 14810113 14810160 + Simple_repeat_(AATCATT)n chr1 14810234 14810493 + LINE/L2_L2a chr1 14810588 14810728 - SINE/MIR_MIRb chr1 14811530 14811643 - SINE/MIR_MIRc chr1 14811644 14811810 + DNA/TcMar-Tc2_MER104 chr1 14811811 14811944 - SINE/MIR_MIRc chr1 14811959 14812139 + DNA/hAT-Tip100_MamTip3 chr1 14812230 14812368 - DNA/hAT-Tip100_MER91A chr1 14812516 14812811 - SINE/Alu_AluJr chr1 14813034 14813152 + SINE/MIR_MIRc chr1 14813198 14813640 + LTR/ERVL-MaLR_MLT1G1 chr1 14813803 14813908 + SINE/MIR_MIRb chr1 14814219 14814513 - SINE/Alu_AluSg chr1 14814897 14815008 + SINE/MIR_MIRb chr1 14815453 14815530 - SINE/MIR_MIRb chr1 14816179 14816561 - LTR/ERVL-MaLR_MLT1E1A chr1 14816590 14816854 + Simple_repeat_(ATAG)n chr1 14816855 14816931 - LTR/ERVL-MaLR_MLT1E1A chr1 14816936 14817107 - SINE/MIR_MIRb chr1 14817233 14817348 + LINE/L2_L2a chr1 14817346 14817425 - LINE/L2_L2c chr1 14817520 14817598 + LINE/L2_L2a chr1 14818287 14818771 - LTR/ERVL_LTR33 chr1 14818773 14819064 + SINE/Alu_AluSq2 chr1 14819305 14819706 + LTR/ERVL_MLT2C2 chr1 14819707 14820008 - SINE/Alu_AluSx1 chr1 14820062 14820226 - DNA/hAT-Charlie_MER5A chr1 14821141 14821250 + LTR/ERVL-MaLR_MLT1B chr1 14821251 14821528 + SINE/Alu_AluSx1 chr1 14821529 14821781 + LTR/ERVL-MaLR_MLT1B chr1 14821782 14821954 - SINE/MIR_MIR1_Amn chr1 14822034 14822055 + Simple_repeat_(TGTT)n chr1 14823183 14823352 - LTR/ERVL-MaLR_MLT1O chr1 14823465 14823634 + DNA/hAT-Charlie_MER20B chr1 14823832 14824131 + SINE/Alu_AluSx chr1 14824687 14824718 + Simple_repeat_(ATC)n chr1 14825498 14825717 - SINE/MIR_MIR chr1 14825718 14825980 + LINE/L2_L2b chr1 14825994 14826076 - SINE/MIR_MIRc chr1 14826623 14826717 + LINE/L2_L2c chr1 14826864 14827037 + SINE/MIR_MIRb chr1 14827259 14827521 - LINE/L1_L1PB1 chr1 14828020 14828105 - DNA/hAT-Charlie_MER117 chr1 14828639 14829161 - LTR/ERVL-MaLR_MLT1J chr1 14829266 14829314 - LINE/L2_L2d2 chr1 14829424 14829585 + SINE/Alu_FRAM chr1 14829646 14829830 - LINE/L2_L2b chr1 14830087 14830443 - LTR/ERVL-MaLR_MLT1K chr1 14830471 14830862 - LTR/ERVL-MaLR_MSTA chr1 14831036 14831159 - SINE/Alu_AluSx chr1 14831340 14831371 + Simple_repeat_(CTG)n chr1 14831839 14831912 - LTR/ERVL-MaLR_MLT1K chr1 14831913 14832211 + SINE/Alu_AluSq2 chr1 14832212 14832479 - LTR/ERVL-MaLR_MLT1K chr1 14832509 14832724 - DNA/hAT-Charlie_MER30 chr1 14832741 14833006 + DNA/hAT-Charlie_MER58B chr1 14833018 14833072 + Simple_repeat_(TTAATAA)n chr1 14833295 14833561 - LTR/ERVL_LTR16C chr1 14833970 14834019 + Simple_repeat_(CA)n chr1 14834034 14834335 - SINE/Alu_AluSx1 chr1 14834357 14834659 - SINE/Alu_AluY chr1 14834695 14834805 - LINE/L2_L2a chr1 14834809 14834837 + Simple_repeat_(TATTAG)n chr1 14835498 14835647 - SINE/MIR_MIRb chr1 14835855 14836246 - LTR/ERVL_LTR79 chr1 14836656 14836691 + Simple_repeat_(CCACCT)n chr1 14837017 14837210 - LINE/L1_L1ME3F chr1 14837211 14837498 - SINE/Alu_AluSp chr1 14837499 14837550 - LINE/L1_L1ME3F chr1 14837551 14837853 - SINE/Alu_AluJb chr1 14837854 14837959 - LINE/L1_L1ME3F chr1 14837960 14838325 - LTR/ERVL-MaLR_MLT1A1 chr1 14838345 14838425 - LINE/L1_L1ME3F chr1 14838639 14838921 - SINE/Alu_AluSz chr1 14839196 14839436 + SINE/MIR_MIR chr1 14839537 14839800 + LINE/L2_L2c chr1 14840803 14840877 - LINE/L2_L2c chr1 14840991 14841089 - LINE/L2_L2a chr1 14841136 14841435 + SINE/Alu_AluYc3 chr1 14841614 14841725 + DNA/hAT-Blackjack_MER63B chr1 14841813 14842275 + LINE/L2_L2a chr1 14842608 14842700 + LINE/CR1_X6B_LINE chr1 14842721 14842753 + Simple_repeat_(TTTTCT)n chr1 14842757 14843163 - LINE/L1_HAL1 chr1 14843275 14843471 - LINE/L2_L2b chr1 14843500 14843638 - LINE/L2_L2b chr1 14844259 14844403 + SINE/MIR_MIR chr1 14844808 14844956 + Simple_repeat_(TGGA)n chr1 14844957 14844991 + Simple_repeat_(ATGG)n chr1 14844992 14845599 + Simple_repeat_(TGGA)n chr1 14845718 14846110 - LTR/ERVL_LTR16B2 chr1 14846168 14846313 - DNA/hAT-Charlie_MER103C chr1 14846499 14846524 + Simple_repeat_(A)n chr1 14846593 14846721 - SINE/MIR_MIRc chr1 14846756 14846939 - SINE/MIR_MIRb chr1 14847584 14847702 + LINE/L2_L2a chr1 14847703 14848002 + SINE/Alu_AluJr chr1 14848003 14848049 + LINE/L2_L2a chr1 14848169 14848247 + Simple_repeat_(CCTCT)n chr1 14848305 14848444 + SINE/MIR_MIRc chr1 14849024 14849071 - SINE/MIR_MIR3 chr1 14849043 14849168 - SINE/MIR_MIRb chr1 14849369 14849433 - SINE/MIR_MIR chr1 14849456 14849938 - DNA/TcMar-Tigger_Tigger9a chr1 14849939 14850233 - SINE/Alu_AluSc chr1 14850234 14850373 - DNA/TcMar-Tigger_Tigger9a chr1 14850411 14850559 - SINE/MIR_MIRb chr1 14850599 14850720 + LINE/L2_L2b chr1 14851598 14851798 + SINE/MIR_MIRb chr1 14851971 14852089 + SINE/MIR_MIRb chr1 14852131 14852181 + Simple_repeat_(TCTCCT)n chr1 14852158 14852186 + Simple_repeat_(CTCCC)n chr1 14852814 14852972 + DNA/hAT-Charlie_MER5A1 chr1 14852975 14853098 - SINE/MIR_MIRb chr1 14853099 14853363 - LINE/L2_L2c chr1 14853375 14853581 - SINE/MIR_MIR chr1 14853586 14853656 - LINE/L2_L2d2 chr1 14853917 14853985 + SINE/MIR_MIRb chr1 14854143 14854213 - SINE/MIR_MIR1_Amn chr1 14854352 14854799 - LTR/ERVL-MaLR_MSTB2 chr1 14855700 14855821 - SINE/MIR_MIR chr1 14856879 14857035 + LINE/CR1_L3b chr1 14857078 14857242 + SINE/MIR_MIRb chr1 14857243 14857554 + SINE/Alu_AluJb chr1 14857811 14858200 - LINE/L1_L1MB8 chr1 14858201 14858512 - SINE/Alu_AluSx1 chr1 14858513 14858653 - LINE/L1_L1MB8 chr1 14858768 14858912 + LINE/RTE-X_L4_A_Mam chr1 14858940 14859228 + SINE/Alu_AluY chr1 14859268 14859465 - SINE/MIR_MIRc chr1 14860109 14860165 + Simple_repeat_(TTCC)n chr1 14860166 14860197 + Simple_repeat_(TC)n chr1 14860198 14860492 - SINE/Alu_AluSz chr1 14860533 14860683 - LINE/L1_L1M5 chr1 14861047 14861096 - DNA/TcMar-Mariner_MADE1 chr1 14861097 14861390 + SINE/Alu_AluSx chr1 14861391 14861429 - DNA/TcMar-Mariner_MADE1 chr1 14861510 14861558 - LINE/RTE-BovB_MamRTE1 chr1 14861585 14862072 + LTR/ERVL-MaLR_MLT1K chr1 14862112 14862318 - LINE/L2_L2c chr1 14862880 14863025 - SINE/MIR_MIR chr1 14863538 14863954 - LINE/L2_L2c chr1 14863958 14864002 + Simple_repeat_(ATTC)n chr1 14864283 14864581 + SINE/Alu_AluSg chr1 14864746 14864936 + SINE/MIR_MIR chr1 14865057 14865136 + LINE/CR1_L3 chr1 14865663 14866084 + LTR/ERVL-MaLR_MLT1C2 chr1 14866087 14866202 + LINE/L2_L2a chr1 14866203 14866225 + LINE/L2_L2b chr1 14866356 14866445 + LINE/L2_L2b chr1 14866447 14866740 + SINE/Alu_AluJr chr1 14867473 14867587 + SINE/MIR_MIR chr1 14868398 14868535 - SINE/MIR_MIR chr1 14868698 14868850 + SINE/Alu_FRAM chr1 14870218 14870334 + SINE/MIR_MIRb chr1 14870335 14870638 - SINE/Alu_AluSz chr1 14870639 14870757 + SINE/MIR_MIRb chr1 14870829 14871197 - LTR/Gypsy_MamGypLTR1b chr1 14871650 14871682 + Simple_repeat_(GT)n chr1 14871736 14871897 - LTR/ERVL-MaLR_MLT1K chr1 14871913 14872048 - LINE/L2_L2 chr1 14872126 14872268 + SINE/MIR_MIRc chr1 14872269 14872457 + DNA/hAT-Charlie_MER3 chr1 14872458 14872514 + SINE/MIR_MIRc chr1 14872733 14873187 + Simple_repeat_(TGGA)n chr1 14873188 14874198 - LINE/L2_L2a chr1 14874200 14874440 - LINE/L2_L2-3_Crp chr1 14874471 14874534 + Simple_repeat_(TGGA)n chr1 14874823 14874855 + Low_complexity_A-rich chr1 14875050 14875355 + SINE/Alu_AluSp chr1 14875483 14875514 + Simple_repeat_(A)n chr1 14875608 14875800 + DNA/hAT-Blackjack_MER63A chr1 14876518 14876785 + SINE/MIR_MIR chr1 14876927 14877094 - LTR/ERVL-MaLR_MLT1K chr1 14877312 14877523 - LTR/ERVL-MaLR_MLT1K chr1 14877568 14877822 + SINE/MIR_MIR chr1 14877901 14877974 - LINE/L2_L2b chr1 14878000 14878166 + SINE/MIR_MIRb chr1 14878251 14878467 - DNA/hAT-Charlie_MER20 chr1 14878478 14878559 + SINE/MIR_MIR3 chr1 14879118 14879369 + SINE/MIR_MIRb chr1 14879391 14879463 + SINE/MIR_MIRc chr1 14879819 14880013 - SINE/MIR_MIRb chr1 14880207 14880407 - SINE/MIR_MIRb chr1 14880421 14880890 - LTR/ERVL-MaLR_MLT1G chr1 14880912 14881025 - LINE/L2_L2b chr1 14881517 14881634 - LINE/L2_L2b chr1 14881658 14881866 + SINE/MIR_MIR chr1 14881892 14882013 + LINE/L2_L2d2 chr1 14882031 14882135 - DNA/hAT-Charlie_MER5A1 chr1 14882337 14882550 + SINE/MIR_MIRc chr1 14882753 14882987 - SINE/MIR_MIR chr1 14883014 14883319 + SINE/Alu_AluSp chr1 14883321 14883426 + Simple_repeat_(AGAA)n chr1 14883557 14883595 + Simple_repeat_(CTCCTCC)n chr1 14883971 14884101 - SINE/MIR_MIRb chr1 14884112 14884402 + SINE/Alu_AluSc chr1 14884615 14884785 - DNA/hAT-Charlie_MER5A1 chr1 14885235 14885511 + LINE/L2_L2a chr1 14885516 14885647 - SINE/Alu_AluJo chr1 14885892 14886120 - DNA/hAT-Charlie_MER20 chr1 14886140 14886422 + SINE/Alu_AluJr4 chr1 14886426 14886464 + Simple_repeat_(AC)n chr1 14886465 14886492 + Simple_repeat_(GA)n chr1 14886510 14886808 + SINE/Alu_AluSp chr1 14886859 14887001 + LINE/L2_L2a chr1 14886983 14887128 + SINE/MIR_MIRb chr1 14887170 14887307 - DNA/hAT-Charlie_Charlie19a chr1 14887422 14887649 - DNA/hAT-Charlie_Charlie19a chr1 14888682 14888912 - LTR/ERVL-MaLR_MLT1I chr1 14888913 14889131 - DNA/hAT-Charlie_MER30 chr1 14889132 14889287 - LTR/ERVL-MaLR_MLT1I chr1 14889344 14889450 + LINE/L1_L1ME3F chr1 14889451 14889515 + LTR/ERVL_LTR16E1 chr1 14889553 14889615 - LINE/CR1_L3 chr1 14889657 14889896 + DNA/hAT-Charlie_MER58B chr1 14890597 14890697 - DNA/hAT-Charlie_MER5A chr1 14890698 14890828 - SINE/Alu_FLAM_C chr1 14890841 14891142 - SINE/Alu_AluY chr1 14891164 14891512 - LINE/L1_L1PB1 chr1 14891513 14891585 + DNA/hAT-Charlie_MER5A chr1 14891716 14891851 + LINE/L1_L1ME3F chr1 14892116 14892226 + SINE/tRNA_LFSINE_Vert chr1 14892369 14892434 + Simple_repeat_(TC)n chr1 14892918 14893065 - LINE/L2_L2a chr1 14893066 14893091 - LINE/L2_L2b chr1 14893092 14893391 + SINE/Alu_AluSg chr1 14893392 14893513 - LINE/L2_L2a chr1 14894317 14894512 + SINE/MIR_MIR chr1 14894553 14894759 + LINE/L2_L2b chr1 14895767 14895817 + LINE/L2_L2 chr1 14895855 14896028 - LINE/L2_L2b chr1 14896042 14896352 - SINE/Alu_AluSc chr1 14896397 14896557 + LTR/ERVL-MaLR_MLT1N2 chr1 14896833 14896973 + SINE/MIR_MIRc chr1 14897215 14897340 - LTR/ERVL-MaLR_MLT1L chr1 14897342 14897395 - LTR/ERVL-MaLR_MLT1L chr1 14897396 14897808 - DNA/hAT-Charlie_Charlie1a chr1 14897994 14898084 + SINE/MIR_MIRb chr1 14898626 14898654 + Simple_repeat_(AC)n chr1 14898783 14898844 + LINE/L2_L2a chr1 14899000 14899116 + SINE/MIR_MIR3 chr1 14899655 14899743 + Simple_repeat_(CCCT)n chr1 14900330 14900465 + SINE/MIR_MIR chr1 14900472 14900768 + SINE/Alu_AluY chr1 14900953 14901693 - LINE/L2_L2a chr1 14902228 14902527 - SINE/Alu_AluY chr1 14902866 14903168 - SINE/Alu_AluSq2 chr1 14903797 14903956 - SINE/MIR_MIR chr1 14903954 14904022 + LINE/L2_L2c chr1 14904085 14904210 + SINE/MIR_MIRb chr1 14904215 14904329 + SINE/Alu_AluSg4 chr1 14904331 14904415 + SINE/MIR_MIRb chr1 14904371 14904427 + LINE/L2_L2a chr1 14904747 14904777 + Simple_repeat_(TGTTTT)n chr1 14904778 14905060 - SINE/Alu_AluY chr1 14905061 14905359 - SINE/Alu_AluSz chr1 14905564 14905845 + SINE/Alu_AluYk3 chr1 14905846 14905875 + Simple_repeat_(AAT)n chr1 14905887 14906171 + SINE/Alu_AluSc chr1 14906178 14906219 + Simple_repeat_(ATA)n chr1 14906345 14906473 - DNA/TcMar-Tigger_Tigger19a chr1 14907015 14907075 + DNA/hAT-Charlie_MER5A1 chr1 14907115 14907423 + SINE/Alu_AluSz chr1 14907424 14907521 + DNA/hAT-Charlie_MER5A1 chr1 14907583 14907785 - LINE/L2_L2c chr1 14907990 14908063 - SINE/MIR_MIRb chr1 14908121 14908258 + SINE/Alu_AluJr chr1 14908259 14908571 + SINE/Alu_AluSx1 chr1 14908572 14908725 + SINE/Alu_AluJr chr1 14908735 14908995 + SINE/Alu_AluSx1 chr1 14909005 14909235 + SINE/MIR_MIR chr1 14909210 14909312 - SINE/MIR_MIRb chr1 14909662 14909756 + LINE/L2_L2d2 chr1 14909764 14910060 + SINE/Alu_AluSx chr1 14910062 14910093 + Simple_repeat_(AATG)n chr1 14910403 14910594 - DNA/hAT-Charlie_MER20 chr1 14910736 14910965 - DNA/hAT-Charlie_Charlie8 chr1 14911176 14911317 + SINE/MIR_MIR3 chr1 14911780 14911870 - SINE/MIR_MIR3 chr1 14911871 14912172 + SINE/Alu_AluSx1 chr1 14912173 14912265 - SINE/MIR_MIR3 chr1 14912344 14912417 - DNA/hAT-Charlie_MER102a chr1 14912487 14912680 - SINE/Alu_AluJr chr1 14912705 14912849 - DNA/hAT-Charlie_MER102a chr1 14912861 14912993 + SINE/MIR_MIRb chr1 14914067 14914250 - DNA/hAT-Charlie_MER5A chr1 14914694 14914908 + SINE/MIR_MIR chr1 14914909 14915213 + SINE/Alu_AluSp chr1 14915214 14915255 + SINE/MIR_MIRc chr1 14915292 14915379 + SINE/MIR_MIRb chr1 14915711 14915880 + SINE/MIR_MIRb chr1 14915889 14916139 + SINE/MIR_MIR chr1 14916165 14916477 - SINE/Alu_AluSx chr1 14916639 14916926 + SINE/Alu_AluJr chr1 14917516 14917732 - SINE/MIR_MIRb chr1 14917744 14917866 + LTR/ERVL-MaLR_MLT1F2 chr1 14917867 14918176 - SINE/Alu_AluSc chr1 14918177 14918398 + LTR/ERVL-MaLR_MLT1F2 chr1 14918399 14918705 + SINE/Alu_AluSx chr1 14918708 14918749 + Simple_repeat_(AT)n chr1 14918750 14919000 + LTR/ERVL-MaLR_MLT1F2 chr1 14919177 14919472 - SINE/Alu_AluSx chr1 14919701 14920036 + DNA/TcMar-Tigger_Tigger3a chr1 14920270 14920296 + Simple_repeat_(T)n chr1 14920623 14920773 - DNA/hAT-Tip100_MER45A chr1 14921154 14921188 + Simple_repeat_(AC)n chr1 14921435 14921699 + DNA/hAT-Charlie_Charlie8 chr1 14921700 14922011 - SINE/Alu_AluSz chr1 14922012 14922210 + DNA/hAT-Charlie_Charlie8 chr1 14922545 14922813 + SINE/Alu_AluSx1 chr1 14923546 14923623 - SINE/MIR_MIR1_Amn chr1 14924036 14924044 + Simple_repeat_(GGGGGC)n chr1 14924045 14924073 + Low_complexity_G-rich chr1 14924583 14924651 + Low_complexity_G-rich chr1 14925413 14925643 - SINE/MIR_MIRb chr1 14925917 14926077 + SINE/MIR_MIR chr1 14927265 14927572 - LINE/L2_L2a chr1 14927955 14928110 - SINE/MIR_MIR chr1 14928169 14928472 + SINE/Alu_AluY chr1 14928935 14929134 + SINE/MIR_MIRb chr1 14930522 14930653 - SINE/MIR_MIRb chr1 14930986 14931143 - SINE/MIR_MIR3 chr1 14931155 14931465 + SINE/Alu_AluSz chr1 14931620 14931692 - DNA/hAT-Charlie_MER5A chr1 14931758 14931811 - SINE/Alu_FAM chr1 14932238 14932268 + Simple_repeat_(CTCCC)n chr1 14932497 14932583 + DNA/hAT-Charlie_MER3 chr1 14932801 14933076 - LINE/L1_L1MB4 chr1 14933092 14933392 - SINE/Alu_AluSp chr1 14933633 14933832 - SINE/Alu_AluJo chr1 14933841 14934154 - SINE/Alu_AluY chr1 14934200 14934504 - SINE/Alu_AluSg7 chr1 14934602 14934731 + LINE/CR1_L3 chr1 14935231 14935356 - SINE/Alu_AluSq chr1 14935445 14935624 + DNA/hAT-Charlie_MER5A chr1 14935665 14935728 + Simple_repeat_(CCTCTCC)n chr1 14935770 14935854 - DNA/hAT-Tip100_MamTip2b chr1 14936096 14936314 + SINE/MIR_MIRc chr1 14936389 14936498 + DNA/hAT-Blackjack_MER81 chr1 14936638 14936736 - LINE/L2_L2a chr1 14938099 14938235 + SINE/MIR_MIR chr1 14938260 14938559 + SINE/Alu_AluSq chr1 14938774 14938860 + SINE/MIR_MIRb chr1 14938962 14939271 - SINE/Alu_AluSx1 chr1 14939274 14939572 - SINE/Alu_AluJr chr1 14940045 14940237 - LTR/ERVL-MaLR_MLT1M chr1 14940252 14940498 - LINE/CR1_L3 chr1 14940899 14940929 + Simple_repeat_(T)n chr1 14940934 14941215 - SINE/Alu_AluSx chr1 14942993 14943051 + Simple_repeat_(GT)n chr1 14943532 14943698 - SINE/MIR_MIR chr1 14943791 14944096 + SINE/Alu_AluSx chr1 14944228 14944245 + Simple_repeat_(A)n chr1 14945024 14945147 + SINE/MIR_MIR chr1 14945529 14945556 + Simple_repeat_(CCTC)n chr1 14945936 14946157 - SINE/MIR_MIR chr1 14946148 14946227 - SINE/MIR_MIR1_Amn chr1 14946324 14946563 - SINE/Alu_AluY chr1 14947285 14947311 + Simple_repeat_(GATG)n chr1 14947595 14947804 + SINE/MIR_MIR chr1 14948156 14948650 - LTR/ERVL-MaLR_MLT1K chr1 14948875 14949155 + SINE/Alu_AluSz chr1 14949156 14949195 + Simple_repeat_(AAT)n chr1 14949286 14949310 + Simple_repeat_(A)n chr1 14950993 14951122 + SINE/MIR_MIR chr1 14951228 14951462 - LTR?_MamRep605 chr1 14951463 14951772 - SINE/Alu_AluSx1 chr1 14951773 14951833 - LTR?_MamRep605 chr1 14952241 14952277 + Simple_repeat_(TG)n chr1 14952486 14952575 - LINE/L1_L1ME2 chr1 14952834 14953079 - SINE/MIR_MIRb chr1 14953117 14953147 - LINE/L2_L2a chr1 14953382 14953624 + SINE/MIR_MIR chr1 14953635 14953670 + Simple_repeat_(TAA)n chr1 14953763 14953957 - LTR/ERVL-MaLR_MLT1J chr1 14954534 14954674 + DNA/hAT-Charlie_MER3 chr1 14954675 14954964 + SINE/Alu_AluY chr1 14954965 14955033 - DNA/hAT-Charlie_MER3 chr1 14955101 14955183 - SINE/MIR_MIR3 chr1 14955258 14955445 - SINE/MIR_MIRb chr1 14955908 14956101 - DNA/hAT_MER53 chr1 14956132 14956217 - SINE/MIR_MIR3 chr1 14956300 14956317 + Simple_repeat_(A)n chr1 14956324 14956636 + SINE/Alu_AluSq2 chr1 14956670 14956785 + DNA/hAT-Tip100_MamTip2b chr1 14957391 14957478 - LINE/L2_L2b chr1 14957623 14957781 - LINE/L2_L2b chr1 14958811 14958854 + Low_complexity_GA-rich chr1 14958939 14959133 - LINE/L2_L2a chr1 14959122 14959452 - LINE/L2_L2b chr1 14960166 14960377 + SINE/MIR_MIRb chr1 14961799 14961875 - LTR/ERVL-MaLR_MLT1J chr1 14961973 14962113 - LTR/ERVL-MaLR_MLT1J chr1 14962944 14963035 + SINE/MIR_MIRb chr1 14963253 14963420 - SINE/MIR_MIRb chr1 14963523 14963601 + SINE/MIR_MIR chr1 14963627 14964195 - LINE/L1_L1PA8 chr1 14964258 14964362 + SINE/MIR_MIR chr1 14965029 14965318 - SINE/Alu_AluSz6 chr1 14965473 14965673 - LINE/L1_L1ME4a chr1 14965758 14965972 - DNA/hAT-Charlie_Charlie1 chr1 14965973 14966275 - SINE/Alu_AluSz chr1 14966276 14966673 - DNA/hAT-Charlie_Charlie1 chr1 14966674 14966968 - SINE/Alu_AluSx chr1 14967448 14967921 + LTR/ERVL-MaLR_MLT1D chr1 14967922 14968462 - DNA/hAT-Charlie_MER1A chr1 14968463 14968514 + LTR/ERVL-MaLR_MLT1D chr1 14969800 14970113 - SINE/Alu_AluSx1 chr1 14970123 14970243 - LINE/L1_L1MD3 chr1 14970251 14970358 - LTR/ERVL-MaLR_MLT1H2 chr1 14970957 14971115 - SINE/MIR_MIRb chr1 14971116 14971422 + SINE/Alu_AluSg chr1 14971423 14971502 - SINE/MIR_MIRb chr1 14971662 14971789 - SINE/Alu_AluYc chr1 14972517 14972814 - SINE/Alu_AluSq2 chr1 14972953 14973085 - SINE/MIR_MIRb chr1 14973218 14973299 - LINE/L2_L2a chr1 14973300 14973794 - LTR/ERV1_LTR10D chr1 14973795 14973843 - LINE/L2_L2a chr1 14973848 14974339 - LTR/ERVL-MaLR_MLT1H1 chr1 14974412 14974962 + LINE/L1_L1ME1 chr1 14975647 14975715 - SINE/MIR_MIRb chr1 14975750 14976061 + SINE/Alu_AluY chr1 14976157 14976309 - SINE/Alu_AluJb chr1 14976772 14977086 + SINE/Alu_AluSx3 chr1 14977884 14978058 - SINE/Alu_AluSp chr1 14978917 14979151 - SINE/Alu_AluSx1 chr1 14979154 14979434 + SINE/Alu_AluSg chr1 14979683 14979873 + SINE/MIR_MIR chr1 14979874 14980188 - SINE/Alu_AluSx chr1 14980189 14980199 + SINE/MIR_MIR chr1 14980322 14980461 - LINE/L2_L2d2 chr1 14981391 14981444 - DNA/hAT-Tip100_MER91B chr1 14981564 14981746 - DNA/hAT-Tip100_MER91A chr1 14982019 14982258 - SINE/MIR_MIR chr1 14982418 14982724 - SINE/Alu_AluSx3 chr1 14983359 14983612 + LINE/L1_L1MC4 chr1 14983660 14983951 + SINE/Alu_AluSq2 chr1 14983975 14984084 + LINE/L1_L1MC4 chr1 14984085 14984396 + SINE/Alu_AluSz6 chr1 14984397 14984858 + LINE/L1_L1MC4 chr1 14984876 14985151 + SINE/Alu_AluSx4 chr1 14985206 14985285 + SINE/Alu_FLAM_C chr1 14985287 14985556 + SINE/Alu_AluJb chr1 14985578 14985648 + LINE/L1_L1MC4 chr1 14985671 14985722 + LINE/L1_L1MC4 chr1 14985723 14986032 + SINE/Alu_AluSg chr1 14986033 14986227 + LINE/L1_L1MC4 chr1 14986957 14987212 - LINE/L2_L2a chr1 14987213 14987508 + SINE/Alu_AluSx3 chr1 14987509 14987903 - LINE/L2_L2a chr1 14989195 14989495 + SINE/Alu_AluSz chr1 14989531 14989775 + SINE/MIR_MIR chr1 14989967 14990141 - SINE/MIR_MIRb chr1 14990155 14990208 + DNA/hAT-Charlie_MER58A chr1 14990209 14990523 - SINE/Alu_AluJb chr1 14990524 14990693 + DNA/hAT-Charlie_MER58A chr1 14990740 14991070 + LTR/ERVL-MaLR_MLT1C chr1 14991071 14991375 + SINE/Alu_AluSx1 chr1 14991376 14991476 + LTR/ERVL-MaLR_MLT1C chr1 14992637 14992943 - SINE/Alu_AluSx1 chr1 14992944 14992991 + LINE/L1_L1MB4 chr1 14992992 14993126 - SINE/Alu_AluSz chr1 14993186 14993483 + SINE/Alu_AluSq2 chr1 14993484 14993501 + Simple_repeat_(ATC)n chr1 14993808 14993934 - LINE/L2_L2d2 chr1 14994687 14994845 + SINE/MIR_MIRb chr1 14995395 14995642 + SINE/Alu_AluSx1 chr1 14995836 14996014 + DNA/hAT-Charlie_MER102a chr1 14996265 14996483 + LINE/L2_L2b chr1 14996866 14997182 + LINE/L2_L2b chr1 14997183 14997204 - SINE/Alu_AluSp chr1 14997205 14997256 + Simple_repeat_(ATTT)n chr1 14997257 14997539 - SINE/Alu_AluSp chr1 14997540 14997661 + LINE/L2_L2b chr1 14997812 14997950 + SINE/MIR_MIRb chr1 14998431 14998541 - SINE/MIR_MIR chr1 14998542 14998849 - SINE/Alu_AluSx chr1 14998850 14998969 - SINE/MIR_MIR chr1 14999026 14999189 + SINE/Alu_AluJr chr1 14999219 14999409 + SINE/MIR_MIRb chr1 14999432 14999466 + Simple_repeat_(TCCT)n chr1 14999940 15000447 + LTR/ERVL-MaLR_MLT1D chr1 15000569 15000699 + SINE/MIR_MIRb chr1 15000822 15001119 + SINE/Alu_AluJo chr1 15001182 15001481 + SINE/Alu_AluSz6 chr1 15001503 15001624 + SINE/MIR_MIRb chr1 15001871 15002179 - SINE/Alu_AluY chr1 15002452 15002588 + SINE/MIR_MIR chr1 15002590 15002621 + Simple_repeat_(TCATCG)n chr1 15002623 15002693 - SINE/MIR_MIR chr1 15002694 15003002 + SINE/Alu_AluSx1 chr1 15003006 15003034 + Simple_repeat_(AC)n chr1 15003035 15003144 - SINE/MIR_MIR chr1 15003228 15003276 + SINE/MIR_MIRb chr1 15003372 15003521 + LINE/L1_L1MA10 chr1 15004175 15004305 + DNA/TcMar-Tc2_MER104 chr1 15004394 15004597 + SINE/MIR_MIRb chr1 15004749 15004889 + SINE/MIR_MIR chr1 15005504 15005804 + SINE/Alu_AluSz chr1 15006162 15006206 - SINE/MIR_MIRc chr1 15006558 15006774 - LINE/L2_L2a chr1 15006775 15007083 + SINE/Alu_AluY chr1 15007140 15007191 - LINE/L2_L2b chr1 15008298 15008485 + SINE/MIR_MIRb chr1 15008518 15008599 - SINE/MIR_MIRc chr1 15009719 15010386 - LINE/L1_L1MC5 chr1 15010387 15010689 - SINE/Alu_AluY chr1 15010690 15010927 - LINE/L1_L1MC5 chr1 15011133 15011254 - SINE/MIR_MIRb chr1 15012089 15012265 - SINE/MIR_MIRc chr1 15012283 15012534 - LINE/CR1_L3b chr1 15012640 15012935 + SINE/Alu_AluSz6 chr1 15013200 15013471 - LINE/L2_L2a chr1 15013472 15013774 + SINE/Alu_AluSx1 chr1 15013775 15013923 - LINE/L2_L2a chr1 15014209 15014379 + SINE/MIR_MIRc chr1 15014914 15015082 + SINE/MIR_MIR chr1 15015133 15015450 - SINE/Alu_AluSg chr1 15015784 15015916 + LINE/L2_L2b chr1 15015958 15016289 + LTR/ERVL-MaLR_MLT1C2 chr1 15016290 15016585 - SINE/Alu_AluSq2 chr1 15016586 15016655 + LTR/ERVL-MaLR_MLT1C2 chr1 15016663 15017015 + LINE/L2_L2b chr1 15017016 15017308 + SINE/Alu_AluSx chr1 15017309 15017378 + LINE/L2_L2b chr1 15017522 15017815 + SINE/Alu_AluSq2 chr1 15017902 15017951 + LINE/CR1_L3 chr1 15018605 15018824 - LTR/ERVL_LTR79 chr1 15018909 15019057 - SINE/MIR_MIRc chr1 15019281 15019376 + SINE/MIR_MIR chr1 15019377 15019688 - SINE/Alu_AluSz6 chr1 15019689 15019821 + SINE/MIR_MIR chr1 15020264 15020784 - LINE/L1_L1ME3A chr1 15020785 15020954 - SINE/MIR_MIR chr1 15021091 15021205 - SINE/MIR_MIRc chr1 15021820 15022132 - LTR/ERVL-MaLR_THE1B chr1 15022417 15022534 - SINE/MIR_MIR chr1 15023286 15024542 - LINE/L2_L2a chr1 15025051 15025203 - LINE/L2_L2a chr1 15025689 15025807 + SINE/MIR_MIRc chr1 15025841 15025926 + SINE/Alu_AluSz chr1 15026300 15026411 - SINE/MIR_MIRb chr1 15026433 15026515 - SINE/MIR_MIR3 chr1 15026657 15026988 + DNA/TcMar-Tigger_MER2 chr1 15026995 15027036 - SINE/MIR_MIRc chr1 15027071 15027379 - SINE/Alu_AluY chr1 15027434 15027538 + SINE/MIR_MIR3 chr1 15027823 15027848 + Simple_repeat_(TGTT)n chr1 15029077 15029188 - LTR/ERVL-MaLR_MLT1J chr1 15029240 15029354 + LINE/L2_L2c chr1 15030026 15030160 + SINE/MIR_MIR3 chr1 15030256 15030560 - SINE/Alu_AluSz chr1 15031554 15031600 + Simple_repeat_(TGTGT)n chr1 15031602 15031907 - SINE/Alu_AluJr chr1 15031922 15032180 - LINE/L1_L1MB8 chr1 15032181 15032490 - SINE/Alu_AluY chr1 15032491 15032598 - LINE/L1_L1MB8 chr1 15032599 15032666 + DNA/TcMar-Tigger_MER2 chr1 15032667 15032972 + SINE/Alu_AluSx1 chr1 15032973 15033246 + DNA/TcMar-Tigger_MER2 chr1 15033250 15033736 - LINE/L1_L1MB8 chr1 15033737 15034038 - SINE/Alu_AluSz chr1 15034039 15034272 - LINE/L1_L1MB8 chr1 15034511 15034686 + SINE/MIR_MIRb chr1 15035076 15035299 + SINE/MIR_MIRb chr1 15035312 15035474 - LINE/L2_L2b chr1 15035566 15035813 + SINE/Alu_AluSz6 chr1 15036205 15036425 + Simple_repeat_(CCCTG)n chr1 15036785 15037028 - SINE/MIR_MIR chr1 15037380 15037525 + SINE/MIR_MIRb chr1 15037581 15037611 + Simple_repeat_(TATCAT)n chr1 15037971 15038280 + SINE/Alu_AluJb chr1 15038420 15038455 - LINE/L2_L2d2 chr1 15038685 15038723 - LINE/L2_L2d2 chr1 15038797 15038844 - LINE/L2_L2a chr1 15038908 15038954 - LINE/L2_L2d2 chr1 15039018 15039064 - LINE/L2_L2d2 chr1 15039125 15039343 - SINE/MIR_MIR chr1 15039583 15039699 - LINE/L1_L1MA5A chr1 15040026 15040186 - SINE/MIR_MIR3 chr1 15040490 15040797 + SINE/Alu_AluSx chr1 15040939 15041260 - SINE/Alu_AluSz chr1 15041325 15041636 - SINE/Alu_AluSp chr1 15041846 15041992 + SINE/MIR_MIR chr1 15042301 15042339 + LINE/L2_L2a chr1 15042392 15042547 - LINE/L2_L2a chr1 15042504 15042693 - SINE/MIR_MIRb chr1 15043303 15043456 - SINE/MIR_MIRb chr1 15043540 15043617 - SINE/MIR_MIR chr1 15043634 15043885 - SINE/Alu_AluY chr1 15044467 15044756 + SINE/Alu_AluJb chr1 15044770 15044940 + LINE/L1_L1ME4a chr1 15045017 15045205 + SINE/MIR_MIRc chr1 15045305 15045409 + SINE/MIR_MIRb chr1 15045419 15045596 - DNA/TcMar-Tigger_Tigger2a chr1 15045597 15045962 - LTR/ERVL-MaLR_THE1D chr1 15045963 15046015 - DNA/TcMar-Tigger_Tigger2a chr1 15046016 15046329 + SINE/Alu_AluSx1 chr1 15046330 15046533 - DNA/TcMar-Tigger_Tigger2a chr1 15046534 15046601 + SINE/MIR_MIRb chr1 15047489 15047783 + SINE/Alu_AluSz chr1 15048413 15048445 + Simple_repeat_(TG)n chr1 15048918 15049026 - LINE/L1_L1MB7 chr1 15049367 15049564 - DNA/hAT-Charlie_MER20 chr1 15049615 15049741 + SINE/MIR_MIRb chr1 15049742 15050016 + SINE/Alu_AluSx1 chr1 15050017 15050126 + Simple_repeat_(AGAGT)n chr1 15050138 15050222 + Simple_repeat_(AATAG)n chr1 15050359 15050459 + SINE/MIR_MIRc chr1 15050830 15050869 + Simple_repeat_(TTGGTCC)n chr1 15051535 15051775 - SINE/MIR_MIR chr1 15051792 15051925 - SINE/MIR_MIRb chr1 15052130 15052165 + Simple_repeat_(TG)n chr1 15052166 15052530 - LTR/ERVL-MaLR_THE1B chr1 15052531 15052646 + Simple_repeat_(TG)n chr1 15053469 15053563 - LINE/L2_L2b chr1 15053600 15053784 + SINE/MIR_MIRc chr1 15053921 15054112 - SINE/MIR_MIR3 chr1 15054400 15054681 + SINE/Alu_AluSx1 chr1 15055080 15055192 - LINE/RTE-X_L4_C_Mam chr1 15055195 15055488 + SINE/Alu_AluSz chr1 15055599 15055738 + SINE/MIR_MIRb chr1 15055782 15055927 + SINE/MIR_MIR chr1 15056409 15056709 - LTR/ERVL-MaLR_MLT1K chr1 15057146 15057443 + SINE/MIR_MIRb chr1 15057445 15057481 + Simple_repeat_(TCA)n chr1 15057643 15057747 - SINE/MIR_MIR3 chr1 15057935 15058149 + LINE/L2_L2c chr1 15058211 15058364 - SINE/MIR_MIRb chr1 15058376 15058489 + LINE/L2_L2c chr1 15058584 15058738 - LINE/L2_L2a chr1 15058739 15059044 + SINE/Alu_AluSg chr1 15059045 15059103 - LINE/L2_L2a chr1 15059104 15059236 - SINE/Alu_FLAM_C chr1 15059237 15059389 - LINE/L2_L2a chr1 15059524 15059714 - LINE/L2_L2a chr1 15060025 15060113 + SINE/MIR_MIR3 chr1 15061106 15061296 + LINE/L1_L1M5 chr1 15061500 15061818 - SINE/Alu_AluSq2 chr1 15064309 15064382 - DNA/hAT-Charlie_MER5B chr1 15064457 15064689 - SINE/MIR_MIRc chr1 15065020 15065327 - SINE/Alu_AluSx1 chr1 15066067 15066160 + Simple_repeat_(TTTTGT)n chr1 15066802 15066838 + Simple_repeat_(TC)n chr1 15069077 15069126 + DNA/hAT-Charlie_MER5B chr1 15069138 15069328 - DNA/hAT-Charlie_MER117 chr1 15069520 15069655 + SINE/Alu_AluYc chr1 15069727 15069855 + DNA/hAT-Tip100_MER91C chr1 15069919 15070129 - SINE/MIR_MIR chr1 15070686 15070839 + SINE/Alu_AluJb chr1 15070913 15071028 - DNA/hAT-Charlie_Charlie20a chr1 15071240 15071554 - SINE/Alu_AluSx chr1 15071556 15072072 - DNA/hAT-Charlie_Charlie20a chr1 15072073 15072220 + SINE/MIR_MIR chr1 15072567 15072803 - SINE/MIR_MIR chr1 15072982 15073149 + DNA/hAT-Charlie_Charlie15a chr1 15073216 15073350 - DNA/hAT-Charlie_MER5A chr1 15073643 15073753 - SINE/MIR_MIRb chr1 15074038 15074128 + SINE/MIR_MIR3 chr1 15074707 15074907 + SINE/MIR_MIR chr1 15074963 15075091 - SINE/MIR_MIRc chr1 15075102 15075250 - SINE/MIR_MIR3 chr1 15075284 15075452 - DNA/hAT-Charlie_MER103C chr1 15076367 15076520 + SINE/MIR_MIRb chr1 15076607 15076688 + SINE/MIR_MIRc chr1 15076945 15077163 - DNA/hAT-Charlie_MER102a chr1 15077879 15077960 - SINE/MIR_MIRb chr1 15077938 15078127 - LINE/L2_L2a chr1 15078071 15078133 - SINE/MIR_MIRc chr1 15078134 15078423 + SINE/Alu_AluSz chr1 15078424 15078479 - SINE/MIR_MIRc chr1 15078609 15078710 + LINE/CR1_X7A_LINE chr1 15078801 15078987 + DNA/hAT-Charlie_Charlie26a chr1 15079762 15079887 + LINE/CR1_L3b chr1 15080173 15080384 + DNA/hAT-Tip100_MamTip1 chr1 15080888 15081000 + DNA/hAT-Charlie_MER20 chr1 15081130 15081163 + Simple_repeat_(CTCC)n chr1 15081203 15081385 + DNA/hAT-Blackjack_MER63A chr1 15081388 15081561 - LINE/L2_L2c chr1 15081562 15081591 + Simple_repeat_(GT)n chr1 15081592 15082078 - LINE/L2_L2c chr1 15082684 15082935 - SINE/Alu_AluSz chr1 15082936 15082996 - DNA/hAT-Charlie_MER5A chr1 15083676 15083713 + Simple_repeat_(CTCT)n chr1 15083904 15084006 + SINE/MIR_MIR1_Amn chr1 15084366 15084617 + LINE/L1_L1MEd chr1 15084738 15085205 + LINE/L1_L1MEd chr1 15085210 15085326 - DNA/hAT-Charlie_MER3 chr1 15085327 15085621 - SINE/Alu_AluSz chr1 15085622 15085738 - DNA/hAT-Charlie_MER3 chr1 15085739 15085981 + LINE/L1_L1MEd chr1 15085982 15086269 - SINE/Alu_AluSx1 chr1 15086270 15086861 + LINE/L1_L1MEd chr1 15086862 15087042 - DNA/hAT-Charlie_MER5A chr1 15087043 15087135 + LINE/L1_L1MEd chr1 15087157 15087392 - DNA/hAT-Charlie_MER33 chr1 15087393 15087686 - SINE/Alu_AluSx chr1 15087687 15087771 - DNA/hAT-Charlie_MER33 chr1 15087822 15088074 - SINE/MIR_MIRb chr1 15088119 15088289 - DNA/hAT-Charlie_MER58A chr1 15089579 15090059 - LTR/ERV1_MER65D chr1 15090071 15090161 - SINE/MIR_MIR chr1 15090785 15090879 - SINE/MIR_MIRc chr1 15091093 15091147 + Simple_repeat_(CACACT)n chr1 15091248 15091295 + Simple_repeat_(TATACA)n chr1 15091336 15091643 - SINE/Alu_AluSc8 chr1 15091806 15091926 - LINE/L1_L1ME4a chr1 15092048 15092374 - SINE/Alu_AluSp chr1 15092416 15092445 + Simple_repeat_(TG)n chr1 15092452 15092490 + Simple_repeat_(TTTGT)n chr1 15092491 15092774 - SINE/Alu_AluSz chr1 15092997 15093186 + LINE/L1_L1ME4b chr1 15093331 15093925 - LTR/ERV1_PRIMA4_LTR chr1 15095505 15095542 + Simple_repeat_(CCTG)n chr1 15096537 15096923 - LTR/ERVL-MaLR_MLT1A chr1 15097085 15097137 + SINE/MIR_MIR3 chr1 15097140 15097233 - DNA/hAT-Charlie_Charlie4z chr1 15097358 15097434 + SINE/MIR_MIRb chr1 15097456 15097536 - SINE/MIR_MIRb chr1 15097537 15097827 + SINE/Alu_AluSq2 chr1 15097828 15097931 - SINE/MIR_MIRb chr1 15097963 15098137 - SINE/MIR_MIRb chr1 15098212 15098552 - DNA/TcMar-Tigger_MamRep434 chr1 15098833 15099134 - LINE/L2_L2c chr1 15099236 15099443 - LINE/L2_L2a chr1 15099631 15099863 - LINE/L2_L2a chr1 15100071 15100106 + Simple_repeat_(GAGCTCA)n chr1 15100618 15100686 - LINE/L2_L2c chr1 15100686 15100776 + SINE/MIR_MIRb chr1 15101098 15101385 + Simple_repeat_(TTCTCT)n chr1 15101847 15101986 + Simple_repeat_(CATC)n chr1 15102256 15102518 - LINE/L2_L2c chr1 15102729 15102820 + SINE/MIR_MIRb chr1 15102979 15103266 + SINE/Alu_AluSx3 chr1 15104311 15104427 - SINE/MIR_MIRc chr1 15104771 15104924 - SINE/MIR_MIRb chr1 15104925 15105607 - LINE/L1_L1M4 chr1 15105673 15105737 - SINE/MIR_MIR3 chr1 15105810 15105943 + DNA/hAT-Charlie_MER5A chr1 15105998 15106305 + SINE/Alu_AluJo chr1 15106672 15106726 - LTR/ERVL?_LTR89B chr1 15107044 15107108 - SINE/MIR_MIR3 chr1 15107449 15107684 + SINE/MIR_MIR chr1 15107756 15107947 + SINE/MIR_MIRb chr1 15108364 15108570 + SINE/MIR_MIRb chr1 15108788 15108865 - DNA/hAT-Tip100_MamTip2b chr1 15109103 15109383 + SINE/Alu_AluJb chr1 15109402 15109517 - SINE/MIR_MIRb chr1 15109574 15110565 + Simple_repeat_(TG)n chr1 15110566 15110630 - SINE/MIR_MIR chr1 15110728 15110913 + SINE/MIR_MIR3 chr1 15111260 15111297 + Simple_repeat_(TGT)n chr1 15111314 15111577 - SINE/Alu_AluSz6 chr1 15111676 15111896 - DNA/hAT-Charlie_MER58A chr1 15111897 15112022 - DNA/hAT-Tip100_MamTip2b chr1 15112077 15112298 + SINE/MIR_MIR chr1 15112330 15112374 + Simple_repeat_(TG)n chr1 15112763 15112949 + DNA/hAT-Charlie_MER5A chr1 15113212 15113472 + DNA/hAT-Charlie_MER106B chr1 15113668 15113870 - SINE/Alu_AluSx1 chr1 15113876 15114060 - SINE/MIR_MIRb chr1 15114290 15114387 - SINE/MIR_MIR chr1 15114410 15114467 + Simple_repeat_(CTT)n chr1 15115681 15115878 + SINE/MIR_MIRc chr1 15118019 15118263 - SINE/MIR_MIRc chr1 15118577 15118712 - DNA/hAT-Charlie_MER5A1 chr1 15119147 15119248 - LINE/L2_L2a chr1 15119254 15119369 - LINE/L2_L2b chr1 15119370 15119667 - SINE/Alu_AluJr chr1 15119668 15119898 - LINE/L2_L2b chr1 15120528 15120772 - SINE/MIR_MIRb chr1 15121133 15121343 - DNA/hAT-Charlie_MER3 chr1 15121500 15121608 + LTR/ERVL-MaLR_MSTB chr1 15121609 15121708 - SINE/Alu_AluYc chr1 15121709 15122082 + LTR/ERVL-MaLR_MSTB chr1 15122085 15122435 - LTR/ERVL-MaLR_MLT1K chr1 15122821 15122870 - DNA/hAT-Charlie_MER20 chr1 15122873 15123005 - SINE/Alu_AluJo chr1 15123006 15123160 - DNA/hAT-Charlie_MER20 chr1 15123162 15123357 - SINE/MIR_MIR chr1 15123682 15123973 - SINE/Alu_AluSx chr1 15124157 15124287 + SINE/MIR_MIR chr1 15125123 15125490 - LTR/ERVL-MaLR_MLT1B chr1 15125776 15126306 + LTR/ERV1_LTR36 chr1 15126681 15126783 - LINE/L2_L2b chr1 15127556 15127856 - SINE/Alu_AluSx chr1 15128088 15128257 - SINE/MIR_MIRb chr1 15128255 15128374 - LINE/CR1_L3 chr1 15128375 15128488 + DNA/hAT-Charlie_MER5B chr1 15128505 15128602 - DNA/hAT-Charlie_MER5A chr1 15129457 15129978 + LTR/ERVL-MaLR_MLT1D chr1 15130071 15130444 - LTR/ERV1_MER57E2 chr1 15130471 15130556 + SINE/Alu_AluJr chr1 15130638 15130826 + LTR/ERVL-MaLR_MLT1F2 chr1 15130827 15130853 + Simple_repeat_(TG)n chr1 15130854 15131193 + LTR/ERVL-MaLR_MLT1F2 chr1 15131363 15131523 - SINE/MIR_MIR chr1 15131529 15131545 + DNA/hAT-Charlie_Charlie16 chr1 15131546 15131679 - SINE/Alu_FLAM_A chr1 15131680 15131773 + DNA/hAT-Charlie_Charlie16 chr1 15132140 15132432 - SINE/Alu_AluSx chr1 15132535 15132583 + Simple_repeat_(AC)n chr1 15132585 15132702 - SINE/Alu_AluJr chr1 15133114 15133246 + DNA/hAT-Charlie_Charlie17a chr1 15133266 15133344 - DNA/hAT-Charlie_MER5A chr1 15133373 15133743 + LTR/ERVL-MaLR_THE1C chr1 15133754 15133902 + DNA/hAT-Charlie_MER5B chr1 15134042 15134084 + Simple_repeat_(TAT)n chr1 15134173 15134428 + LTR/ERVL-MaLR_MLT1F2 chr1 15134436 15134625 - LINE/L2_L2 chr1 15134626 15134925 - SINE/Alu_AluSx1 chr1 15134976 15135188 + LINE/L1_L1PA16 chr1 15135338 15136296 - LTR/ERVK_LTR5_Hs chr1 15136304 15136437 + SINE/MIR_MIR3 chr1 15136555 15136857 - SINE/Alu_AluSx1 chr1 15137067 15137530 + LINE/L1_L1ME3 chr1 15137531 15137828 + SINE/Alu_AluSq2 chr1 15137829 15137894 + LINE/L1_L1ME3 chr1 15138521 15138572 + Simple_repeat_(TCTA)n chr1 15138573 15138927 - SINE/Alu_AluSx1 chr1 15138999 15139152 - SINE/MIR_MIR chr1 15139320 15139336 + Low_complexity_A-rich chr1 15139337 15139646 + SINE/Alu_AluSx chr1 15139647 15139692 + Simple_repeat_(AAAACC)n chr1 15140620 15140717 + SINE/MIR_MIRc chr1 15141373 15141668 + SINE/Alu_AluSg chr1 15141669 15141944 - DNA/hAT-Charlie_Charlie8 chr1 15142079 15142245 + LTR/Gypsy_MamGypLTR1d chr1 15142344 15142584 - LINE/L1_L1MD2 chr1 15142588 15142672 - LINE/L1_L1MD2 chr1 15142673 15142980 + SINE/Alu_AluSz6 chr1 15142981 15142996 - LINE/L1_L1MD2 chr1 15142997 15143805 + LINE/L1_L1MA3 chr1 15143806 15144042 - LINE/L1_L1MD2 chr1 15144043 15144348 - SINE/Alu_AluSz6 chr1 15144349 15144461 - LINE/L1_L1MD2 chr1 15144642 15144877 - SINE/MIR_MIRc chr1 15144985 15145274 - SINE/Alu_AluJb chr1 15145285 15145491 + DNA/hAT-Charlie_MER20 chr1 15145921 15146114 - LTR/Gypsy_MamGypLTR1c chr1 15146517 15146683 - LTR/Gypsy_MamGypLTR2c chr1 15146960 15147216 + SINE/MIR_MIRb chr1 15147497 15147717 + SINE/Alu_AluJr chr1 15147870 15148061 + LTR/ERVL-MaLR_MLT1L chr1 15148810 15149109 + SINE/Alu_AluJr chr1 15149314 15149369 - LINE/L1_L1ME3G chr1 15149370 15149426 - LINE/L1_L1ME3Cz chr1 15149427 15149724 + SINE/Alu_AluSz chr1 15149725 15150003 - LINE/L1_L1ME3G chr1 15150244 15150422 - SINE/MIR_MIRb chr1 15150566 15151080 - LTR/ERVL-MaLR_MLT1D chr1 15151093 15151283 + SINE/MIR_MIR chr1 15151879 15151926 - LINE/L2_L2a chr1 15151960 15152225 + SINE/MIR_MIRb chr1 15153699 15153767 + Simple_repeat_(CCGCCTC)n chr1 15154407 15154428 + Simple_repeat_(GT)n chr1 15155362 15155430 + SINE/MIR_MIRc chr1 15155771 15155862 - LINE/L2_L2a chr1 15155876 15155932 - LINE/L2_L2d chr1 15156675 15156852 - SINE/MIR_MIR3 chr1 15157361 15157509 - SINE/MIR_MIR3 chr1 15157975 15158092 - SINE/Alu_AluJo chr1 15158810 15158995 + DNA/hAT-Charlie_MER112 chr1 15159408 15159547 - DNA/hAT-Tip100_MamRep4096 chr1 15159548 15159835 - SINE/Alu_AluSc chr1 15159836 15159910 - DNA/hAT-Tip100_MamRep4096 chr1 15160266 15160572 - SINE/Alu_AluSz chr1 15160789 15161115 - LINE/L2_L2b chr1 15161225 15161515 + SINE/Alu_AluSx1 chr1 15161522 15161584 + LINE/L1_L1MB3 chr1 15161585 15161656 - LTR/ERVL-MaLR_MSTB1 chr1 15161657 15161942 + SINE/Alu_AluSc chr1 15161943 15162166 - LTR/ERVL-MaLR_MSTB1 chr1 15162167 15162461 - SINE/Alu_AluSz6 chr1 15162462 15162516 - LTR/ERVL-MaLR_MSTB1 chr1 15162517 15163042 + LINE/L1_L1MB3 chr1 15163086 15163270 - LINE/L2_L2a chr1 15163498 15163532 + Simple_repeat_(CTTT)n chr1 15163710 15163848 - LINE/L1_L1MD3 chr1 15163849 15164129 - SINE/Alu_AluSx chr1 15164130 15164598 - LINE/L1_L1MD3 chr1 15164792 15165100 - SINE/Alu_AluSc chr1 15165170 15165323 + SINE/Alu_FRAM chr1 15165330 15165828 + LINE/L1_L1M5 chr1 15165861 15165944 + SINE/Alu_AluSx3 chr1 15166439 15166739 + SINE/Alu_AluSx1 chr1 15166754 15167045 - LINE/L1_L1ME1 chr1 15167046 15167356 + SINE/Alu_AluY chr1 15167357 15167614 - LINE/L1_L1ME1 chr1 15167910 15168088 - LINE/L1_L1ME4b chr1 15168238 15168349 + DNA/hAT-Charlie_Charlie4z chr1 15168350 15168388 + Low_complexity_GA-rich chr1 15168726 15168873 + LINE/CR1_X7C_LINE chr1 15169049 15169114 - LTR/ERVL-MaLR_MLT1J1 chr1 15169126 15169211 - SINE/MIR_MIR chr1 15170347 15170371 + Simple_repeat_(T)n chr1 15170381 15170612 - SINE/Alu_AluSz6 chr1 15170614 15170724 - LINE/L1_L1MB4 chr1 15170735 15170987 - SINE/Alu_AluY chr1 15171017 15171236 - DNA/hAT-Charlie_MER5A chr1 15171284 15171781 - LTR/ERVL_LTR33C chr1 15171894 15172063 + DNA/hAT-Charlie_MER5A chr1 15172097 15172408 + SINE/Alu_AluJb chr1 15172409 15172520 + Low_complexity_GA-rich chr1 15172533 15172637 + SINE/MIR_MIR chr1 15172648 15172912 + DNA/TcMar-Tigger_Tigger7 chr1 15172928 15173000 + DNA/TcMar-Tigger_Tigger7 chr1 15173212 15173520 - SINE/Alu_AluSz chr1 15173677 15173700 + Simple_repeat_(A)n chr1 15174245 15174542 - SINE/Alu_AluJr chr1 15174642 15174987 - LTR/ERVL-MaLR_MLT1A chr1 15175118 15175429 + SINE/Alu_AluSq chr1 15175489 15175532 + LINE/L2_L2b chr1 15175533 15175892 - LTR/ERVL-MaLR_THE1B chr1 15175893 15176195 + LINE/L2_L2b chr1 15176193 15176274 - LINE/L2_L2d2 chr1 15176629 15176711 + LINE/L2_L2d2 chr1 15176998 15177306 - DNA/hAT-Charlie_Charlie8 chr1 15177689 15177724 + Simple_repeat_(TTGA)n chr1 15178385 15178518 - DNA/hAT-Charlie_MER5A chr1 15178589 15178717 - SINE/MIR_MIR chr1 15179173 15179501 + LINE/L1_L1ME2 chr1 15179502 15179800 + SINE/Alu_AluJb chr1 15179801 15180018 + LINE/L1_L1ME2 chr1 15180027 15180149 - DNA/hAT-Charlie_MER5A chr1 15180618 15180734 + SINE/MIR_MIRc chr1 15180773 15180955 - SINE/MIR_MIRb chr1 15181093 15181276 + SINE/MIR_MIR chr1 15181880 15182161 + SINE/Alu_AluSp chr1 15182162 15182207 + Simple_repeat_(AAAT)n chr1 15182257 15182418 + SINE/MIR_MIR chr1 15182474 15182618 + SINE/MIR_MIRb chr1 15182766 15183062 - SINE/Alu_AluSx1 chr1 15183218 15183330 + DNA/TcMar-Tigger_X6b_DNA chr1 15183336 15183610 + SINE/MIR_MIR chr1 15183608 15183663 - LINE/L2_L2c chr1 15183664 15183888 - LINE/L2_L2a chr1 15183889 15183909 - LINE/L2_L2c chr1 15183958 15184001 - DNA/hAT-Charlie_MER5A chr1 15184002 15184045 + Simple_repeat_(TTCC)n chr1 15184049 15184324 - SINE/Alu_AluSc chr1 15184326 15184386 - DNA/hAT-Charlie_MER5A1 chr1 15184387 15184719 - LINE/L2_L2c chr1 15184791 15184998 + DNA/hAT-Charlie_MER20 chr1 15185379 15185472 - SINE/MIR_MIR3 chr1 15186540 15186656 + SINE/MIR_MIRb chr1 15186650 15186887 - LINE/L2_L2 chr1 15187177 15187230 + Simple_repeat_(CCCCCAT)n chr1 15188075 15188408 - LTR/ERVL-MaLR_MLT1K chr1 15188484 15188513 + Simple_repeat_(CCCAG)n chr1 15189051 15189355 - SINE/Alu_AluSx1 chr1 15190146 15190296 + SINE/MIR_MIR chr1 15190569 15190775 + LTR/ERVL-MaLR_MLT1D chr1 15190776 15191086 - SINE/Alu_AluSc8 chr1 15191087 15191381 + LTR/ERVL-MaLR_MLT1D chr1 15191525 15191630 - SINE/MIR_MIRc chr1 15192443 15192750 - SINE/Alu_AluSg7 chr1 15193568 15193856 - SINE/Alu_AluSx chr1 15194018 15194136 - LINE/L2_L2a chr1 15194144 15194351 - DNA/hAT-Charlie_MER33 chr1 15194551 15194838 - LINE/L1_L1ME5 chr1 15194870 15194911 - DNA/hAT-Charlie_MER33 chr1 15194912 15195231 - SINE/Alu_AluJb chr1 15195232 15195471 - DNA/hAT-Charlie_MER33 chr1 15195692 15195802 - SINE/MIR_MIR chr1 15196021 15196157 - LINE/L1_L1ME5 chr1 15196219 15196379 + DNA/hAT-Blackjack_MER63A chr1 15196380 15196420 + Simple_repeat_(GT)n chr1 15196430 15196456 + Low_complexity_GA-rich chr1 15196537 15196653 - SINE/Alu_AluJr chr1 15196902 15197374 - LTR/ERVL-MaLR_MLT1K chr1 15197677 15197739 - SINE/MIR_MIRc chr1 15197935 15198407 - LTR/ERVL-MaLR_MLT1J chr1 15198426 15198717 - SINE/Alu_AluSz chr1 15199136 15199434 - SINE/Alu_AluSq2 chr1 15200023 15200188 + LTR/ERVL-MaLR_MLT1B chr1 15200189 15200432 + SINE/Alu_AluJb chr1 15200433 15200663 + LTR/ERVL-MaLR_MLT1B chr1 15200718 15200838 + Unknown_UCON80_AMi chr1 15201090 15201252 + SINE/MIR_MIR chr1 15202359 15202827 - LTR/ERVL-MaLR_MLT1D chr1 15202856 15202957 + SINE/MIR_MIR3 chr1 15203082 15203252 + LINE/L2_L2a chr1 15203253 15203560 - SINE/Alu_AluSg chr1 15203561 15203756 + LINE/L2_L2a chr1 15203795 15203957 - SINE/MIR_MIRb chr1 15204272 15204546 + SINE/Alu_AluSx chr1 15204642 15204809 + SINE/MIR_MIR chr1 15205574 15205784 + SINE/MIR_MIRb chr1 15205854 15205900 + LINE/L2_L2a chr1 15205963 15206257 + SINE/Alu_AluJo chr1 15206260 15206291 + Simple_repeat_(AAGGG)n chr1 15206341 15206379 + Simple_repeat_(GAGG)n chr1 15206483 15206659 + SINE/MIR_MIRb chr1 15207999 15208317 - LINE/L2_L2a chr1 15208341 15208582 + SINE/Alu_AluJr chr1 15208645 15208896 - LINE/L2_L2a chr1 15208898 15209003 - LINE/L1_L1ME3E chr1 15209066 15209235 - SINE/Alu_AluJb chr1 15209236 15209277 + Simple_repeat_(ATT)n chr1 15209278 15209396 - SINE/Alu_AluJb chr1 15209414 15209665 - LINE/L1_L1ME3E chr1 15209672 15209750 - LINE/L1_L1ME3E chr1 15209751 15210024 + SINE/Alu_AluSx chr1 15210025 15210254 - LINE/L1_L1ME3E chr1 15211192 15211418 - DNA/hAT-Charlie_MER106A chr1 15211458 15211702 - SINE/MIR_MIR chr1 15212029 15212318 - LINE/L1_L1M5 chr1 15213016 15213249 + LINE/L2_L2a chr1 15213849 15214136 - SINE/Alu_AluSc8 chr1 15214137 15214182 - LINE/L2_L2b chr1 15215693 15215744 + LINE/L1_L1PB2 chr1 15215745 15216057 + SINE/Alu_AluSz chr1 15216058 15216109 + LINE/L1_L1PB2 chr1 15216302 15216769 + LINE/L1_L1MC chr1 15216898 15217030 + LINE/L1_L1ME4a chr1 15217050 15217242 + LINE/L1_L1M1 chr1 15217246 15217397 + LTR/ERV1_MER66C chr1 15217408 15217435 - LINE/L1_L1ME4a chr1 15217436 15217917 - LTR/ERVL_MLT2B1 chr1 15217918 15217986 - LINE/L1_L1ME4a chr1 15218407 15218554 + SINE/MIR_MIR chr1 15218556 15219080 + LTR/ERV1_MER66C chr1 15219092 15219200 + SINE/MIR_MIR chr1 15219328 15219354 + Simple_repeat_(AGG)n chr1 15220377 15220408 + Simple_repeat_(A)n chr1 15220541 15220780 - SINE/MIR_MIRb chr1 15221724 15222182 + LTR/ERVL-MaLR_MLT1D chr1 15222614 15222864 + Low_complexity_GA-rich chr1 15223042 15223165 + LTR/ERVL-MaLR_MLT1O chr1 15223466 15223774 - SINE/Alu_AluSp chr1 15224011 15224113 - LINE/L2_L2a chr1 15224202 15224466 + LTR/ERVL-MaLR_MLT1O chr1 15224786 15224892 + SINE/MIR_MIRb chr1 15224898 15225179 + SINE/Alu_AluSz chr1 15225181 15225240 + Low_complexity_GA-rich chr1 15225508 15225550 - LINE/RTE-BovB_MamRTE1 chr1 15225835 15226128 - LINE/L2_L2a chr1 15226129 15226263 + DNA/hAT-Charlie_MER103C chr1 15226274 15226368 - SINE/MIR_MIR3 chr1 15227260 15227448 + DNA/hAT-Charlie_MER20 chr1 15228006 15228314 - SINE/Alu_AluSz6 chr1 15228711 15229015 + SINE/Alu_AluY chr1 15229284 15229577 + SINE/Alu_AluSz chr1 15229966 15230199 - DNA/hAT-Charlie_MER20 chr1 15230555 15230804 + LINE/L1_L1MC5a chr1 15230805 15231105 + SINE/Alu_AluJb chr1 15231106 15231267 + LINE/L1_L1MC5a chr1 15231268 15231567 + SINE/Alu_AluSz chr1 15231576 15232003 + LINE/L1_L1MB2 chr1 15232014 15232321 + SINE/Alu_AluSz chr1 15232322 15233094 + LINE/L1_L1MC5a chr1 15233226 15233361 - LINE/L1_L1PA16 chr1 15233362 15233672 - SINE/Alu_AluSc8 chr1 15233673 15234083 - LINE/L1_L1PA16 chr1 15234455 15235613 + LINE/L1_L1PA16 chr1 15235614 15235909 - SINE/Alu_AluSx1 chr1 15235958 15236038 - LINE/L2_L2b chr1 15236107 15236237 - DNA/hAT-Charlie_MER102a chr1 15236273 15236809 + LTR/ERVL-MaLR_MLT1K chr1 15236810 15237181 + LTR/ERVL-MaLR_THE1C chr1 15237182 15237207 + LTR/ERVL-MaLR_MLT1K chr1 15237222 15237301 - DNA/hAT-Charlie_MER102a chr1 15237323 15237536 + LINE/L2_L2d chr1 15237969 15238269 + SINE/Alu_AluSg chr1 15238741 15238870 - DNA/hAT-Charlie_Charlie4z chr1 15238925 15239206 - SINE/Alu_AluJo chr1 15239216 15239304 - LINE/L2_L2a chr1 15239305 15239616 + SINE/Alu_AluSz chr1 15239617 15239644 - LINE/L2_L2a chr1 15239629 15239710 + DNA/hAT-Charlie_MER5A chr1 15239798 15239817 + LINE/L1_L1ME3A chr1 15239818 15239978 + LINE/L1_L1M5 chr1 15239981 15240347 + LTR/ERVL-MaLR_MLT1F2 chr1 15240348 15240660 + SINE/Alu_AluJr chr1 15240661 15240676 + LTR/ERVL-MaLR_MLT1F2 chr1 15240677 15240984 + SINE/Alu_AluSx4 chr1 15240985 15241200 + LTR/ERVL-MaLR_MLT1F2 chr1 15241201 15241372 + LINE/L1_L1ME3A chr1 15241373 15241676 + SINE/Alu_AluSq2 chr1 15241677 15241952 + LINE/L1_L1ME3A chr1 15241953 15242253 + SINE/Alu_AluSg chr1 15242260 15242457 + SINE/MIR_MIR chr1 15242829 15242967 - SINE/MIR_MIR chr1 15243886 15243974 + SINE/MIR_MIRb chr1 15244667 15244825 - LINE/L2_L2a chr1 15244826 15245247 - LTR/ERVL-MaLR_MSTB1 chr1 15245248 15245285 - LINE/L2_L2a chr1 15245286 15245403 + Simple_repeat_(TTTTTA)n chr1 15245448 15245563 - LINE/L2_L2a chr1 15245778 15245878 + DNA/hAT-Charlie_MER5A chr1 15245962 15246339 - LTR/ERVL-MaLR_MSTA chr1 15246770 15246805 + Simple_repeat_(CCATCC)n chr1 15246992 15247093 + SINE/MIR_MIR chr1 15248169 15248249 - LINE/L2_L2b chr1 15248250 15248579 - LTR/ERVL_LTR40a chr1 15248580 15248887 - SINE/Alu_AluSx chr1 15248888 15249057 - LTR/ERVL_LTR40a chr1 15249913 15250054 + SINE/MIR_MIR chr1 15250308 15250522 + LINE/L1_L1ME2z chr1 15250697 15250840 + SINE/Alu_FRAM chr1 15250940 15251008 - SINE/MIR_MIR chr1 15251009 15251294 + SINE/Alu_AluJo chr1 15251295 15251398 - SINE/MIR_MIR chr1 15252137 15252591 - LTR/ERV1_MER66B chr1 15253000 15253142 - LINE/L1_L1MD2 chr1 15253149 15253185 + Simple_repeat_(TG)n chr1 15253275 15254006 - LINE/L1_L1MD2 chr1 15254261 15254451 + SINE/MIR_MIRb chr1 15255302 15255455 - LINE/L1_L1ME4b chr1 15255536 15255639 - LINE/L2_L2b chr1 15255707 15255987 - LINE/L2_L2b chr1 15256246 15256317 - DNA/hAT-Charlie_MER5A chr1 15256372 15256672 + SINE/Alu_AluSz chr1 15257089 15257374 - LINE/L1_L1ME4b chr1 15257762 15257855 - LINE/L1_L1MB3 chr1 15257856 15258141 - SINE/Alu_AluSz6 chr1 15258142 15258584 - LINE/L1_L1MB3 chr1 15258585 15258879 - SINE/Alu_AluSx chr1 15258880 15259136 - LINE/L1_L1MB3 chr1 15259263 15259455 - SINE/MIR_MIR chr1 15260156 15260378 + SINE/MIR_MIR chr1 15260394 15260964 - LTR/ERVL-MaLR_MLT1E2 chr1 15261116 15261322 - SINE/MIR_MIR chr1 15261869 15262005 + SINE/Alu_FAM chr1 15262014 15262038 + Simple_repeat_(CA)n chr1 15262100 15262251 - SINE/MIR_MIRb chr1 15262256 15262453 - SINE/MIR_MIR chr1 15262454 15262559 - SINE/Alu_AluJo chr1 15262568 15263510 - LINE/L1_L1MB2 chr1 15263511 15263535 + Simple_repeat_(T)n chr1 15263539 15263820 - SINE/Alu_AluY chr1 15263821 15264548 - LINE/L1_L1MB2 chr1 15264549 15264578 + Simple_repeat_(TG)n chr1 15264579 15265178 - LINE/L1_L1MB2 chr1 15265179 15265330 + SINE/MIR_MIRb chr1 15265690 15265989 + SINE/Alu_AluSx chr1 15266103 15266404 - SINE/Alu_AluJb chr1 15266405 15267035 - LINE/L1_L1MC2 chr1 15267038 15267206 + LINE/L1_L1MC chr1 15267216 15267772 - LINE/L1_L1MC2 chr1 15267773 15267962 + Simple_repeat_(AT)n chr1 15267962 15268959 - LINE/L1_L1PA3 chr1 15268996 15269915 - LINE/L1_L1MC2 chr1 15269916 15270210 - SINE/Alu_AluSq2 chr1 15270211 15270291 - LINE/L1_L1MC2 chr1 15270338 15270557 + SINE/MIR_MIRb chr1 15270760 15270872 + SINE/Alu_AluSx4 chr1 15270873 15271163 + SINE/Alu_AluY chr1 15271164 15271322 + SINE/Alu_AluSx4 chr1 15271610 15271840 + LINE/L1_L1ME4a chr1 15271949 15271993 + Low_complexity_A-rich chr1 15272101 15272226 + SINE/MIR_MIRb chr1 15273198 15273320 + LINE/L1_L1ME4b chr1 15273322 15273502 - LINE/L1_L1MA10 chr1 15273514 15273668 - LINE/L1_L1MB7 chr1 15273714 15274215 - LINE/L1_L1MB3 chr1 15274326 15274624 + SINE/Alu_AluSx1 chr1 15274712 15274859 - SINE/MIR_MIRb chr1 15274969 15275261 - SINE/Alu_AluY chr1 15276097 15276265 + SINE/MIR_MIRb chr1 15276519 15276804 + SINE/Alu_AluSz6 chr1 15276827 15276903 + DNA/hAT-Tip100_MER91C chr1 15277284 15277391 + SINE/MIR_MIRb chr1 15277484 15277606 - SINE/MIR_MIR3 chr1 15277856 15278074 - SINE/MIR_MIR chr1 15278075 15278271 + DNA/hAT-Charlie_MER58A chr1 15278272 15278295 - SINE/MIR_MIR chr1 15278476 15278749 - SINE/Alu_AluSx chr1 15279281 15279460 - LTR/ERVL-MaLR_MLT1K chr1 15279440 15279552 + LTR/ERV1_LTR9B chr1 15279555 15279574 + Simple_repeat_(A)n chr1 15279670 15279825 - LTR/ERVL-MaLR_MLT1K chr1 15280928 15281282 - DNA/TcMar-Tigger_Tigger15a chr1 15281478 15281784 + SINE/Alu_AluSz6 chr1 15281820 15282151 - LINE/L1_L1MC4a chr1 15282213 15282252 - LINE/L2_L2b chr1 15282294 15282854 - DNA/hAT-Blackjack_MER63C chr1 15282855 15283342 - LTR/ERV1_MER66B chr1 15283343 15283657 - DNA/hAT-Blackjack_MER63C chr1 15283943 15284153 + SINE/MIR_MIRb chr1 15284199 15284496 + SINE/Alu_AluY chr1 15284905 15284956 + Low_complexity_GA-rich chr1 15285251 15285563 + SINE/Alu_AluSx chr1 15285763 15286215 + LINE/L1_L1MB2 chr1 15286221 15286523 + SINE/Alu_AluJb chr1 15286624 15286678 - LTR/ERVL-MaLR_MLT1K chr1 15287308 15287683 - LTR/ERVL-MaLR_THE1C chr1 15288085 15288180 + SINE/MIR_MIR3 chr1 15288107 15288269 + SINE/MIR_MIRb chr1 15288608 15288719 - LINE/L2_L2a chr1 15288803 15288891 + SINE/MIR_MIRb chr1 15288884 15288978 - LINE/L2_L2c chr1 15288979 15289287 - SINE/Alu_AluJb chr1 15289288 15289350 - LINE/L2_L2c chr1 15289736 15289914 - LINE/L1_L1ME4a chr1 15290020 15290127 - SINE/MIR_MIRb chr1 15290598 15290620 + Simple_repeat_(CAT)n chr1 15290668 15290694 - LINE/L1_L1ME2z chr1 15290695 15291013 - SINE/Alu_AluYa5 chr1 15291014 15291109 - LINE/L1_L1ME2z chr1 15291110 15291381 - SINE/Alu_AluSz chr1 15291382 15291570 - LINE/L1_L1ME2z chr1 15291571 15292017 - LTR/ERVL-MaLR_MLT1F1 chr1 15292031 15292122 + LTR/ERVL-MaLR_MLT1B chr1 15292136 15292321 - SINE/Alu_AluSg4 chr1 15292326 15292585 - SINE/Alu_AluY chr1 15292586 15292812 + LTR/ERVL-MaLR_MLT1B chr1 15292744 15292941 + SINE/Alu_FLAM_C chr1 15292960 15293030 + LTR/ERVL-MaLR_MLT1B chr1 15293031 15293117 - LTR/ERVL-MaLR_MLT1F1 chr1 15293119 15293437 - LINE/L1_L1ME2z chr1 15293438 15293746 + SINE/Alu_AluSg chr1 15293747 15293851 - LINE/L1_L1ME2z chr1 15294064 15294307 - LTR/Gypsy_MamGypLTR4 chr1 15294332 15294403 - DNA/hAT-Charlie_MER20 chr1 15294404 15294692 - SINE/Alu_AluSx1 chr1 15294693 15294842 - DNA/hAT-Charlie_MER20 chr1 15295108 15295271 + SINE/MIR_MIR chr1 15295327 15295606 + SINE/Alu_AluJr chr1 15295635 15295668 + Simple_repeat_(AT)n chr1 15295669 15295733 - DNA/TcMar-Tigger_MER2B chr1 15295766 15295951 - DNA/hAT-Charlie_MER58A chr1 15295964 15296067 - SINE/MIR_MIRb chr1 15296068 15296112 - SINE/MIR_MIRc chr1 15296252 15296552 - SINE/Alu_AluY chr1 15296966 15297182 - SINE/MIR_MIRb chr1 15297578 15297791 - LTR/ERVL-MaLR_MLT1H chr1 15297792 15297831 + Simple_repeat_(TTTG)n chr1 15297881 15297938 - LINE/L1_L1PB1 chr1 15297935 15298488 + LINE/L1_L1PB1 chr1 15298505 15298758 - LTR/ERVL-MaLR_MLT1H chr1 15298833 15298887 - LTR/ERVL-MaLR_MLT1K chr1 15298888 15299229 + SINE/Alu_AluJr chr1 15299230 15299323 - LTR/ERVL-MaLR_MLT1K chr1 15299698 15299774 + LINE/L2_L2b chr1 15300842 15301129 - SINE/Alu_AluSq2 chr1 15301613 15301745 - SINE/MIR_MIRb chr1 15301746 15302034 + SINE/Alu_AluSz chr1 15302035 15302144 - SINE/MIR_MIRb chr1 15302444 15302730 + SINE/Alu_AluYk3 chr1 15302951 15302994 + LINE/L2_L2a chr1 15303024 15303046 + Simple_repeat_(TTCA)n chr1 15303047 15303203 - LINE/L2_L2b chr1 15303569 15303878 + SINE/Alu_AluSx chr1 15304799 15304921 + Unknown_MamRep564 chr1 15305162 15305389 - SINE/MIR_MIR chr1 15305413 15305544 + SINE/MIR_MIRc chr1 15305545 15305895 + LTR/ERVL-MaLR_THE1A chr1 15305896 15307461 + LTR/ERVL-MaLR_THE1A-int chr1 15307462 15307728 + LTR/ERVL-MaLR_THE1A chr1 15307729 15308029 - SINE/Alu_AluY chr1 15308030 15308092 + LTR/ERVL-MaLR_THE1A chr1 15308093 15308212 + SINE/MIR_MIRc chr1 15308763 15308928 - DNA/hAT-Charlie_MER117 chr1 15308929 15308977 + LINE/L2_L2a chr1 15309283 15309307 + Simple_repeat_(ATTC)n chr1 15309310 15309560 - LINE/L2_L2a chr1 15309561 15309791 + DNA/hAT-Charlie_MER30 chr1 15309792 15310075 - LINE/L2_L2a chr1 15310290 15310325 + Simple_repeat_(CCCACTC)n chr1 15310572 15310724 + DNA/hAT-Charlie_MER5A1 chr1 15310725 15310878 - LTR/ERVL-MaLR_MLT1I chr1 15310918 15311577 + LINE/L1_L1MEc chr1 15311775 15312264 - LTR/ERVL-MaLR_MLT1F chr1 15312289 15312378 - SINE/MIR_MIR chr1 15312379 15312670 + SINE/Alu_AluJb chr1 15312671 15312726 - SINE/MIR_MIR chr1 15313788 15314061 + SINE/Alu_AluSq2 chr1 15314062 15314102 + Simple_repeat_(AAT)n chr1 15314599 15314919 + Simple_repeat_(GT)n chr1 15315407 15315480 - LINE/L1_L1M5 chr1 15315481 15315785 - SINE/Alu_AluY chr1 15315786 15315977 - LINE/L1_L1M5 chr1 15316851 15316911 - DNA/hAT-Charlie_MER113 chr1 15316912 15317203 + SINE/Alu_AluSx1 chr1 15317204 15317261 - DNA/hAT-Charlie_MER113 chr1 15318018 15318163 - SINE/MIR_MIRb chr1 15318290 15318300 + DNA/hAT-Charlie_MER3 chr1 15318301 15318360 - DNA/hAT-Charlie_MER3 chr1 15318361 15318667 + SINE/Alu_AluSx1 chr1 15318668 15318788 - DNA/hAT-Charlie_MER3 chr1 15318994 15319129 - SINE/Alu_FLAM_C chr1 15319190 15319480 - DNA_Eutr11 chr1 15319481 15319606 + SINE/Alu_AluJr chr1 15319610 15319989 - DNA_Eutr11 chr1 15319990 15320924 - LINE/L1_L1MEg chr1 15320925 15321229 - SINE/Alu_AluSc chr1 15321230 15322188 - LINE/L1_L1MEg chr1 15322305 15322382 - SINE/MIR_MIRb chr1 15322421 15322526 - SINE/MIR_MIRb chr1 15322539 15322717 - DNA/hAT-Charlie_MER3 chr1 15322832 15323185 - LTR/ERVL-MaLR_THE1A chr1 15324033 15324223 - SINE/MIR_MIR chr1 15325321 15325425 + DNA/hAT-Charlie_Charlie24 chr1 15326519 15326633 - LINE/L2_L2a chr1 15327752 15328063 + SINE/Alu_AluSp chr1 15328242 15328268 + Simple_repeat_(T)n chr1 15329845 15330024 + SINE/MIR_MIR chr1 15329975 15330038 + LINE/L2_L2d2 chr1 15330098 15330295 - LINE/L1_L1ME3G chr1 15330402 15330495 + LINE/L1_L1ME2z chr1 15330766 15331232 - LINE/L2_L2 chr1 15331268 15331341 + LINE/L2_L2a chr1 15331342 15331735 + Simple_repeat_(GGAT)n chr1 15331946 15332036 - LINE/L1_L1ME3G chr1 15332427 15332728 + SINE/Alu_AluJr chr1 15333297 15333409 + Low_complexity_GA-rich chr1 15333413 15333552 - SINE/MIR_MIRb chr1 15333707 15333809 - SINE/MIR_MIRb chr1 15333810 15334125 - SINE/Alu_AluJb chr1 15334126 15334267 - SINE/MIR_MIRb chr1 15334454 15334665 + SINE/MIR_MIR chr1 15335593 15335672 - LINE/L2_L2b chr1 15336132 15336294 + SINE/MIR_MIRb chr1 15337107 15337182 + DNA/hAT-Tip100_MER91A chr1 15337507 15337672 + DNA/hAT-Charlie_MER20 chr1 15337816 15337941 + SINE/MIR_MIRc chr1 15338164 15338623 - LTR/ERVL-MaLR_MLT1D chr1 15338631 15338959 + LINE/L2_L2d2 chr1 15339012 15339320 - SINE/Alu_AluSx chr1 15339838 15340120 - SINE/Alu_AluSx1 chr1 15340280 15340713 - LTR/ERVL-MaLR_MLT1H chr1 15340732 15340902 - DNA/hAT-Charlie_MER58A chr1 15340903 15341205 + SINE/Alu_AluSx1 chr1 15341206 15341274 - DNA/hAT-Charlie_MER58A chr1 15341276 15341395 - SINE/MIR_MIR3 chr1 15341407 15341604 - SINE/MIR_MIR chr1 15342339 15342482 - DNA/hAT-Charlie_MER5A1 chr1 15342526 15342646 + DNA/hAT-Charlie_MER5A chr1 15342667 15342959 - SINE/Alu_AluJr chr1 15342960 15343094 - SINE/Alu_AluJr chr1 15343171 15343311 - DNA/hAT-Charlie_MER5B chr1 15343469 15343866 + DNA/TcMar-Tigger_Tigger16a chr1 15344432 15344618 - DNA/hAT-Charlie_MER58A chr1 15344623 15344738 + SINE/MIR_MIRb chr1 15344749 15344964 - SINE/MIR_MIRb chr1 15345869 15346176 - LINE/CR1_L3 chr1 15346220 15346508 + DNA/hAT-Charlie_MER102c chr1 15346519 15346737 - LINE/CR1_CR1-L3A_Croc chr1 15346749 15346814 + SINE/MIR_MIRb chr1 15347483 15347748 - SINE/Alu_AluSz6 chr1 15348015 15348354 + DNA/TcMar-Tigger_Tigger16a chr1 15348759 15348892 + SINE/MIR_MIRb chr1 15348945 15348986 + Simple_repeat_(TTA)n chr1 15349210 15349373 + SINE/MIR_MIR chr1 15350125 15350271 - LINE/L2_L2a chr1 15350599 15350738 + SINE/MIR_MIR chr1 15351064 15351372 + SINE/Alu_AluSz chr1 15351433 15351611 + SINE/MIR_MIR chr1 15351820 15351877 + Simple_repeat_(GTGG)n chr1 15352020 15352189 - LINE/L1_L1ME4a chr1 15353021 15353244 + SINE/MIR_MIR chr1 15353288 15353346 - SINE/MIR_MIRb chr1 15353423 15353734 + SINE/Alu_AluSx chr1 15354150 15354301 - DNA/hAT-Charlie_MER5A chr1 15354342 15354439 + LINE/CR1_L3 chr1 15354823 15354926 - LINE/RTE-X_L4_A_Mam chr1 15354927 15355227 + SINE/Alu_AluSx chr1 15355228 15355276 - LINE/RTE-X_L4_A_Mam chr1 15355890 15356040 - LINE/L1_L1ME4a chr1 15356166 15356588 - LTR/ERV1_LTR24 chr1 15356590 15356887 + SINE/Alu_AluSx1 chr1 15357059 15357203 - LINE/L1_L1ME4a chr1 15357225 15357265 + SINE/MIR_MIRb chr1 15357344 15357647 + SINE/Alu_AluSc chr1 15357747 15357854 + LINE/CR1_CR1-3_Croc chr1 15357855 15357977 + LINE/CR1_L3 chr1 15358364 15358553 - DNA/hAT-Charlie_MER20 chr1 15359566 15359707 + SINE/Alu_AluSx chr1 15359708 15359963 + SINE/Alu_AluY chr1 15359964 15360138 + SINE/Alu_AluSx chr1 15360175 15360392 - LINE/L2_L2b chr1 15361239 15361450 - DNA/hAT-Charlie_MER20 chr1 15361496 15361596 - LINE/L2_L2a chr1 15361726 15362020 + SINE/Alu_AluSc chr1 15362107 15362169 + LINE/L2_L2c chr1 15362968 15363020 + SINE/MIR_MIRb chr1 15363021 15363430 - LTR/ERVL-MaLR_MSTC chr1 15363431 15363529 + SINE/MIR_MIRb chr1 15364328 15364637 + SINE/Alu_AluSx1 chr1 15365327 15365602 - SINE/Alu_AluJr chr1 15366006 15366309 + SINE/Alu_AluSg7 chr1 15366600 15366702 + SINE/MIR_MIRb chr1 15367070 15367228 + SINE/MIR_MIRb chr1 15367229 15367431 + SINE/Alu_AluSx chr1 15367801 15367955 + LINE/L2_L2c chr1 15367956 15368273 - SINE/Alu_AluSz chr1 15368274 15368393 + LINE/L2_L2c chr1 15368599 15368657 + SINE/MIR_MIR chr1 15368658 15368953 + SINE/Alu_AluSq2 chr1 15368954 15369132 + SINE/MIR_MIR chr1 15369761 15369913 + SINE/MIR_MIR chr1 15369916 15369964 - LINE/L1_L1ME3G chr1 15369965 15370276 - SINE/Alu_AluJb chr1 15370277 15370522 - LINE/L1_L1ME3G chr1 15371093 15371194 + LINE/L2_L2c chr1 15371167 15371257 - LINE/L2_L2b chr1 15371926 15372258 - LINE/L2_L2a chr1 15372405 15372611 + LINE/L2_L2a chr1 15372651 15372707 + Simple_repeat_(ATATCT)n chr1 15372787 15373048 + LINE/L2_L2a chr1 15373076 15373225 + DNA/hAT-Charlie_Charlie4z chr1 15373229 15373521 + SINE/Alu_AluSx3 chr1 15373571 15373839 + SINE/Alu_AluJr chr1 15373856 15373902 + Low_complexity_A-rich chr1 15373929 15374004 - DNA/hAT-Charlie_MER3 chr1 15374708 15374848 - DNA/hAT-Charlie_Charlie18a chr1 15374849 15375155 - SINE/Alu_AluSx4 chr1 15375156 15375237 - DNA/hAT-Charlie_Charlie18a chr1 15375293 15375373 + SINE/MIR_MIR1_Amn chr1 15375406 15375502 + SINE/MIR_MIRc chr1 15376013 15376104 + Simple_repeat_(TTTA)n chr1 15376105 15376389 - SINE/Alu_AluYj4 chr1 15376510 15376738 + SINE/MIR_MIRb chr1 15376754 15377024 + SINE/Alu_AluJr chr1 15377556 15377853 + SINE/Alu_AluSx3 chr1 15377880 15378021 - SINE/MIR_MIRb chr1 15378022 15378312 + SINE/Alu_AluSz6 chr1 15378313 15378405 - SINE/MIR_MIRb chr1 15378726 15378773 + Simple_repeat_(T)n chr1 15379018 15379976 - LTR/ERVK_LTR5_Hs chr1 15380104 15380219 - LINE/L2_L2a chr1 15380220 15380434 - DNA/hAT-Charlie_MER20 chr1 15380435 15380484 - LINE/L2_L2a chr1 15382303 15382562 + SINE/MIR_MIRb chr1 15382531 15382578 + LINE/L2_L2a chr1 15383000 15383031 + Simple_repeat_(CA)n chr1 15383060 15383216 + SINE/MIR_MIRb chr1 15383713 15383921 - SINE/MIR_MIRb chr1 15383963 15384080 + Simple_repeat_(TG)n chr1 15385505 15385792 + SINE/Alu_AluJr chr1 15386013 15386241 + SINE/MIR_MIR chr1 15386645 15386793 + SINE/MIR_MIRb chr1 15386851 15387188 - SINE/Alu_AluSx1 chr1 15387467 15387603 + SINE/Alu_AluJr chr1 15387604 15387902 + SINE/Alu_AluJb chr1 15388242 15388343 + Simple_repeat_(CCTC)n chr1 15388606 15389037 - LINE/RTE-X_L4_A_Mam chr1 15389182 15389468 + SINE/Alu_AluJr chr1 15389749 15389946 + DNA/hAT_MER53 chr1 15390064 15390367 + SINE/Alu_AluSx1 chr1 15390373 15390563 + DNA/hAT-Charlie_MER102a chr1 15391290 15391302 - SINE/Alu_AluY chr1 15391303 15391330 + Simple_repeat_(TTGTT)n chr1 15391331 15391376 - SINE/Alu_AluY chr1 15391386 15391491 - SINE/Alu_AluY chr1 15392251 15392543 + SINE/Alu_AluY chr1 15392853 15392958 - LINE/L2_L2a chr1 15393021 15393068 + DNA/hAT-Charlie_MER106A chr1 15393069 15393362 - SINE/Alu_AluSx4 chr1 15393363 15393415 + DNA/hAT-Charlie_MER106A chr1 15393416 15393455 + Simple_repeat_(AC)n chr1 15393456 15393546 + DNA/hAT-Charlie_MER106A chr1 15393608 15393783 - SINE/Alu_AluSx chr1 15393902 15393961 - LINE/L2_L2a chr1 15394687 15394748 - SINE/MIR_MIRc chr1 15394935 15395031 - LTR/ERVL-MaLR_MLT1H2 chr1 15395032 15395324 + SINE/Alu_AluY chr1 15395325 15395618 - LTR/ERVL-MaLR_MLT1O chr1 15395712 15395903 + DNA/hAT-Charlie_MER30 chr1 15396028 15396351 + SINE/Alu_AluJb chr1 15396507 15396527 + Simple_repeat_(TACAA)n chr1 15396528 15396839 + SINE/Alu_AluSz chr1 15396840 15396859 + Simple_repeat_(TACAA)n chr1 15396903 15397219 + SINE/Alu_AluYm1 chr1 15397851 15398142 + SINE/Alu_AluSg chr1 15398150 15398294 + SINE/Alu_AluSx chr1 15398343 15398584 - SINE/MIR_MIRc chr1 15398824 15399121 - SINE/Alu_AluSq2 chr1 15399285 15399579 + SINE/Alu_AluJr chr1 15399896 15400336 + LTR/ERVL-MaLR_MLT1J chr1 15400310 15400500 - LTR/ERVL-MaLR_MLT1H2 chr1 15400501 15400710 + DNA/hAT-Charlie_MER20 chr1 15400711 15400928 - LTR/ERVL-MaLR_MLT1H2 chr1 15401101 15401237 - LTR/ERVL-MaLR_MLT1N2 chr1 15401263 15401334 + LTR/ERVL-MaLR_MLT1O chr1 15402148 15402205 - LINE/L2_L2a chr1 15402338 15402434 - LINE/L1_L1PA4 chr1 15402579 15402782 - SINE/MIR_MIRb chr1 15402844 15403028 - DNA/hAT-Charlie_MER58A chr1 15403151 15403535 - LTR/ERVL_MER21A chr1 15403536 15403839 + SINE/Alu_AluSq2 chr1 15403840 15404222 - LTR/ERVL_MER21A chr1 15404237 15404295 - LTR/ERVL_MER21A chr1 15404296 15404593 + SINE/Alu_AluJr chr1 15404766 15404814 - LTR?_MamRep605 chr1 15404815 15405118 + SINE/Alu_AluJb chr1 15405119 15405212 - LTR?_MamRep605 chr1 15405624 15405784 - LINE/L2_L2a chr1 15405940 15405971 + Simple_repeat_(CCAGTGC)n chr1 15406358 15406658 + SINE/Alu_AluY chr1 15408418 15408634 - DNA?_UCON58 chr1 15408635 15408687 - LINE/L2_L2d2 chr1 15408864 15409025 - DNA/hAT-Charlie_MER5A chr1 15409471 15409548 - DNA_UCON78 chr1 15409796 15409837 + Simple_repeat_(CG)n chr1 15412348 15412492 - SINE/MIR_MIRb chr1 15412698 15412902 + DNA/hAT-Tip100?_MamRep1879 chr1 15413340 15413438 + SINE/MIR_MIR3 chr1 15413624 15413723 - SINE/MIR_MIR3 chr1 15414365 15414536 + SINE/MIR_MIRb chr1 15414701 15414836 + DNA/hAT-Charlie_Charlie19a chr1 15414863 15415014 + SINE/MIR_MIRb chr1 15415117 15415229 - LINE/L2_L2c chr1 15415442 15415478 - SINE/MIR_MIRc chr1 15415479 15415789 - SINE/Alu_AluSz chr1 15415790 15415905 - SINE/MIR_MIRc chr1 15416542 15416711 - SINE/MIR_MIRc chr1 15416722 15416815 + DNA/hAT-Charlie_MER5A1 chr1 15416816 15417110 - SINE/Alu_AluJr chr1 15417111 15417168 + DNA/hAT-Charlie_MER5A1 chr1 15417547 15417583 + Simple_repeat_(CT)n chr1 15417976 15418289 - SINE/Alu_AluSz chr1 15418300 15418604 - SINE/Alu_AluY chr1 15418607 15418777 + LINE/CR1_L3 chr1 15418800 15419006 + SINE/MIR_MIR chr1 15419518 15419663 + SINE/MIR_MIR3 chr1 15420333 15420508 - SINE/Alu_AluJb chr1 15420509 15420534 + Simple_repeat_(TTAT)n chr1 15420535 15420817 - SINE/Alu_AluSg chr1 15420818 15420941 - SINE/Alu_AluJb chr1 15421003 15421256 - SINE/MIR_MIRb chr1 15421930 15422032 - SINE/MIR_MIRb chr1 15422037 15422085 - DNA/hAT-Charlie_MER20 chr1 15422086 15422389 - SINE/Alu_AluSg chr1 15422390 15422566 - DNA/hAT-Charlie_MER20 chr1 15422580 15422883 + SINE/Alu_AluY chr1 15423482 15423622 + SINE/MIR_MIRb chr1 15423908 15424203 + SINE/Alu_AluJo chr1 15424625 15424667 + Simple_repeat_(CTGCCTC)n chr1 15424905 15425088 + DNA/hAT-Charlie_MER20 chr1 15425243 15425536 + SINE/Alu_AluSz chr1 15425621 15425707 + LINE/CR1_L3 chr1 15426249 15426321 - LINE/L2_L2b chr1 15427101 15427141 + Simple_repeat_(CCCT)n chr1 15428004 15428143 - SINE/MIR_MIRb chr1 15428211 15428521 + SINE/Alu_AluSx1 chr1 15428623 15428673 + Low_complexity_GA-rich chr1 15430479 15430589 - SINE/MIR_MIR chr1 15430590 15430900 + SINE/Alu_AluSx chr1 15430901 15430967 - SINE/MIR_MIR chr1 15431179 15431247 + SINE/MIR_MIR3 chr1 15431540 15431662 + SINE/Alu_AluJr chr1 15431915 15432202 + SINE/Alu_AluJr chr1 15432945 15433025 + LINE/L2_L2b chr1 15433026 15433050 + Simple_repeat_(GAAT)n chr1 15433048 15433080 + Simple_repeat_(ATGTTT)n chr1 15433149 15433444 + SINE/Alu_AluSz chr1 15433894 15434055 + DNA/hAT-Tip100_ORSL chr1 15434056 15434367 - SINE/Alu_AluJr chr1 15434368 15434428 + DNA/hAT-Tip100_ORSL chr1 15434478 15434552 - SINE/MIR_MIR chr1 15434553 15434861 - SINE/Alu_AluSx1 chr1 15434862 15434977 - SINE/MIR_MIR chr1 15435702 15435788 - SINE/MIR_MIR3 chr1 15436174 15436478 + SINE/Alu_AluSz chr1 15436479 15436682 + SINE/Alu_AluSx1 chr1 15437005 15437103 - SINE/MIR_MIR3 chr1 15437292 15437399 + SINE/MIR_MIRc chr1 15437401 15437565 - SINE/Alu_FRAM chr1 15437569 15437687 + SINE/MIR_MIRb chr1 15437718 15438000 + SINE/Alu_AluSx chr1 15438001 15438165 + SINE/Alu_AluSg4 chr1 15438636 15438717 + SINE/MIR_MIRc chr1 15438887 15439014 + SINE/MIR_MIR3 chr1 15439030 15439144 - SINE/MIR_MIRb chr1 15439145 15439453 + SINE/Alu_AluSq2 chr1 15439454 15439623 - SINE/MIR_MIRb chr1 15439732 15439756 + Simple_repeat_(GTTTT)n chr1 15439757 15440041 - SINE/Alu_AluSq2 chr1 15440053 15440245 - SINE/MIR_MIRb chr1 15440726 15440897 - LINE/L2_L2a chr1 15440898 15441202 + SINE/Alu_AluSx1 chr1 15441203 15441613 - LINE/L2_L2a chr1 15441695 15442041 - SINE/Alu_AluJr chr1 15442063 15442164 - LINE/L2_L2a chr1 15442165 15442311 - SINE/MIR_MIRb chr1 15442831 15443015 - SINE/MIR_MIRb chr1 15443676 15443720 - SINE/MIR_MIR chr1 15443681 15443755 - LINE/L2_L2c chr1 15443732 15443818 + SINE/MIR_MIR chr1 15443979 15444288 + SINE/Alu_AluJr chr1 15444910 15445117 + SINE/MIR_MIR chr1 15445317 15445444 - SINE/MIR_MIR1_Amn chr1 15446100 15446173 - LINE/L2_L2c chr1 15447206 15447304 - LINE/L2_L2b chr1 15447335 15447464 - SINE/MIR_MIR3 chr1 15447922 15448110 - LINE/L2_L2c chr1 15448166 15448339 - SINE/Alu_AluSx chr1 15448340 15448645 - SINE/Alu_AluYk3 chr1 15448646 15448790 - SINE/Alu_AluSx chr1 15448931 15449184 - SINE/MIR_MIR chr1 15449269 15449340 + LTR/ERVL-MaLR_MLT1J chr1 15449486 15449574 - LINE/L2_L2c chr1 15449604 15449702 - DNA/hAT-Blackjack_MER81 chr1 15449710 15450035 - LINE/L2_L2c chr1 15450124 15450431 + SINE/Alu_AluJo chr1 15450504 15450808 + SINE/Alu_AluSx1 chr1 15450870 15451345 + LTR/ERVL-MaLR_MLT1D chr1 15451660 15451886 - SINE/MIR_MIR chr1 15452092 15452241 - LTR/ERVL-MaLR_MLT1H chr1 15452311 15452373 - LTR/ERVL-MaLR_MLT1H chr1 15452413 15452569 + SINE/MIR_MIRc chr1 15452645 15452935 - SINE/Alu_AluSq chr1 15453088 15453305 + DNA/hAT-Charlie_Charlie16a chr1 15453317 15453499 + DNA/hAT-Charlie_MER58A chr1 15453586 15453717 - SINE/MIR_MIR chr1 15453735 15453872 - DNA/hAT-Charlie_MER5A1 chr1 15454013 15454297 + SINE/Alu_AluSx1 chr1 15454304 15454424 - SINE/MIR_MIRc chr1 15454449 15454544 + DNA/hAT-Charlie_Charlie16a chr1 15454577 15454650 - SINE/MIR_MIR3 chr1 15454680 15454853 - SINE/MIR_MIRc chr1 15454854 15455162 + SINE/Alu_AluSx4 chr1 15455207 15455508 - SINE/Alu_AluSx chr1 15455662 15455784 - SINE/MIR_MIRc chr1 15455806 15456016 - DNA/hAT-Charlie_MER20 chr1 15456077 15456359 + SINE/Alu_AluSz chr1 15456360 15456391 + Simple_repeat_(AGAA)n chr1 15456428 15456458 + Low_complexity_A-rich chr1 15456459 15456474 + Simple_repeat_(AAC)n chr1 15457330 15457626 + SINE/Alu_AluSx chr1 15458115 15458584 - DNA/TcMar-Tigger_Tigger13a chr1 15458631 15458957 + SINE/Alu_AluSx1 chr1 15458994 15459151 - SINE/Alu_FRAM chr1 15459163 15459450 - SINE/Alu_AluJr chr1 15459464 15459554 - Unknown_MamRep564 chr1 15459756 15459835 + DNA/hAT-Tip100_MER96B chr1 15459836 15459963 + SINE/Alu_FLAM_A chr1 15460016 15460072 - SINE/Alu_Alu chr1 15460088 15460210 - SINE/MIR_MIRb chr1 15460391 15460429 + Low_complexity_A-rich chr1 15460709 15460835 - SINE/Alu_FLAM_A chr1 15460836 15460869 - SINE/Alu_AluJb chr1 15460870 15461247 - LTR/ERVL-MaLR_THE1C chr1 15461248 15461361 - SINE/Alu_AluJb chr1 15461365 15461486 + DNA/hAT-Tip100_MER96B chr1 15461948 15462105 + SINE/MIR_MIRb chr1 15462277 15462426 + SINE/MIR_MIR3 chr1 15463660 15463767 + SINE/MIR_MIR chr1 15463768 15464061 + SINE/Alu_AluSz chr1 15464062 15464127 + SINE/MIR_MIR chr1 15464572 15464657 - Unknown_EUTREP2 chr1 15464765 15464974 + SINE/MIR_MIRc chr1 15465004 15465308 - SINE/Alu_AluSz chr1 15465506 15465883 - LTR/ERVL-MaLR_MLT1O chr1 15466281 15466574 + SINE/Alu_AluSx1 chr1 15466802 15466970 - DNA/hAT-Tip100_MamTip2b chr1 15466997 15467228 + SINE/MIR_MIRb chr1 15467808 15468116 + SINE/Alu_AluSp chr1 15468166 15468283 - LTR/Gypsy_LTR104_Mam chr1 15468287 15468398 - DNA/hAT-Blackjack_MER81 chr1 15468487 15468524 + Simple_repeat_(ATA)n chr1 15468525 15468801 + SINE/Alu_AluJr chr1 15468802 15468812 + Simple_repeat_(ATA)n chr1 15468906 15469206 + SINE/Alu_AluSx chr1 15469298 15469335 + SINE/MIR_MIR3 chr1 15469489 15469742 - DNA/hAT-Charlie_Charlie22a chr1 15470447 15470581 - LINE/L1_L1ME3G chr1 15470582 15470880 - SINE/Alu_AluSx chr1 15470881 15471192 - LINE/L1_L1ME3G chr1 15471265 15471575 + SINE/Alu_AluSz chr1 15471685 15471781 - DNA/hAT-Charlie_Charlie4z chr1 15472141 15472447 + SINE/Alu_AluJb chr1 15472703 15472815 + LTR/ERVL-MaLR_MLT1K chr1 15472929 15473138 + DNA/hAT-Charlie_MER20 chr1 15473211 15473396 + LTR/ERVL-MaLR_MLT1K chr1 15473397 15473703 + SINE/Alu_AluSx1 chr1 15473712 15473823 - SINE/MIR_MIRc chr1 15473850 15473941 + DNA/hAT-Charlie_Charlie16a chr1 15473974 15474047 - SINE/MIR_MIR3 chr1 15474077 15474250 - SINE/MIR_MIRc chr1 15474251 15474448 + SINE/Alu_AluSg4 chr1 15474449 15474608 - SINE/Alu_AluSx3 chr1 15474610 15474919 - SINE/Alu_AluSx chr1 15475037 15475159 - SINE/MIR_MIRc chr1 15475183 15475384 - DNA/hAT-Charlie_MER20 chr1 15475445 15475757 + SINE/Alu_AluSz chr1 15475794 15475827 + Simple_repeat_(A)n chr1 15476702 15476997 + SINE/Alu_AluSx chr1 15477527 15477970 - DNA/TcMar-Tigger_Tigger13a chr1 15478017 15478202 + SINE/Alu_AluSx1 chr1 15478203 15478228 + Simple_repeat_(AT)n chr1 15478230 15478542 - SINE/Alu_AluSp chr1 15478566 15478866 - SINE/Alu_AluSx chr1 15478887 15479055 - SINE/MIR_MIRb chr1 15479263 15479564 + SINE/Alu_AluSx1 chr1 15479572 15480222 - LINE/L2_L2a chr1 15480234 15480406 - SINE/Alu_AluJb chr1 15480407 15480781 - LTR/ERVL-MaLR_THE1C chr1 15480782 15480895 - SINE/Alu_AluJb chr1 15480901 15481026 + DNA/hAT-Tip100_MER96B chr1 15481487 15481635 + SINE/MIR_MIRb chr1 15481841 15481947 + SINE/MIR_MIR3 chr1 15482774 15483066 - SINE/Alu_AluY chr1 15483493 15483645 + SINE/MIR_MIR chr1 15483646 15483941 + SINE/Alu_AluSz chr1 15483942 15484042 + SINE/MIR_MIR chr1 15484473 15484559 - Unknown_EUTREP2 chr1 15484667 15484872 + SINE/MIR_MIRc chr1 15484902 15485194 - SINE/Alu_AluSz chr1 15485387 15485737 - LTR/ERVL-MaLR_MLT1O chr1 15486199 15486481 + SINE/Alu_AluSx1 chr1 15486656 15486876 - DNA/hAT-Tip100_MamTip2b chr1 15486903 15487127 + SINE/MIR_MIRb chr1 15487738 15487867 - LTR/Gypsy_LTR104_Mam chr1 15487871 15487980 - DNA/hAT-Blackjack_MER81 chr1 15488067 15488104 + Simple_repeat_(ATA)n chr1 15488105 15488395 + SINE/Alu_AluJr chr1 15488396 15488406 + Simple_repeat_(ATA)n chr1 15488764 15489016 - DNA/hAT-Charlie_Charlie22a chr1 15489621 15489660 + Low_complexity_GA-rich chr1 15489738 15489869 - LINE/L1_L1ME3G chr1 15489870 15490173 - SINE/Alu_AluSx chr1 15490174 15490220 - LINE/L1_L1ME3G chr1 15490221 15490259 + Simple_repeat_(TATC)n chr1 15490260 15490513 - LINE/L1_L1ME3G chr1 15490586 15490882 + SINE/Alu_AluSz chr1 15490985 15491082 - DNA/hAT-Charlie_Charlie4z chr1 15491476 15491785 + SINE/Alu_AluJb chr1 15491897 15492067 + SINE/MIR_MIRc chr1 15492078 15492174 + SINE/Alu_FLAM_A chr1 15492447 15492649 + DNA/hAT-Charlie_MER20 chr1 15493207 15493247 + Simple_repeat_(AAAATTC)n chr1 15494159 15494262 - SINE/MIR_MIRb chr1 15494284 15494581 + SINE/Alu_AluSz chr1 15494582 15494889 + SINE/Alu_AluY chr1 15494897 15495042 - SINE/MIR_MIRb chr1 15495115 15495193 - SINE/MIR_MIRc chr1 15495499 15495547 + LINE/L1_L1M3 chr1 15495548 15496147 + LTR/ERV1_PRIMA4_LTR chr1 15496150 15496746 + LINE/L1_L1M3 chr1 15496747 15497026 + SINE/Alu_AluJb chr1 15497029 15497339 + SINE/Alu_AluSx1 chr1 15497340 15497362 + LINE/L1_L1M3 chr1 15497363 15497670 + SINE/Alu_AluSc5 chr1 15497671 15498051 + LINE/L1_L1M3 chr1 15498054 15498075 - SINE/Alu_FRAM chr1 15498076 15498360 - SINE/Alu_AluJb chr1 15498361 15498456 - SINE/Alu_FRAM chr1 15498464 15498523 + LINE/L1_L1MB2 chr1 15498743 15499036 - SINE/Alu_AluSc8 chr1 15499086 15499164 - SINE/MIR_MIRb chr1 15499521 15499785 + LINE/L1_L1ME4c chr1 15500830 15500881 + Simple_repeat_(AGCAG)n chr1 15501028 15501390 - DNA/TcMar-Tigger_X1_DNA chr1 15501397 15501437 + Simple_repeat_(ATTAA)n chr1 15501440 15501630 - DNA/TcMar-Tigger_X1_DNA chr1 15501671 15501758 - DNA/TcMar-Tigger_X1_DNA chr1 15501759 15502041 - SINE/Alu_AluJb chr1 15502042 15502105 - DNA/TcMar-Tigger_X1_DNA chr1 15502108 15502339 - LINE/L2_L2c chr1 15502340 15502468 + SINE/Alu_FLAM_C chr1 15502469 15502604 - LINE/L2_L2c chr1 15503107 15503320 + SINE/Alu_AluJr chr1 15503487 15503596 + LINE/L2_L2a chr1 15504030 15504138 - SINE/Alu_AluJo chr1 15505017 15505117 + DNA/hAT-Charlie_MER5A chr1 15505189 15505294 - DNA/hAT-Charlie_MER5A1 chr1 15505323 15505472 - DNA/hAT-Charlie_MER102c chr1 15505506 15505628 - SINE/MIR_MIR3 chr1 15506167 15506452 + SINE/Alu_AluSz chr1 15506593 15506780 - SINE/MIR_MIRb chr1 15507405 15507606 + LINE/L2_L2c chr1 15507996 15508300 + LINE/L1_L1ME2 chr1 15508301 15508401 + LTR/ERV1_LTR9A1 chr1 15508402 15508714 - SINE/Alu_AluSq2 chr1 15508715 15509298 + LTR/ERV1_LTR9A1 chr1 15509299 15509342 + LINE/L1_L1ME2 chr1 15509343 15509658 + SINE/Alu_AluSq2 chr1 15509659 15509767 + LINE/L1_L1ME2 chr1 15509784 15509838 + LINE/L2_L2b chr1 15509933 15510229 - SINE/Alu_AluSx1 chr1 15510285 15510536 + DNA/TcMar-Mariner_MARNA chr1 15510644 15510729 - LINE/L2_L2d chr1 15510940 15511247 - LTR/ERVL_LTR42 chr1 15511248 15511403 - SINE/Alu_AluSx3 chr1 15511407 15511703 - SINE/Alu_AluSz chr1 15511705 15511865 - LTR/ERVL_LTR42 chr1 15511969 15512165 - SINE/MIR_MIRc chr1 15512222 15512676 + LTR/ERVL_MLT2B1 chr1 15512700 15512752 + Simple_repeat_(AT)n chr1 15512753 15512847 - SINE/MIR_MIR chr1 15512876 15513177 + SINE/Alu_AluY chr1 15513222 15513293 - LINE/L2_L2a chr1 15513298 15513354 + LTR/ERVL_MLT2D chr1 15513628 15513728 + LINE/L2_L2b chr1 15513789 15514011 + SINE/MIR_MIR chr1 15514081 15514291 - DNA/hAT-Charlie_MER20 chr1 15514518 15514706 + SINE/MIR_MIRc chr1 15514724 15515033 + SINE/Alu_AluSz chr1 15515496 15515663 + LINE/L1_L1ME2 chr1 15515657 15515769 + LINE/L1_L1MC5a chr1 15515685 15515772 + LINE/L1_L1M5 chr1 15515774 15515919 + SINE/Alu_AluJr4 chr1 15515920 15516219 + SINE/Alu_AluSx1 chr1 15516249 15516495 + SINE/Alu_AluJb chr1 15516511 15516639 - SINE/Alu_FLAM_A chr1 15516656 15516736 + LINE/L1_L1MC5a chr1 15516846 15517050 + SINE/MIR_MIRb chr1 15517163 15517571 + LINE/L1_L1ME3 chr1 15517572 15517711 + SINE/MIR_MIRb chr1 15517863 15517889 + Simple_repeat_(A)n chr1 15518583 15518740 - LINE/L2_L2a chr1 15518864 15519174 - SINE/Alu_AluSz chr1 15519180 15519481 - SINE/Alu_AluSx1 chr1 15519725 15520019 + SINE/Alu_AluJo chr1 15520162 15520285 + DNA/hAT-Charlie_MER3 chr1 15520286 15520672 + LTR/ERVK_LTR13A chr1 15520673 15520875 + SINE/Alu_AluSx3 chr1 15520876 15521182 + SINE/Alu_AluYm1 chr1 15521183 15521289 + SINE/Alu_AluSx3 chr1 15521290 15521872 + LTR/ERVK_LTR13A chr1 15521873 15521957 + DNA/hAT-Charlie_MER3 chr1 15522044 15522303 - SINE/MIR_MIRb chr1 15522324 15522435 + DNA/hAT-Charlie_MER102b chr1 15522436 15522719 + SINE/Alu_AluJr chr1 15522720 15522774 + DNA/hAT-Charlie_MER102b chr1 15522850 15522950 + LINE/L2_L2c chr1 15523057 15523155 + SINE/MIR_MIRc chr1 15523249 15523435 - SINE/MIR_MIRb chr1 15523472 15523861 + LTR/ERV1_MER57F chr1 15524014 15524066 + Low_complexity_G-rich chr1 15524884 15524935 + Simple_repeat_(CCCCGC)n chr1 15525130 15525191 + Simple_repeat_(CCGCC)n chr1 15525526 15525549 + Simple_repeat_(TCCC)n chr1 15525640 15525878 - SINE/MIR_MIRb chr1 15525928 15526130 - LINE/L2_L2a chr1 15526311 15526343 + Low_complexity_A-rich chr1 15526348 15526477 + SINE/MIR_MIR3 chr1 15527392 15527622 - SINE/MIR_MIRb chr1 15528152 15528452 + SINE/Alu_AluSx chr1 15529757 15530110 - DNA/TcMar-Tigger_MER2 chr1 15530135 15530357 + SINE/Alu_AluJo chr1 15530600 15530695 - SINE/MIR_MIR chr1 15530696 15530987 - SINE/Alu_AluSz chr1 15530988 15531036 - SINE/MIR_MIR chr1 15533200 15533322 + SINE/Alu_FLAM_C chr1 15533345 15533652 + SINE/Alu_AluSz chr1 15534442 15534735 + SINE/Alu_AluSx3 chr1 15535465 15535743 + SINE/Alu_AluJb chr1 15535764 15536026 - SINE/Alu_AluJr chr1 15536049 15536088 + Simple_repeat_(TTTCT)n chr1 15536089 15536378 - SINE/Alu_AluSp chr1 15536857 15537150 - SINE/Alu_AluSx1 chr1 15537395 15537455 - LINE/L2_L2c chr1 15537636 15537874 - SINE/MIR_MIR chr1 15537878 15537920 - LINE/L2_L2c chr1 15538116 15538420 + SINE/Alu_AluSg chr1 15538436 15538731 + SINE/Alu_AluSx chr1 15539234 15539544 - SINE/Alu_AluY chr1 15539545 15539697 - SINE/Alu_AluJb chr1 15539746 15540041 + SINE/Alu_AluSz chr1 15540044 15540348 + SINE/Alu_AluSx chr1 15540392 15540464 - SINE/Alu_AluSx chr1 15540474 15540622 + SINE/MIR_MIR chr1 15540646 15540754 - SINE/Alu_FLAM_C chr1 15540809 15540918 + LTR?_MamRep605b chr1 15540955 15541164 + SINE/MIR_MIRb chr1 15541348 15541535 + SINE/MIR_MIR3 chr1 15541621 15541766 + LINE/L1_L1M5 chr1 15541781 15542156 + LTR/ERVL_MER21C chr1 15542338 15542763 + LTR/ERVL_MER21C chr1 15542818 15542930 + SINE/Alu_FLAM_A chr1 15543052 15543759 - LINE/L2_L2a chr1 15543761 15543998 - LINE/L2_L2-1_AMi chr1 15544152 15544186 + Simple_repeat_(AC)n chr1 15544187 15544266 - LINE/L1_L1M5 chr1 15545644 15545838 - LINE/L2_L2 chr1 15546023 15546321 + SINE/Alu_AluJb chr1 15546379 15546421 + Simple_repeat_(GATA)n chr1 15546896 15546934 + Simple_repeat_(T)n chr1 15547034 15547142 - SINE/MIR_MIR chr1 15547143 15547438 - SINE/Alu_AluSx4 chr1 15547445 15547741 - SINE/Alu_AluSz chr1 15547742 15547846 - SINE/MIR_MIR chr1 15548969 15549115 + LINE/L1_L1ME3G chr1 15549178 15549403 - LINE/L2_L2 chr1 15549438 15549533 + DNA/TcMar-Tigger_MER44A chr1 15549534 15549838 + SINE/Alu_AluY chr1 15549839 15550071 + DNA/TcMar-Tigger_MER44A chr1 15550451 15550578 - LINE/L1_L1M5 chr1 15550607 15550866 + DNA/hAT-Charlie_Charlie1a chr1 15550867 15551169 - SINE/Alu_AluSx1 chr1 15551170 15551601 + DNA/hAT-Charlie_Charlie1a chr1 15551602 15551795 - SINE/Alu_AluJb chr1 15551796 15552041 + SINE/Alu_AluSx chr1 15552042 15552171 - SINE/Alu_AluJb chr1 15552172 15552205 + DNA/hAT-Charlie_Charlie1a chr1 15552206 15552499 + SINE/Alu_AluSx4 chr1 15552500 15552609 + DNA/hAT-Charlie_Charlie1a chr1 15552619 15552664 + Simple_repeat_(T)n chr1 15552767 15553069 - SINE/Alu_AluSx chr1 15553070 15553229 - LINE/L1_L1M5 chr1 15553230 15553532 - SINE/Alu_AluSq chr1 15553533 15553553 - LINE/L1_L1M5 chr1 15553561 15553921 - LINE/L1_L1ME3A chr1 15553922 15554223 + SINE/Alu_AluJb chr1 15554224 15554357 - LINE/L1_L1ME3A chr1 15554336 15554533 - LINE/L1_L1ME3A chr1 15554647 15554778 - LINE/L1_L1ME3A chr1 15554780 15554862 + LINE/L1_L1M5 chr1 15554871 15555043 - LINE/L1_L1ME3A chr1 15555094 15555400 + SINE/Alu_AluSx chr1 15555403 15555700 + SINE/Alu_AluSg chr1 15555706 15556011 + SINE/Alu_AluSp chr1 15556022 15556128 + SINE/Alu_AluJo chr1 15556193 15556220 - LINE/L1_L1ME3A chr1 15556221 15556252 + Simple_repeat_(TTGTT)n chr1 15556254 15556523 - SINE/Alu_AluSx chr1 15556524 15557168 - LINE/L1_L1ME3A chr1 15557209 15557263 - LINE/L1_L1ME3A chr1 15557323 15557722 - LINE/L1_L1ME3A chr1 15557723 15557903 - SINE/Alu_AluJb chr1 15557904 15558170 - SINE/Alu_AluSq2 chr1 15558172 15558486 - SINE/Alu_AluJr chr1 15558487 15558631 - SINE/Alu_AluJb chr1 15558632 15558760 - LINE/L1_L1ME3A chr1 15558921 15559237 - SINE/Alu_AluSg chr1 15559782 15560083 + SINE/Alu_AluSp chr1 15560165 15560251 - LINE/L2_L2b chr1 15560466 15560661 - DNA/TcMar_DNA1_Mam chr1 15560732 15560949 + LINE/L2_L2b chr1 15560844 15561267 + LINE/L2_L2a chr1 15561403 15561696 + SINE/Alu_AluSz chr1 15561749 15561846 + SINE/Alu_AluJr chr1 15561855 15561897 + Simple_repeat_(TA)n chr1 15562056 15562127 - LINE/CR1_X17_LINE chr1 15562475 15562781 - SINE/Alu_AluJb chr1 15563247 15563542 + SINE/Alu_AluSp chr1 15563546 15563871 + SINE/Alu_AluY chr1 15564529 15564826 - SINE/Alu_AluSq2 chr1 15564830 15565134 - SINE/Alu_AluSx1 chr1 15565214 15565311 + LINE/L2_L2b chr1 15565338 15565433 + LINE/L2_L2c chr1 15566602 15566899 + SINE/Alu_AluJr chr1 15567277 15567306 + Simple_repeat_(TG)n chr1 15569628 15569948 - SINE/Alu_AluSp chr1 15570373 15570521 - SINE/Alu_FLAM_C chr1 15570783 15570828 - LINE/CR1_L3b chr1 15571605 15571659 + Simple_repeat_(TTTGTGT)n chr1 15571947 15572166 + SINE/Alu_AluSx chr1 15572186 15572229 + Simple_repeat_(GTTTT)n chr1 15572230 15572349 - SINE/Alu_AluSx chr1 15572353 15572659 - SINE/Alu_AluSz chr1 15572820 15572906 - SINE/Alu_AluSx3 chr1 15572976 15573268 + SINE/Alu_AluSq2 chr1 15574215 15574316 + SINE/MIR_MIR chr1 15574379 15574677 - SINE/Alu_AluSx chr1 15574948 15575262 - LINE/L2_L2c chr1 15575497 15575614 - LINE/L2_L2a chr1 15575730 15576055 - SINE/Alu_AluJb chr1 15576106 15576404 - SINE/Alu_AluSp chr1 15576438 15576727 - SINE/Alu_AluSg4 chr1 15576741 15577049 - SINE/Alu_AluYk4 chr1 15577294 15577589 + SINE/Alu_AluSg chr1 15577897 15578067 - SINE/MIR_MIR chr1 15578298 15578594 - SINE/Alu_AluSx chr1 15579403 15579583 - DNA/hAT-Tip100?_MamRep1879 chr1 15580202 15580503 - SINE/Alu_AluSz chr1 15580582 15580684 + SINE/Alu_AluJb chr1 15580695 15581003 + SINE/Alu_AluSp chr1 15581006 15581076 + SINE/Alu_FLAM_A chr1 15581081 15581373 + SINE/Alu_AluJo chr1 15581439 15581592 + DNA/hAT-Charlie_MER3 chr1 15581593 15581868 + SINE/Alu_AluSq2 chr1 15581869 15581915 + DNA/hAT-Charlie_MER3 chr1 15581940 15581986 - SINE/MIR_MIR chr1 15581987 15582288 + SINE/Alu_AluSq2 chr1 15582289 15582501 - SINE/MIR_MIR chr1 15582716 15582996 + SINE/Alu_AluSx1 chr1 15582997 15583017 + Simple_repeat_(CA)n chr1 15583458 15583655 - DNA/hAT-Charlie_MER20 chr1 15583715 15583845 + SINE/MIR_MIRb chr1 15584144 15584448 - SINE/Alu_AluSx3 chr1 15585279 15585402 - SINE/Alu_AluJo chr1 15585543 15585575 + Simple_repeat_(AGCT)n chr1 15585576 15585725 - SINE/Alu_FRAM chr1 15585768 15586050 + SINE/Alu_AluY chr1 15586051 15586093 + Simple_repeat_(AAAT)n chr1 15586332 15586625 + SINE/Alu_AluY chr1 15586914 15586963 - LINE/L2_L2c chr1 15587510 15587590 + LINE/L2_L2c chr1 15587627 15587825 - SINE/Alu_AluJb chr1 15587827 15587991 + SINE/Alu_AluJb chr1 15587992 15588723 - LTR/ERV1_LTR12D chr1 15588615 15588747 - LTR/ERV1_LTR12C chr1 15588724 15588752 - LTR/ERV1_LTR12D chr1 15588753 15588893 + SINE/Alu_AluJb chr1 15588898 15589025 - SINE/Alu_FLAM_C chr1 15589034 15589106 + DNA/hAT-Charlie_Charlie1b chr1 15589107 15589408 - SINE/Alu_AluJr chr1 15589409 15589473 + DNA/hAT-Charlie_Charlie1b chr1 15589491 15589589 + DNA/hAT-Charlie_MER5A1 chr1 15589604 15589896 + SINE/Alu_AluSx1 chr1 15589905 15590212 + SINE/Alu_AluJr chr1 15590222 15590546 + SINE/Alu_AluSx4 chr1 15590556 15590867 + SINE/Alu_AluSg chr1 15590868 15591090 + DNA/hAT-Charlie_Charlie17b chr1 15591091 15591224 + SINE/Alu_AluSx chr1 15591225 15591522 + SINE/Alu_AluSp chr1 15591523 15591675 + SINE/Alu_AluSx chr1 15591676 15591700 + Simple_repeat_(AAAAC)n chr1 15591701 15591718 + DNA/hAT-Charlie_Charlie17b chr1 15591719 15591848 + SINE/Alu_FLAM_C chr1 15591865 15592044 - SINE/Alu_AluSx chr1 15592078 15592277 + DNA/hAT-Charlie_Charlie17 chr1 15592773 15593085 + SINE/Alu_AluSx chr1 15593105 15593327 + SINE/Alu_AluJr chr1 15593329 15593452 - LTR/ERV1_MER31A chr1 15593453 15593972 + LTR/ERV1_MER4C chr1 15593973 15594016 - LTR/ERV1_MER31A chr1 15594019 15594048 - LTR/ERV1_MER31A chr1 15594049 15594346 + SINE/Alu_AluSx1 chr1 15594420 15594720 + SINE/Alu_AluSx3 chr1 15594721 15594858 + SINE/Alu_AluSz chr1 15594865 15595149 + SINE/Alu_AluSz6 chr1 15595151 15595187 + Simple_repeat_(AATAA)n chr1 15595498 15595790 - SINE/Alu_AluSg chr1 15596389 15596407 + Simple_repeat_(A)n chr1 15596411 15596873 + LTR/ERVL_MER21C chr1 15596874 15597218 + LTR/ERVL-MaLR_MSTB1 chr1 15597219 15597525 - SINE/Alu_AluSx1 chr1 15597526 15597578 + LTR/ERVL-MaLR_MSTB1 chr1 15597859 15597889 + Simple_repeat_(TTTGT)n chr1 15597890 15598144 - SINE/Alu_AluSx1 chr1 15598293 15598443 - LINE/L1_L1ME1 chr1 15598629 15598699 - LINE/L2_L2a chr1 15598830 15598981 + LINE/L1_L1MEc chr1 15598982 15599273 + SINE/Alu_AluSx4 chr1 15599274 15599575 + LINE/L1_L1MEc chr1 15599605 15599866 - DNA/hAT-Charlie_MER1B chr1 15599874 15600013 + LINE/L1_L1MEc chr1 15600014 15600315 - SINE/Alu_AluSx chr1 15600351 15600640 - SINE/Alu_AluSg4 chr1 15600650 15600944 - SINE/Alu_AluSq chr1 15601212 15601314 + DNA/hAT-Charlie_MER5A1 chr1 15601315 15601606 + SINE/Alu_AluSg chr1 15601607 15601645 + DNA/hAT-Charlie_MER5A1 chr1 15601676 15601947 + DNA/TcMar-Tigger_Tigger3a chr1 15601948 15602097 + SINE/Alu_AluJo chr1 15602101 15602429 + SINE/Alu_AluJr chr1 15602430 15602499 + DNA/TcMar-Tigger_Tigger3d chr1 15602709 15603019 + SINE/Alu_AluSg7 chr1 15603047 15603063 + Simple_repeat_(AC)n chr1 15604026 15604256 + SINE/Alu_AluSz chr1 15604389 15604525 + SINE/Alu_AluSx chr1 15605324 15605494 + SINE/Alu_AluSx chr1 15605531 15605674 + SINE/MIR_MIR chr1 15605675 15605917 + SINE/MIR_MIR chr1 15605918 15605954 + SINE/MIR_MIR chr1 15606505 15606692 + LINE/L2_L2 chr1 15606696 15606761 - LINE/L1_L1MB8 chr1 15606762 15607057 + SINE/Alu_AluSp chr1 15607058 15607235 - LINE/L1_L1MB8 chr1 15607236 15607543 + SINE/Alu_AluYk2 chr1 15607584 15607607 + Simple_repeat_(AAAT)n chr1 15607609 15607762 - LINE/L1_L1MB8 chr1 15607763 15607805 - snRNA_U7 chr1 15607806 15608111 - SINE/Alu_AluSg4 chr1 15608112 15608144 - snRNA_U7 chr1 15608145 15608367 - LINE/L1_L1MB8 chr1 15608369 15608676 - SINE/Alu_AluSz chr1 15608688 15608812 - SINE/Alu_FLAM_C chr1 15608817 15608947 - LINE/L1_L1MB8 chr1 15608948 15609129 - SINE/Alu_AluSz chr1 15609130 15609436 - SINE/Alu_AluSg chr1 15609437 15609568 - SINE/Alu_AluSz chr1 15609569 15609641 - LINE/L1_L1MB8 chr1 15609644 15609983 - LTR/ERVL-MaLR_MLT1G3 chr1 15610420 15610687 + SINE/Alu_AluJo chr1 15610695 15610985 + SINE/Alu_AluSg chr1 15611067 15611374 + SINE/Alu_AluSp chr1 15611519 15611811 - SINE/Alu_AluSz chr1 15612122 15612418 - SINE/Alu_AluSg4 chr1 15612438 15612589 - SINE/Alu_AluJb chr1 15612600 15612894 + SINE/Alu_AluSq2 chr1 15612904 15613209 + SINE/Alu_AluSx1 chr1 15613210 15613370 + SINE/Alu_AluSx chr1 15613390 15613538 - SINE/Alu_FLAM_C chr1 15613630 15613658 + Simple_repeat_(TAT)n chr1 15613879 15614193 - SINE/Alu_AluSg chr1 15614194 15614502 - SINE/Alu_AluSq2 chr1 15614973 15615349 + SINE/Alu_AluJr chr1 15615352 15615518 - LTR/ERVL-MaLR_MLT1G3 chr1 15615715 15615855 + LTR/ERVL-MaLR_MLT1L chr1 15615940 15616078 + LTR/ERVL-MaLR_MLT1L chr1 15616094 15616396 - SINE/Alu_AluSx4 chr1 15616684 15616755 - LINE/L2_L2d2 chr1 15617022 15617151 + LINE/L2_L2c chr1 15617497 15617555 + Low_complexity_G-rich chr1 15617534 15617564 + Simple_repeat_(GAGC)n chr1 15617628 15617670 + Low_complexity_G-rich chr1 15618252 15618269 + Simple_repeat_(T)n chr1 15619111 15619423 - SINE/Alu_AluSz chr1 15619451 15619748 - SINE/Alu_AluY chr1 15620507 15620556 + Simple_repeat_(TTTCCA)n chr1 15620619 15620732 - DNA/hAT-Charlie_Charlie7a chr1 15620768 15620912 - DNA/hAT-Charlie_Charlie7a chr1 15621400 15621685 - SINE/Alu_AluJr chr1 15621698 15621922 - LINE/L1_L1M5 chr1 15622074 15622323 - SINE/Alu_AluJr chr1 15623382 15623557 - SINE/Alu_AluJr chr1 15623558 15623583 + Simple_repeat_(TTA)n chr1 15623584 15623701 - SINE/Alu_AluJr chr1 15623713 15623778 + LTR?_MamRep605b chr1 15623779 15624072 + SINE/Alu_AluYk2 chr1 15624073 15624218 + LTR?_MamRep605b chr1 15624456 15624705 - SINE/Alu_AluJb chr1 15624707 15624829 + DNA/PiggyBac_MER85 chr1 15624830 15624892 - SINE/Alu_AluJb chr1 15625158 15625319 - SINE/MIR_MIR3 chr1 15625644 15625916 - SINE/Alu_AluSg chr1 15626134 15626624 - LINE/L2_L2a chr1 15626847 15627087 + SINE/MIR_MIRb chr1 15627504 15627663 + DNA/hAT-Charlie_Charlie4z chr1 15628445 15628476 + Simple_repeat_(GTTT)n chr1 15628497 15628591 + DNA/hAT-Ac_EuthAT-2 chr1 15628704 15628974 - LINE/L1_L1MB7 chr1 15629314 15629614 + SINE/Alu_AluSx chr1 15629727 15630031 - SINE/Alu_AluSq2 chr1 15630834 15631142 - SINE/Alu_AluYk2 chr1 15631269 15631414 - SINE/Alu_AluJo chr1 15631577 15631805 - LINE/L1_L1ME2 chr1 15631806 15632102 - SINE/Alu_AluSg chr1 15632103 15632169 - LINE/L1_L1ME2 chr1 15632294 15632546 + SINE/Alu_AluSq2 chr1 15632913 15633228 - SINE/Alu_AluJr chr1 15634112 15634228 + DNA/hAT-Charlie_MER5B chr1 15634523 15634831 - SINE/Alu_AluJb chr1 15634848 15634908 + Retroposon/SVA_SVA_A chr1 15634856 15634930 - SINE/Alu_AluSz chr1 15634978 15635249 + SINE/Alu_AluJr chr1 15635425 15635704 - SINE/Alu_AluSx chr1 15636137 15636419 - SINE/Alu_AluJb chr1 15636426 15636451 + Simple_repeat_(GTTT)n chr1 15636500 15636759 - SINE/Alu_AluJb chr1 15636769 15636936 - DNA/hAT-Charlie_MER58A chr1 15637214 15637373 - SINE/Alu_AluJb chr1 15637381 15637667 - SINE/Alu_AluSx chr1 15638531 15638790 - SINE/Alu_AluSx1 chr1 15639063 15639159 - LINE/L2_L2d chr1 15639481 15639699 - SINE/Alu_AluJr chr1 15639725 15640032 + SINE/Alu_AluJb chr1 15640227 15640374 - SINE/MIR_MIRb chr1 15640367 15640511 - SINE/MIR_MIRb chr1 15640553 15640621 - LINE/L2_L2d chr1 15640837 15641071 - LINE/L2_L2d chr1 15641190 15641485 + SINE/Alu_AluSz chr1 15641673 15641985 + SINE/Alu_AluSq2 chr1 15642500 15642612 - SINE/Alu_AluJo chr1 15642642 15642786 + SINE/Alu_AluSz chr1 15642787 15643081 + SINE/Alu_AluY chr1 15643082 15643166 + SINE/Alu_AluSz chr1 15643736 15643760 + Simple_repeat_(TTG)n chr1 15643901 15643979 - LINE/L1_HAL1 chr1 15643975 15644560 - LINE/L1_HAL1 chr1 15644561 15644892 - SINE/Alu_AluYk3 chr1 15644895 15645191 - SINE/Alu_AluSz chr1 15645192 15645586 - LINE/L1_HAL1 chr1 15645587 15645884 + SINE/Alu_AluJr chr1 15645885 15645977 - LINE/L1_HAL1 chr1 15645965 15646404 - LINE/L1_HAL1 chr1 15646405 15646703 + SINE/Alu_AluSq2 chr1 15646704 15646799 - LINE/L1_HAL1 chr1 15646809 15646965 - LINE/L1_L1ME4a chr1 15647137 15647447 - SINE/Alu_AluSx1 chr1 15647483 15647582 - LINE/L1_L1ME4a chr1 15647684 15647972 + SINE/Alu_AluSx3 chr1 15648333 15648503 + LINE/L1_L1M5 chr1 15648589 15648750 + LINE/L1_L1M5 chr1 15648762 15648851 + SINE/Alu_FLAM_C chr1 15648952 15649075 + SINE/Alu_AluJr chr1 15649082 15649382 + SINE/Alu_AluSx chr1 15649390 15649671 + SINE/Alu_AluSp chr1 15650370 15650411 - rRNA_5S chr1 15650464 15650738 + SINE/Alu_AluJr chr1 15650847 15650871 + Simple_repeat_(TATT)n chr1 15650872 15651151 - SINE/Alu_AluY chr1 15651165 15651203 + Simple_repeat_(AATG)n chr1 15651375 15651618 - SINE/Alu_AluSx chr1 15652059 15652347 - SINE/Alu_AluJr chr1 15652396 15652541 + SINE/Alu_AluSx chr1 15652542 15652854 + SINE/Alu_AluYk2 chr1 15652855 15653018 + SINE/Alu_AluSx chr1 15653254 15653290 + Simple_repeat_(TTTTA)n chr1 15653295 15653436 - SINE/Alu_AluJb chr1 15653561 15653796 - SINE/Alu_AluSz6 chr1 15654375 15654673 + SINE/Alu_AluJb chr1 15655337 15655469 + SINE/Alu_AluJb chr1 15655470 15655776 + SINE/Alu_AluSx1 chr1 15655777 15655942 + SINE/Alu_AluJb chr1 15656023 15656139 - SINE/Alu_AluJr chr1 15657391 15657417 + Simple_repeat_(TTTC)n chr1 15658143 15658449 - SINE/Alu_AluSx1 chr1 15658481 15658769 + SINE/Alu_AluSp chr1 15659335 15659456 - DNA/hAT-Charlie_MER5A1 chr1 15661786 15661817 + Simple_repeat_(AC)n chr1 15664076 15664164 - SINE/Alu_FLAM_C chr1 15664238 15664451 + SINE/Alu_AluSx3 chr1 15664981 15665280 + SINE/Alu_AluSp chr1 15665717 15665739 + Simple_repeat_(AT)n chr1 15666631 15666752 - LINE/L2_L2b chr1 15666929 15667218 + SINE/Alu_AluSx4 chr1 15669268 15669530 + SINE/Alu_AluSx3 chr1 15669543 15669774 + SINE/MIR_MIR chr1 15669854 15670073 - LTR/ERVL-MaLR_MLT1J1 chr1 15670141 15670427 - LTR/ERVL-MaLR_MLT1J1-int chr1 15670428 15670723 + SINE/Alu_AluSc5 chr1 15670724 15671231 - LTR/ERVL-MaLR_MLT1J1-int chr1 15671236 15671531 - SINE/Alu_AluSx4 chr1 15672100 15672268 + SINE/Alu_FRAM chr1 15672269 15672403 + SINE/Alu_AluSx chr1 15672404 15672699 + SINE/Alu_AluSg chr1 15672700 15672722 - DNA/TcMar-Mariner_HSMAR2 chr1 15672723 15672877 - SINE/Alu_AluSz6 chr1 15672878 15673182 - SINE/Alu_AluSp chr1 15673183 15673265 - SINE/Alu_AluSz6 chr1 15673266 15673555 - DNA/TcMar-Mariner_HSMAR2 chr1 15673556 15673709 - SINE/Alu_AluJr chr1 15673710 15674000 - SINE/Alu_AluSp chr1 15674001 15674128 - SINE/Alu_AluJr chr1 15674129 15674309 - DNA/TcMar-Mariner_HSMAR2 chr1 15674310 15674593 - SINE/Alu_AluSc8 chr1 15674594 15674746 - DNA/TcMar-Mariner_HSMAR2 chr1 15675056 15675148 + LINE/L2_L2a chr1 15675149 15675454 + SINE/Alu_AluSq2 chr1 15675455 15675662 + LINE/L2_L2a chr1 15675663 15676016 + Simple_repeat_(CCTA)n chr1 15676017 15676343 + LINE/L2_L2a chr1 15676355 15676430 + Low_complexity_A-rich chr1 15676438 15676568 - SINE/Alu_FRAM chr1 15676682 15676931 + LINE/L2_L2a chr1 15676932 15677489 + LTR/ERV1_LTR19B chr1 15677490 15677518 + LINE/L2_L2a chr1 15677506 15677572 - LINE/CR1_L3 chr1 15677749 15678065 - SINE/Alu_AluJr4 chr1 15678220 15678270 - LTR/ERVL-MaLR_MSTA chr1 15678271 15678567 - SINE/Alu_AluSz chr1 15678568 15678909 - LTR/ERVL-MaLR_MSTA chr1 15678910 15679217 - SINE/Alu_AluSz chr1 15679218 15679268 - LTR/ERVL-MaLR_MSTA chr1 15679332 15679424 - SINE/Alu_AluSx chr1 15679559 15679664 - LTR/ERVL-MaLR_MLT1J2 chr1 15679681 15679823 - LTR/ERVL-MaLR_MLT1J2 chr1 15679857 15679868 + Simple_repeat_(AG)n chr1 15679869 15680151 - SINE/Alu_AluJo chr1 15680152 15680163 + Simple_repeat_(AG)n chr1 15680171 15680340 - LINE/L2_L2 chr1 15680346 15680426 + SINE/Alu_AluSx3 chr1 15680429 15680738 + SINE/Alu_AluSg4 chr1 15680740 15680866 - SINE/MIR_MIR1_Amn chr1 15680969 15681117 - SINE/Alu_FLAM_A chr1 15681923 15682205 + SINE/Alu_AluJr chr1 15682210 15682467 + SINE/Alu_AluSx1 chr1 15682468 15682619 + SINE/Alu_AluSx3 chr1 15682620 15682656 + Simple_repeat_(AAAAC)n chr1 15684300 15684373 + Simple_repeat_(GCC)n chr1 15684501 15684544 + Simple_repeat_(GCG)n chr1 15685393 15685411 + Simple_repeat_(ATA)n chr1 15685468 15685486 + Simple_repeat_(TC)n chr1 15685542 15685590 + Low_complexity_A-rich chr1 15686303 15686467 + SINE/MIR_MIR chr1 15686469 15686648 - SINE/Alu_AluSz chr1 15686649 15686948 - SINE/Alu_AluY chr1 15686949 15687086 - SINE/Alu_AluSz chr1 15687087 15687138 - SINE/Alu_AluSp chr1 15687150 15687182 + Simple_repeat_(TTTTGTT)n chr1 15687183 15687456 - SINE/Alu_AluY chr1 15687971 15688164 - LINE/L1_L1ME3A chr1 15688370 15688672 + SINE/Alu_AluSz chr1 15688967 15689273 + SINE/Alu_AluSp chr1 15689458 15689677 + SINE/MIR_MIRb chr1 15689754 15690055 - SINE/Alu_AluSp chr1 15690060 15690355 - SINE/Alu_AluSz6 chr1 15690399 15690572 - DNA/hAT-Charlie_MER58A chr1 15690748 15691056 - LINE/L2_L2a chr1 15691064 15691371 - SINE/Alu_AluJr chr1 15691831 15692130 + SINE/Alu_AluJo chr1 15692605 15692732 - SINE/Alu_AluJr4 chr1 15692746 15693038 - SINE/Alu_AluSz chr1 15693043 15693338 - SINE/Alu_AluSx1 chr1 15694072 15694378 + SINE/Alu_AluSg chr1 15694747 15695057 - SINE/Alu_AluSz chr1 15695422 15695514 - SINE/MIR_MIR chr1 15695515 15695803 - SINE/Alu_AluSx1 chr1 15695810 15696109 - SINE/Alu_AluJb chr1 15696110 15696245 - SINE/MIR_MIRb chr1 15696340 15696648 - SINE/Alu_AluSq chr1 15696651 15696783 - LINE/L2_L2a chr1 15696816 15696903 - LINE/CR1_X7B_LINE chr1 15697670 15697855 - SINE/Alu_AluJb chr1 15698199 15698504 - SINE/Alu_AluJo chr1 15698520 15698848 - SINE/Alu_AluSx chr1 15699043 15699160 + SINE/Alu_AluJo chr1 15699161 15699185 + Simple_repeat_(TTA)n chr1 15699186 15699671 - LINE/L1_L1PA6 chr1 15699727 15700016 + SINE/Alu_AluJr chr1 15700238 15700305 - SINE/Alu_AluJb chr1 15700466 15700518 + SINE/MIR_MIR3 chr1 15700571 15700614 + Simple_repeat_(TCTCCC)n chr1 15700833 15701129 + SINE/Alu_AluSq2 chr1 15701151 15701458 + SINE/Alu_AluSq2 chr1 15701508 15701808 + SINE/Alu_AluY chr1 15702416 15702717 - SINE/Alu_AluSz chr1 15702718 15703029 - SINE/Alu_AluJr chr1 15703196 15703221 + Simple_repeat_(GCCAG)n chr1 15704215 15704283 + LINE/L2_L2a chr1 15704284 15704323 + Simple_repeat_(CCTC)n chr1 15704324 15704345 + LINE/L2_L2a chr1 15704451 15704668 - LINE/L1_L1MC5 chr1 15704782 15704879 + LINE/L2_L2a chr1 15704884 15704999 - DNA/hAT-Charlie_MER5A chr1 15705107 15705170 + LINE/L2_L2a chr1 15705171 15705481 - SINE/Alu_AluSx3 chr1 15705482 15705809 + LINE/L2_L2a chr1 15705810 15706096 + SINE/Alu_AluJr chr1 15706097 15706392 + LINE/L2_L2a chr1 15706393 15706705 - SINE/Alu_AluSp chr1 15706706 15706883 + LINE/L2_L2a chr1 15707018 15707088 - DNA/hAT-Charlie_MER58A chr1 15707140 15707428 + SINE/Alu_AluJb chr1 15707430 15707537 - DNA/hAT-Charlie_MER58A chr1 15707889 15708184 - SINE/Alu_AluSx1 chr1 15708217 15708514 - SINE/Alu_AluSc chr1 15708595 15708721 - SINE/Alu_AluJr chr1 15708783 15708830 + SINE/MIR_MIRb chr1 15708894 15709131 - SINE/MIR_MIR chr1 15709472 15709518 + DNA/hAT-Charlie_MER3 chr1 15709476 15709525 - DNA/hAT-Charlie_MER3 chr1 15709531 15709673 - SINE/MIR_MIRb chr1 15709774 15709944 + LINE/L1_L1ME3G chr1 15709945 15710245 + SINE/Alu_AluY chr1 15710246 15710316 + LINE/L1_L1ME3G chr1 15710317 15710625 - SINE/Alu_AluSx chr1 15710626 15710649 + LINE/L1_L1ME3G chr1 15710786 15710851 + SINE/Alu_AluJr chr1 15710893 15711219 + SINE/Alu_AluJr chr1 15711334 15711642 + SINE/Alu_AluSx1 chr1 15711680 15711730 + LINE/L1_L1MD chr1 15711821 15711836 + LINE/L1_L1ME3G chr1 15711837 15712139 + SINE/Alu_AluSp chr1 15712140 15712574 + LINE/L1_L1ME3G chr1 15712640 15712837 - SINE/Alu_AluJr chr1 15712838 15713099 - SINE/Alu_AluSz chr1 15713100 15713245 - SINE/Alu_AluJr chr1 15713254 15713299 + Simple_repeat_(TTTC)n chr1 15713614 15713651 + Simple_repeat_(TGTT)n chr1 15713652 15713938 - SINE/Alu_AluY chr1 15713942 15714239 - SINE/Alu_AluSg4 chr1 15714255 15714322 + SINE/MIR_MIR3 chr1 15714349 15714369 + Simple_repeat_(A)n chr1 15715256 15715357 + SINE/Alu_AluJb chr1 15715360 15715672 + SINE/Alu_AluSx1 chr1 15716752 15716798 + SINE/5S-Deu-L2_AmnSINE1 chr1 15716971 15717255 + SINE/Alu_AluJr chr1 15717268 15717423 + SINE/Alu_FRAM chr1 15719168 15719479 + SINE/Alu_AluSx1 chr1 15720129 15720160 + Simple_repeat_(TA)n chr1 15722132 15722443 - SINE/Alu_AluSx chr1 15723471 15723769 + SINE/Alu_AluJo chr1 15724203 15724504 + SINE/Alu_AluSc5 chr1 15724649 15724684 + Simple_repeat_(TGCTCTG)n chr1 15725719 15726054 - LTR/ERVL-MaLR_MLT1A0 chr1 15730047 15730080 + Simple_repeat_(GGGCG)n chr1 15730159 15730459 + SINE/Alu_AluSx1 chr1 15735631 15735849 - SINE/MIR_MIRb chr1 15735917 15735950 + Low_complexity_G-rich chr1 15737287 15737599 + SINE/Alu_AluSx chr1 15737651 15737792 - SINE/MIR_MIRb chr1 15738782 15738826 + Simple_repeat_(AC)n chr1 15738873 15739087 - SINE/MIR_MIRb chr1 15739698 15739897 - SINE/MIR_MIRb chr1 15740009 15740278 - LTR/ERVL-MaLR_MLT1B chr1 15740279 15740577 - SINE/Alu_AluSz chr1 15740578 15740696 - LTR/ERVL-MaLR_MLT1B chr1 15740697 15740982 - SINE/Alu_AluSx3 chr1 15741923 15742226 - SINE/Alu_AluSx3 chr1 15742423 15742481 + Simple_repeat_(CTCCC)n chr1 15743443 15743620 - SINE/MIR_MIRb chr1 15743672 15743984 + SINE/Alu_AluSx1 chr1 15744745 15744967 - LINE/L2_L2c chr1 15745046 15745239 - SINE/Alu_AluJr chr1 15745240 15745545 + SINE/Alu_AluSz6 chr1 15745546 15745610 - SINE/Alu_AluJr chr1 15745611 15745913 + SINE/Alu_AluSq2 chr1 15745914 15746023 - SINE/Alu_AluJr chr1 15746035 15746166 - SINE/Alu_AluSx chr1 15746176 15746199 + Simple_repeat_(CAAA)n chr1 15746202 15746429 + SINE/Alu_AluSz chr1 15746435 15746612 - LINE/L2_L2c chr1 15747145 15747306 + SINE/Alu_AluSx chr1 15748011 15748322 + SINE/Alu_AluSx1 chr1 15748729 15748826 + SINE/MIR_MIRc chr1 15748833 15749077 - SINE/MIR_MIRb chr1 15750423 15750527 - LINE/L2_L2b chr1 15750549 15750858 + SINE/Alu_AluSx chr1 15750908 15751099 - LTR/ERVL-MaLR_MLT1F2 chr1 15751082 15751348 - LTR/ERVL-MaLR_MLT1F2 chr1 15751349 15751506 - LINE/L2_L2b chr1 15751507 15751870 + SINE/Alu_AluSg4 chr1 15751871 15752034 - LINE/L2_L2b chr1 15752054 15752368 - LINE/L2_L2 chr1 15752373 15752509 + SINE/Alu_AluY chr1 15752510 15752779 + SINE/Alu_AluY chr1 15752780 15752958 + SINE/Alu_AluY chr1 15752972 15753051 - DNA/hAT-Tip100_Arthur1C chr1 15753052 15753330 - SINE/Alu_AluSx chr1 15753331 15753581 - DNA/hAT-Tip100_Arthur1C chr1 15754148 15754308 - SINE/Alu_AluSg4 chr1 15754316 15754614 - SINE/Alu_AluSq2 chr1 15754691 15754703 + Simple_repeat_(AAGAAAA)n chr1 15754704 15755019 + SINE/Alu_AluYj4 chr1 15755020 15755050 + Simple_repeat_(AAGAAAA)n chr1 15755068 15755123 + SINE/Alu_FRAM chr1 15755125 15755250 - LTR/ERVL-MaLR_MLT1K chr1 15755372 15755443 - LTR/ERVL-MaLR_MLT1K chr1 15755560 15755807 - SINE/MIR_MIRc chr1 15755840 15755907 + LINE/L2_L2d2 chr1 15755920 15755957 + Simple_repeat_(AGGATGG)n chr1 15755997 15756352 + LINE/L2_L2a chr1 15757263 15757466 + SINE/MIR_MIRb chr1 15758112 15758311 - SINE/MIR_MIRb chr1 15758465 15758535 + Simple_repeat_(CCCCATT)n chr1 15759647 15759791 - SINE/MIR_MIRb chr1 15759942 15760242 + SINE/Alu_AluSx chr1 15760249 15760555 + SINE/Alu_AluSz chr1 15760581 15760603 + Simple_repeat_(T)n chr1 15760685 15760709 + Simple_repeat_(CTGG)n chr1 15761338 15761517 + SINE/MIR_MIRb chr1 15762088 15762256 - SINE/Alu_AluSz chr1 15762257 15762560 - SINE/Alu_AluSg7 chr1 15762561 15762680 - SINE/Alu_AluSz chr1 15762855 15762990 - LINE/L2_L2c chr1 15763066 15763341 - SINE/Alu_AluSq2 chr1 15763355 15763661 + SINE/Alu_AluY chr1 15763662 15763938 - SINE/Alu_AluSx1 chr1 15764152 15764344 + SINE/MIR_MIR chr1 15765306 15765328 + Simple_repeat_(TCAT)n chr1 15766033 15766103 - DNA/hAT-Charlie_MER5A chr1 15766104 15766276 - SINE/Alu_AluJb chr1 15766277 15766307 + Simple_repeat_(TG)n chr1 15766311 15766596 - SINE/Alu_AluSx1 chr1 15766600 15766891 - SINE/Alu_AluSp chr1 15766892 15767192 - SINE/Alu_AluJr chr1 15767194 15767283 - SINE/MIR_MIRb chr1 15767533 15767585 + Simple_repeat_(GCCCCC)n chr1 15767695 15767865 - LINE/CR1_L3b chr1 15767879 15768182 - SINE/Alu_AluSx1 chr1 15768320 15768449 + SINE/MIR_MIRc chr1 15768886 15769194 - LINE/L1_L1ME4b chr1 15769195 15769505 + SINE/Alu_AluSx chr1 15769522 15769590 + SINE/Alu_FLAM_C chr1 15769631 15769931 - SINE/Alu_AluSc chr1 15769934 15770245 - SINE/Alu_AluSz chr1 15770599 15770738 - SINE/MIR_MIRb chr1 15770739 15770921 - LTR/ERVL-MaLR_MLT1D chr1 15770922 15771237 - SINE/Alu_AluSg4 chr1 15771240 15771545 - SINE/Alu_AluSx1 chr1 15771546 15771822 - LTR/ERVL-MaLR_MLT1D chr1 15771823 15771904 - SINE/MIR_MIRb chr1 15772055 15772196 + DNA/hAT-Charlie_MER5A chr1 15772673 15772789 + SINE/Alu_AluJr chr1 15772881 15773084 - SINE/Alu_AluSx1 chr1 15773147 15773396 + SINE/Alu_AluSx chr1 15773399 15773700 + SINE/Alu_AluSp chr1 15773701 15773737 + LINE/L1_L1MB1 chr1 15773747 15773861 - LINE/RTE-X_L4_A_Mam chr1 15773862 15774164 + SINE/Alu_AluSz chr1 15774165 15774338 - LINE/RTE-X_L4_A_Mam chr1 15774901 15774936 - LTR/ERVL_MER21B chr1 15774937 15775246 + SINE/Alu_AluY chr1 15775256 15775554 + SINE/Alu_AluSx4 chr1 15775555 15776121 - LTR/ERVL_MER21A chr1 15776137 15776288 + SINE/Alu_AluSx chr1 15776483 15776554 - LINE/L2_L2c chr1 15776583 15776902 + SINE/Alu_AluSq2 chr1 15777323 15777422 - LINE/L2_L2c chr1 15777448 15777572 - LTR/ERVL-MaLR_MLT1K chr1 15777573 15777872 - SINE/Alu_AluSx chr1 15777873 15778134 - LTR/ERVL-MaLR_MLT1K chr1 15778283 15778417 + SINE/Alu_FLAM_C chr1 15778424 15778628 - LINE/L2_L2c chr1 15778637 15778940 - SINE/Alu_AluSp chr1 15778942 15779235 - SINE/Alu_AluSx chr1 15779292 15779602 - SINE/Alu_AluSz chr1 15779799 15779873 + DNA/hAT-Tip100_MER45A chr1 15779874 15780190 - SINE/Alu_AluJr chr1 15780191 15780203 + DNA/hAT-Tip100_MER45A chr1 15780204 15780494 - SINE/Alu_AluSz chr1 15780495 15780571 + DNA/hAT-Tip100_MER45A chr1 15781087 15781364 + SINE/Alu_AluSc chr1 15781365 15781542 + SINE/Alu_AluSx3 chr1 15781716 15782017 - SINE/Alu_AluY chr1 15782018 15782130 - SINE/MIR_MIRc chr1 15782134 15782424 + SINE/Alu_AluJb chr1 15782811 15783093 - SINE/Alu_AluSg chr1 15783336 15783481 + SINE/MIR_MIR3 chr1 15783567 15783881 - SINE/Alu_AluY chr1 15783911 15784203 + SINE/Alu_AluSz chr1 15785052 15785342 + SINE/Alu_AluSq2 chr1 15785353 15785645 + SINE/Alu_AluSz chr1 15786378 15786585 - DNA/hAT-Charlie_MER113A chr1 15786851 15787134 + SINE/Alu_AluSz chr1 15787152 15787418 + SINE/Alu_AluJb chr1 15787528 15787630 + SINE/MIR_MIRc chr1 15787657 15787964 + SINE/Alu_AluSg chr1 15787965 15788006 + Simple_repeat_(TA)n chr1 15788009 15788305 + SINE/Alu_AluSc chr1 15788308 15788610 + SINE/Alu_AluSp chr1 15788611 15788728 - SINE/Alu_AluJo chr1 15788875 15789089 - LTR/ERV1_MER90a chr1 15789237 15789545 + SINE/Alu_AluSp chr1 15789572 15789664 + Simple_repeat_(AT)n chr1 15789665 15789699 + Simple_repeat_(TG)n chr1 15790020 15790214 + LINE/L1_L1MB3 chr1 15790215 15790518 + SINE/Alu_AluSz chr1 15790519 15790819 + SINE/Alu_AluSx4 chr1 15790820 15791055 + LINE/L1_L1MB3 chr1 15791056 15791323 + SINE/Alu_AluJb chr1 15791332 15791402 + SINE/Alu_AluSx1 chr1 15791403 15791703 + SINE/Alu_AluY chr1 15791704 15791857 + SINE/Alu_AluSx1 chr1 15791858 15791983 + LINE/L1_L1MB3 chr1 15792383 15792713 + SINE/Alu_AluSx chr1 15793363 15793419 + SINE/Alu_AluSx4 chr1 15793420 15793729 + SINE/Alu_AluSp chr1 15793730 15793905 + SINE/Alu_AluSx4 chr1 15794366 15794503 - SINE/MIR_MIR chr1 15794667 15794962 - SINE/Alu_AluJr chr1 15794984 15795116 - SINE/Alu_FLAM_C chr1 15795295 15795547 + LTR/ERV1_LTR37A chr1 15795548 15795844 - SINE/Alu_AluSx chr1 15795845 15795966 + LTR/ERV1_LTR37A chr1 15796123 15796155 + Low_complexity_GA-rich chr1 15796205 15796336 + SINE/Alu_FLAM_C chr1 15796641 15796735 - SINE/MIR_MIRb chr1 15796908 15797069 + LINE/L1_L1MD3 chr1 15797070 15797368 - SINE/Alu_AluY chr1 15797371 15797667 - SINE/Alu_AluSc chr1 15798119 15798235 + SINE/MIR_MIRc chr1 15798282 15798414 + SINE/Alu_AluSx chr1 15798419 15798723 + SINE/Alu_AluSp chr1 15798728 15799033 + SINE/Alu_AluSp chr1 15799468 15799490 + Simple_repeat_(TTCA)n chr1 15799491 15799588 - LINE/L2_L2b chr1 15799617 15799719 - LINE/L2_L2b chr1 15799938 15800084 - SINE/MIR_MIR3 chr1 15800802 15800862 - SINE/MIR_MIRb chr1 15800857 15801248 - LINE/L2_L2b chr1 15801291 15801352 + LINE/L2_L2b chr1 15801412 15801709 - SINE/Alu_AluSx1 chr1 15801731 15801791 - LINE/L1_L1MB7 chr1 15801793 15802092 - SINE/Alu_AluJb chr1 15802123 15802583 + LTR/ERV1_LTR2 chr1 15803086 15803390 - SINE/Alu_AluSg4 chr1 15803423 15803584 - DNA/hAT-Charlie_MER5A1 chr1 15803589 15803875 + SINE/Alu_AluJo chr1 15803984 15804273 - SINE/Alu_AluSx chr1 15804277 15804452 + LINE/L2_L2a chr1 15804508 15804797 - SINE/Alu_AluSz chr1 15804823 15804921 + SINE/Alu_AluJr chr1 15804929 15805235 - SINE/Alu_AluSq4 chr1 15805388 15805524 + SINE/Alu_AluJo chr1 15805527 15805840 + SINE/Alu_AluSp chr1 15805886 15806183 - SINE/Alu_AluY chr1 15806257 15806508 - SINE/Alu_AluJr chr1 15806542 15806839 + SINE/Alu_AluSx1 chr1 15806856 15806975 + SINE/Alu_FLAM_C chr1 15807012 15807181 - SINE/Alu_AluSx chr1 15807576 15807601 + Simple_repeat_(TCC)n chr1 15807698 15807822 - SINE/Alu_AluSx chr1 15807898 15808203 + SINE/Alu_AluSx1 chr1 15808232 15808356 - SINE/Alu_AluSc chr1 15808491 15808791 - SINE/Alu_AluSx chr1 15808850 15808872 + Simple_repeat_(CACG)n chr1 15809304 15809614 - SINE/Alu_AluSz chr1 15809666 15809951 - SINE/Alu_AluJr chr1 15809973 15810168 + SINE/MIR_MIRc chr1 15810521 15810816 + SINE/Alu_AluJb chr1 15810818 15810907 + LINE/CR1_L3 chr1 15810963 15811260 + SINE/Alu_AluY chr1 15811538 15811697 - LINE/L2_L2b chr1 15811778 15812075 - SINE/Alu_AluSx chr1 15812281 15812467 - SINE/MIR_MIRc chr1 15812468 15812523 - SINE/Alu_Alu chr1 15812521 15812575 + SINE/Alu_AluJo chr1 15812550 15812633 + SINE/Alu_FRAM chr1 15812699 15812918 - SINE/Alu_AluJr chr1 15812951 15813056 - SINE/Alu_FRAM chr1 15813059 15813258 + SINE/Alu_AluJr chr1 15813283 15813573 + SINE/Alu_AluSc8 chr1 15813609 15813724 + DNA/hAT-Charlie_MER58B chr1 15813725 15813864 + SINE/Alu_AluSx chr1 15813865 15814163 + SINE/Alu_AluY chr1 15814164 15814330 + SINE/Alu_AluSx chr1 15814331 15814423 + DNA/hAT-Charlie_MER58B chr1 15814425 15814724 - SINE/Alu_AluSg4 chr1 15814725 15815017 - SINE/Alu_AluSx3 chr1 15815018 15815296 - SINE/Alu_AluSx chr1 15815345 15815404 + SINE/Alu_AluYe5 chr1 15815449 15815559 - SINE/MIR_MIR1_Amn chr1 15815881 15815902 + Simple_repeat_(T)n chr1 15816028 15816330 - SINE/Alu_AluSc chr1 15816551 15816610 + SINE/5S-Deu-L2_AmnSINE1 chr1 15816983 15817291 - SINE/Alu_AluSq2 chr1 15817877 15818171 + SINE/Alu_AluSx3 chr1 15818271 15818567 + SINE/Alu_AluJo chr1 15818667 15818763 - SINE/Alu_FLAM_C chr1 15818777 15819086 + SINE/Alu_AluSz chr1 15819246 15819555 + SINE/Alu_AluY chr1 15819557 15819862 + SINE/Alu_AluSx3 chr1 15819898 15819982 - SINE/MIR_MIRc chr1 15820023 15820119 - LINE/L2_L2a chr1 15820227 15820535 - SINE/Alu_AluSx chr1 15820589 15820760 + LINE/L2_L2 chr1 15820773 15820987 + SINE/Alu_AluJr chr1 15821096 15821309 + SINE/Alu_AluSx chr1 15821313 15821608 + SINE/Alu_AluSc chr1 15821609 15821692 + SINE/Alu_AluSx chr1 15821706 15822007 - SINE/Alu_AluY chr1 15822137 15822442 + SINE/Alu_AluYk2 chr1 15822595 15822662 - SINE/Alu_AluSx chr1 15822885 15823191 + SINE/Alu_AluSx chr1 15823197 15823500 + SINE/Alu_AluSp chr1 15823619 15823804 + SINE/Alu_AluSp chr1 15823840 15824147 - SINE/Alu_AluSq2 chr1 15824195 15824330 + SINE/Alu_AluSz chr1 15824331 15824626 + SINE/Alu_AluSc chr1 15824627 15824786 + SINE/Alu_AluSz chr1 15824834 15825138 - SINE/Alu_AluSx chr1 15825140 15825274 - SINE/Alu_AluSx chr1 15825354 15825392 + SINE/Alu_AluSx chr1 15825393 15825700 + SINE/Alu_AluSg chr1 15825701 15825781 + SINE/Alu_FLAM_C chr1 15825967 15826182 + LINE/L2_L2-3_Crp chr1 15826185 15826478 - SINE/Alu_AluSg chr1 15826492 15826791 - SINE/Alu_AluSp chr1 15826792 15826927 - SINE/Alu_AluSz chr1 15826936 15827469 + LINE/L2_L2a chr1 15827470 15827730 + SINE/Alu_AluSx3 chr1 15827731 15827889 + SINE/Alu_AluSx3 chr1 15827891 15828199 + SINE/Alu_AluSx1 chr1 15828749 15828924 + SINE/Alu_AluSx chr1 15828972 15829043 + LINE/L2_L2a chr1 15829044 15829328 - SINE/Alu_AluY chr1 15829329 15829386 + LINE/L2_L2a chr1 15829488 15829642 - SINE/Alu_AluJb chr1 15829644 15829936 - SINE/Alu_AluJr4 chr1 15829943 15830115 + LINE/L2_L2a chr1 15830116 15830275 + SINE/Alu_FRAM chr1 15830276 15830548 + LINE/L2_L2a chr1 15830753 15831047 - SINE/Alu_AluSx3 chr1 15831168 15831252 - LTR?_MER129 chr1 15831351 15831374 + Simple_repeat_(A)n chr1 15831412 15831723 - SINE/Alu_AluY chr1 15832147 15832185 + SINE/tRNA-RTE_MamSINE1 chr1 15832213 15832511 - SINE/Alu_AluSp chr1 15832620 15832893 - SINE/Alu_AluY chr1 15833181 15833305 + SINE/Alu_AluJb chr1 15833306 15833575 + SINE/Alu_AluSz chr1 15833576 15833725 + SINE/Alu_AluJb chr1 15834460 15834478 + Simple_repeat_(T)n chr1 15835912 15835945 + Simple_repeat_(GC)n chr1 15836483 15836777 - SINE/Alu_AluSx1 chr1 15836796 15836907 - SINE/Alu_AluJr chr1 15836911 15836932 + Simple_repeat_(A)n chr1 15837199 15837233 + Low_complexity_A-rich chr1 15837969 15838246 - SINE/Alu_AluJr chr1 15838431 15838727 + SINE/Alu_AluSx1 chr1 15838891 15839039 + SINE/Alu_FRAM chr1 15839190 15839494 - SINE/Alu_AluSp chr1 15839549 15839685 + SINE/Alu_AluJb chr1 15839686 15839977 + SINE/Alu_AluSc chr1 15839978 15840138 + SINE/Alu_AluJb chr1 15840186 15840342 - SINE/Alu_AluSg4 chr1 15840343 15840637 - SINE/Alu_AluSc8 chr1 15840645 15840808 - SINE/Alu_AluSx chr1 15840809 15841106 - SINE/Alu_AluSg chr1 15841107 15841243 - SINE/Alu_AluSx chr1 15841580 15841845 - SINE/Alu_AluSx chr1 15841886 15842185 + SINE/Alu_AluSz chr1 15842233 15842257 + Simple_repeat_(ATTT)n chr1 15842258 15842317 - SINE/Alu_AluSq2 chr1 15842478 15842785 - SINE/Alu_AluSg7 chr1 15842848 15843095 + SINE/Alu_AluSx chr1 15843103 15843140 + Simple_repeat_(TA)n chr1 15843141 15843439 + SINE/Alu_AluSx1 chr1 15843440 15843453 + Simple_repeat_(TA)n chr1 15843454 15843746 - SINE/Alu_AluSc8 chr1 15844552 15844856 - SINE/Alu_AluSx chr1 15844998 15845054 - LINE/CR1_X6A_LINE chr1 15845265 15845424 - SINE/Alu_FAM chr1 15845567 15845873 + SINE/Alu_AluSc chr1 15846020 15846321 + SINE/Alu_AluSq2 chr1 15846324 15846629 + SINE/Alu_AluSp chr1 15846832 15847128 + SINE/Alu_AluSx1 chr1 15847646 15847705 + Simple_repeat_(CG)n chr1 15847934 15847981 + Simple_repeat_(GCC)n chr1 15848302 15848341 + Simple_repeat_(GCGTCGG)n chr1 15850426 15850612 - LINE/L1_L1ME4b chr1 15851112 15851171 + Simple_repeat_(ATTTTT)n chr1 15851703 15851997 + SINE/Alu_AluSx3 chr1 15852920 15853151 - SINE/Alu_AluJb chr1 15853157 15853177 - SINE/Alu_AluSx chr1 15853178 15853485 - SINE/Alu_AluSx chr1 15853486 15853510 + Simple_repeat_(TATTTT)n chr1 15853511 15853660 - SINE/Alu_AluSz chr1 15853661 15853964 - SINE/Alu_AluSg chr1 15853965 15854100 - SINE/Alu_AluSz chr1 15854486 15854780 - SINE/Alu_AluSp chr1 15855579 15855877 + SINE/Alu_AluSx3 chr1 15855991 15856289 - SINE/Alu_AluYk2 chr1 15856318 15856565 + DNA/hAT-Charlie_Charlie4a chr1 15856566 15856867 - SINE/Alu_AluSx1 chr1 15856868 15856955 + DNA/hAT-Charlie_Charlie4a chr1 15856958 15857048 - LINE/L1_L1MC5 chr1 15857067 15857364 - SINE/Alu_AluJb chr1 15857365 15857656 - SINE/Alu_AluSz chr1 15857701 15857856 - SINE/Alu_AluJr chr1 15857857 15858141 + SINE/Alu_AluSc5 chr1 15858142 15858252 - SINE/Alu_AluJr chr1 15858286 15858487 - LINE/L1_L1MC4 chr1 15858490 15858618 + DNA/TcMar-Tigger_Tigger3c chr1 15858654 15858964 + SINE/Alu_AluJb chr1 15859133 15859316 - SINE/Alu_FAM chr1 15859348 15859668 - SINE/Alu_AluYm1 chr1 15859906 15860209 - SINE/Alu_AluY chr1 15860246 15860378 - SINE/Alu_AluJb chr1 15860379 15860476 + Simple_repeat_(GT)n chr1 15860477 15860575 - SINE/Alu_AluJb chr1 15860610 15860897 - SINE/Alu_AluSc8 chr1 15860900 15861128 - SINE/Alu_AluSx4 chr1 15861135 15861429 - SINE/Alu_AluSg chr1 15861638 15861754 + Retroposon/SVA_SVA_E chr1 15861660 15861755 - SINE/Alu_AluJr chr1 15861760 15862068 + SINE/Alu_AluSx3 chr1 15862733 15862762 + SINE/Alu_Alu chr1 15862767 15863060 - SINE/Alu_AluY chr1 15863094 15863261 + SINE/Alu_AluJb chr1 15863265 15863288 + Simple_repeat_(CA)n chr1 15863559 15863861 + SINE/Alu_AluJb chr1 15863872 15864167 + LINE/L1_L1MB3 chr1 15864168 15864468 - SINE/Alu_AluSz6 chr1 15864484 15864586 - LINE/L1_L1ME4b chr1 15864609 15865000 - SINE/Alu_AluJr chr1 15865061 15865351 - SINE/Alu_AluSx chr1 15865421 15865725 - SINE/Alu_AluSx chr1 15865875 15865978 - SINE/Alu_AluSx3 chr1 15866343 15866651 - SINE/Alu_AluY chr1 15866962 15867364 - LINE/L1_L1MB7 chr1 15867365 15867496 - SINE/Alu_FLAM_C chr1 15867497 15867595 - LINE/L1_L1MB7 chr1 15867596 15867643 - SINE/Alu_AluYk3 chr1 15867651 15867851 - LINE/L1_L1MB7 chr1 15867865 15868132 - SINE/Alu_AluSz6 chr1 15868154 15868185 + Simple_repeat_(TA)n chr1 15868186 15868306 - SINE/Alu_AluJr chr1 15868353 15868400 - LINE/L1_L1MB7 chr1 15868439 15868733 - SINE/Alu_AluY chr1 15869064 15869355 - SINE/Alu_AluSx3 chr1 15869581 15869615 + Simple_repeat_(TATT)n chr1 15869616 15869745 - SINE/Alu_AluJb chr1 15869873 15870169 - SINE/Alu_AluSx chr1 15870985 15871267 - SINE/Alu_AluSg chr1 15871341 15871593 - SINE/Alu_AluSz6 chr1 15871953 15872244 + SINE/Alu_AluSx chr1 15872311 15872619 + SINE/Alu_AluY chr1 15876649 15876672 + Simple_repeat_(CAG)n chr1 15877284 15877565 - SINE/Alu_AluSz chr1 15877739 15878044 - SINE/Alu_AluSq2 chr1 15878727 15879034 + SINE/Alu_AluSx chr1 15879057 15879297 + SINE/Alu_AluSx1 chr1 15879676 15879969 - SINE/Alu_AluY chr1 15879972 15880064 + DNA/hAT-Charlie_Charlie8 chr1 15880454 15880756 - SINE/Alu_AluSz chr1 15880988 15881264 - SINE/Alu_AluJr chr1 15882380 15882671 + SINE/Alu_AluSg4 chr1 15883423 15883592 - SINE/Alu_AluSz6 chr1 15883805 15884108 - SINE/Alu_AluYf1 chr1 15884724 15885030 - SINE/Alu_AluSz chr1 15885203 15885248 - SINE/Alu_Alu chr1 15886992 15887272 - SINE/Alu_AluJo chr1 15887277 15887594 - SINE/Alu_AluY chr1 15887695 15887736 + srpRNA_7SLRNA chr1 15887743 15888035 + SINE/Alu_AluSz chr1 15888098 15888235 - SINE/Alu_AluSx3 chr1 15888311 15888623 - SINE/Alu_AluSx chr1 15888639 15888929 - SINE/Alu_AluSp chr1 15889151 15889465 - SINE/Alu_AluSq2 chr1 15889711 15890014 - SINE/Alu_AluSx1 chr1 15890272 15890292 + Simple_repeat_(ATTTTT)n chr1 15890293 15890529 - SINE/Alu_AluSg chr1 15890530 15890544 + Simple_repeat_(ATTTTT)n chr1 15890559 15890642 - SINE/Alu_AluSx3 chr1 15890645 15890943 - LINE/L1_L1MB8 chr1 15890952 15891026 - DNA/hAT-Charlie_MER33 chr1 15891199 15891352 - SINE/Alu_AluJr chr1 15891353 15891649 - SINE/Alu_AluYm1 chr1 15891650 15891787 - SINE/Alu_AluJr chr1 15891923 15892001 + DNA/hAT-Charlie_MER33 chr1 15892002 15892322 - SINE/Alu_AluY chr1 15892323 15892397 + DNA/hAT-Charlie_MER33 chr1 15892817 15893122 + SINE/Alu_AluSp chr1 15893689 15893751 + srpRNA_7SLRNA chr1 15893763 15894021 + SINE/Alu_AluSz6 chr1 15894534 15894832 - SINE/Alu_AluSc chr1 15894878 15894919 - LINE/L1_L1MB3 chr1 15894920 15895211 - SINE/Alu_AluSp chr1 15895212 15895533 - LINE/L1_L1MB3 chr1 15895679 15895983 - SINE/Alu_AluSx chr1 15896188 15896495 - SINE/Alu_AluSx chr1 15896768 15896980 + SINE/Alu_AluJr chr1 15897197 15897355 - SINE/Alu_AluSx chr1 15897356 15897661 - SINE/Alu_AluSp chr1 15897662 15897798 - SINE/Alu_AluSx chr1 15898174 15898210 + Simple_repeat_(TG)n chr1 15898211 15898539 - LINE/L1_L1MB3 chr1 15898540 15898862 - SINE/Alu_AluSx chr1 15898863 15899161 - LINE/L1_L1MB3 chr1 15899162 15899478 - SINE/Alu_AluJb chr1 15899479 15899532 - LINE/L1_L1MB3 chr1 15899538 15899563 + Simple_repeat_(T)n chr1 15899570 15899658 - SINE/Alu_FLAM_A chr1 15899669 15899810 - LINE/L1_L1M3 chr1 15899836 15900145 - SINE/Alu_AluSx chr1 15900771 15900907 - DNA/hAT-Charlie_MER20 chr1 15900910 15901026 - SINE/MIR_MIR3 chr1 15901070 15901358 + SINE/Alu_AluSz chr1 15901361 15901669 + SINE/Alu_AluSz chr1 15901757 15901928 - DNA/hAT-Charlie_Charlie1 chr1 15901929 15902228 - SINE/Alu_AluSz6 chr1 15902229 15902372 - DNA/hAT-Charlie_Charlie1 chr1 15902375 15902437 - DNA/hAT-Charlie_Charlie1 chr1 15902530 15902657 + SINE/Alu_AluJo chr1 15903428 15903644 - SINE/Alu_AluJb chr1 15903646 15903784 - SINE/Alu_AluJo chr1 15904196 15904477 + SINE/Alu_AluSq2 chr1 15904667 15904749 - SINE/Alu_AluSp chr1 15904880 15905187 - SINE/Alu_AluSq2 chr1 15905194 15905231 + Simple_repeat_(ATTTTT)n chr1 15905233 15905521 - SINE/Alu_AluSq2 chr1 15905574 15905872 - SINE/Alu_AluSc chr1 15906430 15906762 - SINE/Alu_AluSz6 chr1 15906766 15907071 - SINE/Alu_AluJr chr1 15907200 15907489 + SINE/Alu_AluSz chr1 15908163 15908188 + Simple_repeat_(TAC)n chr1 15908231 15908363 - SINE/Alu_FLAM_C chr1 15908371 15908415 - SINE/MIR_MIR chr1 15908416 15908709 - SINE/Alu_AluSc chr1 15908710 15908773 - SINE/MIR_MIR chr1 15908895 15909003 - SINE/MIR_MIRb chr1 15909844 15910151 + SINE/Alu_AluY chr1 15910490 15910848 - LINE/L1_L1MC5a chr1 15910896 15911000 + snRNA_U13_ chr1 15911679 15911990 + SINE/Alu_AluSx chr1 15912231 15912306 + DNA/hAT-Charlie_MER5A1 chr1 15912900 15913051 + DNA/hAT-Charlie_Charlie13a chr1 15913061 15913442 + DNA/hAT-Charlie_Charlie13a chr1 15913471 15913625 - SINE/Alu_AluJb chr1 15913626 15913886 + SINE/Alu_AluJr chr1 15913895 15914438 + DNA/hAT-Charlie_Charlie13a chr1 15914541 15914848 + SINE/Alu_AluSq2 chr1 15915229 15915496 + SINE/Alu_AluJb chr1 15915526 15915828 - SINE/Alu_AluJb chr1 15916499 15916728 - SINE/Alu_AluJb chr1 15916845 15917133 + SINE/Alu_AluSz chr1 15917401 15917687 - SINE/Alu_AluSq2 chr1 15918142 15918175 + Simple_repeat_(TTAT)n chr1 15918221 15918518 - SINE/Alu_AluSq2 chr1 15920053 15920360 - SINE/Alu_AluSz6 chr1 15920844 15920864 + Simple_repeat_(TTGT)n chr1 15921044 15921342 + SINE/Alu_AluSx chr1 15922631 15922913 - SINE/Alu_AluJb chr1 15923067 15923094 + Simple_repeat_(TAAA)n chr1 15923674 15923967 - SINE/Alu_AluSx4 chr1 15924461 15924759 - SINE/Alu_AluSp chr1 15925071 15925141 - SINE/MIR_MIRc chr1 15925402 15925490 + LINE/L2_L2d chr1 15925596 15925714 - SINE/Alu_FLAM_C chr1 15926096 15926113 + Simple_repeat_(ATATAT)n chr1 15926114 15926370 + SINE/Alu_AluSx chr1 15926371 15926384 + Simple_repeat_(ATATAT)n chr1 15926385 15926413 + Simple_repeat_(AC)n chr1 15926414 15926464 + Simple_repeat_(ATATAT)n chr1 15926579 15926691 - LINE/L2_L2a chr1 15926692 15926991 - SINE/Alu_AluY chr1 15927117 15927241 - DNA/TcMar-Tigger_X6b_DNA chr1 15927296 15927431 - SINE/MIR_MIR chr1 15927696 15927946 - DNA/hAT-Charlie_MER33 chr1 15935962 15936029 + Simple_repeat_(CTGCCC)n chr1 15936359 15936655 + SINE/Alu_AluJb chr1 15936656 15936778 + SINE/Alu_AluSz chr1 15938136 15938415 - SINE/Alu_AluSz chr1 15938566 15938584 + Simple_repeat_(T)n chr1 15940090 15940127 + Simple_repeat_(TA)n chr1 15945134 15945177 + Simple_repeat_(CCT)n chr1 15946422 15946786 - LINE/L2_L2a chr1 15946837 15946863 - SINE/MIR_MIR3 chr1 15947692 15947805 + SINE/MIR_MIRb chr1 15948730 15948988 - DNA/hAT-Charlie_Charlie18a chr1 15950925 15950989 + Simple_repeat_(GGAT)n chr1 15951221 15951340 + Low_complexity_GA-rich chr1 15952518 15952720 + LTR/ERVL-MaLR_MLT1H chr1 15952815 15952969 + LTR/ERVL-MaLR_MLT1H chr1 15952974 15953057 + LINE/L1_L1ME3D chr1 15953181 15953312 - SINE/Alu_FLAM_C chr1 15953684 15953882 + SINE/MIR_MIR chr1 15954578 15954867 + SINE/Alu_AluJb chr1 15954873 15955179 + SINE/Alu_AluSz chr1 15955668 15955821 + SINE/Alu_AluJb chr1 15955822 15955874 + SINE/Alu_FRAM chr1 15956385 15956681 - DNA/hAT-Charlie_Charlie23a chr1 15956761 15956898 - LINE/L2_L2a chr1 15956899 15957206 + SINE/Alu_AluSx3 chr1 15957207 15957379 - LINE/L2_L2a chr1 15957429 15957950 - LINE/L2_L2a chr1 15957956 15958063 - LINE/L2_L2a chr1 15958025 15958121 + SINE/MIR_MIRb chr1 15958416 15958434 + Simple_repeat_(A)n chr1 15959305 15959447 + LINE/L1_L1ME4b chr1 15959482 15959651 + Low_complexity_GA-rich chr1 15959652 15959802 + SINE/Alu_AluJb chr1 15960247 15960348 + SINE/MIR_MIR1_Amn chr1 15960915 15961127 + SINE/Alu_AluJb chr1 15961142 15961172 + Simple_repeat_(TGAA)n chr1 15961924 15962237 + SINE/Alu_AluJb chr1 15962435 15962638 - SINE/MIR_MIRb chr1 15962804 15962975 + SINE/Alu_FAM chr1 15962981 15963103 + SINE/Alu_FLAM_C chr1 15963223 15963314 + LINE/L1_L1ME3F chr1 15963676 15963828 + LINE/L1_L1ME3F chr1 15963829 15964137 + SINE/Alu_AluJb chr1 15964138 15964278 + LINE/L1_L1ME3F chr1 15964277 15964435 + LINE/L1_L1ME3F chr1 15964436 15964736 + SINE/Alu_AluJb chr1 15964737 15964759 + LINE/L1_L1ME3F chr1 15964817 15964833 + LINE/L1_L1ME3F chr1 15964834 15965132 + SINE/Alu_AluSx3 chr1 15965133 15966042 + LINE/L1_L1ME3F chr1 15966800 15966912 + SINE/Alu_FRAM chr1 15966916 15966937 + Simple_repeat_(AATT)n chr1 15967107 15967385 + SINE/Alu_AluJb chr1 15967397 15967529 + SINE/Alu_FLAM_C chr1 15967696 15967930 - SINE/MIR_MIR chr1 15967931 15968128 + DNA/hAT-Charlie_MER20 chr1 15968324 15968619 - LINE/L2_L2c chr1 15968921 15969471 + DNA/hAT-Charlie_MER1A chr1 15970394 15970549 - SINE/Alu_AluSg4 chr1 15970550 15970846 - SINE/Alu_AluSg4 chr1 15971277 15971300 + Simple_repeat_(TGGG)n chr1 15971322 15971623 + Simple_repeat_(TA)n chr1 15971716 15971824 - SINE/MIR_MIRb chr1 15972184 15972271 + LINE/L2_L2a chr1 15972418 15972539 - DNA/TcMar-Tigger_MamRep137 chr1 15972540 15972796 + LTR/ERVL-MaLR_MLT1J chr1 15973208 15973283 + SINE/MIR_MIRb chr1 15973273 15973406 - LINE/L2_L2b chr1 15973532 15973903 + LINE/L2_L2c chr1 15973908 15974220 + SINE/Alu_AluJr chr1 15974258 15974473 - SINE/Alu_AluJr chr1 15974573 15974875 - SINE/Alu_AluSz chr1 15975106 15975157 + LINE/L2_L2c chr1 15975180 15975399 - SINE/MIR_MIR chr1 15976264 15976536 - SINE/MIR_MIRb chr1 15976615 15976926 + LINE/L2_L2 chr1 15977025 15977322 - SINE/Alu_AluYk3 chr1 15977335 15977461 - SINE/Alu_FLAM_A chr1 15977496 15977622 - LINE/L1_L1PB1 chr1 15977623 15977928 - SINE/Alu_AluY chr1 15977929 15978618 - LINE/L1_L1PB1 chr1 15978619 15978928 + SINE/Alu_AluSx1 chr1 15978929 15979624 - LINE/L1_L1PB1 chr1 15979703 15979790 - LINE/L1_L1PA8 chr1 15979841 15980135 + SINE/Alu_AluJr chr1 15980143 15980599 - LTR/ERV1_MER49 chr1 15980603 15980885 + SINE/Alu_AluSz chr1 15980886 15980930 + Simple_repeat_(AAAT)n chr1 15980973 15981191 - LTR/ERVL-MaLR_THE1C chr1 15981194 15981311 + SINE/Alu_AluSz chr1 15981327 15981523 - LTR/ERVL-MaLR_THE1C chr1 15981524 15981647 - LTR/ERV1_MER49 chr1 15981648 15982081 - LINE/L1_L1MB1 chr1 15982176 15982247 + LTR/ERVL_LTR67B chr1 15982248 15982539 + SINE/Alu_AluSp chr1 15982540 15982680 + LTR/ERVL_LTR67B chr1 15982682 15983526 + LTR/ERVL_ERVL-E-int chr1 15983900 15984097 - DNA/TcMar-Tc2_Kanga2_a chr1 15984118 15984230 - LINE/L1_L1MC5a chr1 15984231 15984540 - SINE/Alu_AluSx chr1 15984541 15985025 - LINE/L1_L1MC5a chr1 15985026 15985323 - SINE/Alu_AluSz chr1 15985324 15985342 - LINE/L1_L1MC5a chr1 15985343 15985654 - SINE/Alu_AluJo chr1 15985655 15985994 - LINE/L1_L1MC5a chr1 15985990 15986339 - LINE/L1_L1MB7 chr1 15986340 15986630 - SINE/Alu_AluSz chr1 15986631 15986807 - LINE/L1_L1MB7 chr1 15986808 15987112 - SINE/Alu_AluSx chr1 15987113 15987128 - LINE/L1_L1MB7 chr1 15987115 15987381 - LINE/L1_L1ME3G chr1 15987389 15987550 - SINE/Alu_AluSz6 chr1 15987662 15987971 + SINE/Alu_AluSx1 chr1 15988519 15988815 + SINE/Alu_AluSx1 chr1 15988958 15989131 + Simple_repeat_(CT)n chr1 15989425 15989466 + Low_complexity_G-rich chr1 15989526 15989567 + Simple_repeat_(CCCCCT)n chr1 15992403 15992435 + Simple_repeat_(CTTCCT)n chr1 15994500 15994743 + LTR/ERVL_HERVL-int chr1 15994858 15994932 + LINE/L2_L2c chr1 15995071 15995159 - LINE/L1_L1ME3A chr1 15995247 15995395 - LINE/L1_L1MD3 chr1 15995396 15995686 + SINE/Alu_AluJo chr1 15995687 15995720 - LINE/L1_L1MD3 chr1 15995882 15996010 - DNA/TcMar-Tigger_Tigger3c chr1 15996011 15996336 + SINE/Alu_AluSq2 chr1 15996337 15996393 - DNA/TcMar-Tigger_Tigger3c chr1 15996394 15996528 + SINE/Alu_FLAM_C chr1 15996570 15996651 - DNA/TcMar-Tigger_Tigger3c chr1 15996653 15996809 + DNA/TcMar-Tigger_MER2 chr1 15996923 15997106 + SINE/MIR_MIRb chr1 15997837 15997890 - SINE/MIR_MIR chr1 15997901 15998194 + LINE/L2_L2a chr1 15998242 15998287 + Low_complexity_GA-rich chr1 15998910 15999007 + SINE/Alu_AluJb chr1 15999012 15999505 - LINE/L2_L2a chr1 15999548 15999854 - SINE/Alu_AluSp chr1 15999924 16000036 - SINE/Alu_AluJr chr1 16000063 16000368 + SINE/Alu_AluSg chr1 16000375 16000522 + SINE/Alu_AluJb chr1 16000542 16000699 - SINE/Alu_AluJb chr1 16000703 16001177 - LINE/L1_L1MD3 chr1 16001212 16001516 + SINE/Alu_AluSq2 chr1 16001522 16001803 + SINE/Alu_AluJr chr1 16001923 16002249 - LTR/ERVL-MaLR_MLT1D chr1 16002250 16002548 - SINE/Alu_AluSx chr1 16002549 16002721 - LTR/ERVL-MaLR_MLT1D chr1 16002745 16002821 + DNA/hAT-Charlie_MER113 chr1 16002855 16003185 + SINE/Alu_AluSz chr1 16003200 16003509 + SINE/Alu_AluSp chr1 16003533 16003842 - SINE/Alu_AluYf1 chr1 16004433 16004737 + SINE/Alu_AluSx3 chr1 16005183 16005214 + SINE/MIR_MIRc chr1 16005246 16005406 + DNA/hAT-Charlie_MER5A1 chr1 16005447 16005499 - SINE/MIR_MIRb chr1 16005593 16005875 + SINE/Alu_AluJr chr1 16006528 16006843 - LINE/L2_L2a chr1 16007197 16007263 - LINE/L2_L2b chr1 16007441 16007735 - SINE/Alu_AluJr chr1 16007740 16007985 - SINE/MIR_MIR chr1 16008013 16008302 - LINE/L2_L2c chr1 16008334 16008489 - LINE/L2_L2a chr1 16008490 16008509 - LINE/L2_L2b chr1 16008491 16008510 - LINE/L2_L2b chr1 16008511 16008841 + SINE/Alu_AluSx1 chr1 16008842 16009065 - LINE/L2_L2b chr1 16009564 16009747 - SINE/MIR_MIR chr1 16010092 16010678 - LTR/ERV1_LTR71B chr1 16010782 16011057 + LTR/ERV1_LTR37A chr1 16011062 16011232 + LINE/L1_L1MB7 chr1 16011233 16011510 + SINE/Alu_AluSq2 chr1 16011511 16011729 + LINE/L1_L1MB7 chr1 16011730 16012051 - SINE/Alu_AluJb chr1 16012053 16012307 - SINE/Alu_AluJb chr1 16012712 16012896 + SINE/MIR_MIRb chr1 16013562 16013658 + SINE/MIR_MIR3 chr1 16013638 16013771 + LINE/L2_L2b chr1 16016766 16016817 + Simple_repeat_(CCCTCAAG)n chr1 16017718 16017746 + Simple_repeat_(TCCCC)n chr1 16017887 16017984 + LINE/CR1_L3 chr1 16018824 16019024 - SINE/MIR_MIRb chr1 16019219 16019525 + LINE/L2_L2d2 chr1 16019750 16019785 + Low_complexity_G-rich chr1 16020077 16020189 - DNA/hAT-Charlie_MER5A chr1 16020227 16020389 - LINE/L2_L2a chr1 16020341 16020460 - SINE/MIR_MIR3 chr1 16020885 16021099 + SINE/MIR_MIRb chr1 16021363 16021560 + SINE/MIR_MIRb chr1 16021594 16021637 + Simple_repeat_(CCCCT)n chr1 16022490 16022508 + Simple_repeat_(AC)n chr1 16023173 16023222 + Simple_repeat_(AGTGCC)n chr1 16024025 16024180 + SINE/MIR_MIR3 chr1 16024423 16024612 - SINE/MIR_MIR chr1 16024989 16025073 - SINE/MIR_MIRc chr1 16025257 16025390 + SINE/MIR_MIRb chr1 16025391 16025702 + SINE/Alu_AluJb chr1 16025703 16025723 + SINE/MIR_MIRc chr1 16025742 16026052 - SINE/Alu_AluSx3 chr1 16026325 16026392 - LINE/L2_L2c chr1 16026924 16026972 + Low_complexity_G-rich chr1 16027026 16027092 + SINE/MIR_MIR chr1 16028899 16029050 - SINE/MIR_MIRb chr1 16029417 16029567 + SINE/MIR_MIRb chr1 16030810 16030840 + SINE/MIR_MIR3 chr1 16030841 16030884 + SINE/MIR_MIRc chr1 16030895 16031061 - SINE/MIR_MIR3 chr1 16031203 16031285 + SINE/MIR_MIRc chr1 16031909 16032060 + SINE/MIR_MIRb chr1 16032313 16032355 + Low_complexity_G-rich chr1 16032759 16032855 + SINE/MIR_MIRc chr1 16034642 16034672 + Simple_repeat_(GACA)n chr1 16035531 16035668 + SINE/MIR_MIR3 chr1 16035847 16036025 + SINE/MIR_MIR chr1 16036044 16036227 - SINE/MIR_MIRc chr1 16037270 16037516 - LINE/L2_L2d chr1 16037864 16037991 + SINE/Alu_AluSz chr1 16037992 16038059 + Simple_repeat_(AC)n chr1 16038060 16038192 + SINE/Alu_AluSz chr1 16038550 16038766 + LTR/ERVL_LTR16A1 chr1 16038767 16039076 + SINE/Alu_AluSx chr1 16039077 16039305 + LTR/ERVL_LTR16A1 chr1 16040207 16040435 + SINE/MIR_MIRb chr1 16040933 16041216 - SINE/Alu_AluSx chr1 16042720 16042934 + SINE/MIR_MIRb chr1 16043198 16043395 + SINE/MIR_MIRb chr1 16044352 16044383 + Simple_repeat_(CACA)n chr1 16045799 16045944 + SINE/MIR_MIR3 chr1 16046195 16046384 - SINE/MIR_MIR chr1 16046741 16046802 - SINE/MIR_MIR3 chr1 16047052 16047160 + SINE/MIR_MIR1_Amn chr1 16047162 16047444 + SINE/Alu_AluJo chr1 16047446 16047480 + Simple_repeat_(AAAC)n chr1 16047495 16047781 + SINE/Alu_AluJr chr1 16048067 16048095 + Simple_repeat_(CCCA)n chr1 16048147 16048190 - LINE/L2_L2c chr1 16048793 16048900 + SINE/MIR_MIR chr1 16050654 16050802 - SINE/MIR_MIRb chr1 16051158 16051314 + SINE/MIR_MIRb chr1 16052159 16052192 + Simple_repeat_(CCCTG)n chr1 16052508 16052535 + SINE/MIR_MIR3 chr1 16052536 16052610 + SINE/MIR_MIR1_Amn chr1 16052621 16052787 - SINE/MIR_MIR3 chr1 16052930 16052978 + SINE/MIR_MIR1_Amn chr1 16052989 16053036 - DNA/hAT-Charlie_Charlie15a chr1 16053037 16053327 - SINE/Alu_AluSz chr1 16053328 16053416 - DNA/hAT-Charlie_Charlie15a chr1 16053838 16053989 + SINE/MIR_MIRb chr1 16054340 16054436 - DNA/hAT-Tip100_MER91A chr1 16054472 16054560 + SINE/MIR_MIR chr1 16055225 16055337 + SINE/MIR_MIR chr1 16055882 16056005 + SINE/MIR_MIRc chr1 16056013 16056113 + SINE/MIR_MIRc chr1 16057586 16057633 + LINE/RTE-BovB_MamRTE1 chr1 16057901 16057931 + Simple_repeat_(GACA)n chr1 16058786 16058921 + SINE/MIR_MIR3 chr1 16059100 16059279 + SINE/MIR_MIR chr1 16059298 16059494 - SINE/MIR_MIRb chr1 16060516 16060642 - LINE/L2_L2d chr1 16061387 16061801 + LTR/ERVL_LTR16A1 chr1 16062381 16062399 + Simple_repeat_(C)n chr1 16062715 16062943 + SINE/MIR_MIRb chr1 16063093 16063211 + Simple_repeat_(AT)n chr1 16063833 16065420 + LINE/L2_L2 chr1 16065613 16065854 + LTR/ERVL_LTR16A2 chr1 16065897 16066186 - SINE/Alu_AluSx1 chr1 16066457 16066522 + SINE/MIR_MIR1_Amn chr1 16066553 16066823 + DNA/hAT-Charlie_MER112 chr1 16067680 16067942 - LINE/CR1_L3b chr1 16067979 16068095 - SINE/MIR_MIR3 chr1 16068230 16068353 + SINE/MIR_MIRc chr1 16068942 16069201 - SINE/MIR_MIRb chr1 16069199 16069356 + LINE/L2_L2c chr1 16069771 16070057 - SINE/Alu_AluJo chr1 16070106 16070293 + SINE/MIR_MIRb chr1 16070510 16070647 + SINE/Alu_AluJr chr1 16070668 16070881 - SINE/MIR_MIR chr1 16071244 16071415 + SINE/MIR_MIRb chr1 16072064 16072195 + LINE/L2_L2b chr1 16073508 16073564 + Low_complexity_G-rich chr1 16073610 16073648 + Simple_repeat_(CGCCT)n chr1 16073649 16073664 + Simple_repeat_(GCC)n chr1 16073681 16073729 + Low_complexity_G-rich chr1 16073937 16074028 - SINE/MIR_MIRc chr1 16074276 16074520 + Simple_repeat_(CA)n chr1 16074443 16074529 + Simple_repeat_(CACTCA)n chr1 16074562 16074698 - SINE/MIR_MIRc chr1 16074741 16074887 + SINE/Alu_AluSz chr1 16074888 16075186 + SINE/Alu_AluSq chr1 16075187 16075360 + SINE/Alu_AluSz chr1 16075540 16075736 + SINE/MIR_MIRb chr1 16075839 16075898 - LINE/L2_L2b chr1 16076354 16076384 + Low_complexity_G-rich chr1 16076485 16076599 - SINE/MIR_MIR3 chr1 16077398 16077467 + SINE/MIR_MIRb chr1 16077457 16077522 + LINE/L2_L2c chr1 16077528 16077565 - SINE/MIR_MIRc chr1 16077599 16077949 - LINE/L2_L2b chr1 16078322 16078422 - LINE/L2_L2 chr1 16078442 16078507 - LINE/L2_L2b chr1 16078523 16078852 + SINE/Alu_AluSx chr1 16079164 16079318 + DNA/hAT-Tip100_MER91A chr1 16079326 16079416 + DNA/hAT-Tip100_MER91A chr1 16079420 16079543 - SINE/MIR_MIR3 chr1 16079844 16079933 - LINE/L2_L2b chr1 16080038 16080348 + SINE/Alu_AluSp chr1 16080398 16080478 - SINE/MIR_MIRb chr1 16080859 16080928 + DNA/hAT-Charlie_MER58A chr1 16080929 16081256 - SINE/Alu_AluSx chr1 16081257 16081377 + DNA/hAT-Charlie_MER58A chr1 16081378 16081619 + SINE/Alu_AluJr chr1 16082120 16082262 + SINE/MIR_MIR3 chr1 16082238 16082446 + SINE/MIR_MIRc chr1 16082560 16082650 - LINE/L2_L2b chr1 16082835 16083056 + SINE/MIR_MIR chr1 16083313 16083410 + SINE/MIR_MIRb chr1 16083603 16083645 + Simple_repeat_(CCCTGCCCAG)n chr1 16084019 16084309 + SINE/Alu_AluSz chr1 16084329 16084480 - SINE/Alu_FRAM chr1 16084709 16084849 + SINE/Alu_AluSq2 chr1 16084850 16085141 + SINE/Alu_AluSp chr1 16085142 16085321 + SINE/Alu_AluSq2 chr1 16085327 16085471 - SINE/MIR_MIRb chr1 16085958 16086252 - SINE/Alu_AluSz chr1 16086255 16086383 - SINE/Alu_FLAM_C chr1 16086401 16086628 - SINE/Alu_AluJb chr1 16086638 16086654 + Simple_repeat_(T)n chr1 16086862 16087170 + SINE/Alu_AluSq chr1 16087175 16087493 + SINE/Alu_AluSq chr1 16087936 16087970 + Simple_repeat_(CCTCCCT)n chr1 16088370 16088451 - SINE/MIR_MIR3 chr1 16089467 16089503 + Low_complexity_GA-rich chr1 16089504 16089844 + SINE/Alu_AluSz6 chr1 16089845 16089939 + Low_complexity_GA-rich chr1 16090451 16090577 + SINE/MIR_MIR chr1 16090571 16090735 - SINE/MIR_MIRb chr1 16091934 16091984 - SINE/MIR_MIRb chr1 16091986 16092079 + LINE/L2_L2c chr1 16092063 16092182 - SINE/MIR_MIRb chr1 16092293 16092327 + Simple_repeat_(CCAC)n chr1 16092366 16092467 + LTR/ERV1_MER90a chr1 16093097 16093184 - SINE/MIR_MIRb chr1 16093200 16093240 + Simple_repeat_(T)n chr1 16093252 16093284 + Simple_repeat_(TG)n chr1 16093285 16093505 - SINE/Alu_AluJr chr1 16093508 16093591 - SINE/MIR_MIR3 chr1 16093567 16093621 + LINE/L2_L2c chr1 16093841 16094397 + LTR/ERV1_MER90 chr1 16094865 16094990 + SINE/MIR_MIRc chr1 16095060 16095357 + SINE/Alu_AluSx chr1 16096001 16096279 + SINE/Alu_AluSx1 chr1 16096280 16096325 + Simple_repeat_(ACAA)n chr1 16096326 16096338 + SINE/Alu_AluSx1 chr1 16096379 16096753 + LINE/L2_L2b chr1 16097032 16097156 + SINE/Alu_AluJr chr1 16097180 16097214 + Simple_repeat_(T)n chr1 16097245 16097541 + SINE/Alu_AluJo chr1 16097552 16097686 + LINE/L1_L1MEg chr1 16097834 16098133 + SINE/Alu_AluJb chr1 16098162 16098215 - DNA/TcMar-Tigger_MERX chr1 16098247 16098323 + LINE/L1_L1MEg chr1 16098324 16098607 + SINE/Alu_AluSx chr1 16098670 16098966 - SINE/Alu_AluSx chr1 16098982 16099019 + Low_complexity_GA-rich chr1 16099020 16099434 - LTR/ERVL_MER21B chr1 16099451 16099509 + LINE/L1_L1MD chr1 16099510 16099699 + LTR/ERVL-MaLR_MLT1A0 chr1 16099700 16100009 - SINE/Alu_AluJb chr1 16100010 16100213 + LTR/ERVL-MaLR_MLT1A0 chr1 16100214 16100418 + LINE/L1_L1MB7 chr1 16100419 16100712 + SINE/Alu_AluYm1 chr1 16100722 16101024 + SINE/Alu_AluSx chr1 16101025 16101188 + LINE/L1_L1MB7 chr1 16101190 16101471 + SINE/Alu_AluSx chr1 16101472 16101529 + Simple_repeat_(AAAAT)n chr1 16102019 16102131 + LINE/L1_L1MB7 chr1 16102133 16102289 - SINE/Alu_AluJb chr1 16102311 16102620 - SINE/Alu_AluSz chr1 16102621 16102810 + LINE/L1_L1MB7 chr1 16102814 16103328 - LTR/ERVK_MER9a2 chr1 16103329 16105518 - LTR/ERVK_HERVK9-int chr1 16105517 16108299 - LTR/ERVK_HERVK9-int chr1 16108300 16108812 - LTR/ERVK_MER9a2 chr1 16108819 16108867 + LINE/L1_L1MB7 chr1 16108919 16109020 + SINE/Alu_AluSz6 chr1 16109315 16109417 - SINE/Alu_FRAM chr1 16109468 16109546 + SINE/Alu_AluJo chr1 16110064 16110289 + SINE/MIR_MIR chr1 16110331 16110673 - LINE/L2_L2a chr1 16110689 16110839 - DNA/hAT-Charlie_MER20 chr1 16111340 16111594 - SINE/Alu_AluJr chr1 16111682 16111704 + Simple_repeat_(CTGT)n chr1 16111915 16112057 + Simple_repeat_(CA)n chr1 16112391 16112521 + SINE/MIR_MIR1_Amn chr1 16113304 16113390 + SINE/MIR_MIRb chr1 16113491 16113585 + SINE/MIR_MIR3 chr1 16113648 16113934 + SINE/Alu_AluSz chr1 16113944 16114179 + SINE/Alu_AluSz6 chr1 16114221 16114525 + SINE/Alu_AluSc chr1 16115234 16115377 - SINE/MIR_MIRb chr1 16115490 16115587 + SINE/MIR_MIR chr1 16116517 16116936 + LINE/L2_L2b chr1 16116952 16117065 - LINE/L2_L2c chr1 16117187 16117482 + SINE/Alu_AluSg chr1 16117731 16117837 + SINE/MIR_MIR chr1 16117838 16117965 - DNA/hAT-Blackjack_MER94 chr1 16117966 16118087 + SINE/MIR_MIR chr1 16118110 16118135 + Simple_repeat_(CA)n chr1 16118276 16118584 - SINE/Alu_AluSx chr1 16119234 16119355 - SINE/MIR_MIR1_Amn chr1 16119444 16119598 + SINE/MIR_MIRb chr1 16119897 16119994 - LINE/L2_L2a chr1 16120581 16120870 + LINE/L2_L2b chr1 16121041 16121209 + SINE/MIR_MIR chr1 16121245 16121517 - LINE/L1_L1MC3 chr1 16121518 16121820 - SINE/Alu_AluSz chr1 16121821 16122190 - LINE/L1_L1MC3 chr1 16122921 16122962 + SINE/MIR_MIR3 chr1 16122967 16123240 + LINE/L2_L2c chr1 16123336 16123391 + SINE/MIR_MIRb chr1 16123392 16123695 + SINE/Alu_AluSc chr1 16123696 16124012 + SINE/MIR_MIRb chr1 16124156 16124200 + Simple_repeat_(CCTGCCC)n chr1 16125605 16125745 - SINE/MIR_MIR chr1 16126031 16126059 + SINE/MIR_MIRc chr1 16126649 16126811 + DNA/hAT-Charlie_MER5A chr1 16126890 16126962 + LINE/L2_L2c chr1 16127187 16127272 - LINE/L2_L2c chr1 16127726 16127763 + Low_complexity_G-rich chr1 16128180 16128261 - SINE/MIR_MIR chr1 16128896 16128962 + SINE/MIR_MIRc chr1 16128979 16129185 + SINE/MIR_MIRb chr1 16129591 16129721 + SINE/MIR_MIR chr1 16129846 16130059 + SINE/MIR_MIR chr1 16130632 16130928 - SINE/Alu_AluJb chr1 16131007 16131111 + LINE/L2_L2b chr1 16131211 16131293 + LINE/L2_L2b chr1 16131298 16131597 + SINE/Alu_AluSq2 chr1 16134296 16134411 - SINE/MIR_MIRc chr1 16135202 16135238 + Simple_repeat_(GGCAG)n chr1 16135309 16135448 - SINE/MIR_MIRb chr1 16135874 16136168 - SINE/Alu_AluJo chr1 16136304 16136335 + Simple_repeat_(AAT)n chr1 16136338 16136626 + SINE/Alu_AluSp chr1 16136627 16136775 + Simple_repeat_(AAG)n chr1 16136794 16137099 - SINE/Alu_AluSp chr1 16137110 16137200 - SINE/MIR_MIR chr1 16137201 16137316 - DNA/hAT-Charlie_MER30 chr1 16137317 16137613 + SINE/Alu_AluSx3 chr1 16137614 16137720 - DNA/hAT-Charlie_MER30 chr1 16137721 16137756 - SINE/MIR_MIR chr1 16138531 16138641 + SINE/MIR_MIRb chr1 16139426 16139521 - LINE/L2_L2a chr1 16140289 16140372 - SINE/MIR_MIR1_Amn chr1 16140398 16140606 - SINE/MIR_MIRb chr1 16140609 16140900 - SINE/Alu_AluSx1 chr1 16140903 16141032 - SINE/Alu_FLAM_C chr1 16141273 16141474 - DNA/hAT-Charlie_Charlie16a chr1 16142061 16142089 + Simple_repeat_(CA)n chr1 16142104 16142120 + LINE/L2_L2c chr1 16142121 16142182 + LINE/L2_L2d chr1 16142229 16142332 - LINE/L2_L2b chr1 16142355 16142483 + LINE/L2_L2c chr1 16142490 16143268 + Simple_repeat_(GATG)n chr1 16144031 16144074 + Simple_repeat_(CCCGCCT)n chr1 16144137 16144160 + Simple_repeat_(GGAG)n chr1 16144659 16144829 - SINE/MIR_MIR3 chr1 16144830 16144953 + SINE/MIR_MIRb chr1 16145822 16146003 + SINE/MIR_MIRb chr1 16146877 16146942 - SINE/MIR_MIR3 chr1 16147365 16147410 + DNA/hAT-Charlie_MER5B chr1 16147425 16147542 - SINE/MIR_MIR3 chr1 16147606 16147857 - SINE/MIR_MIR chr1 16147873 16148175 - SINE/Alu_AluSz6 chr1 16148176 16148190 + Simple_repeat_(ATTC)n chr1 16148217 16148317 - SINE/MIR_MIRb chr1 16148412 16148451 + Simple_repeat_(CTGGCCA)n chr1 16149304 16149482 + SINE/MIR_MIRb chr1 16149631 16149905 + LINE/L2_L2c chr1 16149956 16150072 + SINE/MIR_MIR3 chr1 16150240 16150348 - SINE/MIR_MIR1_Amn chr1 16150418 16150516 - DNA/hAT-Ac_OldhAT1 chr1 16150738 16150847 + SINE/MIR_MIRc chr1 16151110 16151253 - SINE/MIR_MIR3 chr1 16151461 16151543 - DNA/TcMar-Tigger_Tigger17 chr1 16152477 16152530 + Low_complexity_G-rich chr1 16153655 16153684 + SINE/MIR_MIR3 chr1 16154472 16154783 + SINE/Alu_AluSq2 chr1 16155967 16156037 + Simple_repeat_(GCCC)n chr1 16156636 16156803 - SINE/MIR_MIRb chr1 16157360 16157506 + Simple_repeat_(TG)n chr1 16157507 16157808 + SINE/Alu_AluSz chr1 16157809 16158001 + Simple_repeat_(TG)n chr1 16158042 16158189 + SINE/MIR_MIRb chr1 16158362 16158496 - LINE/L2_L2 chr1 16158464 16158520 + LINE/L2_L2a chr1 16158521 16158812 + SINE/Alu_AluSz chr1 16158813 16158867 + LINE/L2_L2a chr1 16159653 16159894 + SINE/Alu_AluJb chr1 16159900 16160034 - SINE/MIR_MIR chr1 16160313 16160375 + LINE/L2_L2d chr1 16160433 16160734 - SINE/Alu_AluSg chr1 16160785 16161080 - SINE/Alu_AluSz chr1 16161096 16161372 - SINE/Alu_AluSc chr1 16161382 16161424 - LINE/L2_L2c chr1 16161396 16161615 - SINE/MIR_MIRb chr1 16161625 16161723 + LINE/L2_L2c chr1 16161746 16161801 + LINE/L2_L2a chr1 16161873 16161992 + SINE/MIR_MIRb chr1 16162087 16162205 + LINE/L2_L2c chr1 16162894 16162920 + Low_complexity_A-rich chr1 16162924 16163237 + SINE/Alu_AluSp chr1 16163350 16163666 + SINE/Alu_AluSq2 chr1 16163679 16163848 + LINE/L2_L2a chr1 16163855 16164028 + SINE/Alu_AluSp chr1 16164035 16164116 + Simple_repeat_(AAAAT)n chr1 16164128 16164371 + LINE/L2_L2a chr1 16164414 16164550 + SINE/MIR_MIR chr1 16164711 16164920 - SINE/MIR_MIRb chr1 16165075 16165247 + SINE/MIR_MIR chr1 16165716 16165740 + Simple_repeat_(CA)n chr1 16165911 16165945 + Simple_repeat_(CTCC)n chr1 16166149 16166251 + Simple_repeat_(TCCA)n chr1 16166252 16166284 + Simple_repeat_(TTCC)n chr1 16166781 16166929 - SINE/Alu_AluSx chr1 16166930 16167242 - SINE/Alu_AluSx chr1 16167489 16167539 + Simple_repeat_(AGGCAGG)n chr1 16168154 16168327 - SINE/Alu_AluJr chr1 16168328 16168601 - SINE/Alu_AluSx3 chr1 16168602 16168742 - SINE/Alu_AluJr chr1 16168753 16168781 + LINE/L2_L2c chr1 16168898 16169011 - SINE/MIR_MIRb chr1 16169001 16169079 + SINE/MIR_MIR3 chr1 16169291 16169322 + SINE/MIR_MIR3 chr1 16169763 16170053 - SINE/Alu_AluSz chr1 16170080 16170244 - SINE/MIR_MIRb chr1 16170455 16170664 - SINE/MIR_MIR chr1 16170820 16171123 - SINE/Alu_AluJr chr1 16171318 16171411 - SINE/MIR_MIRc chr1 16171424 16171457 + Simple_repeat_(GT)n chr1 16171958 16172009 + LINE/L2_L2c chr1 16172513 16172554 + Simple_repeat_(CCCTCCG)n chr1 16172674 16172982 + SINE/Alu_AluSq2 chr1 16173348 16173416 + SINE/MIR_MIRb chr1 16173875 16174086 + SINE/MIR_MIRb chr1 16174090 16174196 - SINE/MIR_MIRc chr1 16174601 16174642 + Simple_repeat_(TC)n chr1 16174695 16174804 + SINE/MIR_MIRc chr1 16175247 16175459 + SINE/MIR_MIRb chr1 16175531 16175560 + Low_complexity_G-rich chr1 16176324 16176397 + Simple_repeat_(TCTATT)n chr1 16176560 16176586 + Simple_repeat_(CTC)n chr1 16176687 16176805 - SINE/MIR_MIR3 chr1 16177051 16177124 + SINE/Alu_AluYj4 chr1 16177152 16177295 + SINE/MIR_MIR3 chr1 16177945 16178041 - SINE/MIR_MIR chr1 16178044 16178189 + DNA/hAT-Blackjack_MER63A chr1 16178190 16178502 - SINE/Alu_AluSx1 chr1 16178503 16178580 + DNA/hAT-Blackjack_MER63A chr1 16178910 16178975 + SINE/MIR_MIRc chr1 16179019 16179187 + SINE/MIR_MIR1_Amn chr1 16179422 16179593 - SINE/MIR_MIRb chr1 16179604 16179918 - SINE/Alu_AluSq2 chr1 16179952 16180125 + LINE/L1_L1MB3 chr1 16180148 16180451 + SINE/Alu_AluSz chr1 16180452 16180550 - LINE/L2_L2a chr1 16180559 16180718 + DNA/hAT-Charlie_MER3 chr1 16180719 16181016 - SINE/Alu_AluSz chr1 16181017 16181055 + DNA/hAT-Charlie_MER3 chr1 16181151 16181301 + DNA/hAT-Charlie_MER5A chr1 16181336 16181593 + SINE/Alu_AluSx1 chr1 16182397 16182516 - LINE/L2_L2a chr1 16182605 16182634 + Simple_repeat_(ATGA)n chr1 16182735 16182856 + SINE/MIR_MIR chr1 16182857 16183151 + SINE/Alu_AluSx1 chr1 16183152 16183269 + SINE/MIR_MIR chr1 16183300 16183344 + Simple_repeat_(TCAC)n chr1 16184037 16184263 - SINE/MIR_MIRb chr1 16185392 16185573 - SINE/Alu_AluSx3 chr1 16185574 16185862 - SINE/Alu_AluYc chr1 16185863 16185998 - SINE/Alu_AluSx3 chr1 16185999 16186175 - SINE/Alu_AluJb chr1 16186176 16186463 - SINE/Alu_AluY chr1 16186464 16186599 - SINE/Alu_AluJb chr1 16186760 16187016 + SINE/MIR_MIRb chr1 16187048 16187341 + SINE/Alu_AluSx chr1 16187344 16187499 + LINE/L1_L1PA7 chr1 16187611 16187636 + Simple_repeat_(AAG)n chr1 16187769 16187825 + Simple_repeat_(CCCTGC)n chr1 16188005 16188060 + Simple_repeat_(ATG)n chr1 16188109 16188290 + SINE/MIR_MIRb chr1 16188291 16188598 + SINE/Alu_AluSz chr1 16188599 16188609 + SINE/MIR_MIRb chr1 16188811 16188963 - SINE/MIR_MIRc chr1 16189203 16189378 - LINE/CR1_Plat_L3 chr1 16189528 16189827 + SINE/Alu_AluSx1 chr1 16190046 16190214 - LINE/L2_L2b chr1 16190225 16190365 + LINE/L2_L2b chr1 16190369 16191007 - LINE/L1_L1ME3A chr1 16191155 16191290 + SINE/Alu_AluSz6 chr1 16191298 16191452 + Low_complexity_G-rich chr1 16192197 16192347 - LINE/CR1_L3 chr1 16192349 16192539 - SINE/MIR_MIRb chr1 16192552 16192717 + Simple_repeat_(TA)n chr1 16192721 16193103 + LINE/L1_L1MD3 chr1 16193104 16193385 + SINE/Alu_AluSx chr1 16193386 16193438 + Simple_repeat_(AAAAT)n chr1 16193439 16193468 + LINE/L1_L1MD3 chr1 16193469 16193794 + DNA/TcMar-Tigger_Tigger3a chr1 16193795 16193860 + LINE/L1_L1MD3 chr1 16193937 16194103 + SINE/Alu_AluJo chr1 16194119 16194252 - SINE/Alu_FLAM_C chr1 16194253 16194343 + SINE/Alu_AluJr chr1 16194354 16194660 + SINE/Alu_AluJb chr1 16194662 16194851 + Low_complexity_GA-rich chr1 16194852 16195161 + SINE/Alu_AluY chr1 16195165 16195750 + LINE/L1_L1MD3 chr1 16195806 16196116 + SINE/Alu_AluSx chr1 16196137 16196443 + SINE/Alu_AluJb chr1 16196538 16196691 - LINE/CR1_L3 chr1 16196768 16197117 + LTR/ERVL-MaLR_MLT1L chr1 16197127 16197387 + SINE/Alu_AluSz chr1 16197388 16197855 + LTR/ERVL-MaLR_MLT1D chr1 16197856 16197941 + LTR/ERVL-MaLR_MLT1L chr1 16198856 16199045 - LINE/CR1_L3b chr1 16199287 16199334 + Low_complexity_G-rich chr1 16199481 16199588 + LINE/L2_L2a chr1 16199663 16199848 + LINE/L2_L2a chr1 16199849 16200070 + LINE/L2_L2b chr1 16200071 16200092 + LINE/L2_L2a chr1 16200166 16200385 + SINE/MIR_MIRb chr1 16200461 16200754 + SINE/Alu_AluSz6 chr1 16200755 16200920 + SINE/Alu_AluSz6 chr1 16200933 16201228 + SINE/Alu_AluJo chr1 16201441 16201546 + SINE/MIR_MIRb chr1 16202866 16202984 - SINE/Alu_AluSx3 chr1 16203055 16203185 + LINE/L2_L2a chr1 16203236 16203993 + LINE/L2_L2c chr1 16204037 16204165 - SINE/Alu_FLAM_C chr1 16204325 16204439 + SINE/MIR_MIRb chr1 16207348 16207397 - LINE/L2_L2c chr1 16208395 16208524 + SINE/MIR_MIRc chr1 16209227 16209445 - SINE/MIR_MIR chr1 16210265 16210306 + Simple_repeat_(GAGG)n chr1 16210614 16210797 - LINE/L2_L2b chr1 16211102 16211146 + SINE/MIR_MIRc chr1 16211892 16212018 + SINE/MIR_MIR3 chr1 16213461 16213576 - SINE/MIR_MIR chr1 16213579 16213710 - DNA/TcMar-Tigger_MER2 chr1 16213714 16213775 + Simple_repeat_(TTTA)n chr1 16213776 16214060 - SINE/Alu_AluSz chr1 16214108 16214392 - SINE/Alu_AluJb chr1 16214394 16214601 - DNA/TcMar-Tigger_MER2 chr1 16214620 16214749 - SINE/MIR_MIRb chr1 16214750 16214882 + SINE/Alu_AluSq10 chr1 16214886 16215199 + SINE/Alu_AluSg chr1 16215420 16215462 + Simple_repeat_(TATT)n chr1 16215463 16215744 - SINE/Alu_AluSp chr1 16216659 16216822 - SINE/MIR_MIR chr1 16216956 16217107 - SINE/MIR_MIR chr1 16218581 16218729 - SINE/MIR_MIR chr1 16218809 16218991 - SINE/MIR_MIRc chr1 16219825 16219904 + LINE/L2_L2b chr1 16220244 16220280 + Low_complexity_A-rich chr1 16220526 16220675 + SINE/Alu_AluSz chr1 16221320 16221354 + Simple_repeat_(TGGA)n chr1 16221505 16221527 + Simple_repeat_(TGGA)n chr1 16221577 16221609 + Simple_repeat_(GGAT)n chr1 16221635 16221658 + Simple_repeat_(TGGA)n chr1 16221659 16221717 + Simple_repeat_(GGAT)n chr1 16221718 16221976 + Simple_repeat_(TGGA)n chr1 16221979 16222266 - LINE/L2_L2a chr1 16222277 16222591 - SINE/Alu_AluSz chr1 16222593 16222729 - SINE/Alu_AluSz6 chr1 16222736 16222756 + Simple_repeat_(TTTTAA)n chr1 16222757 16222913 - SINE/Alu_AluSz6 chr1 16222914 16222955 + Simple_repeat_(TTAT)n chr1 16222956 16223214 - SINE/Alu_AluSc chr1 16223215 16223355 - SINE/Alu_AluSz6 chr1 16223356 16223382 + Simple_repeat_(TTTTAA)n chr1 16223386 16223672 - SINE/Alu_AluJo chr1 16223675 16223737 + DNA/hAT-Tip100_MER91B chr1 16223745 16223856 + LTR/ERVL_LTR33A chr1 16223928 16224217 - LINE/L2_L2a chr1 16224218 16224520 + SINE/Alu_AluSx chr1 16224521 16224969 - LINE/L2_L2a chr1 16225100 16225168 - SINE/MIR_MIRb chr1 16225732 16225771 + Simple_repeat_(CCCA)n chr1 16225907 16225938 + Simple_repeat_(AGCCCC)n chr1 16226279 16226367 - LINE/CR1_L3 chr1 16227256 16227555 - SINE/Alu_AluSx chr1 16227713 16227919 - SINE/MIR_MIR chr1 16228115 16228199 + LINE/CR1_L3b chr1 16228468 16228503 + Simple_repeat_(GGTGCA)n chr1 16228674 16228841 - SINE/MIR_MIR chr1 16229002 16229310 - SINE/Alu_AluSx1 chr1 16229313 16229512 + SINE/Alu_AluJr4 chr1 16229522 16229608 - DNA/hAT-Tip100_MER45R chr1 16231048 16231351 + SINE/Alu_AluSx1 chr1 16231378 16231752 + LTR/ERVL-MaLR_MLT1J1 chr1 16232671 16232819 - SINE/MIR_MIR3 chr1 16233134 16233191 - LINE/L2_L2c chr1 16233344 16233549 - SINE/MIR_MIRb chr1 16233800 16234117 + LTR/ERVL-MaLR_MSTD chr1 16234118 16234422 + SINE/Alu_AluSx1 chr1 16234423 16234468 + LTR/ERVL-MaLR_MSTD chr1 16234548 16234678 - SINE/Alu_AluJo chr1 16234731 16235023 - SINE/Alu_AluSp chr1 16235271 16235391 - DNA/hAT-Charlie_Chap1a_Mam chr1 16235401 16235644 - SINE/MIR_MIR chr1 16235676 16235974 - SINE/Alu_AluSq2 chr1 16236287 16236374 + SINE/MIR_MIRb chr1 16237416 16237516 + SINE/MIR_MIR chr1 16237545 16237657 - SINE/MIR_MIR3 chr1 16237681 16237760 - LINE/L2_L2c chr1 16237784 16237861 + LINE/L2_L2b chr1 16237966 16238174 - SINE/MIR_MIRb chr1 16238175 16238486 + SINE/Alu_AluJo chr1 16238487 16238532 - SINE/MIR_MIRb chr1 16238617 16238731 - LINE/L2_L2c chr1 16239033 16239164 + SINE/Alu_AluSz6 chr1 16239168 16239434 + SINE/Alu_AluSx1 chr1 16239445 16239511 + LTR/ERV1_MER4A chr1 16239512 16239812 - SINE/Alu_AluSc8 chr1 16239813 16240065 + LTR/ERV1_MER4A chr1 16240157 16240428 - LTR/ERVL-MaLR_MLT1B chr1 16240429 16240596 - SINE/Alu_AluJb chr1 16240597 16240910 - SINE/Alu_AluSq2 chr1 16240911 16241045 - SINE/Alu_AluJb chr1 16241059 16241079 + Simple_repeat_(T)n chr1 16241469 16241573 - LTR/ERVL-MaLR_MLT1B chr1 16241830 16242128 - SINE/Alu_AluSc chr1 16242563 16242626 + LINE/L2_L2 chr1 16243404 16243774 + LTR/ERVL-MaLR_MLT1A1 chr1 16243937 16244083 + LINE/L2_L2c chr1 16244647 16244803 - SINE/MIR_MIRb chr1 16244792 16244946 - LINE/CR1_L3 chr1 16245319 16245613 - SINE/Alu_AluSz chr1 16245699 16245820 - SINE/MIR_MIR chr1 16246214 16246500 - SINE/Alu_AluJr chr1 16246510 16246801 - SINE/Alu_AluSg chr1 16249783 16249820 + Low_complexity_A-rich chr1 16250469 16250499 + Simple_repeat_(TA)n chr1 16251830 16252106 - LINE/L2_L2c chr1 16253938 16254093 + SINE/MIR_MIRb chr1 16254920 16254955 + Simple_repeat_(TTTC)n chr1 16255338 16255618 - SINE/Alu_AluSx chr1 16256419 16256556 + DNA/hAT-Charlie_Charlie4z chr1 16256891 16257126 + SINE/MIR_MIR chr1 16257684 16257975 - SINE/Alu_AluSx1 chr1 16257989 16258020 + Simple_repeat_(T)n chr1 16258347 16258656 - SINE/Alu_AluJr chr1 16258753 16259039 - SINE/Alu_AluJr chr1 16259483 16259795 + SINE/Alu_AluSz chr1 16259930 16260017 - DNA/hAT-Charlie_MER3 chr1 16260161 16260209 - SINE/MIR_MIR chr1 16260210 16260508 - SINE/Alu_AluSx chr1 16260509 16260687 - SINE/MIR_MIR chr1 16260695 16261001 - SINE/Alu_AluJr chr1 16261705 16262011 - SINE/Alu_AluY chr1 16262148 16262202 + Simple_repeat_(TA)n chr1 16262203 16262321 - SINE/Alu_FLAM_A chr1 16262429 16262648 + SINE/Alu_AluSz6 chr1 16262701 16262999 - SINE/Alu_AluJb chr1 16263182 16263448 + SINE/Alu_AluY chr1 16263484 16263752 + SINE/Alu_AluSp chr1 16263757 16263786 + Simple_repeat_(AAAAC)n chr1 16263808 16264110 - SINE/Alu_AluSq2 chr1 16265105 16265390 - SINE/Alu_AluSp chr1 16265456 16265585 - SINE/MIR_MIRb chr1 16265648 16265671 + Simple_repeat_(A)n chr1 16266170 16266308 - LTR/ERV1_LTR37B chr1 16266339 16266406 + SINE/Alu_FLAM_A chr1 16267402 16267473 + LINE/L2_L2c chr1 16268682 16268911 + SINE/Alu_AluSp chr1 16268912 16269107 - SINE/Alu_AluSg chr1 16269108 16269386 - SINE/Alu_AluSx3 chr1 16269389 16269521 - SINE/Alu_AluSz6 chr1 16269529 16269824 - SINE/Alu_AluSp chr1 16269827 16269840 - SINE/Alu_AluSx3 chr1 16269841 16269877 + Simple_repeat_(TTTGT)n chr1 16269878 16270155 - SINE/Alu_AluSx3 chr1 16270157 16270339 - LTR/ERVL-MaLR_MSTC chr1 16270557 16270689 + SINE/Alu_FLAM_C chr1 16270690 16270723 + Simple_repeat_(AAGAA)n chr1 16270752 16270792 + Simple_repeat_(AC)n chr1 16270794 16270835 - SINE/MIR_MIRb chr1 16270803 16271220 - LINE/L2_L2a chr1 16271240 16271302 + Simple_repeat_(TG)n chr1 16271308 16271580 - SINE/Alu_AluSx1 chr1 16271587 16271689 + LINE/L2_L2 chr1 16271690 16271703 + LINE/L2_L2c chr1 16271741 16271846 + LINE/L2_L2c chr1 16271857 16272175 + SINE/Alu_AluSx chr1 16272269 16272556 - SINE/Alu_AluSp chr1 16273097 16273247 - SINE/MIR_MIRb chr1 16273481 16273598 + LINE/L1_L1ME3 chr1 16273599 16273894 + SINE/Alu_AluJb chr1 16273895 16274062 + LINE/L1_L1ME3 chr1 16274063 16274336 + SINE/Alu_AluJr chr1 16274337 16274564 + LINE/L1_L1ME3 chr1 16274589 16274888 - SINE/Alu_AluSc chr1 16274930 16275049 + DNA/TcMar-Tigger_X1_DNA chr1 16275056 16275107 + DNA/TcMar-Tigger_X1_DNA chr1 16275339 16275654 + SINE/Alu_AluJr chr1 16275727 16275981 + SINE/Alu_AluSx chr1 16276100 16276409 + SINE/Alu_AluY chr1 16276965 16277038 + LINE/CR1_L3b chr1 16277443 16277747 + SINE/Alu_AluSz chr1 16277759 16278051 + SINE/Alu_AluJb chr1 16278075 16278387 + SINE/Alu_AluSp chr1 16278481 16278512 + Simple_repeat_(T)n chr1 16278766 16279075 - SINE/Alu_AluSp chr1 16279323 16279452 + SINE/Alu_FRAM chr1 16279747 16279843 + LINE/L1_L1MC3 chr1 16279844 16280142 - SINE/Alu_AluSz6 chr1 16280143 16280488 + LINE/L1_L1MC3 chr1 16280489 16280538 + DNA/TcMar-Mariner_MADE1 chr1 16280539 16280624 + LINE/L1_L1MC3 chr1 16280647 16280801 + SINE/Alu_AluJb chr1 16280814 16280957 + SINE/MIR_MIR chr1 16280958 16281261 - SINE/Alu_AluSp chr1 16281262 16281384 + SINE/MIR_MIR chr1 16281459 16281639 - SINE/Alu_AluSx4 chr1 16281649 16281999 - SINE/Alu_AluSz chr1 16282216 16282525 - SINE/Alu_AluSx1 chr1 16282563 16282680 + SINE/Alu_AluJr chr1 16282687 16282698 - DNA/hAT-Charlie_MER5A chr1 16282699 16282996 + SINE/Alu_AluSx1 chr1 16282997 16283093 - DNA/hAT-Charlie_MER5A chr1 16283142 16283279 - SINE/MIR_MIRc chr1 16283495 16283794 - SINE/Alu_AluSg7 chr1 16284270 16284550 + LINE/L2_L2 chr1 16284551 16284685 + SINE/Alu_AluSx3 chr1 16284686 16284995 + SINE/Alu_AluSp chr1 16284996 16285167 + SINE/Alu_AluSx3 chr1 16285168 16285304 + LINE/L2_L2 chr1 16285305 16285567 - SINE/Alu_AluSg chr1 16285568 16285857 + LINE/L2_L2 chr1 16285858 16286168 - SINE/Alu_AluJb chr1 16286169 16286237 + LINE/L2_L2 chr1 16286238 16286605 - LTR/ERVL-MaLR_THE1D chr1 16286606 16287542 + LINE/L2_L2a chr1 16287594 16287749 - SINE/Alu_AluJr chr1 16287750 16287798 - Unknown_UCON71_Crp chr1 16287802 16288149 + SINE/Alu_AluSq2 chr1 16288158 16288454 + SINE/Alu_AluSc chr1 16288455 16288472 + Simple_repeat_(TAA)n chr1 16288540 16288675 + LINE/L2_L2 chr1 16288676 16288974 + SINE/Alu_AluSz chr1 16289122 16289424 + SINE/Alu_AluJr chr1 16289667 16289704 + Simple_repeat_(AAAC)n chr1 16289736 16289921 + SINE/Alu_AluJo chr1 16289971 16290040 + LINE/L2_L2a chr1 16290250 16290410 + LTR/ERVL-MaLR_MLT1A chr1 16290411 16290697 + SINE/Alu_AluSx chr1 16290698 16290916 + LTR/ERVL-MaLR_MLT1A chr1 16291371 16291679 - SINE/Alu_AluSz6 chr1 16291684 16291971 - SINE/Alu_AluJr chr1 16292270 16292314 + Simple_repeat_(TCCC)n chr1 16292320 16292596 - SINE/Alu_AluJr chr1 16292912 16293004 + SINE/Alu_AluJb chr1 16293161 16293419 - SINE/Alu_AluSx1 chr1 16293833 16293953 - SINE/MIR_MIR3 chr1 16295383 16295692 - SINE/Alu_AluSx chr1 16296254 16296287 + Simple_repeat_(AAAAT)n chr1 16296370 16296679 + SINE/Alu_AluSg chr1 16296913 16296942 + Simple_repeat_(CCCCCCA)n chr1 16296943 16297243 - SINE/Alu_AluSx chr1 16297580 16297885 + SINE/Alu_AluY chr1 16297944 16298243 + SINE/Alu_AluSz chr1 16298514 16298809 - SINE/Alu_AluSx1 chr1 16299005 16299310 - SINE/Alu_AluSx3 chr1 16299656 16299942 - SINE/Alu_AluSc chr1 16300894 16301198 - SINE/Alu_AluSz chr1 16301332 16301380 - LINE/L2_L2d2 chr1 16301381 16301700 + SINE/Alu_AluSx1 chr1 16301701 16301770 - LINE/L2_L2d2 chr1 16301833 16301889 - SINE/Alu_FLAM_C chr1 16301955 16302012 - SINE/MIR_MIRc chr1 16302261 16302316 + SINE/Alu_AluJo chr1 16302317 16302513 - LTR/ERVL-MaLR_THE1D chr1 16302514 16302809 - SINE/Alu_AluSx1 chr1 16302810 16302986 - LTR/ERVL-MaLR_THE1D chr1 16302987 16303068 + SINE/Alu_AluJo chr1 16303293 16303400 - DNA/hAT-Charlie_MER20 chr1 16303569 16303731 - SINE/Alu_AluSq chr1 16303744 16304104 - SINE/Alu_AluY chr1 16304114 16304418 - SINE/Alu_AluSq2 chr1 16304687 16304978 + SINE/Alu_AluSx chr1 16305103 16305403 + SINE/Alu_AluJo chr1 16305434 16305730 + SINE/Alu_AluJb chr1 16306019 16306316 - SINE/Alu_AluSg4 chr1 16306581 16306624 + LINE/L2_L2a chr1 16306695 16306865 - SINE/Alu_AluSx3 chr1 16306897 16307191 - SINE/Alu_AluSx chr1 16307217 16307535 + SINE/Alu_AluJr chr1 16307590 16308151 + LINE/L1_L1MC1 chr1 16308152 16308280 - SINE/Alu_FLAM_C chr1 16308281 16308317 + LINE/L1_L1MC1 chr1 16308318 16308492 + SINE/Alu_AluJo chr1 16308495 16308725 - SINE/Alu_AluSx3 chr1 16308741 16309042 - SINE/Alu_AluSz chr1 16309058 16309376 - SINE/Alu_AluSc chr1 16309394 16309613 + LINE/L1_L1MC1 chr1 16309614 16309762 + SINE/Alu_AluJb chr1 16309763 16309913 + SINE/Alu_AluSz chr1 16309914 16310211 + SINE/Alu_AluY chr1 16310212 16310381 + SINE/Alu_AluSz chr1 16310382 16310530 + SINE/Alu_AluJb chr1 16310538 16310712 + LINE/L1_L1MC1 chr1 16310713 16310993 + SINE/Alu_AluSz chr1 16310994 16311044 + LINE/L1_L1MC1 chr1 16311046 16311190 + SINE/Alu_AluYc chr1 16311201 16311503 + SINE/Alu_AluJo chr1 16311504 16311542 + Simple_repeat_(AT)n chr1 16311553 16312077 + LINE/L1_L1MC1 chr1 16312078 16312367 + SINE/Alu_AluJo chr1 16312368 16312787 + LINE/L1_L1MC1 chr1 16312788 16313091 - SINE/Alu_AluSx1 chr1 16313092 16313167 + LINE/L1_L1MC1 chr1 16313258 16313386 + Simple_repeat_(AAAG)n chr1 16313976 16314249 - SINE/Alu_AluSx1 chr1 16314359 16314585 + SINE/MIR_MIR chr1 16314640 16314904 + SINE/Alu_AluY chr1 16315889 16316199 + SINE/Alu_AluSp chr1 16316246 16316453 + DNA/PiggyBac_MER75 chr1 16316454 16316754 + SINE/Alu_AluSx chr1 16316755 16316774 + DNA/PiggyBac_MER75 chr1 16316855 16316998 + LINE/L1_L1ME3A chr1 16316999 16317135 + SINE/Alu_AluSx chr1 16317136 16317436 + SINE/Alu_AluSx chr1 16317437 16317627 + SINE/Alu_AluSx chr1 16317628 16317641 + LINE/L1_L1ME3A chr1 16317642 16317947 + SINE/Alu_AluJr chr1 16317948 16318119 + LINE/L1_L1ME3A chr1 16318138 16318432 + SINE/Alu_AluSx chr1 16318650 16318989 - LTR/ERVL_ERV3-16A3_LTR chr1 16319631 16319939 + SINE/Alu_AluSx4 chr1 16320079 16320379 + SINE/Alu_AluSx chr1 16320500 16320708 - SINE/Alu_AluSx3 chr1 16320727 16320818 - SINE/Alu_AluSz chr1 16321561 16321628 + LINE/L2_L2c chr1 16321694 16321855 - SINE/Alu_FRAM chr1 16321887 16322066 + SINE/Alu_AluSx1 chr1 16322077 16322206 - LTR_EUTREP16 chr1 16322296 16322611 + SINE/Alu_AluSg chr1 16323137 16323442 + SINE/Alu_AluSx3 chr1 16323515 16323821 + SINE/Alu_AluSz chr1 16323830 16323864 + Low_complexity_GA-rich chr1 16324394 16324536 - LINE/L2_L2d2 chr1 16324537 16324845 + SINE/Alu_AluSz6 chr1 16324957 16325255 + SINE/Alu_AluJo chr1 16325651 16325925 - SINE/Alu_AluSx chr1 16326015 16326069 + Simple_repeat_(TG)n chr1 16326070 16326355 - SINE/Alu_AluSp chr1 16326402 16326708 + SINE/Alu_AluJb chr1 16326936 16327134 + LINE/L2_L2c chr1 16327176 16327330 - SINE/tRNA-RTE_MamSINE1 chr1 16327704 16328000 - SINE/Alu_AluSz chr1 16328124 16328882 + LINE/L1_L1MC3 chr1 16328883 16329187 + SINE/Alu_AluSg chr1 16329188 16329301 + LINE/L1_L1MC3 chr1 16329302 16329582 + SINE/Alu_AluSx3 chr1 16329583 16329667 + LINE/L1_L1MC3 chr1 16329668 16329949 + SINE/Alu_AluSx3 chr1 16329950 16330053 + LINE/L1_L1MC3 chr1 16330239 16330506 + SINE/Alu_AluJr chr1 16330527 16330659 + SINE/Alu_AluSx chr1 16330660 16330971 + SINE/Alu_AluSq2 chr1 16330972 16331123 + SINE/Alu_AluSc chr1 16331132 16331422 + SINE/Alu_AluY chr1 16331423 16331452 + Simple_repeat_(AAT)n chr1 16331473 16331765 + SINE/Alu_AluSx1 chr1 16331770 16332073 + SINE/Alu_AluSp chr1 16332536 16332840 - SINE/Alu_AluSz chr1 16333332 16333592 + SINE/Alu_AluJr chr1 16334152 16334440 + SINE/Alu_AluY chr1 16334945 16335077 + SINE/Alu_FLAM_C chr1 16335134 16335239 + srpRNA_7SLRNA chr1 16335241 16335433 - SINE/Alu_AluJb chr1 16335494 16335515 + Simple_repeat_(CTTC)n chr1 16335584 16335885 + SINE/Alu_AluSx1 chr1 16335934 16336237 - SINE/Alu_AluSq2 chr1 16336350 16336655 - SINE/Alu_AluSz chr1 16336702 16336990 + SINE/Alu_AluSq2 chr1 16337163 16337293 - DNA/hAT-Charlie_MER5A1 chr1 16337376 16337511 - SINE/MIR_MIRb chr1 16337601 16337913 + SINE/Alu_AluSx1 chr1 16337926 16337977 + SINE/Alu_FRAM chr1 16337978 16338269 + SINE/Alu_AluYj4 chr1 16338270 16338373 + SINE/Alu_FRAM chr1 16338799 16339108 - SINE/Alu_AluSz chr1 16339311 16339610 - SINE/Alu_AluSc chr1 16339809 16339961 + SINE/Alu_AluJb chr1 16339977 16340209 + SINE/Alu_AluY chr1 16340325 16340618 - SINE/Alu_AluSq2 chr1 16340783 16340946 - SINE/MIR_MIRc chr1 16340990 16341238 + LINE/L1_L1MC5a chr1 16341320 16341634 + SINE/Alu_AluSg chr1 16341689 16341988 + SINE/Alu_AluSp chr1 16341995 16342114 + SINE/Alu_AluJb chr1 16342115 16342417 + SINE/Alu_AluSp chr1 16342418 16342598 + SINE/Alu_AluJb chr1 16342599 16342682 + LTR/ERVL_LTR33A chr1 16342683 16343110 + LTR/ERVL-MaLR_MSTB2 chr1 16343111 16343232 + LTR/ERVL_LTR33A chr1 16343262 16343568 + SINE/Alu_AluSz6 chr1 16343570 16343827 + SINE/Alu_AluSg4 chr1 16343829 16343951 + SINE/Alu_AluJb chr1 16343953 16343977 + Simple_repeat_(A)n chr1 16343978 16343998 + LINE/L1_L1MC5a chr1 16343999 16344273 - SINE/Alu_AluSp chr1 16344274 16344323 + LINE/L1_L1MC5a chr1 16344324 16344631 - SINE/Alu_AluSx chr1 16344632 16344816 + LINE/L1_L1MC5a chr1 16344817 16345122 + SINE/Alu_AluSx1 chr1 16345123 16345142 + LINE/L1_L1MC5a chr1 16345143 16345423 + SINE/Alu_AluSx3 chr1 16345424 16345453 + Simple_repeat_(AAAAT)n chr1 16345454 16345568 + LINE/L1_L1MC5a chr1 16345569 16345865 + SINE/Alu_AluY chr1 16345866 16346217 + LINE/L1_L1MC5a chr1 16346412 16346716 + SINE/Alu_AluY chr1 16346754 16347044 - SINE/Alu_AluSz chr1 16347081 16347132 + Simple_repeat_(AT)n chr1 16347235 16347514 + SINE/Alu_AluSz chr1 16347515 16347544 + Simple_repeat_(AAAC)n chr1 16347573 16347709 + SINE/Alu_AluSx chr1 16347711 16348022 + SINE/Alu_AluY chr1 16348034 16348068 + Simple_repeat_(CTATAAG)n chr1 16348418 16348724 + SINE/Alu_AluSg chr1 16349237 16349304 + LINE/L2_L2c chr1 16349431 16349530 - SINE/MIR_MIRc chr1 16349579 16349873 + SINE/Alu_AluSq2 chr1 16350465 16350757 + SINE/Alu_AluSz chr1 16350758 16350796 + Simple_repeat_(AAAG)n chr1 16352430 16352456 + Simple_repeat_(CGC)n chr1 16352475 16352538 + Simple_repeat_(GC)n chr1 16352890 16353067 - SINE/MIR_MIRb chr1 16353217 16353243 + Simple_repeat_(AACAA)n chr1 16353271 16353551 + LTR/ERV1_MER4B chr1 16353552 16353847 - SINE/Alu_AluJb chr1 16353848 16353960 + LTR/ERV1_MER4B chr1 16353961 16354307 - SINE/Alu_AluSg4 chr1 16354308 16354603 - SINE/Alu_AluSx chr1 16354604 16354785 + LTR/ERV1_MER4B chr1 16354904 16355200 + SINE/Alu_AluJo chr1 16355306 16355620 + SINE/Alu_AluSp chr1 16355778 16356076 + SINE/Alu_AluSx chr1 16356202 16356243 - SINE/Alu_FLAM_A chr1 16356248 16356393 + SINE/Alu_FRAM chr1 16356404 16356689 + SINE/Alu_AluSz6 chr1 16356702 16356824 + SINE/Alu_AluJr chr1 16356864 16356926 - DNA/hAT-Charlie_MER5B chr1 16356980 16357075 + LINE/L2_L2a chr1 16357109 16357150 - LINE/L2_L2b chr1 16357163 16357208 + Simple_repeat_(AT)n chr1 16357209 16357501 - SINE/Alu_AluY chr1 16357506 16357807 - SINE/Alu_AluSz6 chr1 16357808 16357821 + Simple_repeat_(AT)n chr1 16357831 16357932 - LINE/L2_L2a chr1 16357971 16358066 - SINE/MIR_MIR chr1 16358299 16358395 + DNA/TcMar-Tigger_Tigger3a chr1 16358403 16358568 - SINE/Alu_AluSx chr1 16358569 16358923 - SINE/Alu_AluSx chr1 16358924 16359074 - SINE/Alu_AluSx chr1 16359085 16359385 - SINE/Alu_AluY chr1 16359389 16359684 - SINE/Alu_AluSx chr1 16359685 16359771 - LTR/ERV1_MER49 chr1 16359772 16360069 + SINE/Alu_AluSg chr1 16360070 16360280 - LTR/ERV1_MER49 chr1 16360288 16360463 - LINE/L1_L1MC4a chr1 16360470 16360656 - SINE/Alu_AluSx chr1 16360657 16360969 - SINE/Alu_AluSq2 chr1 16360970 16360986 - SINE/Alu_AluSx chr1 16360987 16361287 - SINE/Alu_AluSx3 chr1 16361288 16361423 - SINE/Alu_AluSx chr1 16361512 16361815 + SINE/Alu_AluSp chr1 16361816 16362120 + SINE/Alu_AluSx4 chr1 16362128 16362150 + SINE/Alu_AluSc chr1 16362151 16362225 + SINE/Alu_AluSp chr1 16362226 16362517 + SINE/Alu_AluSx1 chr1 16362518 16362685 + SINE/Alu_AluSp chr1 16362686 16362832 + SINE/Alu_AluSc chr1 16362835 16362861 + Simple_repeat_(AGAG)n chr1 16362910 16363201 - SINE/Alu_AluSc chr1 16363261 16363342 - SINE/MIR_MIR3 chr1 16363651 16363953 - SINE/Alu_AluY chr1 16364131 16364441 - SINE/Alu_AluSg chr1 16364451 16364642 - LINE/L2_L2 chr1 16364789 16365092 + SINE/Alu_AluSx1 chr1 16365297 16365592 - SINE/Alu_AluSz chr1 16365681 16365853 - SINE/MIR_MIR chr1 16365854 16365988 - SINE/Alu_FLAM_C chr1 16366000 16366286 - SINE/Alu_AluSp chr1 16366299 16366594 - SINE/Alu_AluSx chr1 16368642 16368694 - LINE/L2_L2a chr1 16369607 16369911 + SINE/Alu_AluSp chr1 16369982 16370175 - SINE/MIR_MIRb chr1 16370216 16370526 - SINE/Alu_AluSz6 chr1 16370527 16370556 + Simple_repeat_(T)n chr1 16370642 16370800 - SINE/Alu_FRAM chr1 16371192 16371354 - SINE/Alu_FRAM chr1 16371384 16371435 + Simple_repeat_(CTCCC)n chr1 16371436 16371449 - SINE/Alu_AluJr chr1 16371450 16371760 - SINE/Alu_AluY chr1 16371761 16371945 - SINE/Alu_AluJr chr1 16372110 16372138 + Simple_repeat_(T)n chr1 16372192 16372481 + SINE/Alu_AluSx1 chr1 16372915 16373292 - LINE/L2_L2c chr1 16373299 16373600 + SINE/Alu_AluSp chr1 16373605 16373902 - SINE/Alu_AluJb chr1 16374873 16375175 - SINE/Alu_AluSg chr1 16375316 16375612 - SINE/Alu_AluSx chr1 16376524 16376824 + SINE/Alu_AluSx1 chr1 16376858 16376971 - SINE/Alu_FLAM_A chr1 16377025 16377062 + Simple_repeat_(CTCTC)n chr1 16377312 16377589 + SINE/Alu_AluSx chr1 16377644 16377790 + SINE/MIR_MIRb chr1 16378283 16378581 - SINE/Alu_AluSx1 chr1 16378739 16379026 - SINE/Alu_AluSx chr1 16379123 16379402 - SINE/Alu_AluY chr1 16379598 16379733 + SINE/MIR_MIRb chr1 16379761 16379978 - SINE/Alu_AluSx3 chr1 16379996 16380195 - LINE/L2_L2a chr1 16380196 16380309 + SINE/Alu_AluJr chr1 16380310 16380466 - SINE/Alu_AluSz chr1 16380467 16380767 - SINE/Alu_AluSp chr1 16380768 16380906 - SINE/Alu_AluSz chr1 16380907 16381104 + SINE/Alu_AluJr chr1 16381108 16381273 + SINE/Alu_FRAM chr1 16381281 16381571 + SINE/Alu_AluSz chr1 16381572 16381649 - LINE/L2_L2a chr1 16381650 16381947 + SINE/Alu_AluSx1 chr1 16381948 16382175 - LINE/L2_L2a chr1 16382190 16382487 - SINE/Alu_AluSx chr1 16382494 16382790 - SINE/Alu_AluSx chr1 16382870 16383129 - SINE/Alu_AluSx1 chr1 16383150 16383206 + Simple_repeat_(TTTC)n chr1 16383209 16383518 - SINE/Alu_AluJr chr1 16383520 16383607 - DNA/hAT-Charlie_MER33 chr1 16383608 16383917 - SINE/Alu_AluSg chr1 16383918 16383988 - DNA/hAT-Charlie_MER33 chr1 16384259 16384390 + SINE/Alu_FLAM_C chr1 16385364 16385422 + LTR?_MamRep605b chr1 16386944 16387133 + SINE/MIR_MIRc chr1 16388106 16388127 + Simple_repeat_(AT)n chr1 16388128 16388405 - SINE/Alu_AluSz chr1 16388406 16388421 + Simple_repeat_(AT)n chr1 16388505 16388675 + LINE/L1_L1M5 chr1 16388679 16388938 - SINE/Alu_AluSz chr1 16388980 16389072 + LINE/L1_L1M5 chr1 16389073 16389286 - SINE/Alu_AluY chr1 16389287 16389610 - SINE/Alu_AluY chr1 16389611 16389743 - SINE/Alu_AluY chr1 16389744 16389831 + LINE/L1_L1M5 chr1 16389848 16389905 - DNA/hAT-Charlie_Charlie4z chr1 16389906 16390075 - DNA/hAT-Charlie_MER20 chr1 16390076 16390124 - DNA/hAT-Charlie_Charlie4z chr1 16390668 16390864 - LINE/L2_L2c chr1 16390940 16391006 - LINE/L2_L2c chr1 16391650 16391846 + SINE/MIR_MIR3 chr1 16393810 16393970 + SINE/MIR_MIR chr1 16394120 16394262 - LINE/CR1_CR1_Mam chr1 16394697 16394978 + SINE/Alu_AluSz chr1 16395375 16395408 + Low_complexity_G-rich chr1 16395602 16395635 + Simple_repeat_(TTTT)n chr1 16397800 16397845 + Simple_repeat_(CTGCC)n chr1 16398937 16398994 - SINE/MIR_MIRc chr1 16399563 16399768 + SINE/MIR_MIRb chr1 16399844 16399899 + SINE/MIR_MIRb chr1 16399906 16400021 + SINE/MIR_MIRb chr1 16400022 16400314 - SINE/Alu_AluSq2 chr1 16400315 16400419 + SINE/MIR_MIRb chr1 16400980 16401102 + SINE/MIR_MIR3 chr1 16401128 16401292 + SINE/MIR_MIRb chr1 16401313 16401587 - SINE/Alu_AluJr chr1 16401660 16401729 + LINE/L2_L2b chr1 16401859 16401960 - LINE/L2_L2a chr1 16401970 16402244 + LTR/ERVL_LTR16E2 chr1 16402250 16402559 - SINE/Alu_AluYk3 chr1 16402560 16402719 - SINE/Alu_FRAM chr1 16402735 16402778 + SINE/Alu_Alu chr1 16402779 16402919 + SINE/Alu_AluJb chr1 16403255 16403467 + LTR/ERVL-MaLR_MLT1K chr1 16403477 16403612 - SINE/Alu_AluSz6 chr1 16404202 16404499 + SINE/Alu_AluSx chr1 16404539 16404827 + SINE/Alu_AluJr chr1 16404831 16404932 - DNA/hAT-Tip100_MER45R chr1 16405210 16405220 + SINE/Alu_AluJb chr1 16405221 16405512 + SINE/Alu_AluSx chr1 16405513 16405655 + SINE/Alu_AluJb chr1 16405870 16405982 + SINE/MIR_MIRb chr1 16406112 16406283 - SINE/Alu_AluSx chr1 16406314 16406460 + LTR/ERVL-MaLR_MLT1B chr1 16406461 16406763 + SINE/Alu_AluJr chr1 16406764 16406977 + LTR/ERVL-MaLR_MLT1B chr1 16407441 16407466 + Simple_repeat_(TAT)n chr1 16407467 16407755 - SINE/Alu_AluSg chr1 16407769 16408066 - SINE/Alu_AluJr chr1 16408535 16408590 + SINE/MIR_MIRc chr1 16408591 16408889 + SINE/Alu_AluSz chr1 16408890 16408961 + SINE/MIR_MIRc chr1 16409502 16409547 + Low_complexity_G-rich chr1 16409672 16409709 + Low_complexity_G-rich chr1 16410253 16410414 - SINE/Alu_AluJb chr1 16410415 16410710 - SINE/Alu_AluSx chr1 16410711 16410844 - SINE/Alu_AluJb chr1 16410877 16410979 + LINE/L2_L2a chr1 16411074 16411191 + LINE/L2_L2 chr1 16411192 16411473 - SINE/Alu_AluJr chr1 16411474 16411508 + LINE/L2_L2 chr1 16411509 16411818 + SINE/Alu_AluY chr1 16411819 16412420 + LINE/L2_L2 chr1 16412465 16412762 - SINE/Alu_AluSx chr1 16412782 16413081 - SINE/Alu_AluSc chr1 16413087 16413132 - SINE/Alu_FLAM_A chr1 16413133 16413600 - LINE/L1_L1MB2 chr1 16413601 16413912 - SINE/Alu_AluSp chr1 16413913 16414056 - LINE/L1_L1MB2 chr1 16414064 16414368 - SINE/Alu_AluSz6 chr1 16414505 16414549 - LINE/L2_L2 chr1 16414613 16414912 + SINE/Alu_AluSp chr1 16415055 16415346 + SINE/Alu_AluJb chr1 16415553 16415857 - SINE/Alu_AluY chr1 16416294 16416395 + LTR/ERVL_MER70A chr1 16416396 16416698 + SINE/Alu_AluSz chr1 16416699 16417079 + LTR/ERVL_MER70A chr1 16417252 16417425 - SINE/Alu_AluSq2 chr1 16417426 16417734 - SINE/Alu_AluSq2 chr1 16417735 16417873 - SINE/Alu_AluSq2 chr1 16418246 16418266 + Simple_repeat_(TATTTCT)n chr1 16418267 16418567 - SINE/Alu_AluY chr1 16418572 16418841 - SINE/Alu_AluSx chr1 16418842 16418858 + Simple_repeat_(TATTTCT)n chr1 16418905 16419309 - LINE/L2_L2a chr1 16419355 16419657 - LINE/L2_L2a chr1 16419658 16419951 + SINE/Alu_AluSx chr1 16419952 16420364 - LINE/L2_L2a chr1 16420378 16420537 + SINE/Alu_AluSx3 chr1 16420567 16421065 - LINE/L2_L2a chr1 16421299 16421340 + Simple_repeat_(AC)n chr1 16421706 16421859 - SINE/MIR_MIR1_Amn chr1 16422275 16422429 - SINE/MIR_MIRb chr1 16422510 16422807 - SINE/Alu_AluJo chr1 16422881 16423174 + SINE/Alu_AluSz chr1 16423175 16423433 + SINE/Alu_AluSx chr1 16423441 16423539 + LINE/L1_L1ME1 chr1 16423540 16423842 - SINE/Alu_AluSx1 chr1 16423843 16424153 + LINE/L1_L1ME1 chr1 16424167 16424345 + SINE/Alu_AluYc chr1 16424352 16424407 - DNA/hAT-Charlie_MER103C chr1 16424408 16424713 - SINE/Alu_AluJb chr1 16424714 16424845 - DNA/hAT-Charlie_MER103C chr1 16424909 16425211 - SINE/Alu_AluSq2 chr1 16425311 16425436 - SINE/Alu_AluJb chr1 16425903 16425923 + Low_complexity_A-rich chr1 16425924 16426047 - SINE/Alu_AluJo chr1 16426048 16426063 + Low_complexity_A-rich chr1 16426110 16426197 - SINE/MIR_MIRb chr1 16426201 16426254 - LINE/L1_L1ME4a chr1 16426255 16426557 - SINE/Alu_AluJb chr1 16426558 16426579 - LINE/L1_L1ME4a chr1 16426580 16426879 - SINE/Alu_AluSp chr1 16426880 16427195 - LINE/L1_L1ME4a chr1 16427541 16427627 - LINE/L2_L2a chr1 16428391 16428675 - SINE/Alu_AluSz6 chr1 16428677 16428762 + SINE/MIR_MIRb chr1 16428840 16428976 - SINE/MIR_MIRc chr1 16429143 16429428 - SINE/Alu_AluJo chr1 16429429 16429461 + Simple_repeat_(TTTTA)n chr1 16429463 16429772 - SINE/Alu_AluSp chr1 16429887 16430134 + SINE/Alu_AluSz chr1 16430147 16430453 + SINE/Alu_AluJb chr1 16430686 16431156 + LTR/ERVL-MaLR_MLT1K chr1 16431626 16431738 - SINE/MIR_MIRb chr1 16431749 16431907 + DNA/hAT-Charlie_MER103C chr1 16432079 16432111 + Simple_repeat_(TTTCTT)n chr1 16432112 16432421 - SINE/Alu_AluSx1 chr1 16432889 16433060 - SINE/MIR_MIRb chr1 16433833 16434274 - LINE/L1_L1MB8 chr1 16434275 16434595 - SINE/Alu_AluJo chr1 16434596 16434850 - LINE/L1_L1MB8 chr1 16434851 16435151 - SINE/Alu_AluJr chr1 16435152 16435324 - LINE/L1_L1MB8 chr1 16435325 16435616 - SINE/Alu_AluSx chr1 16435623 16435779 - SINE/Alu_FRAM chr1 16435780 16435970 - LINE/L1_L1MB8 chr1 16435971 16436101 + SINE/Alu_AluSx3 chr1 16436106 16436404 + SINE/Alu_AluSp chr1 16436405 16436484 + LINE/L1_L1PB1 chr1 16436485 16436807 + SINE/Alu_AluSz chr1 16436808 16436868 + LINE/L1_L1PB1 chr1 16436881 16436963 - LINE/L1_L1MB8 chr1 16437366 16437484 + SINE/MIR_MIRc chr1 16437497 16437747 + LINE/L2_L2c chr1 16437770 16438062 + SINE/Alu_AluSz chr1 16438074 16438401 + SINE/Alu_AluJr chr1 16438406 16438521 + LINE/L1_L1MB4 chr1 16438524 16438692 + SINE/Alu_AluJb chr1 16438697 16439244 + LINE/L1_L1MB4 chr1 16439300 16439609 + SINE/Alu_AluSx1 chr1 16439739 16439834 + LTR/ERV1?_LTR103_Mam chr1 16439835 16440143 - SINE/Alu_AluSx chr1 16440144 16440223 + LTR/ERV1?_LTR103_Mam chr1 16440347 16440385 + Simple_repeat_(AGGCAG)n chr1 16441980 16442277 - SINE/Alu_AluSp chr1 16442647 16442928 + SINE/Alu_AluJb chr1 16443236 16443542 + DNA/hAT-Charlie_MER58B chr1 16444423 16444640 + SINE/MIR_MIR chr1 16444833 16445123 - SINE/Alu_AluSg7 chr1 16445281 16445501 - DNA/hAT-Charlie_MER20 chr1 16445624 16445925 + SINE/Alu_AluSz6 chr1 16445926 16446227 + SINE/Alu_AluSp chr1 16446286 16446561 + SINE/Alu_AluJr chr1 16446562 16446777 - SINE/Alu_AluJb chr1 16446778 16447078 + SINE/Alu_AluSx chr1 16447079 16447141 - SINE/Alu_AluJb chr1 16448895 16448979 - SINE/MIR_MIRb chr1 16449287 16449610 - LINE/L2_L2c chr1 16449861 16449890 + Simple_repeat_(GGCTG)n chr1 16450208 16450247 - SINE/MIR_MIRb chr1 16450248 16450287 + Simple_repeat_(GTTTT)n chr1 16450288 16450492 - SINE/MIR_MIRb chr1 16450568 16450665 - LINE/L1_L1ME3A chr1 16450666 16450973 + SINE/Alu_AluSp chr1 16450974 16451621 - LINE/L1_L1ME3A chr1 16452401 16452605 - LINE/L2_L2d2 chr1 16452756 16452799 + Simple_repeat_(CCGCC)n chr1 16453100 16453408 - SINE/Alu_AluSp chr1 16453496 16453786 - SINE/Alu_AluSg chr1 16453822 16453963 - LINE/L2_L2a chr1 16453985 16454044 + SINE/MIR_MIR chr1 16454045 16454358 - SINE/Alu_AluSp chr1 16454362 16454655 - SINE/Alu_AluSx chr1 16454656 16454835 + SINE/MIR_MIR chr1 16454838 16454991 - SINE/MIR_MIR chr1 16455001 16455089 + SINE/MIR_MIRc chr1 16455098 16455230 + DNA/TcMar-Tigger_Tigger15a chr1 16455773 16455875 - LINE/L2_L2-2_Mam chr1 16456015 16456324 - SINE/Alu_AluSq2 chr1 16456554 16456586 + Simple_repeat_(TCAGCC)n chr1 16456713 16456728 - DNA/hAT-Charlie_MER58A chr1 16456729 16457001 - SINE/Alu_AluJb chr1 16457002 16457166 - DNA/hAT-Charlie_MER58A chr1 16457174 16457478 + SINE/Alu_AluSx chr1 16457504 16457702 + DNA/PiggyBac_MER75 chr1 16457703 16458010 - SINE/Alu_AluJb chr1 16458011 16458307 + DNA/PiggyBac_MER75 chr1 16458320 16458615 + SINE/Alu_AluJb chr1 16460496 16460606 + SINE/Alu_AluJr chr1 16460607 16460902 - SINE/Alu_AluSz chr1 16460903 16461118 + SINE/Alu_AluJr chr1 16461409 16461701 + SINE/Alu_AluSz chr1 16461721 16461806 + SINE/MIR_MIR chr1 16462223 16462753 + LTR/ERVL-MaLR_MLT1F2 chr1 16462879 16462988 - SINE/MIR_MIR chr1 16462989 16463286 - SINE/Alu_AluSz chr1 16463297 16463479 - LTR/ERVL-MaLR_MLT1A0 chr1 16463482 16463507 + Simple_repeat_(T)n chr1 16463509 16463617 - SINE/Alu_AluJr chr1 16463635 16463917 - SINE/Alu_AluSx4 chr1 16463918 16464064 - LTR/ERVL-MaLR_MLT1A0 chr1 16464074 16464201 - SINE/MIR_MIR chr1 16464380 16464411 + LINE/L2_L2d2 chr1 16464518 16464555 + Simple_repeat_(T)n chr1 16464587 16464898 - SINE/Alu_AluSq2 chr1 16464899 16465337 - LTR/ERVL-MaLR_MLT1F2 chr1 16465338 16465616 + SINE/Alu_AluSc chr1 16465626 16466036 - LTR/ERVL-MaLR_MLT1C2 chr1 16466053 16466258 + LINE/L1_L1ME4b chr1 16466260 16466425 + SINE/Alu_AluSx3 chr1 16466426 16466710 + SINE/Alu_AluSq2 chr1 16466711 16466830 + LINE/L1_L1ME4b chr1 16466835 16466906 - SINE/Alu_FLAM_A chr1 16466916 16466964 + LINE/L1_L1ME4b chr1 16466969 16467182 + SINE/Alu_AluJo chr1 16468312 16468515 - LINE/L1_L1M5 chr1 16468893 16469525 - LTR/ERV1_MER67B chr1 16470035 16470368 + LTR/ERVL_MLT2B4 chr1 16470381 16470418 + Simple_repeat_(GT)n chr1 16470699 16470840 - LINE/L1_L1MD1 chr1 16470841 16471153 + SINE/Alu_AluY chr1 16471154 16471896 - LINE/L1_L1MD1 chr1 16471899 16471948 - LTR/ERVL-MaLR_MLT1B chr1 16471949 16474089 - LINE/L1_L1MD1 chr1 16474581 16474653 + SINE/MIR_MIRc chr1 16474691 16474986 + SINE/Alu_AluSz chr1 16475311 16475346 + Simple_repeat_(ATT)n chr1 16475347 16475620 - SINE/Alu_AluSc8 chr1 16475621 16475746 - SINE/MIR_MIR chr1 16476256 16476304 + Simple_repeat_(GACA)n chr1 16477228 16477292 + SINE/MIR_MIR chr1 16477293 16477590 - SINE/Alu_AluSx chr1 16477591 16477640 + SINE/MIR_MIR chr1 16477728 16477958 + SINE/MIR_MIR chr1 16477983 16478030 + Simple_repeat_(TAT)n chr1 16478032 16478104 + SINE/MIR_MIRb chr1 16478965 16479247 - SINE/Alu_AluY chr1 16479250 16479522 - SINE/Alu_AluSx1 chr1 16479583 16479888 - SINE/Alu_AluSp chr1 16479889 16480022 - SINE/Alu_AluSz6 chr1 16480100 16480217 + SINE/Alu_AluJr chr1 16480119 16480236 - Retroposon/SVA_SVA_A chr1 16480414 16480513 + SINE/MIR_MIRb chr1 16480733 16480991 + SINE/Alu_AluSx1 chr1 16481026 16481325 + SINE/Alu_AluJb chr1 16481333 16481465 + SINE/Alu_FLAM_C chr1 16481497 16481595 + DNA/hAT-Charlie_MER3 chr1 16481604 16481910 - SINE/Alu_AluY chr1 16481911 16482134 - SINE/Alu_AluSx chr1 16482200 16482333 + SINE/Alu_AluJb chr1 16482512 16482619 + SINE/Alu_FRAM chr1 16482620 16482655 + Simple_repeat_(CAAAA)n chr1 16482656 16482673 + SINE/Alu_AluJb chr1 16482678 16482978 + SINE/Alu_AluSg4 chr1 16482979 16483129 + SINE/Alu_AluYc chr1 16483130 16483154 + Simple_repeat_(AAAT)n chr1 16483573 16483648 + Simple_repeat_(CTAGGC)n chr1 16484126 16484171 + Simple_repeat_(CCCGGA)n chr1 16485066 16485151 - SINE/MIR_MIR3 chr1 16485243 16485450 + SINE/MIR_MIRb chr1 16485783 16485881 + SINE/MIR_MIR chr1 16485928 16486217 - SINE/Alu_AluSx chr1 16486574 16486605 + Simple_repeat_(CCAGGC)n chr1 16486731 16487030 + SINE/Alu_AluJb chr1 16487058 16487479 + LINE/L1_L1MC5 chr1 16487480 16487777 + SINE/Alu_AluSx chr1 16487778 16487861 + LINE/L1_L1MC5 chr1 16487862 16488140 - SINE/Alu_AluSx chr1 16488141 16488222 + LINE/L1_L1MC5 chr1 16488254 16488466 + SINE/Alu_AluJb chr1 16488664 16488962 + LINE/L2_L2c chr1 16489033 16489290 + SINE/Alu_AluSx chr1 16489291 16489322 + Simple_repeat_(AAAT)n chr1 16489330 16489524 + SINE/MIR_MIR chr1 16489592 16489632 + Simple_repeat_(TGAAT)n chr1 16490008 16490100 + SINE/MIR_MIR chr1 16490101 16490172 - SINE/Alu_Alu chr1 16490173 16490358 - SINE/Alu_AluSz chr1 16490367 16490423 + SINE/MIR_MIR chr1 16490614 16490661 + Simple_repeat_(CCTCCT)n chr1 16490954 16490981 + Simple_repeat_(GGCAG)n chr1 16491877 16492076 + SINE/MIR_MIRb chr1 16492028 16492095 + LINE/L2_L2d2 chr1 16492117 16492418 + SINE/Alu_AluSp chr1 16492510 16492665 + SINE/MIR_MIR chr1 16492671 16492806 + SINE/Alu_AluSz chr1 16492807 16492990 + SINE/Alu_AluSc chr1 16493219 16493252 + SINE/Alu_AluSx chr1 16493253 16493300 + Simple_repeat_(AAAT)n chr1 16493301 16493452 + SINE/Alu_AluSx chr1 16493453 16493477 + Simple_repeat_(AAAAT)n chr1 16493485 16493617 + SINE/Alu_AluJo chr1 16493719 16493874 + LTR/ERV1_LTR2 chr1 16493945 16494056 - SINE/MIR_MIRb chr1 16494330 16494632 + SINE/Alu_AluSx1 chr1 16495041 16495059 + Simple_repeat_(A)n chr1 16495107 16495285 + SINE/MIR_MIR chr1 16495286 16495689 + LTR/ERVL-MaLR_MLT1J2 chr1 16495690 16495725 + SINE/MIR_MIR chr1 16496015 16496213 - SINE/MIR_MIRb chr1 16496217 16496538 - SINE/Alu_AluSz chr1 16496563 16496864 + SINE/Alu_AluJb chr1 16496952 16497161 + LINE/L2_L2b chr1 16497162 16497478 - SINE/Alu_AluSz chr1 16497479 16497714 + LINE/L2_L2b chr1 16497722 16498001 - SINE/Alu_AluJb chr1 16498691 16498762 + SINE/MIR_MIRb chr1 16499333 16499400 + Simple_repeat_(GGCCGG)n chr1 16499919 16500080 - SINE/Alu_AluSg4 chr1 16500081 16500105 + Simple_repeat_(T)n chr1 16500114 16500365 - SINE/Alu_AluYc3 chr1 16500366 16500499 - SINE/Alu_AluSg4 chr1 16501288 16501446 - SINE/MIR_MIRb chr1 16502004 16502207 + LINE/L1_L1ME4a chr1 16502215 16502503 - SINE/Alu_AluSq2 chr1 16502573 16502674 - LTR/ERV1_MER84 chr1 16502675 16502971 + SINE/Alu_AluSg7 chr1 16502972 16503125 - LTR/ERV1_MER84 chr1 16503153 16503450 - SINE/Alu_AluSx1 chr1 16503463 16503509 - SINE/Alu_AluYe5 chr1 16503534 16504064 - LINE/L1_L1MEc chr1 16504148 16504264 + SINE/Alu_AluJr chr1 16504273 16504296 + Simple_repeat_(CA)n chr1 16504531 16504548 + Simple_repeat_(T)n chr1 16504549 16504881 + SINE/Alu_AluY chr1 16504893 16505125 - SINE/Alu_AluSz chr1 16505790 16506015 + SINE/Alu_AluJb chr1 16506392 16506415 + Simple_repeat_(A)n chr1 16506444 16506476 - snRNA_U1 chr1 16506477 16507011 - LTR/ERV1_LTR4 chr1 16507601 16507770 - SINE/Alu_AluJb chr1 16507906 16508191 - SINE/Alu_AluSz chr1 16508218 16508502 - LTR/ERV1_ERV24_Prim-int chr1 16508780 16508910 - LTR/ERV1_ERV24_Prim-int chr1 16508993 16509073 - SINE/Alu_Alu chr1 16509067 16509592 - LTR/ERV1_MER87 chr1 16509632 16509927 - SINE/Alu_AluSx1 chr1 16509997 16510276 + SINE/Alu_AluSg chr1 16510277 16510307 + Simple_repeat_(AAAT)n chr1 16510541 16510844 + SINE/Alu_AluSz chr1 16510886 16510987 - SINE/Alu_FRAM chr1 16510993 16511098 - SINE/Alu_FLAM_A chr1 16511155 16511394 - SINE/Alu_AluJr chr1 16512479 16512520 + Simple_repeat_(GA)n chr1 16512693 16512856 - SINE/Alu_AluSx chr1 16512857 16512886 + Simple_repeat_(ATT)n chr1 16512887 16512968 - SINE/Alu_AluSx chr1 16513455 16513604 - LTR/ERVL_LTR75 chr1 16513606 16513669 + DNA/hAT-Tip100_MER45B chr1 16514122 16514285 - snRNA_U1 chr1 16514671 16514796 - DNA/hAT-Charlie_Charlie4 chr1 16514977 16515004 + Simple_repeat_(T)n chr1 16515322 16515867 + LTR/ERV1_MER41E chr1 16516156 16516737 - LTR/ERV1_LTR8A chr1 16516738 16517077 - DNA/hAT-Charlie_MER1B chr1 16517078 16517213 - LTR/ERV1_LTR8A chr1 16517243 16517559 - LTR/ERV1_MER31B chr1 16517578 16517732 - LTR/ERV1_MER89 chr1 16517733 16518053 + SINE/Alu_AluY chr1 16518054 16518111 - LTR/ERV1_MER89 chr1 16518112 16518259 + LTR/ERV1_LTR49 chr1 16518276 16518432 + LTR/ERV1_MER72 chr1 16518433 16518592 + LTR/ERV1_MER51D chr1 16518593 16518898 + SINE/Alu_AluSx chr1 16518899 16519376 + LTR/ERV1_MER51D chr1 16519377 16519729 + LTR/ERV1_MER72 chr1 16519733 16519942 + LINE/L1_L1M5 chr1 16519962 16520119 - SINE/Alu_AluSx chr1 16520584 16520658 - tRNA_tRNA-Asn-AAC chr1 16521531 16521556 + Simple_repeat_(TTGT)n chr1 16521806 16521952 - LINE/L1_L1M5 chr1 16521932 16522439 - LINE/L1_L1MEc chr1 16522551 16522593 + Low_complexity_A-rich chr1 16523665 16523772 - LINE/L2_L2 chr1 16523813 16524403 + LTR/ERV1_LTR49 chr1 16524406 16525137 + LTR/ERV1_LTR49-int chr1 16525142 16525339 + SINE/Alu_AluSx chr1 16525344 16525598 + LINE/CR1_L3 chr1 16525832 16525856 + Simple_repeat_(T)n chr1 16525858 16526077 + LTR/ERVL_LTR16A2 chr1 16526342 16526479 - DNA/hAT-Charlie_MER5B chr1 16526742 16527058 - SINE/Alu_AluY chr1 16527194 16527203 + Simple_repeat_(TTTTCC)n chr1 16527204 16527504 + SINE/Alu_AluSp chr1 16527505 16527527 + Simple_repeat_(TTTTCC)n chr1 16527695 16527838 + SINE/MIR_MIR3 chr1 16528316 16528342 + Simple_repeat_(TTTG)n chr1 16529151 16530064 - LTR/ERV1_PRIMA41-int chr1 16530065 16530371 - SINE/Alu_AluSx1 chr1 16530372 16531115 - LTR/ERV1_PRIMA41-int chr1 16531125 16531346 - SINE/Alu_AluYk3 chr1 16531419 16531540 + LINE/L1_L1MEc chr1 16531541 16531825 + SINE/Alu_AluSz chr1 16531826 16531851 + LINE/L1_L1MEc chr1 16531852 16531886 + Simple_repeat_(AAAC)n chr1 16531887 16532038 + LINE/L1_L1MEc chr1 16532257 16532377 - SINE/Alu_AluJb chr1 16532398 16532471 - tRNA_tRNA-Asn-AAC chr1 16533159 16533453 + SINE/Alu_AluSz chr1 16533519 16533586 + tRNA_tRNA-His-CAY_ chr1 16534084 16534220 - snRNA_U1 chr1 16535279 16535350 - tRNA_tRNA-Glu-GAA chr1 16535844 16536004 + LINE/L2_L2 chr1 16536089 16536263 - DNA/hAT-Charlie_MER20 chr1 16537148 16537393 - LTR/ERVL-MaLR_MLT1A0 chr1 16537571 16537922 - LTR/ERVL-MaLR_THE1A chr1 16538006 16538512 + LINE/L1_L1ME2 chr1 16540199 16541110 + LTR/ERV1_MER49 chr1 16541178 16543127 + LINE/L1_L1PA6 chr1 16543142 16543345 + LINE/L1_L1ME2 chr1 16543347 16543533 - SINE/Alu_AluSx chr1 16543537 16543683 + LINE/L1_L1ME2 chr1 16543730 16544064 + LINE/L1_L1ME2 chr1 16544092 16544187 + SINE/Alu_AluSx3 chr1 16544641 16544708 - tRNA_tRNA-His-CAY_ chr1 16544961 16545262 - SINE/Alu_AluSz chr1 16545939 16546009 - tRNA_tRNA-Gly-GGG chr1 16546400 16546463 + Low_complexity_GA-rich chr1 16547663 16547737 - tRNA_tRNA-Val-GTG chr1 16549063 16549367 + SINE/Alu_AluSx3 chr1 16549379 16549519 + LINE/L2_L2d chr1 16549687 16549813 + LTR/ERVL-MaLR_MLT1H2 chr1 16549882 16549929 - SINE/MIR_MIR chr1 16549930 16550231 - SINE/Alu_AluSx chr1 16550232 16550380 - SINE/MIR_MIR chr1 16550388 16550444 + Simple_repeat_(ACTAT)n chr1 16551364 16551580 - SINE/MIR_MIRb chr1 16551815 16552014 - SINE/MIR_MIR chr1 16552207 16552226 + Simple_repeat_(TAT)n chr1 16552787 16552873 + LINE/L2_L2c chr1 16553462 16553533 + tRNA_tRNA-Gln-CAG chr1 16556108 16556185 + Simple_repeat_(AT)n chr1 16556312 16556578 + LINE/L1_L1ME3Cz chr1 16556674 16556802 - SINE/MIR_MIR chr1 16557745 16557876 + Simple_repeat_(TATGTG)n chr1 16557971 16558023 + LINE/L1_L1M5 chr1 16558024 16558321 + SINE/Alu_AluYm1 chr1 16558322 16558376 + LINE/L1_L1M5 chr1 16558396 16558809 - LINE/L1_L1MEc chr1 16558810 16558859 + Simple_repeat_(GT)n chr1 16558860 16558946 - LINE/L1_L1MEc chr1 16558939 16559708 - LINE/L1_L1MEc chr1 16559709 16559974 - SINE/Alu_AluSp chr1 16560269 16560669 + LINE/L1_L1MD chr1 16560670 16560870 + LTR/ERVL_MLT2B4 chr1 16560887 16560955 + LTR/ERVL_MLT2B4 chr1 16561015 16561113 + LINE/L1_L1MC5 chr1 16561436 16561662 + LINE/L1_L1MC4 chr1 16561674 16562104 + LINE/L1_L1MC4 chr1 16562192 16562223 + Simple_repeat_(CA)n chr1 16562584 16563025 + LINE/L1_L1MC4 chr1 16564378 16564421 + Simple_repeat_(GACAGA)n chr1 16565983 16566036 + Low_complexity_GA-rich chr1 16567540 16567586 + Simple_repeat_(AC)n chr1 16569804 16570874 + LINE/L1_L1P2 chr1 16570875 16571519 - LINE/L1_L1PA4 chr1 16571520 16572906 + LINE/L1_L1P2 chr1 16573621 16573895 + SINE/Alu_AluSz chr1 16575857 16575970 - SINE/MIR_MIRc chr1 16576993 16577087 + LINE/L1_L1MB5 chr1 16577603 16577724 - SINE/MIR_MIRc chr1 16578031 16578175 - SINE/MIR_MIR chr1 16578326 16578370 + Simple_repeat_(TTA)n chr1 16578373 16578645 - SINE/Alu_AluSg chr1 16578714 16578806 + LINE/L2_L2d2 chr1 16579727 16580038 - SINE/Alu_AluSx1 chr1 16580045 16580347 - SINE/Alu_AluY chr1 16580434 16580626 - SINE/Alu_AluJb chr1 16582127 16582262 - SINE/MIR_MIRc chr1 16583270 16583364 + LINE/L1_L1MB5 chr1 16583880 16584005 - SINE/MIR_MIRc chr1 16584312 16584458 - SINE/MIR_MIR chr1 16584612 16584662 + Simple_repeat_(TTA)n chr1 16584669 16584941 - SINE/Alu_AluSg chr1 16586024 16586334 - SINE/Alu_AluSx1 chr1 16586341 16586646 - SINE/Alu_AluY chr1 16586733 16586925 - SINE/Alu_AluJb chr1 16588432 16588567 - SINE/MIR_MIRc chr1 16589575 16589669 + LINE/L1_L1MB5 chr1 16590109 16590309 - SINE/MIR_MIRc chr1 16590659 16590823 - SINE/MIR_MIR chr1 16590993 16591299 - SINE/Alu_AluSg chr1 16593649 16593737 - SINE/MIR_MIRc chr1 16593845 16593965 - SINE/MIR_MIRb chr1 16595288 16595453 - DNA/hAT-Charlie_MER5A chr1 16595452 16595609 - SINE/MIR_MIRc chr1 16595717 16596013 - SINE/Alu_AluJr chr1 16596119 16597202 - LINE/L1_L1MB7 chr1 16597823 16598009 - SINE/MIR_MIRb chr1 16598017 16598253 - LINE/L1_L1M8 chr1 16598273 16598476 - LINE/L1_L1ME3 chr1 16598477 16598907 - LTR/ERVL-MaLR_MSTB1 chr1 16598908 16598959 - LINE/L1_L1ME3 chr1 16598963 16599132 + SINE/Alu_AluSx1 chr1 16599166 16599435 - LINE/L1_L1ME3 chr1 16599483 16599542 + Simple_repeat_(ACAC)n chr1 16599563 16600156 - LINE/L1_L1ME3 chr1 16600350 16600429 - LINE/L1_L1MC chr1 16600969 16601160 - LINE/L1_L1ME3 chr1 16601299 16601923 + LTR/ERVL-MaLR_MLT1E3 chr1 16602180 16602372 - LTR/ERVL_ERVL-E-int chr1 16602602 16602686 - LTR/ERVL_LTR16B2 chr1 16602708 16603074 - LTR/ERVL_ERVL-E-int chr1 16603092 16603314 - LTR/ERVL_ERVL-E-int chr1 16603325 16603618 - LTR/ERVL_LTR33 chr1 16604206 16604278 + LINE/CR1_L3 chr1 16605248 16605608 + LINE/L1_L1ME2z chr1 16605694 16605874 + LTR/ERVL-MaLR_MLT1F-int chr1 16605875 16605980 + LINE/L1_L1MC2 chr1 16605981 16606649 + LINE/L1_L1MC2 chr1 16607072 16607287 + Low_complexity_GA-rich chr1 16607737 16607870 + LINE/L2_L2b chr1 16608353 16608400 - LINE/L2_L2a chr1 16608757 16608897 - LINE/L2_L2a chr1 16608949 16609126 + SINE/Alu_AluJo chr1 16609157 16609209 - LINE/L2_L2d2 chr1 16609362 16609473 - LTR/ERVL-MaLR_MLT1I chr1 16609552 16609746 - LINE/L2_L2a chr1 16609800 16610094 + SINE/Alu_AluJb chr1 16610506 16610536 + Simple_repeat_(TTAT)n chr1 16610538 16610591 - SINE/Alu_AluJo chr1 16610593 16610749 + LINE/L1_L1ME2z chr1 16610750 16611062 + SINE/Alu_AluJb chr1 16611063 16611197 + LINE/L1_L1ME2z chr1 16611198 16611503 + SINE/Alu_AluJb chr1 16611504 16611935 + LINE/L1_L1ME2z chr1 16611991 16612287 - SINE/Alu_AluJb chr1 16613393 16613446 + Simple_repeat_(CCGCGCC)n chr1 16613537 16613568 + Simple_repeat_(CCTGCG)n chr1 16614267 16614907 + LINE/L2_L2b chr1 16615322 16615632 + SINE/Alu_AluJb chr1 16615738 16615994 + LINE/L1_L1ME2z chr1 16616012 16616256 - SINE/MIR_MIR chr1 16616288 16616314 + Simple_repeat_(TA)n chr1 16616355 16616390 + Simple_repeat_(GTTTTT)n chr1 16616418 16616716 + SINE/Alu_AluSz chr1 16616717 16616804 - SINE/Alu_FLAM_A chr1 16616917 16616976 + Simple_repeat_(AT)n chr1 16618488 16618647 + LINE/L1_L1ME3Cz chr1 16619309 16619456 - SINE/MIR_MIRb chr1 16619626 16619800 - SINE/MIR_MIRb chr1 16620126 16620339 - LINE/L2_L2c chr1 16620824 16621124 - SINE/Alu_AluSc8 chr1 16621175 16621312 - DNA/hAT-Tip100_MER91B chr1 16621378 16621616 - SINE/MIR_MIR chr1 16622007 16622091 - DNA/hAT-Ac_OldhAT1 chr1 16622249 16622361 + SINE/MIR_MIRc chr1 16622414 16622702 + SINE/Alu_AluSq2 chr1 16622710 16622992 + SINE/Alu_AluSp chr1 16622993 16623025 + Simple_repeat_(AAAC)n chr1 16623030 16623201 - SINE/MIR_MIRb chr1 16623243 16623498 - DNA/TcMar-Tigger_Tigger19a chr1 16623585 16623784 - SINE/MIR_MIRb chr1 16624425 16624453 + Simple_repeat_(CCTGCT)n chr1 16624562 16624638 + SINE/MIR_MIRc chr1 16624670 16624969 + SINE/Alu_AluSz chr1 16625282 16625571 - SINE/Alu_AluSc8 chr1 16625572 16625696 - SINE/MIR_MIR chr1 16626206 16626258 + Simple_repeat_(GACA)n chr1 16627442 16627503 + SINE/MIR_MIR chr1 16627504 16627799 - SINE/Alu_AluSx1 chr1 16627800 16627849 + SINE/MIR_MIR chr1 16627937 16628172 + SINE/MIR_MIR chr1 16628194 16628241 + Simple_repeat_(TAT)n chr1 16628243 16628351 + SINE/MIR_MIR chr1 16629188 16629357 - SINE/Alu_AluY chr1 16629358 16629654 - SINE/Alu_AluY chr1 16629657 16629961 - SINE/Alu_AluSx1 chr1 16629962 16630010 - SINE/Alu_AluY chr1 16631773 16631858 - SINE/MIR_MIR3 chr1 16631944 16632142 + SINE/MIR_MIRb chr1 16632455 16632587 + SINE/MIR_MIR chr1 16632634 16632936 - SINE/Alu_AluSx chr1 16633416 16633728 + SINE/Alu_AluSx chr1 16633739 16633869 + SINE/Alu_FLAM_A chr1 16633886 16634380 + LINE/L1_L1ME4b chr1 16634381 16634427 + Simple_repeat_(GT)n chr1 16634428 16634479 + LINE/L1_L1ME4b chr1 16634480 16634797 + SINE/Alu_AluSx chr1 16634831 16634857 + Simple_repeat_(A)n chr1 16634877 16634968 + LINE/L1_L1ME4b chr1 16635031 16635337 - SINE/Alu_AluSx chr1 16635418 16635610 + SINE/Alu_AluJr chr1 16635650 16636064 + LINE/L1_L1MC5 chr1 16636065 16636382 + SINE/Alu_AluSx chr1 16636383 16636466 + LINE/L1_L1MC5 chr1 16636467 16636771 - SINE/Alu_AluSx chr1 16636772 16636845 + LINE/L1_L1MC5 chr1 16636847 16637077 + SINE/Alu_AluJb chr1 16637271 16637575 + SINE/Alu_AluSp chr1 16637582 16637884 + SINE/Alu_AluSg chr1 16637890 16637958 + SINE/MIR_MIRb chr1 16638836 16639105 + SINE/Alu_AluJr chr1 16639369 16639389 + Simple_repeat_(CAG)n chr1 16639891 16640043 + LINE/L1_L1MA10 chr1 16640120 16640578 + LINE/L1_L1MA2 chr1 16640579 16640886 + SINE/Alu_AluSp chr1 16640887 16641199 + SINE/Alu_AluY chr1 16641200 16641930 + LINE/L1_L1MA2 chr1 16642012 16642314 - SINE/Alu_AluJb chr1 16643316 16643420 + LINE/L2_L2b chr1 16643865 16644061 - DNA/hAT-Charlie_MER3 chr1 16645206 16645262 - SINE/MIR_MIRc chr1 16648016 16648054 + Low_complexity_G-rich chr1 16650295 16650563 - DNA/hAT-Charlie_Charlie1a chr1 16651159 16651296 + SINE/MIR_MIRb chr1 16651297 16651369 + Simple_repeat_(GTA)n chr1 16651671 16651909 + SINE/MIR_MIRb chr1 16652692 16652773 - SINE/MIR_MIR3 chr1 16653377 16653554 - DNA/hAT-Charlie_MER5A chr1 16653932 16653999 - SINE/Alu_Alu chr1 16654000 16654265 - SINE/Alu_AluSz chr1 16656500 16656547 + Simple_repeat_(CCTCCT)n chr1 16657542 16657734 + LINE/L2_L2a chr1 16657952 16658232 + SINE/Alu_AluSz chr1 16658233 16658267 + Simple_repeat_(TTTA)n chr1 16658561 16658699 + SINE/Alu_AluJb chr1 16659106 16659138 - snRNA_U1 chr1 16659139 16659674 - LTR/ERV1_LTR4 chr1 16660239 16660264 - SINE/Alu_AluJb chr1 16660265 16660434 - SINE/Alu_AluJb chr1 16660577 16660862 - SINE/Alu_AluSz chr1 16660889 16661173 - LTR/ERV1_ERV24_Prim-int chr1 16661662 16661752 - SINE/Alu_AluSx3 chr1 16661754 16662271 - LTR/ERV1_MER87 chr1 16662312 16662607 - SINE/Alu_AluSx1 chr1 16662677 16662956 + SINE/Alu_AluSg chr1 16662957 16662987 + Simple_repeat_(AAAT)n chr1 16663212 16663515 + SINE/Alu_AluSz chr1 16663557 16663658 - SINE/Alu_AluJb chr1 16663664 16663769 - SINE/Alu_FLAM_A chr1 16663826 16664064 - SINE/Alu_AluJr chr1 16664971 16665057 - LTR/ERV1_MER92-int chr1 16665149 16665194 + Simple_repeat_(GA)n chr1 16665368 16665530 - SINE/Alu_AluSx chr1 16665531 16665560 + Simple_repeat_(ATT)n chr1 16665561 16665642 - SINE/Alu_AluSx chr1 16666129 16666278 - LTR/ERVL_LTR75 chr1 16666785 16666948 - snRNA_U1 chr1 16667333 16667470 - DNA/hAT-Charlie_Charlie4a chr1 16667639 16667668 + Simple_repeat_(T)n chr1 16667986 16668531 + LTR/ERV1_MER41E chr1 16668647 16668803 + LINE/L1_HAL1 chr1 16668820 16669389 - LTR/ERV1_LTR8A chr1 16669402 16669589 - DNA/hAT-Charlie_MER1B chr1 16670469 16671381 - LTR/ERV1_PRIMA41-int chr1 16671507 16671858 - LTR/ERVL-MaLR_THE1A chr1 16671944 16672450 + LINE/L1_L1M5 chr1 16673643 16673922 + SINE/Alu_AluYc chr1 16673923 16673964 + Simple_repeat_(AAAAG)n chr1 16674432 16674742 + SINE/Alu_AluYb8 chr1 16674777 16675682 + LTR/ERV1_MER49 chr1 16675750 16676420 + LINE/L1_L1P1 chr1 16676427 16676528 + SINE/Alu_AluSx3 chr1 16676976 16677043 - tRNA_tRNA-His-CAY_ chr1 16677298 16677605 - SINE/Alu_AluSz chr1 16678270 16678341 - tRNA_tRNA-Gly-GGG chr1 16678716 16678751 + Low_complexity_GA-rich chr1 16679965 16679999 + Low_complexity_A-rich chr1 16680007 16680078 - tRNA_tRNA-Val-GTG chr1 16681404 16681706 + SINE/Alu_AluSx3 chr1 16682379 16682595 - SINE/MIR_MIRb chr1 16682830 16683029 - SINE/MIR_MIR chr1 16683054 16683090 + Simple_repeat_(T)n chr1 16684458 16684491 + Simple_repeat_(AAACAAA)n chr1 16685034 16685055 + Simple_repeat_(T)n chr1 16685349 16685368 + Simple_repeat_(T)n chr1 16685628 16685683 + Simple_repeat_(TCA)n chr1 16686413 16686450 + Simple_repeat_(TAT)n chr1 16687011 16687110 + LINE/L2_L2c chr1 16687134 16687256 - DNA/hAT-Charlie_MER3 chr1 16687605 16687777 + LINE/L2_L2c chr1 16687957 16687973 + Simple_repeat_(A)n chr1 16688009 16688198 + LINE/L2_L2c chr1 16689022 16689065 + Simple_repeat_(TTA)n chr1 16689066 16689214 - SINE/Alu_AluJb chr1 16689215 16689513 - SINE/Alu_AluSx chr1 16689514 16689524 - SINE/Alu_AluJb chr1 16689525 16689821 - SINE/Alu_AluSx chr1 16689822 16689855 - SINE/Alu_AluJb chr1 16689856 16690155 + SINE/Alu_AluSq2 chr1 16690156 16690451 + SINE/Alu_AluSz chr1 16690452 16690582 - SINE/Alu_AluJb chr1 16690584 16690790 + DNA/hAT-Charlie_MER33 chr1 16691851 16691886 + Simple_repeat_(CTGCACA)n chr1 16694908 16695139 + LINE/L2_L2c chr1 16695257 16695319 + LINE/L2_L2c chr1 16695320 16695629 + SINE/Alu_AluSx1 chr1 16695630 16695662 + LINE/L2_L2c chr1 16695672 16695804 - SINE/MIR_MIRc chr1 16695805 16695968 + DNA/hAT-Charlie_MER5A1 chr1 16696064 16696316 + SINE/Alu_AluSc8 chr1 16696317 16696332 + Simple_repeat_(AAAG)n chr1 16696433 16696458 + Simple_repeat_(TCC)n chr1 16698156 16698193 + Low_complexity_GA-rich chr1 16699131 16699194 - SINE/MIR_MIR3 chr1 16699482 16699559 + Simple_repeat_(GGC)n chr1 16702221 16702251 + Simple_repeat_(CTCC)n chr1 16703136 16703166 + Simple_repeat_(CCCTG)n chr1 16704203 16704282 + SINE/MIR_MIR3 chr1 16705264 16705405 + SINE/MIR_MIR chr1 16705438 16705728 - SINE/Alu_AluSq chr1 16705736 16706050 - SINE/Alu_AluSc chr1 16706086 16706163 + SINE/MIR_MIRb chr1 16706682 16706767 + SINE/MIR_MIRc chr1 16706819 16707000 + SINE/MIR_MIRc chr1 16707141 16707263 + SINE/MIR_MIRb chr1 16707783 16707883 + Simple_repeat_(GCCCC)n chr1 16708314 16708613 - SINE/Alu_AluSx3 chr1 16708957 16709149 - DNA/hAT_MER53 chr1 16709217 16709523 - SINE/Alu_AluSz chr1 16711324 16711618 + SINE/Alu_AluJo chr1 16711647 16711678 + Simple_repeat_(TAA)n chr1 16711679 16711933 - SINE/MIR_MIRb chr1 16711953 16711978 + Simple_repeat_(TCCC)n chr1 16712064 16712252 + SINE/MIR_MIRb chr1 16712881 16712987 + LTR/ERVL_LTR84a chr1 16713608 16713681 - LINE/L2_L2a chr1 16713869 16714155 + SINE/Alu_AluSx chr1 16714167 16714370 - LINE/L2_L2a chr1 16714448 16714612 - SINE/Alu_AluSx4 chr1 16714614 16714904 - SINE/Alu_AluSg chr1 16714918 16715226 - SINE/Alu_AluSg chr1 16716014 16716089 - SINE/MIR_MIR3 chr1 16716119 16716227 + LINE/L2_L2a chr1 16716229 16716288 + SINE/MIR_MIRb chr1 16716331 16716336 + Simple_repeat_(TTCT)n chr1 16716337 16716375 + Simple_repeat_(TTTCC)n chr1 16716378 16716706 - SINE/Alu_AluSx chr1 16717234 16717420 - SINE/Alu_AluSp chr1 16720297 16720379 + SINE/MIR_MIRb chr1 16721187 16721504 - SINE/Alu_AluY chr1 16723524 16723786 + LINE/L1_L1M2 chr1 16724351 16724467 - SINE/Alu_AluSx3 chr1 16725566 16725638 + tRNA_tRNA-Val-GTG chr1 16726879 16726914 + Simple_repeat_(TTC)n chr1 16727285 16727358 + tRNA_tRNA-Gly-GGG chr1 16728032 16728327 + SINE/Alu_AluSz chr1 16728581 16728648 + tRNA_tRNA-His-CAY_ chr1 16729102 16729197 - SINE/Alu_AluSx3 chr1 16729225 16729558 - LINE/L1_L1ME2 chr1 16729605 16729752 - LINE/L1_L1ME2 chr1 16729756 16729939 + SINE/Alu_AluSz6 chr1 16729941 16730148 - LINE/L1_L1ME2 chr1 16730168 16732016 - LINE/L1_L1PA4 chr1 16732084 16732998 - LTR/ERV1_MER49 chr1 16733500 16733540 + Simple_repeat_(TTTCT)n chr1 16733541 16733821 - SINE/Alu_AluYc chr1 16734024 16734070 + Simple_repeat_(GGTGAG)n chr1 16735014 16735520 - LINE/L1_L1ME2 chr1 16735606 16735957 + LTR/ERVL-MaLR_THE1A chr1 16736083 16736995 + LTR/ERV1_PRIMA41-int chr1 16737875 16738062 + DNA/hAT-Charlie_MER1B chr1 16738075 16738644 + LTR/ERV1_LTR8A chr1 16738661 16738817 - LINE/L1_HAL1 chr1 16738933 16739478 - LTR/ERV1_MER41E chr1 16739796 16739825 + Simple_repeat_(A)n chr1 16739994 16740131 + DNA/hAT-Charlie_Charlie4a chr1 16740516 16740679 + snRNA_U1 chr1 16741186 16741335 + LTR/ERVL_LTR75 chr1 16741822 16741903 + SINE/Alu_AluSx chr1 16741904 16741933 + Simple_repeat_(AAT)n chr1 16741934 16742096 + SINE/Alu_AluSx chr1 16742270 16742315 + Simple_repeat_(TC)n chr1 16742407 16742493 + LTR/ERV1_MER92-int chr1 16743400 16743637 + SINE/Alu_AluJr chr1 16743693 16743798 + SINE/Alu_FLAM_A chr1 16743804 16743905 + SINE/Alu_FRAM chr1 16743947 16744250 - SINE/Alu_AluSz chr1 16744475 16744505 + Simple_repeat_(TTAT)n chr1 16744506 16744785 - SINE/Alu_AluSg chr1 16744855 16745150 + SINE/Alu_AluSx1 chr1 16745191 16745708 + LTR/ERV1_MER87 chr1 16745710 16745801 + SINE/Alu_AluSx chr1 16745992 16746032 + Simple_repeat_(CACCTG)n chr1 16746292 16746576 + LTR/ERV1_ERV24_Prim-int chr1 16746603 16746888 + SINE/Alu_AluSz chr1 16747031 16747173 + SINE/Alu_AluJb chr1 16747174 16747200 + Simple_repeat_(AAAC)n chr1 16747201 16747226 + SINE/Alu_AluJb chr1 16747791 16748326 + LTR/ERV1_LTR4 chr1 16748327 16748359 + snRNA_U1 chr1 16748766 16748907 - SINE/Alu_AluJb chr1 16749201 16749235 + Simple_repeat_(AAAT)n chr1 16749236 16749516 - SINE/Alu_AluSz chr1 16749734 16749926 - LINE/L2_L2a chr1 16750921 16750968 + Low_complexity_GA-rich chr1 16753203 16753468 + SINE/Alu_AluSz chr1 16753469 16753536 + SINE/Alu_Alu chr1 16753912 16754089 + DNA/hAT-Charlie_MER5A chr1 16754694 16754775 + SINE/MIR_MIR3 chr1 16755558 16755796 - SINE/MIR_MIRb chr1 16756097 16756169 + Simple_repeat_(TAC)n chr1 16756170 16756308 - SINE/MIR_MIRb chr1 16756604 16756643 + Low_complexity_A-rich chr1 16756904 16757172 + DNA/hAT-Charlie_Charlie1a chr1 16759416 16759451 + Simple_repeat_(CTCAGCC)n chr1 16761842 16761884 - LTR/ERVL-MaLR_MLT1C chr1 16762014 16762696 + LINE/L2_L2d2 chr1 16762558 16762846 + LINE/L2_L2c chr1 16762858 16763053 - SINE/MIR_MIR chr1 16763135 16763158 + Simple_repeat_(TGG)n chr1 16763232 16763367 + LINE/L2_L2c chr1 16764005 16764221 + LINE/L2_L2d2 chr1 16765645 16765675 + Simple_repeat_(AGG)n chr1 16766550 16766753 + DNA/hAT-Charlie_MER33 chr1 16767449 16767736 + DNA/hAT-Charlie_MER33 chr1 16770213 16770347 - SINE/MIR_MIR3 chr1 16770373 16770475 + LINE/RTE-BovB_MamRTE1 chr1 16770629 16770820 + DNA/hAT-Blackjack_MER63A chr1 16771120 16771329 + LINE/L2_L2a chr1 16771338 16771448 + Low_complexity_GA-rich chr1 16771483 16771547 + Simple_repeat_(CA)n chr1 16771552 16771780 + LINE/L2_L2a chr1 16771610 16772084 + LINE/L2_L2a chr1 16772085 16772380 + SINE/Alu_AluSx1 chr1 16773213 16773664 - LTR/ERVL-MaLR_MLT1C2 chr1 16773890 16774355 + LTR/ERVL-MaLR_MLT1D chr1 16774439 16774498 + LINE/L2_L2a chr1 16774503 16774728 - LINE/L2_L2c chr1 16775568 16775770 - SINE/MIR_MIRb chr1 16777506 16777655 - SINE/MIR_MIRb chr1 16777664 16778473 - LINE/L2_L2a chr1 16778474 16778899 - LTR/ERVL-MaLR_MSTA chr1 16778900 16779305 - LINE/L2_L2a chr1 16779614 16779879 + SINE/MIR_MIRc chr1 16780608 16780950 + LTR/ERVL-MaLR_MLT1J chr1 16781035 16781176 + LINE/L2_L2a chr1 16782215 16782236 + Simple_repeat_(CTTG)n chr1 16782576 16784945 - LINE/L1_L1MD2 chr1 16784986 16785099 - LINE/L2_L2d chr1 16785132 16785259 - LINE/L2_L2d chr1 16785333 16785639 + DNA/TcMar-Tigger_Tigger19a chr1 16785808 16786068 - SINE/tRNA-RTE_MamSINE1 chr1 16786071 16786189 + DNA/hAT-Charlie_UCON34 chr1 16786807 16786831 + Simple_repeat_(TGTT)n chr1 16786832 16787116 - SINE/Alu_AluY chr1 16787183 16787416 - SINE/MIR_MIRc chr1 16787993 16788043 + LTR/ERVL-MaLR_MLT1N2 chr1 16788053 16788185 + LINE/L2_L2d2 chr1 16788338 16788561 + LINE/RTE-BovB_MamRTE1 chr1 16788726 16788756 + Simple_repeat_(TCAT)n chr1 16789513 16789677 - DNA/hAT-Charlie_MER5A chr1 16790397 16790651 - SINE/Alu_AluSz chr1 16790807 16790979 - SINE/MIR_MIRb chr1 16790996 16791446 - LINE/L1_L1ME3A chr1 16791447 16791857 - DNA/TcMar-Tigger_Tigger2a chr1 16791858 16793502 - LINE/L1_L1ME3A chr1 16793503 16793803 - SINE/Alu_AluSp chr1 16793804 16794175 - LINE/L1_L1ME3A chr1 16794179 16794498 - LINE/L1_L1ME3A chr1 16794499 16794630 - SINE/Alu_FLAM_C chr1 16795229 16795341 + LINE/RTE-X_L4_B_Mam chr1 16795470 16795540 + DNA/hAT-Tip100_Zaphod3 chr1 16795538 16796185 + DNA/hAT-Tip100_Zaphod3 chr1 16796464 16796637 + SINE/MIR_MIR1_Amn chr1 16797042 16797087 - LINE/L2_L2c chr1 16798394 16798473 + Simple_repeat_(TTTTC)n chr1 16798474 16798506 + Simple_repeat_(CTTC)n chr1 16798515 16798821 - SINE/Alu_AluSq2 chr1 16798863 16799163 - LTR/ERV1_MER39B chr1 16849203 16849560 + LINE/L1_L1M3 chr1 16849580 16849634 - LINE/L1_L1M5 chr1 16849635 16849928 - SINE/Alu_AluJo chr1 16849929 16849985 - LINE/L1_L1M5 chr1 16850080 16850210 + Simple_repeat_(TACATA)n chr1 16851136 16851264 + SINE/MIR_MIR chr1 16851360 16851626 - LINE/L1_L1ME3Cz chr1 16851753 16851830 + Simple_repeat_(TA)n chr1 16853431 16853465 + Simple_repeat_(CCTT)n chr1 16854405 16854476 - tRNA_tRNA-Gln-CAG chr1 16855712 16855731 + Simple_repeat_(TAA)n chr1 16855924 16856123 + SINE/MIR_MIR chr1 16856361 16856577 + SINE/MIR_MIRb chr1 16857497 16857553 + Simple_repeat_(GTATA)n chr1 16857561 16857709 + SINE/MIR_MIR chr1 16857710 16858006 + SINE/Alu_AluSx chr1 16858007 16858054 + SINE/MIR_MIR chr1 16858128 16858249 - LTR/ERVL-MaLR_MLT1H2 chr1 16858417 16858557 - LINE/L2_L2d chr1 16858569 16858874 - SINE/Alu_AluSx3 chr1 16860198 16860272 + tRNA_tRNA-Val-GTG chr1 16861921 16861991 + tRNA_tRNA-Gly-GGG chr1 16862668 16862970 + SINE/Alu_AluSz chr1 16863226 16863293 + tRNA_tRNA-His-CAY_ chr1 16863747 16863842 - SINE/Alu_AluSx3 chr1 16863870 16864162 - LINE/L1_L1ME2 chr1 16864249 16864396 - LINE/L1_L1ME2 chr1 16864400 16864587 + SINE/Alu_AluSx chr1 16864589 16864808 - LINE/L1_L1ME2 chr1 16864808 16866758 - LINE/L1_L1PA6 chr1 16866826 16867736 - LTR/ERV1_MER49 chr1 16869421 16869927 - LINE/L1_L1ME2 chr1 16870011 16870362 + LTR/ERVL-MaLR_THE1A chr1 16870540 16870785 + LTR/ERVL-MaLR_MLT1A0 chr1 16871753 16871844 + DNA/hAT-Charlie_MER20 chr1 16871929 16872089 - LINE/L2_L2 chr1 16872583 16872654 + tRNA_tRNA-Glu-GAA chr1 16873710 16873848 + snRNA_U1 chr1 16874348 16874415 - tRNA_tRNA-His-CAY_ chr1 16874466 16874775 - SINE/Alu_AluSz chr1 16875463 16875536 + tRNA_tRNA-Asn-AAC chr1 16875557 16875680 + SINE/Alu_FLAM_C chr1 16875899 16876058 - LINE/L1_L1MEc chr1 16876059 16876379 - SINE/Alu_AluSz chr1 16876380 16876500 - LINE/L1_L1MEc chr1 16876571 16876791 + SINE/Alu_AluYk3 chr1 16876821 16877538 + LTR/ERV1_PRIMA41-int chr1 16877539 16877844 + SINE/Alu_AluSx chr1 16877845 16878758 + LTR/ERV1_PRIMA41-int chr1 16880070 16880166 - SINE/MIR_MIR3 chr1 16880413 16880591 - SINE/Alu_AluSp chr1 16880592 16880617 + Simple_repeat_(T)n chr1 16880618 16880724 - SINE/Alu_AluSp chr1 16880870 16881176 + SINE/Alu_AluY chr1 16881438 16881575 + DNA/hAT-Charlie_MER5B chr1 16881840 16882068 - LTR/ERVL_LTR16A2 chr1 16882069 16882088 + Simple_repeat_(A)n chr1 16882340 16882578 - LINE/CR1_L3 chr1 16882581 16882612 + Simple_repeat_(TCTT)n chr1 16882613 16882805 - SINE/Alu_AluSx chr1 16883492 16883719 + LINE/L1_L1M4c chr1 16883720 16883999 - SINE/Alu_AluSp chr1 16884000 16884293 + LINE/L1_L1M4c chr1 16884296 16884318 - LTR/ERV1_LTR49-int chr1 16884319 16884621 + SINE/Alu_AluSx1 chr1 16884622 16885969 - LTR/ERV1_LTR49-int chr1 16885972 16886562 - LTR/ERV1_LTR49 chr1 16886603 16886710 + LINE/L2_L2 chr1 16887755 16887799 + Simple_repeat_(TTAATAT)n chr1 16887903 16888414 + LINE/L1_L1M4c chr1 16888394 16888542 + LINE/L1_L1M5 chr1 16889677 16889751 + tRNA_tRNA-Asn-AAC chr1 16890217 16890379 + SINE/Alu_AluSx chr1 16890395 16890608 - LINE/L1_L1M5 chr1 16890612 16890962 - LTR/ERV1_MER72 chr1 16890963 16891441 - LTR/ERV1_MER51D chr1 16891442 16891753 - SINE/Alu_AluSx chr1 16891754 16891913 - LTR/ERV1_MER51D chr1 16891914 16892067 - LTR/ERV1_MER72 chr1 16892083 16892231 - LTR/ERV1_LTR49 chr1 16892232 16892285 + LTR/ERV1_MER89 chr1 16892286 16892581 - SINE/Alu_AluYk3 chr1 16892582 16892736 + LTR/ERV1_MER89 chr1 16892755 16893007 + LTR/ERV1_MER31B chr1 16893062 16893197 + LTR/ERV1_LTR8A chr1 16893198 16893546 + DNA/hAT-Charlie_MER1B chr1 16893547 16894128 + LTR/ERV1_LTR8A chr1 16894145 16894301 - LINE/L1_HAL1 chr1 16894413 16894962 - LTR/ERV1_MER41E chr1 16895279 16895300 + Simple_repeat_(A)n chr1 16895458 16895595 + DNA/hAT-Charlie_Charlie4 chr1 16895980 16896143 + snRNA_U1 chr1 16896598 16896661 - DNA/hAT-Tip100_MER45B chr1 16896667 16896830 + LTR/ERVL_LTR75B chr1 16897305 16897554 + SINE/Alu_AluSx chr1 16897729 16897768 + Simple_repeat_(TC)n chr1 16898856 16899115 + SINE/Alu_AluJr chr1 16899163 16899274 + SINE/Alu_AluJr4 chr1 16899280 16899384 + SINE/Alu_FRAM chr1 16899416 16899715 - SINE/Alu_AluSz chr1 16899942 16899980 + Simple_repeat_(TTAT)n chr1 16899981 16900261 - SINE/Alu_AluSg chr1 16900331 16900626 + SINE/Alu_AluSx1 chr1 16900667 16901163 + LTR/ERV1_MER87 chr1 16901165 16901263 + SINE/Alu_Alu chr1 16901443 16901483 + Simple_repeat_(CACCTG)n chr1 16901743 16902026 + LTR/ERV1_ERV24_Prim-int chr1 16902053 16902337 + SINE/Alu_AluSz chr1 16902467 16902627 + SINE/Alu_AluJb chr1 16903169 16903332 + SINE/Alu_FRAM chr1 16903379 16903562 + LTR/ERV1_LTR39-int chr1 16903708 16904000 + LTR/ERV1_LTR23-int chr1 16904026 16904310 - SINE/Alu_AluYc3 chr1 16904345 16904443 + LTR/ERV1_LTR56 chr1 16904653 16904767 + DNA/hAT-Charlie_Charlie4 chr1 16905331 16905857 + LTR/ERV1_LTR4 chr1 16906288 16906516 - SINE/Alu_AluJr chr1 16906659 16906798 + LTR/ERVL_LTR75B chr1 16906804 16906865 - DNA/hAT-Charlie_MER58D chr1 16907326 16907476 + LINE/L2_L2 chr1 16907531 16907942 + LTR/ERV1_LOR1b chr1 16908758 16908994 + SINE/Alu_AluSz chr1 16909229 16909341 + Simple_repeat_(TA)n chr1 16909343 16909459 - SINE/Alu_AluJr chr1 16909541 16910145 + LINE/L1_L1MEc chr1 16910170 16910214 + SINE/Alu_AluYe5 chr1 16910227 16910520 + SINE/Alu_AluSx1 chr1 16910542 16910705 + LTR/ERV1_MER84 chr1 16910706 16911014 - SINE/Alu_AluSg7 chr1 16911015 16911111 + LTR/ERV1_MER84 chr1 16911188 16911474 + SINE/Alu_AluSq2 chr1 16911506 16911682 - LINE/L1_L1ME4a chr1 16911708 16911785 + DNA/TcMar-Tigger_X9b_DNA chr1 16912240 16912399 + SINE/MIR_MIRb chr1 16913190 16913453 + SINE/Alu_AluSx4 chr1 16913990 16914022 + Simple_repeat_(CCCCGC)n chr1 16914490 16914613 - SINE/MIR_MIR1_Amn chr1 16915396 16915657 + SINE/Alu_AluJb chr1 16915665 16915897 - LINE/L2_L2a chr1 16915898 16916192 + SINE/Alu_AluSz chr1 16916193 16916425 - LINE/L2_L2a chr1 16916512 16916811 - SINE/Alu_AluJb chr1 16916836 16917145 + SINE/Alu_AluSz chr1 16917148 16917346 + SINE/MIR_MIRb chr1 16917636 16917671 - SINE/MIR_MIR chr1 16917672 16918075 - LTR/ERVL-MaLR_MLT1J2 chr1 16918076 16918254 - SINE/MIR_MIR chr1 16918301 16918323 + Simple_repeat_(T)n chr1 16918732 16919034 - SINE/Alu_AluSx1 chr1 16919308 16919419 + SINE/MIR_MIRb chr1 16919490 16919645 - LTR/ERV1_LTR2 chr1 16919747 16919879 - SINE/Alu_AluJo chr1 16919888 16920070 - SINE/Alu_AluSz chr1 16920071 16920356 - SINE/Alu_AluSc chr1 16920357 16920492 - SINE/Alu_AluSz chr1 16920497 16920651 - SINE/MIR_MIR chr1 16920743 16921042 - SINE/Alu_AluSp chr1 16921072 16921278 - SINE/MIR_MIRb chr1 16922882 16922994 - SINE/MIR_MIRc chr1 16923123 16923209 + DNA/TcMar-Tigger_Tigger19b chr1 16923675 16923977 - SINE/Alu_AluSx chr1 16923978 16924085 - SINE/MIR_MIR3 chr1 16924563 16924735 + SINE/MIR_MIRb chr1 16925252 16925471 - SINE/MIR_MIRb chr1 16926173 16926252 + LINE/CR1_L3b chr1 16926261 16926379 - SINE/MIR_MIR3 chr1 16928522 16928806 + SINE/Alu_AluSx3 chr1 16928807 16928846 + Simple_repeat_(AAAT)n chr1 16928847 16929126 - SINE/Alu_AluSz chr1 16929340 16929537 - LINE/L2_L2d chr1 16929840 16929884 + Simple_repeat_(CTGGAG)n chr1 16930790 16930854 - SINE/MIR_MIR chr1 16930855 16930971 + SINE/Alu_FLAM_C chr1 16930972 16931033 - SINE/MIR_MIR chr1 16931559 16931761 - SINE/MIR_MIRb chr1 16931762 16932066 - SINE/Alu_AluSz chr1 16932067 16932087 - SINE/MIR_MIRc chr1 16932162 16932457 + SINE/Alu_AluSp chr1 16932492 16932701 - LINE/L2_L2d chr1 16932878 16933108 - SINE/Alu_AluJb chr1 16933110 16933183 - LINE/L1_L1MC5 chr1 16933184 16933476 + SINE/Alu_AluSx chr1 16933477 16933555 - LINE/L1_L1MC5 chr1 16933556 16933858 - SINE/Alu_AluSx chr1 16933859 16934280 - LINE/L1_L1MC5 chr1 16934309 16934602 - SINE/Alu_AluJb chr1 16934653 16934788 - LINE/L1_L1ME4b chr1 16934809 16934853 + Simple_repeat_(T)n chr1 16934889 16935199 - SINE/Alu_AluSx chr1 16935200 16935414 - LINE/L1_L1ME4b chr1 16935415 16935723 - SINE/Alu_AluYf1 chr1 16935724 16936108 - LINE/L1_L1ME4b chr1 16936112 16936249 - SINE/Alu_FLAM_A chr1 16936259 16936568 - SINE/Alu_AluSx3 chr1 16937073 16937378 + SINE/Alu_AluSx chr1 16937411 16937557 - SINE/MIR_MIRc chr1 16937856 16938081 - SINE/MIR_MIRb chr1 16938155 16938239 + SINE/MIR_MIR3 chr1 16939206 16939243 + Low_complexity_G-rich chr1 16939734 16939809 + Simple_repeat_(GCCTAG)n chr1 16940221 16940390 - SINE/Alu_AluYc chr1 16940391 16940687 - SINE/Alu_AluSg4 chr1 16940688 16940705 - SINE/Alu_AluYc chr1 16940710 16940745 + Simple_repeat_(GTTTT)n chr1 16940754 16940853 - SINE/Alu_FRAM chr1 16940944 16941159 - SINE/Alu_AluJb chr1 16941232 16941446 + SINE/Alu_AluSx chr1 16941448 16941754 + SINE/Alu_AluY chr1 16941755 16941853 - DNA/hAT-Charlie_MER3 chr1 16941877 16942001 - SINE/Alu_AluJr chr1 16942009 16942296 - SINE/Alu_AluJb chr1 16942508 16942731 - SINE/MIR_MIRb chr1 16942752 16942819 - LINE/L1_L1ME4c chr1 16942824 16942956 + SINE/Alu_AluSz chr1 16942961 16943254 + SINE/Alu_AluSx chr1 16943258 16943559 + SINE/Alu_AluY chr1 16943935 16944068 - LINE/L2_L2c chr1 16944399 16944507 - SINE/MIR_MIRb chr1 16944509 16944547 + Simple_repeat_(ATA)n chr1 16944578 16944813 - SINE/MIR_MIR chr1 16944901 16944950 - SINE/MIR_MIR chr1 16944951 16945246 + SINE/Alu_AluSx1 chr1 16945247 16945308 - SINE/MIR_MIR chr1 16946469 16946560 + Simple_repeat_(CTGT)n chr1 16947071 16947192 + SINE/MIR_MIR chr1 16947196 16947468 + SINE/Alu_AluSc8 chr1 16947469 16947501 + Simple_repeat_(AAT)n chr1 16947827 16948127 - SINE/Alu_AluSz chr1 16948160 16948236 - SINE/MIR_MIRc chr1 16948345 16948412 + Simple_repeat_(GAGCAG)n chr1 16949012 16949211 + SINE/MIR_MIRb chr1 16949298 16949552 + DNA/TcMar-Tigger_Tigger19a chr1 16949594 16949762 + SINE/MIR_MIRb chr1 16949765 16950068 - SINE/Alu_AluSp chr1 16950071 16950362 - SINE/Alu_AluSq2 chr1 16950365 16950657 - SINE/Alu_AluSq chr1 16950710 16950833 - SINE/MIR_MIRc chr1 16950977 16951061 + DNA/hAT-Ac_OldhAT1 chr1 16951151 16951214 + SINE/MIR_MIR1_Amn chr1 16951477 16951695 + SINE/MIR_MIR chr1 16951758 16951880 + DNA/hAT-Tip100_MER91B chr1 16951881 16952175 - SINE/Alu_AluSg chr1 16952176 16952203 + DNA/hAT-Tip100_MER91B chr1 16952204 16952501 + SINE/Alu_AluSc8 chr1 16952993 16953199 + LINE/L2_L2c chr1 16953526 16953700 + SINE/MIR_MIRb chr1 16953870 16954017 + SINE/MIR_MIRb chr1 16954341 16954375 + Simple_repeat_(GGCAG)n chr1 16956189 16956280 + SINE/MIR_MIR3 chr1 16956285 16956452 + DNA/hAT-Charlie_Charlie4z chr1 16956468 16956817 - DNA/hAT-Charlie_MER58B chr1 16956834 16956900 - LINE/L2_L2d2 chr1 16956990 16957126 + SINE/Alu_AluJo chr1 16957127 16957414 + SINE/Alu_AluJr chr1 16957431 16957744 - SINE/Alu_AluSq chr1 16958041 16958248 + SINE/MIR_MIR chr1 16958920 16959018 - SINE/MIR_MIRb chr1 16959019 16959319 - SINE/Alu_AluSx1 chr1 16959320 16959429 - SINE/MIR_MIRb chr1 16959403 16959524 + SINE/MIR_MIR1_Amn chr1 16959553 16959603 + LINE/L2_L2b chr1 16959660 16959732 - DNA/hAT-Charlie_Charlie7 chr1 16959745 16959791 + SINE/MIR_MIR chr1 16959817 16959944 + LTR/ERVL_LTR53 chr1 16959948 16959985 + Simple_repeat_(AAAAAC)n chr1 16959986 16960298 + SINE/Alu_AluSz6 chr1 16960301 16960519 + SINE/MIR_MIR chr1 16960884 16960943 + Simple_repeat_(CTGGAG)n chr1 16961028 16961087 + Simple_repeat_(GGCCTC)n chr1 16961206 16961230 + Simple_repeat_(TGTTTT)n chr1 16961363 16961569 - SINE/Alu_AluJr chr1 16961603 16961888 - SINE/Alu_AluJb chr1 16961962 16962052 - DNA/TcMar-Tigger_MER2 chr1 16962053 16962263 - SINE/Alu_AluSx chr1 16962264 16962561 + SINE/Alu_AluYc chr1 16962562 16962672 - SINE/Alu_AluSx chr1 16962689 16962707 + Simple_repeat_(TG)n chr1 16962707 16962735 + Simple_repeat_(TA)n chr1 16962736 16962806 - SINE/Alu_AluSx chr1 16962815 16962899 + LINE/L1_L1MB4 chr1 16962900 16963205 + SINE/Alu_AluSq2 chr1 16963321 16963479 - LINE/L2_L2 chr1 16963728 16963774 + SINE/MIR_MIR chr1 16963795 16964100 - SINE/Alu_AluSx3 chr1 16964104 16964372 - SINE/Alu_AluSz chr1 16964373 16964680 - SINE/Alu_AluSg chr1 16964681 16964980 - SINE/Alu_AluSx chr1 16964981 16965114 - SINE/Alu_AluSz chr1 16965125 16965237 + SINE/MIR_MIRb chr1 16965268 16965553 - LINE/L2_L2b chr1 16965701 16965778 + LINE/Penelope_Penelope1_Vert chr1 16966783 16967077 + SINE/Alu_AluJb chr1 16967100 16967174 + SINE/MIR_MIRb chr1 16967807 16967849 - DNA/hAT-Charlie_MER5A chr1 16967987 16968093 - SINE/MIR_MIR3 chr1 16968287 16968349 + Simple_repeat_(AGCTGC)n chr1 16968444 16968632 - SINE/MIR_MIRb chr1 16968693 16968746 + LINE/RTE-X_L4_C_Mam chr1 16968831 16968874 + Low_complexity_GA-rich chr1 16968979 16969076 - LINE/L2_L2d2 chr1 16970131 16970237 - SINE/MIR_MIRc chr1 16971211 16971252 + Simple_repeat_(GT)n chr1 16971405 16971429 + Simple_repeat_(CA)n chr1 16971504 16971569 + Simple_repeat_(CAGCTG)n chr1 16971846 16972166 - LTR/ERVL-MaLR_MLT1K chr1 16973314 16973498 + SINE/MIR_MIR chr1 16973528 16973726 + SINE/MIR_MIR chr1 16973768 16974071 - SINE/Alu_AluSz6 chr1 16976651 16976724 + Simple_repeat_(GGCTGG)n chr1 16977358 16977779 + LINE/L2_L2b chr1 16980203 16980251 + Simple_repeat_(CT)n chr1 16980268 16980291 + Simple_repeat_(C)n chr1 16980880 16981020 - SINE/MIR_MIRc chr1 16980990 16981106 + LINE/L2_L2b chr1 16981146 16981282 - SINE/Alu_AluJr chr1 16981330 16981567 + LINE/L2_L2b chr1 16981568 16981905 + LTR/ERV1_LTR64 chr1 16981906 16982217 - SINE/Alu_AluJo chr1 16982218 16982377 + LTR/ERV1_LTR64 chr1 16982378 16982670 - SINE/Alu_AluSx chr1 16982681 16982991 - SINE/Alu_AluSx1 chr1 16983297 16983567 + LINE/L2_L2b chr1 16984025 16984335 + SINE/Alu_AluJb chr1 16985216 16985361 + SINE/MIR_MIRb chr1 16985368 16985674 + SINE/Alu_AluSx chr1 16986130 16986174 + Simple_repeat_(GGT)n chr1 16987595 16987658 - LINE/L2_L2c chr1 16987990 16988091 - SINE/MIR_MIRb chr1 16988545 16988605 + LINE/L2_L2b chr1 16988613 16988645 + Simple_repeat_(ATT)n chr1 16988646 16988933 - SINE/Alu_AluSq2 chr1 16988936 16989215 - SINE/Alu_AluSx chr1 16989224 16989510 - SINE/Alu_AluJb chr1 16990351 16990456 + SINE/MIR_MIR chr1 16990519 16990818 - SINE/Alu_AluSx chr1 16990955 16991250 - SINE/Alu_AluSz chr1 16991251 16991384 - SINE/Alu_AluJo chr1 16991422 16991476 - SINE/MIR_MIRb chr1 16991501 16991640 + SINE/MIR_MIRb chr1 16992687 16992893 + SINE/MIR_MIRb chr1 16992909 16992924 + Simple_repeat_(ATT)n chr1 16992925 16993216 - SINE/Alu_AluSx chr1 16993222 16993507 - SINE/Alu_AluJb chr1 16993508 16993518 + Simple_repeat_(ATT)n chr1 16993968 16994118 + LINE/L2_L2c chr1 16994198 16994241 + Simple_repeat_(ATTT)n chr1 16994242 16994532 - SINE/Alu_AluSx chr1 16994664 16994961 - SINE/Alu_AluSx3 chr1 16995472 16995745 - SINE/Alu_AluSz chr1 16995767 16995888 + LINE/L2_L2c chr1 16996820 16996962 - SINE/MIR_MIR3 chr1 16997269 16997348 - SINE/MIR_MIRb chr1 16997560 16997844 + SINE/Alu_AluSx1 chr1 16997847 16997906 + Low_complexity_GA-rich chr1 16998207 16998505 - SINE/Alu_AluSx3 chr1 16998626 16998999 + LINE/L1_L1ME3G chr1 16999100 16999405 + SINE/Alu_AluSp chr1 16999442 16999595 - SINE/MIR_MIR chr1 16999622 16999927 + SINE/Alu_AluSx chr1 17000731 17000965 + SINE/Alu_AluJb chr1 17000978 17001133 - SINE/MIR_MIR chr1 17001137 17001458 + SINE/Alu_AluSq2 chr1 17001571 17001724 + SINE/MIR_MIRc chr1 17002174 17002232 - SINE/MIR_MIRc chr1 17002654 17002841 + SINE/MIR_MIR chr1 17003049 17003267 + LTR/ERVL_LTR16C chr1 17003277 17003584 + SINE/Alu_AluSq2 chr1 17003586 17003631 + Simple_repeat_(CA)n chr1 17003657 17003961 - SINE/Alu_AluSg chr1 17004091 17004284 - SINE/MIR_MIR chr1 17004591 17004683 - SINE/MIR_MIR3 chr1 17005164 17005339 - SINE/MIR_MIR3 chr1 17005855 17006162 + SINE/Alu_AluSx chr1 17006255 17006550 - SINE/Alu_AluSz chr1 17006896 17007176 - SINE/Alu_AluSx chr1 17007328 17007438 + DNA/hAT-Charlie_MER5A chr1 17007544 17007849 - SINE/Alu_AluY chr1 17008039 17008064 + Simple_repeat_(TTCA)n chr1 17008082 17008148 - SINE/Alu_FLAM_C chr1 17008156 17008345 - SINE/Alu_AluJb chr1 17008376 17008438 + SINE/MIR_MIR3 chr1 17008669 17008974 + SINE/Alu_AluY chr1 17009038 17009111 - SINE/MIR_MIRb chr1 17009283 17009321 + Simple_repeat_(ACTG)n chr1 17009388 17009694 - SINE/Alu_AluJr chr1 17010114 17010377 - SINE/Alu_AluSz chr1 17010410 17010586 - SINE/MIR_MIR chr1 17010614 17010753 - SINE/MIR_MIRb chr1 17011760 17011797 + Simple_repeat_(CCGGCC)n chr1 17012090 17012262 + SINE/MIR_MIRb chr1 17012264 17012431 + SINE/Alu_AluSp chr1 17012847 17012991 - LINE/L2_L2a chr1 17013136 17013257 - LINE/L2_L2 chr1 17013500 17013656 + SINE/MIR_MIRc chr1 17013775 17013817 + Simple_repeat_(AAAT)n chr1 17014405 17014737 - SINE/Alu_AluJb chr1 17014972 17015044 + LINE/L2_L2d2 chr1 17015043 17015187 - SINE/MIR_MIR chr1 17015228 17015388 - SINE/Alu_FRAM chr1 17015728 17016012 + SINE/Alu_AluSz chr1 17016310 17016442 + LINE/L2_L2c chr1 17016578 17016688 + LINE/L1_L1ME3G chr1 17016726 17017028 + SINE/Alu_AluY chr1 17017032 17017055 + Simple_repeat_(ACA)n chr1 17017109 17017332 + SINE/Alu_AluYk3 chr1 17017415 17017527 - SINE/Alu_FLAM_C chr1 17017543 17017720 - SINE/Alu_FAM chr1 17017861 17018168 - SINE/Alu_AluSz chr1 17018436 17018599 - SINE/MIR_MIR chr1 17019688 17019748 + Low_complexity_A-rich chr1 17019755 17019954 - SINE/Alu_AluJr chr1 17020050 17020169 + LINE/L1_L1ME4b chr1 17020256 17020439 + SINE/MIR_MIR3 chr1 17020546 17020628 - DNA/hAT-Charlie_MER5B chr1 17020671 17020732 - SINE/MIR_MIR chr1 17020990 17021126 + LTR/ERVL-MaLR_MLT1M chr1 17021127 17021296 + LTR/ERVL-MaLR_MSTD chr1 17021298 17021451 + SINE/Alu_AluJr chr1 17021463 17021762 + SINE/Alu_AluSp chr1 17021776 17021937 + LTR/ERVL-MaLR_MSTD chr1 17021940 17022453 + LTR/ERVL-MaLR_MLT1M chr1 17022963 17023104 + LINE/L2_L2d chr1 17024559 17024759 - SINE/MIR_MIR chr1 17024948 17025404 + LINE/L1_L1ME4a chr1 17025408 17025624 - SINE/Alu_AluJr chr1 17025652 17025693 + LTR/ERV1_MER57B2 chr1 17025823 17026052 + DNA/TcMar-Tigger_Tigger4a chr1 17026359 17026662 - SINE/Alu_AluSz chr1 17026785 17026848 - SINE/Alu_FLAM_C chr1 17026852 17026929 - SINE/Alu_FLAM_A chr1 17026961 17027044 - LINE/L2_L2c chr1 17027385 17027490 + DNA/hAT-Charlie_Charlie8 chr1 17027874 17027905 + Simple_repeat_(AAG)n chr1 17028879 17029056 + LTR/ERVL-MaLR_MLT1J chr1 17029094 17029416 - SINE/Alu_AluJo chr1 17029431 17029562 - SINE/Alu_AluSq2 chr1 17029726 17029919 - SINE/Alu_AluJr chr1 17029920 17030207 + SINE/Alu_AluSx1 chr1 17030208 17030334 - SINE/Alu_AluJr chr1 17030688 17030936 - SINE/Alu_AluSg chr1 17030957 17031088 - SINE/Alu_FLAM_C chr1 17031837 17031984 + SINE/MIR_MIR chr1 17032058 17032198 - DNA/hAT-Charlie_MER113A chr1 17032199 17032486 - SINE/Alu_AluSx1 chr1 17032487 17032618 - DNA/hAT-Charlie_MER113A chr1 17033676 17033864 + SINE/MIR_MIRb chr1 17033902 17034038 + SINE/MIR_MIRb chr1 17034004 17034051 + LINE/L2_L2d2 chr1 17034143 17034433 - SINE/Alu_AluJb chr1 17034434 17034679 - SINE/Alu_AluSz chr1 17034866 17034989 - LINE/L2_L2a chr1 17035570 17035861 + SINE/Alu_AluSc8 chr1 17035862 17035897 + Simple_repeat_(AAAT)n chr1 17036058 17036304 - LINE/L1_L1M4 chr1 17036306 17036517 - SINE/Alu_AluJb chr1 17036521 17036625 - LINE/L1_L1M4 chr1 17036676 17036806 + Simple_repeat_(ATATAA)n chr1 17036808 17036824 + Simple_repeat_(AC)n chr1 17036827 17036957 - SINE/Alu_AluJr chr1 17037202 17037296 - LINE/L1_L1M4 chr1 17037297 17037468 - SINE/Alu_AluJo chr1 17037469 17037756 - SINE/Alu_AluSz6 chr1 17037757 17037854 - SINE/Alu_AluJo chr1 17037855 17038152 + SINE/Alu_AluSp chr1 17038153 17038212 - SINE/Alu_AluJo chr1 17038213 17038336 - LINE/L1_L1M4 chr1 17038369 17038737 - LINE/L1_L1MEf chr1 17038398 17038818 - LINE/L1_L1M2 chr1 17038865 17039302 - LINE/L1_L1MEf chr1 17039303 17039633 + SINE/Alu_AluJr chr1 17039634 17040476 - LINE/L1_L1MEf chr1 17040477 17040763 - SINE/Alu_AluJr chr1 17040764 17040847 - LINE/L1_L1MEf chr1 17040848 17041159 + SINE/Alu_AluSz chr1 17041160 17041388 - LINE/L1_L1MEf chr1 17041390 17041689 + SINE/Alu_AluSx chr1 17041705 17041789 - LINE/L1_L1MEf chr1 17041789 17041919 - LINE/L1_L1MEf chr1 17041920 17042184 - SINE/Alu_AluSg chr1 17042185 17042448 - LINE/L1_L1MEf chr1 17042496 17042784 + SINE/Alu_AluJb chr1 17042939 17043260 - SINE/Alu_AluSx1 chr1 17043476 17043593 - DNA/hAT-Charlie_Charlie15a chr1 17043670 17044061 + LTR/ERVL-MaLR_MLT1E2 chr1 17044326 17044625 - SINE/Alu_AluSx chr1 17045025 17045320 - LINE/L2_L2c chr1 17045340 17045628 + SINE/Alu_AluJb chr1 17045765 17046050 + LTR/ERV1_MER51A chr1 17046380 17046698 - LINE/L1_L1ME1 chr1 17046714 17047016 - SINE/Alu_AluSp chr1 17047017 17047074 - LINE/L1_L1MD1 chr1 17047075 17047385 + SINE/Alu_AluSz chr1 17047386 17047410 - LINE/L1_L1MD1 chr1 17047411 17047697 + SINE/Alu_AluSx chr1 17047698 17047976 - SINE/Alu_AluJr chr1 17047977 17048586 - LINE/L1_L1MD1 chr1 17048692 17049010 - SINE/Alu_AluSq2 chr1 17049011 17049290 - SINE/Alu_AluJo chr1 17049302 17049596 - SINE/Alu_AluSz6 chr1 17049648 17049946 - SINE/Alu_AluSc8 chr1 17050367 17050670 + SINE/Alu_AluSx1 chr1 17050882 17050987 - SINE/MIR_MIRb chr1 17051572 17051688 + SINE/MIR_MIR chr1 17051834 17052145 - SINE/Alu_AluSq2 chr1 17052168 17052305 - LINE/L2_L2d chr1 17052676 17052771 - SINE/MIR_MIR chr1 17052928 17053091 + SINE/MIR_MIRc chr1 17053584 17053778 + SINE/MIR_MIRc chr1 17054378 17054675 - SINE/Alu_AluSx3 chr1 17054678 17054830 - SINE/Alu_AluJo chr1 17054831 17055133 - SINE/Alu_AluSg7 chr1 17055134 17055267 - SINE/Alu_AluJo chr1 17055466 17055668 - LINE/L2_L2c chr1 17055713 17055797 - SINE/MIR_MIRb chr1 17056234 17056325 + SINE/MIR_MIR3 chr1 17056995 17057098 - SINE/MIR_MIRc chr1 17057218 17057531 + SINE/Alu_AluJb chr1 17057753 17058051 - SINE/Alu_AluY chr1 17058099 17058298 + LINE/L2_L2c chr1 17058267 17058522 + LINE/L2_L2c chr1 17058335 17058631 + LINE/L2_L2c chr1 17058687 17058879 - SINE/Alu_AluJo chr1 17058880 17059174 + SINE/Alu_AluSx1 chr1 17059175 17059305 - SINE/Alu_AluJo chr1 17059583 17059744 - SINE/MIR_MIRb chr1 17060561 17060715 + SINE/MIR_MIR chr1 17060864 17061074 - LINE/L1_L1ME3G chr1 17061075 17061398 - SINE/Alu_AluSg chr1 17061399 17061567 - LINE/L1_L1ME3G chr1 17061842 17061876 + Simple_repeat_(TCAT)n chr1 17061955 17062085 + LINE/L2_L2c chr1 17062097 17062224 + DNA/hAT-Blackjack_MER94 chr1 17062568 17062878 - SINE/Alu_AluSz chr1 17062926 17063096 + SINE/MIR_MIRb chr1 17063098 17063172 + DNA/TcMar-Mariner_MADE2 chr1 17063251 17063296 - LINE/L2_L2b chr1 17063267 17063301 - SINE/MIR_MIR chr1 17063329 17063626 - SINE/Alu_AluSx1 chr1 17064634 17064958 + DNA/hAT-Charlie_MER33 chr1 17065164 17065461 + SINE/Alu_AluSc5 chr1 17065877 17066151 + LTR/ERVL_LTR16A1 chr1 17066152 17066460 - SINE/Alu_AluJb chr1 17066461 17066524 + LTR/ERVL_LTR16A1 chr1 17067203 17067231 + Simple_repeat_(A)n chr1 17067622 17067744 + LINE/L2_L2b chr1 17068763 17068789 + Simple_repeat_(GTTGT)n chr1 17069310 17069465 - SINE/MIR_MIR chr1 17069466 17069501 + Simple_repeat_(TGTA)n chr1 17069502 17069571 + Simple_repeat_(TGTACA)n chr1 17069572 17069754 + Simple_repeat_(TGTA)n chr1 17069797 17070000 - SINE/MIR_MIRc chr1 17070434 17070539 - SINE/MIR_MIRb chr1 17070557 17070663 - SINE/MIR_MIR chr1 17070664 17070961 - SINE/Alu_AluSx1 chr1 17070972 17071103 - SINE/Alu_AluJr chr1 17071104 17071169 - SINE/MIR_MIRb chr1 17071380 17071408 + Simple_repeat_(GCCCT)n chr1 17071978 17072218 - SINE/MIR_MIR chr1 17072746 17072776 + Simple_repeat_(TCTA)n chr1 17072911 17073040 + LINE/RTE-BovB_MamRTE1 chr1 17073136 17073433 + SINE/Alu_AluY chr1 17073441 17073564 + LINE/L1_L1MB4 chr1 17073570 17073776 + LINE/L1_L1PA16 chr1 17074110 17074409 + SINE/Alu_AluSp chr1 17074413 17074533 - SINE/Alu_AluJo chr1 17075321 17075517 + DNA/hAT-Charlie_MER33 chr1 17076216 17076510 - SINE/Alu_AluSg chr1 17076573 17076876 - SINE/Alu_AluSc chr1 17076883 17077108 + LINE/L2_L2b chr1 17076974 17077424 + LINE/L2_L2a chr1 17077424 17077664 + LINE/L2_L2b chr1 17078103 17078410 - SINE/Alu_AluSp chr1 17078414 17078687 - SINE/Alu_AluSz6 chr1 17078690 17078991 - SINE/Alu_AluJb chr1 17079609 17079684 + Simple_repeat_(GTGTGA)n chr1 17079794 17080090 - SINE/Alu_AluSz chr1 17080199 17080564 + LTR/ERVL-MaLR_MLT1I chr1 17080644 17080735 - DNA/hAT-Charlie_MER117 chr1 17081230 17081465 - SINE/MIR_MIRb chr1 17081794 17081829 + Simple_repeat_(AAAC)n chr1 17081831 17082118 + SINE/Alu_AluSx chr1 17082353 17082474 - SINE/MIR_MIR3 chr1 17082876 17083172 - SINE/Alu_AluJr chr1 17083187 17083365 + SINE/Alu_AluSx3 chr1 17083401 17083643 - LINE/L1_L1ME4b chr1 17084241 17084288 + LINE/L2_L2c chr1 17084358 17084577 - SINE/MIR_MIRb chr1 17084798 17085065 - SINE/MIR_MIR chr1 17085523 17085736 - SINE/MIR_MIR chr1 17086866 17086952 + SINE/MIR_MIR3 chr1 17087008 17087147 - SINE/MIR_MIRc chr1 17087325 17087488 + LINE/L2_L2b chr1 17087489 17087805 - SINE/Alu_AluSx1 chr1 17087806 17087922 + LINE/L2_L2b chr1 17088549 17088627 + SINE/MIR_MIRb chr1 17088628 17088931 + SINE/Alu_AluSg chr1 17088932 17089026 + SINE/MIR_MIRb chr1 17089038 17089083 + Simple_repeat_(TCAGTAACCA)n chr1 17089930 17090022 - DNA/hAT-Blackjack_MER81 chr1 17090188 17090363 - SINE/MIR_MIRb chr1 17090582 17090618 + Simple_repeat_(TG)n chr1 17091374 17091892 + LINE/L2_L2b chr1 17092632 17092666 - LINE/L1_L1ME4a chr1 17092667 17092978 + SINE/Alu_AluJr chr1 17092979 17093075 - LINE/L1_L1ME4a chr1 17093084 17093393 - SINE/Alu_AluSg chr1 17094114 17094704 + LINE/L2_L2b chr1 17095096 17095168 - LINE/L1_HAL1ME chr1 17095163 17095559 + LINE/L1_L1ME5 chr1 17095647 17095714 - SINE/MIR_MIRb chr1 17096009 17096038 + Simple_repeat_(AGGCAG)n chr1 17096262 17096382 - SINE/MIR_MIR3 chr1 17096691 17096821 + SINE/MIR_MIRb chr1 17096861 17097051 - DNA/hAT-Charlie_MER20 chr1 17097514 17097629 - LINE/L2_L2b chr1 17097609 17097673 - SINE/MIR_MIRb chr1 17098039 17098166 - SINE/MIR_MIR3 chr1 17099382 17099428 + Simple_repeat_(CTCC)n chr1 17099472 17099616 - SINE/MIR_MIR3 chr1 17099735 17099953 - SINE/MIR_MIRb chr1 17100276 17100584 - SINE/Alu_AluSx1 chr1 17100661 17100959 - SINE/Alu_AluSx4 chr1 17100965 17100998 + Simple_repeat_(TTTT)n chr1 17101796 17101982 + SINE/MIR_MIRb chr1 17101954 17101993 + LINE/L2_L2d2 chr1 17102021 17102082 + SINE/MIR_MIRb chr1 17102158 17102226 + SINE/MIR_MIR3 chr1 17102229 17102256 + Low_complexity_A-rich chr1 17102371 17102534 + SINE/MIR_MIR3 chr1 17103674 17103779 + DNA/hAT-Charlie_MER33 chr1 17103782 17103998 + SINE/Alu_AluJo chr1 17104001 17104318 + SINE/Alu_AluY chr1 17104319 17104426 + DNA/hAT-Charlie_MER33 chr1 17104427 17104732 - SINE/Alu_AluYm1 chr1 17104733 17104812 + DNA/hAT-Charlie_MER33 chr1 17105139 17105385 + SINE/Alu_AluJb chr1 17105386 17105692 - SINE/Alu_AluSp chr1 17105693 17105712 + SINE/Alu_AluJb chr1 17105774 17105945 + DNA/hAT-Charlie_MER5A chr1 17106308 17106433 + LTR/ERVL-MaLR_MLT1B chr1 17106434 17106732 - SINE/Alu_AluSx chr1 17106733 17107017 + LTR/ERVL-MaLR_MLT1B chr1 17107230 17107281 + DNA/hAT-Charlie_MER5B chr1 17107282 17107294 - DNA/hAT-Charlie_MER5A chr1 17107420 17107613 + DNA/hAT-Charlie_MER5A chr1 17107950 17108248 - SINE/Alu_AluSx1 chr1 17108267 17108277 + SINE/MIR_MIR3 chr1 17108278 17108347 + SINE/MIR_MIRc chr1 17108494 17108583 + LINE/L2_L2b chr1 17108924 17109020 + SINE/MIR_MIR3 chr1 17109103 17109416 + SINE/Alu_AluSz chr1 17109486 17109790 - SINE/Alu_AluSz chr1 17109791 17109866 + DNA/hAT-Charlie_Charlie15a chr1 17110473 17110498 + Simple_repeat_(AACA)n chr1 17110682 17110786 + LINE/L2_L2b chr1 17110829 17110929 + LINE/L2_L2b chr1 17110963 17111082 - LINE/L2_L2b chr1 17111083 17111392 - SINE/Alu_AluSx1 chr1 17111393 17111605 - LINE/L2_L2b chr1 17111606 17111789 - DNA/hAT-Charlie_MER5A chr1 17111790 17112212 - LINE/L2_L2b chr1 17112778 17112873 + LINE/L2_L2d chr1 17112904 17113096 + LINE/L2_L2d chr1 17113252 17113288 + Simple_repeat_(TAT)n chr1 17113314 17113536 - SINE/MIR_MIR chr1 17114273 17114310 + Simple_repeat_(CATT)n chr1 17114399 17114623 + SINE/MIR_MIR chr1 17114682 17114950 - LINE/L2_L2b chr1 17115042 17115109 + DNA/hAT-Charlie_MER5A chr1 17116114 17116191 - SINE/MIR_MIR3 chr1 17116628 17116798 + SINE/MIR_MIRb chr1 17116940 17117135 + DNA/hAT-Charlie_MER20 chr1 17117144 17117428 + DNA/hAT-Charlie_MER33 chr1 17119709 17119876 - LINE/L2_L2c chr1 17120780 17121079 - SINE/Alu_AluSp chr1 17121462 17121647 - SINE/MIR_MIR chr1 17121686 17121922 + SINE/MIR_MIRb chr1 17122573 17122699 - SINE/MIR_MIR chr1 17122714 17122926 + SINE/MIR_MIRb chr1 17123040 17123181 - LINE/L2_L2c chr1 17123602 17124010 - LTR/ERVL_MLT2C2 chr1 17124047 17124234 - SINE/MIR_MIRb chr1 17124321 17124395 + DNA/hAT-Charlie_MER33 chr1 17124396 17124846 - DNA/hAT-Charlie_MER1A chr1 17124847 17124967 + DNA/hAT-Charlie_MER33 chr1 17124968 17125272 + SINE/Alu_AluJr chr1 17125273 17125368 + DNA/hAT-Charlie_MER33 chr1 17125395 17125703 - SINE/Alu_AluSz chr1 17126268 17126308 + Simple_repeat_(TGTCCTG)n chr1 17126434 17126523 + Simple_repeat_(CA)n chr1 17126629 17126782 + SINE/MIR_MIRb chr1 17126783 17127078 + SINE/Alu_AluY chr1 17127086 17127391 + SINE/Alu_AluSz chr1 17127491 17127505 + LINE/L1_L1MB2 chr1 17127506 17127640 - SINE/Alu_FLAM_C chr1 17127641 17127909 + LINE/L1_L1MB2 chr1 17127962 17128258 + SINE/Alu_AluSx chr1 17128424 17128857 + LTR/ERV1_LTR24B chr1 17128882 17129178 - SINE/Alu_AluJr chr1 17129179 17129229 + LTR/ERV1_LTR24B chr1 17129235 17129470 - LTR?_MamRep1151 chr1 17129471 17129767 - SINE/Alu_AluSx1 chr1 17129768 17129821 - LTR?_MamRep1151 chr1 17129822 17130056 - DNA/hAT-Charlie_MER30 chr1 17130057 17130199 - LTR?_MamRep1151 chr1 17130489 17130597 - LTR/ERVL-MaLR_MSTB1 chr1 17130661 17130970 + SINE/Alu_AluSx1 chr1 17130977 17131363 + LTR/ERVL-MaLR_MSTB-int chr1 17131364 17131640 - SINE/Alu_AluSp chr1 17131641 17131907 + LTR/ERVL-MaLR_MSTB-int chr1 17131908 17132179 + LTR/ERVL-MaLR_MSTB chr1 17132367 17132529 + SINE/Alu_AluJb chr1 17132723 17132871 + DNA/hAT_MER53 chr1 17132881 17133021 + LINE/L2_L2b chr1 17133700 17133763 - LTR/ERV1_LTR56 chr1 17133764 17134066 - SINE/Alu_AluY chr1 17134067 17134419 - LTR/ERV1_LTR56 chr1 17134600 17135150 + Simple_repeat_(CA)n chr1 17135293 17135366 - LTR/ERV1_MER34A chr1 17135409 17135608 - LTR/ERV1_MER34A1 chr1 17135609 17135785 - LTR/ERV1_MER34C2 chr1 17135800 17135895 + DNA/TcMar-Tigger_Tigger5b chr1 17135970 17136076 - LTR/ERV1_MER34C2 chr1 17136797 17136819 + SINE/MIR_MIRc chr1 17136820 17137123 - SINE/Alu_AluSz chr1 17137124 17137230 + SINE/MIR_MIRc chr1 17137243 17137340 - SINE/MIR_MIRb chr1 17137349 17137420 - DNA/TcMar-Mariner_MADE2 chr1 17137421 17137435 - SINE/MIR_MIRb chr1 17137436 17137937 + LTR/ERV1_MER34 chr1 17137938 17138061 - SINE/MIR_MIRb chr1 17138078 17138162 + SINE/MIR_MIRb chr1 17138537 17138697 - SINE/MIR_MIR chr1 17139343 17139627 + SINE/Alu_AluSz chr1 17139628 17139662 + Simple_repeat_(AAAT)n chr1 17139663 17139689 + SINE/Alu_AluSz chr1 17139944 17140400 - LTR/ERVL-MaLR_MSTC chr1 17140622 17140684 - DNA/hAT-Charlie_MER58D chr1 17140683 17140805 - DNA/hAT-Charlie_Cheshire chr1 17140806 17140898 + LINE/L1_L1MB3 chr1 17140899 17141175 + SINE/Alu_AluSx chr1 17141176 17141329 + LINE/L1_L1MB3 chr1 17141330 17141743 + LTR/ERV1_LTR54B chr1 17141744 17141955 + LINE/L1_L1MB3 chr1 17141956 17142353 - DNA/hAT-Charlie_Cheshire chr1 17142452 17142585 - LINE/L2_L2c chr1 17142591 17142980 + LINE/L1_L1MC4 chr1 17142981 17143136 + Simple_repeat_(TA)n chr1 17143144 17143167 + Simple_repeat_(AG)n chr1 17143169 17143302 + LINE/L1_L1MC4 chr1 17143334 17143598 - LINE/L1_L1M5 chr1 17143647 17143930 - LINE/L2_L2-1_AMi chr1 17143970 17144075 - LINE/L2_L2c chr1 17144073 17144227 + LINE/L2_L2b chr1 17144228 17144560 - LTR/ERVL-MaLR_MLT1A1 chr1 17144561 17144614 + LINE/L2_L2b chr1 17144608 17144726 + SINE/MIR_MIR chr1 17144794 17144825 + Simple_repeat_(CT)n chr1 17144826 17144845 + Simple_repeat_(GT)n chr1 17144875 17144995 + DNA/hAT-Charlie_MER20 chr1 17145064 17145656 + LINE/L2_L2b chr1 17145526 17145945 + LINE/L2_L2a chr1 17146049 17146060 - SINE/Alu_AluSc chr1 17146061 17146101 + Simple_repeat_(TTCT)n chr1 17146102 17146367 - SINE/Alu_AluSc chr1 17146763 17147125 - LINE/L2_L2c chr1 17147614 17147849 - SINE/Alu_AluJr chr1 17147881 17147952 + DNA/hAT-Charlie_MER117 chr1 17148045 17148121 + LINE/L2_L2b chr1 17148448 17148486 + Simple_repeat_(AAACA)n chr1 17148490 17148660 + SINE/MIR_MIRb chr1 17148917 17148975 - LINE/L2_L2b chr1 17149069 17149178 + SINE/MIR_MIR3 chr1 17149602 17149711 + SINE/MIR_MIRb chr1 17149824 17149900 - SINE/MIR_MIRc chr1 17150110 17150188 - DNA/TcMar-Mariner_MADE1 chr1 17150418 17150484 - LTR/ERVL-MaLR_MLT1D chr1 17150498 17150811 - SINE/Alu_AluYj4 chr1 17150861 17151311 + LTR/ERVL-MaLR_MSTA chr1 17151321 17151751 - LTR/ERV1_LTR7B chr1 17151748 17153215 + LTR/ERVL-MaLR_MSTA-int chr1 17153221 17153614 + LTR/ERVL-MaLR_MSTA chr1 17153615 17153899 - SINE/Alu_AluJr chr1 17153901 17154333 + LTR/ERV1_LTR56 chr1 17154339 17154768 - LTR/ERVL-MaLR_MLT1D chr1 17154769 17154881 - SINE/MIR_MIRb chr1 17155370 17156055 - LTR/ERV1_LTR76 chr1 17156063 17156570 + LTR/ERV1_MER51A chr1 17156655 17156904 - LTR/ERVL-MaLR_MSTA chr1 17156905 17157213 + SINE/Alu_AluSc chr1 17157214 17157365 - LTR/ERVL-MaLR_MSTA chr1 17157371 17157851 - LTR/ERVL-MaLR_MLT1K chr1 17157970 17157999 + Simple_repeat_(TG)n chr1 17157987 17158015 + Low_complexity_G-rich chr1 17158127 17158221 + SINE/MIR_MIRb chr1 17158223 17158281 + scRNA_HY3 chr1 17158323 17158419 - LINE/L2_L2c chr1 17158509 17158917 + LTR/ERVL-MaLR_MSTB1 chr1 17158936 17158994 - SINE/MIR_MIR3 chr1 17159326 17159356 + Simple_repeat_(TGTG)n chr1 17159393 17159555 + SINE/MIR_MIRc chr1 17159593 17159738 + SINE/MIR_MIR chr1 17159743 17159822 - DNA/TcMar-Tc2_MER104 chr1 17160183 17160317 + LTR/Gypsy_MamGypLTR2b chr1 17160318 17160965 - LTR/ERV1_MER4B chr1 17160966 17161007 + LTR/Gypsy_MamGypLTR2b chr1 17161144 17161559 - LTR/ERVL-MaLR_MLT1J chr1 17161558 17161766 + LTR/ERVL_LTR16 chr1 17161918 17162030 - SINE/MIR_MIRb chr1 17162175 17162252 + SINE/MIR_MIRc chr1 17162302 17162512 - SINE/MIR_MIRb chr1 17163882 17163997 + LINE/L2_L2b chr1 17164558 17164648 + SINE/MIR_MIRb chr1 17164856 17165025 - LINE/L2_L2d2 chr1 17165339 17165660 + LINE/L2_L2c chr1 17165633 17165807 + SINE/MIR_MIR chr1 17165810 17165909 + SINE/MIR_MIR chr1 17166246 17166380 + LINE/L2_L2c chr1 17166958 17167628 + LINE/L2_L2a chr1 17167870 17168026 + SINE/MIR_MIR chr1 17167966 17168064 - SINE/MIR_MIR chr1 17168716 17168737 + Simple_repeat_(A)n chr1 17168825 17169237 - LINE/L1_L1ME1 chr1 17169238 17169536 - SINE/Alu_AluSz chr1 17169537 17169547 - LINE/L1_L1ME1 chr1 17169548 17169835 - SINE/Alu_AluJr chr1 17169836 17170055 - LINE/L1_L1ME1 chr1 17170043 17170303 + LINE/L1_L1MEd chr1 17170323 17170547 - LINE/L1_L1PA8 chr1 17170574 17170680 - SINE/Alu_AluJr chr1 17170682 17170981 + LINE/L1_L1MEd chr1 17170982 17171287 + SINE/Alu_AluJr chr1 17171288 17171338 + LINE/L1_L1MEd chr1 17171342 17171579 + LINE/L1_L1ME1 chr1 17171580 17171890 - SINE/Alu_AluY chr1 17171891 17171904 + LINE/L1_L1ME1 chr1 17171905 17171952 - LTR/ERV1_MER50C chr1 17171953 17172283 + LINE/L1_L1ME1 chr1 17172316 17172618 - SINE/Alu_AluSx1 chr1 17172725 17172996 - SINE/Alu_AluJr chr1 17173009 17173182 + LINE/L2_L2a chr1 17173204 17173779 + LINE/L2_L2a chr1 17173780 17174143 + LTR/ERVL-MaLR_THE1B chr1 17174144 17174359 + LINE/L2_L2a chr1 17174370 17174427 - LINE/L1_L1MC3 chr1 17174431 17174700 + LTR/ERVL-MaLR_MLT1B chr1 17174701 17175081 + LTR/ERVL-MaLR_THE1D chr1 17175082 17175240 + LTR/ERVL-MaLR_MLT1B chr1 17175247 17175494 + LINE/L2_L2a chr1 17175505 17175616 + LINE/L1_L1MEd chr1 17175617 17175905 + SINE/Alu_AluSz chr1 17175907 17175954 + Simple_repeat_(TA)n chr1 17175955 17176209 + LINE/L1_L1MEd chr1 17176197 17179745 + LINE/L1_L1MDa chr1 17179776 17180263 - LINE/L1_L1PB1 chr1 17180264 17181814 + LINE/L1_L1MDa chr1 17182164 17182569 - LINE/L1_L1M4 chr1 17182570 17182876 - SINE/Alu_AluY chr1 17182877 17182929 - LINE/L1_L1M4 chr1 17182940 17182977 - tRNA_tRNA-Met_ chr1 17183005 17183078 + LINE/L1_L1MDa chr1 17183079 17183281 - DNA/hAT-Blackjack_MER63A chr1 17183282 17183344 + LINE/L1_L1MDa chr1 17183640 17183730 - SINE/MIR_MIR3 chr1 17184044 17184292 + SINE/MIR_MIRb chr1 17184536 17184795 + SINE/Alu_AluSx1 chr1 17184843 17184876 + Simple_repeat_(AAAAC)n chr1 17185019 17185133 + SINE/MIR_MIR3 chr1 17185460 17185577 + SINE/MIR_MIR3 chr1 17187196 17187378 + DNA/hAT-Tip100_MER91A chr1 17187395 17187493 - DNA/hAT-Charlie_Charlie16a chr1 17187629 17187934 + SINE/Alu_AluSz chr1 17188066 17188608 - LINE/L1_L1ME3 chr1 17188610 17188951 - LTR/ERVL-MaLR_THE1D chr1 17188954 17189246 - LINE/L1_L1ME3 chr1 17189247 17189547 - SINE/Alu_AluSx chr1 17189548 17189698 - LINE/L1_L1ME3 chr1 17189727 17189857 - LINE/L2_L2c chr1 17190924 17191070 + LTR/ERV1_LTR51 chr1 17191071 17191350 + SINE/Alu_AluSz chr1 17191360 17191657 + SINE/Alu_AluYk2 chr1 17191658 17191783 + LTR/ERV1_LTR51 chr1 17191788 17192013 + Simple_repeat_(TTC)n chr1 17192014 17192298 - SINE/Alu_AluJb chr1 17192299 17192733 + LTR/ERV1_LTR51 chr1 17193978 17194092 + SINE/MIR_MIR3 chr1 17194102 17194199 + SINE/MIR_MIRc chr1 17194974 17195143 - LINE/L2_L2b chr1 17195457 17195497 + SINE/MIR_MIR3 chr1 17196160 17196419 + SINE/MIR_MIRb chr1 17196476 17196618 + LINE/L2_L2c chr1 17197074 17197347 + LINE/RTE-X_L4_A_Mam chr1 17197324 17197545 + LINE/RTE-X_L4_A_Mam chr1 17197765 17197791 + Low_complexity_G-rich chr1 17197955 17198034 - SINE/MIR_MIR chr1 17198035 17198342 + SINE/Alu_AluSx chr1 17198343 17198471 - SINE/MIR_MIR chr1 17198485 17198842 - LINE/L2_L2b chr1 17199346 17199401 + LINE/L2_L2a chr1 17199399 17199553 + SINE/MIR_MIR3 chr1 17199701 17199720 + Simple_repeat_(GT)n chr1 17200060 17200177 + DNA/hAT-Charlie_MER102c chr1 17200240 17200529 - LTR/ERV1_PABL_B chr1 17200530 17200828 - SINE/Alu_AluSz6 chr1 17200829 17201018 - LTR/ERV1_PABL_B chr1 17201300 17201570 + LINE/L2_L2a chr1 17202068 17202175 - LINE/L1_HAL1b chr1 17202195 17202306 + SINE/MIR_MIR chr1 17202317 17202387 + Simple_repeat_(TCCTCTT)n chr1 17202398 17202698 - SINE/Alu_AluSg chr1 17202818 17202928 + SINE/MIR_MIR chr1 17202933 17202970 + Simple_repeat_(TCA)n chr1 17203051 17203123 + Simple_repeat_(TCCA)n chr1 17203564 17203596 + Simple_repeat_(TC)n chr1 17203785 17204103 + LINE/L2_L2a chr1 17204109 17204289 + LINE/L2_L2a chr1 17204516 17204635 + SINE/MIR_MIRc chr1 17204628 17204823 - SINE/MIR_MIR chr1 17205569 17205607 + Low_complexity_G-rich chr1 17205866 17205943 + Simple_repeat_(TGA)n chr1 17206030 17206124 + SINE/MIR_MIR3 chr1 17206527 17206668 + DNA/hAT_MER53 chr1 17206669 17206980 - SINE/Alu_AluSx1 chr1 17206981 17207027 + DNA/hAT_MER53 chr1 17207150 17207275 + SINE/MIR_MIR3 chr1 17207395 17207542 + SINE/MIR_MIRb chr1 17209054 17209412 + DNA/hAT-Tip100_MamRep4096 chr1 17209712 17209784 + SINE/MIR_MIRc chr1 17209842 17210139 - SINE/Alu_AluSx chr1 17210145 17210337 - SINE/Alu_AluJb chr1 17210920 17211015 - LINE/L2_L2d2 chr1 17211200 17211458 - SINE/Alu_AluSx3 chr1 17211463 17211668 - SINE/MIR_MIR chr1 17211710 17211920 + SINE/MIR_MIRc chr1 17211921 17212022 + LINE/L2_L2b chr1 17212600 17212744 + LINE/CR1_L3 chr1 17213110 17213183 + SINE/MIR_MIRc chr1 17213191 17213588 + LTR/ERV1_MER83 chr1 17213606 17213721 + SINE/MIR_MIRb chr1 17214150 17214242 - SINE/MIR_MIRb chr1 17214282 17214323 - LINE/L2_L2d2 chr1 17215379 17215418 + Simple_repeat_(TCA)n chr1 17215637 17215733 + SINE/MIR_MIRb chr1 17215743 17216167 - LINE/L2_L2a chr1 17216191 17216588 - LINE/L2_L2a chr1 17216589 17216896 + SINE/Alu_AluSz chr1 17217008 17217080 + Low_complexity_GA-rich chr1 17217123 17217493 - LTR/ERVL_ERVL-B4-int chr1 17217494 17218401 + DNA/hAT-Blackjack_MER63C chr1 17218413 17220192 - LINE/L2_L2a chr1 17220192 17220248 + SINE/MIR_MIRb chr1 17220480 17220573 - LINE/L2_L2a chr1 17221214 17221358 - LINE/L2_L2a chr1 17221369 17221489 + SINE/Alu_AluSx3 chr1 17221492 17222040 - LINE/L2_L2a chr1 17223007 17223086 + SINE/MIR_MIR chr1 17223216 17223297 - SINE/MIR_MIRb chr1 17224605 17224789 + SINE/MIR_MIRb chr1 17224991 17225054 + DNA/hAT-Tip100_MER91A chr1 17225174 17225267 + SINE/MIR_MIRb chr1 17225584 17225629 + DNA/hAT-Tip100_MER91A chr1 17225680 17225723 + Low_complexity_A-rich chr1 17226238 17226466 - SINE/MIR_MIRb chr1 17226642 17226767 + SINE/Alu_AluSx chr1 17226768 17227047 + SINE/Alu_AluY chr1 17227048 17227263 + SINE/Alu_AluSx chr1 17227266 17227530 + SINE/Alu_AluJb chr1 17227531 17227582 + Simple_repeat_(AAAT)n chr1 17227851 17227993 - LINE/L1_L1MB5 chr1 17227994 17228274 - SINE/Alu_AluJr chr1 17228275 17228369 - LINE/L1_L1MB5 chr1 17228370 17228557 - SINE/MIR_MIRb chr1 17229207 17229242 + Simple_repeat_(GCCCT)n chr1 17229775 17230012 + LINE/L2_L2b chr1 17231078 17231129 - LINE/L2_L2a chr1 17231161 17231392 - SINE/MIR_MIR chr1 17231645 17231781 - LINE/L1_L1M5 chr1 17231752 17231913 - LINE/L1_L1PA5 chr1 17231940 17232245 - SINE/Alu_AluSx3 chr1 17232249 17232402 - SINE/Alu_FRAM chr1 17232403 17232660 - LINE/L1_L1M5 chr1 17233186 17233298 + DNA/hAT-Charlie_Charlie8 chr1 17234067 17234176 + SINE/MIR_MIRb chr1 17234151 17234229 + LINE/L2_L2d2 chr1 17234200 17234276 - LINE/L2_L2a chr1 17234288 17234366 + SINE/MIR_MIRc chr1 17234482 17234641 - SINE/MIR_MIR chr1 17234654 17234788 - LINE/L2_L2a chr1 17234871 17235146 + SINE/Alu_AluSx chr1 17235147 17235310 + Low_complexity_G-rich chr1 17235560 17235709 - Satellite/telo_TAR1 chr1 17235992 17236095 - SINE/MIR_MIRb chr1 17236270 17236414 + SINE/MIR_MIR chr1 17236473 17236784 + SINE/Alu_AluJb chr1 17236785 17236819 + Low_complexity_GA-rich chr1 17236820 17237008 - LINE/L2_L2a chr1 17237023 17237134 + SINE/MIR_MIR chr1 17237682 17237900 - SINE/MIR_MIR chr1 17238037 17238341 - SINE/Alu_AluSx chr1 17238343 17238535 - LINE/L2_L2b chr1 17238982 17239072 - SINE/MIR_MIRc chr1 17239261 17239456 + SINE/MIR_MIRb chr1 17239919 17239966 - LINE/L2_L2c chr1 17239944 17240005 - LINE/L2_L2b chr1 17240362 17240597 + SINE/MIR_MIRb chr1 17240909 17241045 + DNA/hAT-Charlie_MER20 chr1 17242076 17242266 - DNA/hAT-Charlie_MER20 chr1 17242277 17242464 + DNA/hAT-Charlie_MER3 chr1 17242624 17242789 - SINE/MIR_MIR3 chr1 17242816 17242853 + Simple_repeat_(CTGGCC)n chr1 17243368 17243471 + SINE/MIR_MIRb chr1 17243503 17243534 + LINE/L2_L2c chr1 17243544 17243732 + DNA/hAT-Tip100_Zaphod2 chr1 17243741 17243769 + Simple_repeat_(T)n chr1 17246150 17246182 + Simple_repeat_(CAGTGC)n chr1 17246437 17246749 - SINE/Alu_AluJr chr1 17247501 17247663 - SINE/MIR_MIRb chr1 17247861 17247910 - SINE/MIR_MIR chr1 17247911 17248212 + SINE/Alu_AluYk2 chr1 17248213 17248283 - SINE/MIR_MIR chr1 17248300 17248354 - DNA/hAT-Charlie_MER5A chr1 17248355 17248653 + SINE/Alu_AluSp chr1 17248654 17248734 - DNA/hAT-Charlie_MER5A chr1 17249663 17249927 - SINE/MIR_MIR chr1 17250247 17250448 - DNA/hAT-Charlie_MER20 chr1 17251179 17251313 + LINE/CR1_L3 chr1 17251372 17251474 + SINE/MIR_MIRc chr1 17251471 17251691 - SINE/MIR_MIR chr1 17251680 17251786 + SINE/MIR_MIRb chr1 17251897 17252009 - LINE/L2_L2c chr1 17252390 17252703 - SINE/Alu_AluJb chr1 17252986 17253097 - SINE/MIR_MIR3 chr1 17253552 17253723 - DNA/hAT-Charlie_MER5B chr1 17253782 17253903 + SINE/MIR_MIR3 chr1 17254458 17254579 + SINE/MIR_MIRc chr1 17254718 17255015 - SINE/Alu_AluSp chr1 17255173 17255283 + DNA/hAT-Blackjack_MER81 chr1 17255695 17255776 + SINE/MIR_MIR3 chr1 17255805 17255966 - SINE/MIR_MIR chr1 17256185 17256412 - SINE/MIR_MIRc chr1 17256756 17257059 + SINE/Alu_AluSx1 chr1 17257350 17257535 - DNA/hAT_MER53 chr1 17257771 17257909 + SINE/MIR_MIRb chr1 17257981 17258186 + SINE/MIR_MIR chr1 17258561 17258770 + SINE/MIR_MIRb chr1 17259071 17259398 - SINE/Alu_AluSz chr1 17259455 17259521 - LINE/L2_L2d chr1 17259849 17260031 + SINE/MIR_MIR chr1 17260110 17260159 + SINE/MIR_MIR3 chr1 17260445 17260646 - SINE/MIR_MIRb chr1 17260677 17260811 - SINE/MIR_MIRc chr1 17261286 17261524 - SINE/MIR_MIR chr1 17261558 17261782 - SINE/MIR_MIRb chr1 17261791 17261938 - SINE/MIR_MIR3 chr1 17262251 17262460 - LINE/L1_L1ME4b chr1 17262509 17262907 + LINE/L1_L1MCa chr1 17262908 17263213 - SINE/Alu_AluSp chr1 17263214 17263875 + LINE/L1_L1MCa chr1 17263987 17264224 - LINE/L1_L1ME4b chr1 17264305 17264331 - LINE/L1_L1ME3B chr1 17264332 17264363 + Simple_repeat_(AC)n chr1 17264364 17264689 - LINE/L1_L1ME3B chr1 17264729 17265024 + SINE/Alu_AluJb chr1 17265235 17265305 - SINE/MIR_MIRb chr1 17265420 17265470 - LINE/L2_L2d2 chr1 17265988 17266194 - DNA/hAT-Charlie_MER58A chr1 17266211 17266415 - LINE/L2_L2d chr1 17267105 17267241 - SINE/MIR_MIRb chr1 17267458 17267574 - SINE/MIR_MIR3 chr1 17268224 17268463 - LINE/L1_L1MC5a chr1 17268499 17268806 - SINE/Alu_AluSc8 chr1 17268887 17269184 - SINE/Alu_AluJb chr1 17269257 17269446 - LINE/L1_L1ME3G chr1 17269449 17269540 + LINE/L2_L2a chr1 17269570 17269606 - LINE/L1_L1M5 chr1 17269607 17269918 - SINE/Alu_AluSx chr1 17269919 17270111 - LINE/L1_L1M5 chr1 17270445 17270671 + SINE/Alu_AluJb chr1 17270803 17270857 - SINE/MIR_MIRc chr1 17271362 17271454 - LINE/L2_L2b chr1 17271466 17271525 + DNA/hAT-Charlie_MER20 chr1 17271523 17271600 + DNA/hAT-Charlie_MER20 chr1 17271651 17271863 + SINE/Alu_AluJb chr1 17271911 17272014 - LINE/L2_L2b chr1 17272378 17272479 + SINE/MIR_MIR chr1 17272510 17272795 - SINE/Alu_AluJr chr1 17272798 17272930 + SINE/MIR_MIR chr1 17273111 17273162 + SINE/MIR_MIR1_Amn chr1 17273565 17273756 - DNA/hAT_MER53 chr1 17273762 17273932 + SINE/MIR_MIRb chr1 17274982 17275103 - SINE/MIR_MIR3 chr1 17275159 17275459 + SINE/Alu_AluY chr1 17276053 17276363 + SINE/Alu_AluSz chr1 17277117 17277192 + DNA/hAT-Tip100_MER45C chr1 17277193 17277493 - SINE/Alu_AluSp chr1 17277494 17278227 + DNA/hAT-Tip100_MER45C chr1 17278233 17278527 - SINE/Alu_AluJb chr1 17278537 17278561 + Simple_repeat_(T)n chr1 17278718 17278871 - LTR/ERVL?_LTR89B chr1 17279266 17279472 - SINE/MIR_MIRb chr1 17279563 17279770 + SINE/MIR_MIRb chr1 17281147 17281410 - LINE/L2_L2c chr1 17281435 17281752 - SINE/Alu_AluSx chr1 17281753 17281922 - DNA/hAT-Charlie_MER5A chr1 17282171 17282588 - LTR/ERVL-MaLR_MLT1O chr1 17285197 17285315 - LTR/ERVL-MaLR_MLT1D chr1 17285334 17285489 + SINE/Alu_AluJr4 chr1 17285492 17285643 - LTR/ERVL_LTR32 chr1 17285644 17285943 + SINE/Alu_AluSg4 chr1 17285944 17286222 - LTR/ERVL_LTR32 chr1 17286224 17286621 - LTR/ERVL-MaLR_MLT1D chr1 17287141 17287309 + SINE/MIR_MIRb chr1 17287404 17287491 - DNA/hAT-Tip100_MamTip2 chr1 17287801 17288001 - SINE/MIR_MIRb chr1 17288006 17288096 - DNA/hAT-Charlie_MER5B chr1 17288158 17288261 + SINE/Alu_AluSx chr1 17288262 17288552 + SINE/Alu_AluSc8 chr1 17288553 17288724 + SINE/Alu_AluSx chr1 17289044 17289345 + SINE/Alu_AluY chr1 17290178 17290387 + SINE/Alu_AluSx1 chr1 17290401 17290425 + Simple_repeat_(AAT)n chr1 17290437 17290597 - LINE/RTE-X_L4_B_Mam chr1 17290667 17290710 - snRNA_U2 chr1 17290749 17290851 + SINE/MIR_MIRb chr1 17290954 17291094 - LINE/L2_L2b chr1 17291163 17291484 + LINE/L1_L1ME3D chr1 17291485 17291777 + SINE/Alu_AluSz chr1 17291786 17291978 + DNA/hAT-Charlie_MER5A chr1 17291979 17292234 + LINE/L1_L1ME3D chr1 17292235 17292396 - SINE/Alu_AluJr chr1 17292397 17292422 + Simple_repeat_(T)n chr1 17292423 17292540 - SINE/Alu_AluJr chr1 17292541 17292907 + LINE/L1_L1ME3D chr1 17292908 17292939 + Simple_repeat_(GGGAA)n chr1 17292940 17293123 + LINE/L1_L1ME3D chr1 17293134 17293297 - LINE/L1_L1MD chr1 17293428 17293884 + LINE/L1_L1ME3D chr1 17293885 17294382 - LTR/ERVK_LTR3B_ chr1 17294383 17294515 + LINE/L1_L1ME3D chr1 17294516 17294799 + SINE/Alu_AluJr chr1 17294800 17294895 + LINE/L1_L1ME3D chr1 17294896 17295190 + SINE/Alu_AluY chr1 17295191 17295311 + LINE/L1_L1ME3D chr1 17295703 17295826 + DNA/hAT-Charlie_Charlie4z chr1 17295845 17296296 + LTR/ERVL_LTR16C chr1 17296312 17296484 + SINE/MIR_MIRc chr1 17296525 17296925 + LTR/ERVL_MLT2D chr1 17296926 17296968 + Simple_repeat_(TATC)n chr1 17296969 17297001 + LTR/ERVL_MLT2D chr1 17297002 17297069 + LINE/L2_L2b chr1 17297123 17297301 + SINE/MIR_MIRb chr1 17297302 17297596 + SINE/Alu_AluSx chr1 17297597 17297612 + SINE/MIR_MIRb chr1 17298519 17298548 + Simple_repeat_(AAC)n chr1 17299162 17299398 + SINE/Alu_AluSz chr1 17299402 17299422 + Simple_repeat_(AAAC)n chr1 17299423 17299960 + LINE/L1_L1MA9 chr1 17299961 17300267 - SINE/Alu_AluSx chr1 17300268 17300329 + LINE/L1_L1MA9 chr1 17300406 17300714 + SINE/Alu_AluSx1 chr1 17301349 17301554 + SINE/Alu_AluSq2 chr1 17301558 17301703 + SINE/Alu_AluSc chr1 17301715 17302081 + LTR/ERVK_LTR14A chr1 17302088 17302381 + LTR/ERVL-MaLR_MSTA chr1 17302382 17302594 + LINE/L1_L1MD chr1 17302620 17302745 - LINE/L2_L2a chr1 17302886 17302954 + SINE/MIR_MIRb chr1 17303237 17303493 - SINE/Alu_AluSx1 chr1 17303645 17303855 + SINE/MIR_MIRb chr1 17303922 17304069 + SINE/MIR_MIRb chr1 17304113 17304170 + LINE/L2_L2a chr1 17304882 17304956 + LINE/L1_L1M5 chr1 17305161 17305194 + Simple_repeat_(GT)n chr1 17305242 17306330 + LINE/L2_L2a chr1 17306338 17306389 + DNA/TcMar-Tigger_Tigger19a chr1 17306416 17306587 + SINE/MIR_MIR3 chr1 17306736 17306925 + SINE/MIR_MIR chr1 17306900 17306946 - SINE/MIR_MIRb chr1 17306979 17307003 + Simple_repeat_(CAAA)n chr1 17307234 17307465 + SINE/MIR_MIRb chr1 17307466 17307605 + SINE/MIR_MIR1_Amn chr1 17307491 17307632 + SINE/MIR_MIR chr1 17307633 17307715 + SINE/MIR_MIR chr1 17308574 17308707 - SINE/MIR_MIR3 chr1 17308747 17308964 + SINE/MIR_MIRb chr1 17308980 17309036 - LINE/L2_L2b chr1 17309089 17309584 + LTR/ERV1_LTR15 chr1 17310149 17310328 - SINE/MIR_MIRb chr1 17310544 17310643 + SINE/Alu_AluSx3 chr1 17310647 17310679 - SINE/MIR_MIRb chr1 17310680 17310820 + SINE/Alu_AluSz6 chr1 17310821 17311113 + SINE/Alu_AluSc8 chr1 17311114 17311274 + SINE/Alu_AluSz6 chr1 17311275 17311341 - SINE/MIR_MIRb chr1 17311520 17311824 - SINE/Alu_AluYm1 chr1 17311864 17311991 + SINE/MIR_MIRb chr1 17312023 17312177 - LTR/ERV1_MER4CL34 chr1 17312178 17312478 + SINE/Alu_AluSq2 chr1 17312479 17312909 - LTR/ERV1_MER4CL34 chr1 17312910 17313214 + SINE/Alu_AluSc chr1 17313218 17313519 + SINE/Alu_AluSz chr1 17313520 17313562 + Simple_repeat_(AAGGAAAG)n chr1 17313563 17313654 - LTR/ERV1_MER4CL34 chr1 17313949 17314017 + SINE/MIR_MIRb chr1 17314018 17314045 + SINE/MIR_MIRc chr1 17314513 17314689 - SINE/MIR_MIRb chr1 17315164 17315266 + LINE/L2_L2b chr1 17315268 17315293 + Simple_repeat_(TGAA)n chr1 17315308 17315360 + LINE/L2_L2b chr1 17315367 17315433 + LINE/L2_L2c chr1 17316112 17316406 + SINE/Alu_AluSx chr1 17316409 17316709 + SINE/Alu_AluY chr1 17316710 17316755 + Simple_repeat_(TAAT)n chr1 17316874 17317012 + SINE/MIR_MIRc chr1 17317028 17317102 + SINE/MIR_MIRb chr1 17317106 17317224 - SINE/Alu_AluJo chr1 17317288 17317568 - SINE/Alu_AluSq2 chr1 17317675 17317739 - DNA/hAT-Charlie_MER5A chr1 17317740 17318033 + SINE/Alu_AluJr chr1 17318034 17318167 - DNA/hAT-Charlie_MER5A chr1 17318223 17318396 + SINE/MIR_MIRc chr1 17318682 17318987 - SINE/Alu_AluY chr1 17318990 17319108 - SINE/Alu_FLAM_C chr1 17319289 17319519 + SINE/Alu_AluJb chr1 17319559 17319575 - LTR/ERVL-MaLR_MLT1D chr1 17319576 17319978 - LTR/ERV1_MER57B2 chr1 17319979 17320279 - LTR/ERVL-MaLR_MLT1D chr1 17320280 17320908 - LTR/ERV1_MER41B chr1 17320909 17321077 - LTR/ERVL-MaLR_MLT1D chr1 17321096 17321314 - LINE/L2_L2d2 chr1 17321392 17321561 + SINE/MIR_MIR chr1 17321737 17322206 - LTR/ERV1_MER4A chr1 17322207 17322507 + SINE/Alu_AluSx3 chr1 17322508 17322741 - LTR/ERV1_MER4A chr1 17322785 17322872 + Simple_repeat_(CTTC)n chr1 17322881 17323450 - LTR/ERVL-MaLR_MLT1F2 chr1 17323554 17323858 + SINE/Alu_AluSz6 chr1 17323859 17323886 + Simple_repeat_(AAAC)n chr1 17323953 17324497 - LINE/L1_L1MEd chr1 17324502 17324631 - SINE/Alu_AluJr chr1 17324634 17325714 - LINE/L1_L1MEd chr1 17325715 17326003 - SINE/Alu_AluSz6 chr1 17326004 17326090 - LINE/L1_L1MEd chr1 17326121 17326904 - LINE/L1_L1MEd chr1 17326905 17327203 - SINE/Alu_AluSx chr1 17327204 17327528 - LINE/L1_L1MEd chr1 17327529 17327825 - SINE/Alu_AluSz chr1 17327826 17327857 - LINE/L1_L1M5 chr1 17327858 17328011 - LINE/L1_L1MEd chr1 17328012 17328323 - SINE/Alu_AluJo chr1 17328324 17328363 - LINE/L1_L1MEd chr1 17328364 17328662 + SINE/Alu_AluJr chr1 17328663 17328985 - LINE/L1_L1MEd chr1 17329150 17329217 - DNA/TcMar-Tigger_Tigger2b_Pri chr1 17329218 17329339 + SINE/Alu_AluYc chr1 17329344 17329701 - DNA/TcMar-Tigger_Tigger2a chr1 17329702 17329898 - LINE/L1_L1MEd chr1 17329921 17330052 - LINE/L1_L1MEd chr1 17330328 17330888 - LTR/ERVL_MLT2B3 chr1 17331358 17331548 - LINE/L2_L2a chr1 17331636 17331938 + SINE/Alu_AluSx1 chr1 17331951 17332249 - LTR/ERVL-MaLR_MLT1D chr1 17332250 17332549 - SINE/Alu_AluSz chr1 17332550 17332666 - LTR/ERVL-MaLR_MLT1D chr1 17332776 17333049 - LINE/L2_L2a chr1 17333066 17333236 - LINE/L2_L2a chr1 17333608 17333911 + LINE/L2_L2b chr1 17334308 17334602 - SINE/Alu_AluSx1 chr1 17334679 17334820 - DNA/hAT-Charlie_MER113A chr1 17334861 17335147 + SINE/Alu_AluJr chr1 17335148 17335189 + Simple_repeat_(TA)n chr1 17335201 17335229 + Simple_repeat_(TTTCA)n chr1 17335256 17335436 - SINE/MIR_MIR chr1 17337126 17337445 - SINE/Alu_AluSx chr1 17337636 17337939 + SINE/Alu_AluSc chr1 17338190 17338423 - SINE/MIR_MIR3 chr1 17338512 17338686 - SINE/MIR_MIR chr1 17338957 17339170 - SINE/MIR_MIR chr1 17339305 17339604 - LINE/L2_L2a chr1 17339886 17339997 - LINE/L2_L2a chr1 17340053 17340079 + Simple_repeat_(CA)n chr1 17340186 17340729 - LINE/L2_L2a chr1 17340791 17341093 - SINE/Alu_AluSz chr1 17341098 17341398 - SINE/Alu_AluSx3 chr1 17341455 17341680 + LINE/L2_L2b chr1 17342616 17342684 + DNA/hAT-Charlie_MER5A chr1 17342693 17342987 + SINE/Alu_AluSz chr1 17342988 17343209 + SINE/Alu_AluSx3 chr1 17343214 17343253 + Simple_repeat_(AAATT)n chr1 17343418 17343545 + SINE/MIR_MIRb chr1 17343546 17343892 + LTR/ERVL-MaLR_THE1A chr1 17343893 17345468 + LTR/ERVL-MaLR_THE1A-int chr1 17345469 17345815 + LTR/ERVL-MaLR_THE1A chr1 17345816 17345892 + SINE/MIR_MIRb chr1 17346786 17346820 + Simple_repeat_(CCCATTC)n chr1 17346945 17347236 - SINE/Alu_AluSp chr1 17347259 17347335 + DNA/TcMar-Mariner_MADE2 chr1 17348601 17348756 - SINE/MIR_MIRb chr1 17349423 17349732 + SINE/Alu_AluSx chr1 17349800 17350240 + LINE/L2_L2a chr1 17350258 17350540 - DNA/hAT-Charlie_MER113 chr1 17350710 17350905 - LINE/L2_L2a chr1 17350906 17351222 + SINE/Alu_AluJr chr1 17351223 17351309 - LINE/L2_L2a chr1 17351310 17351634 + SINE/Alu_AluJb chr1 17351635 17351888 - LINE/L2_L2a chr1 17352256 17352897 - LINE/L2_L2a chr1 17352902 17353151 - LINE/L2_L2-3_Crp chr1 17353182 17353415 + SINE/Alu_AluJr chr1 17353425 17353531 - DNA/hAT-Charlie_Charlie4z chr1 17353598 17353733 + LTR/ERVL-MaLR_MLT1D chr1 17353748 17353957 + LTR/ERVL-MaLR_MLT1D chr1 17353958 17354256 + SINE/Alu_AluSz chr1 17354814 17354893 + SINE/MIR_MIRb chr1 17354896 17355268 - LINE/L2_L2a chr1 17355270 17355554 + SINE/Alu_AluSx chr1 17355555 17355694 - SINE/MIR_MIRb chr1 17355768 17355850 + SINE/MIR_MIRb chr1 17356534 17356911 - LINE/L2_L2b chr1 17357174 17357465 - SINE/Alu_AluSg chr1 17357651 17357955 + SINE/Alu_AluSg4 chr1 17357966 17358262 + SINE/Alu_AluSx chr1 17358286 17358595 + SINE/Alu_AluYe5 chr1 17358632 17358758 - SINE/MIR_MIRc chr1 17359201 17359277 + Simple_repeat_(CCCCAC)n chr1 17359392 17359447 - LINE/RTE-BovB_MamRTE1 chr1 17359531 17360007 - LTR/ERVL_LTR62 chr1 17360008 17360310 + SINE/Alu_AluSx1 chr1 17360311 17360529 - LTR/ERVL_LTR62 chr1 17360781 17360861 - LINE/L2_L2d2 chr1 17361565 17361779 - SINE/MIR_MIRb chr1 17361864 17362046 - LINE/L2_L2d2 chr1 17362047 17362072 + SINE/MIR_MIR chr1 17362073 17362096 + LINE/L1_L1PA17 chr1 17362097 17362374 + SINE/Alu_AluJb chr1 17362375 17362709 + LINE/L1_L1PA17 chr1 17362710 17362819 + SINE/MIR_MIR chr1 17362820 17363115 - SINE/Alu_AluSx chr1 17363118 17363420 - SINE/Alu_AluJb chr1 17364024 17364044 + Simple_repeat_(TCAT)n chr1 17364207 17364234 + Low_complexity_GA-rich chr1 17364568 17365000 + LINE/L1_L1MB2 chr1 17365001 17365086 - SINE/Alu_FLAM_C chr1 17365087 17365124 + LINE/L1_L1MB2 chr1 17365125 17365449 + SINE/Alu_AluJr chr1 17365450 17365585 + LINE/L1_L1MB2 chr1 17365586 17365884 + SINE/Alu_AluSz chr1 17365885 17366045 + LINE/L1_L1MB2 chr1 17366046 17366344 + SINE/Alu_AluSx chr1 17366435 17366903 + LTR/ERV1_LTR78 chr1 17366878 17367336 - LTR/ERVL-MaLR_MLT1D chr1 17367489 17367512 + LTR/ERV1_LTR78 chr1 17367513 17367913 - LTR/ERVL_LTR16 chr1 17367914 17368055 + LTR/ERV1_LTR78 chr1 17368056 17368236 - LTR/ERV1_LTR38B chr1 17368237 17368548 + SINE/Alu_AluSx1 chr1 17368549 17368759 - LTR/ERV1_LTR38B chr1 17368763 17368781 + Simple_repeat_(ATC)n chr1 17368782 17368901 - SINE/Alu_FLAM_C chr1 17368830 17368912 + Retroposon/SVA_SVA_A chr1 17368900 17369110 - LTR/ERV1_LTR38B chr1 17369165 17369470 - SINE/Alu_AluSx chr1 17369514 17369596 - LINE/L2_L2a chr1 17370173 17370356 + LINE/L1_L1ME4b chr1 17370496 17370668 + LTR/ERV1_LTR38B chr1 17370678 17370850 + LINE/L1_L1ME4b chr1 17370865 17371083 + SINE/Alu_AluJr chr1 17371293 17371383 + DNA/hAT-Charlie_MER5A1 chr1 17371423 17371700 + SINE/Alu_AluJb chr1 17372390 17372412 + Simple_repeat_(GGCA)n chr1 17372521 17372659 + SINE/MIR_MIRb chr1 17373341 17373474 - SINE/MIR_MIR chr1 17373485 17373791 - SINE/Alu_AluSq2 chr1 17373792 17373928 - SINE/MIR_MIRb chr1 17373988 17374167 + DNA/hAT-Charlie_MER5B chr1 17374344 17374633 + SINE/Alu_AluSq chr1 17375071 17375127 + SINE/MIR_MIR chr1 17375638 17375962 + LINE/L2_L2a chr1 17375976 17376272 - SINE/Alu_AluSg chr1 17376305 17376697 - SINE/Alu_AluY chr1 17376838 17376881 + Simple_repeat_(TTTTG)n chr1 17376882 17377036 - SINE/Alu_FRAM chr1 17377080 17377276 - SINE/Alu_AluJr chr1 17377474 17378357 + LINE/L2_L2a chr1 17378599 17378903 - SINE/Alu_AluSx1 chr1 17378948 17379111 - SINE/Alu_AluSx1 chr1 17379112 17379413 - SINE/Alu_AluYe5 chr1 17379414 17379554 - SINE/Alu_AluSx1 chr1 17380188 17380376 - SINE/Alu_AluSx chr1 17380377 17380645 - SINE/Alu_AluY chr1 17381384 17381530 + SINE/MIR_MIRc chr1 17382259 17382340 + SINE/MIR_MIR3 chr1 17382275 17382418 + SINE/MIR_MIRb chr1 17382772 17383082 - SINE/Alu_AluJo chr1 17383566 17383820 + SINE/Alu_AluSx1 chr1 17383868 17384161 + SINE/Alu_AluSx chr1 17384165 17384239 + DNA/hAT-Charlie_MER5A chr1 17384312 17384437 + SINE/Alu_FLAM_C chr1 17384458 17384719 + SINE/Alu_AluSz chr1 17385078 17385143 + SINE/Alu_AluJo chr1 17385198 17385898 - LINE/L2_L2 chr1 17386187 17386302 + SINE/MIR_MIRb chr1 17387162 17387455 + SINE/Alu_AluSx3 chr1 17387467 17387755 + SINE/Alu_AluY chr1 17388129 17388179 + LINE/L2_L2c chr1 17389364 17389682 - LTR/ERVL-MaLR_MLT1J2 chr1 17389996 17390022 - LINE/L2_L2 chr1 17390023 17390316 + SINE/Alu_AluSq chr1 17390319 17390617 + SINE/Alu_AluSx chr1 17390618 17390769 - LINE/L2_L2 chr1 17391219 17391519 - SINE/Alu_AluSx chr1 17391657 17391805 + SINE/Alu_AluSg4 chr1 17392872 17393159 + SINE/Alu_AluSz6 chr1 17393298 17393517 - LINE/L2_L2 chr1 17393534 17393774 - SINE/Alu_AluSz chr1 17393813 17393895 - LINE/L2_L2c chr1 17395197 17395501 - SINE/Alu_AluSx chr1 17396104 17396398 + SINE/Alu_AluSx chr1 17396447 17396565 + DNA/hAT-Charlie_MER5A1 chr1 17396660 17396736 - SINE/MIR_MIR chr1 17398048 17398120 - SINE/MIR_MIRc chr1 17398382 17398537 + SINE/MIR_MIRb chr1 17398649 17398683 + Simple_repeat_(CCCA)n chr1 17399317 17399598 + SINE/Alu_AluSp chr1 17399613 17399754 + SINE/Alu_AluJo chr1 17399755 17400060 + SINE/Alu_AluSz chr1 17400061 17400223 + SINE/Alu_AluJo chr1 17400232 17400500 + SINE/Alu_AluSp chr1 17400502 17400534 + Simple_repeat_(T)n chr1 17403162 17403401 + DNA/hAT-Charlie_Charlie8 chr1 17403463 17403573 + SINE/MIR_MIR chr1 17403652 17403954 + SINE/Alu_AluSz chr1 17403973 17403996 + Simple_repeat_(GGGT)n chr1 17404574 17404858 + SINE/Alu_AluSx chr1 17405262 17405542 + SINE/Alu_AluSz chr1 17405548 17405833 + SINE/Alu_AluSg chr1 17405963 17406024 - LTR/ERV1_HERV-Fc2_LTR chr1 17408774 17408803 + Low_complexity_A-rich chr1 17411295 17411600 + SINE/Alu_AluSx chr1 17413304 17413432 + SINE/Alu_FLAM_A chr1 17413852 17413980 + SINE/Alu_AluSz chr1 17414248 17414561 + SINE/Alu_AluSx chr1 17414626 17414919 - SINE/Alu_AluJb chr1 17415432 17415739 + SINE/Alu_AluSq4 chr1 17415787 17416078 + SINE/Alu_AluSp chr1 17416079 17416097 + Simple_repeat_(G)n chr1 17416824 17417025 + DNA/hAT-Charlie_MER112 chr1 17417386 17417704 + SINE/Alu_AluSx1 chr1 17417828 17418122 + DNA/TcMar-Tigger_MER2 chr1 17418209 17418291 - SINE/Alu_Alu chr1 17418377 17418671 + SINE/Alu_AluSc chr1 17419073 17419357 + SINE/Alu_AluSx1 chr1 17419608 17419662 + SINE/MIR_MIRb chr1 17421225 17421511 + SINE/Alu_AluSc chr1 17421744 17422054 + SINE/Alu_AluSp chr1 17424521 17424849 + LINE/CR1_CR1-16_AMi chr1 17426527 17426562 + Simple_repeat_(CAGCT)n chr1 17426755 17427059 - SINE/Alu_AluSz chr1 17427805 17427845 + Low_complexity_G-rich chr1 17428061 17428188 + SINE/MIR_MIR1_Amn chr1 17428731 17428772 + Simple_repeat_(TTTGACA)n chr1 17428933 17429043 + SINE/MIR_MIRb chr1 17430235 17430502 + SINE/Alu_AluJr chr1 17430506 17430757 + SINE/Alu_AluSx4 chr1 17430764 17430984 - SINE/Alu_AluSx chr1 17431034 17431050 + Simple_repeat_(AT)n chr1 17431051 17431360 + SINE/Alu_AluY chr1 17431361 17431381 + Simple_repeat_(AT)n chr1 17431385 17431516 + SINE/Alu_FLAM_C chr1 17431519 17431543 + Simple_repeat_(AAAG)n chr1 17431816 17432105 + SINE/Alu_AluSq2 chr1 17432812 17432987 + SINE/Alu_AluYk2 chr1 17432990 17433069 + Simple_repeat_(ATAC)n chr1 17434149 17434250 + LTR/ERVL_MER21B chr1 17434264 17434827 + LTR/ERVL_MER21B chr1 17434831 17435101 + SINE/Alu_AluSg chr1 17435668 17435942 + SINE/Alu_AluSq2 chr1 17436232 17436518 + SINE/Alu_AluSz6 chr1 17437103 17437158 + SINE/MIR_MIR1_Amn chr1 17437303 17437387 - DNA/hAT-Charlie_Charlie4z chr1 17437967 17438048 + Simple_repeat_(GCCGCGG)n chr1 17439476 17439497 + Simple_repeat_(A)n chr1 17440310 17440738 - LINE/L2_L2c chr1 17442173 17442491 - SINE/Alu_AluJb chr1 17442508 17442786 - SINE/Alu_AluSg chr1 17442918 17443223 - SINE/Alu_AluJr chr1 17443224 17443315 + DNA/hAT-Charlie_MER33 chr1 17443623 17443734 + LINE/L2_L2d2 chr1 17443752 17443817 - SINE/MIR_MIRc chr1 17443827 17443867 + DNA/hAT-Charlie_MER5A1 chr1 17443868 17444014 + SINE/Alu_AluJb chr1 17444015 17444323 + SINE/Alu_AluSq2 chr1 17444324 17444489 + SINE/Alu_AluJb chr1 17444490 17444535 + DNA/hAT-Charlie_MER5A1 chr1 17444536 17444842 - SINE/Alu_AluSq chr1 17444843 17444894 + DNA/hAT-Charlie_MER5A1 chr1 17444899 17445107 + SINE/Alu_AluSp chr1 17445198 17445500 + SINE/Alu_AluY chr1 17445561 17445601 + LTR/ERVL-MaLR_THE1D chr1 17445602 17445926 - SINE/Alu_AluYf1 chr1 17445934 17446198 - SINE/Alu_AluSz chr1 17446199 17446362 + LTR/ERVL-MaLR_THE1D chr1 17446387 17446437 - LINE/L2_L2b chr1 17446782 17447078 + SINE/Alu_AluSc8 chr1 17447212 17447508 + SINE/Alu_AluSx1 chr1 17447942 17448045 - SINE/MIR_MIRb chr1 17448046 17448430 - LTR/ERVL-MaLR_MLT1B chr1 17448431 17448524 - SINE/MIR_MIRb chr1 17448532 17448810 - SINE/Alu_AluJo chr1 17448841 17448858 + Simple_repeat_(TG)n chr1 17448859 17449142 - SINE/Alu_AluSp chr1 17449406 17449689 + SINE/Alu_AluSx chr1 17449690 17449743 + Simple_repeat_(AAAT)n chr1 17449766 17449866 + snRNA_U13_ chr1 17449898 17450195 + SINE/Alu_AluSz chr1 17450491 17450634 + SINE/Alu_AluJo chr1 17450635 17450946 + SINE/Alu_AluSx1 chr1 17450947 17451124 + SINE/Alu_AluJo chr1 17451256 17451536 - SINE/Alu_AluSg7 chr1 17451600 17451662 - SINE/MIR_MIR3 chr1 17452131 17452427 + SINE/Alu_AluSz chr1 17452430 17452718 + SINE/Alu_AluSg7 chr1 17452773 17452922 - SINE/MIR_MIRc chr1 17452918 17453065 + LINE/L2_L2b chr1 17453072 17453227 + LTR/ERV1_LTR26C chr1 17453228 17453520 - SINE/Alu_AluSx chr1 17453521 17453577 + LTR/ERV1_LTR26C chr1 17453587 17453725 + SINE/Alu_AluSx1 chr1 17453726 17454027 + SINE/Alu_AluY chr1 17454030 17454336 + SINE/Alu_AluSg chr1 17454337 17454630 + SINE/Alu_AluSc8 chr1 17454631 17454785 + SINE/Alu_AluSx1 chr1 17454788 17455100 + SINE/Alu_AluSp chr1 17455101 17455407 + SINE/Alu_AluSg4 chr1 17455408 17455560 + LTR/ERV1_LTR26C chr1 17455747 17455884 + DNA/hAT-Charlie_MER58D chr1 17455885 17456183 + SINE/Alu_AluSz chr1 17456185 17456499 + SINE/Alu_AluY chr1 17456500 17456638 + DNA/hAT-Charlie_MER58D chr1 17456643 17456816 - SINE/Alu_AluJr chr1 17456817 17457109 - SINE/Alu_AluSz chr1 17457110 17457169 - SINE/Alu_AluJr chr1 17457170 17457257 + LINE/L1_L1ME3A chr1 17457258 17457313 + Simple_repeat_(TATC)n chr1 17457314 17457350 + LINE/L1_L1ME3A chr1 17457355 17457662 + SINE/Alu_AluSz6 chr1 17457665 17457962 + SINE/Alu_AluSp chr1 17457976 17458257 + SINE/Alu_AluSq2 chr1 17458258 17458557 - SINE/Alu_AluSp chr1 17458580 17458737 - LINE/L1_L1PA4 chr1 17458981 17459053 + LINE/L2_L2c chr1 17459078 17459267 - SINE/MIR_MIRc chr1 17459316 17459613 + SINE/Alu_AluSx chr1 17459730 17459902 - SINE/Alu_AluSp chr1 17459903 17460195 - SINE/Alu_AluSx chr1 17460223 17460345 - SINE/MIR_MIRb chr1 17460977 17461233 + SINE/Alu_AluSx1 chr1 17461247 17461557 + SINE/Alu_AluY chr1 17461607 17461661 - LINE/L1_L1M5 chr1 17461662 17461971 - SINE/Alu_AluSx3 chr1 17461972 17462010 - LINE/L1_L1M5 chr1 17462115 17462257 + SINE/Alu_AluSx1 chr1 17462258 17462554 + SINE/Alu_AluY chr1 17462555 17462722 + SINE/Alu_AluSx1 chr1 17462805 17463072 - SINE/Alu_AluJb chr1 17463098 17463398 - SINE/Alu_AluSx chr1 17463425 17463767 - LTR/ERV1_MER4E1 chr1 17463768 17464062 + SINE/Alu_AluSp chr1 17464063 17464470 - LTR/ERV1_MER4E1 chr1 17464471 17464772 + SINE/Alu_AluY chr1 17464773 17464789 - LTR/ERV1_MER4E1 chr1 17464792 17464885 - SINE/MIR_MIR chr1 17464903 17465205 + SINE/Alu_AluSq chr1 17465285 17465720 - LTR/ERV1_MER34A1 chr1 17465721 17466031 - SINE/Alu_AluSg chr1 17466114 17466422 - SINE/Alu_AluSp chr1 17466425 17466526 - LTR/ERV1_MER34A1 chr1 17466661 17466692 + SINE/MIR_MIR1_Amn chr1 17466693 17466993 - SINE/Alu_AluSx chr1 17466994 17467111 + SINE/MIR_MIR chr1 17467120 17467133 - SINE/Alu_AluSx chr1 17467134 17467415 + SINE/Alu_AluSx chr1 17467416 17467455 + Simple_repeat_(AAT)n chr1 17467456 17467582 - SINE/Alu_AluJb chr1 17467817 17468130 - SINE/Alu_AluSx chr1 17468154 17468214 - LINE/L1_L1ME2 chr1 17468215 17468512 - SINE/Alu_AluSz chr1 17468513 17468683 - LINE/L1_L1ME2 chr1 17468684 17468990 - SINE/Alu_AluSz chr1 17468991 17469292 - SINE/Alu_AluSx chr1 17469363 17469545 - LTR/ERV1_MER34C_ chr1 17469546 17469845 + SINE/Alu_AluSx1 chr1 17469853 17470153 + SINE/Alu_AluSx1 chr1 17470154 17470521 - LTR/ERV1_MER34C_ chr1 17470526 17470840 - LINE/L1_L1ME2 chr1 17470841 17471145 - SINE/Alu_AluJr chr1 17471146 17471331 - LINE/L1_L1M5 chr1 17471342 17471618 + SINE/Alu_AluJr chr1 17471621 17471894 - SINE/Alu_AluJo chr1 17471926 17472227 + SINE/Alu_AluSx4 chr1 17472279 17472307 + Simple_repeat_(TTA)n chr1 17472308 17472591 - SINE/Alu_AluSx1 chr1 17472598 17472879 - SINE/Alu_AluSz chr1 17472880 17473340 + LTR/ERV1_MER41A chr1 17473367 17473662 + SINE/Alu_AluSx1 chr1 17473716 17473886 - SINE/Alu_AluJr chr1 17473897 17473968 - LINE/L1_L1ME2 chr1 17473972 17474281 - SINE/Alu_AluSx4 chr1 17474285 17474476 - SINE/Alu_AluSx3 chr1 17474477 17474534 - LINE/L1_L1ME2 chr1 17474479 17474545 - LINE/L1_L1MC chr1 17474547 17474719 - SINE/Alu_AluSx chr1 17474726 17474773 - LINE/L1_L1ME2 chr1 17474774 17475068 - SINE/Alu_AluSx4 chr1 17475069 17475197 - LINE/L1_L1ME2 chr1 17475304 17475385 + SINE/Alu_AluJr chr1 17475386 17475685 + SINE/Alu_AluSq2 chr1 17475686 17475896 + SINE/Alu_AluJr chr1 17476047 17476424 - LINE/L1_L1ME2 chr1 17476443 17476747 - SINE/Alu_AluSx chr1 17476779 17477035 - SINE/Alu_AluSz chr1 17477105 17477405 + SINE/Alu_AluSc chr1 17477847 17478041 - SINE/Alu_AluSx chr1 17478045 17478357 - SINE/Alu_AluSx chr1 17478364 17478679 - SINE/Alu_AluSx4 chr1 17478691 17478999 - SINE/Alu_AluSp chr1 17479000 17479145 + LTR/ERV1_LTR56 chr1 17479152 17479248 - SINE/Alu_AluSx3 chr1 17479249 17479511 + SINE/Alu_AluSx1 chr1 17479538 17479661 - LTR/ERV1_MER31B chr1 17479671 17479846 - LTR/ERV1_MER34A1 chr1 17479847 17480158 + SINE/Alu_AluJb chr1 17480159 17480204 - LTR/ERV1_MER34A1 chr1 17480205 17480511 - SINE/Alu_AluSq2 chr1 17480512 17480523 - LTR/ERV1_MER34A1 chr1 17480524 17480819 - SINE/Alu_AluSx4 chr1 17480820 17481059 - LTR/ERV1_MER34A1 chr1 17481060 17481367 - SINE/Alu_AluSx chr1 17481368 17481513 - LTR/ERV1_MER34A1 chr1 17481656 17481955 - SINE/Alu_AluSp chr1 17481995 17482215 + LTR/ERVL-MaLR_THE1C chr1 17482216 17482521 - SINE/Alu_AluSg4 chr1 17482522 17482786 - SINE/Alu_AluSg chr1 17482795 17483088 - SINE/Alu_AluSp chr1 17483089 17483257 + LTR/ERVL-MaLR_THE1C chr1 17483393 17483566 - SINE/Alu_AluSz chr1 17483567 17483857 - SINE/Alu_AluSc chr1 17483858 17483984 - SINE/Alu_AluSz chr1 17484068 17484495 - LINE/L2_L2a chr1 17484496 17484643 - SINE/Alu_AluSx3 chr1 17484644 17484941 - SINE/Alu_AluSc chr1 17484942 17484952 - SINE/Alu_AluSx3 chr1 17484953 17485260 - SINE/Alu_AluY chr1 17485261 17485403 - SINE/Alu_AluSx3 chr1 17485404 17485863 - LINE/L2_L2a chr1 17485865 17486177 + SINE/Alu_AluSc chr1 17486253 17486360 - SINE/MIR_MIR3 chr1 17486373 17486708 + DNA/hAT-Charlie_MER1B chr1 17486713 17487026 + LTR/ERVL-MaLR_MLT1A0 chr1 17487151 17487317 - SINE/Alu_AluSx3 chr1 17487320 17487631 - SINE/Alu_AluSx chr1 17487632 17487933 - SINE/Alu_AluSq chr1 17487935 17488151 - LTR/ERV1_LTR8A chr1 17488152 17488455 + SINE/Alu_AluSp chr1 17488456 17488588 - LTR/ERV1_LTR8A chr1 17488590 17488894 - SINE/Alu_AluSx1 chr1 17488898 17489042 - DNA/hAT-Charlie_MER20 chr1 17489367 17489442 + SINE/MIR_MIRb chr1 17489872 17490170 - SINE/Alu_AluSg chr1 17490173 17490453 - SINE/Alu_AluSz6 chr1 17490482 17490508 + LTR/ERV1_LTR8A chr1 17490509 17490834 + SINE/Alu_AluSp chr1 17490835 17491161 + LTR/ERV1_LTR8A chr1 17491162 17491320 - SINE/Alu_AluSg4 chr1 17491325 17491618 - SINE/Alu_AluSx1 chr1 17491619 17491922 - SINE/Alu_AluSx1 chr1 17491936 17492128 + LTR/ERV1_MER61-int chr1 17492129 17492405 + SINE/Alu_AluSx chr1 17492406 17492870 + LTR/ERV1_MER61-int chr1 17492999 17493066 - Retroposon/SVA_SVA_A chr1 17493067 17493205 + LTR/ERV1_MER61-int chr1 17493206 17493508 + SINE/Alu_AluSx3 chr1 17493550 17493757 + LINE/L2_L2b chr1 17493760 17494063 - SINE/Alu_AluJr chr1 17494167 17494249 + LINE/L2_L2b chr1 17494292 17494364 + LINE/L2_L2b chr1 17494547 17494713 + SINE/MIR_MIRb chr1 17494718 17494895 + SINE/MIR_MIR chr1 17494997 17495290 - SINE/Alu_AluSg chr1 17495293 17495590 - SINE/Alu_AluSx1 chr1 17495595 17495893 - SINE/Alu_AluSx1 chr1 17495897 17496026 - SINE/Alu_AluJr4 chr1 17496035 17496335 - SINE/Alu_AluSp chr1 17496357 17496642 + LTR/ERV1_MER39 chr1 17496664 17496974 - SINE/Alu_AluSz6 chr1 17496981 17497281 - SINE/Alu_AluSx chr1 17497302 17497791 - LINE/L1_L1PB3 chr1 17497792 17498061 - SINE/Alu_AluSx chr1 17498062 17498134 - LINE/L1_L1PB3 chr1 17498135 17498591 + LTR/ERVK_LTR22A chr1 17498592 17499292 - LINE/L1_L1PB3 chr1 17499293 17499598 - SINE/Alu_AluSx1 chr1 17499599 17499908 - LINE/L1_L1PB3 chr1 17499909 17500082 - SINE/Alu_AluSc chr1 17500083 17500386 - SINE/Alu_AluY chr1 17500387 17500397 - SINE/Alu_AluSc chr1 17500398 17500580 - LINE/L1_L1M4 chr1 17500581 17500894 - SINE/Alu_AluSz6 chr1 17500895 17500935 - LINE/L1_L1M4 chr1 17500936 17501069 + SINE/Alu_FLAM_C chr1 17501070 17501362 - LINE/L1_L1M4 chr1 17501363 17501508 + LTR/ERV1_MER39 chr1 17501517 17501694 + SINE/Alu_AluSx chr1 17501695 17501738 + Simple_repeat_(AC)n chr1 17501739 17501749 + SINE/Alu_AluSx chr1 17501887 17502193 - SINE/Alu_AluSg chr1 17502283 17502399 - SINE/MIR_MIRb chr1 17503066 17503446 + LTR/ERVL-MaLR_MLT1H chr1 17503501 17503808 - SINE/Alu_AluYj4 chr1 17503809 17503925 + SINE/MIR_MIRb chr1 17503933 17503979 - LTR/ERVL-MaLR_MLT1I chr1 17503980 17504279 + SINE/Alu_AluSx chr1 17504280 17504320 - LTR/ERVL-MaLR_MLT1I chr1 17504321 17504454 + SINE/Alu_AluSg chr1 17504455 17504757 + SINE/Alu_AluSg chr1 17504758 17504923 + SINE/Alu_AluSg chr1 17504926 17505234 + SINE/Alu_AluSc8 chr1 17505235 17505379 - LTR/ERVL-MaLR_MLT1I chr1 17505576 17505875 - SINE/Alu_AluSx1 chr1 17505907 17506111 - SINE/MIR_MIRb chr1 17506112 17506402 - SINE/Alu_AluSp chr1 17506403 17506425 - SINE/MIR_MIR3 chr1 17506541 17506672 + SINE/Alu_FLAM_C chr1 17506905 17506972 + Simple_repeat_(TTTT)n chr1 17506979 17507056 - LINE/L1_L1PA3 chr1 17507085 17507227 - SINE/MIR_MIR chr1 17507284 17507310 + Simple_repeat_(CA)n chr1 17507312 17507402 - SINE/MIR_MIRc chr1 17507409 17507569 + SINE/Alu_AluSx1 chr1 17507571 17507795 - SINE/Alu_AluSz chr1 17507805 17507951 + SINE/MIR_MIR chr1 17508546 17508835 - SINE/Alu_AluSz chr1 17509108 17509417 - SINE/Alu_AluSx1 chr1 17510137 17510249 + SINE/MIR_MIRc chr1 17510250 17510372 + SINE/Alu_FLAM_C chr1 17510485 17510647 - SINE/Alu_AluSx chr1 17510961 17511159 - LINE/L2_L2a chr1 17511287 17511473 - SINE/MIR_MIRb chr1 17511974 17512092 + SINE/MIR_MIR chr1 17512122 17512414 - SINE/Alu_AluSx1 chr1 17512424 17512712 - SINE/Alu_AluJr chr1 17512713 17512745 + SINE/MIR_MIRc chr1 17512747 17513051 + SINE/Alu_AluSz6 chr1 17513265 17513548 - DNA/TcMar-Mariner_MARNA chr1 17513796 17514123 - SINE/Alu_AluSp chr1 17514126 17514434 - SINE/Alu_AluSx1 chr1 17514639 17514746 + DNA/hAT-Tip100_MamTip1b chr1 17515005 17515129 + SINE/MIR_MIRc chr1 17515130 17515253 + DNA/hAT-Charlie_MER20 chr1 17515282 17515599 - SINE/Alu_AluSx chr1 17515635 17515936 - SINE/Alu_AluSz6 chr1 17516908 17517036 - SINE/MIR_MIR chr1 17517155 17517322 + SINE/MIR_MIRb chr1 17517323 17517603 - SINE/Alu_AluSz6 chr1 17517604 17517636 + SINE/MIR_MIRc chr1 17517656 17517962 - SINE/Alu_AluSx1 chr1 17517992 17518215 - DNA/hAT-Charlie_MER58A chr1 17518510 17518832 + SINE/Alu_AluSx chr1 17518889 17519185 + SINE/Alu_AluSp chr1 17519195 17519236 - SINE/MIR_MIRb chr1 17519237 17519329 - SINE/MIR_MIR chr1 17519333 17519635 + SINE/Alu_AluJb chr1 17519638 17519861 + SINE/Alu_AluSx1 chr1 17519862 17519964 - SINE/MIR_MIRb chr1 17520778 17520970 - SINE/MIR_MIR3 chr1 17521085 17521122 - LINE/CR1_L3 chr1 17521126 17521184 - LINE/CR1_CR1-3_Croc chr1 17521185 17521210 + Simple_repeat_(TGTTT)n chr1 17521211 17521482 - SINE/Alu_AluSp chr1 17521483 17521512 - LINE/CR1_CR1-3_Croc chr1 17521535 17521776 - SINE/MIR_MIR chr1 17522125 17522167 + Simple_repeat_(GGAGTG)n chr1 17522304 17522502 - SINE/MIR_MIRc chr1 17522958 17523052 + SINE/Alu_AluJr chr1 17523074 17523388 - SINE/Alu_AluSx1 chr1 17523406 17523562 - SINE/MIR_MIRb chr1 17523604 17523703 - SINE/MIR_MIR3 chr1 17523717 17523825 + SINE/MIR_MIR3 chr1 17524002 17524324 + SINE/Alu_AluSx chr1 17524330 17524364 + SINE/Alu_AluJb chr1 17524365 17524421 + Simple_repeat_(AC)n chr1 17524422 17524579 + SINE/Alu_AluJb chr1 17524602 17524745 - SINE/MIR_MIRc chr1 17525179 17525200 + Simple_repeat_(GGGA)n chr1 17525366 17525492 + SINE/MIR_MIR3 chr1 17525521 17525686 + SINE/Alu_AluJb chr1 17525721 17526030 + SINE/Alu_AluSz chr1 17526391 17526557 + SINE/MIR_MIRb chr1 17526622 17526929 + SINE/Alu_AluSz6 chr1 17527858 17528159 - SINE/Alu_AluSz6 chr1 17528211 17528510 - SINE/Alu_AluJr chr1 17528534 17528638 + Simple_repeat_(CATC)n chr1 17528639 17528776 - LINE/L2_L2a chr1 17528807 17528947 + SINE/MIR_MIR chr1 17528950 17528998 + Simple_repeat_(TGA)n chr1 17529001 17529302 - SINE/Alu_AluSz chr1 17529825 17530132 - SINE/Alu_AluSq chr1 17530141 17530218 + LINE/L2_L2a chr1 17530267 17530448 + SINE/MIR_MIRb chr1 17530618 17530733 + SINE/MIR_MIRb chr1 17530734 17531034 + SINE/Alu_AluSx1 chr1 17531035 17531133 + SINE/MIR_MIRb chr1 17531444 17531607 + SINE/MIR_MIRb chr1 17532595 17532890 - SINE/Alu_AluSx chr1 17533078 17533133 - LTR/ERVL-MaLR_MLT1M chr1 17533186 17533333 + SINE/MIR_MIR chr1 17533346 17533464 - SINE/Alu_AluJr chr1 17533953 17534128 - SINE/Alu_AluSx1 chr1 17534129 17534421 - SINE/Alu_AluY chr1 17534422 17534560 - SINE/Alu_AluSx1 chr1 17534584 17534888 - SINE/Alu_AluSx chr1 17534912 17535437 - DNA/hAT-Charlie_MER1A chr1 17535449 17535561 + SINE/MIR_MIR1_Amn chr1 17535808 17536140 - LINE/L2_L2b chr1 17536198 17536495 + SINE/Alu_AluJb chr1 17536715 17536870 - DNA/hAT-Charlie_MER102a chr1 17536871 17537038 - SINE/Alu_AluJr chr1 17537039 17537072 + Simple_repeat_(TTTTA)n chr1 17537073 17537192 - SINE/Alu_AluJr chr1 17537193 17537322 - DNA/hAT-Charlie_MER102a chr1 17537406 17537690 - LINE/L2_L2d chr1 17537735 17538085 + SINE/Alu_AluJo chr1 17538316 17538406 + LINE/L2_L2a chr1 17538465 17538513 - LINE/L2_L2a chr1 17538551 17538699 + LINE/L2_L2a chr1 17538966 17539153 + SINE/MIR_MIRb chr1 17539389 17539429 + Low_complexity_G-rich chr1 17540050 17540106 + Simple_repeat_(GTGGCC)n chr1 17541342 17541431 + LINE/L2_L2a chr1 17541549 17541685 + SINE/Alu_AluSz chr1 17541686 17541989 + SINE/Alu_AluSq2 chr1 17541990 17542168 + SINE/Alu_AluSz chr1 17542171 17542450 + SINE/Alu_AluJr chr1 17542495 17542550 - LINE/L2_L2b chr1 17542675 17542895 - SINE/MIR_MIRc chr1 17542897 17542984 - LINE/L2_L2c chr1 17543194 17543300 + DNA/hAT-Blackjack_MER63D chr1 17543301 17543594 + SINE/Alu_AluSq2 chr1 17543595 17543635 + DNA/hAT-Blackjack_MER63D chr1 17543644 17543946 - SINE/Alu_AluSx1 chr1 17543947 17543958 + Simple_repeat_(AT)n chr1 17543959 17544252 - SINE/Alu_AluSq chr1 17544253 17544296 + Simple_repeat_(AT)n chr1 17544297 17544594 - SINE/Alu_AluSp chr1 17544595 17544624 + Simple_repeat_(AT)n chr1 17544734 17545088 - LTR/ERVL-MaLR_THE1B chr1 17545093 17545120 + Simple_repeat_(GT)n chr1 17545125 17545283 + DNA/hAT-Blackjack_MER63B chr1 17545857 17545899 + LINE/L2_L2c chr1 17545920 17545963 - LINE/L2_L2c chr1 17546194 17546295 + DNA/TcMar-Tigger_Tigger19a chr1 17546408 17546594 - SINE/MIR_MIRb chr1 17547526 17547668 - DNA/hAT?_MER121B chr1 17547711 17547839 - LINE/L1_L1ME4a chr1 17547856 17547923 - LINE/L2_L2a chr1 17547917 17548144 + SINE/MIR_MIR chr1 17548147 17548646 - LTR/ERV1_MER34A chr1 17548647 17548948 - SINE/Alu_AluSz chr1 17548949 17548976 - LTR/ERV1_MER34A chr1 17548979 17549018 - LINE/L2_L2a chr1 17549019 17549322 - SINE/Alu_AluSx1 chr1 17549323 17549684 - LINE/L2_L2a chr1 17549812 17549968 - LINE/L2_L2d chr1 17549958 17550360 - LINE/L2_L2a chr1 17550361 17550670 + SINE/Alu_AluSq2 chr1 17550671 17550870 - LINE/L2_L2a chr1 17550896 17551084 - SINE/MIR_MIRb chr1 17551197 17551310 - LINE/L2_L2d2 chr1 17551761 17551834 - SINE/MIR_MIRc chr1 17551993 17552028 + Low_complexity_G-rich chr1 17552175 17552309 + SINE/MIR_MIRc chr1 17552333 17552400 - SINE/MIR_MIR chr1 17552401 17552571 - SINE/Alu_AluSz chr1 17552572 17552603 + Simple_repeat_(T)n chr1 17552604 17552723 - SINE/Alu_AluSz chr1 17552724 17552881 - SINE/MIR_MIR chr1 17553090 17553160 - SINE/MIR_MIRc chr1 17553167 17553360 + SINE/MIR_MIR3 chr1 17553508 17553802 + SINE/Alu_AluJo chr1 17553824 17554024 - SINE/MIR_MIRb chr1 17554409 17554526 - SINE/MIR_MIR3 chr1 17554576 17554890 - SINE/Alu_AluJb chr1 17555034 17555137 + SINE/MIR_MIRc chr1 17555139 17555260 + DNA/hAT-Charlie_MER103C chr1 17555369 17555654 - SINE/Alu_AluSx chr1 17556305 17556474 + SINE/MIR_MIR3 chr1 17556652 17556844 - DNA/hAT-Charlie_MER5A chr1 17556876 17557057 + SINE/Alu_FAM chr1 17557061 17557374 + SINE/Alu_AluSz chr1 17557499 17557638 - DNA/hAT-Charlie_MER5A chr1 17557987 17558111 + Simple_repeat_(CATC)n chr1 17558112 17558214 + Simple_repeat_(TCCA)n chr1 17558215 17558292 + Simple_repeat_(CATC)n chr1 17558302 17558597 - LINE/L2_L2b chr1 17559072 17559291 + SINE/MIR_MIR chr1 17559378 17559529 + SINE/MIR_MIRb chr1 17560163 17560358 - SINE/MIR_MIRb chr1 17560573 17560864 - SINE/Alu_AluSp chr1 17560867 17560949 - LINE/L2_L2c chr1 17560950 17561167 + SINE/MIR_MIRb chr1 17561663 17561688 + Simple_repeat_(TGCCC)n chr1 17561745 17561773 + Simple_repeat_(CTTTTC)n chr1 17561918 17562024 + SINE/MIR_MIRb chr1 17562027 17562103 - LINE/L2_L2a chr1 17562133 17562435 + SINE/Alu_AluJb chr1 17562443 17562552 + SINE/MIR_MIRb chr1 17562790 17562848 + LINE/L2_L2c chr1 17563203 17563232 + LINE/L2_L2b chr1 17563233 17563532 - SINE/Alu_AluJb chr1 17563533 17563632 + LINE/L2_L2b chr1 17563675 17563793 - SINE/MIR_MIRb chr1 17563804 17563900 + LINE/CR1_L3 chr1 17564564 17564810 + SINE/MIR_MIR chr1 17564997 17565319 - LTR/ERVL-MaLR_MLT1A0 chr1 17565852 17565879 + Simple_repeat_(CCCT)n chr1 17565880 17565984 - LINE/L2_L2c chr1 17566163 17566374 - DNA/hAT-Charlie_MER20 chr1 17567375 17567667 - SINE/Alu_AluSz chr1 17568217 17568267 - LINE/L2_L2b chr1 17568235 17568380 - SINE/MIR_MIRb chr1 17568289 17568458 - SINE/MIR_MIR1_Amn chr1 17569034 17569151 + SINE/MIR_MIRc chr1 17570119 17571545 - LINE/L2_L2a chr1 17571546 17571833 - SINE/Alu_AluSx1 chr1 17571834 17571871 - LINE/L2_L2a chr1 17572525 17572602 - SINE/MIR_MIRb chr1 17572654 17572777 + LINE/L2_L2b chr1 17573109 17573244 + SINE/MIR_MIRb chr1 17573573 17573615 + Low_complexity_GA-rich chr1 17574152 17574282 + SINE/MIR_MIRb chr1 17575273 17575347 + DNA/hAT-Charlie_MER5B chr1 17575352 17575730 - LINE/L2_L2c chr1 17576637 17576860 - SINE/MIR_MIRc chr1 17576876 17577007 + SINE/MIR_MIR3 chr1 17577037 17577345 - SINE/Alu_AluSz chr1 17577346 17577482 - SINE/MIR_MIR chr1 17577496 17577597 - LINE/RTE-BovB_MamRTE1 chr1 17577694 17577883 + SINE/MIR_MIRb chr1 17578416 17578674 + SINE/Alu_AluJr chr1 17578757 17579018 + LINE/L1_L1ME4b chr1 17579019 17579318 - SINE/Alu_AluSz chr1 17579319 17579368 + LINE/L1_L1ME4b chr1 17579378 17579591 + SINE/MIR_MIR chr1 17580708 17580812 + SINE/MIR_MIR chr1 17581007 17581033 + Simple_repeat_(AAAAAG)n chr1 17581034 17581339 + SINE/Alu_AluSz chr1 17581340 17581351 + Simple_repeat_(AAAAAG)n chr1 17581353 17581429 + DNA/hAT_MER53 chr1 17581540 17581726 + SINE/MIR_MIRb chr1 17582580 17582808 + LINE/L2_L2b chr1 17582945 17583226 + SINE/Alu_AluJb chr1 17583354 17583439 + Simple_repeat_(TAA)n chr1 17583446 17583661 - SINE/MIR_MIR chr1 17583861 17584022 - SINE/Alu_AluJr chr1 17584038 17584344 - SINE/Alu_AluJr chr1 17584394 17584818 - LINE/L2_L2a chr1 17584858 17584880 + Simple_repeat_(CA)n chr1 17585248 17585341 + SINE/MIR_MIR3 chr1 17585815 17585906 - LINE/L2_L2c chr1 17585928 17585966 - SINE/MIR_MIR chr1 17585967 17586186 + DNA/hAT-Charlie_MER20 chr1 17586187 17586373 - SINE/MIR_MIR chr1 17587062 17587102 + Simple_repeat_(CA)n chr1 17588698 17588826 + SINE/MIR_MIRb chr1 17588850 17588899 + Simple_repeat_(GT)n chr1 17589050 17589195 + SINE/MIR_MIR chr1 17590077 17590193 - SINE/MIR_MIRc chr1 17590499 17590641 + LINE/L2_L2a chr1 17590705 17591012 + SINE/Alu_AluSx1 chr1 17591163 17591213 - LINE/RTE-BovB_MamRTE1 chr1 17591205 17591238 - SINE/MIR_MIRb chr1 17591239 17591408 - SINE/Alu_AluJr chr1 17591409 17591711 - SINE/Alu_AluSq chr1 17591712 17591847 - SINE/Alu_AluJr chr1 17591848 17592015 - SINE/MIR_MIRb chr1 17593240 17593304 - SINE/MIR_MIRc chr1 17593305 17593805 + LTR/ERVL-MaLR_MLT1D chr1 17593806 17593927 - SINE/MIR_MIRc chr1 17594140 17594375 - SINE/MIR_MIR chr1 17594390 17594477 + SINE/MIR_MIRb chr1 17594478 17594784 - SINE/Alu_AluSx1 chr1 17594785 17594879 + SINE/MIR_MIRb chr1 17595027 17595220 - LINE/L1_L1ME4a chr1 17595221 17595245 + Simple_repeat_(T)n chr1 17595246 17595277 - LINE/L1_L1ME4a chr1 17595765 17596060 - SINE/MIR_MIRb chr1 17596071 17596203 + DNA/hAT-Charlie_MER5B chr1 17596204 17596241 + SINE/MIR_MIR3 chr1 17596759 17596928 + SINE/Alu_AluSx chr1 17597106 17597143 + Simple_repeat_(CTGCAT)n chr1 17597606 17597704 + SINE/MIR_MIR chr1 17598036 17598102 - LTR/ERVL-MaLR_MLT1A0 chr1 17598103 17598395 - SINE/Alu_AluSz chr1 17598396 17598715 - LTR/ERVL-MaLR_MLT1A0 chr1 17598885 17599057 + SINE/MIR_MIRb chr1 17599082 17599361 + SINE/Alu_AluSx chr1 17599727 17599817 - SINE/tRNA-RTE_MamSINE1 chr1 17599822 17600003 - DNA/hAT-Charlie_MER3 chr1 17600024 17600141 + SINE/MIR_MIRb chr1 17600189 17600221 + LINE/L2_L2b chr1 17600426 17600772 - LINE/L1_L1MD3 chr1 17600773 17601079 + SINE/Alu_AluSz chr1 17601080 17601414 - LINE/L1_L1MD3 chr1 17601443 17601745 - SINE/Alu_AluSz chr1 17602530 17602935 - LINE/L2_L2b chr1 17603759 17603908 + SINE/MIR_MIRc chr1 17604214 17604282 - SINE/MIR_MIR1_Amn chr1 17604288 17604425 - SINE/MIR_MIRc chr1 17604490 17604540 - LINE/L2_L2c chr1 17605210 17605321 + SINE/MIR_MIRb chr1 17605329 17605434 + SINE/MIR_MIRb chr1 17605620 17605694 + SINE/MIR_MIRc chr1 17606129 17606241 - SINE/MIR_MIRb chr1 17606282 17606593 - SINE/Alu_AluSx chr1 17606629 17606858 - SINE/MIR_MIRc chr1 17607193 17607484 + SINE/Alu_AluJr chr1 17608030 17608065 + Low_complexity_G-rich chr1 17608200 17608379 - DNA/hAT-Charlie_MER117 chr1 17608772 17609082 - SINE/Alu_AluSz6 chr1 17609409 17609563 - LINE/L2_L2b chr1 17610127 17610253 - DNA/hAT-Charlie_MER5A chr1 17610941 17611026 - SINE/tRNA-RTE_MamSINE1 chr1 17611343 17611374 - SINE/MIR_MIRc chr1 17611375 17611494 - SINE/MIR_MIRb chr1 17611534 17611598 + Simple_repeat_(ATCC)n chr1 17611630 17611851 - SINE/MIR_MIR chr1 17611978 17612012 + Simple_repeat_(TCCA)n chr1 17612031 17612106 + Simple_repeat_(CATC)n chr1 17612107 17612305 + DNA/hAT-Blackjack_MER63A chr1 17612306 17612633 + Simple_repeat_(CATC)n chr1 17612640 17612737 - LINE/L2_L2b chr1 17613445 17613575 - DNA/hAT-Charlie_MER5A chr1 17613765 17614005 + SINE/MIR_MIR chr1 17614072 17614181 - SINE/MIR_MIRb chr1 17615025 17615223 - DNA/hAT-Charlie_MER3 chr1 17615602 17615828 - SINE/MIR_MIRc chr1 17616556 17616893 - LINE/L2_L2b chr1 17616900 17617086 - SINE/MIR_MIR chr1 17617072 17617242 - LINE/L2_L2b chr1 17617253 17617360 - LINE/L2_L2a chr1 17617405 17617546 - LINE/L2_L2c chr1 17617632 17617781 + SINE/MIR_MIRb chr1 17617864 17618075 + SINE/MIR_MIRb chr1 17618490 17618520 + Simple_repeat_(CCT)n chr1 17619907 17620223 + SINE/Alu_AluSx3 chr1 17620391 17620476 + DNA/hAT-Charlie_MER20 chr1 17620572 17620609 + Simple_repeat_(GGCCA)n chr1 17620822 17620964 + DNA/hAT-Charlie_MER5B chr1 17621037 17621231 + LINE/CR1_L3 chr1 17621232 17621532 - SINE/Alu_AluSx chr1 17621533 17621631 + LINE/CR1_L3 chr1 17622523 17622634 - LTR/ERVL_LTR79 chr1 17622813 17622888 - LTR/ERVL_LTR79 chr1 17623556 17623722 + SINE/MIR_MIR3 chr1 17624116 17624252 + SINE/MIR_MIR3 chr1 17626647 17627127 - LINE/L1_L1MB3 chr1 17627525 17627576 + Simple_repeat_(CCTGCC)n chr1 17627762 17627926 + SINE/MIR_MIR chr1 17628056 17628358 + SINE/Alu_AluSx chr1 17628680 17628771 + LINE/CR1_L3 chr1 17628795 17628895 - SINE/MIR_MIRc chr1 17628966 17629009 + DNA/hAT-Charlie_Charlie8 chr1 17629010 17629326 - SINE/Alu_AluJo chr1 17629327 17629488 + DNA/hAT-Charlie_Charlie8 chr1 17630614 17630782 + SINE/MIR_MIRb chr1 17631678 17631828 + DNA/hAT-Tip100_MER45A chr1 17631830 17631994 - SINE/MIR_MIRb chr1 17632204 17632251 + SINE/MIR_MIR3 chr1 17632744 17632832 - LINE/CR1_Plat_L3 chr1 17633129 17633182 + Low_complexity_G-rich chr1 17633339 17633647 - SINE/Alu_AluSx chr1 17633995 17634057 - LINE/L2_L2c chr1 17634092 17634127 + Simple_repeat_(CTCA)n chr1 17634236 17634370 + SINE/MIR_MIRb chr1 17634576 17634617 + Simple_repeat_(GGGGCTCT)n chr1 17635446 17635755 + LINE/L2_L2b chr1 17636238 17636597 - DNA/hAT-Charlie_Charlie29a chr1 17636903 17637227 + LINE/L2_L2a chr1 17637501 17637803 - SINE/Alu_AluY chr1 17638831 17638890 + LINE/L2_L2b chr1 17639066 17639141 + LINE/L2_L2b chr1 17639181 17639334 - SINE/MIR_MIRb chr1 17639720 17639743 + Simple_repeat_(ATTC)n chr1 17639749 17639843 - LINE/L2_L2c chr1 17640400 17640546 + SINE/MIR_MIR3 chr1 17640547 17640772 + LINE/L1_L1MB7 chr1 17640773 17640808 + SINE/MIR_MIR3 chr1 17640881 17641108 - SINE/MIR_MIRb chr1 17641350 17641623 + SINE/Alu_AluSc8 chr1 17641624 17641666 + Simple_repeat_(AAAAC)n chr1 17641697 17642010 + SINE/Alu_AluSx chr1 17642037 17642223 - LINE/L2_L2a chr1 17642261 17642585 - LINE/L2_L2a chr1 17643343 17643512 + SINE/MIR_MIR3 chr1 17643548 17643776 + SINE/MIR_MIR chr1 17644449 17644593 + SINE/MIR_MIR3 chr1 17644825 17645016 - SINE/MIR_MIRb chr1 17645574 17645621 + LINE/L2_L2d chr1 17645979 17646035 + LINE/L2_L2d chr1 17646801 17647013 - LINE/L2_L2 chr1 17647169 17647418 + SINE/MIR_MIRb chr1 17647542 17647837 - LINE/L2_L2a chr1 17648158 17648384 - SINE/MIR_MIRc chr1 17648775 17648873 + SINE/MIR_MIR3 chr1 17649080 17649293 - SINE/MIR_MIRb chr1 17649542 17650127 - LINE/L2_L2c chr1 17650128 17650438 + SINE/Alu_AluJr chr1 17650451 17650755 + SINE/Alu_AluSz chr1 17650832 17651240 - DNA/hAT-Tip100_MER96B chr1 17652407 17652544 + SINE/MIR_MIRb chr1 17652983 17653089 + SINE/MIR_MIR chr1 17653086 17653290 - SINE/MIR_MIRb chr1 17653712 17653871 + SINE/MIR_MIR3 chr1 17654253 17654339 + SINE/MIR_MIR3 chr1 17654511 17654573 - DNA/hAT-Charlie_MER5A chr1 17654835 17654902 - LINE/L2_L2b chr1 17655241 17655296 + Simple_repeat_(ATCC)n chr1 17655298 17655484 + Simple_repeat_(CCAT)n chr1 17655485 17655567 - LINE/L2_L2a chr1 17656939 17657172 + SINE/MIR_MIR chr1 17657941 17658331 - LTR/ERVL-MaLR_MLT1C chr1 17658383 17658457 - SINE/MIR_MIR3 chr1 17658604 17659005 - LINE/L2_L2a chr1 17659135 17659181 + Simple_repeat_(CTGG)n chr1 17659913 17659989 - SINE/MIR_MIR3 chr1 17660036 17660060 + Simple_repeat_(TCAT)n chr1 17660278 17660448 + SINE/MIR_MIR chr1 17660929 17661049 - SINE/MIR_MIR3 chr1 17661096 17661377 - SINE/Alu_AluSx chr1 17661898 17662092 + SINE/MIR_MIRb chr1 17662117 17662278 + SINE/MIR_MIR chr1 17662293 17662502 - SINE/MIR_MIRb chr1 17663273 17663580 + SINE/Alu_AluSx1 chr1 17663683 17663900 - SINE/MIR_MIR1_Amn chr1 17663901 17664158 + LINE/L2_L2c chr1 17664160 17664233 + Low_complexity_GA-rich chr1 17665372 17665454 - SINE/MIR_MIR1_Amn chr1 17665704 17666247 - LTR/ERVL_MLT2B4 chr1 17666727 17666760 + Simple_repeat_(CCCCA)n chr1 17666789 17666824 + Simple_repeat_(ACCCC)n chr1 17667330 17667375 - LINE/L2_L2c chr1 17667938 17668170 + SINE/MIR_MIRb chr1 17669345 17669578 - SINE/MIR_MIR chr1 17669681 17669767 + LINE/L2_L2c chr1 17669781 17670118 - LINE/L2_L2c chr1 17670710 17670779 - SINE/MIR_MIRc chr1 17670821 17671126 + LINE/L2_L2b chr1 17671784 17671902 + LINE/L2_L2b chr1 17672690 17672853 + LINE/CR1_L3b chr1 17673481 17673577 + SINE/MIR_MIRb chr1 17673748 17673883 + SINE/MIR_MIR chr1 17674052 17674135 + LINE/L2_L2d2 chr1 17674674 17675012 + DNA/TcMar-Tigger_Tigger3a chr1 17677632 17677780 - SINE/MIR_MIR chr1 17678002 17678028 + Simple_repeat_(TGAGC)n chr1 17678418 17678717 - SINE/Alu_AluSq2 chr1 17679166 17679215 - SINE/MIR_MIR chr1 17679392 17679414 + SINE/MIR_MIRb chr1 17679415 17679527 - DNA/hAT-Blackjack_MER81 chr1 17679528 17679627 + SINE/MIR_MIRb chr1 17679718 17679871 + SINE/MIR_MIR chr1 17680075 17680110 + Simple_repeat_(CCCTGC)n chr1 17680610 17680689 - SINE/MIR_MIR chr1 17680744 17680760 + Simple_repeat_(T)n chr1 17680788 17681072 - SINE/Alu_AluSx1 chr1 17681082 17682163 - LINE/L1_L1MC4 chr1 17682189 17682532 - LINE/L1_L1MC4 chr1 17682534 17682615 - DNA/hAT-Charlie_MER58A chr1 17683618 17683647 + Simple_repeat_(GGCTGG)n chr1 17685081 17685154 - DNA/hAT-Ac_OldhAT1 chr1 17685191 17685726 + LINE/L2_L2c chr1 17685724 17685821 - SINE/MIR_MIRb chr1 17686050 17686078 + Simple_repeat_(TTTTG)n chr1 17686079 17686109 + Simple_repeat_(TTTC)n chr1 17686320 17686639 - LINE/L2_L2c chr1 17686811 17687092 - LINE/L1_HAL1 chr1 17687217 17687390 + DNA/hAT-Blackjack_MER63A chr1 17687883 17687959 - SINE/MIR_MIRb chr1 17688996 17689178 - SINE/MIR_MIRb chr1 17689184 17689320 + SINE/MIR_MIRb chr1 17689446 17689640 + Simple_repeat_(TCCT)n chr1 17689900 17690145 - SINE/MIR_MIRc chr1 17690091 17690222 + SINE/MIR_MIR3 chr1 17691691 17691749 + Simple_repeat_(TA)n chr1 17691825 17691853 + Simple_repeat_(CATC)n chr1 17691854 17692034 - LINE/L2_L2a chr1 17692326 17692995 + LINE/L2_L2b chr1 17693144 17693282 - DNA/hAT-Charlie_MER3 chr1 17693283 17693401 + LINE/L2_L2b chr1 17693628 17693756 + SINE/MIR_MIRc chr1 17693757 17693837 + DNA/hAT-Charlie_MER5A chr1 17694146 17694270 - DNA/TcMar-Tigger_Tigger3a chr1 17694271 17694415 - SINE/MIR_MIR chr1 17695464 17695510 + SINE/MIR_MIRc chr1 17695915 17696228 + SINE/Alu_AluSx chr1 17696283 17696597 - LINE/L2_L2c chr1 17697718 17697760 + Simple_repeat_(GT)n chr1 17697762 17697822 + Simple_repeat_(AT)n chr1 17698000 17698018 + DNA/hAT-Charlie_MER3 chr1 17698019 17698196 - DNA/hAT-Charlie_MER3 chr1 17698756 17698880 - LINE/L2_L2c chr1 17699312 17699484 + SINE/MIR_MIRb chr1 17699896 17700111 - SINE/MIR_MIR chr1 17700207 17700564 - LINE/L2_L2a chr1 17700601 17701435 - LINE/L2_L2a chr1 17702053 17702344 - SINE/Alu_AluSx1 chr1 17702356 17702474 - LTR/Gypsy_LTR81A chr1 17703050 17703247 - LTR/ERVL_LTR16C chr1 17703412 17703526 - LTR/Gypsy_LTR81A chr1 17703527 17703734 - LTR/ERVL-MaLR_MLT1A0 chr1 17703735 17704043 - SINE/Alu_AluSx chr1 17704044 17704200 - LTR/ERVL-MaLR_MLT1A0 chr1 17704293 17704469 + LTR/ERVL-MaLR_THE1D chr1 17704470 17704770 - SINE/Alu_AluY chr1 17704771 17704953 + LTR/ERVL-MaLR_THE1D chr1 17705145 17705316 + SINE/MIR_MIRb chr1 17705337 17705426 - SINE/MIR_MIRb chr1 17705475 17705641 + Simple_repeat_(GGCTGG)n chr1 17705925 17706170 - SINE/Alu_AluJo chr1 17706701 17706886 + DNA/TcMar_UCON44 chr1 17706930 17707006 + SINE/MIR_MIR1_Amn chr1 17707020 17707248 - SINE/MIR_MIR chr1 17707371 17707512 + SINE/MIR_MIR chr1 17707616 17707912 - SINE/Alu_AluSx1 chr1 17708179 17708327 - LINE/L2_L2b chr1 17708330 17708640 + SINE/Alu_AluJo chr1 17708642 17708681 + Low_complexity_A-rich chr1 17708759 17708912 + SINE/MIR_MIRb chr1 17708914 17708939 + Simple_repeat_(TATT)n chr1 17708948 17709195 - SINE/Alu_AluY chr1 17709264 17709306 + Low_complexity_G-rich chr1 17709621 17709661 + Simple_repeat_(ATTT)n chr1 17709715 17709790 - Unknown_UCON47 chr1 17710087 17710141 - SINE/MIR_MIR chr1 17710142 17710560 - LTR/ERV1_LTR19A chr1 17710561 17710716 - SINE/MIR_MIR chr1 17711015 17711071 + LINE/L2_L2b chr1 17711090 17711529 + LTR/ERVL-MaLR_MLT1H1 chr1 17711632 17712072 + LTR/ERVL-MaLR_MLT1K chr1 17712997 17713159 - SINE/MIR_MIR3 chr1 17713327 17713428 + SINE/MIR_MIRc chr1 17713572 17713626 - SINE/MIR_MIRb chr1 17713627 17713938 - SINE/Alu_AluSx1 chr1 17713939 17714090 - SINE/MIR_MIRb chr1 17714975 17714998 + Simple_repeat_(ATTC)n chr1 17714999 17715144 - LINE/L2_L2b chr1 17715266 17715448 - SINE/MIR_MIRb chr1 17715862 17716026 - SINE/MIR_MIR chr1 17716106 17716429 + SINE/Alu_AluJr chr1 17716679 17716776 + Simple_repeat_(CTTC)n chr1 17716777 17716805 + Simple_repeat_(TCTT)n chr1 17716806 17717110 - SINE/Alu_AluSz6 chr1 17717111 17717129 + Simple_repeat_(TCTT)n chr1 17717149 17717277 + LINE/L2_L2a chr1 17718001 17718140 - LTR/ERVL_LTR16A2 chr1 17718452 17718634 - SINE/MIR_MIR chr1 17718692 17718771 - LINE/L2_L2b chr1 17718793 17718873 - LINE/L2_L2a chr1 17718966 17719032 - LTR/ERV1_LTR12C chr1 17719071 17719132 + LINE/L1_L1PB1 chr1 17719136 17719207 + Low_complexity_A-rich chr1 17719244 17719360 - SINE/MIR_MIRc chr1 17719713 17719853 + SINE/MIR_MIRc chr1 17720033 17720134 + SINE/MIR_MIRc chr1 17720470 17720538 - LINE/CR1_CR1_Mam chr1 17720706 17720867 + SINE/MIR_MIRb chr1 17721046 17721143 - LINE/L2_L2a chr1 17721159 17721419 - SINE/MIR_MIRb chr1 17721506 17721915 - LTR/ERVL-MaLR_MLT1K chr1 17722035 17722103 - SINE/MIR_MIRb chr1 17722251 17722346 + LINE/L1_L1M5 chr1 17722347 17722631 + SINE/Alu_AluSp chr1 17722632 17722667 + Simple_repeat_(AAAC)n chr1 17722668 17722921 + LINE/L1_L1M5 chr1 17722957 17723111 - LTR/Gypsy_MamGypLTR1d chr1 17723266 17723430 - LTR/Gypsy_MamGypLTR2b chr1 17723634 17723854 - SINE/MIR_MIR chr1 17724261 17724453 - SINE/MIR_MIRb chr1 17724637 17724943 + SINE/Alu_AluSq2 chr1 17724968 17725125 + SINE/MIR_MIRb chr1 17725176 17725362 - SINE/MIR_MIR chr1 17725566 17725857 - SINE/Alu_AluSz chr1 17726166 17726610 - LTR/ERVL_LTR16E2 chr1 17726612 17727085 + LTR/ERVL_LTR42 chr1 17727249 17727537 - SINE/Alu_AluSz6 chr1 17727622 17727775 + Simple_repeat_(TTTC)n chr1 17727776 17727800 + Simple_repeat_(CTTC)n chr1 17727803 17727844 + Simple_repeat_(TCCC)n chr1 17727845 17727897 - SINE/Alu_AluJr chr1 17727898 17728253 - LTR/ERVL-MaLR_THE1A chr1 17728254 17728500 - SINE/Alu_AluJr chr1 17728509 17728658 - SINE/MIR_MIRb chr1 17728982 17729176 - DNA/hAT-Charlie_Charlie15a chr1 17729179 17729318 - SINE/MIR_MIR3 chr1 17729801 17729931 + SINE/MIR_MIR3 chr1 17730206 17730435 + SINE/MIR_MIR chr1 17730454 17730914 + LTR/ERVL-MaLR_MLT1H chr1 17730949 17731053 + SINE/MIR_MIRb chr1 17731088 17731270 + SINE/MIR_MIRb chr1 17731271 17731410 - LTR/ERVL-MaLR_MLT1H2 chr1 17731509 17731720 - LTR/ERVL-MaLR_MLT1H2 chr1 17731721 17731984 + SINE/Alu_AluSz chr1 17731989 17732019 + Simple_repeat_(AC)n chr1 17732021 17732394 + SINE/Alu_AluSc8 chr1 17732395 17732582 - LTR/ERVL-MaLR_MLT1H2 chr1 17732617 17732690 + SINE/MIR_MIR3 chr1 17733030 17733157 - SINE/MIR_MIRb chr1 17733284 17733494 + SINE/MIR_MIRb chr1 17734187 17734376 - SINE/MIR_MIRb chr1 17734390 17734695 + SINE/Alu_AluSx chr1 17734714 17734835 - SINE/MIR_MIR3 chr1 17734915 17734941 + Simple_repeat_(TCCC)n chr1 17735350 17735755 + LINE/L2_L2c chr1 17736156 17736366 - SINE/MIR_MIRb chr1 17737418 17737650 + SINE/MIR_MIR chr1 17737621 17737760 - SINE/MIR_MIR3 chr1 17737761 17737893 - SINE/Alu_AluJr chr1 17737937 17738053 + SINE/MIR_MIR3 chr1 17738087 17738226 - SINE/MIR_MIR3 chr1 17738442 17738746 + SINE/Alu_AluSc chr1 17739246 17739412 - DNA/hAT-Charlie_MER5A chr1 17739432 17739595 - DNA/hAT-Charlie_MER5B chr1 17739803 17739867 - LINE/L2_L2c chr1 17740263 17740572 - SINE/Alu_AluJo chr1 17741167 17741220 + Simple_repeat_(TCAT)n chr1 17741246 17741323 - LINE/L2_L2c chr1 17741591 17741729 - DNA/hAT?_UCON89 chr1 17742101 17742137 + LINE/CR1_L3b chr1 17742138 17742445 + SINE/Alu_AluSp chr1 17742619 17742832 - DNA/hAT-Charlie_MER58A chr1 17743257 17743317 + Low_complexity_G-rich chr1 17743325 17743348 + Simple_repeat_(CTG)n chr1 17743727 17743763 + Low_complexity_A-rich chr1 17744009 17744209 + SINE/MIR_MIRb chr1 17744323 17744496 + LINE/L2_L2b chr1 17745187 17745321 - SINE/MIR_MIRb chr1 17745417 17745438 + Simple_repeat_(GGCT)n chr1 17745546 17745696 + SINE/MIR_MIR3 chr1 17745969 17746120 + SINE/MIR_MIR chr1 17746121 17746583 + LTR/ERV1_LTR8 chr1 17746584 17746627 + Simple_repeat_(A)n chr1 17746630 17746690 + SINE/MIR_MIR chr1 17746769 17747081 - LTR/ERVL-MaLR_MLT1B chr1 17747082 17747171 + DNA/hAT-Charlie_MER102a chr1 17747172 17747265 - DNA/hAT-Blackjack_MER81 chr1 17747269 17747567 - SINE/Alu_AluSx chr1 17748155 17748305 + SINE/MIR_MIRb chr1 17748388 17748700 - SINE/Alu_AluY chr1 17748703 17748776 - DNA/hAT-Charlie_MER5A1 chr1 17748842 17749041 + Simple_repeat_(ATC)n chr1 17749099 17749184 - SINE/MIR_MIRb chr1 17749188 17749264 + SINE/MIR_MIR3 chr1 17749285 17749341 - SINE/MIR_MIR3 chr1 17749359 17749497 + SINE/MIR_MIRb chr1 17749498 17750026 + LTR/ERVL-MaLR_MLT1F chr1 17750027 17750122 + SINE/MIR_MIRb chr1 17750412 17750447 + Simple_repeat_(GT)n chr1 17750824 17750887 + DNA/hAT-Charlie_Charlie24 chr1 17750888 17751014 - SINE/MIR_MIRc chr1 17751241 17751276 + Simple_repeat_(ACCAGGC)n chr1 17751533 17751759 - SINE/MIR_MIRb chr1 17751761 17751851 - LINE/L2_L2c chr1 17751870 17751988 - LTR/ERVL-MaLR_MLT1H chr1 17752084 17752285 - LTR/ERVL-MaLR_MLT1H chr1 17752346 17752488 - DNA/TcMar-Tc2_Kanga1d chr1 17752500 17752675 - DNA/TcMar-Tc2_Kanga1 chr1 17752677 17752825 - SINE/MIR_MIRb chr1 17752826 17753301 + LTR/ERV1_MER66B chr1 17753302 17753379 - SINE/MIR_MIRb chr1 17753414 17753531 + SINE/MIR_MIR chr1 17753747 17754103 - LTR/ERVL-MaLR_THE1B chr1 17754171 17754387 - LINE/L1_L1ME2 chr1 17754388 17754448 + LTR/ERV1_MER101 chr1 17754449 17754580 + SINE/Alu_AluJo chr1 17754581 17755020 + LTR/ERV1_MER101 chr1 17755021 17755152 - LINE/L1_L1ME2 chr1 17755153 17755695 + LTR/ERVL_LTR41 chr1 17755931 17755986 + LTR/ERVL_LTR41 chr1 17756086 17756150 + LTR/ERVL_LTR41 chr1 17756405 17756635 + LTR/ERVL_LTR41 chr1 17756986 17757141 + SINE/Alu_FRAM chr1 17757179 17757457 + LTR/ERVL_ERVL-E-int chr1 17757725 17758849 + LTR/ERVL_ERVL-E-int chr1 17759156 17759614 + LTR/ERVL_LTR41 chr1 17759725 17759803 + LTR/ERVL_LTR41 chr1 17759784 17760014 + LTR/ERVL_LTR41 chr1 17760015 17760164 - LINE/L1_L1ME2 chr1 17760385 17760500 + SINE/MIR_MIRb chr1 17761141 17761270 - SINE/Alu_FLAM_C chr1 17761273 17761347 + SINE/MIR_MIRb chr1 17761299 17761361 + LINE/L2_L2d2 chr1 17761571 17761870 - SINE/Alu_AluSx1 chr1 17764212 17764321 + DNA/hAT-Charlie_MER20 chr1 17764331 17764370 + LINE/L2_L2a chr1 17765263 17765347 + LINE/L2_L2a chr1 17765382 17765567 - SINE/MIR_MIR chr1 17765645 17765749 + LINE/L2_L2a chr1 17766256 17766425 - SINE/MIR_MIRc chr1 17766547 17766779 - SINE/MIR_MIR chr1 17766868 17766888 + Simple_repeat_(TGAA)n chr1 17766889 17767270 - LINE/L2_L2a chr1 17768012 17768089 - LTR/ERVL-MaLR_MLT1N2 chr1 17768934 17769085 - SINE/MIR_MIRb chr1 17769490 17769591 + SINE/MIR_MIR3 chr1 17769606 17769958 - DNA/hAT-Charlie_MER102a chr1 17770873 17771067 + SINE/MIR_MIR chr1 17771068 17771405 - DNA/hAT-Charlie_MER58B chr1 17771406 17771450 + SINE/MIR_MIR chr1 17771483 17771763 + LINE/CR1_L3 chr1 17772069 17772270 - DNA/TcMar-Tigger_Tigger9b chr1 17773250 17773472 + SINE/MIR_MIR3 chr1 17773655 17773837 + SINE/MIR_MIR chr1 17773985 17774048 - SINE/MIR_MIR1_Amn chr1 17774337 17774394 + LINE/L2_L2b chr1 17774412 17774700 - LINE/L2_L2a chr1 17775119 17775224 - SINE/MIR_MIR3 chr1 17775361 17775377 + Simple_repeat_(GGA)n chr1 17775578 17775836 + LINE/L2_L2 chr1 17775817 17775974 - LINE/L2_L2c chr1 17775965 17776117 - SINE/MIR_MIRb chr1 17776088 17776147 + LINE/L2_L2d chr1 17776593 17776790 - LINE/CR1_CR1_Mam chr1 17776791 17776969 - SINE/Alu_AluJb chr1 17776970 17777005 + Simple_repeat_(T)n chr1 17777006 17777127 - SINE/Alu_AluJb chr1 17777128 17777169 - LINE/CR1_CR1_Mam chr1 17777523 17777695 + DNA/hAT-Charlie_MER117 chr1 17777710 17777997 - SINE/Alu_AluSx chr1 17778559 17778738 + SINE/MIR_MIR3 chr1 17779373 17779464 + SINE/MIR_MIR chr1 17779492 17779824 - LTR/ERVL-MaLR_MLT1J2 chr1 17779925 17780233 + SINE/Alu_AluJr chr1 17780279 17780426 - SINE/MIR_MIR3 chr1 17780729 17780762 + Low_complexity_G-rich chr1 17780923 17781228 - LTR/ERVL-MaLR_MLT1C2 chr1 17781229 17781538 - SINE/Alu_AluSx1 chr1 17781539 17781713 - LTR/ERVL-MaLR_MLT1C2 chr1 17782016 17782162 - DNA/hAT-Charlie_Charlie4z chr1 17782620 17782743 - LINE/L1_L1MC chr1 17783068 17783163 - SINE/MIR_MIRc chr1 17783172 17783362 + LTR/ERVL-MaLR_THE1B chr1 17783921 17784030 + SINE/MIR_MIRc chr1 17784031 17784413 - LTR/ERVL-MaLR_MSTA chr1 17784414 17784446 + SINE/MIR_MIRc chr1 17784620 17784772 - SINE/MIR_MIRb chr1 17784815 17785173 - LTR/ERVL-MaLR_THE1B chr1 17785208 17785277 + SINE/MIR_MIRc chr1 17785455 17785541 + SINE/MIR_MIR chr1 17785756 17786340 - LTR/ERVL-MaLR_MLT1E1A chr1 17786435 17786513 - DNA/TcMar-Tigger_Tigger15a chr1 17786692 17787001 - SINE/Alu_AluJr chr1 17787139 17787272 - LINE/L1_L1ME3G chr1 17787273 17787570 - SINE/Alu_AluSz6 chr1 17787571 17788248 - LINE/L1_L1ME3G chr1 17788402 17788566 + DNA/hAT-Charlie_Charlie17a chr1 17788570 17788647 - LINE/L2_L2c chr1 17788944 17789150 + DNA/hAT-Charlie_Charlie7a chr1 17789162 17789257 + LINE/RTE-BovB_MamRTE1 chr1 17789352 17789603 + LINE/L2_L2a chr1 17789606 17789818 - SINE/MIR_MIRb chr1 17789820 17791332 - LTR/ERV1_MER52A chr1 17791372 17791503 - SINE/MIR_MIRb chr1 17792178 17792311 - SINE/MIR_MIRb chr1 17793369 17793639 - LTR/ERV1_LTR78B chr1 17793957 17794062 - LTR/ERV1_LTR78B chr1 17794070 17794232 - SINE/MIR_MIRb chr1 17794246 17794382 + SINE/MIR_MIR chr1 17794388 17794713 + LINE/RTE-X_L4_A_Mam chr1 17794853 17794888 + Low_complexity_G-rich chr1 17794999 17795360 - LTR/ERVL-MaLR_MLT1J chr1 17795373 17795481 + SINE/MIR_MIRb chr1 17796307 17796337 + Simple_repeat_(TG)n chr1 17796406 17796606 - SINE/MIR_MIRb chr1 17796656 17796737 - SINE/MIR_MIRb chr1 17797513 17797593 + DNA/hAT-Charlie_MER103C chr1 17797631 17797866 + SINE/MIR_MIRb chr1 17799021 17800181 - LINE/L1_L1ME3Cz chr1 17800220 17800583 - LINE/L1_L1ME3Cz chr1 17800584 17800892 - SINE/Alu_AluSg chr1 17800893 17801098 - LINE/L1_L1ME3Cz chr1 17801123 17801274 - SINE/MIR_MIR chr1 17801275 17801617 - DNA/hAT-Tip100_Zaphod chr1 17801614 17802415 - DNA/hAT-Tip100_Zaphod chr1 17802425 17802452 + Simple_repeat_(GT)n chr1 17802528 17802587 - SINE/MIR_MIR chr1 17802701 17803005 + SINE/Alu_AluSx1 chr1 17803019 17803362 + LTR/ERVL-MaLR_THE1C chr1 17803363 17803628 - SINE/Alu_AluSx1 chr1 17803629 17803674 + LTR/ERVL-MaLR_THE1C chr1 17803787 17804003 + SINE/MIR_MIR chr1 17804021 17804503 - LTR/ERVL-MaLR_MLT1K chr1 17804550 17804606 + LINE/L2_L2a chr1 17804607 17804913 - SINE/Alu_AluSq2 chr1 17804914 17805360 + LINE/L2_L2a chr1 17805361 17805677 - SINE/Alu_AluSx4 chr1 17805678 17805838 + LINE/L2_L2a chr1 17806808 17807280 - LTR/ERVL-MaLR_MLT1D chr1 17807330 17807531 + SINE/MIR_MIR chr1 17807543 17807999 + LINE/L1_L1PA7 chr1 17808066 17808143 + SINE/MIR_MIRc chr1 17808441 17808676 + SINE/MIR_MIRb chr1 17809092 17809467 - LINE/L2_L2a chr1 17809474 17809960 + DNA/hAT-Charlie_MER1A chr1 17810002 17810200 + LINE/L2_L2 chr1 17810221 17810524 - LINE/L1_L1MC4 chr1 17810550 17811861 - LINE/L1_L1ME1 chr1 17811862 17812156 - SINE/Alu_AluSz chr1 17812157 17812427 - LINE/L1_L1ME1 chr1 17812428 17812792 - SINE/Alu_AluSx chr1 17812793 17814087 - LINE/L1_L1ME1 chr1 17814093 17814214 + DNA/TcMar-Tigger_Tigger3a chr1 17814215 17814345 + SINE/Alu_AluJr chr1 17814346 17814432 + DNA/TcMar-Tigger_Tigger3a chr1 17814450 17814519 + DNA/TcMar-Tigger_Tigger3c chr1 17814551 17814622 + DNA/TcMar-Tigger_Tigger3c chr1 17814654 17814725 + DNA/TcMar-Tigger_Tigger3c chr1 17814757 17814828 + DNA/TcMar-Tigger_Tigger3c chr1 17814860 17814931 + DNA/TcMar-Tigger_Tigger3c chr1 17814953 17815024 + DNA/TcMar-Tigger_Tigger3c chr1 17815056 17815176 + DNA/TcMar-Tigger_Tigger3c chr1 17815177 17815939 - LINE/L1_L1ME1 chr1 17815962 17816190 - LINE/L1_L1MA7 chr1 17816201 17816503 - SINE/Alu_AluSz chr1 17816536 17817150 - LINE/L1_L1ME1 chr1 17817203 17817342 - LINE/L1_L1MC4 chr1 17817343 17817690 + LINE/L2_L2a chr1 17817691 17818001 - SINE/Alu_AluSx chr1 17818002 17818883 + LINE/L2_L2a chr1 17819749 17820018 + SINE/Alu_AluJb chr1 17820067 17820568 - LINE/L1_L1ME1 chr1 17820569 17820841 - SINE/Alu_AluSp chr1 17820842 17821632 - LINE/L1_L1ME1 chr1 17821633 17821925 - SINE/Alu_AluSx chr1 17821926 17822001 - LINE/L1_L1ME1 chr1 17822022 17822066 - SINE/MIR_MIRc chr1 17822170 17822335 + SINE/MIR_MIRb chr1 17822336 17822414 + DNA/TcMar-Mariner_MADE2 chr1 17822415 17822469 + SINE/MIR_MIRb chr1 17822751 17822878 - LINE/L2_L2c chr1 17823463 17823750 + SINE/Alu_AluJo chr1 17824213 17824313 + DNA/hAT-Charlie_MER58A chr1 17824314 17824343 + Low_complexity_GA-rich chr1 17824452 17824542 + DNA/hAT-Charlie_MER58B chr1 17824543 17824734 - DNA/hAT_MER53 chr1 17824735 17824774 + DNA/hAT-Charlie_MER58B chr1 17824775 17824899 - LINE/L2_L2c chr1 17825240 17825336 + Simple_repeat_(TCTTTTT)n chr1 17825337 17825351 + Simple_repeat_(TCTTT)n chr1 17826593 17826629 + Low_complexity_G-rich chr1 17827329 17827552 - LINE/L2_L2b chr1 17827578 17827663 + LTR/ERVL-MaLR_MLT1J2 chr1 17827725 17827914 + LTR/ERVL-MaLR_MLT1J2 chr1 17827964 17828017 - SINE/MIR_MIR chr1 17828279 17828500 + SINE/MIR_MIRc chr1 17828558 17828721 - SINE/MIR_MIRb chr1 17828751 17828987 - LTR/ERVL-MaLR_MLT1L chr1 17829131 17829203 - SINE/MIR_MIRb chr1 17830181 17830401 + SINE/MIR_MIR chr1 17830591 17830975 + LTR/ERVL-MaLR_MLT1A1 chr1 17831098 17831186 + LINE/L2_L2c chr1 17831876 17832066 + SINE/MIR_MIRb chr1 17832054 17832118 - LINE/L2_L2b chr1 17832119 17832191 + SINE/MIR_MIRb chr1 17832863 17833018 - DNA/Kolobok_UCON29 chr1 17833423 17833501 + SINE/MIR_MIRc chr1 17834013 17834254 + SINE/MIR_MIR chr1 17834613 17834644 - DNA/TcMar-Tigger_Tigger19b chr1 17834667 17835364 + LINE/L2_L2a chr1 17835406 17835606 + LINE/CR1_L3b chr1 17835772 17835853 - LINE/L2_L2c chr1 17836067 17836112 - SINE/MIR_MIRc chr1 17836563 17836691 + SINE/MIR_MIRb chr1 17836711 17836882 + SINE/MIR_MIRb chr1 17836921 17837016 - SINE/MIR_MIR3 chr1 17837394 17837526 + DNA/TcMar-Tigger_MER127 chr1 17837544 17837730 + SINE/MIR_MIR3 chr1 17837826 17838179 - LINE/L2_L2b chr1 17838382 17838751 - LTR/ERVL-MaLR_MLT1B chr1 17838824 17838990 - SINE/MIR_MIR3 chr1 17839065 17839199 + SINE/MIR_MIRc chr1 17839285 17839645 + LTR/ERVL-MaLR_MLT1A0 chr1 17840110 17840158 + Simple_repeat_(TC)n chr1 17840201 17840480 - SINE/Alu_AluY chr1 17840482 17840607 + SINE/MIR_MIRb chr1 17840763 17841628 - LTR/ERV1_LTR78 chr1 17841629 17841925 - SINE/Alu_AluSx1 chr1 17841926 17842102 - LTR/ERV1_LTR78 chr1 17842103 17842481 + LINE/L2_L2a chr1 17843308 17843355 - LINE/L2_L2a chr1 17843620 17843770 + LTR/ERVL_LTR16C chr1 17843778 17843895 - SINE/MIR_MIRb chr1 17844563 17844589 + Simple_repeat_(GGA)n chr1 17844863 17844945 + LINE/L2_L2c chr1 17845169 17845259 + SINE/MIR_MIRb chr1 17845728 17845796 + Simple_repeat_(TG)n chr1 17845901 17845938 + Simple_repeat_(TG)n chr1 17846140 17846222 - SINE/Alu_AluSp chr1 17846241 17846621 + LINE/L2_L2b chr1 17846853 17846978 - SINE/MIR_MIR1_Amn chr1 17846995 17847211 + LINE/L2_L2a chr1 17847212 17847433 - DNA/hAT-Charlie_MER58A chr1 17847434 17847618 + LINE/L2_L2a chr1 17847479 17847815 + LINE/L2_L2a chr1 17847821 17848001 + Simple_repeat_(TCCA)n chr1 17848002 17848366 - LTR/ERVL-MaLR_MLT1A0 chr1 17848367 17848392 + Simple_repeat_(TCCA)n chr1 17848393 17848545 + LINE/L2_L2a chr1 17848748 17849057 + SINE/Alu_AluY chr1 17849579 17849629 + Simple_repeat_(CTTT)n chr1 17849637 17849723 - LINE/L2_L2c chr1 17849746 17849899 + SINE/MIR_MIR chr1 17849900 17850260 + LTR/ERVL-MaLR_THE1B chr1 17850261 17850324 + SINE/MIR_MIR chr1 17850350 17850632 - LINE/L2_L2c chr1 17850636 17851154 + LTR/ERVL-MaLR_MLT1F1 chr1 17851278 17851494 - SINE/MIR_MIR chr1 17851705 17851783 - LINE/L2_L2c chr1 17852739 17852882 + DNA/hAT-Charlie_MER20 chr1 17852883 17853199 - SINE/Alu_AluJb chr1 17853200 17853241 + DNA/hAT-Charlie_MER20 chr1 17853614 17853981 - LTR/Gypsy_MamGypLTR2b chr1 17854040 17854151 + Simple_repeat_(CTC)n chr1 17854479 17854560 - SINE/MIR_MIR1_Amn chr1 17854738 17854855 - SINE/MIR_MIRb chr1 17854878 17855175 - SINE/Alu_AluSx1 chr1 17855448 17855721 - LINE/L1_L1ME3G chr1 17855724 17855765 - SINE/MIR_MIRb chr1 17855783 17856099 + SINE/Alu_AluSq2 chr1 17856260 17856345 - SINE/MIR_MIRc chr1 17856415 17856519 - SINE/MIR_MIRb chr1 17856862 17856900 + Low_complexity_G-rich chr1 17856913 17856968 - LINE/L2_L2c chr1 17856992 17857041 - LINE/L2_L2c chr1 17857179 17857408 + SINE/MIR_MIRb chr1 17857432 17857480 - LINE/L2_L2b chr1 17857481 17857783 - SINE/Alu_AluY chr1 17858610 17858830 - SINE/MIR_MIRb chr1 17859500 17859703 + Low_complexity_GA-rich chr1 17859923 17859983 + Low_complexity_G-rich chr1 17860182 17860229 + Simple_repeat_(CTCTT)n chr1 17860248 17860341 + Simple_repeat_(ACCC)n chr1 17861381 17861599 + SINE/MIR_MIRb chr1 17862214 17862543 - DNA/hAT-Blackjack_MER63B chr1 17862567 17862674 + LINE/L1_L1ME3D chr1 17863030 17863330 + SINE/Alu_AluSz chr1 17863424 17863540 - DNA/hAT-Blackjack_MER63D chr1 17863548 17863693 - DNA/TcMar-Tigger_Tigger19a chr1 17863762 17863848 + SINE/MIR_MIRc chr1 17863866 17863975 - LINE/L2_L2c chr1 17864251 17864323 + Simple_repeat_(ATCC)n chr1 17864337 17864529 + LTR/ERVL_LTR79 chr1 17864523 17864614 + DNA/hAT-Charlie_MER33 chr1 17864620 17864737 + SINE/Alu_AluJb chr1 17864738 17864976 + DNA/hAT-Charlie_MER33 chr1 17865018 17865228 + LTR/ERVL_LTR79 chr1 17865483 17865792 + SINE/Alu_AluJb chr1 17865807 17866025 - DNA/hAT-Charlie_MER20 chr1 17866080 17866161 + LINE/L2_L2c chr1 17866729 17866909 + SINE/MIR_MIR3 chr1 17867133 17867206 + SINE/MIR_MIR chr1 17867195 17867350 - SINE/MIR_MIR1_Amn chr1 17867461 17867515 - LINE/L2_L2a chr1 17867600 17867713 + LINE/L1_L1ME1 chr1 17867714 17867937 - SINE/Alu_AluSp chr1 17867939 17868013 + LINE/L1_L1ME1 chr1 17868016 17868173 - LINE/L2_L2a chr1 17868174 17868603 + LTR/ERVL-MaLR_MSTB chr1 17868604 17868910 - LINE/L2_L2a chr1 17869234 17869310 + LINE/L2_UCON49 chr1 17869645 17869818 + SINE/MIR_MIRb chr1 17869819 17870129 - LTR/ERVL-MaLR_MLT1F1 chr1 17870130 17870172 - LTR/ERVL-MaLR_MLT1F chr1 17870215 17870360 - LINE/L1_L1ME1 chr1 17870361 17870675 - SINE/Alu_AluJb chr1 17870676 17871344 - LINE/L1_L1ME1 chr1 17871345 17871657 - SINE/Alu_AluSp chr1 17871658 17872349 - LINE/L1_L1ME1 chr1 17872456 17872565 - LTR/ERVL-MaLR_MLT1F chr1 17873140 17873429 - LTR/ERVL-MaLR_MLT1H chr1 17873487 17873646 + DNA/hAT-Charlie_Chap1_Mam chr1 17873995 17874168 - SINE/MIR_MIR chr1 17874220 17874470 + LINE/CR1_L3 chr1 17874548 17874719 - SINE/MIR_MIR3 chr1 17875445 17875525 - DNA/TcMar-Mariner_MADE2 chr1 17875829 17875950 - LTR/ERVL-MaLR_MLT1K chr1 17876059 17876329 + LTR/ERVL-MaLR_MLT1J2 chr1 17876330 17876625 - SINE/Alu_AluJr chr1 17876626 17876705 + LTR/ERVL-MaLR_MLT1J2 chr1 17876706 17877019 - LTR/ERVL-MaLR_MLT1K chr1 17877636 17877736 + LINE/L2_L2c chr1 17877876 17878059 - SINE/MIR_MIRb chr1 17878079 17878174 - LINE/L2_L2a chr1 17878330 17878368 - SINE/MIR_MIRc chr1 17878389 17878441 - SINE/MIR_MIR chr1 17878442 17878735 - SINE/Alu_AluSx chr1 17878736 17878929 - SINE/MIR_MIR chr1 17879555 17879645 + LINE/RTE-BovB_MamRTE1 chr1 17879942 17880138 - SINE/MIR_MIRc chr1 17880201 17880294 + LINE/L2_L2c chr1 17880370 17880671 - LTR/ERVL-MaLR_MLT1C2 chr1 17880672 17880974 + SINE/Alu_AluSg chr1 17880979 17881283 + SINE/Alu_AluSx chr1 17881284 17881422 - LTR/ERVL-MaLR_MLT1C2 chr1 17881427 17881613 + SINE/MIR_MIRb chr1 17881734 17881757 + Simple_repeat_(ATTT)n chr1 17881795 17881894 - LTR/ERVL_LTR79 chr1 17882173 17882370 + SINE/MIR_MIR chr1 17882374 17882691 - SINE/Alu_AluYm1 chr1 17883627 17883775 + SINE/MIR_MIRb chr1 17883794 17883912 + Simple_repeat_(GTG)n chr1 17884079 17884279 - LINE/L2_L2c chr1 17884655 17884782 - LINE/L2_L2a chr1 17884982 17885032 + LINE/RTE-BovB_MamRTE1 chr1 17885373 17885857 + LINE/L2_L2b chr1 17886305 17886422 + SINE/MIR_MIR chr1 17886507 17886609 - SINE/MIR_MIRb chr1 17886640 17886758 - DNA/hAT-Charlie_Charlie15a chr1 17887441 17887540 + LINE/CR1_L3 chr1 17887740 17887882 + SINE/MIR_MIR3 chr1 17888373 17888507 + SINE/MIR_MIR1_Amn chr1 17888505 17888576 - LINE/L2_L2b chr1 17888577 17888593 + Simple_repeat_(TAG)n chr1 17888596 17888646 - SINE/MIR_MIR chr1 17888647 17888688 - LTR/ERVL-MaLR_THE1A chr1 17888689 17888991 + SINE/Alu_AluSx chr1 17888992 17889320 - LTR/ERVL-MaLR_THE1A chr1 17889321 17889415 - SINE/MIR_MIR chr1 17889490 17889719 - LINE/L2_L2b chr1 17890230 17890402 - SINE/MIR_MIR1_Amn chr1 17891137 17891293 + SINE/MIR_MIR chr1 17891348 17891382 + Simple_repeat_(TGC)n chr1 17891383 17891390 + Simple_repeat_(TTGCTG)n chr1 17891420 17891523 + LINE/L2_L2d2 chr1 17892102 17892234 - SINE/MIR_MIRc chr1 17892327 17892367 + Simple_repeat_(GCTCTCT)n chr1 17892662 17892733 + SINE/MIR_MIRc chr1 17892746 17893206 + LTR/ERVL-MaLR_MLT1D chr1 17893412 17893491 + Simple_repeat_(CCTCC)n chr1 17893518 17893639 + DNA/hAT-Charlie_MER5B chr1 17893640 17893897 - SINE/Alu_AluJr chr1 17893898 17893958 + DNA/hAT-Charlie_MER5B chr1 17894022 17894602 + LTR/ERVL-MaLR_MLT1K chr1 17894603 17894664 + SINE/MIR_MIR3 chr1 17894665 17894950 + SINE/Alu_AluSc chr1 17894951 17894986 + Simple_repeat_(AAC)n chr1 17894993 17895046 + SINE/MIR_MIR3 chr1 17895440 17895559 + SINE/MIR_MIRb chr1 17895579 17895767 - LTR/ERVL-MaLR_MLT1K chr1 17895848 17895887 + Simple_repeat_(TG)n chr1 17896392 17896608 + SINE/MIR_MIRb chr1 17897254 17897423 - SINE/MIR_MIR3 chr1 17897449 17897636 + Simple_repeat_(CTTT)n chr1 17897685 17901503 + LINE/L1_L1PA17 chr1 17901512 17901943 - LINE/L1_L1MB2 chr1 17901954 17902081 + SINE/MIR_MIR chr1 17902087 17902566 + LTR/ERVL_LTR16A1 chr1 17902567 17902664 - SINE/MIR_MIRb chr1 17902745 17902886 + LTR/ERVL-MaLR_MLT1A chr1 17903095 17903168 + SINE/MIR_MIR3 chr1 17903169 17903358 + DNA/hAT-Charlie_MER117 chr1 17903568 17904002 + LTR/ERVL-MaLR_MLT1K chr1 17904356 17904478 - SINE/MIR_MIRc chr1 17904572 17904621 + SINE/MIR_MIR chr1 17904584 17904637 + LINE/L2_L2d2 chr1 17904645 17904761 - LTR/ERVL_LTR16E2 chr1 17904783 17905294 + LTR/ERVL-MaLR_MLT1F chr1 17905304 17905717 - LTR/ERVL_LTR16E2 chr1 17905719 17905817 + Simple_repeat_(TGAG)n chr1 17905927 17906053 + LTR/ERVL-MaLR_MLT1F1 chr1 17906603 17906705 - SINE/MIR_MIR1_Amn chr1 17906809 17906947 + SINE/MIR_MIR3 chr1 17907062 17907204 - SINE/MIR_MIRb chr1 17907272 17907339 - SINE/MIR_MIRb chr1 17907343 17907774 - LTR/ERVL-MaLR_MLT1K chr1 17907839 17907962 - SINE/MIR_MIRb chr1 17908016 17908391 - LINE/L2_L2d2 chr1 17908473 17908681 + SINE/MIR_MIRc chr1 17908790 17908811 + SINE/MIR_MIRb chr1 17908812 17908896 + SINE/MIR_MIRc chr1 17908897 17908960 + SINE/MIR_MIRb chr1 17909035 17909151 - LTR/ERVL-MaLR_MLT1K chr1 17909175 17909369 + DNA/hAT-Charlie_MER58C chr1 17909370 17909494 - RC/Helitron_Helitron1Na_Mam chr1 17909495 17909992 - LTR/ERVL_LTR33A_ chr1 17909993 17910260 - RC/Helitron_Helitron1Na_Mam chr1 17910366 17910736 - DNA/hAT-Charlie_MER113 chr1 17910768 17910952 + LTR/ERV1_MER4A1 chr1 17910953 17911253 + SINE/Alu_AluSg4 chr1 17911254 17911510 + LTR/ERV1_MER4A1 chr1 17911511 17911679 - SINE/Alu_AluSc chr1 17911680 17911734 + LTR/ERV1_MER4A1 chr1 17911802 17912264 - RC/Helitron_Helitron1Nb_Mam chr1 17912286 17912322 + Simple_repeat_(ATATTAC)n chr1 17912337 17912579 - SINE/MIR_MIRb chr1 17913185 17913280 + Simple_repeat_(CTTC)n chr1 17913346 17913492 + LTR/ERVL-MaLR_THE1B chr1 17914108 17914257 + SINE/MIR_MIR chr1 17914490 17914663 + SINE/MIR_MIR chr1 17914952 17915165 - SINE/MIR_MIRb chr1 17915359 17915802 + LTR/ERV1_MER4A1 chr1 17915806 17915849 + Simple_repeat_(CTGACC)n chr1 17916065 17916111 - LINE/L2_L2 chr1 17916282 17916388 + LINE/L2_L2d2 chr1 17916443 17916675 + SINE/MIR_MIR chr1 17916676 17916692 + Simple_repeat_(ATT)n chr1 17918052 17918463 - LTR/ERVL_LTR16A chr1 17918490 17918524 + LINE/L2_L2b chr1 17918739 17918849 - DNA/TcMar-Tigger_Tigger19a chr1 17918872 17919114 + SINE/MIR_MIR chr1 17919156 17919471 - SINE/Alu_AluJr chr1 17919927 17920011 + SINE/MIR_MIR3 chr1 17920655 17920774 - SINE/MIR_MIRb chr1 17920799 17920970 + SINE/MIR_MIR3 chr1 17921030 17921174 + DNA/hAT-Tip100_MamTip2 chr1 17921432 17921483 + SINE/MIR_MIRb chr1 17922138 17922179 + SINE/MIR_MIRb chr1 17922162 17922566 + LINE/L2_L2a chr1 17922567 17922862 - SINE/Alu_AluY chr1 17922863 17922973 + LINE/L2_L2a chr1 17923153 17923279 - LINE/L2_L2a chr1 17923406 17923497 + SINE/MIR_MIR3 chr1 17924162 17924218 + LINE/CR1_L3b chr1 17924297 17924315 - LINE/L2_L2c chr1 17924316 17924402 + Simple_repeat_(CATC)n chr1 17924403 17924756 - LINE/L2_L2c chr1 17924769 17924917 + LTR/ERVL-MaLR_MLT1K chr1 17924980 17925142 + SINE/MIR_MIR3 chr1 17925677 17925699 + Simple_repeat_(TTTC)n chr1 17926006 17926094 - SINE/MIR_MIR3 chr1 17926496 17926657 + SINE/MIR_MIRb chr1 17926712 17926758 + LINE/L2_L2b chr1 17926974 17927194 - SINE/MIR_MIR chr1 17928443 17928605 - SINE/Alu_AluJb chr1 17928606 17928673 + SINE/MIR_MIRb chr1 17928718 17928799 + SINE/MIR_MIR3 chr1 17928843 17929060 + SINE/MIR_MIR chr1 17929189 17929342 + SINE/MIR_MIRb chr1 17929420 17929512 - LINE/L2_L2b chr1 17929832 17929943 - DNA/TcMar-Mariner_MARNA chr1 17930596 17930759 + SINE/MIR_MIRb chr1 17930912 17931008 - LINE/L2_L2a chr1 17931033 17931216 + LINE/L2_L2a chr1 17931467 17931521 + Simple_repeat_(CCTCTG)n chr1 17931603 17931747 + SINE/MIR_MIRb chr1 17931748 17931958 + SINE/MIR_MIR chr1 17931959 17932319 - LTR/ERVL-MaLR_THE1B chr1 17932320 17932376 + SINE/MIR_MIR chr1 17932456 17932475 + Simple_repeat_(TTG)n chr1 17932664 17932736 - SINE/MIR_MIR chr1 17932819 17933008 - SINE/MIR_MIRc chr1 17933172 17933568 + LTR/ERVL_LTR84b chr1 17933628 17933726 + SINE/MIR_MIR chr1 17933752 17933896 + LTR/ERVL_LTR84b chr1 17933897 17933959 + Simple_repeat_(ACA)n chr1 17934585 17934791 - SINE/MIR_MIRb chr1 17934837 17934990 + SINE/Alu_AluJb chr1 17935081 17935193 - SINE/MIR_MIRc chr1 17935698 17935747 + Simple_repeat_(CTGC)n chr1 17935753 17935818 + Simple_repeat_(CCTC)n chr1 17935853 17935910 + Simple_repeat_(TCCA)n chr1 17935911 17935936 + Simple_repeat_(TTCA)n chr1 17935950 17936023 + LINE/L2_L2a chr1 17936024 17936207 + LTR/ERVL-MaLR_THE1B chr1 17936208 17936502 + SINE/Alu_AluSp chr1 17936509 17936565 + Simple_repeat_(AAATA)n chr1 17936566 17936736 + LTR/ERVL-MaLR_THE1B chr1 17936737 17936787 + Simple_repeat_(TATC)n chr1 17936788 17936937 + LINE/L2_L2a chr1 17937145 17937541 + LINE/L2_L2c chr1 17937573 17937882 - SINE/Alu_AluY chr1 17937935 17938166 - SINE/MIR_MIR chr1 17938090 17938265 + SINE/MIR_MIR3 chr1 17938512 17938803 + LTR/ERVL-MaLR_MLT1H chr1 17939089 17939384 - SINE/Alu_AluSz chr1 17939672 17939964 + LTR/ERVL?_LTR89B chr1 17940019 17940433 - LTR/ERVL-MaLR_MLT1D chr1 17940436 17940559 - SINE/MIR_MIRc chr1 17940701 17940811 - SINE/MIR_MIRc chr1 17940823 17941278 + LTR/ERVL_LTR16A1 chr1 17941282 17941925 - LTR/ERVL_MLT2F chr1 17941931 17944543 - LTR/ERVL_ERVL-E-int chr1 17944540 17944795 - LTR/ERVL_ERVL-E-int chr1 17944818 17944864 - LTR/ERVL_MLT2E chr1 17945105 17945192 + SINE/Alu_AluJr chr1 17945193 17945220 + Simple_repeat_(TAA)n chr1 17945272 17945440 - SINE/MIR_MIRb chr1 17946291 17946794 - LINE/L2_L2a chr1 17946814 17946824 - SINE/Alu_AluJb chr1 17946825 17947122 - SINE/Alu_AluJb chr1 17947123 17947346 - SINE/Alu_AluJb chr1 17947349 17947686 - LINE/L2_L2a chr1 17947702 17947909 + SINE/MIR_MIR chr1 17947940 17947990 + DNA/hAT-Charlie_MER58A chr1 17947991 17948301 + SINE/Alu_AluY chr1 17948302 17948429 + DNA/hAT-Charlie_MER58A chr1 17948898 17949007 + SINE/MIR_MIRb chr1 17949109 17949170 + LINE/L2_L2b chr1 17949510 17949601 + Simple_repeat_(AGAT)n chr1 17949907 17950071 - SINE/Alu_AluSc chr1 17950072 17950364 - SINE/Alu_AluY chr1 17950386 17950422 + Simple_repeat_(T)n chr1 17950413 17950462 + Simple_repeat_(CT)n chr1 17950423 17950435 + Simple_repeat_(TCTTT)n chr1 17950463 17950603 - SINE/Alu_FLAM_C chr1 17950624 17950681 + SINE/MIR_MIR3 chr1 17951062 17951215 - DNA/hAT-Tip100_MER45A chr1 17951236 17951472 + SINE/MIR_MIR chr1 17951934 17952158 - LINE/L2_L2d2 chr1 17952414 17952867 - LINE/L2_L2d2 chr1 17952908 17953044 - DNA/hAT-Tip100_MamTip2b chr1 17953277 17953745 - LTR/ERVL_LTR33 chr1 17953793 17954080 + SINE/Alu_AluSx chr1 17954083 17954125 + Simple_repeat_(AAAT)n chr1 17954925 17955258 - SINE/Alu_AluSx4 chr1 17955455 17955658 + SINE/MIR_MIR chr1 17955688 17955842 - SINE/MIR_MIR3 chr1 17956472 17956659 - SINE/Alu_AluYf1 chr1 17956660 17956685 + Simple_repeat_(T)n chr1 17956686 17956811 - SINE/Alu_AluYf1 chr1 17956825 17957006 - SINE/MIR_MIR3 chr1 17957082 17957300 - SINE/MIR_MIR chr1 17957598 17957750 + SINE/MIR_MIR3 chr1 17958390 17958446 - LINE/L2_L2b chr1 17958467 17958642 + SINE/MIR_MIRb chr1 17958831 17959215 + LTR/ERVL-MaLR_MSTA chr1 17959804 17959866 + DNA/TcMar-Mariner_MADE1 chr1 17959896 17960079 - SINE/MIR_MIR3 chr1 17960110 17960175 + LINE/L2_L2c chr1 17960595 17960838 + LINE/L2_L2d2 chr1 17960929 17961064 - SINE/MIR_MIRb chr1 17961299 17961414 + SINE/MIR_MIRc chr1 17961447 17961725 + LINE/L2_L2b chr1 17961759 17961868 + LINE/L2_L2b chr1 17961887 17962303 + LTR/ERVL-MaLR_MLT1H1 chr1 17962475 17962786 + LTR/ERVL-MaLR_MLT1J1 chr1 17962827 17962876 + LINE/L2_L2b chr1 17963070 17963208 + LINE/L2_L2b chr1 17964043 17964117 - SINE/MIR_MIRb chr1 17964555 17964646 - SINE/MIR_MIRb chr1 17964730 17964852 + DNA/hAT-Charlie_Charlie15a chr1 17964853 17965147 + SINE/Alu_AluSg7 chr1 17965163 17965292 - SINE/MIR_MIR3 chr1 17965310 17965593 + SINE/Alu_AluSz6 chr1 17965783 17965981 + LTR/ERVL-MaLR_MLT1K chr1 17966227 17966373 + SINE/MIR_MIR3 chr1 17966361 17966577 + LINE/L2_L2c chr1 17967233 17967262 + Simple_repeat_(TCAT)n chr1 17967263 17967326 - LINE/L2_L2b chr1 17967355 17967478 - SINE/MIR_MIR chr1 17967637 17967707 - LINE/L2_L2b chr1 17968491 17968825 - DNA/hAT-Charlie_Charlie8 chr1 17968826 17968845 - SINE/Alu_AluJb chr1 17968846 17968925 + Simple_repeat_(TATTA)n chr1 17968926 17969214 - SINE/Alu_AluJb chr1 17969215 17969319 - DNA/hAT-Charlie_Charlie8 chr1 17969432 17969830 - LTR/ERVL-MaLR_MLT1H chr1 17969837 17969915 + SINE/MIR_MIR3 chr1 17970362 17970473 + Simple_repeat_(CATC)n chr1 17970492 17970792 - SINE/Alu_AluY chr1 17971555 17971643 - DNA/hAT-Charlie_Charlie4z chr1 17972625 17972714 + SINE/MIR_MIRb chr1 17972715 17972881 - SINE/Alu_AluJo chr1 17972882 17972913 + Simple_repeat_(TTA)n chr1 17972914 17973031 - SINE/Alu_AluJo chr1 17973032 17973093 + SINE/MIR_MIRb chr1 17973106 17973198 - DNA/hAT-Tip100_MER91C chr1 17973356 17973526 + SINE/MIR_MIR chr1 17973652 17973815 + LINE/L1_L1ME5 chr1 17974068 17974168 - SINE/MIR_MIR3 chr1 17974255 17974370 + LTR/Gypsy?_LTR85b chr1 17974403 17974538 + LTR/Gypsy?_LTR85b chr1 17975042 17975358 - SINE/Alu_AluSz6 chr1 17975543 17975580 + Simple_repeat_(CA)n chr1 17976013 17976259 + LINE/L2_L2a chr1 17976227 17976426 - SINE/MIR_MIRb chr1 17976783 17976920 + SINE/MIR_MIR chr1 17976921 17977266 + LTR/ERVL-MaLR_MLT1J chr1 17977355 17977634 + LTR/ERVL-MaLR_MLT1J-int chr1 17977722 17977987 + LTR/ERVL-MaLR_MLT1J-int chr1 17978122 17978302 - SINE/MIR_MIR3 chr1 17978372 17978674 + SINE/Alu_AluSp chr1 17978790 17979016 + SINE/MIR_MIR chr1 17979162 17979310 - LTR/ERVL-MaLR_MLT1J1 chr1 17979334 17979514 + SINE/MIR_MIRb chr1 17979516 17979606 + Simple_repeat_(TTCC)n chr1 17980137 17980358 - SINE/MIR_MIRb chr1 17980401 17980515 + LINE/L2_L2c chr1 17980751 17980967 + Simple_repeat_(CATC)n chr1 17981008 17981354 + LINE/L1_L1MB3 chr1 17981566 17981803 - SINE/MIR_MIR chr1 17981863 17981976 + LINE/L2_L2c chr1 17981979 17982147 + DNA/hAT-Tip100_MER96B chr1 17982321 17982524 + SINE/MIR_MIR chr1 17983396 17983588 - SINE/MIR_MIRc chr1 17983776 17983942 - SINE/MIR_MIR3 chr1 17983966 17984134 + SINE/MIR_MIRc chr1 17984217 17984574 + LINE/L2_L2b chr1 17984550 17984652 + SINE/MIR_MIRc chr1 17984708 17984809 - DNA/hAT-Tip100_MamTip2b chr1 17985443 17985541 + LTR/ERVL-MaLR_MLT1H2 chr1 17985662 17985772 + LTR/ERVL-MaLR_MLT1H2 chr1 17985856 17985915 + SINE/MIR_MIR3 chr1 17985933 17986007 - SINE/MIR_MIRb chr1 17986036 17986066 - SINE/MIR_MIRb chr1 17986118 17986207 - SINE/MIR_MIR chr1 17986167 17986327 + LINE/L2_L2a chr1 17986439 17986641 + DNA/hAT-Charlie_MER20 chr1 17986718 17986924 - LTR/ERVL-MaLR_MLT1C2 chr1 17986925 17987288 - LTR/ERVL-MaLR_THE1B chr1 17987289 17987532 - LTR/ERVL-MaLR_MLT1C2 chr1 17987586 17987748 - SINE/MIR_MIRb chr1 17988308 17988927 - LINE/L1_L1ME1 chr1 17988928 17989228 + SINE/Alu_AluJr4 chr1 17989229 17989768 - LINE/L1_L1ME1 chr1 17989752 17989950 - LINE/L1_L1ME1 chr1 17990033 17990218 - SINE/MIR_MIRb chr1 17990460 17990536 - LINE/CR1_L3 chr1 17990734 17991361 - LINE/CR1_L3 chr1 17991444 17991672 - SINE/MIR_MIR chr1 17992117 17992538 + LTR/ERVL-MaLR_MLT1K chr1 17992640 17992841 - LINE/L2_L2c chr1 17992935 17993155 + SINE/MIR_MIRb chr1 17993519 17993564 + Simple_repeat_(TCAGT)n chr1 17994045 17994142 + Simple_repeat_(CACT)n chr1 17994611 17994951 + LTR/ERVL-MaLR_THE1C chr1 17994958 17995238 - SINE/Alu_AluSz chr1 17995375 17995492 + LTR/ERVL?_LTR89B chr1 17995562 17995587 + Simple_repeat_(CATT)n chr1 17995603 17996059 - LTR/ERVL-MaLR_MLT1C2 chr1 17996752 17997125 - LTR/ERVL-MaLR_THE1C chr1 17997607 17997723 + LINE/L2_L2a chr1 17997747 17998057 + SINE/Alu_AluSz chr1 17998402 17998609 - SINE/Alu_AluJr chr1 17998623 17998656 + Simple_repeat_(AATAAC)n chr1 17998715 17998778 - LINE/L2_L2a chr1 17998817 17998946 + Simple_repeat_(TATTA)n chr1 17998949 17999072 - SINE/MIR_MIRb chr1 17999163 17999293 + SINE/MIR_MIRb chr1 17999551 17999741 + SINE/MIR_MIR chr1 18000140 18000175 + Simple_repeat_(TTATTTT)n chr1 18000176 18000442 - LINE/L1_L1MB8 chr1 18000449 18000603 - SINE/MIR_MIR3 chr1 18000705 18000747 + Simple_repeat_(TCAT)n chr1 18000761 18001140 - LINE/L2_L2a chr1 18001344 18001624 + SINE/Alu_AluSx1 chr1 18001625 18001660 + Simple_repeat_(CAA)n chr1 18002326 18002580 - SINE/MIR_MIRb chr1 18002687 18003000 + SINE/Alu_AluSz chr1 18004008 18004195 - SINE/MIR_MIRb chr1 18004413 18004563 - LINE/L2_L2b chr1 18004604 18004688 - LTR/ERVL_LTR67B chr1 18004812 18005110 - LTR/ERVL_LTR67B chr1 18005169 18005323 - LINE/L2_L2b chr1 18005364 18005482 + LTR/ERVL-MaLR_MLT1H chr1 18005597 18005693 + LTR/ERVL-MaLR_MLT1H chr1 18005748 18005852 - LINE/L2_L2b chr1 18005839 18005986 - SINE/MIR_MIR3 chr1 18006072 18006171 - SINE/MIR_MIRc chr1 18006766 18006896 + LTR/ERVL-MaLR_MLT1I chr1 18006978 18007121 + LTR/ERVL-MaLR_MLT1I chr1 18007139 18007210 - DNA/hAT-Charlie_MER103C chr1 18007211 18007410 + Low_complexity_A-rich chr1 18007630 18007759 + SINE/MIR_MIR1_Amn chr1 18008630 18008967 + SINE/Alu_AluJb chr1 18008969 18009047 + Low_complexity_A-rich chr1 18010188 18010340 - LTR/ERVL-MaLR_MLT1K chr1 18010394 18010794 - LTR/ERVL-MaLR_MLT1K chr1 18011091 18011211 + LINE/L2_L2c chr1 18011234 18011560 + LINE/L1_L1PA6 chr1 18011635 18012054 - LINE/CR1_L3 chr1 18012183 18012211 + Simple_repeat_(TTC)n chr1 18012226 18012482 - SINE/Alu_AluSp chr1 18012488 18012672 - SINE/MIR_MIRb chr1 18013599 18013793 + SINE/MIR_MIRb chr1 18013818 18013961 - SINE/MIR_MIRb chr1 18014113 18014195 - SINE/MIR_MIR3 chr1 18014542 18014811 + LINE/L2_L2c chr1 18014812 18015184 - LTR/ERVL-MaLR_THE1B chr1 18015185 18015265 + LINE/L2_L2c chr1 18016020 18016197 - LINE/L2_L2b chr1 18016503 18016560 + Simple_repeat_(TGGTGA)n chr1 18016668 18016701 + Simple_repeat_(GGT)n chr1 18017225 18017482 + LINE/L2_L2c chr1 18017531 18017906 - LTR/ERVL-MaLR_MLT1A chr1 18018423 18018576 - LINE/L2_L2b chr1 18018642 18018789 + SINE/MIR_MIRb chr1 18018808 18019123 + SINE/Alu_AluSz chr1 18019347 18019517 + LINE/L2_L2a chr1 18019735 18019904 - SINE/MIR_MIRc chr1 18019963 18020096 + SINE/MIR_MIRc chr1 18020546 18020862 + Simple_repeat_(TC)n chr1 18020866 18020905 + Simple_repeat_(AC)n chr1 18021019 18021427 + LTR/ERVL-MaLR_MLT1I chr1 18022226 18022353 - LINE/CR1_L3b chr1 18022527 18022761 - SINE/MIR_MIRb chr1 18022762 18022899 - SINE/MIR_MIR3 chr1 18023401 18023417 + Simple_repeat_(AAAAT)n chr1 18023418 18023466 + LINE/L1_L1PB1 chr1 18023467 18023729 + SINE/Alu_AluSg chr1 18023730 18023918 + LINE/L1_L1PB1 chr1 18023919 18023938 + Simple_repeat_(AAAAT)n chr1 18024388 18024787 - LTR/ERVL-MaLR_MSTA chr1 18025152 18025359 - SINE/MIR_MIRb chr1 18025542 18025749 - LINE/CR1_Plat_L3 chr1 18025757 18026455 - LINE/L1_L1ME3B chr1 18026488 18026789 - SINE/Alu_AluSq2 chr1 18027137 18027307 + LTR/ERVL-MaLR_MLT1H2 chr1 18027342 18027475 + LTR/ERVL-MaLR_MLT1H2 chr1 18027478 18027501 + Simple_repeat_(TCA)n chr1 18027508 18027691 - SINE/MIR_MIRb chr1 18027978 18028209 + SINE/MIR_MIRc chr1 18029455 18029645 + LINE/L2_L2b chr1 18030103 18030401 + SINE/Alu_AluSq2 chr1 18030734 18030974 - SINE/MIR_MIRb chr1 18031506 18031691 - SINE/MIR_MIRb chr1 18031982 18032038 - SINE/MIR_MIR chr1 18033049 18033305 + LINE/L2_L2d2 chr1 18033419 18033504 - LINE/L2_L2a chr1 18033869 18033904 + SINE/MIR_MIRc chr1 18034046 18034091 + LINE/L2_L2d2 chr1 18034233 18034367 - LINE/L2_L2a chr1 18034368 18034509 + Simple_repeat_(CTAT)n chr1 18034510 18034633 - LINE/L2_L2a chr1 18034644 18034742 - SINE/MIR_MIR chr1 18034783 18034813 + SINE/MIR_MIRb chr1 18034816 18034839 + Simple_repeat_(TGA)n chr1 18035412 18035489 - SINE/MIR_MIR chr1 18035521 18035713 + SINE/MIR_MIR chr1 18035715 18035756 + Simple_repeat_(TATTGTTAC)n chr1 18035778 18036183 - LINE/L2_L2a chr1 18036220 18036364 - SINE/MIR_MIRb chr1 18036480 18036572 - DNA/TcMar-Tigger_MamRep434 chr1 18036680 18036860 - SINE/MIR_MIRb chr1 18037274 18037380 + LINE/L2_L2a chr1 18037382 18037446 - SINE/MIR_MIR chr1 18037447 18037624 - DNA/hAT-Charlie_MER5B chr1 18037625 18037785 - SINE/MIR_MIR chr1 18038239 18038285 + Simple_repeat_(TTCA)n chr1 18038368 18038676 - LTR_LTR90B chr1 18039012 18039111 + SINE/MIR_MIR chr1 18039025 18039153 + SINE/MIR_MIRb chr1 18039418 18039503 + LINE/L2_L2a chr1 18039541 18039595 + Low_complexity_GA-rich chr1 18039798 18039863 + Low_complexity_A-rich chr1 18039873 18040186 - SINE/Alu_AluSz6 chr1 18040241 18040418 - DNA/hAT_MER53 chr1 18041176 18041282 + SINE/MIR_MIRb chr1 18042056 18042080 + Simple_repeat_(AC)n chr1 18042386 18042693 - SINE/Alu_AluY chr1 18043772 18044089 + LINE/L2_L2c chr1 18044116 18044211 + SINE/MIR_MIR3 chr1 18044334 18044877 + LINE/L1_L1ME5 chr1 18044983 18045194 - DNA/hAT-Charlie_MER20 chr1 18045867 18046072 - SINE/MIR_MIRb chr1 18046291 18046504 + LINE/L2_L2c chr1 18046654 18046767 + LINE/L2_L2d2 chr1 18046782 18046831 + Simple_repeat_(ATGG)n chr1 18046898 18046968 + Simple_repeat_(TAGA)n chr1 18047026 18047100 + Low_complexity_G-rich chr1 18048219 18048459 + SINE/MIR_MIR chr1 18048583 18048692 - SINE/MIR_MIRb chr1 18049196 18049999 + Simple_repeat_(TGATGG)n chr1 18050166 18050323 - SINE/MIR_MIRb chr1 18050371 18050468 + SINE/MIR_MIR3 chr1 18051046 18051279 - SINE/MIR_MIRb chr1 18051388 18051426 + SINE/MIR_MIRb chr1 18051630 18051958 + LINE/L2_L2c chr1 18051970 18051997 + Simple_repeat_(ATGA)n chr1 18052855 18053012 - DNA/TcMar-Tigger_Tigger19b chr1 18053606 18053801 - SINE/MIR_MIR chr1 18054000 18054278 - SINE/Alu_AluSz6 chr1 18054290 18054949 + LINE/L2_L2b chr1 18054968 18055037 + LINE/L2_L2b chr1 18055640 18055745 - SINE/MIR_MIR chr1 18055853 18055879 + Simple_repeat_(TCAT)n chr1 18056162 18056379 + SINE/MIR_MIR chr1 18056383 18056535 - SINE/MIR_MIR chr1 18056911 18056967 + LINE/L2_L2c chr1 18057552 18057771 - LINE/CR1_L3 chr1 18058331 18058586 + SINE/MIR_MIRb chr1 18058724 18058783 + Simple_repeat_(CCCAG)n chr1 18059153 18059169 + SINE/MIR_MIRc chr1 18059170 18059212 + SINE/MIR_MIRb chr1 18059216 18059655 - LTR/ERVL-MaLR_MLT1C chr1 18059660 18059810 + SINE/MIR_MIRc chr1 18060023 18060065 - SINE/MIR_MIRb chr1 18060066 18060143 - DNA/TcMar-Mariner_MADE1 chr1 18060144 18060248 - SINE/MIR_MIRb chr1 18060417 18060478 - SINE/MIR_MIRb chr1 18060843 18061033 + SINE/MIR_MIRb chr1 18061120 18061422 + SINE/Alu_AluSx4 chr1 18061544 18062005 + LTR/ERVL_LTR16C chr1 18062009 18062164 + SINE/MIR_MIR chr1 18062200 18062407 + LINE/L2_L2a chr1 18062404 18062543 + LINE/L2_L2d2 chr1 18062544 18062856 - SINE/Alu_AluYk3 chr1 18062857 18062883 + LINE/L2_L2d2 chr1 18063011 18063217 - SINE/MIR_MIRb chr1 18063324 18063367 + Simple_repeat_(TG)n chr1 18063371 18063396 + Simple_repeat_(GAGA)n chr1 18063899 18064012 - LINE/L2_L2a chr1 18064093 18064124 - DNA/hAT-Ac_OldhAT1 chr1 18065238 18065446 + SINE/MIR_MIR chr1 18065455 18065957 + LTR/ERVL-MaLR_MLT1D chr1 18066424 18066488 - LINE/L2_L2a chr1 18067017 18067117 - DNA/hAT-Charlie_MER5A chr1 18067283 18067436 - SINE/MIR_MIRb chr1 18067807 18067850 + Simple_repeat_(CCAGCT)n chr1 18068207 18068268 + Low_complexity_GA-rich chr1 18068396 18068552 - DNA/hAT-Charlie_MER20 chr1 18068627 18068957 + LTR/ERVL-MaLR_MLT1H chr1 18069127 18069242 - LINE/L2_L2b chr1 18069389 18069709 + SINE/Alu_AluJb chr1 18069712 18069961 - SINE/MIR_MIRb chr1 18070370 18070438 - DNA/hAT-Charlie_MER5B chr1 18070439 18070741 - SINE/Alu_AluSz chr1 18070742 18070809 - DNA/hAT-Charlie_MER5B chr1 18070978 18071107 + LINE/L2_L2c chr1 18071141 18071365 + SINE/MIR_MIRb chr1 18071362 18071675 + LINE/L2_L2c chr1 18071676 18071981 + SINE/Alu_AluYb8 chr1 18071982 18072107 + LINE/L2_L2c chr1 18072123 18072326 + LINE/L2_L2c chr1 18072488 18073000 + LTR/ERVL-MaLR_MLT1H chr1 18073627 18073647 + Simple_repeat_(TCTT)n chr1 18073648 18073674 + Simple_repeat_(GA)n chr1 18074132 18074670 + LINE/L1_L1MB2 chr1 18074680 18074977 + SINE/Alu_AluSx chr1 18074989 18075146 + SINE/Alu_AluJr chr1 18075360 18075463 - LINE/L1_L1ME3G chr1 18075473 18075775 + SINE/Alu_AluSq2 chr1 18075809 18076000 - SINE/MIR_MIR chr1 18076025 18076295 - LINE/L1_L1ME4b chr1 18076665 18076821 + SINE/MIR_MIR3 chr1 18076889 18077117 + SINE/MIR_MIR chr1 18077513 18077733 + SINE/MIR_MIRb chr1 18077963 18078096 + DNA/hAT-Charlie_MER5A1 chr1 18078991 18079028 + Simple_repeat_(AC)n chr1 18079299 18079349 + Low_complexity_G-rich chr1 18079427 18079455 + Simple_repeat_(AC)n chr1 18079504 18079690 - SINE/MIR_MIR chr1 18079727 18079763 + Simple_repeat_(GAGAGGT)n chr1 18080054 18080181 - LINE/CR1_L3b chr1 18080238 18080281 + Low_complexity_GA-rich chr1 18081275 18081479 - SINE/MIR_MIRb chr1 18082073 18082254 + SINE/MIR_MIRb chr1 18082403 18082551 + LINE/CR1_L3 chr1 18083102 18083154 + Low_complexity_A-rich chr1 18083156 18083255 + LINE/L2_L2a chr1 18083262 18083480 - SINE/MIR_MIR chr1 18083548 18083930 - LTR/ERVL-MaLR_MLT1B chr1 18084135 18084730 - DNA/hAT-Charlie_Charlie20a chr1 18084976 18085048 + SINE/MIR_MIR3 chr1 18085154 18085718 - LTR/ERVL-MaLR_MLT1F2 chr1 18086040 18086080 + Simple_repeat_(GCTCCA)n chr1 18086727 18086760 + Simple_repeat_(AATC)n chr1 18086765 18086879 + LINE/L2_L2a chr1 18086933 18087013 + SINE/MIR_MIR chr1 18087014 18087390 + LTR/ERVL-MaLR_THE1D chr1 18087391 18087554 + SINE/MIR_MIR chr1 18088499 18088525 + Simple_repeat_(CAAA)n chr1 18088526 18088593 - LTR/ERV1_MER67B chr1 18088561 18088672 - LTR/ERV1_MER31A chr1 18088741 18088791 + Simple_repeat_(AAT)n chr1 18088792 18088979 + DNA/TcMar-Tigger_MER2 chr1 18088981 18089163 - LTR/ERV1_MER31A chr1 18089363 18089440 - LINE/L2_L2a chr1 18089588 18089712 + SINE/MIR_MIR1_Amn chr1 18090098 18090261 + SINE/MIR_MIR chr1 18090299 18090460 - SINE/Alu_FRAM chr1 18090492 18091024 - LTR/ERVL-MaLR_MLT1F2 chr1 18091033 18091169 + LINE/L2_L2a chr1 18091540 18092072 + LINE/L2_L2b chr1 18092162 18092571 + LINE/L2_L2b chr1 18092576 18092715 + SINE/MIR_MIRb chr1 18093553 18094181 + LINE/L1_L1MB2 chr1 18094718 18095095 - LTR/ERVL-MaLR_THE1B chr1 18095527 18095554 + Simple_repeat_(CT)n chr1 18095849 18096043 + SINE/MIR_MIRb chr1 18096162 18096382 - SINE/MIR_MIRc chr1 18096674 18096722 - SINE/5S-Deu-L2_AmnSINE1 chr1 18097311 18097630 + Simple_repeat_(TCCC)n chr1 18097631 18097707 + Simple_repeat_(TCTT)n chr1 18097708 18097742 + Simple_repeat_(TCCC)n chr1 18097743 18098127 + LTR/ERVL-MaLR_MSTB chr1 18098310 18098339 + Low_complexity_G-rich chr1 18098442 18098554 - LINE/L2_L2 chr1 18098883 18098988 + LINE/L2_L2d2 chr1 18099651 18099874 + LINE/L2_L2c chr1 18099972 18100052 - SINE/MIR_MIR3 chr1 18101037 18101191 - SINE/MIR_MIR chr1 18101195 18101383 + Simple_repeat_(TTCC)n chr1 18101593 18101788 + Simple_repeat_(CATC)n chr1 18101818 18102210 - LINE/L2_L2c chr1 18102381 18102599 + SINE/MIR_MIR chr1 18102692 18102948 + DNA/hAT-Charlie_MER103C chr1 18103372 18103407 + Low_complexity_GA-rich chr1 18103480 18103612 + SINE/MIR_MIRb chr1 18103656 18104328 + Simple_repeat_(TG)n chr1 18104338 18104579 + SINE/MIR_MIR chr1 18105333 18105392 - LINE/L2_L2a chr1 18105404 18105434 + Simple_repeat_(TCAT)n chr1 18105437 18105516 - LINE/L2_L2a chr1 18106301 18106330 + Simple_repeat_(A)n chr1 18106376 18106401 + Simple_repeat_(TTA)n chr1 18106984 18107018 + Simple_repeat_(GT)n chr1 18108072 18108178 + Simple_repeat_(CCG)n chr1 18108986 18109015 + Simple_repeat_(GT)n chr1 18111412 18111546 - DNA/hAT-Charlie_Charlie15a chr1 18111700 18111780 - SINE/MIR_MIRb chr1 18112015 18112045 + Low_complexity_GA-rich chr1 18112245 18112370 - LINE/L2_L2b chr1 18112424 18112649 - SINE/MIR_MIRb chr1 18113049 18113119 - SINE/MIR_MIRb chr1 18113587 18113623 + Simple_repeat_(CACCCC)n chr1 18113794 18113871 + SINE/MIR_MIRb chr1 18114282 18114383 + LINE/L2_L2b chr1 18114617 18114843 - SINE/MIR_MIRc chr1 18114922 18115059 - SINE/MIR_MIRb chr1 18115127 18115161 + Simple_repeat_(CTC)n chr1 18115409 18115489 + LINE/L2_L2b chr1 18115752 18115842 + Low_complexity_GA-rich chr1 18115843 18115903 + Simple_repeat_(AAGA)n chr1 18115904 18115929 + Low_complexity_GA-rich chr1 18115930 18116122 - SINE/MIR_MIRb chr1 18116131 18116280 + DNA/hAT-Blackjack_MER63B chr1 18116367 18116466 + SINE/MIR_MIRc chr1 18116804 18116899 - LINE/L2_L2b chr1 18117405 18117642 + SINE/MIR_MIRb chr1 18117783 18117885 - SINE/MIR_MIR3 chr1 18118868 18118949 + Simple_repeat_(TCCC)n chr1 18119176 18119295 - LINE/L2_L2a chr1 18119298 18119501 - DNA/hAT-Charlie_MER3 chr1 18119538 18119989 + DNA/hAT-Charlie_MER113 chr1 18120023 18120074 + SINE/MIR_MIRb chr1 18120075 18120511 - LTR/ERVL_LTR33 chr1 18120512 18120613 + SINE/MIR_MIRb chr1 18122163 18122490 - SINE/Alu_AluSz6 chr1 18122615 18122926 - SINE/Alu_AluSx chr1 18123042 18123177 + SINE/MIR_MIR3 chr1 18123376 18123845 - LINE/L2_L2c chr1 18124246 18124502 - SINE/MIR_MIR chr1 18125122 18125281 + LINE/L2_L2b chr1 18125274 18125337 - LINE/L2_L2c chr1 18125282 18125418 + LINE/L2_L2b chr1 18125601 18125837 - LINE/L2_L2a chr1 18125977 18126052 - LINE/L2_L2a chr1 18126084 18126302 + DNA/hAT-Charlie_Charlie15b chr1 18126339 18126493 - SINE/MIR_MIR3 chr1 18127092 18127182 + SINE/MIR_MIR3 chr1 18127189 18127388 - LINE/L2_L2a chr1 18127468 18127620 + SINE/MIR_MIRb chr1 18127621 18127919 + SINE/Alu_AluJb chr1 18127920 18127977 + SINE/MIR_MIR3 chr1 18128102 18128385 - LINE/L2_L2c chr1 18128625 18128783 - SINE/MIR_MIR chr1 18128789 18128813 + Simple_repeat_(TG)n chr1 18129670 18129715 - SINE/MIR_MIRb chr1 18129716 18130048 + LTR/ERVL-MaLR_MSTD chr1 18130049 18130170 - SINE/MIR_MIRb chr1 18130392 18130489 + SINE/MIR_MIR3 chr1 18130518 18130719 - SINE/MIR_MIRb chr1 18131367 18131461 + SINE/MIR_MIRb chr1 18131560 18131899 - LTR/ERVL-MaLR_MLT1J2 chr1 18132006 18132160 + LINE/L2_L2a chr1 18132162 18132205 + Simple_repeat_(TGGA)n chr1 18132228 18132259 + Simple_repeat_(TGACCTA)n chr1 18132319 18132438 + LINE/L2_L2b chr1 18132909 18132943 + Low_complexity_GA-rich chr1 18133352 18133548 + SINE/MIR_MIR chr1 18133890 18133970 + SINE/MIR_MIR chr1 18133972 18134083 - LINE/L2_L2c chr1 18134140 18134176 + Simple_repeat_(ATGA)n chr1 18134941 18135102 - SINE/MIR_MIRb chr1 18135178 18135233 - LTR/ERVL-MaLR_MSTA chr1 18135234 18135261 + Simple_repeat_(TTAT)n chr1 18135262 18137008 - LINE/L1_L1PA4 chr1 18137009 18137393 - LTR/ERVL-MaLR_MSTA chr1 18137423 18137607 - SINE/MIR_MIRb chr1 18137664 18137782 - SINE/MIR_MIRc chr1 18137775 18137996 - SINE/MIR_MIR chr1 18138439 18138565 - LTR/ERVL_LTR16E1 chr1 18138587 18138682 - LTR/ERVL_LTR16E1 chr1 18138775 18138957 - SINE/MIR_MIRb chr1 18138943 18138999 - LINE/L2_L2b chr1 18139579 18139727 + SINE/MIR_MIR chr1 18139728 18139957 + LTR/ERVL_LTR16C chr1 18139958 18140223 - SINE/Alu_AluJb chr1 18140224 18140456 + LTR/ERVL_LTR16C chr1 18140457 18140519 + SINE/MIR_MIR chr1 18140605 18140897 + LINE/L2_L2b chr1 18141561 18141779 + SINE/MIR_MIR1_Amn chr1 18141787 18142074 + SINE/Alu_AluJr chr1 18142224 18142413 - SINE/MIR_MIRb chr1 18143277 18143483 + LINE/L2_L2 chr1 18144752 18144907 + SINE/MIR_MIRb chr1 18146012 18146178 + SINE/MIR_MIRb chr1 18146344 18146438 + SINE/MIR_MIR1_Amn chr1 18146791 18146927 - SINE/MIR_MIRb chr1 18146947 18147425 + LINE/L2_L2b chr1 18147288 18147650 + LINE/L2_L2a chr1 18147678 18147793 - SINE/MIR_MIR chr1 18147794 18148129 + LTR/ERVL-MaLR_THE1B chr1 18148130 18148431 - SINE/Alu_AluY chr1 18148432 18148473 + LTR/ERVL-MaLR_THE1B chr1 18148474 18148599 - SINE/MIR_MIR chr1 18148600 18148713 + LINE/L2_L2b chr1 18149735 18149924 + SINE/MIR_MIRb chr1 18149971 18150243 + SINE/Alu_AluJr chr1 18150245 18150404 + SINE/MIR_MIRb chr1 18150905 18151040 - SINE/MIR_MIRc chr1 18151087 18151196 + LINE/L2_L2a chr1 18151204 18151587 - LTR/ERVL-MaLR_MLT1J2 chr1 18151629 18151801 - LTR/ERVL-MaLR_MLT1J2 chr1 18151846 18151971 - DNA/hAT-Blackjack_MER94 chr1 18151985 18152148 + LINE/L2_L2a chr1 18152391 18152436 - LINE/L2_L2a chr1 18152439 18152605 + SINE/MIR_MIRb chr1 18152834 18152992 + SINE/MIR_MIRb chr1 18153593 18153720 - SINE/MIR_MIRb chr1 18153918 18154016 - SINE/MIR_MIR3 chr1 18154114 18154226 + DNA/hAT-Charlie_MER102c chr1 18154775 18154818 + Simple_repeat_(CCCT)n chr1 18154800 18154846 + Simple_repeat_(TTCACT)n chr1 18155145 18155192 + Simple_repeat_(GCACTT)n chr1 18155310 18155424 + SINE/MIR_MIRb chr1 18156376 18156502 - SINE/MIR_MIRc chr1 18156871 18157148 + SINE/Alu_AluSx1 chr1 18157689 18157757 - SINE/MIR_MIRc chr1 18157905 18157950 + LINE/L2_L2c chr1 18158097 18158167 + LINE/L2_L2c chr1 18158675 18158687 + SINE/MIR_MIRc chr1 18158688 18158768 + SINE/MIR_MIRb chr1 18158966 18159178 + LINE/L2_L2b chr1 18159386 18159685 + LTR/ERVL-MaLR_THE1B chr1 18159686 18159980 - SINE/Alu_AluSg7 chr1 18159981 18160022 + LTR/ERVL-MaLR_THE1B chr1 18160233 18160424 + SINE/MIR_MIRb chr1 18160510 18160613 + SINE/MIR_MIR chr1 18160716 18160853 + SINE/MIR_MIRc chr1 18161295 18161338 - SINE/MIR_MIR chr1 18161790 18161981 + SINE/MIR_MIRb chr1 18161982 18162027 + DNA/TcMar-Tigger_MER47B chr1 18162028 18162346 - SINE/Alu_AluJb chr1 18162347 18162719 + DNA/TcMar-Tigger_MER47B chr1 18162720 18162782 + SINE/MIR_MIRb chr1 18163143 18163229 + SINE/MIR_MIRb chr1 18163382 18163428 + Low_complexity_G-rich chr1 18164106 18164272 - SINE/MIR_MIRb chr1 18164273 18164447 + Simple_repeat_(TTCC)n chr1 18164455 18164745 - LINE/L1_L1ME4b chr1 18164746 18165119 - LINE/L1_L1PA5 chr1 18165120 18165152 - LINE/L1_L1ME4b chr1 18165173 18165572 - LTR/ERVL-MaLR_MLT1B chr1 18165573 18166162 + LTR/ERV1_LTR10C chr1 18166350 18166598 + SINE/MIR_MIRb chr1 18166806 18166915 - LINE/L2_L2c chr1 18167304 18167451 - SINE/MIR_MIRb chr1 18168166 18168279 - LINE/CR1_L3b chr1 18168700 18168844 - SINE/MIR_MIRb chr1 18169641 18169764 + DNA/hAT-Charlie_Charlie19a chr1 18170104 18170156 - LINE/L2_L2c chr1 18170413 18170479 - DNA/hAT-Charlie_MER5A chr1 18170480 18170738 - SINE/MIR_MIR chr1 18170856 18171035 - LINE/L2_L2d chr1 18171481 18171688 + SINE/MIR_MIRb chr1 18171704 18172264 + LTR/ERVL-MaLR_MLT1L chr1 18172304 18172372 - SINE/MIR_MIRb chr1 18172373 18172764 - LTR/ERVL-MaLR_MLT1B chr1 18172765 18172909 - SINE/MIR_MIRb chr1 18173007 18173314 + SINE/Alu_AluY chr1 18173315 18173700 - LINE/L1_L1ME1 chr1 18173708 18173746 + Simple_repeat_(ATTTT)n chr1 18173760 18174053 - SINE/Alu_AluSx chr1 18174316 18174382 - LINE/CR1_L3b chr1 18174525 18174900 + LTR/ERVL_LTR16C chr1 18174932 18175071 - SINE/MIR_MIR chr1 18175145 18175319 + SINE/MIR_MIR3 chr1 18175331 18175546 + LINE/CR1_X7A_LINE chr1 18176334 18176555 + SINE/MIR_MIRb chr1 18176905 18177036 - SINE/MIR_MIRb chr1 18177099 18177193 + DNA/TcMar-Tigger_Tigger16b chr1 18177393 18177436 + Simple_repeat_(GT)n chr1 18178097 18178144 + Low_complexity_GA-rich chr1 18178377 18178596 + SINE/MIR_MIR chr1 18179378 18179624 - DNA/hAT-Tip100_ORSL chr1 18179935 18180324 + LINE/L2_L2a chr1 18180497 18180591 - LTR?_Eutr18 chr1 18180706 18180912 - SINE/MIR_MIR chr1 18180869 18181024 + SINE/MIR_MIR3 chr1 18181437 18181492 + SINE/MIR_MIR chr1 18181923 18182020 - SINE/MIR_MIRb chr1 18182174 18182478 - SINE/Alu_AluSx chr1 18182649 18182688 + Simple_repeat_(TGAA)n chr1 18183064 18183143 - LINE/CR1_CR1-1_Amn chr1 18183577 18183721 - LTR/ERVL-MaLR_MLT1H chr1 18183854 18183932 - LTR/ERVL-MaLR_MLT1H chr1 18184023 18184229 + SINE/MIR_MIR chr1 18184364 18184487 - LINE/CR1_L3b chr1 18184861 18185060 + SINE/MIR_MIR chr1 18185059 18185185 - SINE/MIR_MIR3 chr1 18185601 18185820 + SINE/MIR_MIR chr1 18185819 18186032 + SINE/MIR_MIRc chr1 18186392 18186701 + LINE/L2_L2b chr1 18186741 18186960 + LINE/L2_L2b chr1 18187033 18187316 - LTR/ERVL-MaLR_MLT1A1 chr1 18187317 18187679 - LTR/ERVL-MaLR_THE1B chr1 18187680 18187814 - LTR/ERVL-MaLR_MLT1A1 chr1 18187831 18187913 + LINE/L2_L2b chr1 18187916 18187961 + Simple_repeat_(AATG)n chr1 18188658 18188874 - SINE/MIR_MIR chr1 18189037 18189193 - SINE/MIR_MIR3 chr1 18189362 18189852 - DNA/hAT-Charlie_MER1A chr1 18189980 18190019 - DNA/hAT-Charlie_MER1A chr1 18190064 18190229 + LTR/ERVL_LTR16 chr1 18190349 18190479 + SINE/MIR_MIRb chr1 18191205 18191351 + SINE/MIR_MIRc chr1 18192086 18192242 + SINE/MIR_MIRb chr1 18192290 18192480 + SINE/MIR_MIR chr1 18192681 18192867 + SINE/MIR_MIRc chr1 18192882 18193352 - LINE/L2_L2b chr1 18193356 18193790 - LTR/ERVL_MLT2A1 chr1 18193905 18193926 + Simple_repeat_(TG)n chr1 18193945 18194023 + LINE/CR1_L3 chr1 18194249 18194327 - SINE/MIR_MIRb chr1 18194329 18194767 - LTR/ERVL-MaLR_MLT1C2 chr1 18194805 18195152 - LTR/ERVL-MaLR_THE1B chr1 18195174 18195288 - SINE/MIR_MIR3 chr1 18195803 18195941 - LINE/L2_L2b chr1 18196413 18196452 + Simple_repeat_(TGCAGTG)n chr1 18196578 18196817 - SINE/MIR_MIR chr1 18197364 18197443 + SINE/MIR_MIR3 chr1 18197647 18197863 + SINE/MIR_MIR chr1 18197932 18197989 + SINE/MIR_MIRc chr1 18198683 18198703 + Simple_repeat_(AG)n chr1 18199161 18199375 + SINE/MIR_MIRb chr1 18199668 18199702 + Simple_repeat_(CCTC)n chr1 18200083 18200293 + SINE/MIR_MIRc chr1 18200322 18200754 - LTR/ERVL-MaLR_MLT1C2 chr1 18202404 18202782 - LTR/ERVL-MaLR_THE1D chr1 18203298 18203608 - SINE/Alu_AluSz chr1 18203781 18203981 - DNA/hAT-Charlie_MER58C chr1 18203989 18204102 + SINE/MIR_MIRb chr1 18204129 18204339 - SINE/MIR_MIR chr1 18205102 18205195 + Low_complexity_GA-rich chr1 18205203 18205318 + LINE/L1_L1ME4a chr1 18205953 18206266 - LINE/L2_L2a chr1 18206267 18206565 + SINE/Alu_AluJr chr1 18206566 18206845 - LINE/L2_L2a chr1 18206861 18207440 - LTR/ERVL-MaLR_MLT1E2 chr1 18208374 18208395 + Simple_repeat_(AT)n chr1 18208396 18208696 - SINE/Alu_AluY chr1 18208724 18209159 + LINE/L1_L1ME3 chr1 18209211 18209342 - SINE/MIR_MIRb chr1 18209445 18209759 - SINE/Alu_AluSx3 chr1 18209953 18209992 + Simple_repeat_(CACCC)n chr1 18210624 18210839 - DNA/hAT-Charlie_MER20 chr1 18210860 18211171 - SINE/Alu_AluSz chr1 18211450 18211772 + DNA/TcMar-Tigger_X11_DNA chr1 18212151 18212351 - SINE/MIR_MIRb chr1 18212429 18212615 - SINE/MIR_MIRb chr1 18212955 18213129 + SINE/MIR_MIR3 chr1 18213204 18213228 + Simple_repeat_(TTCA)n chr1 18213293 18213598 - LTR/ERVL-MaLR_MLT1J2 chr1 18213881 18214041 + SINE/MIR_MIR1_Amn chr1 18214055 18214157 + Simple_repeat_(TAA)n chr1 18214521 18214723 - LTR/ERVL-MaLR_THE1D chr1 18214724 18215039 - SINE/Alu_AluSc chr1 18215040 18215212 - LTR/ERVL-MaLR_THE1D chr1 18215230 18215258 + Simple_repeat_(TTCA)n chr1 18215261 18215826 - LINE/L2_L2a chr1 18215923 18216601 - LINE/L2_L2c chr1 18217225 18217287 - LINE/L2_L2c chr1 18217749 18217961 + SINE/MIR_MIRc chr1 18217969 18218042 - LINE/L2_L2a chr1 18218043 18218418 - LTR/ERVL-MaLR_MSTA chr1 18218429 18218607 + LINE/L1_L1MDa chr1 18218622 18219050 + LINE/L1_L1MDa chr1 18219074 18219450 - LINE/L2_L2a chr1 18219459 18219645 - LINE/L2_L2a chr1 18220511 18220578 - LINE/L2_L2a chr1 18220649 18220917 - LINE/L2_L2c chr1 18221059 18221078 + Simple_repeat_(CAC)n chr1 18221154 18221224 - SINE/MIR_MIR1_Amn chr1 18221248 18221361 + DNA/hAT-Blackjack_MER81 chr1 18221934 18222092 - SINE/MIR_MIR3 chr1 18222556 18222681 - SINE/MIR_MIRb chr1 18223411 18223557 + SINE/MIR_MIRb chr1 18223654 18223717 - LINE/L2_L2b chr1 18224039 18224178 + DNA/hAT-Tip100_MER96 chr1 18224301 18224663 + LINE/L2_L2a chr1 18224784 18225064 + SINE/Alu_AluSz chr1 18225065 18225085 + Simple_repeat_(CT)n chr1 18225086 18225129 + Simple_repeat_(CA)n chr1 18225852 18226071 + SINE/MIR_MIR chr1 18228326 18228473 - DNA/hAT-Tip100_MamTip2 chr1 18228781 18229115 - DNA/hAT-Charlie_MER58B chr1 18230334 18230525 + Unknown_UCON47 chr1 18231557 18231756 - DNA/hAT-Charlie_MER3 chr1 18231923 18231961 + Simple_repeat_(GT)n chr1 18232408 18232575 - SINE/MIR_MIR chr1 18232580 18232787 + LINE/L2_L2c chr1 18233470 18233634 + SINE/MIR_MIRb chr1 18234472 18234828 - LTR/ERVL-MaLR_THE1C chr1 18235034 18235182 + DNA/hAT-Charlie_MER5B chr1 18235251 18236104 - LINE/L2_L2a chr1 18236105 18236459 + LTR/ERVL-MaLR_THE1B chr1 18236460 18236947 - LINE/L2_L2a chr1 18237550 18237684 + LINE/L2_L2c chr1 18237993 18238103 + DNA/hAT-Charlie_Charlie4z chr1 18238813 18238912 + SINE/MIR_MIR3 chr1 18239399 18239759 + LINE/L2_L2c chr1 18239911 18240003 - LINE/L2_L2a chr1 18240004 18240310 + SINE/Alu_AluJb chr1 18240311 18240439 - LINE/L2_L2a chr1 18240491 18240586 + LINE/L2_L2c chr1 18240593 18240636 + Simple_repeat_(GGAT)n chr1 18240651 18240874 + LINE/CR1_L3 chr1 18241212 18241343 + LINE/L2_L2 chr1 18241427 18241551 - SINE/MIR_MIRb chr1 18241734 18241883 - SINE/MIR_MIRb chr1 18242466 18242768 - DNA/TcMar-Tigger_MamRep434 chr1 18243034 18243066 + Low_complexity_GA-rich chr1 18243182 18243312 + LINE/L2_L2-1_AMi chr1 18243314 18243610 + LINE/L2_L2 chr1 18243920 18243986 + SINE/MIR_MIR chr1 18244000 18244110 + DNA/hAT-Blackjack_MER94 chr1 18244600 18244661 - LINE/L2_L2b chr1 18244808 18244826 + Simple_repeat_(AAT)n chr1 18244845 18245035 + SINE/MIR_MIR chr1 18245057 18245386 - LINE/L1_L1M5 chr1 18245674 18245730 - LINE/L2_L2d2 chr1 18245736 18245755 + Simple_repeat_(CTG)n chr1 18246884 18247043 - SINE/MIR_MIRb chr1 18247046 18247125 - SINE/Alu_Alu chr1 18247553 18247647 + SINE/MIR_MIRb chr1 18247683 18247914 - SINE/MIR_MIR chr1 18248068 18248157 + DNA_MARE8 chr1 18248710 18248994 - LINE/L2_L2b chr1 18249077 18249365 - LINE/L2_L2b chr1 18250023 18250053 + Simple_repeat_(TC)n chr1 18250054 18250133 + Simple_repeat_(CCCT)n chr1 18250134 18250146 + Simple_repeat_(TC)n chr1 18250599 18250773 + SINE/MIR_MIRb chr1 18250746 18251310 - LINE/L2_L2a chr1 18251311 18251719 - LTR/ERVL-MaLR_MLT1J2 chr1 18251769 18251852 - LINE/L2_L2a chr1 18252046 18252348 - SINE/Alu_AluY chr1 18252375 18252537 - SINE/MIR_MIR3 chr1 18252737 18252973 + SINE/MIR_MIRb chr1 18253808 18253962 - LINE/L2_L2d chr1 18253975 18254203 - SINE/MIR_MIR chr1 18254358 18254391 + Simple_repeat_(AACCCT)n chr1 18254434 18254491 + Simple_repeat_(TAACCC)n chr1 18254844 18254904 + Simple_repeat_(ATCC)n chr1 18255653 18256379 + LINE/L2_L2c chr1 18257369 18257519 - SINE/MIR_MIRc chr1 18258458 18258577 + SINE/MIR_MIR3 chr1 18258645 18258842 + SINE/MIR_MIRb chr1 18259197 18259395 + SINE/MIR_MIRb chr1 18259916 18260129 + SINE/MIR_MIRb chr1 18260332 18260840 - LTR/ERV1_MER66B chr1 18261164 18261258 + SINE/MIR_MIRb chr1 18261312 18261649 + LTR/ERV1_MER66B chr1 18261782 18261984 - LTR/ERVL_LTR16C chr1 18261993 18262157 - DNA/hAT-Charlie_MER5A chr1 18262151 18262280 - LTR/ERVL_LTR16C chr1 18262396 18262584 + SINE/MIR_MIRb chr1 18262552 18262594 + LINE/L2_L2b chr1 18263659 18263752 + DNA/hAT-Charlie_MER5A chr1 18264321 18264400 + SINE/MIR_MIR chr1 18264447 18264518 + SINE/MIR_MIR3 chr1 18264784 18264910 + SINE/MIR_MIRb chr1 18265007 18265390 + DNA/TcMar-Tigger_MER46C chr1 18266096 18266352 - SINE/MIR_MIR chr1 18266494 18266795 - LTR/ERVL-MaLR_MLT1H chr1 18267793 18267985 + DNA/hAT-Charlie_MER58A chr1 18267992 18268115 + LINE/L2_L2a chr1 18268187 18268330 - SINE/MIR_MIR3 chr1 18269409 18269426 + Simple_repeat_(GGA)n chr1 18269753 18269882 + LINE/CR1_L3b chr1 18269797 18269907 + SINE/MIR_MIRb chr1 18271159 18271336 - LINE/CR1_L3 chr1 18271410 18271518 + DNA/hAT-Blackjack_MER81 chr1 18271706 18271789 + SINE/MIR_MIR chr1 18271948 18271966 + SINE/MIR_MIR chr1 18271967 18272352 - LTR/ERVL-MaLR_THE1B chr1 18272353 18272537 + SINE/MIR_MIRb chr1 18273040 18273330 - SINE/Alu_AluSz chr1 18273332 18273535 + Simple_repeat_(TTCCT)n chr1 18273926 18274002 + SINE/MIR_MIR3 chr1 18274124 18274196 - LINE/L2_L2b chr1 18274462 18274667 + DNA/hAT-Charlie_MER58A chr1 18274744 18275052 + SINE/Alu_AluSz chr1 18275055 18275191 - DNA/hAT-Tip100_MER45A chr1 18275734 18276077 + LTR/ERVL_LTR33 chr1 18276190 18276623 - LTR/ERVL-MaLR_MLT1H1 chr1 18277015 18277151 + SINE/MIR_MIR1_Amn chr1 18277173 18277319 + SINE/MIR_MIRb chr1 18277428 18277502 + LINE/L2_L2b chr1 18277526 18278207 + LINE/L1_L1ME2z chr1 18278208 18278256 + Simple_repeat_(ATTT)n chr1 18278257 18278525 - SINE/Alu_AluSx1 chr1 18278532 18278855 - SINE/Alu_AluJb chr1 18278856 18278971 + LINE/L1_L1ME2z chr1 18279139 18279405 + LINE/L2_L2a chr1 18279495 18279673 - SINE/MIR_MIR3 chr1 18280308 18280459 + LINE/L2_L2 chr1 18280509 18280642 - SINE/MIR_MIR chr1 18281103 18281195 + Simple_repeat_(ATGA)n chr1 18281279 18281584 + SINE/Alu_AluSx1 chr1 18281915 18282084 + SINE/MIR_MIR3 chr1 18282192 18282369 + DNA/hAT-Charlie_MER5A chr1 18282636 18282681 + Simple_repeat_(AC)n chr1 18283773 18283901 + SINE/MIR_MIR chr1 18283892 18283991 - LINE/L2_L2a chr1 18284072 18284120 + LINE/L2_L2a chr1 18284313 18284398 - LINE/L2_L2b chr1 18284505 18284710 - SINE/MIR_MIRc chr1 18285185 18285209 + Simple_repeat_(T)n chr1 18285846 18285892 + SINE/MIR_MIR chr1 18285911 18286127 - SINE/MIR_MIRb chr1 18286226 18286314 + SINE/MIR_MIR1_Amn chr1 18286317 18286370 - LINE/L2_L2a chr1 18286730 18286791 + LINE/L2_L2c chr1 18286807 18286893 - LINE/L2_L2a chr1 18286900 18287209 + SINE/Alu_AluYa5 chr1 18287232 18287424 + SINE/Alu_AluYc chr1 18288051 18288157 - SINE/MIR_MIRb chr1 18288264 18288369 + LINE/L2_L2d2 chr1 18289034 18289072 + Simple_repeat_(GT)n chr1 18289466 18289575 + SINE/MIR_MIRc chr1 18289613 18289839 - DNA/hAT-Charlie_MER20 chr1 18290171 18290202 + Simple_repeat_(TCC)n chr1 18291349 18291793 + LINE/L2_L2c chr1 18292224 18292394 - SINE/MIR_MIRb chr1 18292651 18292811 + LINE/L2_L2b chr1 18293148 18293262 + LINE/L2_L2b chr1 18293928 18294101 + SINE/MIR_MIR chr1 18294106 18294273 + SINE/MIR_MIRb chr1 18294368 18294466 - SINE/MIR_MIRc chr1 18294533 18294618 - SINE/MIR_MIR3 chr1 18295187 18295224 + SINE/MIR_MIR3 chr1 18295311 18295622 - LINE/L2_L2a chr1 18295699 18295892 + LTR/ERVL_LTR16C chr1 18295895 18295989 + LINE/L2_L2b chr1 18296122 18296253 + LTR/ERVL_LTR16C chr1 18296250 18296709 - LTR/ERV1_MER39 chr1 18296840 18296940 - SINE/MIR_MIRc chr1 18296936 18297080 + LINE/L2_L2c chr1 18296955 18297253 + LINE/L2_L2b chr1 18297370 18297574 - LINE/L2_L2a chr1 18297575 18297895 - DNA/TcMar-Tigger_MER2 chr1 18297896 18298160 - LINE/L2_L2a chr1 18298314 18298385 - LTR/ERV1_MER39 chr1 18298801 18298951 + LINE/L2_L2c chr1 18299662 18299759 - DNA/hAT-Charlie_Chap1_Mam chr1 18299953 18300074 + SINE/Alu_FLAM_C chr1 18300148 18300278 - DNA/hAT-Charlie_MER5A chr1 18300888 18301015 + LINE/L2_L2b chr1 18301045 18301246 - SINE/MIR_MIR1_Amn chr1 18301321 18301639 - SINE/Alu_AluSx1 chr1 18303106 18303193 + LINE/L2_L2c chr1 18303220 18303362 - SINE/MIR_MIRc chr1 18303459 18303525 - DNA/hAT-Tip100_MamTip2b chr1 18303697 18303752 - LINE/L2_L2a chr1 18303832 18303896 + DNA/hAT-Charlie_MER5B chr1 18304007 18304397 - LINE/L2_L2 chr1 18304426 18304591 - SINE/MIR_MIR3 chr1 18304697 18304747 + Simple_repeat_(AC)n chr1 18304763 18304985 + DNA_X23_DNA chr1 18305182 18305624 + Simple_repeat_(GGAT)n chr1 18305787 18305844 - DNA/TcMar-Tigger_Tigger19b chr1 18305825 18305987 - SINE/MIR_MIRc chr1 18306017 18306322 + LINE/L2_L2c chr1 18306162 18306599 + LINE/L2_L2a chr1 18306693 18306821 + SINE/MIR_MIRc chr1 18307350 18307573 - SINE/MIR_MIR chr1 18307687 18307861 + SINE/MIR_MIRb chr1 18308073 18308199 + LINE/L2_L2c chr1 18308350 18308446 - LINE/L2_L2b chr1 18308466 18308596 - SINE/MIR_MIRc chr1 18309283 18309517 - SINE/MIR_MIR chr1 18310245 18310363 - LINE/L1_L1ME3 chr1 18310387 18310648 - LINE/L1_L1ME3 chr1 18310649 18311114 - LTR/ERVL-MaLR_MLT1C2 chr1 18311115 18311194 - LINE/L1_L1ME3 chr1 18311564 18311583 + LINE/L2_L2a chr1 18311584 18311945 - LTR/ERVL-MaLR_THE1B chr1 18311946 18312238 + LINE/L2_L2a chr1 18312372 18312828 - LTR/ERVL-MaLR_MLT1D chr1 18312889 18313006 + LINE/L2_L2a chr1 18313011 18313067 + LINE/L2_L2c chr1 18313354 18313576 + SINE/MIR_MIR chr1 18313579 18313830 - SINE/MIR_MIRb chr1 18314145 18314207 + SINE/tRNA-Deu_AmnSINE2 chr1 18314460 18314730 - SINE/MIR_MIRb chr1 18315228 18315543 + LINE/L2_L2a chr1 18315544 18315872 + Simple_repeat_(GGAT)n chr1 18315882 18315913 + Simple_repeat_(TGGA)n chr1 18316679 18316879 + SINE/MIR_MIR chr1 18316878 18316952 - LINE/L2_L2a chr1 18317004 18317184 + SINE/MIR_MIRb chr1 18317285 18317473 - SINE/MIR_MIRb chr1 18317783 18318010 - SINE/MIR_MIR chr1 18318310 18318414 - DNA/TcMar-Tigger_UCON11 chr1 18318452 18318490 + Simple_repeat_(GT)n chr1 18318707 18319000 + DNA/hAT-Charlie_Charlie8 chr1 18319008 18319465 - LTR/ERVL-MaLR_MLT1J chr1 18319718 18320036 + LINE/L2_L2a chr1 18320730 18320880 + SINE/MIR_MIRb chr1 18320997 18321220 + LINE/RTE-X_L4_C_Mam chr1 18321321 18321421 + SINE/MIR_MIR chr1 18321423 18321451 + SINE/MIR_MIR chr1 18321452 18321803 - LTR/ERVL-MaLR_MLT1A0 chr1 18321804 18321977 + SINE/MIR_MIR chr1 18321976 18322089 - SINE/MIR_MIR chr1 18322085 18322182 + SINE/MIR_MIRb chr1 18323321 18323390 - SINE/MIR_MIR3 chr1 18324518 18324613 + SINE/MIR_MIR3 chr1 18326400 18326593 + DNA/TcMar-Tigger_MamRep434 chr1 18326718 18326746 + Simple_repeat_(CCTC)n chr1 18327066 18327316 + DNA/TcMar-Tigger_Tigger15a chr1 18327324 18327374 + LINE/L1_L1MC chr1 18327487 18327892 + LINE/L1_L1ME1 chr1 18328139 18328504 - DNA/TcMar-Tigger_MER47A chr1 18328510 18328588 + LINE/L2_L2a chr1 18328633 18328697 - SINE/MIR_MIR chr1 18328839 18328990 + LINE/CR1_CR1_Mam chr1 18329008 18329149 + LINE/CR1_X7A_LINE chr1 18329318 18329475 - SINE/MIR_MIRc chr1 18329640 18329691 - LINE/L2_L2c chr1 18330331 18330445 + LINE/L1_L1MD3 chr1 18330763 18331013 - LINE/L1_L1MC1 chr1 18331118 18331603 - LINE/L1_L1PB1 chr1 18331619 18331953 - LINE/L2_L2c chr1 18332335 18332371 + Simple_repeat_(CTGTCAC)n chr1 18333448 18333683 - SINE/MIR_MIRc chr1 18333793 18334108 + DNA/hAT-Charlie_MER58B chr1 18334425 18334489 + SINE/MIR_MIRb chr1 18334710 18334752 + LINE/L2_L2b chr1 18335451 18335499 + SINE/MIR_MIR1_Amn chr1 18335493 18335580 + SINE/MIR_MIRb chr1 18335503 18335625 + SINE/MIR_MIR3 chr1 18335978 18336097 - LINE/L2_L2b chr1 18336079 18336338 - LINE/L2_L2a chr1 18336339 18336484 - SINE/MIR_MIR chr1 18336691 18336741 + SINE/MIR_MIR3 chr1 18336742 18337096 - LTR/ERVL-MaLR_THE1B chr1 18337097 18337154 + SINE/MIR_MIR3 chr1 18337165 18337234 - SINE/MIR_MIR chr1 18337230 18337368 + SINE/MIR_MIRb chr1 18337732 18337908 - SINE/MIR_MIRc chr1 18337999 18338119 - SINE/MIR_MIRb chr1 18338118 18338206 + SINE/MIR_MIRb chr1 18339089 18339290 - SINE/MIR_MIRb chr1 18339452 18339752 + SINE/Alu_AluJb chr1 18339928 18339974 + SINE/MIR_MIRc chr1 18340130 18340362 - LINE/L2_L2a chr1 18340429 18340561 - LINE/L2_L2a chr1 18340583 18340879 - LTR/ERVL-MaLR_MLT1C2 chr1 18340885 18340963 + Simple_repeat_(CCTTCT)n chr1 18340964 18341078 + Simple_repeat_(CTT)n chr1 18341079 18341130 + Simple_repeat_(CCTTCT)n chr1 18341131 18341254 - SINE/Alu_FLAM_C chr1 18341270 18341441 - LTR/ERVL-MaLR_MLT1C2 chr1 18341522 18341704 - SINE/MIR_MIR chr1 18342052 18342089 + Simple_repeat_(TTGT)n chr1 18342095 18342393 - SINE/Alu_AluSz chr1 18342412 18343817 - LINE/L1_L1MB8 chr1 18344199 18344385 - SINE/Alu_AluJb chr1 18345376 18345682 - SINE/Alu_AluSx chr1 18345684 18345902 - SINE/MIR_MIR chr1 18345900 18346060 + SINE/MIR_MIR chr1 18346233 18346426 + SINE/MIR_MIR chr1 18347310 18347428 - SINE/MIR_MIRb chr1 18347502 18347573 - SINE/MIR_MIRc chr1 18347858 18347958 - LINE/L2_L2d2 chr1 18347995 18348219 - SINE/MIR_MIR chr1 18349006 18349258 - SINE/MIR_MIRb chr1 18349264 18349555 - LINE/L2_L2d2 chr1 18349653 18349774 - SINE/MIR_MIR chr1 18349776 18349904 + SINE/Alu_FLAM_C chr1 18349930 18350049 - SINE/MIR_MIRb chr1 18350502 18350639 + LINE/L1_L1ME2z chr1 18351031 18351182 - SINE/MIR_MIR3 chr1 18352335 18352520 - DNA/hAT-Charlie_MER5A chr1 18353391 18353860 - LINE/L2_L2c chr1 18353940 18354385 - LINE/L2_L2c chr1 18354901 18355117 - SINE/MIR_MIRc chr1 18355239 18355633 - LINE/RTE-X_L5 chr1 18355726 18355808 + SINE/MIR_MIRc chr1 18355830 18356134 - SINE/Alu_AluSp chr1 18356142 18356173 + Simple_repeat_(TTACTA)n chr1 18356703 18357531 + Simple_repeat_(GATGGA)n chr1 18358047 18358065 + Simple_repeat_(GT)n chr1 18358102 18358230 + SINE/MIR_MIR1_Amn chr1 18358239 18358528 - DNA/hAT-Charlie_MER33 chr1 18358546 18358639 + LINE/L2_L2c chr1 18358668 18358879 - SINE/MIR_MIR chr1 18358983 18359307 + SINE/Alu_AluJr chr1 18359308 18359462 + Low_complexity_GA-rich chr1 18359463 18359514 + Simple_repeat_(GGAA)n chr1 18359909 18360107 + SINE/MIR_MIR chr1 18360223 18360397 + LINE/L2_L2d chr1 18360396 18360549 + LINE/L2_L2b chr1 18360769 18360981 - SINE/MIR_MIRb chr1 18361189 18361267 + LINE/L2_L2c chr1 18361410 18361497 - DNA/hAT-Blackjack_MER63D chr1 18361595 18361654 + SINE/Alu_AluSc chr1 18361697 18361935 + LINE/L2_L2c chr1 18362551 18362665 - SINE/MIR_MIR3 chr1 18362699 18362757 + Low_complexity_GA-rich chr1 18363099 18363150 - DNA/hAT-Charlie_Charlie15b chr1 18364899 18365056 - SINE/MIR_MIRc chr1 18367091 18367198 - LINE/RTE-BovB_MamRTE1 chr1 18367281 18367500 - SINE/MIR_MIRb chr1 18367604 18367902 - SINE/Alu_AluSx1 chr1 18367944 18368162 - SINE/MIR_MIR chr1 18368224 18368527 + SINE/Alu_AluSg chr1 18369167 18369309 + SINE/MIR_MIRc chr1 18369276 18369508 - SINE/MIR_MIR chr1 18369779 18369821 + LINE/L2_L2b chr1 18369822 18369878 + LINE/L2_L2a chr1 18369921 18370250 + LINE/L2_L2a chr1 18370251 18371289 + LINE/L1_L1ME2 chr1 18371290 18371586 + SINE/Alu_AluSz chr1 18371587 18371728 + LINE/L1_L1ME2 chr1 18371729 18371889 + LINE/L2_L2a chr1 18371977 18372097 - LINE/L1_L1ME2 chr1 18372098 18372211 + LINE/L2_L2a chr1 18372229 18372319 + SINE/MIR_MIRb chr1 18372312 18372400 + LINE/L2_L2a chr1 18372454 18372908 + Simple_repeat_(TGGA)n chr1 18373064 18373270 - SINE/MIR_MIRc chr1 18373713 18373921 + SINE/MIR_MIR chr1 18374235 18374365 + SINE/MIR_MIRb chr1 18374385 18374533 - SINE/MIR_MIRc chr1 18374978 18375326 + LINE/L2_L2a chr1 18375333 18375488 + SINE/MIR_MIR3 chr1 18375696 18375830 + SINE/MIR_MIRb chr1 18375947 18376153 + LINE/L2_L2b chr1 18376160 18376182 + Simple_repeat_(TGAA)n chr1 18377191 18377322 + SINE/MIR_MIRb chr1 18378095 18378175 - SINE/MIR_MIRc chr1 18378735 18379117 - LTR/ERVL-MaLR_MLT1H chr1 18379310 18379377 - LTR/ERVL-MaLR_MLT1J2 chr1 18379378 18379799 + LTR/ERVL-MaLR_MSTB chr1 18379800 18379938 - LTR/ERVL-MaLR_MLT1J2 chr1 18379939 18380420 + LTR/ERVL-MaLR_MLT1D chr1 18380421 18380576 - LTR/ERVL-MaLR_MLT1J2 chr1 18381021 18382028 + LINE/L2_L2a chr1 18382045 18382172 - LINE/L2_L2b chr1 18382933 18383222 - LINE/L2_L2c chr1 18384909 18385429 + Low_complexity_GA-rich chr1 18386006 18386249 + SINE/MIR_MIR chr1 18386345 18386499 + DNA/hAT-Charlie_MER5A1 chr1 18386606 18386773 - SINE/MIR_MIRb chr1 18386913 18387222 + SINE/Alu_AluSx1 chr1 18387447 18387585 - SINE/MIR_MIR3 chr1 18387620 18387852 - LTR/ERVL_LTR16C chr1 18388121 18388428 + LTR/ERVL-MaLR_MLT1A0 chr1 18388717 18388856 + SINE/MIR_MIRb chr1 18388904 18389137 - SINE/MIR_MIRb chr1 18389145 18389192 + LINE/L2_L2b chr1 18389292 18389546 + SINE/MIR_MIR chr1 18389735 18389949 - SINE/MIR_MIRb chr1 18390195 18390378 - SINE/MIR_MIR3 chr1 18390815 18390836 + Simple_repeat_(TG)n chr1 18391446 18391490 + LINE/L2_L2b chr1 18391483 18391656 - SINE/MIR_MIR chr1 18391662 18392019 - LTR/ERVL-MaLR_MLT1B chr1 18392033 18392117 + LINE/L2_L2 chr1 18392270 18392311 - LTR/ERVL-MaLR_MLT1C chr1 18392312 18392429 + SINE/Alu_AluJo chr1 18392443 18392979 - LINE/L1_L1PA3 chr1 18392980 18393032 + SINE/Alu_FLAM_C chr1 18393218 18393500 + LINE/L1_L1ME3A chr1 18393500 18393657 + LINE/L1_L1MC4a chr1 18393773 18393823 + LINE/L1_L1ME3A chr1 18393888 18394118 - SINE/MIR_MIRb chr1 18394895 18394978 + DNA/hAT-Charlie_MER103C chr1 18395119 18395234 - SINE/MIR_MIRc chr1 18395257 18395668 + LTR/ERVL-MaLR_MSTB1 chr1 18395798 18395992 - SINE/MIR_MIRc chr1 18396531 18397077 + LTR/ERVL_MER68 chr1 18397120 18397454 + LTR/ERVL-MaLR_MLT1A0 chr1 18397455 18397574 - DNA/hAT-Charlie_MER5A chr1 18398275 18398541 - LINE/L2_L2a chr1 18399480 18399635 - SINE/MIR_MIR3 chr1 18399705 18399877 + SINE/MIR_MIRb chr1 18400031 18400050 + Simple_repeat_(A)n chr1 18400268 18400303 - DNA/hAT-Charlie_MER5A chr1 18400304 18400621 + SINE/Alu_AluJr chr1 18400622 18400725 - DNA/hAT-Charlie_MER5A chr1 18400951 18401167 + SINE/MIR_MIRb chr1 18401382 18401418 + Simple_repeat_(TGAA)n chr1 18401566 18401861 - SINE/Alu_AluJb chr1 18401888 18402221 - LTR/ERVL-MaLR_MLT1H chr1 18402335 18402436 - LINE/L2_L2a chr1 18402465 18402524 + SINE/MIR_MIR3 chr1 18402539 18402602 - SINE/MIR_MIRc chr1 18403205 18403379 - SINE/MIR_MIRb chr1 18403436 18403501 + Low_complexity_GA-rich chr1 18403503 18403548 - LINE/L2_L2c chr1 18403560 18403701 - LINE/L2_L2c chr1 18403725 18404174 + LINE/L2_L2c chr1 18404174 18404258 - LINE/L2_L2a chr1 18404435 18404551 + LTR/ERVL_LTR16C chr1 18404554 18405016 - LTR/ERVL-MaLR_MLT1H2 chr1 18405020 18405277 + LTR/ERVL_LTR16C chr1 18406131 18406222 - SINE/MIR_MIR chr1 18406223 18406337 + SINE/Alu_AluSp chr1 18406339 18406379 + SINE/Alu_AluSq2 chr1 18406382 18406453 - SINE/MIR_MIR chr1 18406484 18406559 - LINE/L2_L2c chr1 18406535 18406620 - SINE/MIR_MIR chr1 18406647 18407089 - LINE/L2_L2b chr1 18407160 18407339 - DNA/hAT-Charlie_MER103C chr1 18407503 18407745 - SINE/MIR_MIR chr1 18407923 18407976 - LINE/L2_L2b chr1 18408113 18408214 - DNA/hAT-Charlie_Charlie15a chr1 18408309 18408611 - SINE/Alu_AluJr chr1 18408849 18408885 + Low_complexity_G-rich chr1 18409435 18409690 + SINE/MIR_MIRc chr1 18409913 18409972 - DNA/hAT-Charlie_MER5A chr1 18410026 18410219 - SINE/MIR_MIRb chr1 18411093 18411306 - SINE/MIR_MIRb chr1 18411307 18411379 + SINE/MIR_MIRc chr1 18411580 18411639 - LINE/L2_L2 chr1 18411646 18411754 - SINE/MIR_MIRb chr1 18411848 18412139 + LTR/ERV1_LTR10B2 chr1 18412141 18412241 - SINE/MIR_MIRb chr1 18412327 18412442 + DNA/hAT-Blackjack_MER81 chr1 18413200 18413361 + SINE/MIR_MIRc chr1 18413586 18413597 + Simple_repeat_(TCCCC)n chr1 18413598 18413629 + Simple_repeat_(C)n chr1 18414070 18414502 + LINE/L2_L2b chr1 18414522 18414969 + LTR/ERVL_LTR16E1 chr1 18414971 18415103 + LINE/L2_L2b chr1 18415116 18415205 + LTR/ERV1_LTR29 chr1 18415180 18415288 + LTR/ERV1_MER39 chr1 18415291 18415414 + SINE/Alu_AluJr chr1 18415423 18415516 + SINE/Alu_AluJb chr1 18415517 18416156 + LTR/ERV1_MER41B chr1 18416157 18416218 + SINE/Alu_AluJb chr1 18416219 18416325 + LTR/ERV1_MER39 chr1 18416326 18416628 + SINE/Alu_AluSc chr1 18416629 18416846 + LTR/ERV1_MER39 chr1 18416847 18416904 - LTR/ERV1_MER4B chr1 18416905 18416997 + SINE/Alu_AluSq2 chr1 18416998 18417346 - LTR/ERV1_MER4B chr1 18417436 18418010 - LTR/ERV1_MER34C_ chr1 18418011 18418194 + LINE/L2_L2b chr1 18418042 18418339 + LINE/L2_L2c chr1 18418370 18418409 + Low_complexity_GA-rich chr1 18418410 18418733 - LTR/ERV1_LTR8A chr1 18418734 18419041 + SINE/Alu_AluSz chr1 18419042 18419410 - LTR/ERV1_LTR8A chr1 18419692 18419741 + LINE/L2_L2b chr1 18419847 18419979 - SINE/MIR_MIRc chr1 18419989 18420652 - LTR/ERV1_LTR8A chr1 18420705 18420741 + LTR/ERV1_LTR29 chr1 18420743 18420917 - SINE/Alu_AluSz6 chr1 18420918 18421211 - SINE/Alu_AluSx chr1 18421214 18421771 + LTR/ERV1_MER4B chr1 18421777 18421833 + LTR/ERV1_MER41-int chr1 18421836 18421926 + LINE/Penelope_Penelope1_Vert chr1 18421927 18422036 + LTR/ERV1_MER41-int chr1 18421942 18422180 + LTR/ERV1_MER34-int chr1 18422181 18422855 + LTR/ERV1_MER41B chr1 18422856 18423422 + LTR/ERV1_MER34-int chr1 18423424 18423508 + LTR/ERV1_MER4B chr1 18423509 18423815 + SINE/Alu_AluSz chr1 18423816 18424347 + LTR/ERV1_MER4B chr1 18424355 18424477 + LTR/ERV1_LTR49-int chr1 18424478 18425966 + LTR/ERV1_LTR49-int chr1 18425967 18426256 + SINE/Alu_AluJb chr1 18426257 18426397 + LTR/ERV1_LTR49-int chr1 18426398 18426456 + LINE/CR1_L3 chr1 18426457 18427544 + LTR/ERV1_LTR49-int chr1 18427545 18427832 - SINE/Alu_AluJr chr1 18427833 18428371 + LTR/ERV1_LTR49-int chr1 18428372 18428496 - DNA/PiggyBac_MER85 chr1 18428497 18429854 + LTR/ERV1_LTR49-int chr1 18429834 18430017 + LTR/ERV1_LTR49-int chr1 18430090 18430185 + LTR/ERV1_MER34-int chr1 18430186 18430782 + LTR/ERV1_LTR48B chr1 18430896 18430960 - LINE/L1_L1ME1 chr1 18430973 18431269 - SINE/Alu_AluSx4 chr1 18431280 18431538 - SINE/Alu_AluJb chr1 18431788 18432042 - SINE/MIR_MIR chr1 18432249 18432463 - SINE/MIR_MIRc chr1 18432932 18433069 - SINE/MIR_MIRb chr1 18433158 18433197 - DNA/hAT-Charlie_MER5A chr1 18433248 18433597 + LTR/ERVL-MaLR_THE1A chr1 18433598 18433620 - SINE/MIR_MIRc chr1 18433621 18433921 + SINE/Alu_AluSz chr1 18433922 18433978 - SINE/MIR_MIRc chr1 18434630 18434749 - SINE/MIR_MIR chr1 18434766 18434930 + SINE/Alu_AluJb chr1 18434931 18435383 + LTR/ERVL-MaLR_MLT1G chr1 18435384 18435460 - SINE/MIR_MIR chr1 18435548 18435759 - LTR/ERV1?_LTR103_Mam chr1 18436028 18436321 + SINE/Alu_AluSx chr1 18436400 18436528 + LINE/L2_L2 chr1 18437326 18437657 - SINE/Alu_AluJo chr1 18437682 18437806 - SINE/MIR_MIRb chr1 18437962 18438263 - SINE/Alu_AluSg4 chr1 18438268 18438446 + SINE/MIR_MIR chr1 18438518 18438691 + LINE/L1_L1M5 chr1 18438747 18438818 + LINE/L2_L2a chr1 18438821 18438846 + Simple_repeat_(TGAA)n chr1 18439143 18439193 + Simple_repeat_(GT)n chr1 18439352 18439389 + Low_complexity_A-rich chr1 18439760 18439842 - SINE/MIR_MIRb chr1 18439958 18440161 - SINE/MIR_MIR chr1 18440446 18440593 + LTR/ERVL_MER74A chr1 18440630 18440670 + SINE/MIR_MIR chr1 18440745 18441113 + LTR/ERVL_MER74A chr1 18441291 18441400 + SINE/MIR_MIR chr1 18441444 18441524 - SINE/MIR_MIRb chr1 18441609 18441760 + DNA/hAT-Tip100_MER96 chr1 18441913 18441958 + Simple_repeat_(GT)n chr1 18442181 18442361 - LTR/ERV1?_LTR103_Mam chr1 18442429 18442508 - LTR/ERV1?_LTR103_Mam chr1 18442518 18442825 + SINE/Alu_AluJr chr1 18442947 18443196 + LINE/L2_L2a chr1 18443197 18443753 - LTR/ERV1_MER41D chr1 18443754 18443867 + LINE/L2_L2a chr1 18443868 18444169 + SINE/Alu_AluSz6 chr1 18444170 18444299 + LINE/L2_L2a chr1 18444412 18444561 + LINE/L2_L2a chr1 18444605 18444806 - LTR/ERVL-MaLR_MLT1L chr1 18445147 18445206 - SINE/MIR_MIRb chr1 18445207 18445611 + LTR/ERVL-MaLR_MSTA chr1 18445612 18445683 - SINE/MIR_MIRb chr1 18446234 18446403 + SINE/MIR_MIR chr1 18446420 18446667 + LINE/L2_L2b chr1 18447120 18447310 - DNA/TcMar-Tigger_Tigger19a chr1 18447311 18447348 + Simple_repeat_(TG)n chr1 18447589 18447836 + Simple_repeat_(CA)n chr1 18448091 18448196 + SINE/MIR_MIRc chr1 18448267 18448446 - SINE/MIR_MIRb chr1 18448756 18448823 - LINE/L2_L2b chr1 18449275 18449428 + SINE/MIR_MIR3 chr1 18449458 18449582 - LINE/L2_L2a chr1 18449597 18449660 - DNA/hAT-Charlie_MER117 chr1 18449727 18449901 - LTR/ERVL_MLT2C2 chr1 18449902 18450209 - SINE/Alu_AluSz chr1 18450210 18450252 - LTR/ERVL_MLT2C2 chr1 18450446 18450637 - LTR/ERVL_MLT2C2 chr1 18450932 18452229 + LINE/L1_L1PA8 chr1 18452457 18452844 - LTR/ERVL-MaLR_MLT1I chr1 18452952 18453062 - DNA/TcMar-Tigger_Tigger15a chr1 18453116 18453156 + DNA/hAT-Charlie_Chap1a_Mam chr1 18453157 18453186 + Simple_repeat_(AAAC)n chr1 18453187 18453236 + DNA/hAT-Charlie_Chap1a_Mam chr1 18453470 18453690 + SINE/MIR_MIRb chr1 18453836 18454025 + LINE/L1_L1ME4c chr1 18454120 18454179 + Simple_repeat_(ATTTA)n chr1 18454306 18454474 + SINE/MIR_MIRb chr1 18454895 18454952 + LINE/CR1_L3 chr1 18455013 18455312 + LINE/L2_L2b chr1 18455379 18455582 - SINE/MIR_MIRb chr1 18455590 18455902 - SINE/Alu_AluJo chr1 18456025 18456427 - LINE/L1_L1MB7 chr1 18456428 18456737 - SINE/Alu_AluSp chr1 18456738 18457082 - LINE/L1_L1MB7 chr1 18457083 18457375 - SINE/Alu_AluJb chr1 18457376 18457423 - LINE/L1_L1MB7 chr1 18457628 18457749 + SINE/MIR_MIRb chr1 18457753 18458017 - LINE/L2_L2a chr1 18458018 18458486 - LTR/ERVL-MaLR_MLT1J chr1 18458487 18458663 - LINE/L2_L2a chr1 18459244 18459503 + SINE/MIR_MIR chr1 18459611 18459797 + LINE/L2_L2a chr1 18459808 18459922 - SINE/MIR_MIRb chr1 18459930 18460335 + LTR/ERVL_LTR16 chr1 18460362 18460500 - SINE/MIR_MIRc chr1 18460581 18460658 + LINE/L2_L2a chr1 18460678 18460711 + Simple_repeat_(TGAA)n chr1 18460712 18460798 + LINE/L2_L2c chr1 18460836 18461260 + LINE/L2_L2c chr1 18461288 18461416 - SINE/MIR_MIR chr1 18462092 18463028 + LINE/L1_L1PA6 chr1 18463029 18463062 + Simple_repeat_(ATA)n chr1 18463296 18463803 - LTR/ERVL-MaLR_MLT1D chr1 18463930 18464134 - DNA/hAT-Tip100_ORSL chr1 18464284 18464455 - SINE/MIR_MIR3 chr1 18464468 18464490 + Simple_repeat_(CA)n chr1 18464576 18464721 - SINE/MIR_MIR3 chr1 18465093 18465153 + SINE/MIR_MIRb chr1 18465154 18465471 + SINE/Alu_AluJr chr1 18465472 18465567 + SINE/MIR_MIRb chr1 18465534 18465572 + LINE/L2_L2a chr1 18465904 18466017 - SINE/MIR_MIR3 chr1 18466193 18466230 - LINE/L1_L1PA10 chr1 18466231 18466387 + LINE/L1_L1PA10 chr1 18466449 18466627 + LINE/L2_L2c chr1 18466775 18466838 - LINE/L2_L2b chr1 18467066 18467203 + SINE/MIR_MIRb chr1 18467340 18467629 - SINE/Alu_AluSx chr1 18467665 18467744 + SINE/MIR_MIR3 chr1 18467843 18468262 - LTR/ERVL-MaLR_MSTB chr1 18468810 18469035 + LINE/L2_L2b chr1 18469052 18469153 + SINE/MIR_MIRb chr1 18469164 18469524 - LTR/ERVL-MaLR_MLT1J1 chr1 18469528 18469614 + SINE/MIR_MIRb chr1 18470240 18470332 - SINE/MIR_MIRb chr1 18470508 18470625 + DNA/hAT-Charlie_Charlie4z chr1 18470789 18470862 - LINE/L2_L2d2 chr1 18471133 18471397 + SINE/MIR_MIRb chr1 18471419 18471550 - SINE/MIR_MIR chr1 18471736 18471860 - DNA/hAT-Charlie_MER5A1 chr1 18471870 18472176 - SINE/Alu_AluSz chr1 18472235 18472409 + SINE/MIR_MIRb chr1 18472410 18472640 + Simple_repeat_(AATTAGT)n chr1 18472673 18472719 + Simple_repeat_(TAATAC)n chr1 18472730 18472788 + Simple_repeat_(TATTAA)n chr1 18473337 18473449 + SINE/MIR_MIRb chr1 18473775 18473944 + DNA/hAT_MamRep38 chr1 18474070 18474344 + SINE/Alu_AluSx chr1 18474462 18474523 + Simple_repeat_(CTCTCTCTGT)n chr1 18474610 18474660 + Simple_repeat_(ATGTGC)n chr1 18475120 18475208 + DNA/hAT-Ac_OldhAT1 chr1 18475331 18475343 + LTR/ERVL_MER74A chr1 18475344 18475646 - SINE/Alu_AluJo chr1 18475647 18476201 + LTR/ERVL_MER74A chr1 18476474 18476715 + LINE/L2_L2b chr1 18477277 18477501 + SINE/MIR_MIRc chr1 18477637 18477745 + SINE/MIR_MIR chr1 18477916 18477993 + DNA/hAT-Charlie_MER5A chr1 18478001 18478298 + SINE/Alu_AluSx chr1 18478300 18478515 + SINE/Alu_AluSx chr1 18478516 18478670 + Low_complexity_GA-rich chr1 18478671 18478723 + Simple_repeat_(AGGC)n chr1 18478884 18479048 + SINE/MIR_MIRc chr1 18479533 18479692 + DNA/hAT-Charlie_MER102a chr1 18479928 18480131 - SINE/MIR_MIR chr1 18484565 18484660 + SINE/MIR_MIRc chr1 18485511 18485621 - DNA/hAT-Charlie_MER117 chr1 18486012 18486367 - LINE/L2_L2a chr1 18486778 18487085 - LINE/L1_L1ME4a chr1 18487382 18487540 - SINE/MIR_MIRb chr1 18487541 18487837 + SINE/Alu_AluSz chr1 18487838 18487895 - SINE/MIR_MIRc chr1 18488002 18488124 + SINE/MIR_MIRb chr1 18488149 18488448 + SINE/Alu_AluJb chr1 18489581 18490056 + LTR/ERVL-MaLR_MLT1H chr1 18490057 18490218 - SINE/MIR_MIRb chr1 18490539 18490722 + LTR/ERVL_LTR16C chr1 18491092 18491240 - DNA/TcMar-Tigger_MamRep434 chr1 18491541 18491627 - LINE/L1_L1MB4 chr1 18491653 18491789 - LINE/L1_L1ME4a chr1 18491801 18491998 - LINE/L1_L1MB3 chr1 18492689 18492975 - LINE/L2_L2c chr1 18493002 18493204 - LINE/L2_L2a chr1 18493214 18493312 - LINE/L1_L1MC3 chr1 18493313 18493656 + DNA/hAT-Charlie_MER1B chr1 18493657 18493859 - LINE/L1_L1MC3 chr1 18493860 18494117 - LTR/ERV1_MER66B chr1 18494118 18494387 + SINE/Alu_AluSz chr1 18494388 18494618 - LTR/ERV1_MER66B chr1 18494619 18495401 - LINE/L1_L1MC3 chr1 18495434 18495652 - LTR/ERV1_MER34B chr1 18495686 18495869 - LINE/L2_L2a chr1 18496026 18496122 + LINE/L2_L2b chr1 18496139 18496474 + DNA/TcMar-Tigger_MamRep434 chr1 18496521 18496585 + DNA/TcMar-Tigger_MamRep434 chr1 18496586 18496886 + SINE/Alu_AluSx chr1 18496887 18497182 + DNA/TcMar-Tigger_MamRep434 chr1 18497183 18497258 + SINE/MIR_MIR chr1 18497376 18497419 + SINE/MIR_MIRc chr1 18497420 18497773 - LTR/ERVL-MaLR_THE1A chr1 18497774 18497860 + SINE/MIR_MIRc chr1 18497920 18498397 - LTR/ERV1_MER34A chr1 18498398 18498681 - SINE/Alu_AluJr chr1 18498682 18498708 - LTR/ERV1_MER34A chr1 18498874 18498960 - LINE/L2_L2b chr1 18499017 18499144 - LTR/ERVL_LTR16C chr1 18499108 18499855 + LTR/ERV1_LTR1F1 chr1 18499927 18500136 - LTR/ERVL_LTR16C chr1 18500315 18501286 - LINE/L1_L1MC4 chr1 18501332 18501345 - LTR/ERVL_LTR16A1 chr1 18501346 18501645 + SINE/Alu_AluY chr1 18501646 18501690 - LTR/ERVL_LTR16A1 chr1 18501770 18501848 - LTR/ERVL_LTR16A1 chr1 18501895 18502108 - DNA/hAT-Charlie_MER58A chr1 18502291 18502385 + DNA/hAT-Tip100_MamTip3 chr1 18502415 18502573 + LINE/L1_L1MB7 chr1 18502574 18502885 + SINE/Alu_AluYf1 chr1 18502887 18503015 + Simple_repeat_(AAAG)n chr1 18503016 18503116 + LINE/L1_L1MB7 chr1 18503117 18503500 + LTR/ERVL_MLT2C1 chr1 18503501 18503517 + LINE/L1_L1MB7 chr1 18503519 18503829 + SINE/Alu_AluSc chr1 18503830 18503918 + Low_complexity_GA-rich chr1 18503919 18504847 + LINE/L1_L1MB7 chr1 18504848 18505224 - LTR/ERVL-MaLR_THE1D chr1 18505225 18505256 + LINE/L1_L1MB7 chr1 18505330 18505685 + LTR/ERVL-MaLR_MLT1J chr1 18505711 18505947 - LTR/ERVL-MaLR_MLT1G1 chr1 18505948 18506282 - SINE/Alu_AluJb chr1 18506283 18506414 - LTR/ERVL-MaLR_MLT1G1 chr1 18506415 18506496 - LINE/L1_L1M5 chr1 18506498 18507068 - LTR/ERVL-MaLR_MLT1D chr1 18507069 18507365 - LINE/L1_L1MEf chr1 18507367 18507519 + SINE/Alu_AluJo chr1 18507529 18508207 + LINE/L1_L1PA17 chr1 18508208 18509220 - LINE/L1_L1MEf chr1 18509221 18509503 + SINE/Alu_AluJb chr1 18509549 18509731 + SINE/MIR_MIRb chr1 18510429 18510452 + Simple_repeat_(A)n chr1 18510511 18510538 + Simple_repeat_(GTTT)n chr1 18510539 18510810 - SINE/Alu_AluY chr1 18511373 18511449 + Simple_repeat_(GT)n chr1 18512191 18512208 + Simple_repeat_(A)n chr1 18512392 18512489 + SINE/MIR_MIR chr1 18512538 18512624 + Simple_repeat_(ATG)n chr1 18512587 18512632 + Simple_repeat_(TGACGG)n chr1 18512766 18513002 - SINE/MIR_MIRc chr1 18513394 18513688 + SINE/Alu_AluSc chr1 18514034 18514245 - LTR/ERVL-MaLR_MLT1H2 chr1 18514960 18516257 + LINE/L1_L1MB7 chr1 18516258 18516561 + SINE/Alu_AluSx4 chr1 18516562 18517099 + LINE/L1_L1MB7 chr1 18517100 18517175 + DNA/TcMar-Mariner_MADE1 chr1 18517176 18517207 + LINE/L1_L1MB7 chr1 18517208 18518398 - DNA/TcMar-Tigger_Tigger3b chr1 18518399 18518764 + LINE/L1_L1MB7 chr1 18518767 18520515 + LINE/L1_L1MB7 chr1 18520520 18520668 + LINE/L1_L1ME3Cz chr1 18520669 18520765 - LINE/L1_L1PA8 chr1 18520766 18521562 + LINE/L1_L1ME3Cz chr1 18521563 18522126 - LINE/L1_L1PB2 chr1 18522127 18522466 + LINE/L1_L1ME3Cz chr1 18522467 18522773 + SINE/Alu_AluSx1 chr1 18522774 18522857 + LINE/L1_L1ME3Cz chr1 18522936 18522953 + Simple_repeat_(A)n chr1 18522983 18523590 + LINE/L1_L1ME3Cz chr1 18523602 18523699 + Simple_repeat_(CTTC)n chr1 18523821 18524056 - LINE/L2_L2c chr1 18524126 18524209 - LTR/ERVL_LTR105_Mam chr1 18524209 18524582 - LTR/ERVL_LTR105_Mam chr1 18524657 18524771 - DNA/hAT-Tip100_ORSL chr1 18524897 18525041 - DNA/hAT-Tip100_ORSL chr1 18525743 18525789 + Simple_repeat_(AC)n chr1 18526071 18526255 - DNA/hAT-Charlie_MER5A chr1 18526360 18526478 - LINE/L1_L1PA3 chr1 18526805 18526940 - SINE/MIR_MIRc chr1 18526950 18527320 - LTR/ERVL-MaLR_MLT1L chr1 18527603 18527741 + SINE/MIR_MIRb chr1 18528051 18528080 + Simple_repeat_(G)n chr1 18528151 18528445 + SINE/Alu_AluJr chr1 18528447 18528577 - SINE/MIR_MIR chr1 18528900 18529078 - LINE/L2_L2b chr1 18529177 18529271 - SINE/MIR_MIR chr1 18529272 18529634 + LTR/ERVL-MaLR_THE1B chr1 18529635 18529779 - SINE/MIR_MIR chr1 18530431 18530654 - LINE/RTE-X_L4_A_Mam chr1 18530856 18530945 - SINE/MIR_MIR chr1 18530945 18531711 + LINE/L2_L2c chr1 18531848 18532347 + LTR/ERVL-MaLR_MLT1D chr1 18532356 18532418 - SINE/MIR_MIRc chr1 18532933 18533165 - SINE/MIR_MIRb chr1 18533427 18533724 - DNA/TcMar-Tigger_Tigger19b chr1 18533727 18533811 + LTR/ERVL-MaLR_MLT1D chr1 18533812 18534117 - LTR/Gypsy?_LTR85a chr1 18534118 18534421 + SINE/Alu_AluY chr1 18534422 18534742 - LTR/Gypsy?_LTR85a chr1 18534835 18535163 + LTR/ERVL-MaLR_MLT1J chr1 18535648 18535872 + SINE/MIR_MIRb chr1 18536141 18536197 + LINE/L2_L2c chr1 18536483 18536664 + SINE/MIR_MIR3 chr1 18536909 18536935 + Simple_repeat_(TCAG)n chr1 18536939 18537132 - LINE/L2_L2b chr1 18537170 18537220 + DNA/hAT-Blackjack_MER81 chr1 18538222 18538360 + SINE/MIR_MIRc chr1 18538349 18538409 - LINE/L2_L2a chr1 18538510 18538561 + Simple_repeat_(CTCCTT)n chr1 18538572 18538823 - SINE/MIR_MIR chr1 18538960 18539161 + SINE/MIR_MIRb chr1 18539207 18539419 + LTR/ERVL_LTR16A1 chr1 18539533 18539809 + SINE/Alu_AluSx chr1 18539811 18540094 + Simple_repeat_(AAGG)n chr1 18540619 18540676 - LINE/L2_L2a chr1 18540747 18540963 + SINE/MIR_MIRb chr1 18541523 18541838 - LTR/ERVL-MaLR_MLT1C2 chr1 18541848 18541993 - SINE/MIR_MIRb chr1 18542033 18542225 - SINE/MIR_MIRb chr1 18542414 18542420 + Simple_repeat_(CACACT)n chr1 18542421 18542458 + Simple_repeat_(AC)n chr1 18543076 18543132 + SINE/MIR_MIR3 chr1 18543151 18543277 + SINE/MIR_MIRc chr1 18543278 18543786 + LTR/ERVL-MaLR_MLT1D chr1 18543787 18543848 + SINE/MIR_MIRc chr1 18544013 18544032 + LINE/L2_L2a chr1 18544033 18544269 - SINE/Alu_AluJr chr1 18544270 18544414 + LINE/L2_L2a chr1 18544511 18544644 + SINE/MIR_MIRc chr1 18545104 18545299 + DNA/hAT-Blackjack_MER63A chr1 18545935 18546081 - SINE/MIR_MIRc chr1 18546587 18546706 + SINE/Alu_AluSz6 chr1 18546994 18547195 + LINE/L2_L2c chr1 18547056 18547214 + LINE/L2_L2a chr1 18547409 18547741 - LINE/L2_L2b chr1 18548334 18548428 - SINE/MIR_MIR3 chr1 18548584 18548775 - LTR/ERVL-MaLR_MLT1I chr1 18548798 18548982 - SINE/MIR_MIRb chr1 18549537 18549627 + LINE/L2_L2c chr1 18549628 18549692 - LTR/Gypsy?_LTR85b chr1 18549703 18550086 - LTR/Gypsy?_LTR85b chr1 18550415 18550603 - SINE/MIR_MIRb chr1 18550749 18550950 - SINE/MIR_MIRb chr1 18551427 18551729 + SINE/Alu_AluSx chr1 18551730 18551822 + Low_complexity_GA-rich chr1 18551823 18551844 + Simple_repeat_(GA)n chr1 18551874 18551963 - SINE/MIR_MIR chr1 18551968 18552156 + DNA/hAT-Charlie_Charlie15a chr1 18552311 18553626 + LINE/L2_L2c chr1 18553575 18553902 + LINE/L2_L2b chr1 18553955 18554128 - SINE/MIR_MIRc chr1 18554413 18554553 - SINE/MIR_MIRc chr1 18554671 18554793 + LINE/L2_L2c chr1 18554906 18555101 + LINE/L1_L1MC5 chr1 18555177 18555305 - SINE/MIR_MIRb chr1 18555470 18555587 - DNA/TcMar-Mariner_MARNA chr1 18555919 18556364 + LINE/L2_L2b chr1 18556396 18556511 + SINE/MIR_MIRb chr1 18556469 18556624 - LINE/L2_L2a chr1 18556625 18557107 - DNA/hAT-Charlie_MER1A chr1 18557108 18557195 - LINE/L2_L2a chr1 18557196 18557489 - SINE/Alu_AluSp chr1 18557490 18557679 - LINE/L2_L2a chr1 18559101 18559377 - LINE/L2_L2a chr1 18559378 18559603 - SINE/Alu_AluSp chr1 18559604 18559742 - LINE/L2_L2a chr1 18560219 18560354 - SINE/MIR_MIR chr1 18560355 18560643 + SINE/Alu_AluSx1 chr1 18560644 18560655 - SINE/MIR_MIR1_Amn chr1 18560808 18560840 + Simple_repeat_(TCACC)n chr1 18561239 18561404 + DNA/hAT-Blackjack_MER63A chr1 18561544 18561557 + Low_complexity_A-rich chr1 18561558 18561592 + Simple_repeat_(AT)n chr1 18561600 18561680 + DNA/TcMar-Mariner_MADE2 chr1 18561725 18561911 + SINE/MIR_MIRb chr1 18562358 18562448 + SINE/MIR_MIR1_Amn chr1 18562449 18562742 - SINE/Alu_AluJo chr1 18562743 18562774 + SINE/MIR_MIR1_Amn chr1 18562918 18563068 + SINE/MIR_MIRb chr1 18563082 18563411 + SINE/Alu_AluSz chr1 18563419 18563666 - SINE/MIR_MIR chr1 18564158 18564462 + SINE/Alu_AluSz chr1 18564463 18564668 + SINE/MIR_MIRb chr1 18565442 18565668 + SINE/MIR_MIRb chr1 18565958 18566257 - DNA/hAT-Charlie_MER102b chr1 18566687 18566836 - SINE/MIR_MIR3 chr1 18567260 18567365 - SINE/MIR_MIR chr1 18567726 18567972 - LINE/L1_L1ME3 chr1 18567977 18568496 + LTR/ERVL-MaLR_MLT1G1 chr1 18568589 18568773 + Low_complexity_GA-rich chr1 18568780 18568823 - LTR/ERVL-MaLR_MLT1K chr1 18568824 18569246 - LTR/ERVL-MaLR_MLT1C chr1 18569247 18569385 - LTR/ERVL-MaLR_MLT1K chr1 18569626 18569780 - DNA/hAT?_MER121 chr1 18569995 18570229 - LINE/L2_L2a chr1 18570522 18570819 + SINE/Alu_AluJb chr1 18570857 18570976 + LINE/L1_L1ME3A chr1 18570991 18571114 + LINE/L1_L1MA5A chr1 18571149 18571266 + LINE/L1_L1ME5 chr1 18571419 18571521 + SINE/Alu_FLAM_A chr1 18572314 18572452 + LINE/L2_L2b chr1 18572461 18572517 - SINE/MIR_MIRc chr1 18572629 18572659 + Simple_repeat_(ATGCAA)n chr1 18573000 18573133 - SINE/MIR_MIRb chr1 18573499 18573879 + LINE/L2_L2b chr1 18574298 18574497 + SINE/MIR_MIR chr1 18574744 18574763 + Simple_repeat_(A)n chr1 18574768 18575064 + SINE/Alu_AluJr chr1 18575465 18575591 - SINE/MIR_MIR3 chr1 18576085 18576223 + LTR/ERVL-MaLR_MLT1H chr1 18576233 18576531 + SINE/Alu_AluJb chr1 18576602 18576670 + Simple_repeat_(CAT)n chr1 18577136 18577234 + SINE/MIR_MIRb chr1 18577657 18577894 + SINE/Alu_AluSz chr1 18577983 18578513 - LINE/L2_L2a chr1 18578514 18578802 + SINE/Alu_AluJr chr1 18578805 18579094 + SINE/Alu_AluY chr1 18579095 18579266 - LINE/L2_L2a chr1 18579267 18579639 - LTR/ERVL-MaLR_THE1C chr1 18579640 18579674 - LINE/L2_L2a chr1 18579675 18579730 - SINE/MIR_MIRc chr1 18579731 18579774 + Simple_repeat_(TTTTAT)n chr1 18579777 18580059 - SINE/Alu_AluJb chr1 18580060 18580096 - SINE/MIR_MIRb chr1 18580637 18580793 - SINE/MIR_MIRb chr1 18581063 18581279 - SINE/MIR_MIR chr1 18581688 18581752 - DNA/hAT-Charlie_MER113A chr1 18581753 18581795 + Simple_repeat_(TAT)n chr1 18581796 18582079 - SINE/Alu_AluSz chr1 18582080 18582264 - DNA/hAT-Charlie_MER113A chr1 18582365 18582691 - LINE/L2_L2b chr1 18582828 18583023 - SINE/MIR_MIRb chr1 18583045 18583172 + SINE/MIR_MIR3 chr1 18583174 18583371 - SINE/MIR_MIR chr1 18583991 18584031 - LINE/L2_L2c chr1 18584439 18584756 - SINE/MIR_MIR chr1 18584977 18585175 + LINE/L1_L1ME1 chr1 18585176 18585484 + SINE/Alu_AluSz chr1 18585485 18586032 + LINE/L1_L1ME1 chr1 18586033 18586345 + SINE/Alu_AluSz chr1 18586346 18586421 + LINE/L1_L1ME1 chr1 18586606 18586663 - SINE/MIR_MIR3 chr1 18586729 18586763 + Simple_repeat_(GCCATG)n chr1 18586852 18586997 + SINE/MIR_MIRb chr1 18587008 18587261 - LTR/ERVL-MaLR_MLT1L chr1 18587713 18588632 - LINE/L1_L1ME4b chr1 18588633 18588936 - SINE/Alu_AluSx chr1 18588937 18588961 - LINE/L1_L1ME4b chr1 18588962 18589245 + SINE/Alu_AluYh3a3 chr1 18589246 18589883 - LINE/L1_L1ME4b chr1 18589887 18590093 - LINE/L1_L1ME3B chr1 18590094 18590205 + DNA/TcMar-Mariner_MADE1 chr1 18590206 18590232 - LINE/L1_L1ME3B chr1 18590232 18591012 - LINE/L1_L1PA5 chr1 18591090 18591286 - SINE/Alu_AluJb chr1 18591291 18591358 + Simple_repeat_(TTC)n chr1 18591359 18591636 - SINE/Alu_AluSc8 chr1 18591637 18591776 - SINE/Alu_AluJb chr1 18591785 18592008 - LINE/L1_L1ME3B chr1 18592009 18592301 + SINE/Alu_AluJr chr1 18592302 18592486 - LINE/L1_L1ME3B chr1 18592488 18592919 + LTR/ERVL-MaLR_MLT1J2 chr1 18593087 18593577 - LINE/L1_L1ME4b chr1 18593752 18593787 + tRNA_tRNA-Gln-CAG chr1 18593789 18594224 - LINE/L1_L1ME4b chr1 18594891 18595166 + SINE/Alu_AluY chr1 18595213 18595297 + DNA/TcMar-Tigger_MER2 chr1 18595299 18595407 + SINE/Alu_AluSx chr1 18595621 18595827 + SINE/Alu_AluSx chr1 18595828 18595911 + DNA/TcMar-Tigger_MER2B chr1 18595943 18596135 - LINE/L2_L2a chr1 18596178 18596191 + Low_complexity_A-rich chr1 18596192 18596522 + SINE/Alu_AluSz chr1 18596527 18596707 + SINE/Alu_AluJb chr1 18596708 18597013 + SINE/Alu_AluSx chr1 18597101 18597238 - LINE/L2_L2a chr1 18597774 18597874 - SINE/MIR_MIRc chr1 18598587 18598882 - SINE/Alu_AluJr chr1 18598890 18599040 + LINE/L2_L2a chr1 18599463 18599550 + Low_complexity_G-rich chr1 18599552 18599590 + Low_complexity_A-rich chr1 18599847 18599917 - SINE/MIR_MIR3 chr1 18600209 18600360 + SINE/MIR_MIR3 chr1 18600529 18600830 + SINE/Alu_AluSq chr1 18600844 18601046 - SINE/MIR_MIRb chr1 18601278 18601362 - DNA/hAT_MER53 chr1 18601657 18601726 + SINE/MIR_MIR3 chr1 18602137 18602270 + LINE/L2_L2b chr1 18602221 18602426 - SINE/MIR_MIR chr1 18602446 18602456 + Simple_repeat_(ATGC)n chr1 18602457 18602792 + Simple_repeat_(AATG)n chr1 18602793 18602808 + Simple_repeat_(ATGC)n chr1 18603071 18603215 + SINE/MIR_MIRb chr1 18603623 18603722 + SINE/MIR_MIR3 chr1 18603974 18604131 - SINE/MIR_MIR chr1 18604241 18604412 - SINE/MIR_MIRb chr1 18604445 18604739 - SINE/Alu_AluJr chr1 18605194 18605268 + LINE/L2_L2b chr1 18607418 18607500 + SINE/MIR_MIR3 chr1 18607507 18607708 - SINE/MIR_MIR chr1 18608093 18608191 - DNA/hAT-Charlie_MER20 chr1 18608246 18608409 + SINE/MIR_MIR chr1 18608723 18608942 + DNA/hAT-Charlie_MER20 chr1 18608959 18609115 + LINE/RTE-X_L4_C_Mam chr1 18609726 18609851 + LINE/L1_L1MB5 chr1 18609852 18610147 + SINE/Alu_AluSx1 chr1 18610148 18610403 + LINE/L1_L1MB5 chr1 18610413 18610435 + Low_complexity_GA-rich chr1 18610436 18610567 + SINE/Alu_AluJr chr1 18610568 18610840 + SINE/Alu_AluSz chr1 18610841 18611056 + SINE/Alu_AluJr chr1 18611057 18611074 + Low_complexity_GA-rich chr1 18611185 18611352 + Simple_repeat_(GAAG)n chr1 18612563 18612844 + SINE/Alu_AluSx1 chr1 18612847 18613152 + SINE/Alu_AluSq2 chr1 18613301 18613418 - SINE/MIR_MIRb chr1 18613554 18613837 + SINE/Alu_AluSx1 chr1 18613863 18614053 - SINE/MIR_MIRb chr1 18614435 18614584 - DNA/hAT-Charlie_Charlie17a chr1 18614900 18615194 + SINE/Alu_AluJb chr1 18615266 18615301 + Simple_repeat_(CA)n chr1 18615438 18615583 - SINE/MIR_MIR3 chr1 18615855 18615899 - LINE/CR1_X8_LINE chr1 18615967 18616028 - DNA/hAT-Charlie_MER5A chr1 18616029 18616327 + SINE/Alu_AluJb chr1 18616328 18616474 - DNA/hAT-Charlie_MER5A chr1 18616623 18616701 + LINE/L2_L2b chr1 18616929 18617095 - LINE/L1_L1ME4a chr1 18617233 18617539 + SINE/Alu_AluSp chr1 18617550 18617858 + SINE/Alu_AluJb chr1 18617869 18618166 + SINE/Alu_AluYk2 chr1 18618177 18618410 + SINE/MIR_MIR chr1 18618467 18619006 - LINE/L1_L1ME4a chr1 18619506 18619737 - SINE/MIR_MIRb chr1 18619728 18619923 + SINE/MIR_MIR chr1 18620090 18620199 - LINE/L2_L2c chr1 18620237 18620533 - SINE/Alu_AluSx chr1 18620739 18621046 + SINE/Alu_AluSx1 chr1 18621175 18621307 + LINE/L1_L1ME3Cz chr1 18621308 18621594 + SINE/Alu_AluJb chr1 18621595 18621607 + LINE/L1_L1ME3Cz chr1 18621608 18622091 - LTR/ERVL-MaLR_MLT1D chr1 18622092 18622177 + LINE/L1_L1ME3Cz chr1 18622264 18622438 + SINE/MIR_MIRb chr1 18622450 18622679 - SINE/Alu_AluSz6 chr1 18622680 18622980 - SINE/Alu_AluSx chr1 18622981 18623071 - SINE/Alu_AluSz6 chr1 18623164 18623191 + Simple_repeat_(CA)n chr1 18623930 18624016 - LINE/L2_L2b chr1 18624096 18624272 + SINE/Alu_AluSp chr1 18624303 18624472 + SINE/MIR_MIRb chr1 18624901 18625000 - LINE/L2_L2a chr1 18626123 18626330 + Low_complexity_GA-rich chr1 18626331 18626631 + SINE/Alu_AluSx1 chr1 18626632 18626841 + Low_complexity_GA-rich chr1 18628064 18628213 + Low_complexity_GA-rich chr1 18629147 18629240 + Simple_repeat_(AT)n chr1 18630804 18630834 + Simple_repeat_(CCT)n chr1 18631057 18631085 + Low_complexity_GA-rich chr1 18631371 18631413 + Simple_repeat_(CCTCC)n chr1 18631441 18631491 + Low_complexity_G-rich chr1 18631484 18631524 + Low_complexity_GA-rich chr1 18634262 18634300 + Simple_repeat_(CCTTCT)n chr1 18635428 18635576 + Low_complexity_GA-rich chr1 18635805 18635837 + Simple_repeat_(TG)n chr1 18636287 18636321 + Low_complexity_GA-rich chr1 18636753 18636791 + Low_complexity_G-rich chr1 18637140 18637210 + LINE/L1_X9_LINE chr1 18638243 18638275 + Low_complexity_G-rich chr1 18642131 18642169 + Simple_repeat_(TA)n chr1 18642913 18643012 + Low_complexity_G-rich chr1 18644302 18644414 + SINE/MIR_MIRc chr1 18644889 18645029 - LINE/L2_L2c chr1 18645097 18645158 + Simple_repeat_(TG)n chr1 18646431 18646529 + SINE/MIR_MIRc chr1 18646867 18646909 + Simple_repeat_(CGCGG)n chr1 18646969 18647048 + Low_complexity_G-rich chr1 18647489 18647540 + Low_complexity_GA-rich chr1 18648330 18648642 - SINE/Alu_AluJb chr1 18649081 18649167 - SINE/MIR_MIR3 chr1 18649358 18649463 - SINE/MIR_MIR1_Amn chr1 18649826 18649920 + SINE/MIR_MIR3 chr1 18649928 18649980 + DNA/hAT-Charlie_Charlie15a chr1 18650301 18650406 + SINE/MIR_MIR1_Amn chr1 18650662 18650789 - SINE/MIR_MIR3 chr1 18651019 18651161 + SINE/MIR_MIRc chr1 18651507 18651539 + Simple_repeat_(TCTA)n chr1 18651796 18651840 + Simple_repeat_(C)n chr1 18652291 18652594 - LINE/L2_L2a chr1 18652631 18652765 - SINE/MIR_MIRb chr1 18652836 18653048 - SINE/MIR_MIRb chr1 18653179 18653214 + Simple_repeat_(T)n chr1 18653704 18653835 - SINE/MIR_MIRb chr1 18654753 18654933 - SINE/MIR_MIRb chr1 18655771 18655810 + Simple_repeat_(GT)n chr1 18656019 18656172 + SINE/MIR_MIR chr1 18656214 18656517 + SINE/Alu_AluSz chr1 18656543 18656602 + Simple_repeat_(TTTGAAG)n chr1 18656713 18657012 + SINE/Alu_AluJb chr1 18657753 18657774 + Simple_repeat_(CT)n chr1 18658200 18658260 + Simple_repeat_(CCTCCT)n chr1 18658404 18658566 - SINE/MIR_MIRb chr1 18658926 18659126 + Simple_repeat_(GT)n chr1 18659155 18659306 + LINE/L2_L2d2 chr1 18659708 18659811 + DNA/hAT-Blackjack_MER81 chr1 18660170 18660269 + SINE/MIR_MIRb chr1 18660338 18660365 + Simple_repeat_(TCTTC)n chr1 18660878 18660996 + SINE/MIR_MIRb chr1 18661488 18661673 + SINE/MIR_MIRb chr1 18662165 18662314 + SINE/MIR_MIRc chr1 18662417 18662451 + Simple_repeat_(TTCA)n chr1 18662561 18662872 - SINE/Alu_AluSx chr1 18662912 18662960 + Low_complexity_A-rich chr1 18663263 18663371 - SINE/MIR_MIR chr1 18663372 18663681 - SINE/Alu_AluSz6 chr1 18663682 18663721 - SINE/MIR_MIR3 chr1 18664137 18664264 - SINE/MIR_MIR3 chr1 18664515 18664682 - SINE/MIR_MIRc chr1 18664663 18664766 - SINE/MIR_MIR3 chr1 18664948 18665175 + SINE/MIR_MIR chr1 18665365 18665672 - SINE/Alu_AluSc chr1 18665673 18665970 - SINE/Alu_AluSx3 chr1 18666027 18666144 + SINE/Alu_AluJo chr1 18667008 18667107 - LINE/CR1_L3b chr1 18667141 18667249 + SINE/MIR_MIRc chr1 18667348 18667452 + Simple_repeat_(GGAA)n chr1 18668041 18668205 + DNA/hAT-Charlie_MER5B chr1 18668264 18668404 + SINE/MIR_MIR chr1 18668407 18668418 + Simple_repeat_(AAT)n chr1 18668419 18668707 + SINE/Alu_AluJr chr1 18668708 18668736 + Simple_repeat_(AAT)n chr1 18669183 18669262 - SINE/MIR_MIRb chr1 18669451 18669588 + SINE/MIR_MIR3 chr1 18669682 18669843 + SINE/MIR_MIRb chr1 18669844 18670104 + SINE/Alu_AluSg chr1 18670105 18670137 + SINE/MIR_MIRb chr1 18670168 18670302 + SINE/MIR_MIRb chr1 18671603 18671701 + SINE/MIR_MIRb chr1 18671702 18672002 + SINE/Alu_AluJr chr1 18672003 18672107 + SINE/MIR_MIRb chr1 18672597 18672903 - SINE/Alu_AluSx chr1 18673091 18673297 + SINE/MIR_MIRb chr1 18674122 18674232 - LINE/L2_L2a chr1 18674821 18674975 + DNA/hAT-Charlie_MER5A chr1 18675350 18675486 - SINE/MIR_MIR3 chr1 18676491 18676515 + Simple_repeat_(GGGGA)n chr1 18677803 18678111 + SINE/Alu_AluSx4 chr1 18678131 18678415 + SINE/Alu_AluSp chr1 18678531 18678776 - LINE/L2_L2d2 chr1 18678901 18679083 + SINE/MIR_MIR chr1 18679784 18679821 + Simple_repeat_(TTCA)n chr1 18679824 18679909 - LINE/L2_L2a chr1 18679957 18680144 - SINE/MIR_MIR chr1 18680210 18680363 - SINE/MIR_MIRc chr1 18680503 18680635 - SINE/MIR_MIRb chr1 18680877 18681193 + SINE/Alu_AluSp chr1 18681692 18681766 + Simple_repeat_(TTTAT)n chr1 18681767 18682037 - SINE/Alu_AluSp chr1 18682223 18682290 + SINE/MIR_MIRb chr1 18682544 18682669 + SINE/MIR_MIRb chr1 18683423 18683488 - LINE/L2_L2c chr1 18683558 18683857 + SINE/Alu_AluSz6 chr1 18684078 18684149 - LINE/L2_L2b chr1 18685187 18685304 + SINE/MIR_MIRc chr1 18685354 18685695 - LINE/L2_L2c chr1 18685899 18685937 + Simple_repeat_(CCATCA)n chr1 18686879 18687190 - SINE/Alu_AluSx chr1 18687200 18687381 - SINE/MIR_MIRc chr1 18687443 18687550 + SINE/MIR_MIRb chr1 18688061 18688364 - DNA/hAT-Charlie_MER113 chr1 18688376 18688577 - DNA/hAT-Charlie_MER3 chr1 18688619 18688943 + SINE/Alu_AluSx chr1 18688973 18689075 + LINE/L2_L2c chr1 18689778 18689839 + LINE/L2_L2c chr1 18690083 18690200 + LINE/L2_L2c chr1 18690241 18690319 + Low_complexity_GA-rich chr1 18690344 18690376 + Simple_repeat_(CTCTTC)n chr1 18690835 18690961 - SINE/MIR_MIR3 chr1 18690985 18691268 - SINE/Alu_AluJr chr1 18691284 18691396 - LINE/L1_L1ME1 chr1 18691467 18691494 + Simple_repeat_(TGTT)n chr1 18691525 18691610 - LINE/L1_L1ME3G chr1 18692158 18692259 + Low_complexity_GA-rich chr1 18692260 18692563 + SINE/Alu_AluSx1 chr1 18692564 18692578 + Low_complexity_GA-rich chr1 18693251 18693475 - SINE/MIR_MIRb chr1 18694185 18694461 + SINE/Alu_AluSp chr1 18694462 18694525 + Simple_repeat_(AAAT)n chr1 18694529 18694567 + LINE/CR1_L3 chr1 18694650 18694802 + SINE/MIR_MIR3 chr1 18694820 18694896 + LINE/CR1_L3 chr1 18695059 18695275 + SINE/MIR_MIR chr1 18695335 18695465 - LINE/L2_L2a chr1 18696057 18696366 - SINE/Alu_AluSq2 chr1 18696367 18696534 - SINE/MIR_MIRb chr1 18696535 18697055 + LINE/L1_L1MA3 chr1 18697056 18697139 - SINE/MIR_MIRb chr1 18697426 18697629 - SINE/MIR_MIR chr1 18697722 18697953 + LINE/L2_L2c chr1 18697975 18698028 + Low_complexity_GA-rich chr1 18698225 18698272 - DNA/hAT-Charlie_MER113 chr1 18698273 18698310 + Simple_repeat_(AC)n chr1 18698311 18698585 - DNA/hAT-Charlie_MER113 chr1 18699090 18699115 + Simple_repeat_(TTCA)n chr1 18699117 18699461 - LINE/L2_L2c chr1 18699543 18699564 + SINE/MIR_MIR chr1 18699565 18699860 - SINE/Alu_AluY chr1 18699861 18700025 + SINE/MIR_MIR chr1 18700078 18700117 + Simple_repeat_(GT)n chr1 18700312 18700522 + SINE/MIR_MIRc chr1 18701043 18701182 - DNA/hAT-Charlie_MER5A1 chr1 18702051 18702348 + SINE/Alu_AluSz chr1 18702534 18702696 + SINE/MIR_MIRb chr1 18702796 18702846 - LINE/L2_L2c chr1 18703878 18704108 + SINE/MIR_MIRb chr1 18704189 18704338 + DNA/hAT-Charlie_MER58A chr1 18704339 18704648 + SINE/Alu_AluSx1 chr1 18704649 18704701 + DNA/hAT-Charlie_MER58A chr1 18704847 18705001 + SINE/MIR_MIRb chr1 18706451 18706782 - SINE/Alu_AluSx1 chr1 18707070 18707247 - LINE/L2_L2a chr1 18707335 18707367 - SINE/MIR_MIRb chr1 18707381 18707724 - SINE/Alu_AluSz chr1 18708278 18708558 + SINE/Alu_AluJr chr1 18708770 18708819 - LINE/L2_L2c chr1 18709636 18709659 - SINE/MIR_MIR3 chr1 18709685 18709749 - LINE/L2_L2b chr1 18709746 18709809 - SINE/MIR_MIR chr1 18710070 18710272 - SINE/MIR_MIRb chr1 18711113 18711354 + SINE/MIR_MIRb chr1 18711409 18711454 + Simple_repeat_(CCTTC)n chr1 18711667 18711830 - LINE/RTE-BovB_MamRTE1 chr1 18712800 18713092 + SINE/Alu_AluSz chr1 18713573 18713806 - LINE/L2_L2d chr1 18713868 18713929 + SINE/MIR_MIRb chr1 18713930 18714206 + SINE/Alu_AluSz chr1 18714207 18714239 + Simple_repeat_(AAAT)n chr1 18714240 18714251 + SINE/Alu_AluSz chr1 18714252 18714438 + SINE/MIR_MIRb chr1 18714528 18714700 + SINE/MIR_MIR3 chr1 18715114 18715354 + LINE/L2_L2c chr1 18715379 18715556 - SINE/MIR_MIR chr1 18715573 18715786 + SINE/MIR_MIR chr1 18715801 18715909 - SINE/MIR_MIR3 chr1 18716491 18716518 + Simple_repeat_(T)n chr1 18716752 18716791 + Simple_repeat_(TC)n chr1 18716912 18717007 + Low_complexity_G-rich chr1 18717012 18717064 + Simple_repeat_(CCG)n chr1 18717388 18717726 + LINE/L2_L2b chr1 18717743 18717927 - LINE/L2_L2b chr1 18718179 18718355 + LINE/CR1_L3 chr1 18718375 18718469 - DNA/hAT-Charlie_MER5A chr1 18718644 18718939 - SINE/Alu_AluSg chr1 18719013 18719078 - SINE/MIR_MIR3 chr1 18720134 18720193 + DNA/hAT-Charlie_MER5A chr1 18720520 18720540 + Simple_repeat_(CAGA)n chr1 18720717 18720774 + Low_complexity_GA-rich chr1 18721965 18722157 + SINE/MIR_MIR chr1 18722967 18723087 - LINE/L2_L2c chr1 18723355 18723408 - DNA/hAT-Charlie_Charlie16 chr1 18724646 18724767 + SINE/MIR_MIRb chr1 18724814 18724876 - DNA/TcMar-Tc2_Kanga1a chr1 18725071 18725097 - SINE/MIR_MIR1_Amn chr1 18725400 18725446 + Simple_repeat_(CCCTCCC)n chr1 18725980 18726045 + Simple_repeat_(GT)n chr1 18726140 18726179 + Simple_repeat_(GT)n chr1 18726423 18726507 - LINE/L2_L2c chr1 18726655 18726796 - LINE/CR1_L3b chr1 18727016 18727341 + Simple_repeat_(CA)n chr1 18727341 18727369 + Simple_repeat_(CT)n chr1 18727372 18727410 + Simple_repeat_(AC)n chr1 18727493 18727694 + SINE/MIR_MIRc chr1 18728518 18728578 - DNA/hAT-Charlie_MER5B chr1 18728579 18728883 + SINE/Alu_AluSp chr1 18728884 18728912 - DNA/hAT-Charlie_MER5B chr1 18729368 18729411 + SINE/MIR_MIR1_Amn chr1 18730092 18730277 - SINE/MIR_MIR chr1 18730887 18730966 + Low_complexity_G-rich chr1 18731373 18731562 + SINE/MIR_MIRb chr1 18732034 18732358 + LINE/L2_L2a chr1 18732362 18732480 - LINE/L2_L2b chr1 18732477 18732535 + LINE/L2_L2c chr1 18733848 18733983 + SINE/MIR_MIRc chr1 18733972 18734030 - SINE/MIR_MIRc chr1 18734592 18734663 + LINE/L2_L2c chr1 18735281 18735305 + Simple_repeat_(TTCA)n chr1 18735308 18735407 - LINE/L2_L2c chr1 18735958 18736091 - SINE/MIR_MIR3 chr1 18736105 18736182 - DNA/hAT-Charlie_Charlie5 chr1 18736183 18736482 + SINE/Alu_AluSz6 chr1 18736483 18736781 - DNA/hAT-Charlie_Charlie5 chr1 18736799 18736923 - DNA/hAT-Charlie_Charlie5 chr1 18737459 18737514 + Simple_repeat_(ATGT)n chr1 18738531 18738644 + Simple_repeat_(TCC)n chr1 18738807 18738884 + SINE/MIR_MIR1_Amn chr1 18739153 18739259 - SINE/MIR_MIRc chr1 18739457 18739594 - SINE/MIR_MIRb chr1 18740435 18740619 + SINE/MIR_MIRb chr1 18740647 18741166 + LINE/L1_L1MA4A chr1 18741167 18741478 + SINE/Alu_AluSx1 chr1 18741479 18741606 + LINE/L1_L1MA4A chr1 18741877 18742105 + SINE/MIR_MIR chr1 18742149 18742453 - SINE/Alu_AluY chr1 18742869 18743007 - SINE/MIR_MIRc chr1 18743101 18743332 - SINE/MIR_MIR chr1 18743488 18743545 - SINE/MIR_MIRc chr1 18744415 18744741 + Simple_repeat_(GATG)n chr1 18747008 18747085 - LINE/CR1_L3b chr1 18748968 18749068 + LINE/L2_L2b chr1 18749287 18749458 - SINE/Alu_AluJr chr1 18749459 18749888 - LTR/ERVL_LTR16A1 chr1 18750292 18750429 + SINE/MIR_MIR3 chr1 18750478 18750549 + LINE/RTE-BovB_MamRTE1 chr1 18750657 18750787 - LTR/ERVL-MaLR_MLT1J chr1 18751265 18751329 + SINE/MIR_MIRc chr1 18751357 18751429 - LTR/ERVL-MaLR_MLT1I chr1 18751449 18751662 - LTR/ERVL-MaLR_MLT1I chr1 18751840 18752037 - SINE/MIR_MIRb chr1 18752332 18752542 - SINE/MIR_MIRb chr1 18752622 18752726 - LTR/ERVL-MaLR_MLT1H chr1 18752736 18753326 + LTR/ERVL-MaLR_MLT1E1A chr1 18753461 18753567 - LTR/ERVL-MaLR_MLT1H chr1 18754106 18754275 + SINE/MIR_MIR chr1 18754283 18754373 - DNA/hAT-Charlie_MER103C chr1 18754385 18754760 - LINE/RTE-X_L4_A_Mam chr1 18754887 18755492 - LINE/L1_L1MEf chr1 18755493 18755765 + LTR/ERVL-MaLR_MSTB chr1 18755951 18756030 + LTR/ERVL-MaLR_MLT1I chr1 18756107 18756211 + LINE/L1_L1MC1 chr1 18756212 18756620 + LTR/ERVL-MaLR_MSTA chr1 18756621 18756810 + LINE/L1_L1MC1 chr1 18756812 18757074 + SINE/Alu_AluY chr1 18757079 18757344 - LINE/L1_HAL1b chr1 18757360 18758665 - LINE/L1_L1MEf chr1 18758936 18759220 + SINE/Alu_AluSx1 chr1 18759221 18759282 + Simple_repeat_(AGG)n chr1 18759283 18759306 + SINE/Alu_AluSx1 chr1 18759508 18759799 - SINE/Alu_AluSx3 chr1 18759807 18760058 - SINE/Alu_AluSx3 chr1 18760411 18760522 + DNA/hAT-Charlie_MER5B chr1 18760523 18760798 - SINE/Alu_AluJb chr1 18760799 18760859 + DNA/hAT-Charlie_MER5B chr1 18761237 18761484 - LINE/L1_L1ME5 chr1 18761682 18762110 - LTR/ERVL_MER77B chr1 18762128 18762229 - LTR/ERVL-MaLR_MLT1I chr1 18762266 18762409 - LTR/ERVL-MaLR_MLT1I chr1 18763068 18763262 - SINE/MIR_MIRb chr1 18763085 18763289 - SINE/MIR_MIR chr1 18763330 18763485 - LINE/L1_L1MC5 chr1 18763620 18763847 + SINE/MIR_MIRb chr1 18763842 18764035 + SINE/MIR_MIR chr1 18764171 18764387 + DNA/hAT-Charlie_MER20 chr1 18764811 18764906 + SINE/MIR_MIRb chr1 18764935 18765001 - SINE/MIR_MIRc chr1 18765002 18765033 + Simple_repeat_(TTCA)n chr1 18765045 18765764 - LINE/L2_L2 chr1 18766039 18766151 + SINE/MIR_MIR1_Amn chr1 18766160 18766229 - SINE/MIR_MIRb chr1 18766420 18766495 + DNA/hAT-Charlie_MER5B chr1 18766496 18766539 + Simple_repeat_(ATC)n chr1 18766540 18766587 + DNA/hAT-Charlie_MER5B chr1 18766826 18766985 - SINE/MIR_MIRc chr1 18767058 18767113 + LINE/L2_L2a chr1 18767624 18767781 - DNA/hAT-Charlie_MER58B chr1 18767782 18768039 - SINE/Alu_AluSx chr1 18768040 18768220 - DNA/hAT-Charlie_MER58B chr1 18768224 18768395 - SINE/MIR_MIR chr1 18768448 18768572 - SINE/MIR_MIRc chr1 18769147 18769218 + Simple_repeat_(CCCCTC)n chr1 18770085 18770223 - SINE/MIR_MIRc chr1 18770412 18770581 + SINE/MIR_MIRb chr1 18770859 18771099 - SINE/MIR_MIR chr1 18771362 18771519 + SINE/MIR_MIR3 chr1 18771654 18771961 - SINE/Alu_AluY chr1 18772625 18772838 - SINE/MIR_MIRb chr1 18772884 18772930 + LINE/L2_L2c chr1 18773020 18773148 - SINE/MIR_MIRb chr1 18773405 18773709 + LINE/L1_L1ME4b chr1 18773861 18773913 + Low_complexity_A-rich chr1 18774260 18774464 - SINE/MIR_MIRb chr1 18774549 18774774 + LINE/L1_L1ME4b chr1 18774782 18774818 + LINE/L1_L1ME4b chr1 18775190 18775546 - LINE/L2_L2b chr1 18775610 18775669 + SINE/MIR_MIRc chr1 18775744 18775920 + DNA/hAT-Charlie_MER5A chr1 18776199 18776504 + SINE/Alu_AluSz chr1 18776505 18776539 + Simple_repeat_(AC)n chr1 18776544 18776708 + SINE/MIR_MIR chr1 18776744 18777176 + LINE/L2_L2c chr1 18777816 18777885 - SINE/MIR_MIRb chr1 18777870 18777983 - SINE/MIR_MIR3 chr1 18778136 18778228 - SINE/MIR_MIRb chr1 18778330 18778401 - SINE/MIR_MIRc chr1 18779217 18779335 - SINE/MIR_MIRc chr1 18779256 18779724 - LINE/L2_L2c chr1 18779831 18779876 - LTR/ERVL-MaLR_MSTA chr1 18779877 18780171 + SINE/Alu_AluSz chr1 18780172 18780555 - LTR/ERVL-MaLR_MSTA chr1 18780649 18780801 - SINE/MIR_MIRc chr1 18781134 18781396 + SINE/MIR_MIRb chr1 18781728 18781941 - SINE/MIR_MIRb chr1 18782026 18782318 + SINE/Alu_AluSz chr1 18782444 18782680 + SINE/MIR_MIR chr1 18783062 18783214 - LINE/L2_L2a chr1 18783215 18783539 + SINE/Alu_AluY chr1 18783540 18783641 - LINE/L2_L2a chr1 18783887 18783994 + SINE/MIR_MIRb chr1 18784950 18785184 - SINE/MIR_MIRb chr1 18785438 18785542 + LINE/CR1_L3 chr1 18785592 18785962 - LTR/ERVL-MaLR_THE1D chr1 18785995 18786061 - DNA/hAT_MER53 chr1 18786150 18786393 + LTR/ERVL-MaLR_MLT1J chr1 18786448 18786473 + Simple_repeat_(AAAC)n chr1 18786550 18786662 + LINE/L2_L2c chr1 18786721 18786920 - SINE/MIR_MIR chr1 18786987 18787186 - LINE/L2_L2a chr1 18787187 18787352 + LINE/L1_L1ME2 chr1 18787353 18787635 - SINE/Alu_AluSx1 chr1 18787636 18787717 + LINE/L1_L1ME2 chr1 18787840 18788077 + Low_complexity_GA-rich chr1 18788424 18788760 + LINE/L1_L1ME2 chr1 18788761 18789072 - SINE/Alu_AluY chr1 18789073 18789210 + LINE/L1_L1ME2 chr1 18789086 18789433 + LINE/L1_L1M5 chr1 18789628 18789747 - LINE/L2_L2a chr1 18789748 18789873 + LINE/L1_L1ME2 chr1 18789882 18790253 + LINE/L1_L1ME3A chr1 18790436 18790461 + Simple_repeat_(GA)n chr1 18790630 18790664 + Simple_repeat_(AATG)n chr1 18790665 18790961 + SINE/Alu_AluJb chr1 18790962 18790977 + Simple_repeat_(AATG)n chr1 18791201 18791231 + Simple_repeat_(AC)n chr1 18791232 18791250 + Simple_repeat_(GA)n chr1 18791341 18791374 + Simple_repeat_(CATT)n chr1 18791443 18791739 + SINE/Alu_AluJr chr1 18791744 18791810 - LINE/L2_L2c chr1 18792037 18792151 - SINE/MIR_MIR chr1 18792126 18792277 - LINE/L2_L2c chr1 18792278 18792805 + LTR/ERV1_MER89 chr1 18792806 18792933 - LINE/L2_L2c chr1 18793750 18793835 - LINE/L2_L2c chr1 18793854 18793933 + SINE/MIR_MIRc chr1 18793960 18794134 + SINE/MIR_MIRc chr1 18794124 18794319 - SINE/MIR_MIR chr1 18795019 18795146 + LINE/L1_L1PA8 chr1 18795147 18795240 + Simple_repeat_(TAAAA)n chr1 18795293 18795398 - SINE/MIR_MIRc chr1 18795399 18795704 + LINE/L2_L2b chr1 18796142 18796281 + SINE/MIR_MIR3 chr1 18796456 18796754 + LINE/L1_L1MD2 chr1 18796755 18797077 + SINE/Alu_AluJb chr1 18797078 18797319 + LINE/L1_L1MD2 chr1 18797320 18797631 + SINE/Alu_AluSz chr1 18797632 18797828 + LINE/L1_L1MD2 chr1 18797941 18798068 - SINE/MIR_MIRb chr1 18798157 18798236 + LINE/L1_L1ME4a chr1 18798395 18798557 + SINE/MIR_MIRc chr1 18799425 18799575 + SINE/MIR_MIR3 chr1 18799573 18799994 - LINE/L2_L2b chr1 18800351 18800365 + LINE/L2_L2b chr1 18800366 18800491 + LINE/L2_L2d2 chr1 18800492 18800521 + LINE/L2_L2b chr1 18800566 18800587 + Simple_repeat_(CATT)n chr1 18800591 18800653 - LINE/L2_L2b chr1 18800672 18800753 + LINE/L2_L2b chr1 18801161 18801266 + Simple_repeat_(AACACC)n chr1 18801267 18801295 + Simple_repeat_(CAC)n chr1 18801296 18802679 + Simple_repeat_(AACACC)n chr1 18802799 18803000 - SINE/MIR_MIRb chr1 18803061 18803270 + SINE/MIR_MIRb chr1 18803370 18803545 - SINE/MIR_MIR chr1 18803553 18803655 - SINE/MIR_MIRb chr1 18804329 18804358 - LINE/L2_L2b chr1 18804333 18804429 - SINE/MIR_MIRb chr1 18804508 18804650 - SINE/MIR_MIR3 chr1 18804857 18804935 + LINE/L2_L2d2 chr1 18805599 18805753 + SINE/MIR_MIRc chr1 18805963 18805989 - SINE/MIR_MIR chr1 18806469 18806606 + LINE/L2_L2d2 chr1 18806581 18806826 - LINE/L2_L2b chr1 18806827 18806988 - SINE/Alu_FRAM chr1 18806989 18807041 - LINE/L2_L2b chr1 18807054 18807120 - SINE/MIR_MIR3 chr1 18807646 18807698 - LINE/CR1_L3 chr1 18807745 18807848 - SINE/MIR_MIRc chr1 18808157 18808301 + SINE/MIR_MIR3 chr1 18808433 18808565 - SINE/MIR_MIR3 chr1 18809106 18809135 + Simple_repeat_(TTCA)n chr1 18809328 18809558 + SINE/MIR_MIR chr1 18809595 18809618 + Simple_repeat_(TGAA)n chr1 18809887 18809931 + Low_complexity_GA-rich chr1 18810097 18810232 + SINE/MIR_MIR1_Amn chr1 18810642 18810798 - SINE/MIR_MIR3 chr1 18811133 18811341 - SINE/MIR_MIR chr1 18811861 18812069 + SINE/MIR_MIRb chr1 18812092 18812213 + LINE/L2_L2a chr1 18812399 18813053 - LINE/L2_L2a chr1 18813056 18813351 + SINE/Alu_AluJb chr1 18813511 18813681 + SINE/MIR_MIRc chr1 18813873 18814063 + SINE/MIR_MIR3 chr1 18814085 18814382 + SINE/Alu_AluSx1 chr1 18814853 18815001 + SINE/MIR_MIRb chr1 18815069 18815181 - SINE/MIR_MIR chr1 18815750 18815844 - SINE/MIR_MIR3 chr1 18816062 18816073 - LINE/L1_L1PA3 chr1 18816074 18816107 + Simple_repeat_(TTA)n chr1 18816108 18816277 - LINE/L1_L1PA3 chr1 18816421 18816453 + Simple_repeat_(TCA)n chr1 18816624 18816841 - SINE/MIR_MIRb chr1 18816938 18817025 + Simple_repeat_(ACC)n chr1 18817309 18817740 - LINE/L2_L2b chr1 18818652 18818738 - SINE/MIR_MIRc chr1 18818739 18818935 - SINE/MIR_MIRb chr1 18819176 18819314 + LINE/L2_L2b chr1 18819521 18819648 + LINE/L2_L2b chr1 18819725 18819830 - SINE/MIR_MIRb chr1 18819954 18820150 + SINE/MIR_MIRb chr1 18820802 18820823 + Simple_repeat_(GT)n chr1 18821623 18821757 - SINE/MIR_MIR1_Amn chr1 18822033 18822171 + LTR/ERVL-MaLR_MLT1A0 chr1 18822172 18822469 - SINE/Alu_AluY chr1 18822471 18822793 - SINE/Alu_AluSp chr1 18822794 18823026 + LTR/ERVL-MaLR_MLT1A0 chr1 18823088 18823230 - SINE/MIR_MIRc chr1 18823319 18823924 - LINE/L2_L2a chr1 18823925 18824343 - LTR/ERVL-MaLR_MSTA chr1 18824344 18824500 - LINE/L2_L2a chr1 18824501 18824540 + Simple_repeat_(AC)n chr1 18824744 18824917 - SINE/MIR_MIR3 chr1 18825220 18826109 + Simple_repeat_(CATCAC)n chr1 18826656 18826740 + LTR/ERVL-MaLR_MLT1I chr1 18826741 18827157 - LTR/ERVL_LTR16A chr1 18827158 18827403 + LTR/ERVL-MaLR_MLT1J chr1 18827547 18828063 + DNA/hAT-Charlie_MER1A chr1 18828132 18828300 + SINE/MIR_MIRb chr1 18828496 18828714 + SINE/MIR_MIRb chr1 18829564 18829900 + LTR/ERVL-MaLR_MLT1I chr1 18829901 18830062 - SINE/MIR_MIRb chr1 18830063 18830281 + SINE/Alu_AluJb chr1 18830340 18830424 - SINE/MIR_MIRb chr1 18831253 18831658 + LTR/ERVL-MaLR_MSTA chr1 18831886 18832010 + LINE/L2_L2c chr1 18832126 18832210 - SINE/MIR_MIR chr1 18832241 18832318 + Simple_repeat_(TC)n chr1 18832319 18832681 + LTR/ERVL-MaLR_THE1B chr1 18832682 18832893 + Simple_repeat_(TC)n chr1 18832898 18833369 + LINE/L2_L2c chr1 18833373 18833405 + Simple_repeat_(TGAA)n chr1 18833526 18833552 + Simple_repeat_(TTCA)n chr1 18833554 18834129 - LINE/L2_L2b chr1 18834183 18834542 - LTR/ERV1_MER4D chr1 18834551 18834850 + SINE/Alu_AluSx1 chr1 18834912 18835163 - LTR/ERV1_MER49 chr1 18835165 18835223 - LINE/L2_L2b chr1 18835619 18835850 - LINE/L2_L2c chr1 18835891 18836104 + DNA/TcMar-Tigger_MER6B chr1 18836255 18836378 + LINE/CR1_L3 chr1 18836401 18836481 + SINE/MIR_MIRb chr1 18836488 18836978 - LTR/ERVL-MaLR_MLT1K chr1 18837015 18837105 + SINE/MIR_MIRb chr1 18837163 18837436 - SINE/Alu_AluJr chr1 18837444 18837557 + LINE/CR1_L3 chr1 18837732 18837750 + Simple_repeat_(A)n chr1 18838628 18838810 + SINE/MIR_MIR chr1 18838833 18838907 - SINE/MIR_MIRb chr1 18839129 18839189 + Simple_repeat_(CCCAG)n chr1 18840669 18840814 - SINE/MIR_MIRb chr1 18840887 18840980 - LINE/L2_L2c chr1 18841257 18841365 - LINE/L2_L2b chr1 18841385 18841410 + Simple_repeat_(CATT)n chr1 18841546 18841614 - LINE/L2_L2c chr1 18841669 18841705 + Simple_repeat_(CAGGGG)n chr1 18841991 18842478 + LINE/L1_L1MCa chr1 18842474 18842683 + LINE/L1_L1MCa chr1 18842684 18842969 + LINE/L1_L1MB2 chr1 18842970 18843090 + LINE/L1_L1MCa chr1 18843102 18843683 + LTR/ERVL_ERV3-16A3_I-int chr1 18843819 18844470 + LTR/ERVL_ERV3-16A3_I-int chr1 18844471 18844781 + SINE/Alu_AluSz chr1 18844782 18845941 + LTR/ERVL_ERV3-16A3_I-int chr1 18846058 18846135 + LTR/ERVL_LTR16E2 chr1 18846226 18846539 + LTR/ERVL_LTR16E2 chr1 18846543 18846777 + LTR/ERVL-MaLR_MLT1C chr1 18846784 18847189 + LTR/ERVL-MaLR_MSTA chr1 18847191 18847425 + LTR/ERVL-MaLR_MLT1C chr1 18847429 18847761 + LINE/L1_HAL1 chr1 18847764 18848127 - LTR/ERVL-MaLR_THE1B chr1 18848199 18848366 + SINE/MIR_MIR chr1 18848505 18849035 + DNA/hAT-Charlie_MER1A chr1 18849290 18849331 + Simple_repeat_(GGCCCCA)n chr1 18849688 18849925 + SINE/MIR_MIR chr1 18850149 18850267 + SINE/MIR_MIR3 chr1 18850941 18851143 - SINE/MIR_MIRb chr1 18851241 18851316 + SINE/MIR_MIR3 chr1 18851326 18851628 - SINE/Alu_AluSx3 chr1 18851861 18852022 + SINE/MIR_MIRb chr1 18852021 18852224 - LINE/L2_L2a chr1 18852262 18852438 + DNA/hAT-Tip100_MER91A chr1 18852432 18852488 + DNA/hAT-Tip100_Zaphod2 chr1 18852915 18852948 + LINE/L2_L2c chr1 18853070 18853275 + SINE/MIR_MIRb chr1 18853477 18853780 + LTR/Gypsy?_LTR85a chr1 18855148 18855209 - SINE/MIR_MIRb chr1 18855214 18855272 - SINE/MIR_MIRb chr1 18855420 18856639 + LINE/L2_L2a chr1 18856706 18856746 - LINE/L2_L2b chr1 18856831 18856896 - SINE/MIR_MIRb chr1 18856838 18856942 - LINE/L2_L2d2 chr1 18856944 18857188 - SINE/MIR_MIRb chr1 18859146 18859225 + SINE/MIR_MIR3 chr1 18860122 18860326 - SINE/MIR_MIR chr1 18860350 18860371 + Simple_repeat_(A)n chr1 18860807 18861124 + SINE/Alu_AluSx chr1 18861126 18861339 + LINE/L2_L2d2 chr1 18861340 18861532 + SINE/MIR_MIRb chr1 18861919 18861983 + SINE/MIR_MIRc chr1 18862512 18862809 - SINE/Alu_AluJr chr1 18862828 18863043 - SINE/MIR_MIRb chr1 18863569 18863634 + SINE/MIR_MIR3 chr1 18863887 18864029 - SINE/MIR_MIR3 chr1 18864262 18864332 - SINE/MIR_MIRb chr1 18864333 18864633 - SINE/Alu_AluSx chr1 18864634 18864752 - SINE/MIR_MIRb chr1 18864895 18865009 + SINE/MIR_MIR3 chr1 18864902 18865074 + SINE/MIR_MIR1_Amn chr1 18865138 18865194 + SINE/MIR_MIR3 chr1 18866208 18866285 + LINE/CR1_L3 chr1 18866313 18866439 - SINE/MIR_MIRb chr1 18866440 18866761 - SINE/Alu_AluJr chr1 18866762 18866808 - SINE/MIR_MIRb chr1 18866816 18866938 + LINE/CR1_L3 chr1 18866931 18867119 - SINE/MIR_MIR chr1 18867166 18867384 + LINE/CR1_L3 chr1 18867546 18867848 - SINE/Alu_AluSz chr1 18867917 18868003 + SINE/MIR_MIRb chr1 18868398 18868532 + SINE/MIR_MIRb chr1 18868769 18869019 + SINE/MIR_MIRb chr1 18869561 18869759 + SINE/MIR_MIRb chr1 18870162 18870284 + SINE/MIR_MIR chr1 18870309 18870708 - LTR/ERVL_MLT2D chr1 18870752 18871052 - SINE/Alu_AluSz chr1 18871061 18871277 - SINE/MIR_MIR chr1 18873214 18873275 + SINE/MIR_MIRc chr1 18873293 18874076 + LTR/ERVL_MER21C chr1 18874077 18874177 + SINE/MIR_MIRc chr1 18874240 18874381 + SINE/MIR_MIRb chr1 18874716 18874832 + SINE/MIR_MIRb chr1 18875907 18876002 + SINE/MIR_MIRb chr1 18876114 18876230 + SINE/MIR_MIRb chr1 18876662 18876680 + Simple_repeat_(TG)n chr1 18877748 18877841 - SINE/MIR_MIR3 chr1 18877847 18878004 - SINE/MIR_MIR1_Amn chr1 18878105 18878145 + SINE/MIR_MIR3 chr1 18878180 18878339 - SINE/MIR_MIRb chr1 18878578 18878801 - DNA/hAT-Charlie_MER103C chr1 18878968 18879176 - DNA/hAT-Charlie_MER3 chr1 18879605 18879762 + SINE/MIR_MIRb chr1 18880371 18880411 + Simple_repeat_(CAACCC)n chr1 18880609 18880683 - LINE/L2_L2 chr1 18880860 18880949 + LINE/L2_L2c chr1 18881147 18881393 + LINE/L2_L2c chr1 18881411 18881632 - SINE/MIR_MIRc chr1 18881926 18881961 + Simple_repeat_(CCACC)n chr1 18882560 18882754 + SINE/MIR_MIRb chr1 18882819 18883001 - SINE/MIR_MIRb chr1 18883875 18884121 + LTR/ERVL_LTR86A1 chr1 18884700 18885009 - LINE/L2_L2d2 chr1 18885208 18885253 + Simple_repeat_(GACAGAG)n chr1 18885419 18885473 + Simple_repeat_(CACCC)n chr1 18886711 18886768 - LINE/L2_L2c chr1 18886786 18886978 + SINE/MIR_MIRb chr1 18887105 18887161 + LINE/RTE-X_L4_A_Mam chr1 18887162 18887317 + LTR/ERV1_LTR46 chr1 18887318 18887611 + SINE/Alu_AluY chr1 18887612 18887916 + LTR/ERV1_LTR46 chr1 18887917 18888011 + LINE/RTE-X_L4_A_Mam chr1 18888080 18888198 - DNA/hAT-Charlie_MER3 chr1 18888209 18888309 + LINE/RTE-X_L4_A_Mam chr1 18888349 18888509 + LINE/RTE-X_L4_A_Mam chr1 18888864 18889144 + LINE/RTE-X_L4_A_Mam chr1 18889545 18889761 - DNA/hAT-Charlie_MER102a chr1 18890260 18890564 + SINE/Alu_AluSz chr1 18890572 18890761 + SINE/MIR_MIRb chr1 18891108 18891232 + SINE/MIR_MIR chr1 18891431 18891653 - LTR/ERV1_MER90 chr1 18891686 18891745 - LTR/ERV1_MER90 chr1 18891746 18892044 + SINE/Alu_AluSz chr1 18892045 18892168 - LTR/ERV1_MER90 chr1 18892256 18892605 - LINE/L2_L2b chr1 18892653 18892682 + Simple_repeat_(CCTCC)n chr1 18892695 18892854 + SINE/MIR_MIRb chr1 18893154 18893284 + SINE/MIR_MIRb chr1 18893285 18893503 + LTR/ERV1_LTR23 chr1 18893504 18893803 - SINE/Alu_AluSx1 chr1 18893804 18894015 + LTR/ERV1_LTR23 chr1 18894016 18894047 + SINE/MIR_MIRb chr1 18894259 18894558 + SINE/Alu_AluSz chr1 18894843 18894886 + Simple_repeat_(T)n chr1 18895109 18895209 - LINE/L2_L2c chr1 18895235 18895290 - SINE/MIR_MIRc chr1 18895758 18895987 + LINE/L2_L2 chr1 18896309 18896364 - SINE/MIR_MIRc chr1 18896612 18896916 + SINE/Alu_AluSz chr1 18896969 18897066 - SINE/MIR_MIRb chr1 18897067 18897260 + DNA/TcMar-Tigger_Tigger4a chr1 18897261 18897569 + SINE/Alu_AluSz chr1 18897570 18897609 + DNA/TcMar-Tigger_Tigger4a chr1 18897610 18897714 - SINE/MIR_MIRb chr1 18897816 18897937 - SINE/MIR_MIR3 chr1 18898034 18898342 + SINE/Alu_AluJb chr1 18898532 18898761 - SINE/MIR_MIR chr1 18898763 18898821 - LINE/L2_L2b chr1 18898983 18899085 - SINE/MIR_MIR chr1 18899591 18899738 + SINE/MIR_MIRb chr1 18899855 18899944 - LINE/L2_L2a chr1 18899946 18900158 + LINE/L1_L1ME4a chr1 18900429 18900667 + DNA/hAT-Charlie_MER20 chr1 18900919 18901109 + LINE/L2_L2d2 chr1 18901468 18901571 + LINE/L2_L2c chr1 18901628 18901719 + SINE/MIR_MIRb chr1 18901738 18901872 - SINE/MIR_MIR chr1 18901931 18901947 + Simple_repeat_(A)n chr1 18903525 18903672 + SINE/MIR_MIRb chr1 18903978 18904157 + SINE/MIR_MIRc chr1 18905689 18905720 + Simple_repeat_(GT)n chr1 18906056 18906109 + Simple_repeat_(AGGC)n chr1 18906898 18906920 + Simple_repeat_(TC)n chr1 18907947 18907980 + Simple_repeat_(TCCT)n chr1 18908841 18908993 + LINE/L2_L2d chr1 18909004 18909150 + SINE/MIR_MIR1_Amn chr1 18909035 18909221 + SINE/MIR_MIRb chr1 18909440 18909778 + LTR/ERVL-MaLR_THE1C chr1 18909769 18909914 - SINE/Alu_FLAM_C chr1 18909891 18909936 + LTR/ERVL-MaLR_THE1C chr1 18910026 18910113 + LINE/L2_L2c chr1 18910116 18910168 + SINE/MIR_MIRb chr1 18910188 18910215 + Simple_repeat_(TGGA)n chr1 18910216 18910250 + Simple_repeat_(CGGA)n chr1 18910902 18911066 + SINE/MIR_MIR chr1 18911134 18911251 - SINE/MIR_MIRc chr1 18911503 18911838 - SINE/Alu_AluSx1 chr1 18912207 18912315 + LINE/L1_L1ME3G chr1 18912386 18912623 - SINE/MIR_MIRc chr1 18912624 18912832 - SINE/MIR_MIR chr1 18912870 18912920 - DNA/hAT-Blackjack_MER81 chr1 18912934 18913089 - SINE/MIR_MIR3 chr1 18913805 18913851 + Simple_repeat_(TTGT)n chr1 18913852 18914130 - SINE/Alu_AluYe6 chr1 18914535 18914840 + SINE/Alu_AluSz chr1 18915163 18915219 + SINE/MIR_MIR3 chr1 18915394 18915494 - DNA/hAT-Blackjack_MER63A chr1 18915527 18915725 + SINE/MIR_MIRb chr1 18915843 18916133 + SINE/Alu_AluSz chr1 18916328 18916475 - DNA/hAT-Charlie_MER102b chr1 18916484 18916788 + SINE/Alu_AluJr chr1 18918018 18918192 + SINE/MIR_MIR1_Amn chr1 18920057 18920133 + DNA/TcMar-Mariner_MADE2 chr1 18920456 18920576 - SINE/MIR_MIR1_Amn chr1 18920707 18920868 - SINE/MIR_MIR3 chr1 18921257 18921394 - LINE/L2_L2c chr1 18921557 18921708 - SINE/MIR_MIRb chr1 18921746 18921805 - SINE/MIR_MIRb chr1 18921961 18922180 - SINE/MIR_MIR chr1 18922754 18922876 - SINE/MIR_MIR3 chr1 18923306 18923455 - SINE/MIR_MIRc chr1 18923478 18923709 - SINE/MIR_MIRb chr1 18923710 18923846 - DNA/hAT-Charlie_MER5A1 chr1 18924585 18924669 - SINE/MIR_MIRb chr1 18924865 18924976 - SINE/MIR_MIRb chr1 18925104 18925232 - SINE/MIR_MIRb chr1 18925237 18925267 + Simple_repeat_(TCC)n chr1 18925681 18925748 + LINE/L2_L2a chr1 18925749 18926152 + Simple_repeat_(ATGG)n chr1 18926328 18926366 + Simple_repeat_(AGGCC)n chr1 18926956 18927159 + SINE/MIR_MIRc chr1 18928668 18928794 + SINE/MIR_MIR3 chr1 18929032 18929100 - LINE/L2_L2c chr1 18929192 18929287 + SINE/MIR_MIRb chr1 18929469 18929576 - LINE/RTE-BovB_MamRTE1 chr1 18929762 18929898 + SINE/MIR_MIR3 chr1 18930016 18930233 + SINE/MIR_MIRb chr1 18930280 18930393 - SINE/MIR_MIRb chr1 18930889 18931187 + SINE/Alu_AluJr4 chr1 18931449 18931590 + LINE/L2_L2c chr1 18931611 18931751 - SINE/MIR_MIRb chr1 18932045 18932177 + SINE/MIR_MIRb chr1 18932446 18932664 + SINE/MIR_MIR chr1 18932866 18932943 + LINE/L2_L2c chr1 18933043 18933107 - LINE/L2_L2c chr1 18933493 18933778 + SINE/Alu_AluJb chr1 18933781 18934041 - LINE/L1_L1MB3 chr1 18934042 18934371 - SINE/Alu_AluJb chr1 18934372 18934471 - LINE/L1_L1MB3 chr1 18934586 18934813 - LINE/L1_L1ME4a chr1 18935422 18935693 + LINE/L2_L2b chr1 18935694 18936006 - SINE/Alu_AluJb chr1 18936007 18936111 + LINE/L2_L2b chr1 18936770 18936925 + SINE/MIR_MIRb chr1 18937018 18937105 - SINE/MIR_MIRb chr1 18937646 18937722 - SINE/MIR_MIR3 chr1 18938113 18938295 + SINE/MIR_MIRb chr1 18938477 18938541 + LINE/L2_L2b chr1 18938558 18938656 + SINE/MIR_MIR3 chr1 18939396 18939520 + SINE/MIR_MIR3 chr1 18939736 18939972 + LINE/CR1_L3b chr1 18940270 18940426 + SINE/MIR_MIR3 chr1 18940531 18940562 + Simple_repeat_(GGAC)n chr1 18940563 18940609 + Simple_repeat_(GGAT)n chr1 18940610 18940633 + Simple_repeat_(GGAC)n chr1 18940789 18940835 + Simple_repeat_(ACATAC)n chr1 18940978 18941213 + SINE/MIR_MIRb chr1 18941676 18941836 + SINE/MIR_MIRc chr1 18942464 18942604 - SINE/MIR_MIRb chr1 18942827 18943008 - SINE/Alu_AluSx chr1 18943086 18943387 + SINE/Alu_AluJr chr1 18943603 18943712 - SINE/MIR_MIRb chr1 18944045 18944145 + SINE/MIR_MIR3 chr1 18944866 18944976 + Low_complexity_GA-rich chr1 18945050 18945198 + LINE/L2_L2c chr1 18945848 18945967 - SINE/MIR_MIRc chr1 18946191 18946320 - SINE/MIR_MIRc chr1 18946366 18946405 + SINE/MIR_MIR chr1 18946406 18946716 - SINE/Alu_AluSg4 chr1 18946717 18946902 + SINE/MIR_MIR chr1 18946910 18946980 - LINE/L2_L2b chr1 18947421 18947569 - SINE/MIR_MIR3 chr1 18947954 18948161 + SINE/MIR_MIRb chr1 18948189 18948394 - SINE/MIR_MIRc chr1 18948848 18949008 - SINE/MIR_MIRb chr1 18949221 18949385 - LINE/L2_L2b chr1 18949753 18949819 + Simple_repeat_(GT)n chr1 18950420 18950569 - SINE/MIR_MIRb chr1 18951065 18951130 + SINE/MIR_MIR3 chr1 18951950 18952099 - SINE/MIR_MIR3 chr1 18952371 18952453 + DNA/hAT-Charlie_MER113A chr1 18952470 18952877 + LINE/L1_L1MB8 chr1 18953245 18953295 - SINE/MIR_MIR3 chr1 18953385 18953561 - DNA/hAT-Charlie_Charlie15b chr1 18953562 18953627 - SINE/MIR_MIR3 chr1 18953893 18953949 + Simple_repeat_(CACAA)n chr1 18954304 18954414 - SINE/MIR_MIRb chr1 18955208 18955410 + SINE/MIR_MIRc chr1 18955934 18955990 + Simple_repeat_(GGCGCCG)n chr1 18956044 18956115 + Simple_repeat_(GCT)n chr1 18956192 18956278 + Simple_repeat_(CCG)n chr1 18956346 18956417 + Simple_repeat_(GGCCCCG)n chr1 18957601 18957626 + Simple_repeat_(CATT)n chr1 18957727 18957788 - SINE/MIR_MIR chr1 18957894 18958069 + LINE/L2_L2d2 chr1 18958422 18958496 + DNA/hAT-Charlie_Charlie4a chr1 18958497 18958812 - SINE/Alu_AluSp chr1 18958813 18959184 + DNA/hAT-Charlie_Charlie4a chr1 18959685 18959792 - DNA/hAT-Blackjack_MER81 chr1 18959842 18959978 - SINE/MIR_MIR3 chr1 18960323 18960443 + SINE/MIR_MIRb chr1 18960453 18960832 + LINE/L2_L2c chr1 18960920 18961231 - LINE/L1_L1MB8 chr1 18961271 18961326 + SINE/MIR_MIR chr1 18961327 18961606 + SINE/Alu_AluSz6 chr1 18961612 18961917 + SINE/Alu_AluJr chr1 18961918 18962016 + SINE/MIR_MIR chr1 18962391 18962411 + Simple_repeat_(A)n chr1 18962753 18962928 + SINE/MIR_MIR chr1 18962933 18962987 - SINE/MIR_MIR chr1 18963119 18963335 - SINE/MIR_MIR3 chr1 18963506 18963611 + LINE/L2_L2b chr1 18963669 18964331 - LINE/L1_L1ME3 chr1 18964637 18964936 - SINE/Alu_AluSz6 chr1 18964986 18965128 + SINE/MIR_MIRb chr1 18965400 18965606 - SINE/MIR_MIRb chr1 18966216 18966410 + LINE/L1_L1ME4b chr1 18966561 18966671 - SINE/MIR_MIRb chr1 18966756 18966914 - LINE/L2_L2c chr1 18966928 18967143 - SINE/MIR_MIRb chr1 18967245 18967343 + SINE/MIR_MIRb chr1 18967480 18967536 - LINE/L2_L2d chr1 18967633 18967779 - SINE/MIR_MIRb chr1 18967857 18967999 + SINE/MIR_MIRb chr1 18968000 18968334 - SINE/Alu_AluY chr1 18968335 18968442 + SINE/MIR_MIRb chr1 18968443 18968820 + LTR/ERVL-MaLR_MSTA chr1 18968826 18968865 + Simple_repeat_(ATTACT)n chr1 18968887 18969015 - DNA/hAT-Charlie_MER102c chr1 18969125 18969762 + LTR/ERVL_MER21C chr1 18969826 18970120 + SINE/Alu_AluJr chr1 18970316 18970385 - DNA/hAT-Charlie_MER102c chr1 18970457 18970566 - SINE/MIR_MIR chr1 18971075 18971326 + DNA/hAT-Charlie_MER113 chr1 18971327 18971619 - SINE/Alu_AluSx chr1 18971620 18971747 + DNA/hAT-Charlie_MER113 chr1 18971748 18971826 + DNA/hAT-Charlie_MER58B chr1 18971827 18972119 + SINE/Alu_AluSc chr1 18972120 18972189 + DNA/hAT-Charlie_MER58B chr1 18972190 18972466 + SINE/Alu_AluJr chr1 18972467 18972684 + DNA/hAT-Charlie_MER58B chr1 18972685 18972726 + DNA/hAT-Charlie_MER113 chr1 18972744 18972885 - SINE/MIR_MIRb chr1 18973029 18973136 - SINE/Alu_FLAM_A chr1 18973311 18973540 + LINE/L2_L2c chr1 18973648 18973869 + SINE/MIR_MIR chr1 18973979 18974029 + LINE/L2_L2c chr1 18974034 18974079 + Simple_repeat_(TCCCTCC)n chr1 18974220 18974540 + SINE/Alu_AluSx1 chr1 18974541 18974586 + Low_complexity_A-rich chr1 18974761 18974778 + Simple_repeat_(A)n chr1 18974966 18975136 + SINE/MIR_MIRb chr1 18975139 18975471 + DNA/TcMar-Tigger_MER44A chr1 18975502 18975810 - SINE/Alu_AluJb chr1 18976127 18976187 + SINE/Alu_AluSx chr1 18976205 18976553 - LTR/ERVL_LTR16C chr1 18976554 18976651 - SINE/MIR_MIRb chr1 18976653 18976685 + Simple_repeat_(TTTA)n chr1 18976716 18977014 + SINE/Alu_AluSx chr1 18977140 18977389 - LINE/L1_L1MB7 chr1 18977390 18977820 - LTR/ERVL-MaLR_MSTB chr1 18977821 18978419 - LINE/L1_L1MB7 chr1 18978969 18979365 - LINE/L2_L2c chr1 18979500 18979739 - SINE/Alu_AluSx1 chr1 18979778 18980161 + LINE/L2_L2 chr1 18980162 18980534 + LTR/ERVL-MaLR_THE1C chr1 18980535 18980628 + LINE/L2_L2 chr1 18980629 18980916 + LINE/L1_L1ME4a chr1 18980917 18981431 + LTR/ERVL-MaLR_MLT1D chr1 18981432 18981578 + LINE/L1_L1ME4a chr1 18981589 18981602 + Low_complexity_polypurine chr1 18981603 18981913 + SINE/Alu_AluJb chr1 18981914 18982187 + Low_complexity_GA-rich chr1 18982188 18982601 + LINE/L1_L1ME4a chr1 18982587 18983203 + LINE/L1_L1ME4a chr1 18983204 18983481 + SINE/Alu_AluSc8 chr1 18983482 18983746 + LINE/L1_L1ME4a chr1 18983612 18984107 + LINE/L1_L1ME3E chr1 18984108 18984390 + SINE/Alu_AluJo chr1 18984391 18984794 + LINE/L1_L1ME3E chr1 18984795 18984841 + Simple_repeat_(TTA)n chr1 18985324 18985609 + SINE/Alu_AluJb chr1 18985610 18985640 + Simple_repeat_(CAAA)n chr1 18986037 18986434 + LINE/L1_L1ME4a chr1 18986435 18986590 - SINE/Alu_AluJb chr1 18986591 18986896 - SINE/Alu_AluSz chr1 18986897 18987036 - SINE/Alu_AluJb chr1 18987037 18987138 + LINE/L1_L1ME4a chr1 18987148 18987329 - LINE/L1_L1ME3G chr1 18987339 18987751 + LINE/L2_L2b chr1 18987752 18987809 + Simple_repeat_(GAAG)n chr1 18988219 18988262 + LTR/Gypsy?_LTR85c chr1 18988311 18988329 + Low_complexity_GA-rich chr1 18988330 18988636 + SINE/Alu_AluSz chr1 18988637 18988818 + Low_complexity_GA-rich chr1 18988849 18989019 + SINE/MIR_MIRb chr1 18989116 18989287 + DNA/hAT-Tip100_MER45A chr1 18989719 18989873 + LINE/L2_L2b chr1 18990074 18990207 + SINE/MIR_MIR3 chr1 18990215 18990315 + DNA/hAT-Charlie_MER3 chr1 18990343 18990638 - SINE/Alu_AluSz chr1 18990639 18990731 + DNA/hAT-Charlie_Charlie5 chr1 18990733 18990936 + SINE/Alu_AluJr chr1 18990941 18991162 + DNA/hAT-Charlie_Charlie5 chr1 18991164 18991474 + SINE/Alu_AluSg4 chr1 18991708 18991998 + LTR/ERVL-MaLR_MLT1K chr1 18992101 18992304 + LINE/L2_L2-1_Crp chr1 18992314 18992419 - LINE/L2_L2a chr1 18992424 18993073 + LINE/L2_L2b chr1 18993087 18993609 + LINE/L1_L1ME3Cz chr1 18993738 18994043 + SINE/Alu_AluSz chr1 18994044 18994254 + LINE/L2_L2b chr1 18994094 18994440 + LINE/L2_L2a chr1 18994437 18994522 + LINE/L2_L2b chr1 18994563 18994851 - SINE/Alu_AluSz chr1 18994857 18995007 + DNA/hAT-Charlie_Charlie4z chr1 18995039 18995191 + LINE/L2_L2b chr1 18995194 18995225 + Simple_repeat_(AATG)n chr1 18995454 18995489 + Simple_repeat_(CCT)n chr1 18996093 18996301 - SINE/MIR_MIR chr1 18996364 18996510 - SINE/MIR_MIRb chr1 18996645 18996681 + SINE/MIR_MIR chr1 18996684 18996730 + Simple_repeat_(ATGA)n chr1 18996965 18997147 - SINE/MIR_MIRc chr1 18997616 18997657 + Simple_repeat_(CCAGC)n chr1 18998126 18998257 - SINE/MIR_MIR3 chr1 18998653 18998817 - LINE/L2_L2c chr1 18998904 18998986 + LINE/L1_L1MEc chr1 18998987 18999299 + SINE/Alu_AluSx chr1 18999300 18999665 + LINE/L1_L1MEc chr1 18999765 18999898 + SINE/Alu_FLAM_C chr1 18999899 19000144 + Low_complexity_GA-rich chr1 19000230 19000973 + LINE/L1_L1MEc chr1 19000974 19001193 - SINE/Alu_AluYk3 chr1 19001194 19001742 + LINE/L1_L1MEc chr1 19001754 19002383 + LTR/ERVL_MLT2F chr1 19002435 19002464 + Simple_repeat_(TTA)n chr1 19002465 19002605 - SINE/Alu_AluJb chr1 19002618 19003448 + LINE/L1_L1MEc chr1 19004374 19004651 - SINE/Alu_AluSg chr1 19004703 19004807 + DNA/hAT-Blackjack_MER81 chr1 19004980 19005090 + DNA/hAT-Charlie_Charlie7a chr1 19005091 19005333 - SINE/Alu_AluJr chr1 19005334 19005363 + DNA/hAT-Charlie_Charlie7a chr1 19005340 19005463 + DNA/hAT-Charlie_Charlie7 chr1 19005549 19005599 + LINE/L2_L2c chr1 19005600 19005960 + LTR/ERVL-MaLR_THE1B chr1 19005961 19006292 + LINE/L2_L2c chr1 19006368 19006548 - SINE/MIR_MIR chr1 19006558 19006650 + Simple_repeat_(GAAT)n chr1 19006881 19007073 - SINE/MIR_MIR3 chr1 19007229 19007357 - SINE/MIR_MIRb chr1 19007359 19007469 + DNA/hAT-Blackjack_MER81 chr1 19007625 19007661 + Simple_repeat_(GGAGTGG)n chr1 19008609 19008662 - DNA/TcMar-Mariner_MADE1 chr1 19008691 19008985 - SINE/Alu_AluSg chr1 19009226 19009312 + DNA/hAT-Charlie_MER5A1 chr1 19009344 19009522 - SINE/MIR_MIRb chr1 19009565 19009768 - SINE/MIR_MIR chr1 19010197 19010349 + SINE/MIR_MIRb chr1 19010613 19010983 + LTR/Gypsy?_LTR88a chr1 19011369 19011520 + SINE/MIR_MIRc chr1 19011560 19011608 - LINE/L2_L2d chr1 19011768 19012089 - LINE/L2_L2d chr1 19012296 19012427 - DNA/hAT-Charlie_Charlie22a chr1 19012649 19012945 - SINE/Alu_AluSq2 chr1 19013165 19013306 - DNA/TcMar-Tigger_X26_DNA chr1 19013307 19013605 + SINE/Alu_AluSc chr1 19013606 19013691 - DNA/TcMar-Tigger_X26_DNA chr1 19013700 19013736 + Low_complexity_A-rich chr1 19013942 19014142 + SINE/MIR_MIRb chr1 19014830 19014926 + LINE/L1_L1ME4a chr1 19014927 19015150 + SINE/Alu_AluJb chr1 19015151 19015248 + LINE/L1_L1M5 chr1 19015293 19015393 + SINE/Alu_AluSz6 chr1 19015489 19015793 - SINE/Alu_AluJb chr1 19016000 19016103 - SINE/MIR_MIRb chr1 19016123 19016305 + DNA/hAT-Charlie_MER5A chr1 19016382 19016679 + SINE/Alu_AluSc8 chr1 19016685 19017339 - LTR/ERV1_LTR65 chr1 19017459 19017530 + Simple_repeat_(ATTA)n chr1 19017567 19017848 + SINE/Alu_AluJb chr1 19017982 19018049 - SINE/MIR_MIRb chr1 19018019 19018139 + SINE/MIR_MIRb chr1 19018140 19018210 - DNA/TcMar-Mariner_MADE1 chr1 19018211 19018228 + SINE/MIR_MIR3 chr1 19018289 19018351 + LINE/L1_L1ME4a chr1 19018352 19018666 + SINE/Alu_AluSx chr1 19018667 19018807 + LINE/L1_L1ME4a chr1 19018858 19019140 + LINE/L1_L1M4 chr1 19019160 19020026 - LINE/L1_L1PA13 chr1 19020025 19020505 + LINE/L1_L1M4 chr1 19020509 19020818 + SINE/Alu_AluSg chr1 19020819 19020935 + Low_complexity_A-rich chr1 19020939 19020993 + LINE/L1_L1PA8 chr1 19021008 19021303 + SINE/Alu_AluJb chr1 19021304 19021422 + LINE/L1_L1MB3 chr1 19021460 19021583 - SINE/Alu_AluY chr1 19021830 19022010 + LINE/L1_L1M4a1 chr1 19022078 19022304 + LINE/L1_L1M4a1 chr1 19022303 19022551 + LINE/L1_L1M4a1 chr1 19022552 19022782 + SINE/Alu_AluSx chr1 19022784 19022965 + LINE/L1_L1M4a1 chr1 19022966 19023244 + SINE/Alu_AluSz chr1 19023245 19024777 + LINE/L1_L1M4a1 chr1 19024769 19024931 + LINE/L1_L1MEc chr1 19024932 19025237 + SINE/Alu_AluSx1 chr1 19025238 19025848 + LINE/L1_L1MEc chr1 19025849 19025887 + LTR/ERV1_MER4D0 chr1 19025888 19025915 + LINE/L1_L1MEc chr1 19025916 19026178 - SINE/Alu_AluSx1 chr1 19026179 19026374 + LINE/L1_L1MEc chr1 19026375 19026684 - SINE/Alu_AluSp chr1 19026685 19026719 + LINE/L1_L1MEc chr1 19026736 19026881 + LINE/L1_L1MA10 chr1 19026892 19027558 + LINE/L1_L1M4a1 chr1 19027559 19027874 + SINE/Alu_AluSx1 chr1 19027875 19028003 + LINE/L1_L1M4a1 chr1 19028423 19028591 - DNA/hAT-Charlie_UCON34 chr1 19028924 19028982 + LINE/L2_L2d chr1 19029026 19029220 - SINE/MIR_MIRb chr1 19029332 19029360 + Simple_repeat_(TGCC)n chr1 19029621 19029661 + Simple_repeat_(CCTGCCG)n chr1 19030175 19030390 + Low_complexity_GA-rich chr1 19030391 19030689 + SINE/Alu_AluSx chr1 19030690 19030724 + Low_complexity_G-rich chr1 19030725 19031096 + Low_complexity_A-rich chr1 19031137 19031202 - LINE/L1_L1ME4a chr1 19031203 19031405 - DNA/hAT-Charlie_MER58C chr1 19031406 19031607 - LINE/L1_L1ME4a chr1 19031845 19032131 - SINE/Alu_AluSz6 chr1 19032600 19032784 - SINE/MIR_MIRc chr1 19032897 19033028 + SINE/MIR_MIRc chr1 19033307 19033502 + LINE/L2_L2c chr1 19033540 19033757 + LINE/L2_L2c chr1 19034289 19034315 + Low_complexity_A-rich chr1 19035153 19035586 + LTR/ERVL-MaLR_MLT1C chr1 19035591 19035752 + DNA/hAT-Tip100_MER45A chr1 19036771 19036851 + LINE/L2_L2c chr1 19036912 19037204 + SINE/Alu_AluJo chr1 19037340 19037460 - LINE/L2_L2 chr1 19038421 19038549 - LINE/L2_L2c chr1 19038792 19038826 + LINE/L2_L2c chr1 19038927 19038974 + LINE/L2_L2c chr1 19039266 19039479 - LINE/L1_L1MA8 chr1 19039523 19039653 + DNA/hAT-Charlie_MER5B chr1 19039654 19039942 + SINE/Alu_AluJb chr1 19040025 19040079 - LINE/L2_L2c chr1 19040100 19040161 - LINE/CR1_L3 chr1 19040224 19040433 - SINE/MIR_MIRb chr1 19040544 19040770 - SINE/Alu_AluJr chr1 19041074 19041204 - SINE/MIR_MIRb chr1 19041245 19041408 + DNA/hAT-Charlie_MER112 chr1 19041409 19041442 + Simple_repeat_(TG)n chr1 19041443 19041481 + DNA/hAT-Charlie_MER112 chr1 19041482 19041788 - SINE/Alu_AluSx chr1 19041789 19041833 + DNA/hAT-Charlie_MER112 chr1 19042189 19042261 + SINE/MIR_MIR3 chr1 19042262 19042555 - DNA/hAT-Charlie_MER33 chr1 19042556 19042585 + SINE/MIR_MIR3 chr1 19042733 19042840 + SINE/MIR_MIRb chr1 19042830 19043073 - SINE/MIR_MIR chr1 19043588 19043716 - LTR/ERVL-MaLR_MLT1K chr1 19043717 19044048 + SINE/Alu_AluJr chr1 19044049 19044314 - LTR/ERVL-MaLR_MLT1K chr1 19044828 19044938 + LINE/L1_L1PA4 chr1 19045598 19045806 - SINE/MIR_MIRb chr1 19045801 19046141 + LINE/CR1_L3b chr1 19046277 19046825 - LTR/ERVL-MaLR_MLT1G1 chr1 19046895 19047206 + LINE/L2_L2b chr1 19047194 19047326 + LINE/L2_L2c chr1 19047609 19047752 - DNA/hAT-Charlie_MER5A chr1 19047982 19048114 + DNA/hAT-Tip100_Arthur1 chr1 19048124 19048387 + SINE/Alu_AluSg chr1 19048388 19048471 + Simple_repeat_(AAGG)n chr1 19048472 19048484 + SINE/Alu_AluSg chr1 19048503 19048570 + DNA/hAT-Tip100_Arthur1C chr1 19048581 19048884 + SINE/Alu_AluSg chr1 19048887 19049048 + SINE/Alu_AluSx chr1 19049075 19049143 + SINE/MIR_MIRb chr1 19049162 19049330 + SINE/MIR_MIR chr1 19049485 19049600 + LINE/CR1_L3b chr1 19050073 19050370 - SINE/Alu_AluJb chr1 19050446 19050753 - SINE/Alu_AluSx3 chr1 19050796 19051396 - LINE/L1_L1MC4 chr1 19051397 19051711 - SINE/Alu_AluSc chr1 19051712 19052014 - LINE/L1_L1MC4 chr1 19052015 19052051 + DNA/TcMar-Tigger_Tigger2b_Pri chr1 19052052 19052174 - LINE/L1_L1MC4 chr1 19052175 19052483 + SINE/Alu_AluSg chr1 19052484 19052573 - LINE/L1_L1MC4 chr1 19052988 19053049 - SINE/MIR_MIRb chr1 19053050 19053341 - SINE/Alu_AluSz chr1 19053632 19053825 - SINE/MIR_MIRc chr1 19055682 19056221 + LINE/L1_L1MA8 chr1 19056222 19056491 + SINE/Alu_AluJo chr1 19056492 19056654 + LINE/L1_L1MA8 chr1 19056661 19056946 + SINE/Alu_AluJb chr1 19056947 19057258 + SINE/Alu_AluSx chr1 19057336 19057612 - LINE/L2_L2a chr1 19058154 19058221 + Low_complexity_G-rich chr1 19058566 19058868 + LINE/L2_L2c chr1 19059202 19059277 + SINE/MIR_MIR3 chr1 19061121 19061292 + SINE/MIR_MIR chr1 19061294 19061499 + Simple_repeat_(ACC)n chr1 19061509 19061562 + Simple_repeat_(ATC)n chr1 19061667 19061743 + LINE/L2_L2b chr1 19062275 19062361 + LINE/L2_L2d2 chr1 19062512 19062709 - SINE/MIR_MIRb chr1 19062817 19062920 + LTR/ERVL-MaLR_MLT1N2 chr1 19063541 19063589 + SINE/MIR_MIRc chr1 19063678 19063706 - SINE/MIR_MIRc chr1 19063707 19064018 - SINE/Alu_AluSz chr1 19064019 19064189 - SINE/MIR_MIRb chr1 19064318 19064460 - DNA/hAT-Charlie_MER58C chr1 19064689 19064791 + SINE/MIR_MIRb chr1 19064901 19064979 + SINE/MIR_MIR3 chr1 19065494 19065651 - SINE/MIR_MIRb chr1 19065719 19065839 - LINE/CR1_L3 chr1 19066476 19066563 + DNA/hAT_MER53 chr1 19066812 19066890 + SINE/MIR_MIRb chr1 19067567 19067715 + SINE/MIR_MIRb chr1 19069230 19069540 - SINE/Alu_AluSx1 chr1 19069659 19069695 - SINE/MIR_MIR chr1 19069994 19070171 + SINE/MIR_MIRb chr1 19070513 19070554 + Low_complexity_G-rich chr1 19070798 19071194 - LINE/L2_L2a chr1 19071705 19071806 + LINE/L2_L2a chr1 19071827 19071965 + SINE/MIR_MIRb chr1 19073275 19073351 - SINE/MIR_MIRb chr1 19075106 19075223 - DNA/TcMar-Tigger_Tigger19b chr1 19075326 19075355 + Simple_repeat_(CCT)n chr1 19075805 19075894 + LINE/L1_L1ME4a chr1 19076075 19076137 + DNA/TcMar-Tigger_Tigger19a chr1 19076375 19076553 + SINE/MIR_MIR chr1 19077148 19077182 + Simple_repeat_(CATTCC)n chr1 19077452 19077737 + SINE/Alu_AluSp chr1 19077738 19077776 + Simple_repeat_(AAACC)n chr1 19081919 19082043 - SINE/MIR_MIRc chr1 19082205 19082223 + Simple_repeat_(ATG)n chr1 19082238 19082452 - SINE/MIR_MIR chr1 19083540 19083818 - SINE/Alu_AluJo chr1 19085147 19085239 + SINE/MIR_MIRb chr1 19085240 19085534 + SINE/Alu_AluSq2 chr1 19086332 19086360 + Low_complexity_G-rich chr1 19087406 19087501 + SINE/MIR_MIRb chr1 19088386 19088506 + SINE/MIR_MIRb chr1 19089153 19089281 + SINE/MIR_MIRb chr1 19089963 19090037 - LINE/L2_L2a chr1 19090044 19090345 + LINE/L2_L2c chr1 19090417 19090573 + LINE/L2_L2c chr1 19090813 19091113 + SINE/Alu_AluSq2 chr1 19091322 19091409 + LINE/CR1_L3b chr1 19091467 19092341 + LINE/L1_L1MD3 chr1 19092684 19092728 + LINE/CR1_L3b chr1 19093247 19093288 + Low_complexity_GA-rich chr1 19093527 19093673 + LINE/L2_L2 chr1 19094126 19094245 + LINE/RTE-BovB_MamRTE1 chr1 19094481 19094671 + SINE/MIR_MIR3 chr1 19096627 19096652 + Simple_repeat_(CTT)n chr1 19097462 19097544 - SINE/MIR_MIRb chr1 19097546 19097616 - SINE/MIR_MIR chr1 19097617 19097630 - SINE/MIR_MIRb chr1 19097630 19097697 - SINE/MIR_MIRc chr1 19097982 19098126 - SINE/MIR_MIRc chr1 19100128 19100326 - SINE/MIR_MIRb chr1 19101862 19101983 + LINE/L2_L2c chr1 19102148 19102373 + SINE/Alu_AluJb chr1 19102904 19103297 + LINE/L1_L1MEd chr1 19103298 19103585 + SINE/Alu_AluSp chr1 19103586 19103836 + LINE/L1_L1MEd chr1 19104312 19104394 + LINE/L2_L2c chr1 19105437 19105487 + SINE/MIR_MIRb chr1 19105547 19105582 + Simple_repeat_(CACCAC)n chr1 19107374 19107498 - DNA/hAT-Charlie_Charlie1 chr1 19107499 19107806 + SINE/Alu_AluSx3 chr1 19107807 19108827 - DNA/hAT-Charlie_Charlie1 chr1 19109339 19109472 - LINE/L2_L2c chr1 19109541 19109762 - SINE/MIR_MIR chr1 19109771 19109856 - LINE/L2_L2c chr1 19111296 19111546 + LINE/L2_L2a chr1 19111561 19111615 - SINE/Alu_AluSz chr1 19111616 19111919 - SINE/Alu_AluSp chr1 19111920 19112054 - SINE/Alu_AluSz chr1 19112055 19112197 - SINE/MIR_MIR chr1 19112952 19113125 - SINE/MIR_MIRb chr1 19114144 19114405 - SINE/MIR_MIRb chr1 19115194 19115230 + Simple_repeat_(CACA)n chr1 19116121 19116348 - SINE/MIR_MIR chr1 19117539 19117661 - SINE/Alu_AluJo chr1 19118419 19118638 - SINE/Alu_AluJr chr1 19120548 19120587 + tRNA_tRNA-Gly-GGA chr1 19120828 19120965 - SINE/MIR_MIRb chr1 19121191 19121228 + Simple_repeat_(TCTTTT)n chr1 19121719 19121902 + LINE/L2_L2a chr1 19123170 19123472 + SINE/Alu_AluSz chr1 19123838 19124023 + LTR/ERVL_LTR50 chr1 19124131 19124203 + LTR/ERVL_LTR50 chr1 19125280 19125590 - LINE/L2_L2c chr1 19125848 19126081 - SINE/MIR_MIRb chr1 19127951 19128088 + LINE/L2_L2a chr1 19128700 19128829 + SINE/MIR_MIR3 chr1 19129030 19129143 - DNA/hAT-Ac_OldhAT1 chr1 19130064 19130202 + SINE/Alu_AluJr chr1 19130276 19130526 + SINE/Alu_AluJr4 chr1 19130911 19131208 - SINE/Alu_AluJb chr1 19131244 19131273 + Simple_repeat_(A)n chr1 19131392 19131433 + Simple_repeat_(AATT)n chr1 19131594 19131895 + SINE/Alu_AluSp chr1 19131910 19132177 + LINE/L1_L1MC chr1 19132178 19132480 - SINE/Alu_AluSz6 chr1 19132481 19133646 + LINE/L1_L1MC chr1 19133669 19133796 + SINE/Alu_AluJb chr1 19133797 19134114 + SINE/Alu_AluSz6 chr1 19134115 19134280 + SINE/Alu_AluJr4 chr1 19134282 19134444 + SINE/Alu_AluJb chr1 19134940 19135640 - LINE/L1_L1M5 chr1 19135635 19136713 + LINE/L1_L1MEd chr1 19137017 19137314 + SINE/Alu_AluJb chr1 19138025 19138056 + Simple_repeat_(TCTCCT)n chr1 19139254 19139287 + Simple_repeat_(AAAC)n chr1 19141741 19141769 + Simple_repeat_(TCA)n chr1 19142300 19142571 + LINE/RTE-X_L4_B_Mam chr1 19142579 19142841 + DNA/hAT-Charlie_MER33 chr1 19142842 19143114 + SINE/Alu_AluSz chr1 19143115 19143321 + Low_complexity_A-rich chr1 19143347 19143455 + LINE/RTE-X_L4_A_Mam chr1 19143456 19143492 + Simple_repeat_(TGTATTG)n chr1 19143493 19143848 + LINE/RTE-X_L4_A_Mam chr1 19144529 19144610 - LINE/L2_L2b chr1 19145024 19145056 + Simple_repeat_(ACAC)n chr1 19145509 19145543 + Simple_repeat_(AC)n chr1 19149810 19149834 + Simple_repeat_(A)n chr1 19155798 19155910 - LINE/L2_L2c chr1 19156019 19156183 - SINE/Alu_AluJb chr1 19158172 19158341 + DNA/hAT-Charlie_Charlie18a chr1 19158453 19158761 - SINE/Alu_AluJb chr1 19158860 19159156 + SINE/Alu_AluSz chr1 19159272 19159330 + LINE/CR1_L3 chr1 19159604 19159886 - SINE/Alu_AluJb chr1 19160287 19160333 + Low_complexity_GA-rich chr1 19160482 19160675 + DNA/hAT-Charlie_MER105 chr1 19161384 19161426 + SINE/MIR_MIR3 chr1 19165854 19165943 + LINE/L2_L2b chr1 19166596 19166910 + SINE/Alu_AluJb chr1 19168696 19168995 + SINE/Alu_AluY chr1 19169090 19169224 - SINE/MIR_MIRb chr1 19170199 19170396 + SINE/Alu_AluJr chr1 19170398 19170443 + Low_complexity_GA-rich chr1 19170471 19170670 + SINE/MIR_MIRb chr1 19171049 19171266 - DNA/hAT-Charlie_MER103C chr1 19171317 19171416 + LINE/L2_L2a chr1 19171560 19171855 + SINE/Alu_AluSx chr1 19171942 19172109 - DNA/hAT-Charlie_MER102a chr1 19172235 19172525 + SINE/Alu_AluSx chr1 19175125 19175335 - DNA/hAT-Charlie_MER20 chr1 19175783 19176086 - SINE/Alu_AluJb chr1 19176091 19176392 - SINE/Alu_AluSz chr1 19177325 19177365 + Low_complexity_A-rich chr1 19177935 19177999 + SINE/Alu_AluJo chr1 19179801 19179819 + Simple_repeat_(CA)n chr1 19180182 19180440 - SINE/Alu_AluSx chr1 19181098 19181382 + SINE/Alu_AluJr4 chr1 19181603 19181680 + LINE/L2_L2a chr1 19181771 19181782 - SINE/Alu_AluJo chr1 19181783 19182355 - LINE/L1_L1MB3 chr1 19182356 19182537 - SINE/Alu_AluJo chr1 19182538 19182720 - LINE/L1_L1M3 chr1 19182704 19183002 + LINE/L1_L1ME3F chr1 19183124 19183252 - LINE/L1_L1M5 chr1 19183440 19183740 + SINE/Alu_AluSx chr1 19184732 19184897 - SINE/MIR_MIRb chr1 19185190 19185215 + Simple_repeat_(GGA)n chr1 19185386 19185460 + LINE/L1_L1ME1 chr1 19185529 19185562 + LINE/L1_L1ME1 chr1 19185563 19185869 - SINE/Alu_AluSx4 chr1 19187077 19187117 + Simple_repeat_(TA)n chr1 19187605 19187736 - DNA/hAT-Charlie_Charlie4z chr1 19187704 19187775 - DNA/hAT-Charlie_Charlie4 chr1 19187781 19188070 + SINE/Alu_AluJr4 chr1 19188272 19188575 + SINE/Alu_AluJb chr1 19188630 19188685 + LINE/L1_L1M5 chr1 19188686 19189000 + SINE/Alu_AluJr4 chr1 19189001 19189486 + LINE/L1_L1M5 chr1 19189566 19189701 + LINE/L1_HAL1b chr1 19189830 19190004 + LINE/L2_L2b chr1 19190005 19190313 + SINE/Alu_AluSz6 chr1 19190314 19190448 + LINE/L2_L2b chr1 19190454 19191062 + LINE/L2_L2a chr1 19190938 19191187 + LINE/L2_L2a chr1 19191188 19191491 + SINE/Alu_AluSz6 chr1 19191492 19191697 + LINE/L2_L2a chr1 19191905 19192025 - SINE/MIR_MIRc chr1 19192665 19192781 + LINE/L2_L2a chr1 19192788 19193076 - SINE/Alu_AluSp chr1 19193138 19193206 + LINE/L2_L2a chr1 19193688 19194340 + LINE/L1_L1MC2 chr1 19194342 19194512 + SINE/Alu_FRAM chr1 19194520 19194808 + SINE/Alu_AluSq2 chr1 19194809 19194838 + Simple_repeat_(TAAT)n chr1 19194867 19194963 + SINE/Alu_FLAM_C chr1 19194979 19195266 + SINE/Alu_AluY chr1 19195267 19195291 + Simple_repeat_(AAT)n chr1 19195667 19195863 + LINE/L2_L2d2 chr1 19195970 19196018 + Simple_repeat_(AC)n chr1 19196096 19196340 + LINE/L2_L2d2 chr1 19196656 19196906 + LINE/L2_L2c chr1 19197347 19197639 + SINE/Alu_AluJb chr1 19198932 19198970 + Low_complexity_A-rich chr1 19199050 19199351 + DNA/hAT-Charlie_MER102b chr1 19199376 19199582 + DNA/hAT-Charlie_MER58A chr1 19199961 19200166 - SINE/MIR_MIR chr1 19200170 19200384 - DNA/hAT-Charlie_MER30 chr1 19200441 19200607 + SINE/Alu_AluSz6 chr1 19200624 19200674 + Low_complexity_A-rich chr1 19200780 19200815 - DNA/hAT-Charlie_MER30B chr1 19200872 19201145 + SINE/Alu_AluJo chr1 19201829 19201958 - SINE/MIR_MIR chr1 19201959 19202199 + SINE/Alu_AluSx chr1 19202200 19202274 - SINE/MIR_MIR chr1 19202805 19203101 + SINE/Alu_AluSg7 chr1 19203229 19203514 + SINE/Alu_AluSx chr1 19203763 19203799 - LINE/L2_L2d2 chr1 19204006 19204300 - SINE/Alu_AluSg chr1 19204522 19204550 + Simple_repeat_(AT)n chr1 19206179 19206336 + SINE/Alu_FRAM chr1 19206339 19206635 - SINE/Alu_AluSq2 chr1 19207347 19207652 + SINE/Alu_AluSx chr1 19207705 19208000 + DNA/hAT-Charlie_MER58B chr1 19208186 19208489 + SINE/Alu_AluY chr1 19208586 19208711 - DNA/TcMar-Tigger_Tigger19b chr1 19210179 19210246 + Simple_repeat_(CCG)n chr1 19211271 19211558 - SINE/Alu_AluJb chr1 19212416 19212563 + LINE/L1_L1ME3 chr1 19213127 19213290 + LINE/L1_L1MC4 chr1 19214823 19215011 + SINE/Alu_AluSx chr1 19215019 19215315 + SINE/Alu_AluSx chr1 19215679 19215988 - SINE/Alu_AluSz chr1 19216091 19216242 + SINE/Alu_FRAM chr1 19216477 19216545 + LINE/L1_L1MA2 chr1 19216692 19216989 - SINE/Alu_AluJb chr1 19217266 19217529 + SINE/Alu_AluSz chr1 19220255 19220685 + LINE/L2_L2a chr1 19221070 19221118 + Simple_repeat_(AAGATA)n chr1 19221354 19221653 + SINE/Alu_AluSc8 chr1 19221790 19222045 + SINE/Alu_AluSz chr1 19222179 19222421 + SINE/Alu_AluJb chr1 19223065 19223278 - SINE/MIR_MIR chr1 19223729 19223911 - SINE/MIR_MIRb chr1 19223931 19224922 + LINE/L2_L2a chr1 19224784 19225232 + LINE/L2_L2a chr1 19225235 19225371 + SINE/Alu_AluJb chr1 19225372 19225673 + SINE/Alu_AluSc chr1 19225674 19225833 + SINE/Alu_AluJr chr1 19225839 19226013 - SINE/MIR_MIRb chr1 19226335 19226464 + SINE/Alu_AluJr chr1 19226556 19226873 - SINE/Alu_AluJb chr1 19226881 19227059 + SINE/Alu_AluJb chr1 19227063 19227285 - SINE/MIR_MIRb chr1 19227586 19227859 + SINE/Alu_AluSx1 chr1 19227924 19228231 + SINE/Alu_AluSp chr1 19228435 19228482 + LINE/L1_L1ME4b chr1 19228481 19228665 + LINE/L1_L1ME3A chr1 19228770 19229061 + SINE/Alu_AluSz chr1 19229108 19229290 + LINE/L1_L1ME3G chr1 19229729 19229944 - SINE/MIR_MIR chr1 19230044 19230205 + SINE/tRNA-RTE_MamSINE1 chr1 19230289 19230589 + SINE/Alu_AluSx3 chr1 19230592 19230704 + LINE/L2_L2a chr1 19231625 19231921 - SINE/Alu_AluJb chr1 19231980 19232222 + SINE/Alu_AluSx chr1 19233738 19233959 + SINE/MIR_MIRb chr1 19234056 19234073 + Simple_repeat_(AC)n chr1 19234251 19234318 + LINE/L2_L2a chr1 19234567 19234865 + SINE/Alu_AluSg chr1 19235286 19235420 + SINE/Alu_AluJb chr1 19235421 19235729 + SINE/Alu_AluY chr1 19235730 19235917 + SINE/Alu_AluJb chr1 19236094 19236373 + SINE/Alu_AluSq2 chr1 19236376 19236675 + SINE/Alu_AluSp chr1 19236686 19236991 + SINE/Alu_AluSz chr1 19237607 19237848 + SINE/MIR_MIR chr1 19241222 19241503 + DNA/hAT-Charlie_MER102a chr1 19241511 19241806 - SINE/Alu_AluJb chr1 19242018 19242051 + Simple_repeat_(AGAACT)n chr1 19242550 19242629 + SINE/MIR_MIRb chr1 19242675 19242894 + LINE/L2_L2c chr1 19242956 19243262 + SINE/Alu_AluSg chr1 19243414 19243461 + LINE/L2_L2a chr1 19244421 19244661 - SINE/Alu_AluJb chr1 19245157 19245452 + SINE/Alu_AluSp chr1 19245624 19245925 - SINE/Alu_AluSq10 chr1 19246014 19246106 + SINE/Alu_AluJb chr1 19246109 19246409 + SINE/Alu_AluSz chr1 19246503 19246809 + SINE/Alu_AluSz chr1 19246834 19247192 + LTR/ERVL-MaLR_MLT1B chr1 19247212 19247337 - SINE/Alu_FLAM_A chr1 19247369 19247743 + DNA/TcMar-Tigger_Tigger3b chr1 19247744 19248034 + SINE/Alu_AluSx chr1 19248035 19248192 + DNA/TcMar-Tigger_Tigger3b chr1 19248193 19248412 - SINE/Alu_AluJb chr1 19248413 19248717 - SINE/Alu_AluSc chr1 19248718 19248810 - SINE/Alu_AluJb chr1 19248811 19249607 + DNA/TcMar-Tigger_Tigger3b chr1 19249643 19249921 + SINE/Alu_AluJb chr1 19249936 19250247 + SINE/Alu_AluSp chr1 19250341 19250363 + Simple_repeat_(CATT)n chr1 19250561 19250608 - LINE/L2_L2d2 chr1 19250623 19250853 - SINE/MIR_MIR chr1 19250874 19250895 + Simple_repeat_(TGAA)n chr1 19251202 19251331 + DNA/hAT-Tip100_MER45A chr1 19252153 19252339 + LINE/L2_L2b chr1 19252355 19252415 - SINE/Alu_AluJo chr1 19252416 19252718 + SINE/Alu_AluSx1 chr1 19252719 19252938 - SINE/Alu_AluJo chr1 19253014 19253115 - LINE/L2_L2a chr1 19253140 19253658 - LINE/L2_L2a chr1 19254091 19254413 + SINE/Alu_AluJb chr1 19255025 19255201 - SINE/MIR_MIRb chr1 19255212 19255378 + SINE/Alu_AluJb chr1 19255379 19255420 + SINE/Alu_AluJb chr1 19256234 19256520 + SINE/Alu_AluSx1 chr1 19257320 19257414 - DNA/hAT-Charlie_MER20 chr1 19258062 19258130 + SINE/MIR_MIRb chr1 19258213 19258318 + SINE/Alu_AluJb chr1 19258981 19259280 + SINE/Alu_AluSc8 chr1 19259363 19259556 + SINE/Alu_AluJr chr1 19259894 19259933 + Simple_repeat_(TTTTTG)n chr1 19259938 19260063 - SINE/Alu_AluSz chr1 19261225 19261322 + SINE/Alu_AluJo chr1 19261323 19261616 + SINE/Alu_AluY chr1 19261617 19261829 + SINE/Alu_AluJo chr1 19261838 19261959 + SINE/MIR_MIRb chr1 19262520 19262816 + SINE/Alu_AluJr chr1 19263576 19263781 - SINE/Alu_AluJr chr1 19263979 19264149 + DNA/hAT-Blackjack_BLACKJACK chr1 19264150 19264442 + SINE/Alu_AluSg7 chr1 19264443 19264498 + DNA/hAT-Blackjack_BLACKJACK chr1 19264724 19265029 - SINE/Alu_AluY chr1 19265539 19265629 + LTR/ERVL-MaLR_MLT1G1 chr1 19265780 19265928 - SINE/Alu_AluJo chr1 19265938 19266305 - LINE/L1_L1MB7 chr1 19266306 19266602 - SINE/Alu_AluJr chr1 19266603 19266905 - LINE/L1_L1MB7 chr1 19266910 19267131 + LTR/ERVL-MaLR_MSTB chr1 19267658 19267755 + SINE/MIR_MIRb chr1 19267663 19267766 + SINE/MIR_MIR1_Amn chr1 19267805 19267884 - LINE/L2_L2a chr1 19267988 19268093 + SINE/MIR_MIRb chr1 19268117 19268426 + SINE/Alu_AluSx chr1 19269847 19269952 - LINE/L2_L2c chr1 19270017 19270064 + LINE/L2_L2a chr1 19270964 19271168 + SINE/Alu_AluSx chr1 19271170 19271473 + SINE/Alu_AluY chr1 19271474 19271576 + SINE/Alu_AluSx chr1 19271637 19271789 + SINE/Alu_AluSz6 chr1 19271806 19272105 + SINE/Alu_AluJb chr1 19272425 19272715 + SINE/Alu_AluJb chr1 19272908 19272971 - SINE/MIR_MIR chr1 19274583 19274644 + LINE/L1_L1MEc chr1 19274648 19274795 + SINE/Alu_FLAM_C chr1 19274829 19274917 + SINE/Alu_AluJb chr1 19274925 19275129 + LINE/L1_L1MEc chr1 19275130 19275429 + SINE/Alu_AluSp chr1 19275430 19275534 + LINE/L1_L1MEc chr1 19275535 19275835 + SINE/Alu_AluJb chr1 19275836 19276082 + LINE/L1_L1MEc chr1 19276083 19276377 + SINE/Alu_AluSg7 chr1 19276378 19276519 + LINE/L1_L1MEc chr1 19276520 19276823 + SINE/Alu_AluSz chr1 19276830 19277142 + SINE/Alu_AluSx chr1 19277143 19277522 + LINE/L1_L1MEc chr1 19277524 19277815 - SINE/Alu_AluSz6 chr1 19277823 19278157 - LINE/L1_L1M4 chr1 19278158 19278262 - SINE/Alu_FLAM_C chr1 19278277 19278589 + SINE/Alu_AluSc8 chr1 19278597 19278950 - LINE/L1_L1MB7 chr1 19278951 19279250 - SINE/Alu_AluJr chr1 19279251 19279813 - LINE/L1_L1MB7 chr1 19279814 19280042 + DNA/hAT-Charlie_MER30 chr1 19280043 19280238 - LINE/L1_L1MB7 chr1 19280240 19280416 - SINE/Alu_AluSz6 chr1 19280417 19280554 - LINE/L1_L1MB7 chr1 19280555 19280586 + Simple_repeat_(TTCTT)n chr1 19280587 19280869 - SINE/Alu_AluY chr1 19280870 19281022 - LINE/L1_L1MB7 chr1 19281026 19281219 - SINE/Alu_AluSg4 chr1 19281220 19281355 - LINE/L1_L1MB7 chr1 19281356 19281666 + SINE/Alu_AluSx chr1 19281667 19281738 - LINE/L1_L1MB7 chr1 19281746 19282124 + LTR/ERVL-MaLR_MSTB-int chr1 19282143 19282564 + LTR/ERVL-MaLR_MSTB chr1 19283089 19283186 + SINE/MIR_MIRb chr1 19283094 19283197 + SINE/MIR_MIR1_Amn chr1 19283236 19283315 - LINE/L2_L2a chr1 19283419 19283524 + SINE/MIR_MIRb chr1 19283548 19283853 + SINE/Alu_AluSx chr1 19285284 19285436 - LINE/L2_L2c chr1 19285453 19285500 + LINE/L2_L2a chr1 19286400 19286702 + SINE/Alu_AluSg chr1 19287033 19287322 + SINE/Alu_AluJb chr1 19287507 19287570 - SINE/MIR_MIR chr1 19288269 19288309 + Low_complexity_GA-rich chr1 19289215 19289277 + LINE/L1_L1MEc chr1 19289283 19289424 + SINE/Alu_FLAM_C chr1 19289447 19289533 + SINE/Alu_AluJb chr1 19289541 19289756 + LINE/L1_L1MEc chr1 19289576 19289838 + LINE/L1_L1M5 chr1 19289859 19290073 + SINE/Alu_AluJr chr1 19290101 19290492 + LINE/L1_L1MEc chr1 19290493 19290776 + SINE/Alu_AluSx chr1 19290777 19290804 + Simple_repeat_(AAAT)n chr1 19290805 19290874 + LINE/L1_L1MEc chr1 19290875 19291188 + SINE/Alu_AluSz chr1 19291189 19291517 + LINE/L1_L1MEc chr1 19291518 19291823 - SINE/Alu_AluSz6 chr1 19291824 19291914 + LINE/L1_L1MEc chr1 19291915 19292048 + SINE/Alu_AluSx3 chr1 19292049 19292222 + LINE/L1_L1MEc chr1 19292223 19292276 + Simple_repeat_(TTTAAATGACA)n chr1 19292277 19292425 + LINE/L1_L1MEc chr1 19292426 19292726 + SINE/Alu_AluSz chr1 19292733 19293027 - SINE/Alu_AluJo chr1 19293028 19293572 + LINE/L1_L1MEc chr1 19293573 19293703 + SINE/Alu_AluJo chr1 19293704 19293838 + LINE/L1_L1MEc chr1 19293841 19294040 - SINE/Alu_AluJb chr1 19294096 19294270 + SINE/Alu_AluSx3 chr1 19294272 19294461 - LTR/ERV1_HUERS-P3-int chr1 19294464 19295631 - LTR/ERV1_HUERS-P3-int chr1 19295641 19295844 - LTR/ERV1_HUERS-P3-int chr1 19295845 19295902 - LTR/ERV1_LTR9D chr1 19295903 19296217 - LTR/ERV1_LTR9D chr1 19296218 19296531 - SINE/Alu_AluSq chr1 19296532 19296835 - LTR/ERV1_LTR9D chr1 19297210 19297489 - SINE/Alu_AluSg chr1 19297941 19298445 + LTR/ERVL_MLT2A2 chr1 19298446 19298747 - SINE/Alu_AluSx1 chr1 19298748 19298809 + LTR/ERVL_MLT2A2 chr1 19299114 19299153 + Low_complexity_A-rich chr1 19299154 19299478 + SINE/Alu_AluY chr1 19299613 19299907 - SINE/Alu_AluSx4 chr1 19299915 19300013 - LINE/L1_L1MA8 chr1 19300252 19300543 + LTR/ERVL-MaLR_MLT1G3 chr1 19300565 19301036 - LINE/L1_L1MB8 chr1 19301037 19301338 - SINE/Alu_AluSx chr1 19301339 19301679 - LINE/L1_L1MB8 chr1 19301680 19301989 - SINE/Alu_AluJb chr1 19301990 19302687 - LINE/L1_L1MB8 chr1 19302692 19302987 - SINE/Alu_AluSz chr1 19303152 19303295 + SINE/Alu_AluJb chr1 19304654 19304952 + SINE/Alu_AluJo chr1 19305076 19305181 - snRNA_U6 chr1 19305326 19305630 - SINE/Alu_AluJb chr1 19305755 19305798 + SINE/MIR_MIRc chr1 19306440 19306731 - SINE/Alu_AluJb chr1 19307595 19307941 - LINE/L2_L2c chr1 19308647 19308819 + SINE/MIR_MIR chr1 19308830 19309069 - LINE/L2_L2b chr1 19309637 19309772 + SINE/Alu_AluJo chr1 19309793 19310103 + SINE/Alu_AluSx chr1 19310416 19310698 + SINE/Alu_AluSz chr1 19311197 19311289 + LINE/L2_L2b chr1 19311939 19311969 + Simple_repeat_(GGCGC)n chr1 19313066 19313181 - SINE/MIR_MIRb chr1 19313626 19313693 - LINE/L2_L2c chr1 19313902 19313996 + DNA/hAT-Charlie_MER5A chr1 19314562 19314692 - SINE/MIR_MIRb chr1 19314657 19314746 + SINE/MIR_MIRb chr1 19314898 19315208 - SINE/Alu_AluSz chr1 19315709 19315752 + Simple_repeat_(TCTC)n chr1 19315805 19315986 + SINE/MIR_MIR chr1 19316354 19316394 + Simple_repeat_(TG)n chr1 19316395 19316651 - SINE/Alu_AluJb chr1 19316667 19316847 - SINE/Alu_AluJb chr1 19316848 19316888 + Simple_repeat_(TTCT)n chr1 19316920 19317050 - SINE/Alu_FLAM_C chr1 19317192 19317389 - SINE/MIR_MIR chr1 19318051 19318248 - SINE/MIR_MIR chr1 19318252 19318411 - LINE/L2_L2c chr1 19318399 19318551 + SINE/MIR_MIRb chr1 19318666 19318801 + SINE/Alu_FLAM_C chr1 19318821 19318942 - LINE/L2_L2c chr1 19319122 19319605 - LINE/L1_L1MB7 chr1 19319606 19319621 - SINE/Alu_AluJb chr1 19319622 19319903 + SINE/Alu_AluSx chr1 19319904 19320200 - SINE/Alu_AluJb chr1 19320201 19320404 - LINE/L1_L1MB7 chr1 19320411 19320714 - SINE/Alu_AluY chr1 19320719 19321012 - SINE/Alu_AluSx1 chr1 19321013 19321162 - LINE/L1_L1MB7 chr1 19321163 19321476 - SINE/Alu_AluJr chr1 19321477 19321567 - LINE/L1_L1MB7 chr1 19321568 19321860 - SINE/Alu_AluSx chr1 19321861 19321934 - LINE/L1_L1MB7 chr1 19321952 19322125 - LINE/L2_L2a chr1 19322886 19323178 - SINE/Alu_AluJr chr1 19323180 19323220 - SINE/Alu_AluYj4 chr1 19323396 19323707 - SINE/Alu_AluSz6 chr1 19323900 19323968 + SINE/MIR_MIR chr1 19324909 19325016 + SINE/MIR_MIR3 chr1 19325322 19325483 - SINE/MIR_MIRb chr1 19325822 19325916 - LINE/L2_L2b chr1 19326046 19326183 - SINE/MIR_MIR chr1 19326690 19326824 + SINE/MIR_MIR1_Amn chr1 19327140 19327191 - LINE/L2_L2b chr1 19327488 19327529 + Simple_repeat_(CATC)n chr1 19327530 19327556 + Simple_repeat_(TCCC)n chr1 19327574 19327869 - LINE/L2_L2b chr1 19327891 19328113 + SINE/MIR_MIRb chr1 19328466 19328493 + Low_complexity_G-rich chr1 19328975 19329275 + SINE/Alu_AluSz chr1 19329612 19329752 + SINE/MIR_MIRb chr1 19329769 19329914 - LINE/L2_L2b chr1 19329986 19330130 - SINE/Alu_FRAM chr1 19330540 19330685 - LINE/L2_L2b chr1 19330917 19331014 - SINE/MIR_MIRc chr1 19331015 19331309 - SINE/Alu_AluSx1 chr1 19331310 19331380 - SINE/MIR_MIRc chr1 19331920 19331977 - LINE/L2_L2d2 chr1 19332206 19332419 + SINE/MIR_MIRb chr1 19332488 19332623 + SINE/Alu_AluJb chr1 19333211 19333461 - SINE/Alu_AluJb chr1 19333670 19333763 + SINE/Alu_AluJo chr1 19333764 19333899 + SINE/Alu_AluJb chr1 19333900 19333939 + Simple_repeat_(AAACA)n chr1 19334082 19334130 + SINE/MIR_MIR chr1 19334156 19334232 + LINE/L2_L2c chr1 19334194 19334278 - LINE/L2_L2d chr1 19334298 19334373 + LINE/L1_L1MB8 chr1 19334545 19334841 - SINE/Alu_AluSz6 chr1 19335150 19335293 + SINE/Alu_FRAM chr1 19335294 19335603 + SINE/Alu_AluSx4 chr1 19335613 19335630 + Simple_repeat_(ACA)n chr1 19336077 19336373 + SINE/Alu_AluSx1 chr1 19336392 19336522 + SINE/Alu_AluJr chr1 19336667 19336964 - LINE/L1_L1MCb chr1 19336975 19337110 + LTR/ERV1_MER31A chr1 19337111 19337396 - SINE/Alu_AluJb chr1 19337397 19337670 + LTR/ERV1_MER31A chr1 19337998 19338281 + SINE/Alu_AluSp chr1 19338789 19338823 + Simple_repeat_(TTTCT)n chr1 19340457 19340527 - SINE/MIR_MIRc chr1 19340528 19340544 - SINE/MIR_MIRb chr1 19340550 19340670 + SINE/Alu_AluJr chr1 19340692 19340715 - SINE/MIR_MIR1_Amn chr1 19340716 19340759 - SINE/MIR_MIRc chr1 19341115 19341200 - DNA_Eutr2B chr1 19343557 19343662 - SINE/MIR_MIRc chr1 19344231 19344357 - SINE/MIR_MIRb chr1 19344967 19345003 + Simple_repeat_(CTGGAG)n chr1 19345767 19345872 - SINE/MIR_MIRc chr1 19346447 19346810 + LINE/CR1_L3 chr1 19346811 19346990 - SINE/Alu_AluSq2 chr1 19346991 19347021 + Simple_repeat_(TTTTTA)n chr1 19347022 19347140 - SINE/Alu_AluSq2 chr1 19347141 19347431 + LINE/CR1_L3 chr1 19347841 19347903 - SINE/MIR_MIRb chr1 19347909 19348168 + LINE/CR1_L3 chr1 19348190 19348267 + LINE/CR1_L3 chr1 19348268 19348383 - SINE/Alu_FLAM_A chr1 19348384 19348634 + LINE/CR1_L3 chr1 19350613 19350914 - SINE/Alu_AluJb chr1 19350981 19351277 - SINE/Alu_AluSg chr1 19351289 19351588 - SINE/Alu_AluJr chr1 19351616 19351735 - SINE/MIR_MIRb chr1 19352852 19352884 + Simple_repeat_(CTGG)n chr1 19353458 19353506 + Simple_repeat_(CTGCCCCT)n chr1 19353577 19353724 + LINE/L2_L2 chr1 19354059 19354217 + SINE/MIR_MIRb chr1 19355132 19355211 + DNA/hAT-Charlie_Charlie23a chr1 19355212 19355512 + SINE/Alu_AluSz chr1 19355513 19355637 + DNA/hAT-Charlie_Charlie23a chr1 19356078 19356234 - DNA/hAT-Charlie_MER5A1 chr1 19356862 19357164 - SINE/Alu_AluSx1 chr1 19357749 19357854 + SINE/MIR_MIRc chr1 19358136 19358437 - SINE/Alu_AluSz chr1 19358861 19358942 + SINE/MIR_MIRb chr1 19359104 19359273 + LINE/L1_L1MC4a chr1 19360918 19361172 - LINE/L1_L1ME4c chr1 19362186 19362238 - SINE/Alu_AluYe5 chr1 19362239 19362519 - SINE/Alu_AluSg chr1 19362549 19362669 + SINE/Alu_FLAM_C chr1 19363084 19363320 - SINE/Alu_AluJo chr1 19364205 19364289 + SINE/MIR_MIR3 chr1 19364333 19364834 - LINE/CR1_L3 chr1 19364835 19365136 - SINE/Alu_AluJr chr1 19365137 19365328 - LINE/CR1_L3 chr1 19365549 19365860 + SINE/Alu_AluSp chr1 19366165 19366283 + SINE/Alu_FLAM_C chr1 19366284 19366310 + Simple_repeat_(AT)n chr1 19366311 19366321 + SINE/Alu_FLAM_C chr1 19366407 19366718 + SINE/Alu_AluSq chr1 19367267 19367344 + SINE/MIR_MIRb chr1 19368088 19368230 - SINE/MIR_MIRb chr1 19368362 19368510 + SINE/Alu_FLAM_C chr1 19368664 19368826 - SINE/Alu_FRAM chr1 19370265 19370782 - LTR/ERVL_MER68 chr1 19370819 19371050 + LINE/L2_L2c chr1 19371100 19371174 - SINE/MIR_MIRc chr1 19371480 19371593 - SINE/MIR_MIRb chr1 19373674 19373958 - SINE/Alu_AluJr4 chr1 19375728 19375886 + SINE/MIR_MIRb chr1 19376506 19376832 + LINE/L2_L2b chr1 19376822 19376997 + SINE/MIR_MIRb chr1 19377322 19377486 + SINE/MIR_MIRb chr1 19377535 19377667 + DNA/hAT-Charlie_MER58C chr1 19378016 19378248 + SINE/MIR_MIRb chr1 19378821 19379049 + SINE/MIR_MIRb chr1 19379087 19379398 - SINE/Alu_AluSz6 chr1 19380585 19380751 + DNA/hAT-Charlie_MER3 chr1 19380757 19380885 + LINE/L2_L2a chr1 19380888 19381189 + SINE/Alu_AluSx3 chr1 19381585 19381649 - SINE/Alu_AluJb chr1 19381685 19381816 - SINE/Alu_AluSx3 chr1 19382558 19382589 - LINE/L1_X9_LINE chr1 19382834 19382935 + LINE/L2_L2c chr1 19382997 19383163 + SINE/Alu_FLAM_C chr1 19383164 19383466 + SINE/Alu_AluSz chr1 19383467 19383554 + SINE/Alu_AluJb chr1 19383625 19383964 + LINE/L2_L2c chr1 19384388 19384421 + Simple_repeat_(CCCT)n chr1 19384446 19384603 - SINE/MIR_MIRb chr1 19384801 19385021 + LINE/L2_L2c chr1 19385206 19385275 + LINE/L2_L2c chr1 19385328 19385376 + Low_complexity_GA-rich chr1 19387043 19387266 - SINE/MIR_MIRb chr1 19387640 19387865 + SINE/MIR_MIRb chr1 19388210 19388388 + LINE/L2_L2a chr1 19388684 19388761 + SINE/MIR_MIR3 chr1 19389324 19389422 + DNA/hAT_MER53 chr1 19389430 19389725 - SINE/Alu_AluY chr1 19389763 19389830 + LINE/L2_L2d chr1 19390264 19390479 + DNA_MARE8 chr1 19390840 19391011 - SINE/MIR_MIRb chr1 19391627 19391712 + SINE/MIR_MIRc chr1 19391901 19392206 + SINE/Alu_AluSz chr1 19392529 19392547 + Simple_repeat_(A)n chr1 19392558 19392800 + SINE/Alu_AluJr chr1 19392804 19392885 - LINE/L1_L1ME4b chr1 19393996 19394285 - SINE/Alu_AluSx chr1 19394439 19394744 + SINE/Alu_AluSx chr1 19394819 19395169 + DNA/TcMar-Tigger_MER47B chr1 19396659 19396970 + SINE/Alu_AluSz chr1 19397163 19397243 - DNA/TcMar-Tigger_MER6B chr1 19397265 19397493 - SINE/MIR_MIRc chr1 19398884 19399156 - SINE/Alu_AluSq2 chr1 19399157 19399256 + LINE/L1_L1MB7 chr1 19401193 19401221 + Low_complexity_A-rich chr1 19402766 19403072 + SINE/Alu_AluSq2 chr1 19403098 19403228 + LINE/L2_L2b chr1 19403216 19403295 - LINE/L2_L2c chr1 19403593 19403696 - LINE/L2_L2c chr1 19403993 19404258 + SINE/Alu_AluSz chr1 19404262 19404564 + SINE/Alu_AluSz chr1 19404829 19405039 - SINE/MIR_MIR chr1 19405207 19405285 - LINE/L2_L2a chr1 19405542 19405565 + Simple_repeat_(A)n chr1 19406593 19406774 + SINE/MIR_MIRb chr1 19406853 19407019 - SINE/MIR_MIRb chr1 19408283 19408456 - SINE/MIR_MIRb chr1 19408691 19408814 - SINE/MIR_MIR3 chr1 19409147 19409191 + DNA/hAT-Charlie_MER103C chr1 19409409 19409551 - LINE/L1_L1M5 chr1 19409553 19409761 + SINE/MIR_MIR chr1 19411062 19411199 + SINE/Alu_AluJo chr1 19412448 19412571 - SINE/Alu_FLAM_A chr1 19412947 19413069 + LINE/L2_L2a chr1 19413206 19413297 + LINE/L2_L2b chr1 19413353 19413632 - SINE/Alu_AluJr chr1 19415714 19415838 - SINE/Alu_AluSz6 chr1 19416417 19416540 + SINE/MIR_MIRb chr1 19416625 19416888 + SINE/Alu_AluJr chr1 19417856 19418165 + SINE/Alu_AluSx1 chr1 19418181 19418370 + DNA/hAT-Charlie_MER102b chr1 19418439 19418584 - DNA/TcMar-Tigger_Tigger12c chr1 19418746 19418808 + DNA/hAT-Charlie_Charlie22a chr1 19419026 19419346 + DNA/hAT-Charlie_Charlie1 chr1 19420421 19420730 - SINE/Alu_AluJb chr1 19420831 19420858 + Simple_repeat_(ATAA)n chr1 19421682 19421874 + SINE/MIR_MIR chr1 19421978 19421994 + Simple_repeat_(TC)n chr1 19422140 19422166 + Simple_repeat_(CA)n chr1 19422181 19422362 + DNA/hAT-Charlie_MER20 chr1 19422489 19422729 + SINE/Alu_AluY chr1 19422730 19422761 + Simple_repeat_(AAC)n chr1 19423373 19423513 + LINE/L1_L1ME3F chr1 19423517 19423691 - SINE/Alu_AluSz chr1 19423692 19423976 - SINE/Alu_AluSz chr1 19423977 19424111 - SINE/Alu_AluSz chr1 19424384 19424599 + srpRNA_7SLRNA chr1 19424616 19424751 + SINE/MIR_MIR3 chr1 19424864 19424960 - LINE/L2_L2c chr1 19425320 19425492 - LINE/L2_L2a chr1 19426346 19426597 + LTR/ERVL-MaLR_MLT1G3 chr1 19426667 19426868 + LTR/ERVL-MaLR_MLT1G3 chr1 19427152 19427466 - DNA/TcMar-Tigger_MamRep434 chr1 19428101 19428120 - LINE/L1_L1MB4 chr1 19428121 19428446 + SINE/Alu_AluSz chr1 19428447 19428515 - LINE/L1_L1MB4 chr1 19429151 19429312 - DNA/TcMar-Tigger_Tigger15a chr1 19429452 19429627 + SINE/MIR_MIRb chr1 19431176 19431370 + DNA/TcMar-Tigger_Tigger4a chr1 19431418 19431750 + SINE/Alu_AluY chr1 19431803 19432072 + SINE/Alu_AluJr chr1 19432161 19432463 + SINE/Alu_AluSz6 chr1 19432595 19432789 - SINE/MIR_MIRb chr1 19434183 19434416 + SINE/MIR_MIRb chr1 19434418 19434583 + SINE/Alu_FLAM_C chr1 19434655 19434905 + SINE/Alu_AluJb chr1 19435569 19435806 - SINE/MIR_MIRb chr1 19435919 19436078 + SINE/MIR_MIR chr1 19436079 19436600 + DNA/TcMar-Tigger_MER44C chr1 19436603 19436686 + DNA/TcMar-Tigger_Tigger7 chr1 19436689 19436783 + SINE/MIR_MIRb chr1 19439407 19439782 - LINE/CR1_L3 chr1 19439873 19439906 + Simple_repeat_(TTCTTT)n chr1 19440183 19440336 - SINE/Alu_AluJb chr1 19440779 19441059 + SINE/Alu_AluY chr1 19441060 19441089 + Simple_repeat_(AAAACAA)n chr1 19441726 19442028 + SINE/Alu_AluSx1 chr1 19442311 19442471 + LINE/L1_L1MC5 chr1 19443105 19443421 + SINE/Alu_AluJb chr1 19443605 19443628 + Simple_repeat_(GTT)n chr1 19443664 19443746 - LINE/L2_L2b chr1 19443701 19443820 - SINE/MIR_MIR1_Amn chr1 19443850 19443933 - DNA/hAT-Charlie_Charlie4z chr1 19443976 19444172 + SINE/Alu_AluSx chr1 19444737 19445042 - SINE/Alu_AluSx1 chr1 19445067 19445267 + LINE/L1_L1ME4a chr1 19445297 19445409 + LINE/L1_L1ME4a chr1 19445441 19445536 - LINE/L1_L1ME4a chr1 19445640 19445886 + LINE/L1_L1MB8 chr1 19446480 19446670 + LINE/L1_L1ME4c chr1 19447273 19447296 + Simple_repeat_(T)n chr1 19447316 19447534 - SINE/Alu_AluSx chr1 19449506 19449800 + SINE/Alu_AluSz6 chr1 19449802 19449858 + Low_complexity_GA-rich chr1 19449859 19450173 + SINE/Alu_AluJb chr1 19450174 19450194 + Low_complexity_GA-rich chr1 19450229 19450467 - DNA/TcMar-Tigger_Tigger4a chr1 19450474 19450658 - SINE/MIR_MIR chr1 19451566 19451684 - SINE/MIR_MIRb chr1 19451857 19451936 - SINE/MIR_MIR chr1 19451944 19451971 + Simple_repeat_(TTA)n chr1 19452315 19452441 + LINE/L2_L2b chr1 19452698 19452759 + Simple_repeat_(TCCAC)n chr1 19452794 19453103 - SINE/Alu_AluSx1 chr1 19453250 19453560 - SINE/Alu_AluSx chr1 19453813 19454002 - SINE/MIR_MIR chr1 19454069 19454103 + LINE/L2_L2d2 chr1 19454514 19454687 - SINE/MIR_MIRb chr1 19455916 19456211 - SINE/Alu_AluSz6 chr1 19457535 19458111 + LINE/L1_L1ME4c chr1 19458180 19458259 + DNA/TcMar-Mariner_MADE2 chr1 19458339 19458655 - SINE/Alu_AluSq chr1 19459676 19460264 + DNA/TcMar-Tigger_Tigger7 chr1 19460271 19460572 - SINE/Alu_AluYk2 chr1 19460582 19460702 - SINE/Alu_FLAM_C chr1 19460716 19460789 + DNA/TcMar-Tigger_Tigger7 chr1 19460992 19461101 - DNA/TcMar-Tigger_Tigger19b chr1 19461191 19461281 - SINE/MIR_MIRc chr1 19461729 19461970 + SINE/MIR_MIRb chr1 19462020 19462166 + SINE/Alu_FAM chr1 19462171 19462486 + SINE/Alu_AluY chr1 19462488 19462543 - SINE/MIR_MIR3 chr1 19462848 19463010 - DNA/hAT-Charlie_MER3 chr1 19463795 19463882 + LINE/L1_L1ME4b chr1 19463898 19464215 + SINE/Alu_AluSz chr1 19464230 19464263 + Low_complexity_A-rich chr1 19464269 19464289 + LINE/L1_L1ME1 chr1 19464290 19464591 - SINE/Alu_AluY chr1 19464592 19465053 + LINE/L1_L1ME1 chr1 19465151 19465496 + LTR/ERVL-MaLR_MLT1A0 chr1 19465788 19466067 - SINE/MIR_MIR chr1 19466249 19466333 - SINE/MIR_MIRb chr1 19466585 19466702 - SINE/MIR_MIRc chr1 19466708 19466876 + DNA/hAT-Charlie_MER113A chr1 19466902 19467174 - SINE/Alu_AluJr chr1 19467957 19468110 + SINE/Alu_FRAM chr1 19468144 19468297 + LINE/L1_L1ME1 chr1 19468571 19468600 + Simple_repeat_(GCAGG)n chr1 19468980 19469003 + Simple_repeat_(GTCT)n chr1 19469235 19469470 + DNA/hAT-Charlie_MER113 chr1 19469742 19469785 + Simple_repeat_(AC)n chr1 19470001 19470239 + SINE/Alu_AluJb chr1 19470573 19470667 + SINE/MIR_MIRb chr1 19470678 19470758 + SINE/MIR_MIRb chr1 19470969 19471195 - DNA/TcMar-Tigger_MER44D chr1 19471177 19471257 - DNA/TcMar-Tigger_MER44D chr1 19471583 19471895 + SINE/Alu_AluY chr1 19472710 19473016 - SINE/Alu_AluY chr1 19473212 19473399 + SINE/MIR_MIRb chr1 19473582 19473872 + SINE/Alu_AluSp chr1 19473999 19474294 - SINE/Alu_AluSz chr1 19474298 19474449 - SINE/MIR_MIRb chr1 19475162 19475335 - SINE/MIR_MIR chr1 19475722 19475802 + DNA/TcMar-Mariner_MADE2 chr1 19476041 19476168 + SINE/Alu_AluJo chr1 19476169 19476227 + Simple_repeat_(TAGA)n chr1 19476228 19476416 + SINE/Alu_AluJo chr1 19476850 19476884 + SINE/MIR_MIR3 chr1 19476905 19477100 + SINE/MIR_MIRb chr1 19477756 19477882 - SINE/Alu_FLAM_A chr1 19478205 19478291 + SINE/MIR_MIRb chr1 19478328 19478430 - SINE/MIR_MIRb chr1 19478828 19478908 + SINE/MIR_MIR chr1 19478928 19479230 + SINE/Alu_AluSx3 chr1 19480022 19480093 - LINE/L2_L2d chr1 19480270 19480461 - SINE/MIR_MIRb chr1 19480489 19480600 - LINE/L2_L2b chr1 19480848 19480972 + SINE/MIR_MIRb chr1 19480964 19481070 - SINE/MIR_MIR chr1 19481414 19481541 + SINE/MIR_MIRb chr1 19481884 19481966 + LINE/L2_L2a chr1 19482593 19482719 + DNA/TcMar-Tigger_Tigger19a chr1 19483034 19483115 - LINE/L2_L2a chr1 19483292 19483510 + SINE/MIR_MIRb chr1 19485439 19485458 + Simple_repeat_(GGC)n chr1 19485680 19485798 + Simple_repeat_(GCGGC)n chr1 19485986 19486032 + Simple_repeat_(GCCCA)n chr1 19486152 19486295 - LINE/L2_L2a chr1 19486296 19486580 + SINE/Alu_AluSc chr1 19486581 19486599 - LINE/L2_L2a chr1 19486686 19486715 + Simple_repeat_(TC)n chr1 19487009 19487143 - LINE/L2_L2a chr1 19487154 19487291 + SINE/Alu_AluSz chr1 19487293 19487582 + SINE/Alu_AluYf1 chr1 19487642 19488246 - LINE/L2_L2a chr1 19488247 19488562 - SINE/Alu_AluJr chr1 19488564 19488927 - LINE/L1_L1MC1 chr1 19488928 19489066 - LINE/L2_L2a chr1 19489067 19489367 - SINE/Alu_AluSz chr1 19489368 19489379 - LINE/L2_L2a chr1 19489380 19489688 - SINE/Alu_AluY chr1 19489689 19489722 - LINE/L2_L2a chr1 19489723 19490020 + SINE/Alu_AluSx chr1 19490021 19490289 - LINE/L2_L2a chr1 19490328 19490429 - LTR/ERVL-MaLR_MLT1M chr1 19490488 19490756 + SINE/Alu_AluJo chr1 19490764 19490805 + Simple_repeat_(AAAT)n chr1 19490810 19490941 - SINE/Alu_AluJo chr1 19491029 19491265 + LINE/L2_L2c chr1 19491266 19491549 + SINE/Alu_AluJr chr1 19491550 19491807 + LINE/L2_L2c chr1 19491808 19492116 + SINE/Alu_AluSx1 chr1 19492117 19492850 + LINE/L2_L2 chr1 19493106 19493148 + LINE/L2_L2a chr1 19493198 19493290 - SINE/MIR_MIRb chr1 19493291 19493586 - SINE/Alu_AluJb chr1 19493587 19493713 - SINE/MIR_MIRb chr1 19493686 19493768 + SINE/MIR_MIRc chr1 19493773 19493837 + LINE/L2_L2a chr1 19494313 19494609 + SINE/Alu_AluSx chr1 19494886 19494993 - DNA/hAT-Charlie_MER103C chr1 19495001 19495304 + LINE/L2_L2c chr1 19495305 19495444 - SINE/Alu_FLAM_C chr1 19495445 19495737 + LINE/L2_L2c chr1 19495743 19495852 - LTR/ERVL-MaLR_MLT1K chr1 19496464 19496682 + LINE/L2_L2c chr1 19496922 19497009 + LINE/L1_L1M5 chr1 19497010 19497304 - SINE/Alu_AluSp chr1 19497305 19497518 + LINE/L1_L1M5 chr1 19497519 19497820 - SINE/Alu_AluJr chr1 19497821 19497864 + LINE/L1_L1M5 chr1 19497865 19498158 - SINE/Alu_AluSg7 chr1 19498159 19498483 + LINE/L1_L1M5 chr1 19498484 19498660 - SINE/Alu_AluJb chr1 19498661 19498958 - SINE/Alu_AluJb chr1 19498959 19499079 - SINE/Alu_AluJb chr1 19499080 19499328 + LINE/L1_L1M5 chr1 19500004 19500303 - SINE/Alu_AluJo chr1 19500432 19500729 + LTR/ERVL-MaLR_MLT1J2 chr1 19500817 19501108 + SINE/Alu_AluY chr1 19501146 19501279 - SINE/MIR_MIRb chr1 19501680 19501753 + SINE/MIR_MIRb chr1 19501754 19501764 - SINE/Alu_AluSx chr1 19501765 19501803 + Simple_repeat_(TG)n chr1 19501804 19502091 - SINE/Alu_AluSq2 chr1 19502092 19502393 - SINE/Alu_AluSx chr1 19502396 19502544 + LTR/ERVL-MaLR_MSTA chr1 19502545 19502709 + LTR/ERV1_HERV35I-int chr1 19502712 19502994 + LTR/ERV1_LTR8A chr1 19503085 19503393 + SINE/Alu_AluSz chr1 19503402 19503706 + SINE/Alu_AluSx chr1 19503813 19504069 - SINE/Alu_AluSx4 chr1 19504070 19504363 - SINE/Alu_AluSx1 chr1 19504444 19504564 - SINE/Alu_AluJo chr1 19504581 19504820 - LINE/L1_L1M5 chr1 19504859 19504884 + Simple_repeat_(A)n chr1 19506130 19506175 + SINE/MIR_MIRb chr1 19506253 19506372 - LINE/L2_L2a chr1 19506462 19506606 + LINE/L2_L2a chr1 19506607 19506965 - LTR/ERVL-MaLR_MLT1A0 chr1 19506966 19506995 + LINE/L2_L2a chr1 19507141 19507302 + SINE/MIR_MIRb chr1 19507911 19508221 - SINE/Alu_AluY chr1 19508251 19508324 + SINE/Alu_AluJb chr1 19508325 19508459 - LTR/ERV1_MER41B chr1 19508460 19508777 + SINE/Alu_AluYb8 chr1 19508778 19509281 - LTR/ERV1_MER41B chr1 19509282 19509509 + SINE/Alu_AluJr chr1 19509512 19509674 - SINE/MIR_MIRb chr1 19509675 19509973 + SINE/Alu_AluSz chr1 19509974 19510094 - SINE/MIR_MIRb chr1 19510261 19510585 + SINE/Alu_AluSx1 chr1 19510593 19510711 + snRNA_U4 chr1 19510712 19511006 - SINE/Alu_AluSq2 chr1 19511150 19511346 - DNA/hAT-Charlie_MER3 chr1 19511425 19511463 + LINE/CR1_L3b chr1 19511652 19511694 - LINE/L2_L2b chr1 19511837 19511945 - LTR/ERVL-MaLR_MLT1K chr1 19512037 19512112 + SINE/Alu_AluJb chr1 19512132 19512204 - SINE/Alu_AluJo chr1 19512306 19512586 + SINE/Alu_AluSg chr1 19512587 19512620 + Simple_repeat_(AAAAC)n chr1 19512748 19513129 + LTR/ERVL-MaLR_MSTA chr1 19513214 19513510 - SINE/Alu_AluJb chr1 19513848 19514118 - SINE/Alu_AluY chr1 19514122 19514430 - SINE/Alu_AluSg7 chr1 19514992 19515283 + SINE/Alu_AluSq2 chr1 19515292 19515424 - LTR/ERVL_LTR79 chr1 19515442 19515648 - SINE/Alu_AluJr chr1 19515649 19515685 - LTR/ERVL-MaLR_MSTA chr1 19515686 19515987 + SINE/Alu_AluSx1 chr1 19515988 19516029 - LTR/ERVL-MaLR_MSTA chr1 19516030 19516341 - SINE/Alu_AluSz chr1 19516342 19516652 - LTR/ERVL-MaLR_MSTA chr1 19516653 19516744 - SINE/Alu_AluJr chr1 19516759 19517081 - LTR/ERVL_LTR79 chr1 19517223 19517473 + SINE/Alu_AluJr chr1 19517544 19517899 - LTR/ERVL-MaLR_THE1B chr1 19518324 19518466 + SINE/Alu_AluJr4 chr1 19518468 19518746 + SINE/Alu_AluSq2 chr1 19518899 19519018 - LTR/ERVL-MaLR_MLT1A0 chr1 19519026 19519113 - LINE/L2_L2 chr1 19519115 19519246 + SINE/Alu_FLAM_C chr1 19519364 19519607 - LTR/ERVL-MaLR_MLT1A0 chr1 19519704 19519836 - SINE/Alu_FLAM_C chr1 19520484 19520784 - SINE/Alu_AluSx chr1 19520793 19520971 - SINE/Alu_AluJb chr1 19521102 19521238 - SINE/Alu_FLAM_C chr1 19521444 19521726 - LTR/ERVL_MLT2D chr1 19521766 19521897 + LINE/L1_L1MC5a chr1 19521898 19522193 + SINE/Alu_AluSx1 chr1 19522194 19522444 + LINE/L1_L1MC5a chr1 19522445 19522752 - SINE/Alu_AluSx chr1 19522753 19522783 + LINE/L1_L1MC5a chr1 19522784 19523053 + SINE/Alu_AluJb chr1 19523054 19523128 + LINE/L1_L1MC5a chr1 19523236 19523299 - DNA/hAT-Charlie_Charlie5 chr1 19523300 19523605 + SINE/Alu_AluJo chr1 19523606 19523669 - DNA/hAT-Charlie_MER33 chr1 19523670 19523954 - SINE/Alu_AluSx1 chr1 19523955 19524161 - DNA/hAT-Charlie_MER33 chr1 19524208 19524530 - SINE/Alu_AluJr chr1 19524727 19524749 + Simple_repeat_(A)n chr1 19524933 19525237 + SINE/Alu_AluJr chr1 19525323 19525636 - SINE/Alu_AluJb chr1 19525746 19525876 + SINE/MIR_MIRb chr1 19525919 19525994 - DNA/hAT-Charlie_Charlie15a chr1 19526041 19526303 - SINE/MIR_MIR chr1 19526743 19526871 + DNA/hAT-Charlie_MER33 chr1 19526872 19527097 + LINE/L1_L1MB3 chr1 19527102 19527720 + Simple_repeat_(TA)n chr1 19527721 19528048 + SINE/Alu_AluSx chr1 19528049 19528134 + Simple_repeat_(TA)n chr1 19528186 19528229 + Simple_repeat_(AT)n chr1 19528349 19528435 + SINE/MIR_MIRb chr1 19528465 19528655 - SINE/MIR_MIRb chr1 19528656 19528767 + DNA/hAT-Tip100_ORSL chr1 19528805 19528917 + SINE/Alu_AluJo chr1 19528984 19529083 + DNA/hAT-Tip100_ORSL chr1 19529207 19529505 - SINE/Alu_AluSz chr1 19529506 19529656 + SINE/MIR_MIR chr1 19529964 19530092 + SINE/MIR_MIRb chr1 19530145 19530186 + SINE/MIR_MIR chr1 19530187 19530543 + LTR/ERVL-MaLR_MLT1A0 chr1 19530544 19530744 + SINE/MIR_MIR chr1 19530758 19531036 - SINE/Alu_AluJb chr1 19531108 19531149 + LINE/L2_L2b chr1 19531150 19531181 + Simple_repeat_(GAAT)n chr1 19531300 19531832 + LTR/ERVL-MaLR_MLT1F1 chr1 19531844 19532143 - SINE/Alu_AluJb chr1 19532167 19532275 - snRNA_U13 chr1 19532889 19533023 + SINE/Alu_AluSz chr1 19533121 19533291 - LINE/L2_L2c chr1 19533292 19533595 + SINE/Alu_AluJo chr1 19533596 19534169 - LINE/L2_L2c chr1 19534462 19534589 + LINE/L2_L2a chr1 19534590 19534745 - SINE/Alu_FLAM_C chr1 19534752 19535071 - SINE/Alu_AluJr chr1 19535072 19535354 + LINE/L2_L2a chr1 19535377 19535542 - LINE/L2_L2a chr1 19535543 19535848 + SINE/Alu_AluJr chr1 19535858 19536001 + LINE/L2_L2a chr1 19536197 19536505 + SINE/Alu_AluJb chr1 19536559 19536611 + LINE/L1_HAL1 chr1 19536644 19536691 - LTR/ERVL-MaLR_MLT1N2 chr1 19536693 19536768 + LINE/L1_HAL1ME chr1 19536769 19536947 - SINE/Alu_AluSx chr1 19536948 19536983 + Simple_repeat_(AT)n chr1 19536984 19537291 - SINE/Alu_AluSp chr1 19537294 19537573 - SINE/Alu_AluSx chr1 19537574 19537877 - LINE/L1_L1MC4 chr1 19537878 19538166 + SINE/Alu_AluSg chr1 19538167 19538347 - LINE/L1_L1MC4 chr1 19538348 19538653 - SINE/Alu_AluJb chr1 19538662 19538947 - SINE/Alu_AluJb chr1 19538948 19539067 - LINE/L1_L1MC4 chr1 19539069 19539397 + SINE/Alu_AluJr chr1 19539403 19539707 + SINE/Alu_AluSp chr1 19539712 19539797 - LINE/L1_L1MC4 chr1 19539798 19540095 - SINE/Alu_AluSp chr1 19540096 19540430 - LINE/L1_L1MC4 chr1 19540431 19540732 - SINE/Alu_AluSx1 chr1 19540733 19540853 - LINE/L1_L1MC4 chr1 19540854 19540892 + Simple_repeat_(TTAT)n chr1 19540893 19541177 - SINE/Alu_AluJb chr1 19541178 19541226 + Simple_repeat_(AT)n chr1 19541227 19541270 + Simple_repeat_(AG)n chr1 19541278 19541451 - SINE/Alu_AluJb chr1 19541477 19541984 + LTR/ERVK_MER9a3 chr1 19541985 19542507 + LTR/ERVK_HERVK9-int chr1 19542502 19547188 + LTR/ERVK_HERVK9-int chr1 19547189 19547695 + LTR/ERVK_MER9a3 chr1 19547696 19547800 - SINE/Alu_AluJb chr1 19547920 19548148 + LINE/RTE-X_L4_A_Mam chr1 19548166 19548304 - SINE/Alu_AluSx chr1 19548350 19548662 + SINE/Alu_AluJb chr1 19548708 19548799 - SINE/tRNA-RTE_MamSINE1 chr1 19548856 19548995 - LTR/ERVL-MaLR_MLT1N2 chr1 19549026 19549055 - DNA/hAT-Tip100_MER91C chr1 19549056 19549124 + DNA/hAT-Charlie_MER58A chr1 19549125 19549443 + SINE/Alu_AluSx1 chr1 19549444 19549633 + DNA/hAT-Charlie_MER58A chr1 19549634 19549699 - DNA/hAT-Tip100_MER91C chr1 19550121 19551154 - LTR/ERVK_MER11C chr1 19551165 19551347 + LINE/L2_L2a chr1 19551381 19551450 - LTR/ERVL-MaLR_MSTA chr1 19551451 19551754 + SINE/Alu_AluSx chr1 19551755 19552067 - LTR/ERVL-MaLR_MSTA chr1 19552068 19552340 + SINE/Alu_AluSx1 chr1 19552353 19552603 + LINE/L2_L2a chr1 19552443 19552895 + LINE/L2_L2a chr1 19553163 19553174 + LINE/L2_L2a chr1 19553175 19553351 - SINE/Alu_AluJb chr1 19553352 19553656 - SINE/Alu_AluSc chr1 19553657 19553793 - SINE/Alu_AluJb chr1 19553794 19553833 + LINE/L2_L2a chr1 19553906 19554376 + LTR/ERVL-MaLR_MLT1K chr1 19554543 19554849 + SINE/Alu_AluSq2 chr1 19554869 19555156 + SINE/Alu_AluJr chr1 19555157 19555620 + LTR/ERV1_LTR37-int chr1 19555632 19555745 - DNA/hAT-Charlie_Charlie1a chr1 19555746 19555902 - SINE/Alu_AluJr chr1 19555903 19556202 - SINE/Alu_AluY chr1 19556203 19556330 - SINE/Alu_AluJr chr1 19556331 19556420 - DNA/hAT-Charlie_Charlie1b chr1 19556393 19556451 - DNA/hAT-Charlie_Charlie1b chr1 19556522 19557183 + LTR/ERV1_LTR37-int chr1 19557184 19557478 - SINE/Alu_AluJb chr1 19557479 19557542 + LTR/ERV1_LTR37-int chr1 19557577 19557659 + LTR/ERV1_LTR37-int chr1 19557660 19557874 + Simple_repeat_(CTTCC)n chr1 19557887 19558193 - SINE/Alu_AluJb chr1 19558194 19558362 + LTR/ERV1_LTR37-int chr1 19558363 19558656 - SINE/Alu_AluSx chr1 19558657 19558719 + LTR/ERV1_LTR37-int chr1 19558720 19559016 + SINE/Alu_AluJo chr1 19559017 19559301 + LTR/ERV1_LTR37-int chr1 19559302 19559587 - SINE/Alu_AluSx chr1 19559588 19560162 + LTR/ERV1_LTR37-int chr1 19560163 19560458 + SINE/Alu_AluSp chr1 19560459 19560684 + LTR/ERV1_LTR37-int chr1 19560685 19560983 + SINE/Alu_AluSx1 chr1 19560985 19561301 + SINE/Alu_AluSp chr1 19561302 19561658 + LTR/ERV1_LTR37-int chr1 19561659 19561954 - SINE/Alu_AluJb chr1 19561955 19562093 + LTR/ERV1_LTR37-int chr1 19562141 19562217 + LTR/ERV1_LTR37A chr1 19562218 19562530 - SINE/Alu_AluSp chr1 19562531 19562799 + LTR/ERV1_LTR37A chr1 19562801 19562872 + SINE/MIR_MIR chr1 19562852 19563044 - SINE/MIR_MIR3 chr1 19563224 19563344 + SINE/Alu_FLAM_C chr1 19564492 19564557 + SINE/MIR_MIR1_Amn chr1 19564721 19565035 - SINE/Alu_AluJb chr1 19565053 19565346 - SINE/Alu_AluSg chr1 19565351 19565538 - SINE/MIR_MIRb chr1 19565562 19565812 - LINE/L2_L2a chr1 19565813 19566069 + SINE/Alu_AluSz chr1 19566070 19566356 - LINE/L2_L2a chr1 19566357 19566646 + SINE/Alu_AluSz chr1 19566647 19566660 - LINE/L2_L2a chr1 19566661 19566801 + SINE/Alu_FLAM_C chr1 19566802 19567116 - LINE/L2_L2a chr1 19567266 19567670 + DNA/hAT-Charlie_Charlie4a chr1 19567671 19567987 + SINE/Alu_AluJb chr1 19567988 19568071 + DNA/hAT-Charlie_Charlie4a chr1 19568135 19568205 - SINE/MIR_MIRb chr1 19568206 19568499 - SINE/Alu_AluSq chr1 19568500 19568625 - SINE/MIR_MIRb chr1 19568685 19568903 + SINE/MIR_MIR chr1 19568904 19569180 + SINE/Alu_AluJo chr1 19569210 19569516 - SINE/Alu_AluY chr1 19569533 19569759 + SINE/MIR_MIR chr1 19569930 19570030 - LINE/L2_L2a chr1 19570042 19570331 - SINE/Alu_AluSq2 chr1 19570343 19570480 - SINE/Alu_AluJb chr1 19570696 19570769 + LTR/ERVL-MaLR_MLT1A1 chr1 19570770 19571072 + SINE/Alu_AluJb chr1 19571073 19571403 + LTR/ERVL-MaLR_MLT1A1 chr1 19571430 19571653 - LTR/ERVL_HERV16-int chr1 19571654 19571675 - LTR/ERVL_LTR16 chr1 19571676 19572014 + LTR/ERVL-MaLR_MLT1A0 chr1 19572015 19572219 - LTR/ERVL_LTR16 chr1 19572220 19572522 - SINE/Alu_AluSg chr1 19572523 19572549 - LTR/ERVL_LTR16 chr1 19572550 19572895 + LTR/ERVL-MaLR_THE1B chr1 19572896 19573076 - LTR/ERVL_LTR16 chr1 19573079 19573120 - LINE/L1_L1M5 chr1 19573121 19573433 - SINE/Alu_AluSq2 chr1 19573434 19573633 - LINE/L1_L1M5 chr1 19573634 19573931 + SINE/Alu_AluSx1 chr1 19573932 19574031 - LINE/L1_L1M5 chr1 19574208 19574536 + LINE/L2_L2 chr1 19574578 19574877 - SINE/Alu_AluSg chr1 19574883 19574930 - LINE/L1_L1MC1 chr1 19574931 19575264 + SINE/Alu_AluJo chr1 19575265 19575470 - LINE/L1_L1MC1 chr1 19575471 19575767 + SINE/Alu_AluSz6 chr1 19575768 19575904 - LINE/L1_L1MC1 chr1 19575905 19576216 - SINE/Alu_AluSx1 chr1 19576217 19576331 - LINE/L1_L1MC1 chr1 19576430 19576525 - SINE/MIR_MIR chr1 19576750 19577049 + SINE/Alu_AluSx3 chr1 19577597 19577693 + LINE/L2_L2a chr1 19578317 19578626 - SINE/Alu_AluSg chr1 19578637 19578945 - SINE/Alu_AluSz chr1 19578947 19579371 + LINE/L2_L2a chr1 19579372 19579482 + LINE/L2_L2a chr1 19579493 19579920 - LTR/ERVL_LTR16D chr1 19580072 19580091 + Simple_repeat_(TCT)n chr1 19580094 19580393 - SINE/Alu_AluSp chr1 19580486 19581124 + LINE/L2_L2a chr1 19581153 19581451 - SINE/Alu_AluSz chr1 19581665 19581958 + SINE/Alu_AluSp chr1 19582482 19582641 - DNA/hAT-Charlie_Charlie4z chr1 19582805 19583070 + SINE/Alu_AluJb chr1 19583090 19583343 - SINE/Alu_AluSz chr1 19583386 19583553 - SINE/Alu_FAM chr1 19583565 19583864 - SINE/Alu_AluJb chr1 19583870 19584156 - SINE/Alu_AluSz chr1 19584269 19584555 + SINE/Alu_AluJb chr1 19584651 19584897 - SINE/Alu_AluJb chr1 19585230 19585534 + SINE/Alu_AluSz chr1 19585612 19585840 - LINE/L2_L2a chr1 19585863 19585911 - LTR/ERVL_LTR33 chr1 19585947 19585983 + Simple_repeat_(ATAA)n chr1 19585998 19586288 + SINE/Alu_AluJb chr1 19586300 19586335 + Simple_repeat_(ATAG)n chr1 19586336 19586652 + SINE/Alu_AluSx3 chr1 19586660 19586780 - LTR/ERVL_LTR33 chr1 19586786 19587040 - DNA/TcMar-Tigger_Tigger3a chr1 19587041 19587347 - SINE/Alu_AluSx chr1 19587348 19587456 - DNA/TcMar-Tigger_Tigger3a chr1 19587478 19587764 + SINE/Alu_AluSg chr1 19587833 19587900 - SINE/MIR_MIR3 chr1 19587901 19588249 - LTR/ERVL-MaLR_MLT1C chr1 19588250 19588558 - SINE/Alu_AluJr chr1 19588559 19588575 - LTR/ERVL-MaLR_MLT1C chr1 19588576 19588887 - SINE/Alu_AluSq2 chr1 19588888 19588981 - LTR/ERVL-MaLR_MLT1C chr1 19589128 19589380 + LINE/L1_L1ME3B chr1 19589381 19589665 + SINE/Alu_AluJb chr1 19589666 19589743 + LINE/L1_L1ME3B chr1 19589744 19590048 + SINE/Alu_AluSx1 chr1 19590049 19590339 + LINE/L1_L1ME3B chr1 19590340 19590622 + SINE/Alu_AluJo chr1 19590623 19590728 + LINE/L1_L1ME3B chr1 19590751 19590911 - SINE/Alu_FRAM chr1 19590934 19591208 - SINE/Alu_AluJb chr1 19591446 19591756 - SINE/Alu_AluJo chr1 19592178 19592461 + SINE/Alu_AluJb chr1 19592462 19592497 + Simple_repeat_(TAA)n chr1 19592499 19592664 + SINE/Alu_FRAM chr1 19592703 19592844 - LINE/L1_L1MC4a chr1 19592956 19593041 - LINE/L2_L2a chr1 19593088 19593156 - SINE/MIR_MIR chr1 19593260 19593403 + SINE/MIR_MIRb chr1 19593405 19593553 - SINE/Alu_AluJb chr1 19593677 19593981 - SINE/Alu_AluYk3 chr1 19594001 19594196 - LINE/L2_L2c chr1 19594200 19594324 + SINE/Alu_AluJo chr1 19594363 19594668 + SINE/Alu_AluSx chr1 19594800 19595087 + SINE/Alu_AluSx1 chr1 19595101 19595156 - LINE/L2_L2d2 chr1 19595383 19595685 - SINE/Alu_AluJr chr1 19595788 19595933 - DNA/TcMar-Tigger_MER2 chr1 19595956 19596029 - SINE/Alu_FLAM_A chr1 19596033 19596114 - DNA/TcMar-Tigger_MER2 chr1 19596212 19596263 + Simple_repeat_(TGTGTG)n chr1 19597826 19597942 - SINE/MIR_MIRc chr1 19598048 19598156 + LINE/L2_L2a chr1 19598195 19598336 + SINE/MIR_MIR chr1 19598496 19598625 + SINE/Alu_FLAM_C chr1 19598628 19598857 + SINE/MIR_MIRb chr1 19598919 19599044 - SINE/Alu_AluJo chr1 19599058 19599356 - SINE/Alu_AluJb chr1 19599691 19599777 - SINE/MIR_MIR chr1 19599967 19600070 - LINE/L2_L2c chr1 19600071 19600123 - DNA/TcMar-Mariner_MADE1 chr1 19600261 19600288 + SINE/MIR_MIR3 chr1 19600606 19600900 - SINE/Alu_AluJb chr1 19601297 19601602 + SINE/Alu_AluJr chr1 19602036 19602220 + LINE/L2_L2a chr1 19602911 19602991 + LINE/CR1_L3 chr1 19603048 19603355 + SINE/Alu_AluSp chr1 19603741 19603847 - LINE/L2_L2 chr1 19604090 19604223 + SINE/MIR_MIRb chr1 19604225 19604525 + SINE/Alu_AluSz6 chr1 19604680 19604790 - LINE/L2_L2d2 chr1 19604735 19604801 - LINE/L2_L2c chr1 19604937 19605034 - SINE/MIR_MIRb chr1 19605248 19605726 - LINE/L1_L1MB8 chr1 19605727 19605852 - SINE/Alu_FLAM_A chr1 19605854 19605956 - LINE/L1_L1MB8 chr1 19605957 19606305 - LINE/L2_L2d2 chr1 19606464 19606772 + SINE/Alu_AluSx chr1 19607570 19607703 - SINE/MIR_MIRb chr1 19607706 19608069 + LINE/L1_L1ME1 chr1 19608610 19608860 + LINE/L1_L1ME1 chr1 19608884 19608994 - SINE/Alu_AluJr chr1 19608995 19609165 - SINE/Alu_AluJb chr1 19609215 19609792 + LINE/L1_L1ME1 chr1 19609863 19610156 + SINE/Alu_AluSc chr1 19610368 19610539 - SINE/Alu_AluJb chr1 19610936 19611251 - SINE/Alu_AluSx1 chr1 19611470 19611635 - SINE/Alu_AluJb chr1 19611690 19611985 + SINE/Alu_AluSz chr1 19612030 19612348 - SINE/Alu_AluY chr1 19612442 19612710 + SINE/Alu_AluSx chr1 19613218 19613401 + LINE/L2_L2c chr1 19613493 19613676 + LINE/L2_L2c chr1 19613813 19614000 + SINE/MIR_MIR chr1 19614072 19614177 - SINE/Alu_AluSx3 chr1 19614346 19614469 + Simple_repeat_(AC)n chr1 19614484 19614616 - SINE/Alu_AluY chr1 19614822 19615269 + LINE/L2_L2d2 chr1 19615312 19615533 - SINE/MIR_MIR chr1 19615573 19615763 - SINE/Alu_AluJb chr1 19615912 19616215 - SINE/Alu_AluSx chr1 19616722 19616771 + SINE/MIR_MIRb chr1 19617907 19617935 + Simple_repeat_(AT)n chr1 19618113 19618441 - SINE/Alu_AluJr chr1 19618487 19618700 - SINE/MIR_MIR chr1 19621475 19621580 + SINE/MIR_MIRb chr1 19622921 19623220 - SINE/Alu_AluSc chr1 19623983 19624192 - SINE/Alu_AluSx1 chr1 19624272 19624587 - SINE/Alu_AluSx chr1 19624744 19624837 + LINE/L2_L2c chr1 19624994 19625215 + SINE/MIR_MIRb chr1 19625212 19625332 - SINE/MIR_MIRc chr1 19625909 19626106 - SINE/MIR_MIRb chr1 19627360 19627761 - LINE/L2_L2c chr1 19628067 19628374 - SINE/Alu_AluSp chr1 19628402 19628701 + SINE/Alu_AluSc chr1 19630203 19630282 + SINE/MIR_MIRc chr1 19630297 19630593 + SINE/Alu_AluJo chr1 19630595 19630629 + Simple_repeat_(CA)n chr1 19630993 19631151 - SINE/MIR_MIR chr1 19631306 19631391 - DNA/hAT-Charlie_Charlie19a chr1 19631500 19631813 - SINE/Alu_AluJo chr1 19631814 19632201 - LINE/L1_L1MC4 chr1 19632252 19632288 + Simple_repeat_(GT)n chr1 19632296 19632357 - LINE/L1_L1MC4 chr1 19632570 19632605 + Simple_repeat_(TTTG)n chr1 19632607 19632710 - LINE/L1_L1PA3 chr1 19632711 19633011 - SINE/Alu_AluSx1 chr1 19633154 19633237 + DNA/hAT-Charlie_Charlie30b chr1 19633290 19633388 + SINE/MIR_MIRc chr1 19633903 19634073 + LINE/L2_L2 chr1 19634109 19634339 + SINE/MIR_MIR chr1 19634365 19634654 + LINE/L2_L2a chr1 19634662 19634970 - SINE/Alu_AluSc8 chr1 19635055 19635356 - SINE/Alu_AluSx chr1 19635398 19635452 + LINE/L2_L2a chr1 19635453 19635644 - SINE/Alu_AluSz chr1 19635645 19635673 + Simple_repeat_(TTA)n chr1 19635674 19635794 - SINE/Alu_AluSz chr1 19635795 19635886 + LINE/L2_L2d2 chr1 19636234 19636340 - LTR/ERVL-MaLR_MLT1O chr1 19636618 19636908 + SINE/Alu_AluSx1 chr1 19637060 19637368 + SINE/Alu_AluSq4 chr1 19637369 19637675 + SINE/Alu_AluY chr1 19638037 19638358 - SINE/Alu_AluJr chr1 19638566 19638704 + SINE/MIR_MIR1_Amn chr1 19638705 19638999 + SINE/Alu_AluSx chr1 19639000 19639017 + SINE/MIR_MIR chr1 19639006 19639253 - SINE/MIR_MIRb chr1 19639258 19639339 - SINE/MIR_MIRb chr1 19639460 19639738 - SINE/Alu_AluSx chr1 19639777 19640066 - SINE/Alu_AluSz chr1 19640132 19640291 + LINE/L2_L2a chr1 19640300 19640463 - SINE/MIR_MIR chr1 19640836 19640889 + Simple_repeat_(GGCCCA)n chr1 19641049 19641277 + SINE/MIR_MIRb chr1 19641620 19641665 - LINE/L2_L2c chr1 19641714 19641725 + SINE/MIR_MIR1_Amn chr1 19641726 19641763 + SINE/MIR_MIR chr1 19641764 19641785 + SINE/MIR_MIR1_Amn chr1 19641859 19641942 - SINE/Alu_FRAM chr1 19641969 19642279 - SINE/Alu_AluSz chr1 19642285 19642388 + SINE/MIR_MIR chr1 19642460 19642749 + SINE/Alu_AluSq2 chr1 19642751 19642777 + Simple_repeat_(AAC)n chr1 19642778 19642993 - LINE/L2_L2c chr1 19643045 19643273 - LINE/L2_L2c chr1 19644743 19644767 + Simple_repeat_(GGGA)n chr1 19644774 19644833 + Simple_repeat_(GGCCG)n chr1 19644997 19645034 + Simple_repeat_(TC)n chr1 19645035 19645069 + Simple_repeat_(TGTCTC)n chr1 19646272 19646386 - SINE/MIR_MIR3 chr1 19647027 19647184 - SINE/MIR_MIR chr1 19647185 19647454 + SINE/Alu_AluJr chr1 19647455 19647492 - SINE/MIR_MIR chr1 19647851 19647913 + Simple_repeat_(GT)n chr1 19648357 19648418 + DNA/hAT-Charlie_MER5B chr1 19649151 19649293 + SINE/MIR_MIR chr1 19649311 19649347 + DNA/TcMar-Tigger_Tigger3a chr1 19649348 19649374 + Simple_repeat_(ATT)n chr1 19649375 19649657 - SINE/Alu_AluSz chr1 19649658 19649705 + DNA/TcMar-Tigger_Tigger3a chr1 19649713 19649960 - SINE/Alu_AluJr chr1 19649970 19650098 - SINE/Alu_AluYc chr1 19650102 19650285 + DNA/TcMar-Tigger_Tigger3c chr1 19651065 19651222 - SINE/MIR_MIRb chr1 19651638 19651681 + Simple_repeat_(CCTC)n chr1 19651697 19651900 - SINE/Alu_AluJr chr1 19651901 19651926 + Simple_repeat_(TATTT)n chr1 19651927 19652042 - SINE/Alu_AluJr chr1 19652706 19652994 + SINE/Alu_AluSz chr1 19653006 19653306 + SINE/Alu_AluY chr1 19653546 19653609 - DNA/hAT-Charlie_MER5A chr1 19654128 19654424 + SINE/Alu_AluJb chr1 19654597 19654743 - SINE/MIR_MIR chr1 19654744 19654958 - SINE/MIR_MIR chr1 19655669 19656191 + LINE/L2_L2c chr1 19656195 19656223 + Low_complexity_GA-rich chr1 19656224 19656467 - LINE/L2_L2b chr1 19657014 19657056 + Simple_repeat_(CCCACC)n chr1 19657803 19657835 + Low_complexity_G-rich chr1 19658810 19658836 + Simple_repeat_(CCTGG)n chr1 19658910 19659059 - SINE/MIR_MIR chr1 19659084 19659186 - SINE/MIR_MIRb chr1 19659303 19659590 + SINE/Alu_AluJr chr1 19659591 19659654 + Low_complexity_A-rich chr1 19660069 19660109 + Simple_repeat_(GTTTTT)n chr1 19660110 19660388 - SINE/Alu_AluSc chr1 19660532 19660754 + SINE/MIR_MIR chr1 19660817 19660915 - LINE/L2_L2a chr1 19661186 19661497 - SINE/Alu_AluY chr1 19661592 19661890 + SINE/Alu_AluJr4 chr1 19661950 19662045 - LINE/L1_L1ME3Cz chr1 19662048 19662243 - SINE/Alu_AluJr chr1 19662391 19662476 - SINE/MIR_MIRb chr1 19662602 19662639 + Simple_repeat_(TTTTTTTG)n chr1 19662640 19662920 - SINE/Alu_AluSx chr1 19662954 19663142 - SINE/MIR_MIRc chr1 19663361 19663495 + SINE/MIR_MIR3 chr1 19663621 19663680 + Simple_repeat_(TTTC)n chr1 19663682 19663992 - SINE/Alu_AluSx1 chr1 19663994 19664172 + LINE/L2_L2a chr1 19664471 19664549 - SINE/MIR_MIR1_Amn chr1 19664826 19664865 + Simple_repeat_(GCGCG)n chr1 19666591 19667374 + LINE/L2_L2a chr1 19667396 19667647 - SINE/Alu_AluSg4 chr1 19668240 19668345 - SINE/Alu_AluJb chr1 19668375 19668466 - SINE/Alu_AluJr4 chr1 19668554 19668602 - DNA/hAT-Charlie_MER113 chr1 19668603 19668713 - SINE/Alu_FLAM_A chr1 19668714 19669096 - DNA/hAT-Charlie_MER113 chr1 19669330 19669523 - SINE/MIR_MIRb chr1 19669550 19669760 + LINE/L2_L2a chr1 19669773 19669935 - SINE/Alu_AluSx chr1 19670051 19670454 + LINE/L2_L2a chr1 19670455 19670763 - SINE/Alu_AluSx3 chr1 19670764 19670848 + LINE/L2_L2a chr1 19670969 19671160 - LINE/L2_L2a chr1 19672271 19672343 + Simple_repeat_(CTTCTC)n chr1 19672522 19672753 + SINE/MIR_MIRb chr1 19672756 19672960 + SINE/Alu_AluJr chr1 19673149 19673395 - SINE/MIR_MIR chr1 19673413 19673426 - LINE/L1_L1ME3G chr1 19673427 19673725 + SINE/Alu_AluSp chr1 19673726 19673858 - LINE/L1_L1ME3G chr1 19673859 19674167 - SINE/Alu_AluJb chr1 19674168 19674399 - LINE/L1_L1ME3G chr1 19674400 19674705 - SINE/Alu_AluSz chr1 19674706 19674792 - LINE/L1_L1ME3G chr1 19675117 19675339 + SINE/MIR_MIRb chr1 19675359 19675462 + SINE/MIR_MIRc chr1 19676155 19676387 - SINE/MIR_MIRb chr1 19677163 19677181 + Simple_repeat_(TG)n chr1 19677216 19677242 + Simple_repeat_(GT)n chr1 19677417 19677463 + Simple_repeat_(TCTTCC)n chr1 19677756 19678039 - SINE/Alu_AluSx1 chr1 19678217 19678263 + Low_complexity_G-rich chr1 19678823 19678846 + Simple_repeat_(GT)n chr1 19679176 19679236 + Simple_repeat_(CTCAGG)n chr1 19679761 19679924 + DNA/hAT-Charlie_MER5A1 chr1 19680003 19680226 + SINE/MIR_MIRb chr1 19681014 19681162 + SINE/MIR_MIR3 chr1 19681221 19681521 + SINE/Alu_AluSq2 chr1 19681524 19681773 - SINE/Alu_AluJr chr1 19681895 19681981 + SINE/MIR_MIRc chr1 19682024 19682253 + SINE/MIR_MIRb chr1 19682408 19682495 + DNA/hAT-Tip100_MER91C chr1 19682886 19682925 + Simple_repeat_(TTCCAT)n chr1 19683759 19683788 + Simple_repeat_(T)n chr1 19683797 19684060 - SINE/Alu_AluSz chr1 19684064 19684088 + Simple_repeat_(T)n chr1 19684151 19684424 - SINE/Alu_AluJr chr1 19684456 19684586 + SINE/MIR_MIR chr1 19684625 19685038 + LTR/ERVL-MaLR_MLT1J chr1 19685043 19685349 + SINE/Alu_AluJb chr1 19685526 19685561 + LINE/L2_L2a chr1 19685619 19685706 - SINE/MIR_MIRb chr1 19685707 19686026 - SINE/Alu_AluY chr1 19686027 19686057 - SINE/MIR_MIRb chr1 19686054 19686208 - SINE/MIR_MIRb chr1 19686711 19686862 + SINE/MIR_MIRc chr1 19686940 19687252 - SINE/Alu_AluSx chr1 19687253 19687386 - SINE/Alu_AluJr4 chr1 19687414 19687524 + SINE/MIR_MIRb chr1 19687576 19687642 - LTR/ERVL-MaLR_MLT1J chr1 19688133 19688304 + LINE/L2_L2b chr1 19688460 19688577 + SINE/MIR_MIR chr1 19688633 19688770 + SINE/MIR_MIRb chr1 19688812 19688856 - LINE/L2_L2b chr1 19689206 19689358 - Unknown_UCON70 chr1 19689484 19689653 - SINE/MIR_MIRb chr1 19689866 19690644 + LTR/ERV1_LTR28B chr1 19690754 19690862 + SINE/MIR_MIRb chr1 19690865 19691167 - SINE/Alu_AluY chr1 19691414 19691541 + LTR/Gypsy?_LTR85c chr1 19691747 19692105 + LTR/ERVL-MaLR_THE1B chr1 19692329 19692431 + SINE/MIR_MIRc chr1 19692448 19692774 + SINE/Alu_AluSq2 chr1 19692890 19693194 + SINE/Alu_AluSz chr1 19693201 19693507 + SINE/Alu_AluSx1 chr1 19693605 19693730 - DNA/hAT-Charlie_MER5A chr1 19693913 19693995 + SINE/MIR_MIRb chr1 19694007 19694300 - SINE/Alu_AluJb chr1 19694782 19694879 + LINE/L2_L2a chr1 19695562 19695729 + SINE/MIR_MIR3 chr1 19696269 19696582 + SINE/Alu_AluJo chr1 19696918 19697072 + SINE/MIR_MIRb chr1 19697183 19697271 + LINE/L2_L2c chr1 19697272 19697596 - SINE/Alu_AluJo chr1 19697602 19697913 - SINE/Alu_AluJb chr1 19697914 19698067 + LINE/L2_L2c chr1 19698330 19698535 + SINE/MIR_MIRc chr1 19698915 19699208 + SINE/Alu_AluY chr1 19699252 19699469 - SINE/MIR_MIRb chr1 19699481 19699514 + Simple_repeat_(AT)n chr1 19699600 19699678 - SINE/Alu_AluSx3 chr1 19699687 19699728 + LINE/L2_L2b chr1 19699877 19699963 - SINE/MIR_MIR chr1 19700651 19700733 + LINE/L2_L2c chr1 19700979 19701042 + Simple_repeat_(CACATG)n chr1 19701081 19701161 - SINE/MIR_MIRb chr1 19701162 19701471 - SINE/Alu_AluSx chr1 19701472 19701571 - SINE/MIR_MIRb chr1 19701848 19701962 + SINE/Alu_AluJo chr1 19701963 19702268 - SINE/Alu_AluY chr1 19702269 19702309 + SINE/Alu_AluJo chr1 19702310 19702607 + SINE/Alu_AluSz6 chr1 19702608 19702783 + SINE/Alu_AluJo chr1 19702940 19703143 + LINE/L2_L2a chr1 19703183 19703361 + SINE/MIR_MIRb chr1 19703409 19703532 + Simple_repeat_(CTTC)n chr1 19703533 19703538 + Simple_repeat_(TCTTT)n chr1 19703540 19703847 - SINE/Alu_AluSx chr1 19703966 19703997 + Simple_repeat_(GAGACA)n chr1 19704006 19704118 + DNA/hAT-Charlie_Charlie15a chr1 19704338 19704504 - SINE/MIR_MIR3 chr1 19705090 19705171 - DNA/hAT-Charlie_MER30 chr1 19705172 19705452 + SINE/Alu_AluSx1 chr1 19705453 19705487 + Simple_repeat_(AAAT)n chr1 19705494 19705647 - DNA/hAT-Charlie_MER30 chr1 19705915 19706204 - SINE/Alu_AluJr chr1 19706860 19707009 + LINE/L1_L1MB7 chr1 19707010 19707155 + LINE/L2_L2 chr1 19707161 19707341 + LTR/ERVL-MaLR_MSTB chr1 19707342 19707629 + SINE/Alu_AluSz6 chr1 19707701 19708083 - LTR/ERVL-MaLR_MSTA chr1 19708384 19708415 + Simple_repeat_(A)n chr1 19708481 19708590 - DNA/TcMar-Tigger_X6b_DNA chr1 19709710 19709736 + Simple_repeat_(T)n chr1 19709776 19710080 - SINE/Alu_AluSp chr1 19710102 19710389 - SINE/Alu_AluJo chr1 19710418 19710459 + Simple_repeat_(ATCATC)n chr1 19710460 19710638 - SINE/MIR_MIRb chr1 19711154 19711287 + DNA/TcMar-Tigger_Tigger1 chr1 19711248 19711489 - DNA/TcMar-Tigger_Tigger1 chr1 19711490 19711655 - SINE/Alu_AluJb chr1 19711656 19711954 - SINE/Alu_AluSp chr1 19711955 19712095 - SINE/Alu_AluJb chr1 19712096 19712338 - DNA/TcMar-Tigger_Tigger1 chr1 19712339 19712646 + SINE/Alu_AluSx3 chr1 19712647 19712716 - DNA/TcMar-Tigger_Tigger1 chr1 19712718 19712766 - DNA/TcMar-Tigger_Tigger1 chr1 19712780 19713275 - LTR/ERVL-MaLR_MLT1H chr1 19713556 19713709 + SINE/Alu_FRAM chr1 19713710 19713739 + Simple_repeat_(CAA)n chr1 19713767 19713934 - LINE/L1_L1MEd chr1 19713935 19714236 - SINE/Alu_AluSz6 chr1 19714237 19714620 - LINE/L1_L1MEd chr1 19714641 19714924 + SINE/Alu_AluSq2 chr1 19714925 19714954 + Simple_repeat_(AAAT)n chr1 19714961 19715239 + SINE/Alu_AluJr chr1 19715240 19715442 - SINE/Alu_AluJr chr1 19716035 19716152 + SINE/Alu_AluJo chr1 19716153 19716187 + Simple_repeat_(TTA)n chr1 19716188 19716359 - SINE/Alu_AluSg chr1 19716360 19716403 + Simple_repeat_(T)n chr1 19716404 19716529 - SINE/Alu_AluSg chr1 19716530 19716562 + Simple_repeat_(TTA)n chr1 19716581 19716772 + SINE/MIR_MIRb chr1 19716906 19717198 + SINE/Alu_AluSq2 chr1 19717332 19717427 + LINE/L2_L2b chr1 19717530 19717898 + LINE/L2_L2d chr1 19717907 19718049 + SINE/MIR_MIRb chr1 19718051 19718363 + SINE/Alu_AluSx1 chr1 19718398 19718679 + SINE/Alu_AluJr chr1 19718760 19718796 + SINE/MIR_MIRb chr1 19719438 19719477 + Simple_repeat_(AATAAC)n chr1 19719520 19719542 + Simple_repeat_(TGAT)n chr1 19719543 19719711 - SINE/Alu_AluJo chr1 19719712 19720005 + SINE/Alu_AluSx1 chr1 19720006 19720148 - SINE/Alu_AluJo chr1 19720171 19720301 - SINE/Alu_AluJr chr1 19720304 19720481 - SINE/MIR_MIR chr1 19721507 19721788 + SINE/Alu_AluJb chr1 19721789 19721823 + Simple_repeat_(AAACA)n chr1 19722070 19722221 + LINE/CR1_L3 chr1 19722392 19722542 - SINE/MIR_MIRb chr1 19723105 19723311 - DNA/TcMar-Tigger_Tigger4a chr1 19723675 19723750 + SINE/MIR_MIR3 chr1 19724249 19724317 - SINE/MIR_MIRb chr1 19724397 19724528 - LINE/L2_L2b chr1 19724457 19724598 + SINE/MIR_MIR chr1 19724543 19724751 - SINE/MIR_MIRc chr1 19724753 19725044 - SINE/Alu_AluSc chr1 19726051 19726232 - LINE/L2_L2b chr1 19726263 19726378 + SINE/MIR_MIRb chr1 19726379 19726476 + SINE/Alu_AluSx3 chr1 19726502 19726676 + SINE/MIR_MIRb chr1 19727327 19727357 - LINE/L2_L2c chr1 19727358 19727407 - SINE/MIR_MIRb chr1 19727443 19727555 + LINE/L2_L2a chr1 19727665 19728217 + LINE/L1_L1PREC2 chr1 19728262 19728419 + SINE/MIR_MIRb chr1 19728848 19728973 - SINE/MIR_MIR3 chr1 19729064 19729153 + LINE/L1_L1MA2 chr1 19729154 19729447 - SINE/Alu_AluSz6 chr1 19729448 19729485 + LINE/L1_L1MA2 chr1 19729486 19729765 + SINE/Alu_AluSx chr1 19729766 19729802 + Simple_repeat_(AAAT)n chr1 19729803 19729950 + LINE/L1_L1MA2 chr1 19729979 19730047 + SINE/MIR_MIRb chr1 19730065 19730431 + LTR/ERVL-MaLR_MLT1I chr1 19730448 19730566 - LINE/L2_L2c chr1 19731379 19731541 - SINE/MIR_MIRb chr1 19732196 19732465 - SINE/Alu_AluJb chr1 19732609 19732739 + SINE/Alu_FLAM_C chr1 19732910 19732944 + Simple_repeat_(T)n chr1 19732984 19733277 + SINE/Alu_AluSx1 chr1 19733453 19733535 - LINE/L2_L2a chr1 19733556 19733619 - DNA/hAT-Charlie_MER5B chr1 19733800 19734019 + SINE/MIR_MIR chr1 19734049 19734080 + Simple_repeat_(TTA)n chr1 19734104 19734170 - LINE/L2_L2a chr1 19734733 19734940 + SINE/MIR_MIR chr1 19735016 19735175 - LINE/L2_L2b chr1 19735204 19735417 - SINE/MIR_MIRb chr1 19735963 19736166 + LTR/ERVL-MaLR_MLT1H2 chr1 19736165 19736420 - LTR/ERVL-MaLR_THE1B chr1 19736769 19737096 + LTR/ERVL-MaLR_MLT1C chr1 19737093 19737198 - DNA/hAT-Charlie_Chap1a_Mam chr1 19738281 19738410 + LTR/ERVL-MaLR_MLT1D chr1 19738453 19738552 + LTR/ERVL-MaLR_MLT1D chr1 19738582 19738640 + LTR/ERVL-MaLR_MLT1C chr1 19738737 19738914 + LTR/ERVL-MaLR_MLT1J chr1 19738959 19739153 + LTR/ERVL-MaLR_MLT1J chr1 19740175 19740448 + LTR/ERVL-MaLR_MLT1H chr1 19740523 19740567 + LTR/ERVL-MaLR_MLT1H chr1 19740575 19740681 + LINE/L2_L2b chr1 19741118 19741302 + SINE/MIR_MIR chr1 19741447 19741520 + SINE/MIR_MIRc chr1 19741726 19742036 - SINE/Alu_AluSx1 chr1 19742115 19742359 + LINE/L2_L2b chr1 19742622 19742804 + SINE/MIR_MIRc chr1 19742805 19743078 + SINE/Alu_AluSc chr1 19743079 19743108 + SINE/MIR_MIRc chr1 19743110 19743331 + SINE/MIR_MIRb chr1 19743754 19743809 + SINE/MIR_MIRb chr1 19743756 19743820 + SINE/MIR_MIR1_Amn chr1 19744382 19744544 - SINE/MIR_MIR chr1 19744492 19744968 + LINE/L2_L2b chr1 19744976 19745358 + Simple_repeat_(ATGG)n chr1 19745381 19745449 + SINE/MIR_MIRc chr1 19746643 19746945 + SINE/Alu_AluY chr1 19748134 19748230 + SINE/MIR_MIRb chr1 19748264 19748349 + SINE/MIR_MIRb chr1 19748543 19748823 + SINE/Alu_AluSx1 chr1 19748824 19748858 + Simple_repeat_(GAAT)n chr1 19748859 19748881 + SINE/Alu_AluSx1 chr1 19749213 19749360 + LTR/ERVL-MaLR_MLT1I chr1 19749361 19749665 - SINE/Alu_AluSz6 chr1 19749666 19749914 + LTR/ERVL-MaLR_MLT1I chr1 19749930 19750107 + SINE/MIR_MIR chr1 19750354 19750469 + SINE/MIR_MIR3 chr1 19750554 19750602 + Simple_repeat_(TGCC)n chr1 19750732 19750901 + LINE/L2_L2c chr1 19750922 19751080 + DNA/hAT-Charlie_MER5A chr1 19751316 19751605 + SINE/Alu_AluJb chr1 19751627 19751766 - DNA/hAT-Ac_EuthAT-2 chr1 19751767 19752061 + SINE/Alu_AluY chr1 19752062 19752418 - DNA/hAT-Ac_EuthAT-2 chr1 19752758 19752899 + SINE/MIR_MIRb chr1 19752940 19753259 - SINE/Alu_AluSp chr1 19753297 19753490 - SINE/MIR_MIR chr1 19753580 19753747 - SINE/MIR_MIR3 chr1 19753941 19754141 - SINE/MIR_MIR chr1 19754811 19755011 - SINE/MIR_MIRb chr1 19755072 19755121 - SINE/MIR_MIR3 chr1 19755122 19755413 - SINE/Alu_AluSz chr1 19755414 19755551 - SINE/MIR_MIR3 chr1 19755965 19756261 + SINE/Alu_AluJb chr1 19756272 19756671 + LINE/L1_L1ME4a chr1 19756718 19756851 + SINE/Alu_FLAM_C chr1 19757028 19757208 + SINE/Alu_AluSz6 chr1 19757225 19757584 - LTR/ERVL-MaLR_MLT1D chr1 19758003 19758051 + DNA/hAT-Charlie_MER5A chr1 19758813 19759129 + SINE/Alu_AluSz chr1 19759133 19759646 + LINE/L2_L2a chr1 19759500 19759918 + LINE/L2_L2a chr1 19760167 19760289 - SINE/Alu_FLAM_A chr1 19760322 19760417 + LINE/L2_L2a chr1 19760418 19760667 - SINE/Alu_AluJr chr1 19760668 19760834 + LINE/L2_L2a chr1 19760995 19761217 - SINE/MIR_MIR chr1 19761623 19761765 + DNA/hAT-Charlie_MER5A chr1 19761937 19762144 - SINE/MIR_MIRb chr1 19762610 19762764 - DNA/hAT-Charlie_MER20 chr1 19762950 19763068 + SINE/MIR_MIRb chr1 19763069 19763377 - SINE/Alu_AluSx1 chr1 19763378 19763491 + SINE/MIR_MIRb chr1 19764214 19764453 + SINE/MIR_MIR chr1 19764462 19764527 + LINE/L2_L2b chr1 19764578 19764868 + SINE/Alu_AluSp chr1 19765185 19765288 + SINE/MIR_MIRb chr1 19765420 19765608 + DNA/hAT-Charlie_MER20 chr1 19766173 19766277 - LINE/L2_L2a chr1 19766407 19766556 - SINE/MIR_MIRb chr1 19766573 19766735 - SINE/MIR_MIR chr1 19766830 19766921 + SINE/MIR_MIR3 chr1 19767172 19767347 + SINE/MIR_MIRb chr1 19767675 19767809 + SINE/MIR_MIR3 chr1 19767811 19768113 + SINE/Alu_AluSz chr1 19768153 19768274 + SINE/MIR_MIRb chr1 19768749 19768905 - DNA/hAT-Charlie_MER5A1 chr1 19768926 19768978 - LTR/ERVL-MaLR_MLT1L chr1 19768988 19769213 - LTR/ERVL_LTR82A chr1 19769806 19769879 - DNA/hAT-Charlie_MER1B chr1 19769940 19770316 - LINE/L2_L2a chr1 19770315 19770390 + SINE/MIR_MIRb chr1 19770659 19770764 - LINE/L2_L2a chr1 19770920 19771148 + SINE/MIR_MIR chr1 19771652 19771957 - SINE/Alu_AluSq chr1 19772909 19773215 + SINE/Alu_AluSx chr1 19773242 19773639 - LTR/ERVL_LTR16A chr1 19773645 19773802 + SINE/MIR_MIR chr1 19773862 19774086 + SINE/MIR_MIR chr1 19774302 19774516 + SINE/MIR_MIR chr1 19774702 19774886 - SINE/MIR_MIR chr1 19775061 19775376 - SINE/Alu_AluSp chr1 19775386 19775481 - SINE/MIR_MIRc chr1 19775485 19775702 + DNA/hAT-Charlie_MER58A chr1 19775902 19776207 - SINE/Alu_AluSz chr1 19776394 19776479 + LINE/L2_L2b chr1 19776749 19777054 + SINE/Alu_AluSz chr1 19777112 19777347 + LTR/ERVL-MaLR_MLT1L chr1 19777348 19777646 - SINE/Alu_AluSz6 chr1 19777647 19777771 + LTR/ERVL-MaLR_MLT1L chr1 19777772 19778063 + SINE/Alu_AluJr chr1 19778064 19778086 + LTR/ERVL-MaLR_MLT1L chr1 19778087 19778150 - SINE/MIR_MIR chr1 19778172 19778244 - SINE/MIR_MIRb chr1 19778245 19778289 + Simple_repeat_(TATT)n chr1 19778290 19778563 - SINE/Alu_AluSx chr1 19778564 19778735 - SINE/MIR_MIRb chr1 19778776 19779105 - LINE/L2_L2b chr1 19779569 19779789 + LINE/L2_L2c chr1 19779798 19779934 + LINE/L2_L2b chr1 19779957 19779990 + LINE/L2_L2d2 chr1 19779995 19780204 - DNA/hAT-Charlie_MER1B chr1 19780206 19780325 + SINE/MIR_MIRb chr1 19780330 19780451 - DNA/hAT-Charlie_MER1B chr1 19780802 19781075 + LINE/L1_L1ME3 chr1 19781076 19781169 + SINE/Alu_Alu chr1 19781179 19781477 + SINE/Alu_AluJr chr1 19781478 19781637 + LINE/L1_L1ME3 chr1 19781638 19781944 - SINE/Alu_AluY chr1 19781945 19782543 + LINE/L1_L1ME3 chr1 19782544 19782578 + Low_complexity_GA-rich chr1 19782593 19782633 + Simple_repeat_(TCCT)n chr1 19782791 19783037 + SINE/MIR_MIR chr1 19783076 19783121 + SINE/MIR_MIRb chr1 19783120 19783363 - SINE/MIR_MIRb chr1 19783461 19783546 - SINE/MIR_MIRc chr1 19783555 19783774 - SINE/MIR_MIRc chr1 19783773 19783858 - SINE/MIR_MIR1_Amn chr1 19783792 19783893 - SINE/MIR_MIR3 chr1 19784252 19784555 + SINE/Alu_AluSz chr1 19784607 19784745 + LINE/L2_L2a chr1 19784746 19784981 - DNA/hAT-Charlie_MER30 chr1 19784982 19785216 + LINE/L2_L2a chr1 19785217 19785290 - DNA/TcMar-Mariner_MADE1 chr1 19785291 19785516 + LINE/L2_L2a chr1 19785487 19785662 - LINE/L2_L2a chr1 19785659 19785744 - SINE/MIR_MIR chr1 19785925 19785976 - LINE/L2_L2c chr1 19785977 19786289 + SINE/Alu_AluJr chr1 19786290 19786642 - LINE/L2_L2c chr1 19786666 19786760 + LINE/L1_L1MD3 chr1 19786878 19787011 - LINE/L2_L2a chr1 19787310 19787400 - SINE/MIR_MIRb chr1 19787355 19787514 + LINE/L2_L2c chr1 19787675 19787723 + Low_complexity_A-rich chr1 19787747 19788056 - SINE/Alu_AluSz6 chr1 19788401 19788565 + LINE/L1_L1ME4a chr1 19788608 19788734 + DNA/hAT-Blackjack_MER63B chr1 19788748 19788793 + LINE/L1_L1ME4a chr1 19788794 19789104 + SINE/Alu_AluY chr1 19789105 19789120 + LINE/L1_L1ME4a chr1 19789121 19789435 + SINE/Alu_AluSx chr1 19789436 19789610 + LINE/L1_L1ME4a chr1 19789771 19790074 + SINE/Alu_AluJb chr1 19790077 19791308 + LINE/L1_L1PA10 chr1 19791309 19791338 + Simple_repeat_(TTAAAA)n chr1 19791843 19792187 + LTR/ERVL-MaLR_THE1B chr1 19792473 19792523 + LINE/L2_L2d2 chr1 19792566 19792630 - DNA/TcMar-Tigger_MamRep137 chr1 19792766 19793069 - SINE/Alu_AluSz chr1 19793129 19793195 + LINE/L2_L2b chr1 19793796 19794179 + LINE/L2_L2a chr1 19794020 19794415 + LINE/L2_L2a chr1 19794605 19795165 + LINE/L1_L1MB2 chr1 19795189 19795504 + SINE/Alu_AluSq2 chr1 19795538 19795724 - SINE/MIR_MIRb chr1 19795897 19796079 + LINE/L2_L2d2 chr1 19796109 19796294 - SINE/MIR_MIRb chr1 19796531 19796566 + LINE/L2_L2a chr1 19796567 19796872 - SINE/Alu_AluSc chr1 19796873 19797049 + LINE/L2_L2a chr1 19797349 19797587 - LINE/L2_L2b chr1 19797589 19797889 + SINE/Alu_AluSx4 chr1 19797890 19797956 + Simple_repeat_(GAGA)n chr1 19797957 19798018 + Simple_repeat_(AGAAA)n chr1 19798019 19798029 + Simple_repeat_(GAGA)n chr1 19798488 19798558 - SINE/MIR_MIRb chr1 19798551 19798627 - SINE/MIR_MIRc chr1 19798763 19798880 + LINE/L2_L2a chr1 19798965 19799036 + SINE/MIR_MIRc chr1 19799099 19799206 + SINE/MIR_MIRb chr1 19799451 19799532 + SINE/MIR_MIR3 chr1 19799720 19799795 + Low_complexity_G-rich chr1 19799857 19799888 + Simple_repeat_(GGAGGC)n chr1 19799902 19800014 + Simple_repeat_(CGGCC)n chr1 19800376 19800441 - SINE/MIR_MIRc chr1 19800440 19800664 + LINE/L2_L2b chr1 19800665 19800689 + Simple_repeat_(T)n chr1 19800697 19800966 - SINE/Alu_AluSx chr1 19800967 19801096 + LINE/L2_L2b chr1 19801049 19801248 - SINE/MIR_MIRb chr1 19801280 19801483 + SINE/MIR_MIRc chr1 19801910 19802072 - LINE/L2_L2a chr1 19802073 19802358 - SINE/Alu_AluSx3 chr1 19802359 19802640 - LINE/L2_L2a chr1 19802658 19802919 + SINE/Alu_AluSz chr1 19802923 19802948 + Simple_repeat_(AT)n chr1 19802965 19803154 + DNA/hAT_MER53 chr1 19803157 19803235 - DNA/hAT-Charlie_MER5B chr1 19803237 19803264 + Simple_repeat_(TTTCT)n chr1 19803266 19803289 + Simple_repeat_(AAC)n chr1 19803296 19803597 + SINE/Alu_AluSx1 chr1 19803633 19804060 - LINE/L1_L1PREC2 chr1 19804105 19804287 - LINE/L2_L2a chr1 19804294 19804324 - LINE/L2_L2a chr1 19804379 19804470 + DNA/hAT-Charlie_MER5B chr1 19804550 19804736 - SINE/MIR_MIR chr1 19805287 19805587 - SINE/Alu_AluSz chr1 19805717 19806021 + DNA/hAT-Charlie_MER113 chr1 19806022 19806265 - LINE/L1_L1MD3 chr1 19806267 19806566 - SINE/Alu_AluJb chr1 19806591 19806798 - LINE/L2_L2a chr1 19806904 19807185 + SINE/Alu_AluSc chr1 19807251 19807379 - LINE/L1_L1ME3B chr1 19807380 19807676 + SINE/Alu_AluJo chr1 19807677 19807743 - LINE/L1_L1ME3B chr1 19807908 19808071 - SINE/MIR_MIRb chr1 19808072 19808385 - SINE/Alu_AluSx1 chr1 19808386 19808439 - SINE/MIR_MIRb chr1 19808513 19808804 + SINE/MIR_MIR chr1 19809263 19809395 - LTR/ERVL-MaLR_MLT1N2 chr1 19809524 19809737 + DNA/hAT-Charlie_MER1A chr1 19809778 19809840 + LINE/L2_L2b chr1 19809870 19810183 - SINE/Alu_AluSx chr1 19810715 19810949 + SINE/MIR_MIR chr1 19811103 19811262 - SINE/MIR_MIRb chr1 19811528 19811695 - SINE/MIR_MIRb chr1 19811955 19812078 - SINE/MIR_MIRb chr1 19812121 19812140 + Simple_repeat_(TTCTT)n chr1 19812141 19812456 - SINE/Alu_AluSx1 chr1 19812457 19812479 + Simple_repeat_(TTCTT)n chr1 19812633 19812877 + SINE/MIR_MIRb chr1 19815528 19815614 - SINE/Alu_AluYk2 chr1 19816026 19816067 - SINE/MIR_MIRb chr1 19816138 19816226 - LTR?_MamRep605 chr1 19816706 19817158 + LTR/ERV1_MER4A1 chr1 19817231 19817788 + LTR/ERVL-MaLR_MLT1K chr1 19818089 19818355 - SINE/Alu_AluSx chr1 19818356 19818448 - LTR?_MamRep605 chr1 19818684 19818793 + SINE/MIR_MIR chr1 19818795 19819104 + SINE/Alu_AluJb chr1 19819292 19819484 - DNA/hAT-Charlie_Charlie16a chr1 19820240 19820257 + Simple_repeat_(T)n chr1 19820275 19820407 + SINE/MIR_MIRc chr1 19820480 19820785 - SINE/Alu_AluSq2 chr1 19820826 19820959 - LINE/L1_L1ME4a chr1 19820960 19821272 - SINE/Alu_AluSq2 chr1 19821273 19821556 - LINE/L1_L1ME4a chr1 19821994 19822036 + Simple_repeat_(TGACCC)n chr1 19822387 19822513 + LINE/RTE-X_L4_B_Mam chr1 19822514 19822586 + DNA/hAT-Charlie_Charlie14a chr1 19822596 19822835 - DNA/hAT-Charlie_MER103C chr1 19822838 19823331 + LINE/RTE-X_L4_B_Mam chr1 19823332 19823630 - SINE/Alu_AluSz6 chr1 19823631 19823681 + LINE/RTE-X_L4_B_Mam chr1 19823682 19823705 + Low_complexity_GA-rich chr1 19823706 19824015 + SINE/Alu_AluSx chr1 19824016 19824038 + Low_complexity_GA-rich chr1 19824603 19824723 - LTR/ERVL-MaLR_MLT1K chr1 19824837 19824990 + SINE/MIR_MIRb chr1 19825243 19825673 + LINE/L2_L2 chr1 19825674 19825976 - SINE/Alu_AluSz chr1 19825977 19826499 + LINE/L2_L2 chr1 19826500 19826781 - SINE/Alu_AluJr chr1 19826782 19826929 + LINE/L2_L2 chr1 19826928 19827132 - LINE/L2_L2 chr1 19827134 19827406 + LINE/L1_L1ME2z chr1 19827407 19827708 - SINE/Alu_AluSx chr1 19827709 19827761 + LINE/L1_L1ME2z chr1 19827741 19827789 - LINE/L1_L1MB4 chr1 19827790 19828087 - SINE/Alu_AluSp chr1 19828088 19828645 - LINE/L1_L1MB4 chr1 19828646 19828813 - SINE/Alu_FRAM chr1 19828814 19829035 - LINE/L1_L1MB4 chr1 19829051 19829739 - LINE/L1_L1MC1 chr1 19829740 19830052 - SINE/Alu_AluSz chr1 19830053 19830227 - LINE/L1_L1MC1 chr1 19830228 19830539 - SINE/Alu_AluSx chr1 19830540 19830740 - LINE/L1_L1MC1 chr1 19830741 19831031 + LINE/L2_L2 chr1 19831053 19832323 + LINE/L1_L1MB7 chr1 19832324 19832629 + SINE/Alu_AluY chr1 19832630 19833314 + LINE/L1_L1MB7 chr1 19833315 19833624 + SINE/Alu_AluSx1 chr1 19833627 19833934 + SINE/Alu_AluSp chr1 19833935 19834416 + LINE/L1_L1MB7 chr1 19834417 19834721 + SINE/Alu_AluSq4 chr1 19834722 19834795 + LINE/L1_L1MB7 chr1 19834796 19835109 + SINE/Alu_AluSx1 chr1 19835110 19835816 + LINE/L1_L1MB7 chr1 19835818 19835926 + SINE/Alu_AluJr chr1 19835927 19836225 + SINE/Alu_AluSq2 chr1 19836226 19836402 + SINE/Alu_AluJr chr1 19836415 19836709 + SINE/Alu_AluSg chr1 19836714 19837942 + LINE/L1_L1MB7 chr1 19837943 19838074 + SINE/Alu_FLAM_A chr1 19838075 19838253 + LINE/L1_L1MB7 chr1 19838254 19838312 + Simple_repeat_(TCCT)n chr1 19838315 19838620 - SINE/Alu_AluSx1 chr1 19838621 19839745 + LINE/L1_L1MB7 chr1 19839746 19840049 + SINE/Alu_AluJb chr1 19840050 19841029 + LINE/L1_L1MB7 chr1 19841035 19841300 + LINE/L1_L1MB7 chr1 19841301 19841583 + SINE/Alu_AluSz6 chr1 19841584 19841618 + Simple_repeat_(AATA)n chr1 19841619 19841885 + LINE/L1_L1MC4 chr1 19841886 19841901 + LINE/L1_L1MB7 chr1 19841902 19842190 + SINE/Alu_AluSp chr1 19842191 19842366 + LINE/L1_L1MB7 chr1 19842367 19842645 + SINE/Alu_AluSx1 chr1 19842646 19843130 + LINE/L1_L1MB7 chr1 19843163 19843310 + DNA/hAT-Blackjack_MER63B chr1 19843376 19843504 + SINE/Alu_AluJr chr1 19843627 19843925 - SINE/Alu_AluSz chr1 19843996 19844049 + SINE/MIR_MIRb chr1 19844133 19844176 - LINE/L1_L1ME3G chr1 19844203 19844481 + SINE/Alu_AluSg7 chr1 19844502 19844620 + SINE/Alu_FLAM_C chr1 19845053 19845101 + Simple_repeat_(AAAAC)n chr1 19845511 19845805 + SINE/Alu_AluSx chr1 19845908 19845986 + LTR/Gypsy_MamGypLTR1a chr1 19846209 19846364 + LTR/Gypsy_MamGypLTR1b chr1 19846411 19846496 + LTR/Gypsy_MamGypLTR1b chr1 19847068 19847294 - SINE/MIR_MIRb chr1 19847484 19847753 - LTR/ERV1_MER41A chr1 19847758 19847834 + SINE/Alu_FRAM chr1 19847836 19848798 - LTR/ERV1_HUERS-P1-int chr1 19848799 19849113 + SINE/Alu_AluSp chr1 19849114 19850962 - LTR/ERV1_HUERS-P1-int chr1 19851043 19851274 - LTR/ERV1_MER41A chr1 19851275 19851583 - SINE/Alu_AluSq4 chr1 19851584 19851912 - LTR/ERV1_MER41A chr1 19852060 19852143 + DNA/hAT-Charlie_MER103C chr1 19852422 19852483 - SINE/MIR_MIR3 chr1 19852572 19852881 - SINE/Alu_AluSx1 chr1 19852966 19853092 - DNA/hAT_MER53 chr1 19853163 19853193 + Simple_repeat_(TTGTT)n chr1 19853194 19853476 - SINE/Alu_AluJb chr1 19853493 19853663 + SINE/MIR_MIRb chr1 19853888 19853979 - DNA/hAT-Tip100_Arthur1A chr1 19853980 19854287 + SINE/Alu_AluSg chr1 19854288 19854388 - DNA/hAT-Tip100_Arthur1A chr1 19854400 19854917 + LTR/ERVL-MaLR_MLT1J chr1 19854921 19855039 + SINE/MIR_MIRb chr1 19855043 19855077 + Simple_repeat_(ATATT)n chr1 19855102 19855455 + LINE/L1_L1ME3Cz chr1 19855456 19855749 - SINE/Alu_AluSx3 chr1 19855750 19855872 + LINE/L1_L1ME3Cz chr1 19855873 19856078 + DNA/hAT-Charlie_MER33 chr1 19856079 19856106 + Simple_repeat_(AT)n chr1 19856107 19856412 - SINE/Alu_AluSz chr1 19856413 19856524 + DNA/hAT-Charlie_MER33 chr1 19856525 19856833 + LINE/L1_L1ME3Cz chr1 19856874 19856964 - DNA/hAT_MamRep1894 chr1 19856965 19857267 - SINE/Alu_AluSx chr1 19857268 19857292 - DNA/hAT_MamRep1894 chr1 19857293 19857369 + LINE/L1_L1ME3Cz chr1 19857461 19857858 + LINE/L1_L1ME5 chr1 19857868 19858055 + DNA/hAT_MER53 chr1 19858392 19858586 + SINE/MIR_MIRb chr1 19858635 19858725 + Simple_repeat_(AT)n chr1 19858730 19858796 + LINE/L1_L1ME4a chr1 19859303 19859601 + SINE/Alu_AluSp chr1 19859613 19859893 + SINE/Alu_AluSc8 chr1 19859894 19859935 + Simple_repeat_(AAACAA)n chr1 19860077 19860188 - DNA/hAT-Charlie_Charlie22a chr1 19860196 19860376 - DNA/hAT-Tip100_MamTip2 chr1 19860826 19860998 + SINE/MIR_MIRb chr1 19861642 19861854 + SINE/MIR_MIR chr1 19861935 19862065 + SINE/MIR_MIRb chr1 19862079 19862246 + LINE/L1_L1MEd chr1 19862091 19862267 + LINE/L1_L1MEg2 chr1 19862351 19862537 + LINE/L1_L1MEd chr1 19862546 19862739 + SINE/Alu_AluSg chr1 19862740 19862798 + SINE/Alu_AluSx chr1 19862799 19863088 + SINE/Alu_AluSz chr1 19863096 19863568 + LINE/L1_L1MEd chr1 19863569 19863866 + SINE/Alu_AluSp chr1 19863867 19863978 + LINE/L1_L1MEd chr1 19864098 19864192 + LINE/L2_L2b chr1 19864193 19864489 - SINE/Alu_AluSg chr1 19864490 19864634 + LINE/L2_L2b chr1 19864634 19864738 + SINE/MIR_MIRc chr1 19864782 19864837 + SINE/MIR_MIRb chr1 19864920 19864985 - SINE/MIR_MIRb chr1 19865530 19865793 - LTR/ERVL-MaLR_MLT1J1 chr1 19865874 19865905 + Simple_repeat_(ATC)n chr1 19865912 19866043 - SINE/MIR_MIRb chr1 19866159 19866440 + LINE/L2_L2a chr1 19866473 19866550 + Simple_repeat_(ATTC)n chr1 19867432 19867797 - LINE/L1_L1ME4a chr1 19867861 19868149 - SINE/Alu_AluSz6 chr1 19868661 19868968 - SINE/Alu_AluSx1 chr1 19869229 19869403 - SINE/MIR_MIRb chr1 19869613 19869923 + SINE/Alu_AluJr chr1 19870336 19870422 + LINE/L1_L1ME3Cz chr1 19870423 19870718 + SINE/Alu_AluSx chr1 19870719 19871023 + LINE/L1_L1ME3Cz chr1 19871062 19871366 + SINE/Alu_AluSx3 chr1 19871448 19871472 + LINE/L1_L1ME4a chr1 19871473 19871552 - DNA/TcMar-Mariner_MADE2 chr1 19871553 19871663 + LINE/L1_L1ME4a chr1 19871665 19871973 - SINE/Alu_AluSx1 chr1 19872087 19872235 + SINE/MIR_MIR3 chr1 19872628 19872726 - DNA/hAT-Charlie_Charlie30a chr1 19872727 19872782 + Simple_repeat_(TCTT)n chr1 19872783 19872828 + Simple_repeat_(CCTT)n chr1 19872829 19873116 - SINE/Alu_AluSg7 chr1 19873317 19873515 - SINE/MIR_MIRb chr1 19873689 19874003 + SINE/Alu_AluSx chr1 19874080 19874171 + Simple_repeat_(CAC)n chr1 19874277 19874652 + LTR/ERVL-MaLR_THE1D chr1 19874718 19875017 + SINE/Alu_AluJr chr1 19875026 19875316 + SINE/Alu_AluSc8 chr1 19875317 19875346 + Simple_repeat_(AAT)n chr1 19875453 19875760 + SINE/Alu_AluSz chr1 19876082 19876217 + SINE/Alu_FLAM_C chr1 19876241 19876534 + SINE/Alu_AluSq2 chr1 19876707 19876824 + SINE/Alu_FRAM chr1 19876826 19877005 - LINE/L1_L1ME3G chr1 19877006 19877120 - LINE/L1_L1PA8 chr1 19877121 19877263 - LINE/L1_L1ME3G chr1 19877608 19877630 + LTR/ERV1_LTR43B chr1 19877631 19877908 + SINE/Alu_AluJb chr1 19877909 19878129 + LTR/ERV1_LTR43B chr1 19878130 19878724 - LTR/ERV1_LTR9 chr1 19878725 19878833 + LTR/ERV1_LTR43B chr1 19878904 19879008 - LTR/ERV1_LTR9 chr1 19879072 19879407 + SINE/Alu_AluSx chr1 19879419 19879512 - LINE/L1_L1ME3G chr1 19879516 19879609 + DNA/TcMar-Tigger_MamRep434 chr1 19879625 19879908 - SINE/Alu_AluJr chr1 19880141 19880370 - SINE/Alu_AluSp chr1 19880615 19881316 + LINE/L1_L1PA16 chr1 19881317 19881444 - SINE/Alu_AluSx chr1 19881455 19881762 - SINE/Alu_AluY chr1 19881764 19881843 + DNA/hAT-Blackjack_MER81 chr1 19881921 19882082 + SINE/MIR_MIRb chr1 19882621 19882661 + Simple_repeat_(GCG)n chr1 19882997 19883098 - LINE/L2_L2b chr1 19883657 19883780 - SINE/Alu_AluJr chr1 19883997 19884074 + LINE/L2_L2a chr1 19884199 19884347 - LTR?_MamRep605b chr1 19884506 19884624 - DNA/hAT-Charlie_Charlie1b chr1 19884631 19884834 - DNA/hAT-Charlie_Charlie1 chr1 19885473 19885751 - SINE/Alu_AluSx3 chr1 19886484 19886665 + DNA/hAT-Charlie_MER5B chr1 19886689 19886884 - DNA/hAT-Charlie_MER5A chr1 19886893 19887070 - DNA/hAT-Charlie_MER33 chr1 19887071 19887382 - SINE/Alu_AluSp chr1 19887383 19887517 - DNA/hAT-Charlie_MER33 chr1 19887609 19888041 - LINE/CR1_L3 chr1 19888046 19888270 + DNA/hAT-Charlie_MER20 chr1 19888422 19888482 - LINE/CR1_L3 chr1 19888609 19888776 + SINE/MIR_MIR chr1 19888788 19888852 + Simple_repeat_(TGTGTC)n chr1 19889034 19889204 - DNA/hAT-Charlie_Charlie22a chr1 19889210 19889283 - LINE/CR1_L3 chr1 19890071 19890194 - SINE/MIR_MIRb chr1 19890643 19890715 - LINE/L2_L2a chr1 19890933 19891045 + SINE/MIR_MIR chr1 19891287 19891327 - LINE/L2_L2a chr1 19891385 19891727 - LINE/L2_L2 chr1 19892320 19892467 + LINE/L2_L2c chr1 19892573 19892962 + LINE/L2_L2b chr1 19893004 19893037 + Simple_repeat_(TC)n chr1 19893176 19893592 - LTR/ERVL_MER77B chr1 19893642 19893767 - LTR/ERVL_MER77B chr1 19893788 19893844 + LINE/L2_L2b chr1 19893854 19894051 + SINE/MIR_MIR chr1 19894889 19895053 - LINE/L1_L1ME4a chr1 19895266 19895545 - LINE/L1_L1ME1 chr1 19896223 19896394 - LINE/L1_L1ME4a chr1 19896799 19896831 + DNA/hAT-Tip100_MER91B chr1 19897021 19897199 + DNA/hAT-Tip100_Arthur1A chr1 19897208 19897298 - DNA/hAT-Tip100_Arthur1B chr1 19897948 19898247 - SINE/Alu_AluSp chr1 19898447 19898744 + SINE/Alu_AluSp chr1 19898814 19899109 - SINE/Alu_AluJb chr1 19899113 19900199 - LINE/L1_L1ME4b chr1 19900200 19900488 - SINE/Alu_AluJb chr1 19900489 19900904 - LINE/L1_L1ME4b chr1 19900905 19901209 - SINE/Alu_AluSz chr1 19901210 19901408 - LINE/L1_L1ME4b chr1 19901504 19902213 - LINE/L1_L1MB5 chr1 19902214 19902504 - SINE/Alu_AluY chr1 19902505 19902913 - LINE/L1_L1MB5 chr1 19902923 19903040 - LINE/L1_L1ME4a chr1 19903041 19903351 - SINE/Alu_AluJb chr1 19903352 19903423 - LINE/L1_L1ME4a chr1 19904862 19904884 + Simple_repeat_(GTTT)n chr1 19905461 19905755 + SINE/Alu_AluSg chr1 19905963 19906098 + DNA/hAT-Charlie_MER3 chr1 19906046 19906107 - DNA/hAT-Charlie_MER3 chr1 19906877 19907125 - LINE/L1_L1M5 chr1 19911389 19911687 - SINE/Alu_AluSg chr1 19913870 19913958 + LINE/CR1_L3 chr1 19914457 19914789 - LINE/L2_L2b chr1 19914910 19915201 + SINE/Alu_AluJo chr1 19915349 19915419 - LINE/L2_L2c chr1 19915458 19915600 - SINE/MIR_MIRb chr1 19915717 19915995 + SINE/Alu_AluJr chr1 19916808 19916883 + SINE/MIR_MIR chr1 19916916 19917038 - SINE/MIR_MIRb chr1 19917051 19917211 - DNA/hAT-Charlie_MER5A1 chr1 19917413 19917717 + SINE/Alu_AluY chr1 19917880 19918015 + LINE/L2_L2 chr1 19918409 19918708 - SINE/Alu_AluSp chr1 19919312 19919404 - DNA/hAT-Tip100_MamTip2b chr1 19919473 19919784 + SINE/Alu_AluY chr1 19919835 19919948 + LINE/L2_L2b chr1 19921326 19921491 - SINE/MIR_MIRb chr1 19922761 19922787 + Simple_repeat_(GAGAGG)n chr1 19924143 19924292 + SINE/MIR_MIRc chr1 19924321 19924459 - SINE/MIR_MIR chr1 19924502 19924728 + DNA/hAT-Tip100_ORSL chr1 19924966 19925270 + SINE/Alu_AluSz chr1 19925404 19925441 - SINE/MIR_MIR1_Amn chr1 19925929 19926152 + SINE/MIR_MIRc chr1 19926411 19926560 + Simple_repeat_(CATCAT)n chr1 19926563 19926709 + LTR/ERVL-MaLR_MSTA chr1 19926887 19927423 - LTR/ERVK_LTR14 chr1 19927433 19930516 - LTR/ERVK_HERVK14C-int chr1 19930517 19932159 - LTR/ERVK_HERVK14C-int chr1 19932160 19932710 - LTR/ERVK_LTR14 chr1 19932714 19932840 + LTR/ERVL-MaLR_MSTA chr1 19932843 19933104 + Simple_repeat_(ATCATC)n chr1 19933284 19933466 - LINE/L2_L2b chr1 19933467 19933767 + SINE/Alu_AluSx4 chr1 19933768 19933994 - LINE/L2_L2b chr1 19934674 19934734 - LTR/ERVL-MaLR_MLT1H chr1 19934735 19935036 - SINE/Alu_AluSz chr1 19935037 19935523 - LTR/ERVL-MaLR_MLT1H chr1 19935581 19935797 - DNA/hAT-Charlie_MER58A chr1 19935930 19936037 - LINE/L2_L2a chr1 19936040 19936455 - LTR/ERVL-MaLR_MLT1K chr1 19936754 19936926 - SINE/MIR_MIR3 chr1 19936905 19936986 + LINE/L2_L2d2 chr1 19937124 19937215 + SINE/MIR_MIRb chr1 19937439 19937743 + SINE/Alu_AluSx1 chr1 19937872 19938149 - LTR/ERVL-MaLR_MLT1J1 chr1 19938150 19938613 - LINE/L1_L1PA13 chr1 19938614 19938793 - LTR/ERVL-MaLR_MLT1J1 chr1 19938827 19939027 + LINE/L2_L2c chr1 19939033 19939081 + Simple_repeat_(AT)n chr1 19939083 19939302 - LTR/ERVL-MaLR_MLT1K chr1 19939412 19939598 - LTR/ERVL-MaLR_MLT1K chr1 19939866 19940164 - SINE/Alu_AluY chr1 19940661 19940841 - LINE/L2_L2b chr1 19940858 19941031 + LTR/ERVL-MaLR_MLT1K chr1 19941224 19941290 + LTR/ERVL-MaLR_MLT1K chr1 19941584 19941667 + LINE/L2_L2a chr1 19941747 19941888 + LTR/ERVL_ERVL-E-int chr1 19941979 19942096 - DNA/TcMar-Tigger_Tigger19b chr1 19942097 19942317 + LTR/ERVL_ERVL-E-int chr1 19942547 19942782 - LINE/L1_HAL1ME chr1 19942783 19942968 + DNA/hAT-Charlie_MER58A chr1 19942969 19943350 - LINE/L1_HAL1ME chr1 19943351 19943652 - SINE/Alu_AluYm1 chr1 19943653 19943744 - LINE/L1_HAL1ME chr1 19943745 19944020 - SINE/Alu_AluSp chr1 19944021 19944331 - LINE/L1_HAL1ME chr1 19944510 19944555 + Simple_repeat_(TG)n chr1 19944625 19945000 + LTR/ERVL_MLT2A1 chr1 19945001 19945070 + Simple_repeat_(AT)n chr1 19945071 19945109 + LTR/ERVL_MLT2A1 chr1 19945118 19945606 + LTR/ERVL-MaLR_MLT1K chr1 19945823 19946030 + LTR/Gypsy_MamGypLTR1b chr1 19946045 19946079 + Simple_repeat_(ATC)n chr1 19946569 19946730 - LINE/L2_L2c chr1 19947361 19947398 - LINE/L1_L1PREC2 chr1 19947399 19947694 - SINE/Alu_AluSz chr1 19947696 19947983 - SINE/Alu_AluSz chr1 19947984 19948169 - LINE/L1_L1PREC2 chr1 19948170 19948464 - SINE/Alu_AluYk3 chr1 19948465 19948640 - LINE/L1_L1PREC2 chr1 19948642 19948903 + LINE/L1_L1PREC2 chr1 19948949 19949413 + LTR/ERVL_LTR79 chr1 19949429 19949749 + SINE/Alu_AluSz6 chr1 19950360 19950656 - SINE/Alu_AluSq2 chr1 19950669 19950954 - SINE/Alu_AluSg4 chr1 19950955 19951027 - DNA/hAT-Charlie_MER5A1 chr1 19951054 19951144 + DNA/hAT-Charlie_MER5A1 chr1 19951271 19951475 + DNA/hAT-Charlie_MER20 chr1 19951650 19951948 + SINE/Alu_AluJr chr1 19951949 19952000 + Simple_repeat_(AAGG)n chr1 19952286 19952468 + DNA/hAT-Charlie_MER5A chr1 19952714 19952828 - DNA/TcMar-Tc2_Kanga1c chr1 19953060 19953310 - LTR/ERVL-MaLR_MLT1J chr1 19953318 19953470 - LINE/L2_L2 chr1 19953752 19954122 + LTR/ERVL_LTR33 chr1 19954123 19954579 - LTR/ERV1_LOR1b chr1 19954580 19954700 + LTR/ERVL_LTR33 chr1 19954703 19954789 + LINE/L2_L2a chr1 19954797 19955299 + LINE/L2_L2-1_AMi chr1 19955302 19955729 + LINE/L2_L2a chr1 19955730 19956090 + LTR/ERV1_MER66A chr1 19956091 19956410 + LINE/L2_L2a chr1 19956411 19956666 - SINE/Alu_AluSx3 chr1 19956667 19956744 + LINE/L2_L2a chr1 19958128 19958280 - LTR/ERVL-MaLR_MLT1D chr1 19958269 19958443 - LTR/ERVL-MaLR_MLT1D chr1 19958744 19958815 - LTR/ERV1_LOR1b chr1 19959004 19959432 - LTR/ERV1_LTR7B chr1 19960034 19960179 - SINE/MIR_MIRb chr1 19960284 19960378 + LTR/ERV1_LTR9B chr1 19960379 19960544 - LINE/L1_L1PA4 chr1 19960545 19961062 + LTR/ERV1_LTR9B chr1 19961063 19961349 + SINE/Alu_AluY chr1 19961350 19961376 + Simple_repeat_(AAAT)n chr1 19961377 19961401 + SINE/Alu_AluY chr1 19961405 19961493 + LTR/ERV1_LTR9B chr1 19961708 19961760 + Simple_repeat_(CCCACC)n chr1 19962181 19962540 - LINE/L2_L2 chr1 19962807 19962895 + LINE/L2_L2c chr1 19963038 19963059 + Simple_repeat_(C)n chr1 19963598 19963690 - LINE/L2_L2b chr1 19963710 19963996 + LINE/L2_L2a chr1 19964099 19964566 - LTR/ERVL_LTR16B1 chr1 19964669 19965078 + LINE/L1_L1ME2z chr1 19965079 19965125 - Retroposon/SVA_SVA_C chr1 19965126 19965222 + LINE/L1_L1ME2z chr1 19965223 19965609 + LTR/ERVL-MaLR_MLT1B chr1 19965610 19965773 + LINE/L1_L1ME2z chr1 19965781 19965797 + Simple_repeat_(ATG)n chr1 19965813 19965948 + LINE/L1_L1MC5a chr1 19965981 19966302 - SINE/Alu_AluSx4 chr1 19966399 19966610 + SINE/MIR_MIRb chr1 19966617 19966928 - SINE/Alu_AluSq2 chr1 19966930 19967051 - LTR/ERVL-MaLR_MLT1K chr1 19967059 19967094 + Simple_repeat_(TCC)n chr1 19967286 19967732 + LINE/L2_L2a chr1 19967732 19967835 + SINE/MIR_MIRb chr1 19968350 19968517 + DNA/hAT-Tip100_MER115 chr1 19968894 19969192 - SINE/Alu_AluSg chr1 19969447 19969479 + DNA/TcMar-Tigger_Tigger19b chr1 19969601 19969662 + SINE/MIR_MIRb chr1 19969726 19969781 - LINE/L1_L1MC chr1 19970019 19970963 + LINE/L1_L1MEh chr1 19970970 19971270 + srpRNA_7SLRNA chr1 19971274 19971579 - SINE/Alu_AluSp chr1 19971602 19972102 + LINE/L1_L1MEh chr1 19972417 19972715 + SINE/Alu_AluJr chr1 19972755 19973014 + DNA/hAT-Charlie_MER5C chr1 19973050 19973317 - DNA/hAT-Charlie_MER58B chr1 19973394 19974151 - LINE/L1_L1ME4b chr1 19974155 19974355 - DNA/hAT-Tip100_MER91A chr1 19974493 19974627 + SINE/Alu_AluJr chr1 19974634 19974799 - DNA/hAT-Tip100_MER45A chr1 19975056 19975203 - SINE/MIR_MIR chr1 19975210 19975367 - SINE/Alu_FLAM_C chr1 19975631 19975660 + Low_complexity_G-rich chr1 19975920 19975964 - LINE/L2_L2c chr1 19976074 19976585 + LTR/ERVL-MaLR_MLT1L chr1 19976713 19977995 + LINE/L2_L2c chr1 19978974 19979001 + Simple_repeat_(CA)n chr1 19979424 19979470 + Simple_repeat_(CACT)n chr1 19980087 19980142 - LINE/L2_L2b chr1 19980244 19980514 - LTR/Gypsy_MamGyp-int chr1 19980833 19980917 + LTR/ERVL-MaLR_MLT1I chr1 19980922 19980975 - LTR/Gypsy_MamGyp-int chr1 19980976 19981429 + LTR/ERVL-MaLR_MLT1C chr1 19981430 19981746 - LTR/Gypsy_MamGyp-int chr1 19981776 19981938 - LINE/L2_L2a chr1 19981939 19982511 + LTR/ERV1_LTR19B chr1 19982512 19982594 - LINE/L2_L2a chr1 19982710 19983016 - SINE/Alu_AluSc chr1 19983101 19983700 - LINE/L1_L1MC4a chr1 19983701 19985016 - LTR/ERV1_LTR12C chr1 19985017 19985296 - LINE/L1_L1MC4a chr1 19985297 19985537 - SINE/Alu_AluSx3 chr1 19985541 19985583 - LINE/L1_L1MC4a chr1 19985595 19985881 - SINE/Alu_AluYk3 chr1 19985962 19986255 - SINE/Alu_AluY chr1 19986257 19986761 - LINE/L1_L1MC4a chr1 19986762 19986828 - LTR/ERVL-MaLR_MLT1E1A chr1 19986831 19987133 + SINE/Alu_AluSp chr1 19987212 19987445 - LTR/ERVL-MaLR_MLT1E1A chr1 19987485 19988110 - LINE/L1_L1MC4a chr1 19988145 19988460 - LINE/L2_L2c chr1 19988472 19988557 + LTR/ERVL_LTR67B chr1 19988577 19989410 - LINE/L1_L1MC1 chr1 19989411 19989641 + LTR/ERV1_LOR1a chr1 19989642 19990689 + LTR/ERVK_MER11C chr1 19990731 19991000 + LTR/ERV1_LOR1a chr1 19991001 19991076 - LINE/L1_L1PA10 chr1 19991077 19991375 - SINE/Alu_AluSz chr1 19991376 19991490 - LINE/L1_L1PA10 chr1 19991491 19991693 - LINE/L1_L1PA8 chr1 19991694 19993348 - LINE/L1_L1PA10 chr1 19993349 19993380 + Simple_repeat_(TGTT)n chr1 19993381 19995927 - LINE/L1_L1PA10 chr1 19995929 19997410 + LINE/L1_L1PA6 chr1 19997411 19997923 + LTR/ERVK_MER9a2 chr1 19997924 19998514 + LTR/ERVK_HERVK9-int chr1 19998515 20003214 + LTR/ERVK_HERVK9-int chr1 20003219 20003727 + LTR/ERVK_MER9a2 chr1 20003728 20007079 + LINE/L1_L1PA6 chr1 20007092 20007770 + LINE/L1_L1PA6 chr1 20007776 20007859 - LINE/L1_L1P3 chr1 20007866 20007980 - LINE/L1_L1MC chr1 20008172 20008293 - LTR/ERVL_LTR16B1 chr1 20008299 20008682 - LTR/ERV1_MER66B chr1 20008683 20008772 + LTR/ERV1_LTR1C3 chr1 20008788 20008866 - LTR/ERV1_LTR1C3 chr1 20008867 20008950 - LTR/ERV1_MER66B chr1 20008951 20009268 - LTR/ERVL_LTR16B1 chr1 20009364 20009699 + LINE/L1_L1ME2 chr1 20009701 20009791 - LINE/L1_L1ME2 chr1 20009855 20009944 - LTR/ERVL-MaLR_MLT1A1 chr1 20009946 20010085 - LTR/ERVL-MaLR_MLT1A1-int chr1 20010160 20010820 - LTR/ERVL_MER54A chr1 20010846 20011145 + SINE/Alu_AluSg chr1 20011146 20011172 + Simple_repeat_(AACAA)n chr1 20011217 20011388 + SINE/Alu_AluSc chr1 20011507 20011704 - LINE/L1_L1M1 chr1 20011705 20012107 - LTR/ERVL-MaLR_MLT1A1 chr1 20012112 20013854 - LTR/ERVL-MaLR_MLT1A1-int chr1 20013855 20014254 - LTR/ERVL-MaLR_MLT1A1 chr1 20014255 20016330 - LINE/L1_L1M1 chr1 20016302 20016326 - LINE/L1_L1M1 chr1 20016327 20016415 - LINE/L1_L1M2 chr1 20016420 20016474 - DNA/TcMar-Tigger_Tigger2a chr1 20016477 20016679 + DNA/TcMar-Tigger_Tigger2a chr1 20016690 20017172 - LINE/L1_L1M1 chr1 20017173 20017458 - SINE/Alu_AluSz chr1 20017459 20018048 - LINE/L1_L1M1 chr1 20018040 20019351 - LINE/L1_L1MA5 chr1 20019352 20019626 - SINE/Alu_AluJb chr1 20019627 20019873 - LINE/L1_L1MA5 chr1 20019877 20020008 - SINE/Alu_AluJr chr1 20020009 20020560 - LINE/L1_L1MA5 chr1 20020561 20020842 + SINE/Alu_AluSc8 chr1 20020843 20021325 - LINE/L1_L1MA5 chr1 20021926 20022226 + SINE/Alu_AluYk3 chr1 20022252 20023490 - LINE/L1_L1PB1 chr1 20023491 20023780 - SINE/Alu_AluSz chr1 20023781 20025326 - LINE/L1_L1PB1 chr1 20025332 20025390 - LINE/L1_L1PB1 chr1 20025391 20025693 - SINE/Alu_AluY chr1 20025694 20026977 - LINE/L1_L1PB1 chr1 20026983 20028115 - LINE/L1_L1PB1 chr1 20028127 20028464 - LTR/ERV1_LTR46 chr1 20028465 20028814 - LTR/ERV1_LTR46-int chr1 20028815 20029712 - LTR/ERV1_LTR1A2 chr1 20029713 20030027 - LTR/ERV1_LTR46-int chr1 20030028 20031015 - LTR/ERVK_LTR5B chr1 20031016 20031187 - LTR/ERV1_LTR46-int chr1 20031176 20034775 - LTR/ERV1_LTR46-int chr1 20034773 20035106 - LTR/ERV1_LTR46-int chr1 20035108 20035516 - LTR/ERV1_LTR46 chr1 20035518 20036654 - LINE/L1_L1PB1 chr1 20036655 20036964 - SINE/Alu_AluSp chr1 20036965 20038529 - LINE/L1_L1PB1 chr1 20038530 20038792 + SINE/Alu_AluJo chr1 20038798 20039214 - LINE/L1_L1M2a chr1 20039652 20039690 + Low_complexity_GA-rich chr1 20040079 20040100 + Simple_repeat_(AAAC)n chr1 20040101 20040144 - LINE/L1_L1M2a chr1 20040205 20040342 - LINE/L1_L1M2a chr1 20040360 20040577 - LINE/L1_L1MC4a chr1 20040578 20040617 + Simple_repeat_(AC)n chr1 20040618 20040670 - LINE/L1_L1MC4a chr1 20040685 20041135 + LTR/ERV1_MER4A1 chr1 20041139 20041280 + LTR/ERV1_MER4-int chr1 20041281 20041436 - LTR/ERV1_LTR7 chr1 20041447 20042413 - LTR/ERV1_HERVH-int chr1 20042408 20045782 - LTR/ERV1_HERVH-int chr1 20045806 20045884 + LTR/ERV1_LTR7B chr1 20045885 20046234 + LTR/ERV1_MER4-int chr1 20046234 20046407 + LTR/ERV1_MER4-int chr1 20046464 20046792 + LTR/ERV1_MER4-int chr1 20046813 20047718 + LTR/ERV1_MER4-int chr1 20047719 20047763 + Simple_repeat_(TG)n chr1 20047786 20048513 + LTR/ERV1_MER4-int chr1 20048514 20048754 - LTR/ERV1_LTR8 chr1 20048755 20049450 + LTR/ERV1_MER4-int chr1 20049451 20049808 + LTR/ERVL-MaLR_THE1B chr1 20049809 20050010 + LTR/ERV1_MER4-int chr1 20050015 20052119 + LTR/ERV1_MER4-int chr1 20052120 20052195 + SINE/Alu_Alu chr1 20052196 20052716 + LTR/ERV1_MER4-int chr1 20052724 20053194 + LTR/ERV1_MER4A1 chr1 20053195 20053725 - LINE/L1_L1MC4a chr1 20053754 20053902 - LINE/L1_L1MC4a chr1 20053921 20054015 - LTR/ERVL-MaLR_MLT1F2 chr1 20054102 20054322 - LTR/ERVL-MaLR_MLT1F2 chr1 20054367 20054563 - LINE/L1_L1MC4a chr1 20054637 20054985 - LINE/L1_L1MC4a chr1 20055056 20055222 - LINE/L2_L2c chr1 20055223 20055839 + LTR/ERVL_LTR33C chr1 20055840 20055981 - LINE/L2_L2c chr1 20055998 20056079 + LTR/ERVL_LTR67B chr1 20056098 20056232 - LINE/L1_L1MC1 chr1 20056234 20056342 - LINE/L1_L1P4e chr1 20056378 20057522 - LINE/L1_L1MC chr1 20057523 20057794 - SINE/Alu_AluSx chr1 20057795 20058546 - LINE/L1_L1MC chr1 20058548 20058623 + LTR/ERVL_LTR67B chr1 20058892 20059090 + SINE/Alu_AluSz chr1 20059091 20059140 + SINE/Alu_Alu chr1 20059151 20059446 + SINE/Alu_AluJr chr1 20059578 20059762 + LTR/ERVL_LTR67B chr1 20059879 20059997 + LTR/ERVL_LTR67B chr1 20060238 20060552 - SINE/Alu_AluSx chr1 20060726 20061016 - SINE/Alu_AluSp chr1 20061305 20061610 + SINE/Alu_AluJb chr1 20061868 20062245 + LTR/ERVL-MaLR_MSTA chr1 20062250 20062305 + LINE/L2_L2c chr1 20062512 20062648 + SINE/Alu_FLAM_C chr1 20063050 20063206 + SINE/Alu_FRAM chr1 20064277 20064291 + Low_complexity_A-rich chr1 20064292 20064611 + SINE/Alu_AluSx3 chr1 20064612 20064743 + SINE/Alu_AluJr chr1 20064744 20064756 + Low_complexity_A-rich chr1 20064807 20064973 + DNA/hAT-Charlie_MER5A1 chr1 20065105 20065859 - LINE/L1_L1MC2 chr1 20065862 20065986 + SINE/Alu_FAM chr1 20065997 20066422 - LINE/L1_L1MC2 chr1 20066425 20066582 + DNA/hAT-Blackjack_MER63B chr1 20066583 20066724 + DNA/hAT-Charlie_Charlie17 chr1 20066725 20066998 + SINE/Alu_AluJr chr1 20066999 20067298 - SINE/Alu_AluJb chr1 20067299 20067334 + SINE/Alu_AluJr chr1 20067342 20067405 + LINE/L1_L1ME3G chr1 20067406 20067702 + SINE/Alu_AluSx3 chr1 20067703 20067836 + LINE/L1_L1M5 chr1 20067837 20068141 + SINE/Alu_AluSq2 chr1 20068142 20068451 + LINE/L1_L1ME3G chr1 20068452 20068750 - SINE/Alu_AluSp chr1 20068751 20068834 + LINE/L1_L1ME3G chr1 20068974 20069258 + LINE/L1_L1ME3G chr1 20069248 20069392 + LINE/L1_L1MC4 chr1 20069393 20069661 + SINE/Alu_AluJb chr1 20069664 20069718 + Simple_repeat_(AAAG)n chr1 20069719 20069912 + LINE/L1_L1MC4 chr1 20070861 20070950 - LINE/L2_L2b chr1 20070967 20071021 + Simple_repeat_(TC)n chr1 20071026 20071259 + SINE/MIR_MIR chr1 20071273 20071385 - LINE/L2_L2c chr1 20071400 20071606 - LINE/L2_L2c chr1 20071666 20071826 + LINE/L2_L2a chr1 20071827 20072122 + SINE/Alu_AluSz chr1 20072123 20072401 + LINE/L2_L2a chr1 20072485 20072518 + Simple_repeat_(GA)n chr1 20072924 20073002 + LINE/L2_L2d2 chr1 20073554 20073600 - LINE/L2_L2a chr1 20073601 20073881 + SINE/Alu_AluSz chr1 20073882 20074176 - LINE/L2_L2a chr1 20074406 20074625 - Unknown_MamRep564 chr1 20074664 20074950 - LINE/CR1_L3 chr1 20075216 20075253 + Simple_repeat_(GATCAA)n chr1 20075402 20075615 - SINE/MIR_MIR chr1 20075660 20075859 - DNA/hAT-Charlie_Charlie8 chr1 20075860 20076170 - SINE/Alu_AluSx1 chr1 20076171 20076312 - DNA/hAT-Charlie_Charlie8 chr1 20076376 20076460 - LINE/CR1_CR1-3_Croc chr1 20076483 20076702 + SINE/MIR_MIRb chr1 20076762 20076881 - LINE/L2_L2c chr1 20077083 20077204 - LINE/L2_L2a chr1 20077349 20077386 + Simple_repeat_(TCTA)n chr1 20077457 20077569 - LINE/L2_L2c chr1 20077589 20077798 - SINE/MIR_MIRb chr1 20077932 20078147 - LINE/L2_L2c chr1 20078482 20078652 + LINE/L2_L2a chr1 20078825 20079135 + SINE/Alu_AluJb chr1 20079265 20079464 - SINE/MIR_MIR chr1 20079495 20079635 - SINE/Alu_AluJb chr1 20079648 20079847 - SINE/MIR_MIRc chr1 20080324 20080602 + SINE/Alu_AluSz chr1 20081357 20081692 - LTR/ERVL_MER74B chr1 20081716 20081786 - LTR/ERV1_MER50 chr1 20081787 20082053 + SINE/Alu_AluSc8 chr1 20082054 20082676 - LTR/ERV1_MER50 chr1 20082677 20082822 - LTR/ERVL_MER74B chr1 20082892 20082944 + SINE/MIR_MIR3 chr1 20082982 20083068 + LINE/L2_L2b chr1 20083729 20083896 + SINE/MIR_MIRb chr1 20083901 20084162 + DNA/hAT-Tip100_ORSL chr1 20084199 20084320 - LINE/L2_L2b chr1 20084915 20085104 + SINE/MIR_MIRb chr1 20085449 20085567 + SINE/MIR_MIR3 chr1 20085697 20085927 - SINE/MIR_MIRb chr1 20086264 20086402 - SINE/MIR_MIRb chr1 20086407 20086900 - LTR/ERVL-MaLR_MLT1G1 chr1 20086912 20087138 + LINE/L1_L1MC3 chr1 20086914 20087167 + LINE/L1_L1MD3 chr1 20087169 20087253 + LINE/L1_L1MC3 chr1 20087288 20087386 - DNA/TcMar-Tigger_MER2B chr1 20087401 20087525 - SINE/Alu_AluYc chr1 20087542 20087911 + LINE/L1_L1MC3 chr1 20087915 20088075 + LINE/L1_L1MC3 chr1 20088076 20088373 + SINE/Alu_AluSp chr1 20088374 20088646 + LINE/L1_L1MC3 chr1 20088693 20088802 - DNA/TcMar-Tigger_MER2 chr1 20088803 20089099 - SINE/Alu_AluJb chr1 20089100 20089299 - DNA/TcMar-Tigger_MER2 chr1 20089348 20089598 - LTR/ERVL-MaLR_MLT1J2 chr1 20090085 20090237 - SINE/MIR_MIRb chr1 20090335 20090383 + LINE/L2_L2c chr1 20091117 20091249 + DNA/TcMar-Tigger_Tigger19a chr1 20091395 20091519 + SINE/MIR_MIR chr1 20091656 20091779 - DNA/hAT-Charlie_Charlie18a chr1 20092320 20092486 - SINE/MIR_MIRb chr1 20093149 20093248 + Simple_repeat_(TTC)n chr1 20093251 20093532 - SINE/Alu_AluSq2 chr1 20093574 20093721 - SINE/MIR_MIRb chr1 20093730 20093976 - SINE/Alu_AluJr chr1 20094082 20094704 + LTR/ERVL_MLT2F chr1 20094722 20095038 + LTR/ERVL_ERVL-E-int chr1 20095046 20095168 + SINE/Alu_AluJo chr1 20095194 20096476 + LTR/ERVL_ERVL-E-int chr1 20096488 20096749 + LTR/ERVL_ERVL-E-int chr1 20096756 20097380 + LTR/ERVL_MLT2F chr1 20097394 20097461 + LINE/L2_L2b chr1 20097478 20097594 - LINE/L2_L2c chr1 20098190 20098354 + LINE/L2_L2b chr1 20098375 20098605 - SINE/MIR_MIRb chr1 20098605 20098686 + LINE/L2_L2b chr1 20099089 20099377 + SINE/Alu_AluY chr1 20099390 20099511 + SINE/MIR_MIR3 chr1 20099531 20099596 + DNA/hAT-Charlie_Charlie9 chr1 20099931 20100242 + SINE/Alu_AluSx1 chr1 20100427 20100653 + SINE/MIR_MIRb chr1 20101167 20101214 - LINE/L2_L2b chr1 20101331 20101620 + SINE/Alu_AluJr chr1 20101922 20101999 - DNA/hAT-Charlie_MER5A chr1 20102340 20102652 + SINE/Alu_AluJr chr1 20102667 20102780 - DNA/TcMar-Tigger_Tigger10 chr1 20103290 20103455 - SINE/MIR_MIRc chr1 20103613 20103724 + LTR/ERV1_MER34A chr1 20103731 20103917 + SINE/Alu_AluJb chr1 20104017 20104207 - LTR/ERV1_MER34A chr1 20104317 20104623 + SINE/Alu_AluSx1 chr1 20104953 20105011 + DNA/hAT-Charlie_MER5A chr1 20105044 20105243 - LTR/ERV1_MER4B chr1 20105244 20105797 + LTR/ERV1_MER41A chr1 20105798 20106166 - LTR/ERV1_MER4B chr1 20106167 20106297 + DNA/hAT-Charlie_MER5A chr1 20106302 20106864 + LTR/ERV1_LTR26D chr1 20106990 20107222 - SINE/Alu_AluSz chr1 20107225 20107408 + SINE/MIR_MIRb chr1 20107894 20107959 - SINE/MIR_MIRb chr1 20107960 20108511 + LTR/ERV1_MER41A chr1 20108512 20108608 - SINE/MIR_MIRb chr1 20108597 20108961 - LINE/L2_L2a chr1 20109558 20109718 + SINE/MIR_MIRb chr1 20110044 20110149 - LINE/L2_L2a chr1 20110389 20110664 + SINE/Alu_AluY chr1 20110665 20110699 + Simple_repeat_(AAAC)n chr1 20110781 20111005 + LTR/ERVL-MaLR_MLT1L chr1 20111488 20111772 + SINE/Alu_AluJb chr1 20111777 20111819 - SINE/Alu_AluJo chr1 20111921 20111982 + Simple_repeat_(TTATT)n chr1 20111983 20112266 - SINE/Alu_AluSx1 chr1 20112267 20112393 - LINE/L1_L1MC1 chr1 20112621 20113064 - LTR/ERVL_LTR16A1 chr1 20113094 20113363 - LINE/L2_L2c chr1 20113494 20113636 - LINE/L2_L2b chr1 20114453 20114493 - LINE/L2_L2c chr1 20114739 20114862 + SINE/MIR_MIR chr1 20114955 20115134 + SINE/MIR_MIRc chr1 20115160 20115306 - SINE/MIR_MIRb chr1 20115750 20115986 - SINE/MIR_MIR chr1 20115975 20116021 + SINE/MIR_MIRc chr1 20115981 20116032 + SINE/MIR_MIR chr1 20116157 20116233 + LINE/L2_L2c chr1 20116620 20116686 + SINE/Alu_FLAM_C chr1 20116682 20116734 - SINE/Alu_Alu chr1 20116736 20116971 + SINE/Alu_AluJb chr1 20117058 20117231 - LINE/L1_L1MD3 chr1 20117241 20117330 - SINE/MIR_MIR chr1 20117512 20117606 + LTR/ERVL_LTR101_Mam chr1 20118545 20118784 - SINE/MIR_MIRb chr1 20118796 20118934 - DNA/TcMar-Tigger_Tigger23a chr1 20119053 20119129 + SINE/MIR_MIRc chr1 20119699 20119754 + Simple_repeat_(ATTT)n chr1 20119756 20120021 - SINE/Alu_AluJr4 chr1 20120042 20120313 - LINE/L1_L1ME4a chr1 20120343 20120631 + SINE/Alu_AluSg7 chr1 20120793 20120916 - LINE/L1_L1ME4a chr1 20121623 20121814 + SINE/MIR_MIRb chr1 20121837 20122094 + SINE/MIR_MIR chr1 20122108 20122146 + Simple_repeat_(TCA)n chr1 20122168 20122299 - LINE/L1_L1MEc chr1 20122338 20122623 - SINE/Alu_AluSx chr1 20122655 20122732 + DNA/TcMar-Tigger_MER44A chr1 20122733 20122764 + Simple_repeat_(GTTT)n chr1 20122765 20123037 - SINE/Alu_AluSc chr1 20123038 20123309 + DNA/TcMar-Tigger_MER44A chr1 20123370 20123447 - LINE/L1_L1ME4c chr1 20123451 20123593 + SINE/Alu_AluJb chr1 20123594 20123903 + SINE/Alu_AluJb chr1 20123904 20124076 + SINE/Alu_AluJb chr1 20124389 20124588 - SINE/MIR_MIRb chr1 20124638 20124685 - SINE/MIR_MIR chr1 20125118 20125525 + LINE/L1_L1ME3A chr1 20125539 20125565 + Simple_repeat_(ATG)n chr1 20125589 20125858 + SINE/Alu_AluJr chr1 20125942 20126145 + SINE/MIR_MIRb chr1 20126492 20126650 + SINE/MIR_MIRb chr1 20126716 20126855 - LINE/L2_L2d2 chr1 20127657 20127821 - LINE/L1_L1MB3 chr1 20127848 20128158 + SINE/Alu_AluSc chr1 20128172 20128289 - LINE/L1_L1MB7 chr1 20128290 20128593 - SINE/Alu_AluJb chr1 20128594 20128621 - LINE/L1_L1MB7 chr1 20128739 20129115 + LTR/ERVL-MaLR_MLT1B chr1 20129272 20129419 + SINE/MIR_MIR1_Amn chr1 20129513 20129571 - LINE/L1_L1ME1 chr1 20129706 20130470 - LINE/L1_L1ME1 chr1 20130471 20130782 - SINE/Alu_AluSz6 chr1 20130783 20130823 - LINE/L1_L1ME1 chr1 20130824 20131126 - SINE/Alu_AluSq2 chr1 20131127 20131300 - LINE/L1_L1ME1 chr1 20131309 20131681 - LINE/L1_L1MB3 chr1 20131708 20131842 + SINE/Alu_FLAM_C chr1 20131845 20132285 - LINE/L1_L1MB7 chr1 20132286 20132652 - LTR/ERVL-MaLR_THE1B chr1 20132653 20133257 - LINE/L1_L1MB7 chr1 20133259 20133563 - SINE/Alu_AluSx1 chr1 20133575 20133598 + Simple_repeat_(GTTT)n chr1 20133599 20133835 - SINE/Alu_AluJr chr1 20133836 20134004 - LINE/L1_L1MB7 chr1 20134072 20134252 + Simple_repeat_(TCTC)n chr1 20134253 20134453 - LINE/L1_L1M4 chr1 20134454 20134749 - SINE/Alu_AluJo chr1 20134750 20135481 - LINE/L1_L1M4 chr1 20135482 20135785 - SINE/Alu_AluSx chr1 20135786 20136232 - LINE/L1_L1M4 chr1 20136233 20136637 + LTR/ERVL-MaLR_MLT1B chr1 20136638 20136833 - LINE/L1_L1M4 chr1 20138167 20138292 - SINE/Alu_AluJr chr1 20138402 20138531 - SINE/MIR_MIR chr1 20138705 20139106 + LTR/ERV1_MER31A chr1 20139659 20139787 - SINE/MIR_MIR3 chr1 20140564 20140607 + Simple_repeat_(TG)n chr1 20141290 20141534 + Simple_repeat_(GT)n chr1 20141710 20141822 - SINE/MIR_MIRb chr1 20141881 20141999 - SINE/MIR_MIRc chr1 20142276 20142404 + SINE/MIR_MIRb chr1 20142957 20143195 + SINE/MIR_MIR chr1 20143956 20144082 + LINE/L2_L2d chr1 20144349 20144520 + SINE/MIR_MIR3 chr1 20144767 20144795 + DNA/hAT_MER53 chr1 20144796 20145108 + SINE/Alu_AluSz chr1 20145109 20145209 + DNA/hAT_MER53 chr1 20145252 20145284 - DNA/hAT_MER53 chr1 20145285 20145579 - SINE/Alu_AluSc chr1 20145580 20145604 - DNA/hAT_MER53 chr1 20145605 20145632 + Simple_repeat_(TTAA)n chr1 20145633 20145906 - SINE/Alu_AluJr chr1 20145907 20145945 - DNA/hAT_MER53 chr1 20146077 20146157 - DNA/hAT-Charlie_MER5A chr1 20146158 20146344 + SINE/MIR_MIR3 chr1 20147127 20147301 + DNA/hAT-Charlie_MER5A chr1 20147447 20147748 - SINE/Alu_AluSq chr1 20147857 20147929 + SINE/MIR_MIR3 chr1 20147931 20148092 - DNA/hAT-Charlie_MER5A chr1 20150737 20151041 - SINE/Alu_AluSx1 chr1 20151166 20151286 + SINE/MIR_MIRc chr1 20151490 20151800 - SINE/Alu_AluY chr1 20152694 20152901 + SINE/MIR_MIRb chr1 20153095 20153141 + Low_complexity_G-rich chr1 20153438 20153455 + Simple_repeat_(A)n chr1 20153465 20153783 + SINE/Alu_AluY chr1 20153885 20153957 - SINE/MIR_MIRb chr1 20154110 20154320 + LTR/ERV1_LTR2C chr1 20154322 20156816 + LTR/ERV1_HERVE-int chr1 20156817 20159981 + LTR/ERV1_HERVE-int chr1 20160012 20160102 + LTR/ERV1_HERVE-int chr1 20160087 20160163 + LTR/ERV1_HERVE-int chr1 20160162 20160228 + LTR/ERV1_HERVE-int chr1 20160392 20160513 + LTR/ERV1_LTR2C chr1 20160506 20160610 + LTR/ERV1_LTR2C chr1 20160611 20160727 - SINE/MIR_MIR chr1 20160763 20160920 + SINE/MIR_MIR3 chr1 20160893 20161013 - LINE/L2_L2a chr1 20161035 20161245 - LINE/L2_L2a chr1 20161304 20161451 - LINE/L2_L2a chr1 20161480 20161764 + LINE/L2_L2b chr1 20161953 20162239 + SINE/Alu_AluSx1 chr1 20162276 20162318 + Simple_repeat_(ACCCTC)n chr1 20162746 20162842 + LINE/L2_L2a chr1 20163016 20163512 - LTR/ERVL-MaLR_MLT1H chr1 20163648 20163804 + LINE/L2_L2a chr1 20163911 20163977 - LINE/L2_L2c chr1 20164292 20164437 + Simple_repeat_(GTGTGT)n chr1 20164611 20165540 + LINE/L2_L2a chr1 20165669 20165842 + Simple_repeat_(TG)n chr1 20165941 20165992 + SINE/MIR_MIR chr1 20166698 20166738 + Simple_repeat_(CTCTCC)n chr1 20167119 20167795 + LINE/L2_L2b chr1 20167756 20168031 + LINE/L2_L2a chr1 20168125 20168290 + SINE/MIR_MIRc chr1 20168277 20168367 - LINE/L2_L2b chr1 20168427 20168546 - LINE/L2_L2b chr1 20169171 20169479 - SINE/Alu_AluJo chr1 20169646 20169768 - SINE/MIR_MIRb chr1 20170005 20170193 + SINE/MIR_MIRb chr1 20170192 20170241 - SINE/MIR_MIR chr1 20170315 20170428 + SINE/MIR_MIR3 chr1 20170399 20170526 + LINE/L2_L2b chr1 20170567 20170620 - SINE/MIR_MIR3 chr1 20171753 20172062 - SINE/Alu_AluYi6 chr1 20172350 20172424 + LINE/L2_L2b chr1 20172521 20172570 - LINE/L2_L2b chr1 20172659 20172736 + LINE/L1_L1MD chr1 20173004 20173026 - SINE/MIR_MIR chr1 20173027 20173345 + SINE/Alu_AluJr chr1 20173346 20173529 - SINE/MIR_MIRc chr1 20173531 20173656 - LINE/L2_L2b chr1 20173915 20174138 + SINE/MIR_MIRb chr1 20174448 20174528 + LTR/ERVL-MaLR_MLT1O chr1 20174608 20174772 - DNA/hAT-Tip100_MER45A chr1 20174774 20174834 - DNA/hAT-Tip100_MER45R chr1 20175659 20175767 + LINE/L1_L1M5 chr1 20175903 20176218 - SINE/Alu_AluSq2 chr1 20177858 20177916 + SINE/MIR_MIRc chr1 20177933 20178061 - SINE/MIR_MIR chr1 20178060 20178132 + SINE/MIR_MIRc chr1 20178940 20179182 + LTR/ERVL_LTR16 chr1 20179213 20179237 + Simple_repeat_(TG)n chr1 20179736 20179768 + Simple_repeat_(GT)n chr1 20180130 20180248 + LTR/ERVL_LTR16 chr1 20180265 20180387 - LINE/L2_L2d2 chr1 20180411 20180602 - SINE/MIR_MIRb chr1 20180954 20181046 + LINE/L2_L2b chr1 20181059 20181207 - SINE/MIR_MIR chr1 20181232 20181254 + Simple_repeat_(ATGA)n chr1 20181327 20181504 - SINE/MIR_MIRb chr1 20181618 20181676 + DNA/TcMar-Tigger_Tigger15a chr1 20181909 20182072 + SINE/MIR_MIRc chr1 20182090 20182184 + DNA/TcMar-Tigger_Tigger10 chr1 20182185 20182583 + DNA/TcMar-Tigger_Tigger3a chr1 20182584 20182638 + DNA/TcMar-Tigger_Tigger10 chr1 20183028 20183141 - LINE/L2_L2d2 chr1 20183150 20183328 - SINE/MIR_MIRb chr1 20183613 20183883 - LINE/L2_L2d2 chr1 20184192 20184228 + Simple_repeat_(CA)n chr1 20184609 20184871 - LINE/L2_L2d2 chr1 20184872 20185163 + SINE/Alu_AluJb chr1 20185997 20186029 + Simple_repeat_(CCCCA)n chr1 20188126 20188409 - LINE/L2_UCON86 chr1 20188914 20189204 - SINE/MIR_MIR3 chr1 20189618 20189757 + SINE/Alu_AluSz6 chr1 20189814 20189949 + SINE/MIR_MIR3 chr1 20189974 20190165 + DNA/hAT-Charlie_MER58B chr1 20190254 20190389 + LINE/L2_L2a chr1 20190390 20190416 + Simple_repeat_(AATG)n chr1 20195352 20195505 - LINE/L2_L2 chr1 20196485 20196794 - SINE/Alu_AluSx3 chr1 20196859 20197033 - LINE/L2_L2a chr1 20197034 20197329 + SINE/Alu_AluSg chr1 20197330 20197863 - LINE/L2_L2a chr1 20198041 20198689 - LINE/L1_L1ME4c chr1 20198943 20199062 - LINE/L2_L2c chr1 20199915 20200038 + DNA/hAT-Charlie_MER5B chr1 20200039 20200091 + LINE/CR1_L3 chr1 20200338 20200490 + SINE/MIR_MIRc chr1 20200529 20200663 - LINE/L2_L2c chr1 20200690 20200776 + LINE/L2_L2a chr1 20200877 20200986 + LINE/L2_L2b chr1 20201649 20201749 - LINE/L2_L2c chr1 20202004 20202121 + LINE/CR1_Plat_L3 chr1 20202587 20202802 + LINE/L2_L2a chr1 20203025 20203251 + SINE/MIR_MIR chr1 20203277 20203325 - SINE/MIR_MIRb chr1 20203915 20204097 - SINE/MIR_MIRb chr1 20204788 20205088 + SINE/Alu_AluY chr1 20205182 20205530 - LINE/L2_L2b chr1 20206146 20206196 - SINE/MIR_MIRb chr1 20206208 20206350 - SINE/Alu_FLAM_A chr1 20206541 20206703 - SINE/Alu_FRAM chr1 20206719 20207033 - SINE/Alu_AluSz chr1 20207591 20207670 - DNA/TcMar-Mariner_MADE1 chr1 20207977 20208194 + SINE/Alu_AluJr chr1 20208805 20208919 + SINE/Alu_AluJr chr1 20208938 20209059 + SINE/MIR_MIRc chr1 20210202 20210356 - SINE/MIR_MIR3 chr1 20210375 20210408 + LINE/L2_L2a chr1 20210409 20210719 - SINE/Alu_AluJb chr1 20210720 20210811 + LINE/L2_L2a chr1 20211273 20211418 + DNA/TcMar-Tigger_X26_DNA chr1 20212779 20212962 + SINE/MIR_MIR3 chr1 20212971 20212992 + Simple_repeat_(A)n chr1 20213913 20214202 + SINE/Alu_AluSp chr1 20214498 20214659 - LINE/L1_L1ME4c chr1 20216019 20216179 - SINE/MIR_MIRb chr1 20216152 20216384 + SINE/MIR_MIR chr1 20216435 20216775 + LTR/ERVL-MaLR_MLT1I chr1 20216943 20217258 - SINE/Alu_AluJr chr1 20217354 20217630 + SINE/Alu_AluJr chr1 20218293 20218389 + SINE/MIR_MIR3 chr1 20218480 20218774 - SINE/Alu_AluSz6 chr1 20218776 20218906 + SINE/MIR_MIRc chr1 20219017 20219263 - LINE/L1_L1ME4a chr1 20219264 20219606 + SINE/Alu_AluSq2 chr1 20219607 20219795 - LINE/L1_L1ME4a chr1 20220738 20220886 - SINE/MIR_MIRb chr1 20220887 20221180 + SINE/Alu_AluSq10 chr1 20221186 20221270 + SINE/MIR_MIRb chr1 20222166 20222467 - LINE/L2_L2c chr1 20222907 20223688 + LINE/L2_L2d2 chr1 20223822 20224081 + LINE/L2_L2d2 chr1 20225700 20226119 - LTR/ERVL-MaLR_MSTA chr1 20226619 20226802 + SINE/MIR_MIRb chr1 20227426 20227709 + SINE/Alu_AluSz chr1 20227710 20227746 + Simple_repeat_(AAAAC)n chr1 20227747 20227758 + SINE/Alu_AluSz chr1 20227925 20228079 - LINE/L2_L2 chr1 20228075 20228305 - SINE/MIR_MIR chr1 20228375 20228600 - LTR/ERVL_LTR16 chr1 20228729 20228778 + SINE/MIR_MIR1_Amn chr1 20228779 20228902 + SINE/MIR_MIR chr1 20229654 20229773 - SINE/MIR_MIR chr1 20229774 20230074 + SINE/Alu_AluSx chr1 20230075 20230149 - SINE/MIR_MIR chr1 20230828 20230925 + SINE/MIR_MIRc chr1 20231062 20231190 + DNA/hAT-Charlie_MER113 chr1 20231191 20231490 - SINE/Alu_AluSx1 chr1 20231491 20231823 + DNA/hAT-Charlie_MER113 chr1 20231841 20231907 + SINE/MIR_MIRb chr1 20232037 20232340 + SINE/Alu_AluSx1 chr1 20232361 20233359 - LTR/ERVK_MER11A chr1 20233370 20233439 + SINE/MIR_MIR3 chr1 20233378 20233460 + SINE/MIR_MIRb chr1 20233459 20233683 - SINE/MIR_MIRb chr1 20233683 20233767 + SINE/MIR_MIRb chr1 20235769 20235827 + LINE/L1_L1ME4c chr1 20236301 20236808 + LTR/ERVL-MaLR_MLT1K chr1 20236882 20237010 + LINE/L2_L2d2 chr1 20237119 20237237 + LINE/L2_L2d2 chr1 20237420 20238338 + LTR/ERV1_MER49 chr1 20238408 20238423 - LINE/L2_L2 chr1 20238424 20238728 + SINE/Alu_AluY chr1 20238729 20238939 - LINE/L2_L2 chr1 20238978 20239181 + LTR/ERV1_MER4D1 chr1 20239182 20239475 + SINE/Alu_AluSz chr1 20239476 20239578 + LTR/ERV1_MER4D1 chr1 20239579 20239877 - SINE/Alu_AluSx1 chr1 20239878 20240438 + LTR/ERV1_MER4D1 chr1 20240439 20240904 - LINE/L2_L2 chr1 20240957 20241071 + LINE/L2_L2d2 chr1 20241280 20241492 - SINE/MIR_MIRb chr1 20241523 20241696 - LINE/L2_L2d chr1 20242068 20242165 + LINE/L2_L2d2 chr1 20242178 20242288 - LINE/L2_L2 chr1 20242656 20242745 - SINE/MIR_MIRc chr1 20242780 20242852 - SINE/MIR_MIRc chr1 20242884 20242942 + SINE/MIR_MIR3 chr1 20243468 20243675 + SINE/MIR_MIR chr1 20243676 20244057 - LTR_LTR107_Mam chr1 20244062 20244292 + LTR/ERVL-MaLR_MLT1H chr1 20244293 20244405 - DNA/hAT-Blackjack_MER81 chr1 20244406 20244729 + LTR/ERVL-MaLR_MLT1H chr1 20244826 20245289 - LINE/L2_L2b chr1 20245415 20245784 - LTR/ERVL-MaLR_MSTD chr1 20246212 20246453 - LINE/L2_L2a chr1 20246860 20247002 + LINE/L2_L2a chr1 20247003 20247532 - LTR/ERVL-MaLR_MLT1H chr1 20247555 20247786 + LINE/L2_L2a chr1 20247774 20247888 - LINE/L2_L2d2 chr1 20248244 20248564 + LTR/ERV1_MER41A chr1 20248565 20248776 + LINE/L1_L1PA7 chr1 20248777 20249026 + LTR/ERV1_MER41A chr1 20249072 20249363 + LINE/L2_L2c chr1 20249481 20249549 + SINE/MIR_MIR chr1 20249574 20249617 + LINE/RTE-X_L4_C_Mam chr1 20249816 20250325 + LTR/ERVL-MaLR_MLT1D chr1 20251123 20251767 - LINE/L1_L1PA10 chr1 20251774 20251999 + LINE/L1_L1PA10 chr1 20252022 20252336 + LINE/L2_L2a chr1 20252383 20252810 + LTR/ERVL-MaLR_MLT1B chr1 20252885 20253210 - LTR/ERVL_LTR33 chr1 20253226 20253337 - LTR/ERVL_LTR33 chr1 20253342 20253462 + DNA/TcMar_X4b_DNA chr1 20253710 20253741 - LINE/L2_L2b chr1 20253774 20253908 + SINE/Alu_FLAM_C chr1 20253952 20254039 - SINE/MIR_MIRb chr1 20254098 20254326 - SINE/MIR_MIR chr1 20254543 20254578 - DNA/hAT-Charlie_MER20 chr1 20254582 20254870 - SINE/Alu_AluSx chr1 20255315 20255442 - DNA/hAT-Charlie_MER20 chr1 20255623 20255811 + SINE/MIR_MIRb chr1 20256348 20256622 + LINE/L2_L2c chr1 20256624 20257103 + LTR/ERVL-MaLR_MLT1J chr1 20257297 20257600 - SINE/Alu_AluSq2 chr1 20257830 20257996 - LINE/L2_L2c chr1 20257985 20258204 + SINE/MIR_MIRb chr1 20258664 20258729 + SINE/MIR_MIR3 chr1 20258731 20258901 + DNA/hAT-Charlie_MER5B chr1 20258939 20259113 + SINE/MIR_MIR chr1 20259455 20259590 - SINE/MIR_MIR3 chr1 20259593 20259631 + Simple_repeat_(A)n chr1 20260192 20260308 + SINE/MIR_MIR chr1 20260334 20260789 - LTR/ERVL_LTR33 chr1 20260907 20261087 + LINE/RTE-X_L5 chr1 20261298 20261514 + SINE/MIR_MIRb chr1 20261489 20261624 + LINE/L2_L2a chr1 20261619 20261769 - SINE/MIR_MIRb chr1 20261770 20261935 - DNA/hAT-Charlie_MER5A chr1 20261936 20261955 - SINE/MIR_MIR1_Amn chr1 20261956 20262002 + LINE/L2_L2d2 chr1 20262312 20262435 + Simple_repeat_(CCTT)n chr1 20262475 20262786 - SINE/Alu_AluSz chr1 20262871 20263210 + LTR/ERVL_LTR79 chr1 20263266 20263358 - LTR/ERVL-MaLR_MLT1L chr1 20263492 20263693 + SINE/MIR_MIRb chr1 20264023 20264458 - LTR/ERVL-MaLR_MLT1C chr1 20264479 20264746 + LINE/L2_L2d2 chr1 20264747 20264797 + SINE/MIR_MIR chr1 20264798 20264957 + DNA/hAT-Charlie_MER5A1 chr1 20264958 20264969 + LINE/L2_L2d2 chr1 20264970 20265129 + SINE/MIR_MIR chr1 20265328 20265403 - SINE/tRNA-RTE_MamSINE1 chr1 20265523 20265541 + Simple_repeat_(AG)n chr1 20265649 20265856 - SINE/MIR_MIRb chr1 20266040 20266344 - SINE/Alu_AluSx chr1 20266350 20266393 - LINE/CR1_L3 chr1 20266626 20266773 - SINE/MIR_MIRb chr1 20267143 20267390 - DNA/hAT-Charlie_MER113A chr1 20267408 20267564 - SINE/MIR_MIRb chr1 20267752 20268069 - LINE/L1_L1PA5 chr1 20269020 20269246 + LTR/ERVL-MaLR_MLT1G1 chr1 20269308 20269375 + LTR/ERVL-MaLR_MLT1B chr1 20269436 20269730 + LTR/ERVL-MaLR_MLT1B chr1 20269839 20269932 + LINE/L2_L2c chr1 20270103 20270324 - LTR/ERVL-MaLR_MLT1I chr1 20270443 20270696 + LINE/L2_L2c chr1 20270697 20271059 + LTR/ERVL-MaLR_THE1B chr1 20271060 20271137 + LINE/L2_L2c chr1 20271189 20271570 + LTR/ERVL-MaLR_MLT1B chr1 20271625 20271756 + LINE/L2_L2c chr1 20272964 20273176 + SINE/MIR_MIRb chr1 20273177 20273461 - DNA/TcMar_UCON44 chr1 20273617 20273738 + LINE/L2_L2c chr1 20273739 20274244 + Simple_repeat_(GGAT)n chr1 20274498 20274607 + SINE/MIR_MIRb chr1 20274853 20275035 + SINE/MIR_MIRb chr1 20275049 20275356 + SINE/Alu_AluY chr1 20275571 20275612 + Low_complexity_GA-rich chr1 20275898 20276450 + LINE/L2_L2c chr1 20276458 20276502 - LINE/L2_L2d2 chr1 20277069 20277275 + SINE/MIR_MIR chr1 20277581 20277724 - SINE/MIR_MIRb chr1 20278259 20278388 - SINE/Alu_AluSg7 chr1 20278578 20279002 - LTR/ERVL-MaLR_MSTB1 chr1 20279382 20279451 - LINE/L2_L2a chr1 20279436 20279939 - LINE/L2_L2a chr1 20279958 20279977 + Simple_repeat_(TTG)n chr1 20280338 20280388 - SINE/MIR_MIRc chr1 20280522 20280566 + Simple_repeat_(ATCTATT)n chr1 20280822 20280908 - DNA/TcMar-Tigger_MamRep434 chr1 20281020 20281100 + LINE/L2_L2d2 chr1 20281109 20281213 - LINE/L2_L2a chr1 20281239 20282068 + LINE/L2_L2b chr1 20282215 20282293 + SINE/MIR_MIR chr1 20282941 20283068 + SINE/MIR_MIR chr1 20283129 20283793 - LINE/L2_L2c chr1 20283836 20283930 - SINE/MIR_MIRc chr1 20284180 20284266 + SINE/MIR_MIRb chr1 20284499 20284666 - DNA/hAT-Tip100_Zaphod3 chr1 20284811 20285483 - LINE/L2_L2b chr1 20285529 20285880 + LTR/ERVL-MaLR_THE1B chr1 20285885 20286389 - LTR/ERVL-MaLR_MLT1H chr1 20286448 20286472 + Simple_repeat_(GTTT)n chr1 20287032 20287235 + SINE/MIR_MIR1_Amn chr1 20287201 20287399 - SINE/MIR_MIR chr1 20287497 20287540 + LTR/ERVL-MaLR_MLT1C2 chr1 20287541 20287924 - LINE/L1_L1PA8 chr1 20287925 20288355 + LTR/ERVL-MaLR_MLT1C2 chr1 20288782 20289003 + SINE/MIR_MIRc chr1 20289778 20289976 + SINE/MIR_MIRb chr1 20289994 20290156 + SINE/MIR_MIRb chr1 20290791 20290824 + Low_complexity_GA-rich chr1 20291166 20291212 + Simple_repeat_(GT)n chr1 20291329 20291551 + Simple_repeat_(CT)n chr1 20292052 20292167 + Simple_repeat_(TTCTTT)n chr1 20292168 20292215 + Simple_repeat_(TTCT)n chr1 20293782 20293999 - LINE/L2_L2d2 chr1 20294046 20294130 - LINE/L2_L2d2 chr1 20294089 20294133 - LINE/L2_L2d2 chr1 20294468 20294753 - SINE/Alu_AluSp chr1 20294757 20294888 - SINE/Alu_AluSp chr1 20294896 20295000 - SINE/MIR_MIRc chr1 20295057 20295329 - LINE/L2_L2d2 chr1 20295752 20295793 + LINE/L2_L2b chr1 20295853 20295874 + Simple_repeat_(TGTAGGT)n chr1 20295875 20296162 - SINE/Alu_AluSp chr1 20296163 20296175 + Simple_repeat_(TGTAGGT)n chr1 20296181 20296522 + DNA/TcMar-Tigger_Tigger18a chr1 20296526 20296867 + DNA/TcMar-Tigger_Tigger18a chr1 20296993 20297116 - LINE/L2_L2a chr1 20297347 20297524 - LTR/ERVL-MaLR_MLT1I chr1 20297819 20297974 + SINE/MIR_MIRc chr1 20297997 20298306 - SINE/Alu_AluJr chr1 20298527 20298711 - SINE/MIR_MIRb chr1 20298907 20299118 + SINE/MIR_MIR chr1 20299392 20299703 - SINE/Alu_AluSx1 chr1 20300035 20300228 + SINE/MIR_MIR chr1 20300494 20300743 + SINE/MIR_MIR chr1 20301294 20301521 - SINE/MIR_MIRb chr1 20302434 20302486 - LINE/L2_L2d2 chr1 20302556 20302651 + LINE/L2_L2a chr1 20302740 20302913 - LINE/L2_L2d2 chr1 20303506 20303590 - SINE/MIR_MIR chr1 20303882 20303965 + SINE/MIR_MIRb chr1 20304059 20304533 - LINE/L2_L2b chr1 20304553 20304596 + Simple_repeat_(GT)n chr1 20304661 20304704 + Low_complexity_GA-rich chr1 20304763 20304997 + SINE/MIR_MIR chr1 20304960 20305222 - LINE/L2_L2a chr1 20305947 20306259 - LINE/L2_L2a chr1 20306345 20306492 - LINE/L2_L2a chr1 20306620 20306703 - LINE/L2_L2a chr1 20307471 20307596 + SINE/MIR_MIRb chr1 20307619 20307745 + LINE/L2_L2b chr1 20307868 20308237 - LINE/L1_L1PA16 chr1 20309386 20309907 - LINE/L2_L2c chr1 20309908 20309956 + Simple_repeat_(TG)n chr1 20309957 20310048 - LINE/L2_L2c chr1 20310107 20310184 - SINE/MIR_MIR3 chr1 20310240 20310395 + SINE/MIR_MIR3 chr1 20310790 20310919 + SINE/MIR_MIRc chr1 20310977 20311267 - SINE/Alu_AluJb chr1 20311980 20311990 - SINE/MIR_MIRc chr1 20311991 20312028 - SINE/MIR_MIR3 chr1 20312111 20312266 + DNA/hAT-Charlie_MER5A1 chr1 20312304 20312471 - SINE/MIR_MIRb chr1 20312555 20312641 + DNA/hAT-Charlie_MER102b chr1 20313077 20313264 + SINE/MIR_MIRc chr1 20313283 20313398 + SINE/MIR_MIR chr1 20313505 20313550 - SINE/MIR_MIRc chr1 20313586 20313630 - SINE/MIR_MIR3 chr1 20314108 20314330 + SINE/MIR_MIRb chr1 20314866 20315239 - LINE/L2_L2b chr1 20315502 20315803 - LINE/L2_L2a chr1 20316117 20316234 + LINE/L1_L1M5 chr1 20316349 20316642 + LINE/L1_L1ME5 chr1 20316656 20316814 - SINE/MIR_MIRb chr1 20316817 20316916 + SINE/MIR_MIRb chr1 20316917 20317179 + SINE/Alu_AluYk3 chr1 20317180 20317350 + SINE/MIR_MIRb chr1 20317975 20318134 - DNA/hAT-Charlie_MER5B chr1 20318372 20318472 - SINE/MIR_MIR3 chr1 20319076 20319194 - SINE/MIR_MIR3 chr1 20319572 20319644 - SINE/MIR_MIR1_Amn chr1 20321114 20321137 + Simple_repeat_(A)n chr1 20321217 20321304 - LINE/L2_L2c chr1 20321305 20321608 + SINE/Alu_AluSc5 chr1 20321609 20322262 - LINE/L2_L2c chr1 20322273 20322345 + Simple_repeat_(AGACAC)n chr1 20322497 20322609 - LINE/L2_L2b chr1 20322687 20322813 - SINE/MIR_MIRb chr1 20323015 20323176 - SINE/MIR_MIRb chr1 20323832 20324045 - SINE/MIR_MIRb chr1 20324077 20324124 - LINE/L2_L2d2 chr1 20324137 20324271 - LINE/L2_L2b chr1 20325016 20325196 - SINE/MIR_MIR3 chr1 20325285 20325387 + SINE/MIR_MIR3 chr1 20325395 20325588 - DNA/hAT-Charlie_MER117 chr1 20326459 20326748 - SINE/Alu_AluSg chr1 20326768 20326882 + SINE/MIR_MIRb chr1 20326997 20327045 + Simple_repeat_(ATC)n chr1 20327151 20327361 + SINE/MIR_MIR chr1 20327458 20327572 + SINE/MIR_MIRc chr1 20327629 20327664 + Simple_repeat_(TGA)n chr1 20327665 20327697 + Simple_repeat_(TGG)n chr1 20328526 20328569 + Low_complexity_GA-rich chr1 20328615 20328760 + SINE/Alu_AluJb chr1 20328875 20329251 - LINE/L1_L1ME4a chr1 20329263 20329578 - SINE/Alu_AluSz chr1 20329705 20329861 - SINE/MIR_MIR1_Amn chr1 20331042 20331116 - SINE/MIR_MIR1_Amn chr1 20331429 20331866 - LTR/ERVL_ERV3-16A3_LTR chr1 20331936 20332113 - SINE/MIR_MIR3 chr1 20332102 20332203 + SINE/MIR_MIR3 chr1 20332204 20332479 + SINE/Alu_AluSx1 chr1 20332480 20332549 + Simple_repeat_(AAAAT)n chr1 20332550 20332611 + SINE/MIR_MIR3 chr1 20333160 20333227 + DNA/hAT-Charlie_MER5A chr1 20333228 20333491 + SINE/Alu_AluSx1 chr1 20333492 20333610 + DNA/hAT-Charlie_MER5A chr1 20333876 20333954 - DNA/hAT-Charlie_MER102c chr1 20333942 20334185 - DNA/hAT-Charlie_Charlie8 chr1 20334397 20334528 + LINE/L1_L1ME4c chr1 20334529 20334823 + SINE/Alu_AluSx1 chr1 20334824 20334993 + LINE/L1_L1ME4c chr1 20335026 20335330 + SINE/Alu_AluJr chr1 20335465 20335901 - DNA/TcMar-Tigger_Tigger2 chr1 20335886 20335954 - DNA/TcMar-Tigger_Tigger2b_Pri chr1 20336510 20336716 - SINE/MIR_MIRb chr1 20336734 20336855 - LINE/L2_L2c chr1 20337124 20337427 - SINE/Alu_AluSz6 chr1 20338030 20338295 - SINE/MIR_MIRc chr1 20338339 20338517 - SINE/Alu_AluSz6 chr1 20338518 20338829 - SINE/Alu_AluSx chr1 20338830 20338964 - SINE/Alu_AluSz6 chr1 20339004 20339213 - DNA/hAT-Charlie_MER20 chr1 20339260 20339574 + SINE/Alu_AluJb chr1 20339795 20339884 + SINE/MIR_MIRc chr1 20339908 20339979 - LINE/L2_L2a chr1 20339971 20340037 + SINE/MIR_MIRb chr1 20340200 20340220 + Simple_repeat_(CA)n chr1 20340957 20341057 - LINE/L1_L1ME4b chr1 20341062 20341731 - LINE/L1_L1ME4b chr1 20342361 20342404 + Simple_repeat_(TCC)n chr1 20343348 20343390 + Simple_repeat_(CGCGAG)n chr1 20344348 20344647 - LINE/L2_L2a chr1 20344912 20345044 + SINE/MIR_MIRb chr1 20345052 20345324 + DNA/hAT-Charlie_Charlie18a chr1 20345833 20346079 - LINE/L1_HAL1b chr1 20346168 20346395 - SINE/MIR_MIR chr1 20346425 20346787 - LINE/L1_L1MC5a chr1 20347102 20347384 + DNA/hAT-Charlie_MER103C chr1 20347430 20347762 - SINE/Alu_AluJr chr1 20347951 20348124 - SINE/MIR_MIRb chr1 20349367 20349680 - SINE/Alu_AluSx1 chr1 20349732 20349753 + Simple_repeat_(T)n chr1 20349779 20349973 - SINE/MIR_MIRb chr1 20350094 20350127 + Low_complexity_G-rich chr1 20350328 20350515 - SINE/MIR_MIR1_Amn chr1 20350745 20350852 + SINE/MIR_MIR3 chr1 20351139 20351182 + DNA/hAT-Tip100_MamTip2b chr1 20351239 20351371 + SINE/Alu_AluJr chr1 20351372 20351673 + SINE/Alu_AluSg chr1 20351674 20351823 + SINE/Alu_AluJr chr1 20351850 20351991 + SINE/MIR_MIR3 chr1 20352314 20352583 - DNA/hAT-Charlie_Charlie16a chr1 20352839 20353185 - LINE/L2_L2b chr1 20354273 20354320 + Low_complexity_G-rich chr1 20354377 20354515 + SINE/MIR_MIR3 chr1 20356392 20356466 + SINE/MIR_MIR3 chr1 20356811 20357108 - DNA/TcMar-Tigger_MamRep434 chr1 20357159 20357304 + SINE/MIR_MIR3 chr1 20357553 20357653 - LINE/L2_L2b chr1 20359355 20359567 - DNA_X30_DNA chr1 20359630 20359650 - LTR/ERVL_LTR16C chr1 20359651 20359761 - DNA/hAT-Blackjack_MER81 chr1 20359762 20360140 - LTR/ERVL_LTR16C chr1 20360221 20360352 - DNA/hAT-Tip100_Zaphod4a chr1 20360432 20360525 - LTR/ERVL_LTR16C chr1 20360587 20360729 - RC?/Helitron?_Helitron2Na_Mam chr1 20361096 20361152 + Simple_repeat_(TG)n chr1 20361401 20361595 + DNA/TcMar_X4b_DNA chr1 20362284 20362328 + LINE/L2_L2d2 chr1 20362458 20362798 + LINE/L2_L2c chr1 20363003 20363081 + Simple_repeat_(AATAA)n chr1 20363082 20363200 - SINE/MIR_MIRb chr1 20363217 20363290 - LINE/L2_L2b chr1 20363650 20363773 - SINE/MIR_MIR3 chr1 20363783 20363834 + DNA/hAT-Tip100_MamTip2b chr1 20363835 20364020 + LTR/ERVL-MaLR_MSTC chr1 20364021 20364321 + SINE/Alu_AluSq chr1 20364322 20364555 + LTR/ERVL-MaLR_MSTC chr1 20364556 20364598 + DNA/hAT-Tip100_MamTip2b chr1 20364893 20364998 + SINE/MIR_MIR3 chr1 20365348 20365480 + SINE/MIR_MIR1_Amn chr1 20367083 20367156 + Low_complexity_GA-rich chr1 20367209 20367824 + LINE/L2_L2a chr1 20367859 20368040 - SINE/Alu_AluSc8 chr1 20368042 20368168 + SINE/Alu_AluSx3 chr1 20368169 20368282 + Low_complexity_GA-rich chr1 20368286 20368844 + LINE/L2_L2a chr1 20368845 20368924 + Low_complexity_GA-rich chr1 20369097 20369134 + Simple_repeat_(CACCCC)n chr1 20369446 20369507 - LINE/CR1_L3 chr1 20369664 20369852 - SINE/MIR_MIRb chr1 20369996 20370112 + SINE/MIR_MIRb chr1 20370507 20370722 - SINE/MIR_MIR chr1 20371179 20371350 + Unknown_MamRep564 chr1 20371712 20371753 + Simple_repeat_(TCTTTT)n chr1 20371947 20372054 + SINE/MIR_MIR3 chr1 20372287 20372437 + DNA/hAT-Charlie_MER103C chr1 20372441 20372677 - SINE/MIR_MIR chr1 20372898 20372926 + Simple_repeat_(TTTA)n chr1 20372969 20373070 - LINE/L1_L1MA4 chr1 20373743 20373760 + Simple_repeat_(A)n chr1 20374092 20374223 + SINE/MIR_MIRc chr1 20374555 20374852 + LINE/L1_L1ME4c chr1 20375018 20375226 + SINE/MIR_MIRb chr1 20375998 20376164 + Simple_repeat_(CTTCT)n chr1 20376278 20376468 - LINE/L2_L2c chr1 20376538 20376688 - SINE/MIR_MIRb chr1 20377211 20377314 + SINE/MIR_MIRb chr1 20377393 20377840 - LINE/L2_L2b chr1 20377877 20377921 - SINE/MIR_MIR3 chr1 20378152 20378451 + SINE/Alu_AluJr chr1 20378644 20378786 + Simple_repeat_(TC)n chr1 20378788 20378831 + Simple_repeat_(GATT)n chr1 20378890 20379255 - LTR/ERVL-MaLR_THE1B chr1 20379521 20379633 + DNA/hAT-Charlie_MER5B chr1 20379706 20379824 - LINE/L2_L2a chr1 20380095 20380301 + SINE/MIR_MIR chr1 20380549 20380681 + LINE/L2_L2b chr1 20380698 20380905 - LTR/ERV1?_LTR103b_Mam chr1 20381333 20381409 + SINE/MIR_MIR3 chr1 20381533 20381834 - SINE/Alu_AluSx chr1 20381989 20382121 + LINE/L2_L2c chr1 20382149 20382331 + SINE/MIR_MIR chr1 20382345 20382614 + Simple_repeat_(TGATGA)n chr1 20383012 20383150 - LINE/L2_L2d2 chr1 20383163 20383302 - LINE/L2_L2b chr1 20383336 20383572 - SINE/MIR_MIRb chr1 20383569 20383816 - LINE/L2_L2b chr1 20384012 20384265 + SINE/MIR_MIRb chr1 20384629 20385193 + LINE/L2_L2c chr1 20385053 20385477 + LINE/L2_L2c chr1 20385572 20385640 - SINE/MIR_MIR3 chr1 20386249 20386320 - DNA/hAT-Charlie_MER5A chr1 20386339 20386561 + LINE/L2_L2c chr1 20386601 20386682 + Simple_repeat_(TGAA)n chr1 20387039 20387077 + Simple_repeat_(GAGGCA)n chr1 20387940 20388243 - LTR/ERVL-MaLR_MLT1K chr1 20388282 20388471 - SINE/MIR_MIR chr1 20388473 20388582 - DNA/hAT-Charlie_MER5A chr1 20388681 20388812 - LTR/ERVL-MaLR_MLT1K chr1 20388818 20388985 + SINE/MIR_MIR chr1 20389308 20389539 + SINE/MIR_MIR chr1 20389795 20389874 + Unknown_UCON41 chr1 20390122 20390274 + SINE/MIR_MIRc chr1 20390275 20390582 + SINE/Alu_AluSc chr1 20390583 20390672 + SINE/MIR_MIRc chr1 20390880 20390987 + LINE/L2_L2a chr1 20390999 20391075 - LINE/L2_L2c chr1 20391683 20391732 + Simple_repeat_(ATGG)n chr1 20391823 20391910 + Simple_repeat_(TGGA)n chr1 20392111 20392131 + Simple_repeat_(CATT)n chr1 20392133 20392231 - LINE/L2_L2a chr1 20392776 20394763 + LINE/L1_L1MDa chr1 20394780 20394992 - SINE/Alu_AluJb chr1 20394993 20395632 + LINE/L1_L1MDa chr1 20395600 20395925 + LINE/L1_L1MD2 chr1 20395926 20395946 + LTR/ERVL_LTR52 chr1 20395947 20396184 - SINE/Alu_AluSz chr1 20396185 20396316 + LTR/ERVL_LTR52 chr1 20396416 20396547 + LTR/ERVL_LTR52 chr1 20396615 20396661 + LINE/L2_L2b chr1 20396976 20397088 + LINE/L2_L2b chr1 20397146 20397474 + SINE/Alu_AluSz chr1 20397940 20398081 + SINE/MIR_MIR chr1 20398254 20398321 + Simple_repeat_(ATGATG)n chr1 20398359 20398498 - SINE/MIR_MIRb chr1 20398648 20399070 - LINE/L2_L2c chr1 20399428 20399633 + SINE/MIR_MIRb chr1 20399615 20399681 - SINE/MIR_MIR1_Amn chr1 20400135 20400279 + SINE/MIR_MIR3 chr1 20400890 20401117 + SINE/MIR_MIRb chr1 20401298 20401380 - SINE/MIR_MIR1_Amn chr1 20401405 20401579 - SINE/MIR_MIR3 chr1 20402113 20402308 + SINE/MIR_MIRb chr1 20402324 20402418 - SINE/MIR_MIR1_Amn chr1 20402443 20402645 - SINE/MIR_MIR chr1 20402634 20402704 + LINE/CR1_L3b chr1 20402784 20402972 + SINE/MIR_MIRc chr1 20403214 20403413 - SINE/MIR_MIRb chr1 20403431 20403562 - SINE/MIR_MIRb chr1 20403629 20403747 - SINE/MIR_MIRc chr1 20403660 20403767 + SINE/MIR_MIRb chr1 20403714 20403823 + SINE/MIR_MIR3 chr1 20403843 20403946 - DNA/hAT-Charlie_MER5B chr1 20404260 20404409 - LINE/L2_L2a chr1 20404487 20404519 - SINE/MIR_MIRc chr1 20404779 20404910 - LINE/L2_L2d2 chr1 20404964 20405145 - SINE/MIR_MIR chr1 20405694 20405793 + SINE/MIR_MIR chr1 20406826 20406927 - SINE/MIR_MIRb chr1 20406929 20406950 + LTR/ERV1_MER34B chr1 20406951 20407018 + LTR/ERV1_MER34B chr1 20407019 20407150 + SINE/Alu_FLAM_C chr1 20407182 20408655 + LINE/L1_L1PA11 chr1 20408664 20408847 + SINE/Alu_AluJb chr1 20408848 20409321 + LTR/ERV1_MER34B chr1 20409396 20409729 - LINE/L2_L2d chr1 20409826 20409858 + Simple_repeat_(TGGA)n chr1 20409972 20410078 + SINE/MIR_MIR3 chr1 20411366 20411451 - SINE/tRNA-RTE_MamSINE1 chr1 20411660 20411869 + LINE/L2_L2d2 chr1 20412057 20412224 + LINE/L2_L2d2 chr1 20412222 20412266 - SINE/MIR_MIR3 chr1 20412277 20412428 - LINE/L1_L1PA16 chr1 20412429 20412581 + DNA/hAT-Charlie_Charlie15a chr1 20412606 20413040 - LINE/L2_L2a chr1 20413105 20413133 + Simple_repeat_(ATAG)n chr1 20413142 20413254 - SINE/MIR_MIR3 chr1 20413922 20414127 + SINE/MIR_MIRb chr1 20414334 20414373 + Simple_repeat_(TG)n chr1 20414545 20414716 - LINE/L2_L2b chr1 20414599 20414793 + LINE/CR1_L3b chr1 20414850 20415245 - LINE/L2_L2a chr1 20415406 20415465 - LINE/L2_L2b chr1 20415466 20415482 - LINE/L2_L2a chr1 20415483 20415527 - LINE/L2_L2a chr1 20415531 20415751 + SINE/MIR_MIR chr1 20415757 20416254 + LINE/L1_L1MD3 chr1 20416400 20416695 - SINE/Alu_AluSx1 chr1 20416702 20416786 + LINE/CR1_L3b chr1 20417066 20417235 + SINE/MIR_MIR chr1 20417253 20417400 - LINE/L2_L2c chr1 20418007 20418238 - SINE/MIR_MIR chr1 20418255 20418339 - LINE/L2_L2b chr1 20418639 20418724 + SINE/MIR_MIRb chr1 20418859 20419601 - LINE/L2_L2a chr1 20419604 20421610 + LINE/L1_HAL1 chr1 20421611 20422579 + LINE/L1_L1MB7 chr1 20422573 20423482 + LINE/L1_L1MB7 chr1 20423479 20424265 + LINE/L1_L1MB7 chr1 20424266 20424556 + SINE/Alu_AluJb chr1 20424557 20426197 + LINE/L1_L1MB7 chr1 20426227 20426428 + LINE/L1_HAL1 chr1 20426620 20426743 + SINE/tRNA-RTE_MamSINE1 chr1 20426776 20426987 - SINE/MIR_MIRb chr1 20427370 20427540 - SINE/MIR_MIR3 chr1 20427696 20427985 + SINE/Alu_AluSz chr1 20427986 20428145 + SINE/Alu_AluSg4 chr1 20428288 20428443 + SINE/MIR_MIRb chr1 20428444 20428557 + SINE/Alu_FLAM_C chr1 20428558 20428577 + SINE/MIR_MIR1_Amn chr1 20428739 20428865 + LINE/L2_L2d chr1 20428969 20429036 - SINE/MIR_MIRc chr1 20429006 20429040 - SINE/MIR_MIR3 chr1 20429374 20429682 + LINE/L2_L2b chr1 20429683 20429974 + SINE/Alu_AluJb chr1 20429975 20430148 + LINE/L2_L2b chr1 20430200 20430343 - SINE/MIR_MIRb chr1 20430627 20430752 + Low_complexity_G-rich chr1 20431221 20431299 + DNA?_UCON21 chr1 20431596 20431717 + DNA/hAT-Tip100_MER45A chr1 20431799 20431868 - SINE/MIR_MIR chr1 20431879 20431978 - DNA/hAT-Charlie_MER5A1 chr1 20431979 20432282 - SINE/Alu_AluSx chr1 20432283 20432351 - DNA/hAT-Charlie_MER5A1 chr1 20432401 20432703 + LTR/ERVL_LTR16A1 chr1 20432704 20432830 - SINE/MIR_MIR chr1 20433039 20433061 + Simple_repeat_(ATTC)n chr1 20433089 20433152 + LINE/L2_L2b chr1 20433153 20433348 + LTR/ERVL-MaLR_MSTA chr1 20433349 20433655 + SINE/Alu_AluSp chr1 20433656 20433726 + LTR/ERVL-MaLR_MSTA chr1 20433727 20433782 + Simple_repeat_(AGGA)n chr1 20433783 20433996 + LTR/ERVL-MaLR_MSTA chr1 20433997 20434177 + LINE/L2_L2b chr1 20434169 20434255 + SINE/MIR_MIRb chr1 20434309 20434608 - SINE/Alu_AluY chr1 20434611 20434935 - LINE/L2_L2c chr1 20434936 20435212 + SINE/Alu_AluSx chr1 20435213 20435454 - LINE/L2_L2c chr1 20435817 20435957 + SINE/MIR_MIR3 chr1 20435958 20436318 + LTR/ERVL-MaLR_MLT1K chr1 20436385 20436504 + LINE/L2_L2c chr1 20436509 20436795 - DNA/hAT-Charlie_MER103C chr1 20436851 20436943 - LINE/L2_L2b chr1 20436966 20437080 + SINE/MIR_MIR3 chr1 20437081 20437190 + LTR/ERVL-MaLR_MLT1K chr1 20437270 20437579 + SINE/Alu_AluSx1 chr1 20437639 20437760 - DNA/TcMar-Tigger_Tigger20a chr1 20437784 20438000 + SINE/MIR_MIRb chr1 20438324 20438878 + LTR/ERVL-MaLR_MLT1F2 chr1 20438879 20438989 - LTR/ERVL-MaLR_MLT1K chr1 20438990 20439248 - LTR/ERVL_LTR16E1 chr1 20439362 20439582 - LTR/ERVL-MaLR_MLT1K chr1 20439730 20439820 + DNA/hAT-Charlie_MER112 chr1 20439821 20440041 - DNA/hAT-Charlie_MER5A chr1 20440042 20440154 + DNA/hAT-Charlie_MER112 chr1 20440155 20440464 - SINE/Alu_AluSz chr1 20440497 20440612 - SINE/MIR_MIRc chr1 20440681 20441014 - LTR/ERVL-MaLR_MLT1J chr1 20441220 20441501 + DNA/hAT-Tip100_Zaphod4a chr1 20441502 20441685 - DNA/TcMar-Tigger_Tigger20a chr1 20442168 20442351 - SINE/MIR_MIRb chr1 20444171 20444255 + Simple_repeat_(CA)n chr1 20444591 20444648 + SINE/5S-Deu-L2_AmnSINE1 chr1 20444626 20444742 + SINE/MIR_MIR1_Amn chr1 20444834 20445125 + SINE/Alu_AluSz chr1 20445371 20445538 + SINE/MIR_MIR3 chr1 20445636 20445794 - SINE/MIR_MIRb chr1 20445801 20446013 - SINE/MIR_MIR chr1 20446312 20446375 - LTR/ERVL-MaLR_MLT1H chr1 20446394 20446478 - LTR/ERVL-MaLR_MLT1H chr1 20446483 20446515 + Simple_repeat_(AGAC)n chr1 20446517 20446737 - LTR/ERVL-MaLR_MLT1H chr1 20446788 20446903 - SINE/MIR_MIR3 chr1 20447207 20447430 - SINE/MIR_MIR chr1 20447593 20447881 + SINE/Alu_AluSz chr1 20447897 20448133 + SINE/MIR_MIR chr1 20448284 20448458 - DNA/hAT-Tip100_MamTip1 chr1 20448951 20448997 + Simple_repeat_(CCCCCCA)n chr1 20449258 20449675 + LINE/L2_L2b chr1 20449699 20449782 - SINE/MIR_MIRb chr1 20449803 20450314 + LINE/L1_L1ME1 chr1 20450318 20450865 + LTR/ERVL_MER21C chr1 20450866 20451157 + SINE/Alu_AluSz chr1 20451158 20451504 + LTR/ERVL_MER21C chr1 20451549 20451660 + LINE/L1_L1ME1 chr1 20451662 20451741 + SINE/MIR_MIR3 chr1 20451944 20452063 - DNA/hAT-Tip100_MER115 chr1 20452054 20452193 + LTR/ERVL_MER21C chr1 20452238 20452415 - DNA/hAT-Tip100_MER115 chr1 20452646 20452903 - SINE/MIR_MIRb chr1 20453001 20453150 - SINE/Alu_FRAM chr1 20453411 20453715 + SINE/Alu_AluSx1 chr1 20454878 20455117 + SINE/MIR_MIRb chr1 20455158 20455223 - LINE/L2_L2c chr1 20455315 20455515 + SINE/MIR_MIRc chr1 20455588 20455889 + SINE/Alu_AluSx chr1 20456343 20456475 + SINE/MIR_MIR chr1 20456606 20456807 - LINE/L2_L2c chr1 20456808 20457122 - SINE/Alu_AluSq2 chr1 20457123 20457330 - LINE/L2_L2c chr1 20457356 20457662 - SINE/Alu_AluSc8 chr1 20457677 20457941 + LINE/L1_L1MD3 chr1 20457948 20458236 - LINE/L2_L2c chr1 20458422 20458728 - SINE/Alu_AluSx1 chr1 20458831 20459096 - LTR/ERVL-MaLR_MLT1K chr1 20459733 20459978 + SINE/MIR_MIRb chr1 20460028 20460296 + Simple_repeat_(ATGG)n chr1 20460297 20460443 + LTR/ERVL-MaLR_MSTD chr1 20460444 20460740 - SINE/Alu_AluY chr1 20460741 20460985 + LTR/ERVL-MaLR_MSTD chr1 20460986 20461041 + Simple_repeat_(ATGG)n chr1 20461050 20461087 + Simple_repeat_(TGGA)n chr1 20461096 20461220 - LINE/L2_L2c chr1 20461246 20461299 - LINE/L2_L2c chr1 20461267 20461300 - LINE/L2_L2d2 chr1 20461812 20461952 + SINE/MIR_MIR3 chr1 20462016 20462179 + SINE/MIR_MIRc chr1 20462278 20462392 - SINE/MIR_MIR3 chr1 20462659 20462684 + Simple_repeat_(CTG)n chr1 20462823 20463020 + SINE/MIR_MIR chr1 20463028 20463389 - LTR/ERVL_LTR16 chr1 20463424 20463785 + Simple_repeat_(ATC)n chr1 20463786 20463974 - SINE/MIR_MIRb chr1 20464364 20464764 + LINE/L2_L2b chr1 20465045 20465127 - SINE/MIR_MIR3 chr1 20465320 20465365 + Simple_repeat_(CCCTCCT)n chr1 20465941 20466437 + LINE/L2_L2b chr1 20466518 20467043 + LTR/ERVL_MLT2F chr1 20467109 20467261 + LINE/L2_L2b chr1 20468663 20468883 - SINE/MIR_MIRc chr1 20469220 20469343 + LINE/L2_L2b chr1 20469347 20469404 - SINE/MIR_MIR chr1 20469444 20469784 - LTR/ERVL-MaLR_MSTD chr1 20469796 20469937 + Simple_repeat_(TGGA)n chr1 20470261 20470318 - LINE/L2_L2d chr1 20470319 20470372 + Simple_repeat_(CTGG)n chr1 20470985 20471193 - SINE/MIR_MIR chr1 20471305 20471344 + Simple_repeat_(ATT)n chr1 20471346 20471627 - SINE/Alu_AluSq2 chr1 20472027 20472194 + SINE/MIR_MIRc chr1 20472200 20472608 - LINE/L2_L2b chr1 20472991 20473110 - SINE/MIR_MIRb chr1 20473715 20473766 + SINE/MIR_MIR chr1 20473877 20474005 + SINE/MIR_MIR chr1 20474006 20474319 + SINE/Alu_AluJr chr1 20474320 20474356 + SINE/MIR_MIR chr1 20474376 20474483 + SINE/MIR_MIRb chr1 20474498 20474573 - SINE/MIR_MIR chr1 20474603 20474820 - DNA/hAT-Charlie_MER20 chr1 20475184 20475254 + LINE/L2_L2b chr1 20475369 20475392 + SINE/MIR_MIR3 chr1 20475393 20475694 + SINE/Alu_AluSq10 chr1 20475695 20475917 + SINE/MIR_MIR chr1 20475964 20476113 - SINE/MIR_MIRc chr1 20476720 20476782 - SINE/MIR_MIR3 chr1 20476770 20477009 + SINE/MIR_MIR chr1 20477029 20477071 - SINE/MIR_MIR3 chr1 20477528 20477568 + Simple_repeat_(CCTC)n chr1 20478273 20478935 + LINE/L1_L1MC3 chr1 20479108 20479197 + LINE/L2_L2a chr1 20479309 20479455 + DNA/hAT-Tip100?_MamRep1879 chr1 20479694 20479822 - SINE/MIR_MIR chr1 20480696 20480826 + SINE/MIR_MIR chr1 20480863 20481020 + SINE/MIR_MIRc chr1 20481898 20482095 - SINE/MIR_MIRb chr1 20482857 20482892 + Simple_repeat_(GT)n chr1 20483460 20483494 + Simple_repeat_(CCT)n chr1 20483905 20483946 + Simple_repeat_(CCAGCCT)n chr1 20485449 20485525 + Low_complexity_G-rich chr1 20485544 20485614 + Simple_repeat_(GC)n chr1 20485582 20485631 + Simple_repeat_(GCTGC)n chr1 20487509 20487752 + SINE/MIR_MIRc chr1 20487819 20487976 - SINE/MIR_MIR3 chr1 20488083 20488298 - SINE/MIR_MIRc chr1 20488638 20488842 - SINE/MIR_MIRc chr1 20489105 20489157 + SINE/MIR_MIRc chr1 20489158 20489450 + SINE/Alu_AluJr chr1 20489451 20489633 + SINE/MIR_MIR chr1 20489869 20489915 + SINE/MIR_MIR3 chr1 20490515 20490565 + Simple_repeat_(CA)n chr1 20490635 20490681 - LINE/L2_L2b chr1 20490714 20490918 - DNA/hAT-Charlie_Charlie15a chr1 20490921 20490973 - SINE/MIR_MIR3 chr1 20491589 20491686 - SINE/MIR_MIRc chr1 20491950 20492035 - SINE/MIR_MIRb chr1 20492190 20492297 - DNA/hAT-Charlie_Charlie4z chr1 20492576 20492755 - SINE/MIR_MIRc chr1 20492787 20492931 + SINE/MIR_MIRb chr1 20494160 20494221 + LTR/Gypsy?_LTR88a chr1 20494225 20494270 + LINE/L2_L2d2 chr1 20494772 20494962 - SINE/MIR_MIR3 chr1 20495201 20495426 + SINE/MIR_MIR chr1 20495489 20495725 - LINE/L2_L2c chr1 20495726 20496036 - SINE/Alu_AluJr chr1 20496037 20496274 - LINE/L2_L2c chr1 20496471 20496897 + LTR/ERVL-MaLR_MSTA chr1 20496949 20497042 + LINE/L2_L2c chr1 20497055 20497342 + SINE/Alu_AluSp chr1 20498397 20498432 + Simple_repeat_(GAATTC)n chr1 20498859 20499102 + SINE/MIR_MIRb chr1 20501750 20501774 - DNA/hAT-Charlie_MER5A chr1 20501866 20501903 + DNA/hAT-Charlie_MER5A1 chr1 20502267 20502548 + SINE/Alu_AluJr chr1 20502549 20502833 - SINE/Alu_AluSz chr1 20502834 20502844 + SINE/Alu_AluJr chr1 20502948 20503082 - SINE/MIR_MIRb chr1 20503875 20504169 - SINE/Alu_AluJb chr1 20504341 20504520 - LINE/L2_L2b chr1 20504631 20504781 + LINE/L2_L2a chr1 20504783 20504957 - SINE/MIR_MIRb chr1 20505015 20505213 + SINE/MIR_MIRb chr1 20505303 20505609 + SINE/Alu_AluJr chr1 20505616 20505721 + Low_complexity_A-rich chr1 20506079 20506128 - SINE/Alu_AluSq2 chr1 20506266 20506433 - LINE/L2_L2b chr1 20506440 20506567 + SINE/MIR_MIRc chr1 20506568 20506877 + SINE/Alu_AluSx1 chr1 20506878 20506941 + Low_complexity_A-rich chr1 20506945 20507239 + SINE/Alu_AluSx chr1 20507362 20507665 + LINE/L2_L2a chr1 20508476 20508646 - SINE/MIR_MIRb chr1 20508730 20508948 - SINE/MIR_MIRb chr1 20509035 20509051 - LINE/L1_L1ME4b chr1 20509052 20509340 - SINE/Alu_AluJr chr1 20509341 20509505 - LINE/L1_L1ME4b chr1 20509506 20509805 - SINE/Alu_AluSz chr1 20509806 20509848 - LINE/L1_L1ME4b chr1 20510191 20510262 + LINE/L1_L1MC chr1 20510361 20510517 + LINE/L1_L1ME4b chr1 20510539 20510684 + SINE/Alu_AluSx chr1 20510685 20510996 + SINE/Alu_AluSp chr1 20511006 20511530 - LINE/L2_L2c chr1 20512314 20512440 - LINE/L2_L2d2 chr1 20512538 20512839 + SINE/Alu_AluSz chr1 20512865 20513097 + SINE/Alu_AluSz chr1 20513151 20513204 + Low_complexity_GA-rich chr1 20513205 20513363 + SINE/Alu_FRAM chr1 20513364 20513444 + Low_complexity_GA-rich chr1 20513614 20513911 + SINE/Alu_AluSc chr1 20514298 20514600 - SINE/Alu_AluSx1 chr1 20514609 20514702 - LINE/L1_L1MA10 chr1 20514703 20515079 - LTR/ERV1_LOR1b chr1 20515102 20515402 - SINE/Alu_AluSg chr1 20515422 20515728 - SINE/Alu_AluSz chr1 20515729 20515882 + LTR/ERV1_HUERS-P1-int chr1 20515883 20516190 + SINE/Alu_AluSp chr1 20516191 20516512 + LTR/ERV1_HUERS-P1-int chr1 20516513 20516797 + SINE/Alu_AluSq2 chr1 20516798 20516834 + LTR/ERV1_HUERS-P1-int chr1 20516835 20517146 + SINE/Alu_AluSx1 chr1 20517147 20517927 + LTR/ERV1_HUERS-P1-int chr1 20517928 20518213 + SINE/Alu_AluSz chr1 20518214 20518407 + LTR/ERV1_HUERS-P1-int chr1 20518408 20518701 + SINE/Alu_AluSq2 chr1 20518708 20518995 + SINE/Alu_AluSx chr1 20518996 20519665 + LTR/ERV1_HUERS-P1-int chr1 20519666 20520164 - LTR/ERV1_LTR8 chr1 20520165 20520760 - LTR/ERV1_HUERS-P1-int chr1 20520761 20521017 + SINE/Alu_AluSp chr1 20521025 20521183 - SINE/MIR_MIR3 chr1 20521307 20521391 + LTR/ERV1_LTR8 chr1 20521432 20521642 - LINE/L1_L1MD chr1 20521649 20521949 + SINE/Alu_AluY chr1 20521979 20522103 - SINE/MIR_MIRb chr1 20522835 20522993 + SINE/MIR_MIR3 chr1 20523001 20523313 - SINE/Alu_AluSx1 chr1 20523392 20523524 + SINE/MIR_MIRc chr1 20523586 20523644 + LINE/L2_L2a chr1 20523711 20523775 - LINE/CR1_CR1-3_Croc chr1 20524103 20524328 + LTR/ERVL-MaLR_MLT1L chr1 20524551 20524852 - SINE/Alu_AluY chr1 20524858 20524958 - LTR/ERVL-MaLR_MLT1K chr1 20525015 20525243 - LTR/ERVL-MaLR_MLT1K chr1 20525612 20525649 + Simple_repeat_(TTTATTT)n chr1 20525650 20525931 - SINE/Alu_AluSq2 chr1 20526993 20527132 - DNA/hAT-Tip100_MER96 chr1 20527133 20527247 - LTR/ERVL-MaLR_MLT1K chr1 20527264 20527322 + Simple_repeat_(CTCTTCT)n chr1 20527764 20528074 + SINE/Alu_AluSx chr1 20528689 20528878 + LTR/ERVL-MaLR_MLT1J chr1 20528879 20529187 - SINE/Alu_AluSp chr1 20529188 20529473 + LTR/ERVL-MaLR_MLT1J chr1 20529498 20529716 + LINE/L2_L2a chr1 20529696 20529864 - SINE/MIR_MIR chr1 20530051 20530201 - DNA/TcMar-Tc2_Kanga11a chr1 20530426 20530560 + LINE/L2_L2b chr1 20531413 20531534 - LINE/L2_L2a chr1 20531535 20531652 + SINE/Alu_AluJo chr1 20531653 20531712 - LINE/L2_L2a chr1 20531717 20531912 + LTR/ERVL-MaLR_MLT1D chr1 20531914 20532030 + SINE/Alu_FLAM_A chr1 20532061 20532211 - SINE/Alu_FRAM chr1 20532266 20532390 + LTR/ERVL-MaLR_MLT1D chr1 20532391 20532693 + SINE/Alu_AluJr chr1 20532694 20532704 + LTR/ERVL-MaLR_MLT1D chr1 20532705 20532994 + SINE/Alu_AluSx chr1 20532995 20533114 + LTR/ERVL-MaLR_MLT1D chr1 20533115 20533632 - LINE/L2_L2a chr1 20533633 20534083 + LTR/ERVK_LTR22A chr1 20534084 20534336 - LINE/L2_L2a chr1 20534337 20534483 - SINE/Alu_FRAM chr1 20534484 20535626 - LINE/L2_L2a chr1 20535628 20535739 + LTR/ERVL-MaLR_MLT1D chr1 20535740 20535845 + SINE/Alu_FLAM_A chr1 20535846 20536264 + LTR/ERVL-MaLR_MLT1D chr1 20536283 20536480 - LINE/L2_L2a chr1 20536481 20536764 + SINE/Alu_AluSx chr1 20536765 20536982 + LTR/ERVL-MaLR_MLT1F chr1 20537012 20537143 + LINE/L2_L2b chr1 20537144 20537278 + SINE/Alu_AluSz chr1 20537279 20537592 + SINE/Alu_AluY chr1 20537593 20537763 + SINE/Alu_AluSz chr1 20537764 20537920 + LINE/L2_L2b chr1 20538019 20538283 + SINE/Alu_AluJr chr1 20538735 20538943 + SINE/MIR_MIRb chr1 20539083 20539129 + Simple_repeat_(CA)n chr1 20539164 20539203 + Simple_repeat_(ATCACTT)n chr1 20539409 20539465 - SINE/tRNA-RTE_MamSINE1 chr1 20539466 20539685 - DNA/hAT-Charlie_MER30 chr1 20539686 20539717 - SINE/tRNA-RTE_MamSINE1 chr1 20539751 20540236 - LINE/L2_L2b chr1 20540250 20540782 - LTR/ERVL-MaLR_MLT1F chr1 20540872 20541138 + SINE/Alu_AluJo chr1 20541302 20541811 - LTR/ERVL_MER21A chr1 20541812 20542062 - SINE/Alu_AluSz chr1 20542063 20542342 - LTR/ERVL_MER21A chr1 20542694 20543061 + LINE/L1_HAL1 chr1 20543173 20543245 + LINE/L1_HAL1 chr1 20543280 20543568 - SINE/Alu_AluSx chr1 20543651 20543948 + SINE/Alu_AluJo chr1 20543950 20544221 + LINE/L1_L1MB2 chr1 20544222 20544513 + SINE/Alu_AluSx1 chr1 20544514 20544937 + LINE/L1_L1MB2 chr1 20544938 20545232 - SINE/Alu_AluSq2 chr1 20545233 20545706 + LINE/L1_L1MB2 chr1 20545725 20546285 + LINE/L1_HAL1 chr1 20546286 20546661 + LTR/ERVL-MaLR_MSTA chr1 20546662 20547007 + LINE/L1_HAL1 chr1 20547008 20547321 - SINE/Alu_AluY chr1 20547322 20547501 + LINE/L1_HAL1 chr1 20547502 20547813 + SINE/Alu_AluY chr1 20547814 20548002 + LINE/L1_HAL1 chr1 20548003 20548285 + SINE/Alu_AluSz chr1 20548286 20548405 + LINE/L1_HAL1 chr1 20548661 20548887 + DNA/hAT-Charlie_MER1B chr1 20548888 20549189 + SINE/Alu_AluSx chr1 20549190 20549319 + DNA/hAT-Charlie_MER1B chr1 20549624 20549667 - DNA/hAT_MER53 chr1 20549668 20549962 + SINE/Alu_AluSz6 chr1 20549963 20550026 - DNA/hAT_MER53 chr1 20550069 20550185 + SINE/Alu_AluJr chr1 20550186 20550485 - SINE/Alu_AluSp chr1 20550486 20550679 + SINE/Alu_AluJr chr1 20551670 20551824 + SINE/MIR_MIRc chr1 20552450 20552480 + Simple_repeat_(GGGGA)n chr1 20552667 20552696 + Simple_repeat_(CCAGCC)n chr1 20553764 20553817 + Simple_repeat_(AGG)n chr1 20554022 20554061 + Simple_repeat_(CCCCGG)n chr1 20556149 20556295 + SINE/Alu_FRAM chr1 20556384 20556418 + Simple_repeat_(AC)n chr1 20556488 20556628 + SINE/MIR_MIRc chr1 20556961 20557006 + SINE/MIR_MIR chr1 20557014 20557213 + DNA/hAT-Charlie_Charlie15a chr1 20557548 20557864 + LINE/L2_L2d2 chr1 20557873 20558177 - SINE/Alu_AluSp chr1 20558178 20558305 + LINE/L2_L2d2 chr1 20558724 20558855 + SINE/MIR_MIRb chr1 20558856 20559166 + SINE/Alu_AluSq2 chr1 20559167 20559185 + SINE/MIR_MIR3 chr1 20559191 20559503 - SINE/Alu_AluY chr1 20559512 20559688 - SINE/Alu_AluJb chr1 20559689 20559735 + Simple_repeat_(GT)n chr1 20559737 20559767 + Simple_repeat_(T)n chr1 20559768 20559882 - SINE/Alu_AluJb chr1 20559933 20560084 - SINE/Alu_AluJb chr1 20560107 20560182 + SINE/MIR_MIRb chr1 20560420 20560527 + LINE/L2_L2b chr1 20561048 20561185 + LTR/ERVL-MaLR_MLT1J2 chr1 20561197 20561408 + SINE/Alu_AluJr4 chr1 20561422 20561638 + LTR/ERVL-MaLR_MLT1J2 chr1 20561640 20562106 + LTR/ERVL-MaLR_MLT1K chr1 20562107 20562401 + SINE/Alu_AluSx3 chr1 20562402 20562467 + LTR/ERVL-MaLR_MLT1K chr1 20562986 20563198 + SINE/MIR_MIRc chr1 20563599 20563631 + Low_complexity_G-rich chr1 20563955 20563978 + Simple_repeat_(A)n chr1 20564031 20564324 + SINE/Alu_AluSx1 chr1 20564334 20564373 + Simple_repeat_(TTCCCCA)n chr1 20564459 20564723 - SINE/Alu_AluSx chr1 20564871 20564917 + LINE/CR1_X7B_LINE chr1 20564924 20565081 - SINE/MIR_MIR3 chr1 20565122 20565244 - SINE/Alu_FRAM chr1 20565391 20565779 + LINE/L2_L2a chr1 20565975 20566282 - SINE/Alu_AluSg chr1 20566629 20566706 + SINE/MIR_MIR3 chr1 20566711 20567024 + SINE/Alu_AluJb chr1 20567145 20567454 - SINE/Alu_AluSx chr1 20567779 20568131 - LINE/L1_L1MA4 chr1 20568132 20568435 - SINE/Alu_AluSx chr1 20568436 20568608 - LINE/L1_L1MA4 chr1 20568610 20568839 - SINE/Alu_AluSx1 chr1 20568840 20569303 + LINE/L1_L1M1 chr1 20569304 20569539 - LINE/L2_L2a chr1 20569614 20569933 + LTR/ERVL-MaLR_MLT1J2 chr1 20570132 20570291 - LINE/L2_L2a chr1 20570305 20570595 - SINE/Alu_AluSz6 chr1 20570615 20570710 - SINE/MIR_MIR chr1 20570885 20570988 + SINE/MIR_MIR chr1 20570989 20571299 + SINE/Alu_AluY chr1 20571300 20571311 + SINE/MIR_MIR chr1 20571312 20571757 + LTR/ERVL-MaLR_MLT1H2 chr1 20571758 20571864 + SINE/MIR_MIR chr1 20572238 20572876 - LINE/L2_L2a chr1 20572888 20573256 - LINE/L1_L1PB1 chr1 20573257 20573346 + Simple_repeat_(AT)n chr1 20573347 20573390 - LINE/L1_L1P5 chr1 20573401 20573912 - LINE/L2_L2a chr1 20573921 20574045 + SINE/Alu_AluJr4 chr1 20574049 20574230 + Low_complexity_GA-rich chr1 20574343 20574388 + SINE/Alu_Alu chr1 20574389 20574443 + Low_complexity_A-rich chr1 20574809 20574903 + LINE/L2_L2d2 chr1 20575031 20575289 - LINE/L2_L2b chr1 20576017 20576185 + SINE/Alu_AluSx chr1 20576342 20576612 - SINE/Alu_AluSz chr1 20576693 20577004 + LINE/L2_L2a chr1 20577005 20577309 + SINE/Alu_AluSz chr1 20577310 20577428 + LINE/L2_L2a chr1 20577573 20577860 - SINE/Alu_AluYc chr1 20577873 20578184 - SINE/Alu_AluJb chr1 20578576 20578764 - LTR/ERVL_LTR67B chr1 20578979 20579115 - LTR/ERVL_LTR67B chr1 20579256 20579360 - LTR/ERVL-MaLR_MLT1J-int chr1 20579363 20579504 - LTR/ERVL_ERVL-E-int chr1 20579505 20579802 + SINE/Alu_AluSz6 chr1 20579803 20579902 + DNA/hAT-Charlie_Charlie4 chr1 20579903 20580207 + SINE/Alu_AluSg chr1 20580216 20580524 + SINE/Alu_AluSx chr1 20580525 20580589 + DNA/hAT-Charlie_Charlie4 chr1 20580611 20580886 + LINE/L1_L1M5 chr1 20580887 20581187 - SINE/Alu_AluY chr1 20581188 20581374 + LINE/L1_L1M5 chr1 20581342 20581520 + LINE/L1_L1M5 chr1 20581552 20581674 - SINE/Alu_AluJo chr1 20581696 20581791 + LINE/L1_L1M5 chr1 20581806 20582194 + LINE/L1_L1M5 chr1 20582199 20582514 + SINE/Alu_AluSx chr1 20582528 20582944 - LINE/L1_L1M5 chr1 20582977 20583287 - SINE/Alu_AluJb chr1 20583414 20583519 - LINE/L1_L1M chr1 20583520 20583827 + SINE/Alu_AluY chr1 20583828 20583838 - LINE/L1_L1M chr1 20584098 20584414 + SINE/Alu_AluSc8 chr1 20584440 20584688 + LINE/L2_L2b chr1 20584665 20584769 - SINE/MIR_MIRb chr1 20584796 20585125 - LTR/ERVL-MaLR_MLT1K chr1 20585243 20585394 - SINE/MIR_MIR chr1 20585466 20585509 + Simple_repeat_(TAT)n chr1 20585510 20585788 - SINE/Alu_AluJr chr1 20585810 20585961 + SINE/MIR_MIR chr1 20586201 20586509 - SINE/Alu_AluJr chr1 20586582 20586664 - DNA/hAT-Tip100?_MamRep1879 chr1 20586665 20586958 + SINE/Alu_AluSz6 chr1 20586959 20586979 - DNA/hAT-Tip100?_MamRep1879 chr1 20587140 20587184 + Low_complexity_G-rich chr1 20587466 20587505 + Low_complexity_G-rich chr1 20587636 20587679 + Simple_repeat_(TCCCCCT)n chr1 20587854 20588046 - SINE/MIR_MIRb chr1 20588064 20588210 - SINE/MIR_MIRb chr1 20588281 20588553 - SINE/Alu_AluJo chr1 20589336 20589384 + Low_complexity_GA-rich chr1 20589684 20589815 - SINE/MIR_MIR3 chr1 20590202 20590356 - SINE/MIR_MIRb chr1 20590434 20590478 + Simple_repeat_(ACC)n chr1 20590568 20590802 + LINE/L2_L2b chr1 20591059 20591367 + SINE/Alu_AluY chr1 20591457 20591559 - LINE/L2_L2a chr1 20591691 20591825 + SINE/MIR_MIRb chr1 20591840 20592135 - SINE/Alu_AluSx1 chr1 20592303 20592386 + SINE/MIR_MIRb chr1 20592601 20592699 + LINE/L2_L2b chr1 20592811 20593121 + SINE/Alu_AluJr chr1 20593569 20593874 - SINE/Alu_AluJr chr1 20594491 20594505 + Low_complexity_A-rich chr1 20594506 20594840 + SINE/Alu_AluSx1 chr1 20595635 20595862 + SINE/Alu_AluJb chr1 20595863 20596160 + SINE/Alu_AluSq2 chr1 20596320 20596470 + DNA/hAT-Charlie_MER5A chr1 20596468 20596570 + SINE/Alu_FLAM_C chr1 20596757 20597061 - SINE/Alu_AluJr chr1 20597167 20597455 + SINE/Alu_AluY chr1 20597507 20597643 + SINE/MIR_MIRb chr1 20597879 20597909 + Simple_repeat_(T)n chr1 20598047 20598083 + LTR/ERVL-MaLR_MLT1A1 chr1 20598158 20598506 + LTR/ERVL-MaLR_MLT1A1 chr1 20598507 20598840 - LTR/ERVL-MaLR_MLT1A chr1 20598866 20599334 - LINE/L2_L2a chr1 20599337 20599616 + SINE/Alu_AluSx4 chr1 20599618 20599676 + Simple_repeat_(AAAAT)n chr1 20599789 20600010 - SINE/MIR_MIRb chr1 20600474 20600783 + SINE/Alu_AluSz chr1 20600998 20601202 - LTR/ERVL-MaLR_MLT1K chr1 20601847 20602127 + SINE/Alu_AluJo chr1 20602128 20602184 + Low_complexity_GA-rich chr1 20602210 20602361 - LTR/ERVL-MaLR_MLT1J chr1 20602419 20602719 - SINE/Alu_AluSx4 chr1 20602723 20603037 - LTR/ERVL-MaLR_MLT1J-int chr1 20603037 20603216 - LTR/ERVL-MaLR_MLT1J-int chr1 20603227 20603714 - LTR/ERVL-MaLR_MLT1J chr1 20603863 20604015 - LTR/ERVL-MaLR_MLT1J-int chr1 20604044 20604097 + Simple_repeat_(TTGTTAG)n chr1 20604192 20604547 - LTR/ERVL-MaLR_MLT1A0 chr1 20605117 20605276 - LINE/L2_L2a chr1 20605379 20605673 + SINE/Alu_AluY chr1 20605688 20605845 + SINE/Alu_AluJb chr1 20606200 20606346 + SINE/Alu_AluYc chr1 20606349 20606529 - SINE/MIR_MIRc chr1 20606912 20606997 + DNA/TcMar-Tigger_MamRep434 chr1 20607540 20607689 + DNA_Eulor1 chr1 20608177 20608319 + SINE/MIR_MIR3 chr1 20608199 20608377 + SINE/MIR_MIRb chr1 20608394 20608427 + Simple_repeat_(GTTTT)n chr1 20608435 20608709 - SINE/Alu_AluSz6 chr1 20608782 20608979 + SINE/MIR_MIRb chr1 20609203 20609496 + SINE/Alu_AluY chr1 20609526 20609587 - LINE/L2_L2 chr1 20609636 20609754 + LINE/L2_L2a chr1 20609777 20609929 + RNA_7SK chr1 20609997 20610200 + LINE/L2_L2a chr1 20610189 20610259 + SINE/MIR_MIRb chr1 20610262 20610584 - SINE/Alu_AluSg7 chr1 20610754 20610958 - SINE/MIR_MIRc chr1 20610959 20611260 + SINE/Alu_AluJo chr1 20611374 20611678 - SINE/Alu_AluSc chr1 20611826 20611858 + LINE/L2_L2d2 chr1 20611859 20612162 - SINE/Alu_AluSx3 chr1 20612233 20612288 + SINE/MIR_MIRb chr1 20612289 20612679 + LTR/ERV1_LTR7C chr1 20612680 20612962 + SINE/Alu_AluSc8 chr1 20612963 20613001 + LTR/ERV1_LTR7C chr1 20613002 20613091 + SINE/MIR_MIRb chr1 20613089 20613199 + LINE/L2_L2d2 chr1 20613207 20613503 - SINE/Alu_AluSq2 chr1 20613547 20613619 + LINE/L2_L2d2 chr1 20613972 20613995 + Simple_repeat_(GCAG)n chr1 20614077 20614343 - LINE/L2_L2a chr1 20614344 20614564 + DNA/hAT-Charlie_MER58A chr1 20614565 20614863 - LINE/L2_L2a chr1 20614868 20615159 - SINE/Alu_AluSg chr1 20615160 20615220 + LINE/L1_L1MB3 chr1 20615338 20615352 + Low_complexity_A-rich chr1 20615353 20615517 + SINE/Alu_FLAM_C chr1 20615723 20616008 + SINE/Alu_AluJb chr1 20616013 20616210 - SINE/MIR_MIRb chr1 20617150 20617220 - SINE/MIR_MIRc chr1 20617275 20617376 + SINE/MIR_MIR chr1 20617379 20617682 + LTR/ERVL-MaLR_THE1B chr1 20617683 20617996 - SINE/Alu_AluSx1 chr1 20617997 20618069 + LTR/ERVL-MaLR_THE1B chr1 20618228 20618446 - SINE/MIR_MIR chr1 20619238 20619415 - SINE/MIR_MIRb chr1 20619436 20619632 - SINE/MIR_MIRb chr1 20619633 20619932 + SINE/Alu_AluSx1 chr1 20619933 20619962 - SINE/MIR_MIRb chr1 20620565 20620872 - SINE/Alu_AluSg4 chr1 20620899 20621228 + SINE/Alu_AluJr chr1 20621231 20621542 + SINE/Alu_AluSp chr1 20621659 20621959 - SINE/Alu_AluSx1 chr1 20621978 20622286 - SINE/Alu_AluY chr1 20622287 20622435 - LINE/L2_L2c chr1 20622476 20622612 + SINE/Alu_AluJb chr1 20622613 20622911 + SINE/Alu_AluSg chr1 20622912 20623099 + SINE/Alu_AluJb chr1 20623476 20623785 - SINE/Alu_AluSp chr1 20623907 20624216 + SINE/Alu_AluSx chr1 20624257 20624761 - LTR/ERVL-MaLR_MLT1F2 chr1 20624947 20624960 + SINE/Alu_AluJb chr1 20624961 20625093 + SINE/Alu_AluJr4 chr1 20625094 20625110 + SINE/Alu_AluJb chr1 20625111 20625824 - LTR/ERV1_LTR8A chr1 20625825 20625970 + SINE/Alu_AluJb chr1 20625971 20626222 + Simple_repeat_(GGAA)n chr1 20626226 20626407 + LINE/L1_HAL1 chr1 20626435 20626551 + SINE/Alu_FLAM_C chr1 20626698 20626838 + LTR/ERVL_LTR79 chr1 20626851 20627022 - LINE/L2_L2b chr1 20627893 20628503 + LTR/ERV1_MER4B chr1 20628758 20628804 + LINE/L1_L1MA3 chr1 20628826 20629131 + SINE/Alu_AluSp chr1 20629158 20629512 - LTR/ERVL-MaLR_THE1A chr1 20629561 20629831 + SINE/Alu_AluJr chr1 20629832 20629860 + Simple_repeat_(ATAA)n chr1 20629872 20630130 + LTR/ERVL_LTR84a chr1 20630539 20631419 - LTR/ERV1_MER4D chr1 20631450 20631726 - SINE/Alu_AluJr chr1 20632078 20632128 + LTR/ERVL_LTR47A chr1 20632129 20632432 - SINE/Alu_AluSz chr1 20632433 20632714 + LTR/ERVL_LTR47A chr1 20632858 20632890 + Simple_repeat_(TG)n chr1 20632898 20632972 + SINE/MIR_MIR3 chr1 20633070 20633248 + SINE/MIR_MIR chr1 20633486 20633543 + Simple_repeat_(CGG)n chr1 20633687 20633737 + Simple_repeat_(GGCTCG)n chr1 20634238 20634386 - SINE/MIR_MIRb chr1 20634564 20634779 - LTR/ERVL_MLT2D chr1 20634807 20634947 - SINE/MIR_MIR3 chr1 20635221 20635499 + SINE/Alu_AluSg4 chr1 20635567 20635878 + SINE/Alu_AluY chr1 20635886 20636148 + SINE/Alu_AluJr chr1 20636181 20636297 + Simple_repeat_(AT)n chr1 20636345 20636641 - SINE/Alu_AluSx3 chr1 20636695 20636927 - LINE/L2_L2d2 chr1 20638359 20638667 + SINE/Alu_AluJb chr1 20638671 20638784 - DNA/hAT-Charlie_MER5A1 chr1 20640003 20640112 + LINE/L2_L2b chr1 20640462 20640648 - LINE/L2_L2b chr1 20640768 20641039 + SINE/Alu_AluJb chr1 20641698 20641812 + LINE/L2_L2a chr1 20641718 20641875 + LINE/L2_L2b chr1 20642032 20642109 - LTR/ERVL-MaLR_MLT1M chr1 20642211 20642667 - LTR/ERV1_LTR45C chr1 20642668 20642955 - SINE/Alu_AluJb chr1 20642956 20642988 - LTR/ERV1_LTR45C chr1 20643039 20643270 - LTR/ERVL-MaLR_MLT1M chr1 20643422 20643534 - LINE/L2_L2a chr1 20643551 20643685 + LINE/L2_L2b chr1 20643684 20643753 - SINE/MIR_MIRb chr1 20643830 20644140 - DNA/TcMar-Tigger_MER44A chr1 20644161 20644205 - SINE/MIR_MIR chr1 20644231 20644301 + Simple_repeat_(GGAT)n chr1 20644311 20644390 + LINE/L2_L2a chr1 20645207 20645504 + SINE/Alu_AluSx1 chr1 20645992 20646325 + SINE/Alu_AluJr chr1 20646364 20646650 + SINE/Alu_AluSc chr1 20646665 20646830 + LINE/CR1_L3 chr1 20646852 20647046 - SINE/Alu_AluSz6 chr1 20647047 20647331 - SINE/Alu_AluY chr1 20647332 20647455 - SINE/Alu_AluSz6 chr1 20647467 20647768 - SINE/Alu_AluSg chr1 20647821 20648107 - SINE/Alu_AluSx chr1 20649483 20649786 + SINE/Alu_AluSz chr1 20651802 20652124 - SINE/Alu_AluY chr1 20654859 20655159 - SINE/Alu_AluSx chr1 20655163 20655195 + Simple_repeat_(T)n chr1 20655198 20655310 - SINE/Alu_AluJr chr1 20656522 20656963 - DNA/TcMar-Tc2_Kanga2_a chr1 20657001 20657626 - LINE/L2_L2a chr1 20657665 20657739 - DNA/TcMar-Tc2_Kanga2_a chr1 20657884 20658084 + DNA/hAT-Charlie_MER20 chr1 20658138 20658189 + SINE/MIR_MIR1_Amn chr1 20658457 20658843 + DNA/hAT-Charlie_MER113 chr1 20658844 20659157 - SINE/Alu_AluJr chr1 20659158 20659286 + DNA/hAT-Charlie_MER113 chr1 20659790 20659930 + SINE/Alu_AluJb chr1 20660015 20660301 + LINE/CR1_L3 chr1 20661974 20662096 + SINE/MIR_MIR1_Amn chr1 20662946 20663242 + SINE/Alu_AluSx1 chr1 20663435 20663570 - SINE/MIR_MIRb chr1 20663792 20663870 - DNA/hAT-Charlie_Charlie4z chr1 20665014 20665134 + SINE/MIR_MIRb chr1 20665220 20665246 + Simple_repeat_(T)n chr1 20665253 20665394 - SINE/Alu_AluSz6 chr1 20665395 20665700 - SINE/Alu_AluSz chr1 20666050 20666152 - SINE/MIR_MIRb chr1 20666905 20667068 + LTR/ERVL-MaLR_MLT1K chr1 20667069 20667602 + DNA/hAT-Charlie_MER1A chr1 20667603 20667725 + LTR/ERVL-MaLR_MLT1K chr1 20667726 20668025 + SINE/Alu_AluSp chr1 20668029 20668324 + SINE/Alu_AluY chr1 20668325 20668472 + LTR/ERVL-MaLR_MLT1K chr1 20668703 20668913 + DNA/hAT-Charlie_MER58A chr1 20669257 20669563 + SINE/Alu_AluY chr1 20669565 20669591 + Simple_repeat_(AAATA)n chr1 20669592 20669894 + SINE/Alu_AluSc chr1 20669895 20669907 + Simple_repeat_(AAATA)n chr1 20669988 20670008 + Simple_repeat_(A)n chr1 20670959 20671087 - SINE/MIR_MIR3 chr1 20671067 20671237 + SINE/MIR_MIRb chr1 20671246 20671323 - SINE/MIR_MIR chr1 20671324 20671634 - SINE/Alu_AluJb chr1 20671635 20671779 - SINE/MIR_MIR chr1 20672335 20672373 + Simple_repeat_(CCAT)n chr1 20672943 20673214 + SINE/Alu_AluSz chr1 20673215 20673245 + Simple_repeat_(AAAT)n chr1 20673378 20673404 + Simple_repeat_(TTTG)n chr1 20673409 20673526 - SINE/Alu_AluJo chr1 20673534 20673828 - SINE/Alu_AluSx chr1 20673923 20674227 - SINE/Alu_AluJr chr1 20674289 20674559 - SINE/Alu_AluJb chr1 20674561 20674867 - LINE/L1_L1M4 chr1 20674868 20675164 + SINE/Alu_AluSx chr1 20675166 20675463 + SINE/Alu_AluY chr1 20675464 20675887 - LINE/L1_L1M4 chr1 20675892 20676056 + SINE/Alu_AluSz6 chr1 20676141 20676549 + LINE/L1_L1MC5a chr1 20676550 20676860 + SINE/Alu_AluSz chr1 20676861 20676928 + LINE/L1_L1MC5a chr1 20676929 20677227 + SINE/Alu_AluJb chr1 20677228 20677571 + LINE/L1_L1MC5a chr1 20677572 20677870 + SINE/Alu_AluSx chr1 20677871 20677981 + LINE/L1_L1MC5a chr1 20678008 20678370 - LTR/ERVL-MaLR_THE1B chr1 20678442 20678628 + LINE/L1_L1MC4 chr1 20679002 20679020 + Simple_repeat_(A)n chr1 20679031 20679152 + SINE/Alu_AluJo chr1 20679159 20679506 + SINE/Alu_AluJo chr1 20679559 20679977 + LTR/ERVL-MaLR_MLT1E2 chr1 20680000 20680148 - SINE/Alu_AluSx chr1 20680287 20680430 + LINE/L1_L1MC4 chr1 20680431 20680591 + SINE/Alu_FRAM chr1 20680592 20680749 + LINE/L1_L1MC4 chr1 20680842 20681156 + SINE/Alu_AluY chr1 20681182 20681206 + Simple_repeat_(GTTTT)n chr1 20681207 20681491 - SINE/Alu_AluSz chr1 20681886 20681976 + LINE/L1_L1MC5a chr1 20682092 20682214 + Simple_repeat_(ACAC)n chr1 20682215 20682323 + LINE/L2_L2a chr1 20682325 20682524 + SINE/Alu_AluJb chr1 20682985 20683054 - SINE/MIR_MIR3 chr1 20683230 20683370 + DNA/TcMar-Tigger_Tigger15a chr1 20683373 20683676 + SINE/Alu_AluSz chr1 20683802 20683870 + LINE/L2_L2b chr1 20683949 20684190 - SINE/MIR_MIR chr1 20684312 20684452 - SINE/MIR_MIRc chr1 20685232 20685520 + LINE/L2_L2d chr1 20685903 20685969 - SINE/MIR_MIRb chr1 20686390 20686457 - SINE/MIR_MIR3 chr1 20686473 20686518 + Simple_repeat_(AC)n chr1 20686561 20686860 - SINE/Alu_AluSx chr1 20688035 20688348 - SINE/Alu_AluY chr1 20688469 20688584 + LINE/L2_L2d chr1 20689044 20689215 + SINE/MIR_MIRb chr1 20689369 20689689 + SINE/Alu_AluSz6 chr1 20689894 20690114 + SINE/MIR_MIR chr1 20690519 20690690 - SINE/Alu_AluJr chr1 20690691 20690986 - SINE/Alu_AluY chr1 20690987 20691018 - SINE/Alu_AluJr chr1 20691019 20691321 + SINE/Alu_AluSz6 chr1 20691322 20691446 - SINE/Alu_AluJr chr1 20691456 20691768 - SINE/Alu_AluSq2 chr1 20692215 20692341 + LINE/L2_L2a chr1 20692342 20692599 - SINE/Alu_AluSg chr1 20692600 20692691 + LINE/L2_L2a chr1 20692771 20693071 - SINE/Alu_AluSg chr1 20693245 20693565 - SINE/Alu_AluJr chr1 20694104 20694314 - SINE/Alu_AluSx3 chr1 20694816 20694968 + LINE/L2_L2d2 chr1 20695177 20695488 - SINE/Alu_AluSz chr1 20695719 20695877 + LINE/L2_L2d2 chr1 20695995 20696183 - SINE/MIR_MIRc chr1 20696273 20696308 + LINE/L2_L2a chr1 20697326 20697629 + SINE/Alu_AluJr chr1 20697996 20698081 + LINE/L2_L2d2 chr1 20698163 20698258 + SINE/MIR_MIRb chr1 20698639 20698780 - LINE/L2_L2a chr1 20698840 20698945 - LINE/L2_L2a chr1 20698946 20699248 - SINE/Alu_AluJr chr1 20699249 20699274 - LINE/L2_L2a chr1 20699275 20699582 + SINE/Alu_AluSx1 chr1 20699583 20700061 - LINE/L2_L2a chr1 20700062 20700370 - SINE/Alu_AluSz chr1 20700371 20700589 - LINE/L2_L2a chr1 20700627 20701103 + LTR/ERVL_MER74B chr1 20701152 20701453 + SINE/Alu_AluSc8 chr1 20701462 20701959 - LINE/L2_L2a chr1 20702074 20702222 - SINE/MIR_MIRb chr1 20702562 20702996 + LINE/L2_L2c chr1 20703026 20703742 + Simple_repeat_(ATGG)n chr1 20703812 20703943 + SINE/Alu_AluYc chr1 20704141 20704176 + Low_complexity_G-rich chr1 20704938 20705162 - SINE/MIR_MIR chr1 20705306 20705457 + SINE/MIR_MIRb chr1 20705458 20705892 + LTR/ERV1_LTR64 chr1 20705894 20705920 + Simple_repeat_(T)n chr1 20705924 20706203 - SINE/Alu_AluJr chr1 20706298 20706308 + Simple_repeat_(AACA)n chr1 20706309 20706607 + SINE/Alu_AluSx chr1 20706610 20706918 + SINE/Alu_AluJb chr1 20706919 20706947 + Simple_repeat_(AAC)n chr1 20706948 20707103 + SINE/Alu_FRAM chr1 20707115 20707302 + LINE/L1_L1ME4c chr1 20707493 20707781 + SINE/Alu_AluJr chr1 20707782 20707836 + Simple_repeat_(TG)n chr1 20707845 20707932 + Simple_repeat_(TA)n chr1 20708344 20708466 - SINE/Alu_AluJb chr1 20708597 20708829 + SINE/MIR_MIRb chr1 20708955 20709108 + SINE/MIR_MIRb chr1 20709109 20709402 + SINE/Alu_AluJr chr1 20709403 20709429 + SINE/MIR_MIRb chr1 20710009 20710189 + SINE/MIR_MIRb chr1 20711051 20711218 + SINE/MIR_MIRb chr1 20711782 20711954 + SINE/MIR_MIR chr1 20712414 20712526 + SINE/Alu_FLAM_A chr1 20712527 20713009 + Simple_repeat_(AT)n chr1 20713010 20713292 - SINE/Alu_AluSz chr1 20713854 20713906 + Simple_repeat_(AAAAC)n chr1 20713907 20714045 + SINE/Alu_AluSx1 chr1 20714046 20714340 + SINE/Alu_AluY chr1 20714341 20714519 + SINE/Alu_AluSx1 chr1 20714521 20714815 + SINE/Alu_AluSx1 chr1 20715070 20715216 + SINE/MIR_MIRb chr1 20715308 20715383 + DNA/hAT-Charlie_Charlie22a chr1 20715768 20715820 - LINE/L2_L2d2 chr1 20715965 20716261 + SINE/Alu_AluSx1 chr1 20716583 20716655 - LINE/L2_L2d2 chr1 20716777 20716859 - LINE/L2_L2b chr1 20717058 20717264 + SINE/Alu_AluSx1 chr1 20717769 20717850 + Low_complexity_G-rich chr1 20718509 20718788 + SINE/Alu_AluSq2 chr1 20718903 20719066 - LINE/L2_L2c chr1 20719067 20719309 - SINE/Alu_AluJr chr1 20719310 20719430 - LINE/L2_L2c chr1 20722286 20722359 + SINE/MIR_MIRc chr1 20723035 20723203 + SINE/Alu_FRAM chr1 20723338 20723508 - SINE/MIR_MIR chr1 20724722 20724793 + LINE/L2_L2b chr1 20724827 20724902 - LINE/L2_L2b chr1 20724976 20725152 + SINE/MIR_MIRb chr1 20725257 20725316 + LINE/L2_L2b chr1 20725492 20725677 - LINE/L2_L2c chr1 20726263 20726478 + SINE/MIR_MIRb chr1 20726598 20726654 + Simple_repeat_(CAG)n chr1 20727710 20727814 + SINE/MIR_MIRb chr1 20728153 20728442 - LINE/L2_L2a chr1 20729348 20729453 + SINE/MIR_MIRc chr1 20730977 20731107 - SINE/MIR_MIR3 chr1 20731330 20731457 + SINE/MIR_MIRc chr1 20731920 20732119 + Simple_repeat_(GCGCCCC)n chr1 20733143 20733345 + SINE/MIR_MIRb chr1 20733404 20733451 - SINE/Alu_FRAM chr1 20733462 20733504 - SINE/Alu_FRAM chr1 20733515 20733558 - SINE/Alu_FRAM chr1 20733569 20733775 - SINE/Alu_AluJb chr1 20734270 20734326 - LINE/L2_L2a chr1 20734350 20734648 + SINE/Alu_AluSq chr1 20734819 20735093 + SINE/Alu_AluY chr1 20735094 20735360 - SINE/Alu_AluSz chr1 20736235 20736538 + SINE/Alu_AluSp chr1 20736546 20736828 + SINE/Alu_AluJr chr1 20736850 20737234 + LTR/ERVL-MaLR_MLT1B chr1 20737256 20737375 + SINE/Alu_FLAM_A chr1 20737481 20737582 - SINE/MIR_MIR3 chr1 20737668 20737882 - DNA/hAT-Charlie_Charlie16a chr1 20738565 20738717 + SINE/MIR_MIRb chr1 20738906 20739010 - SINE/MIR_MIRc chr1 20739663 20739691 + Simple_repeat_(ACAA)n chr1 20739842 20740138 + SINE/Alu_AluSx chr1 20740539 20740838 + SINE/Alu_AluJr chr1 20742048 20742145 - LINE/CR1_L3 chr1 20743101 20743133 + Simple_repeat_(T)n chr1 20743382 20743690 + SINE/Alu_AluSc8 chr1 20743786 20744099 + SINE/Alu_AluSx chr1 20745561 20745614 + Simple_repeat_(TCTTCA)n chr1 20745904 20745984 - LINE/L2_L2a chr1 20746187 20746227 + Simple_repeat_(TG)n chr1 20746236 20746357 - SINE/Alu_FLAM_A chr1 20746554 20746630 + LINE/L2_L2a chr1 20746749 20747031 + SINE/Alu_AluJr chr1 20747211 20747440 + DNA/TcMar-Tigger_MER8 chr1 20747849 20747873 + Simple_repeat_(TACA)n chr1 20747874 20747879 + Simple_repeat_(TATA)n chr1 20747899 20748015 - SINE/Alu_AluJo chr1 20748020 20748119 - SINE/MIR_MIR3 chr1 20748105 20748168 - SINE/MIR_MIRc chr1 20748465 20748775 + SINE/Alu_AluY chr1 20749342 20749658 - SINE/Alu_AluSx3 chr1 20750214 20750322 + SINE/Alu_AluSx1 chr1 20750323 20750379 + Simple_repeat_(AC)n chr1 20750380 20750563 + SINE/Alu_AluSx1 chr1 20750616 20750807 + DNA/TcMar-Tigger_Tigger2a chr1 20750808 20751116 - SINE/Alu_AluSx1 chr1 20751117 20751361 + DNA/TcMar-Tigger_Tigger2a chr1 20751447 20751754 + SINE/Alu_AluJr chr1 20751877 20751978 + DNA/hAT-Charlie_Charlie1a chr1 20751979 20752271 + SINE/Alu_AluSx chr1 20752272 20752283 + DNA/hAT-Charlie_Charlie1a chr1 20752284 20752589 - SINE/Alu_AluSx chr1 20752590 20752947 + DNA/hAT-Charlie_Charlie1a chr1 20752948 20753241 - SINE/Alu_AluSc8 chr1 20753242 20753722 + DNA/hAT-Charlie_Charlie1a chr1 20753723 20755285 + LINE/L1_L1MB7 chr1 20755286 20755588 + SINE/Alu_AluJb chr1 20755589 20756170 + LINE/L1_L1MB7 chr1 20756171 20756407 + DNA/hAT-Charlie_Charlie1a chr1 20756436 20756577 + SINE/Alu_AluJb chr1 20756601 20756708 + DNA/hAT-Charlie_Charlie1a chr1 20756709 20757007 - SINE/Alu_AluSx chr1 20757008 20757136 + DNA/hAT-Charlie_Charlie1a chr1 20757261 20757325 + Low_complexity_A-rich chr1 20757361 20757669 - SINE/Alu_AluSp chr1 20757759 20757863 - DNA/TcMar-Tigger_MamRep137 chr1 20757864 20758175 + SINE/Alu_AluSx3 chr1 20758176 20758249 - DNA/TcMar-Tigger_MamRep137 chr1 20758251 20758350 - SINE/MIR_MIR chr1 20758353 20758645 - SINE/Alu_AluSc8 chr1 20758646 20758951 - SINE/Alu_AluJb chr1 20758977 20759112 + LINE/L1_L1MC5a chr1 20759119 20759419 + SINE/Alu_AluSp chr1 20759508 20759653 + LTR/ERVL-MaLR_MLT1A0 chr1 20759668 20759880 - LINE/L2_L2a chr1 20759881 20760186 - SINE/Alu_AluSg chr1 20760187 20760258 - LINE/L2_L2a chr1 20760259 20760554 + SINE/Alu_AluJr chr1 20760561 20760854 + SINE/Alu_AluSz chr1 20760855 20761040 - LINE/L2_L2a chr1 20761041 20761340 - SINE/Alu_AluSx chr1 20761341 20762062 - LINE/L2_L2a chr1 20762075 20762159 - DNA/TcMar-Tigger_MamRep137 chr1 20762465 20762909 + LTR/ERV1_MER4A1 chr1 20762925 20763248 + LTR/ERVL-MaLR_MSTB chr1 20763859 20763914 + Simple_repeat_(AT)n chr1 20763916 20763978 - SINE/Alu_AluY chr1 20763954 20763991 - SINE/Alu_Alu chr1 20764351 20764516 - SINE/Alu_AluSq2 chr1 20764692 20764708 + Simple_repeat_(A)n chr1 20764715 20764886 + SINE/Alu_AluSq2 chr1 20764887 20765197 + SINE/Alu_AluSq2 chr1 20765690 20765988 + SINE/Alu_AluSz6 chr1 20766011 20766295 + SINE/Alu_AluSp chr1 20766656 20766969 + SINE/Alu_AluSx chr1 20767038 20767326 + SINE/Alu_AluSx1 chr1 20767369 20767468 + SINE/MIR_MIR chr1 20767482 20767524 + LINE/L2_L2d2 chr1 20767786 20767931 + SINE/MIR_MIR1_Amn chr1 20768165 20768459 + SINE/Alu_AluY chr1 20768558 20768836 + SINE/Alu_AluSx1 chr1 20769093 20769275 - SINE/Alu_AluJr chr1 20769276 20769584 + SINE/Alu_AluJb chr1 20769585 20769701 - SINE/Alu_AluJr chr1 20769953 20770094 + SINE/MIR_MIR3 chr1 20770318 20770614 - SINE/Alu_AluJo chr1 20772075 20772114 + Low_complexity_A-rich chr1 20773921 20774151 + SINE/MIR_MIRc chr1 20774222 20774339 + SINE/Alu_AluJr chr1 20774344 20774383 + Simple_repeat_(AC)n chr1 20774495 20774702 + DNA/TcMar-Tigger_Tigger3b chr1 20774703 20775005 + SINE/Alu_AluSc chr1 20775006 20775936 + DNA/TcMar-Tigger_Tigger3b chr1 20776010 20776042 + Simple_repeat_(A)n chr1 20776253 20776402 + DNA/TcMar-Tigger_Tigger19b chr1 20777277 20777322 + Low_complexity_A-rich chr1 20777449 20777756 - SINE/Alu_AluSc8 chr1 20778683 20778752 + DNA/hAT-Charlie_MER117 chr1 20778786 20779089 - SINE/Alu_AluSx chr1 20779183 20779226 + Low_complexity_A-rich chr1 20781627 20781925 - SINE/Alu_AluSx chr1 20782161 20782288 + SINE/Alu_AluJb chr1 20782302 20782612 + SINE/Alu_AluJb chr1 20782621 20782927 + SINE/Alu_AluSx4 chr1 20782948 20783113 + SINE/Alu_AluJb chr1 20783114 20783134 + Simple_repeat_(TAA)n chr1 20783239 20783550 + SINE/Alu_AluSx chr1 20783648 20783794 + SINE/Alu_AluSx3 chr1 20783980 20784269 - SINE/Alu_AluJb chr1 20784345 20784447 + LINE/L2_L2a chr1 20784470 20784775 - SINE/Alu_AluY chr1 20784943 20785210 - SINE/Alu_AluJb chr1 20787240 20787362 + Simple_repeat_(CGCCGC)n chr1 20788537 20788817 + SINE/Alu_AluJr chr1 20788829 20788969 + SINE/Alu_AluJb chr1 20788970 20789263 + SINE/Alu_AluSc8 chr1 20789264 20789407 + SINE/Alu_AluJb chr1 20789415 20789704 + SINE/Alu_AluSg chr1 20789709 20790019 + SINE/Alu_AluSx1 chr1 20790382 20790677 - SINE/Alu_AluSx chr1 20790823 20790908 + DNA/hAT-Tip100_MamTip2 chr1 20790942 20791065 - SINE/Alu_FLAM_A chr1 20791486 20791775 - SINE/Alu_AluSx1 chr1 20792009 20792276 - SINE/Alu_AluJo chr1 20792791 20792842 + DNA/TcMar-Tigger_MER46C chr1 20792847 20793087 - DNA/TcMar-Tigger_MER44A chr1 20793088 20793215 + SINE/Alu_AluJo chr1 20793217 20793275 - DNA/TcMar-Tigger_MER44A chr1 20794021 20794078 - DNA/hAT-Blackjack_MER81 chr1 20794079 20794381 - SINE/Alu_AluSg chr1 20794382 20794435 - DNA/hAT-Blackjack_MER81 chr1 20794523 20794657 + SINE/Alu_AluSx chr1 20794658 20794906 + SINE/Alu_AluSx chr1 20794907 20795073 + SINE/Alu_AluSx chr1 20795305 20795596 - SINE/Alu_AluSc chr1 20795871 20795896 + Simple_repeat_(ATTT)n chr1 20795897 20796177 - SINE/Alu_AluSc8 chr1 20796528 20796831 + SINE/Alu_AluY chr1 20796904 20797217 + SINE/Alu_AluSx chr1 20797354 20797454 - SINE/MIR_MIRb chr1 20797455 20797720 + SINE/Alu_AluJb chr1 20798189 20798359 - DNA/hAT-Charlie_MER5A chr1 20798563 20798872 + SINE/Alu_AluSx chr1 20799366 20799383 + Simple_repeat_(GA)n chr1 20799384 20799502 - DNA/hAT-Charlie_MER5B chr1 20800103 20800199 - SINE/Alu_AluJr4 chr1 20800412 20800703 - SINE/Alu_AluSz6 chr1 20800781 20801083 - SINE/Alu_AluSx1 chr1 20801093 20801112 + Simple_repeat_(AC)n chr1 20801123 20801418 - SINE/Alu_AluSc5 chr1 20801519 20802194 + LINE/L1_L1MC3 chr1 20802195 20802470 - SINE/Alu_AluJr chr1 20802471 20802884 + LINE/L1_L1MC3 chr1 20803073 20803229 + SINE/Alu_AluSx chr1 20803632 20803743 + LINE/L2_L2a chr1 20803932 20804236 + SINE/Alu_AluSx chr1 20805076 20805228 + SINE/Alu_FRAM chr1 20805240 20805438 + DNA/hAT-Charlie_MER1B chr1 20806347 20806377 + Simple_repeat_(CT)n chr1 20807746 20807914 - SINE/Alu_AluSx1 chr1 20807916 20807967 - SINE/Alu_AluSx chr1 20807968 20808183 - SINE/Alu_AluSx1 chr1 20808400 20808689 + SINE/Alu_AluY chr1 20808826 20808882 + Simple_repeat_(ATAGTT)n chr1 20808886 20808979 - DNA/TcMar-Tigger_Tigger19a chr1 20808980 20809229 - SINE/Alu_AluSq chr1 20809230 20809286 - DNA/TcMar-Tigger_Tigger19a chr1 20809705 20809715 + DNA/hAT-Charlie_MER3 chr1 20809716 20809894 - DNA/hAT-Charlie_MER3 chr1 20809977 20810143 - SINE/Alu_AluSz chr1 20810144 20810453 - SINE/Alu_AluSc8 chr1 20810454 20810587 - SINE/Alu_AluSz chr1 20810945 20811253 - SINE/Alu_AluSx1 chr1 20811255 20811276 + Simple_repeat_(TTC)n chr1 20811273 20811294 + Simple_repeat_(TTCAATT)n chr1 20811295 20811412 - SINE/Alu_AluJo chr1 20811413 20811427 + Simple_repeat_(TTCAATT)n chr1 20811668 20811814 + LINE/L1_L1MA7 chr1 20812138 20812272 + SINE/Alu_FLAM_C chr1 20813283 20813582 + SINE/Alu_AluJr chr1 20813585 20813872 + SINE/Alu_AluSq2 chr1 20813948 20814263 - SINE/Alu_AluSx3 chr1 20814749 20814798 + Simple_repeat_(CTCCCC)n chr1 20814804 20815773 + Retroposon/SVA_SVA_D chr1 20815742 20817259 + Retroposon/SVA_SVA_E chr1 20817262 20817298 + Low_complexity_A-rich chr1 20817682 20817973 - SINE/Alu_AluSz chr1 20818357 20818656 + SINE/Alu_AluJb chr1 20818941 20819065 - SINE/Alu_FLAM_A chr1 20819259 20819292 + Simple_repeat_(TTTA)n chr1 20819326 20819409 - SINE/Alu_AluJb chr1 20819416 20819443 - SINE/Alu_AluJb chr1 20819444 20819477 + Simple_repeat_(TATT)n chr1 20819478 20819550 - SINE/Alu_AluJb chr1 20819551 20819857 + LINE/L1_L1PA13 chr1 20819858 20819868 - SINE/Alu_AluJb chr1 20819869 20819965 + Low_complexity_GA-rich chr1 20819972 20821128 + LTR/ERV1_MER52C chr1 20821235 20821553 - LINE/L1_L1M4c chr1 20821872 20821889 + Simple_repeat_(ATT)n chr1 20821898 20822174 - SINE/Alu_AluSg chr1 20822181 20822355 - LINE/L1_L1M5 chr1 20822356 20822459 - SINE/Alu_AluYk2 chr1 20822460 20822588 - LINE/L1_L1MEd chr1 20822589 20822723 - SINE/Alu_FLAM_C chr1 20822724 20822899 - LINE/L1_L1MEd chr1 20822903 20823317 - LINE/L1_L1MEd chr1 20823372 20823674 - SINE/Alu_AluJr chr1 20823681 20824014 - LINE/L1_L1MEd chr1 20824323 20824502 - LINE/L1_L1MEi chr1 20824517 20824984 - LINE/L1_L1MEi chr1 20825240 20825270 + Simple_repeat_(A)n chr1 20825311 20825459 + SINE/Alu_FRAM chr1 20826009 20826087 - SINE/MIR_MIR chr1 20826180 20826461 - SINE/Alu_AluSx1 chr1 20826482 20826787 - SINE/Alu_AluSc chr1 20827303 20827508 - RC?/Helitron?_Helitron2Na_Mam chr1 20828030 20828329 - SINE/Alu_AluSc5 chr1 20828469 20828661 + DNA/hAT-Charlie_MER103C chr1 20829347 20829626 - LINE/L2_L2c chr1 20830255 20830907 - LINE/RTE-BovB_MamRTE1 chr1 20831171 20831273 + DNA/hAT-Charlie_Charlie15a chr1 20831279 20831297 + Simple_repeat_(T)n chr1 20831520 20831553 + Simple_repeat_(T)n chr1 20831608 20833653 - LINE/L1_L1PA2 chr1 20833653 20833941 + LINE/L1_L1PA2 chr1 20834036 20834166 + LINE/L1_L1M1 chr1 20834167 20834463 + SINE/Alu_AluJb chr1 20834464 20835657 + LINE/L1_L1M1 chr1 20835658 20835954 + SINE/Alu_AluJr chr1 20835955 20836081 + LINE/L1_L1M1 chr1 20836083 20836248 - SINE/Alu_AluJo chr1 20836249 20836546 - SINE/Alu_AluJr chr1 20836903 20836929 + Simple_repeat_(CTAC)n chr1 20837213 20837502 - SINE/Alu_AluSc5 chr1 20838686 20838976 - SINE/Alu_AluJb chr1 20838988 20839276 - SINE/Alu_AluSc8 chr1 20839277 20839409 - SINE/Alu_AluSg chr1 20839514 20839788 - SINE/Alu_AluSz6 chr1 20839859 20840122 + DNA/TcMar-Tigger_MamRep137 chr1 20841242 20841298 + snRNA_U7 chr1 20841862 20841924 + Simple_repeat_(GGTA)n chr1 20842181 20842368 - LINE/L1_L1MC4 chr1 20842370 20842664 - SINE/Alu_AluSp chr1 20842747 20842837 - LINE/L1_HAL1 chr1 20842845 20843138 - SINE/Alu_AluJr chr1 20843234 20843545 + SINE/Alu_AluSz chr1 20843646 20844559 - LINE/L1_L1PA12 chr1 20844560 20844868 + SINE/Alu_AluSq2 chr1 20844869 20845380 - LINE/L1_L1PA12 chr1 20845381 20845691 + SINE/Alu_AluY chr1 20845692 20846382 - LINE/L1_L1PA12 chr1 20846423 20846726 + LINE/L2_L2a chr1 20846729 20846936 + SINE/MIR_MIRb chr1 20847043 20847284 + SINE/MIR_MIR chr1 20847965 20848265 - SINE/Alu_AluJb chr1 20848375 20848566 + LINE/L2_L2c chr1 20848628 20848756 + SINE/MIR_MIR chr1 20848766 20849075 + SINE/Alu_AluSx1 chr1 20849158 20849212 - SINE/tRNA-RTE_MamSINE1 chr1 20849857 20850228 + LINE/L2_L2c chr1 20850598 20850790 - SINE/MIR_MIR chr1 20851736 20852022 + SINE/Alu_AluSx1 chr1 20852043 20852301 - SINE/Alu_AluSp chr1 20852569 20852647 + DNA/TcMar-Tigger_Tigger15a chr1 20852745 20852859 + DNA/TcMar-Tigger_Tigger10 chr1 20852903 20852993 + SINE/Alu_FLAM_A chr1 20853354 20853378 + Simple_repeat_(ATCCC)n chr1 20853705 20853836 - DNA/hAT-Charlie_MER5A1 chr1 20854424 20854706 + SINE/Alu_AluJb chr1 20854707 20854745 + Simple_repeat_(AAAT)n chr1 20856881 20857184 + SINE/Alu_AluY chr1 20857327 20857345 + Simple_repeat_(TG)n chr1 20857920 20858414 + LINE/L2_L2d chr1 20858252 20858687 + LINE/L2_L2a chr1 20858715 20859003 + SINE/Alu_AluSg7 chr1 20859598 20859892 - SINE/Alu_AluSz chr1 20859900 20860166 + LINE/L2_L2d chr1 20861383 20861412 + Simple_repeat_(A)n chr1 20861470 20861585 - SINE/MIR_MIRb chr1 20861628 20861664 + Simple_repeat_(AATTT)n chr1 20861692 20861988 + SINE/Alu_AluSz chr1 20862422 20862712 - SINE/Alu_AluJb chr1 20862774 20863088 - SINE/Alu_AluJo chr1 20863099 20863243 + LTR/ERVL-MaLR_MLT1J chr1 20863251 20863385 + SINE/Alu_AluJr chr1 20863432 20863742 - SINE/Alu_AluSp chr1 20863881 20863976 + LTR/ERVL-MaLR_MLT1J chr1 20866061 20867027 + LINE/RTE-BovB_MamRTE1 chr1 20867028 20867209 + SINE/MIR_MIRc chr1 20867256 20867380 + SINE/MIR_MIRc chr1 20867409 20867645 + SINE/MIR_MIRb chr1 20868086 20868106 + Simple_repeat_(T)n chr1 20868228 20868576 - LTR/ERVL-MaLR_MLT1A chr1 20869315 20869459 - SINE/Alu_FLAM_A chr1 20869494 20869797 + SINE/Alu_AluYk2 chr1 20870459 20870588 - LINE/L2_L2a chr1 20870948 20870991 + Simple_repeat_(TATGTG)n chr1 20871282 20871478 - DNA/hAT-Charlie_MER20 chr1 20871812 20871992 - SINE/Alu_AluSz6 chr1 20872005 20872107 - LINE/L1_L1ME3A chr1 20872141 20872422 - SINE/Alu_AluJo chr1 20872586 20872713 - LINE/L1_L1ME3A chr1 20872714 20873036 - SINE/Alu_AluSx chr1 20873037 20873506 - LINE/L1_L1ME3A chr1 20873741 20874487 - LINE/L1_L1PA8A chr1 20874542 20874855 - LINE/L1_L1ME3A chr1 20874979 20875283 - SINE/Alu_AluJr chr1 20875329 20875398 + LINE/L1_L1ME3A chr1 20875400 20875553 + DNA/hAT-Blackjack_MER63C chr1 20875633 20875903 + SINE/Alu_AluJr chr1 20875904 20875939 + Low_complexity_A-rich chr1 20875940 20876249 + SINE/Alu_AluJb chr1 20876301 20876322 + Simple_repeat_(GA)n chr1 20876323 20876450 + LINE/L1_L1ME3G chr1 20876716 20876989 + SINE/Alu_AluJo chr1 20877182 20877232 + SINE/MIR_MIR3 chr1 20877344 20877563 + SINE/MIR_MIRb chr1 20877625 20877821 - SINE/MIR_MIRb chr1 20877847 20878166 - SINE/Alu_AluSx chr1 20878244 20878404 + LINE/L2_L2d2 chr1 20879707 20879891 + LINE/L1_L1ME4c chr1 20880013 20880470 + LINE/L1_L1MD chr1 20880471 20880773 + SINE/Alu_AluSz6 chr1 20880774 20881504 + LINE/L1_L1MD chr1 20881505 20881793 + SINE/Alu_AluJb chr1 20881795 20881839 + Simple_repeat_(AATAA)n chr1 20881840 20881880 + LINE/L1_L1MD chr1 20881881 20882174 + SINE/Alu_AluJb chr1 20882177 20882215 + Simple_repeat_(AC)n chr1 20882243 20882339 + SINE/Alu_AluJr chr1 20882340 20882649 + SINE/Alu_AluSz chr1 20882650 20882807 + SINE/Alu_AluJr chr1 20882810 20883095 + SINE/Alu_AluJo chr1 20883138 20883225 - LINE/L2_L2d2 chr1 20883337 20883646 + SINE/Alu_AluSc8 chr1 20884347 20884460 + SINE/MIR_MIRb chr1 20884849 20885200 - DNA/hAT-Charlie_MER1B chr1 20885262 20885391 - SINE/MIR_MIRb chr1 20888555 20888736 + SINE/MIR_MIRc chr1 20889491 20889789 - SINE/Alu_AluYk2 chr1 20889967 20890258 - SINE/Alu_AluSz6 chr1 20890454 20890755 - SINE/Alu_AluJb chr1 20891491 20891803 + SINE/Alu_AluY chr1 20892850 20892933 - SINE/Alu_AluJb chr1 20892935 20893231 - SINE/Alu_AluJr chr1 20893390 20893413 + Simple_repeat_(TCT)n chr1 20894521 20894647 - DNA/PiggyBac_MER85 chr1 20895548 20896174 + DNA/TcMar-Tigger_Tigger3b chr1 20896175 20896464 + SINE/Alu_AluJr chr1 20896465 20897047 + DNA/TcMar-Tigger_Tigger3b chr1 20897280 20897340 + Low_complexity_A-rich chr1 20897870 20897958 + Simple_repeat_(AATATAA)n chr1 20898240 20898334 + SINE/Alu_AluJo chr1 20898389 20898623 - DNA/hAT-Charlie_MER112 chr1 20898729 20899029 - SINE/Alu_AluSp chr1 20900513 20900552 + Low_complexity_A-rich chr1 20901403 20901497 + DNA/TcMar-Tc1_X32_DNA chr1 20902067 20902361 - SINE/Alu_AluSg chr1 20902647 20902940 - SINE/Alu_AluSz chr1 20903037 20903325 + DNA/hAT-Charlie_MER58B chr1 20904348 20904647 - SINE/Alu_AluJr chr1 20905257 20905420 + SINE/MIR_MIR3 chr1 20906812 20906845 + Simple_repeat_(AC)n chr1 20907320 20907350 + Low_complexity_A-rich chr1 20907394 20908337 - LINE/L1_L1PA4 chr1 20908868 20909167 + SINE/Alu_AluSz chr1 20909642 20909671 + Simple_repeat_(T)n chr1 20909779 20910074 - SINE/Alu_AluJr chr1 20910304 20910611 + SINE/Alu_AluSg chr1 20911286 20911588 + SINE/Alu_AluJr chr1 20911893 20911956 + Simple_repeat_(ATATAA)n chr1 20911957 20912004 + SINE/Alu_Alu chr1 20912008 20912285 + SINE/Alu_AluJb chr1 20912298 20912348 + LTR/ERV1_MER67C chr1 20913286 20913535 + SINE/Alu_AluJr4 chr1 20913619 20913800 - LINE/L1_L1M5 chr1 20913801 20914107 - SINE/Alu_AluY chr1 20914108 20914173 - LINE/L1_L1M5 chr1 20914176 20914651 + LTR/ERVL-MaLR_MLT1D chr1 20914663 20914995 - LINE/L1_L1MEc chr1 20915006 20915163 - LINE/L1_L1MEc chr1 20915166 20915283 - LTR/ERVL-MaLR_MLT1D chr1 20915284 20916200 + LINE/L1_L1ME1 chr1 20916201 20916476 + SINE/Alu_AluY chr1 20916477 20916792 + LINE/L1_L1ME1 chr1 20916870 20917214 + LINE/L1_L1ME1 chr1 20917215 20917514 + SINE/Alu_AluJb chr1 20917515 20917675 + LINE/L1_L1ME1 chr1 20918003 20918053 + Simple_repeat_(TTTTTC)n chr1 20918219 20918507 - SINE/Alu_AluJr chr1 20918702 20918843 - SINE/Alu_FLAM_A chr1 20918994 20919134 - LINE/L1_L1M chr1 20919208 20919504 - SINE/Alu_AluJb chr1 20919911 20920205 - SINE/Alu_AluSp chr1 20921215 20921361 + LINE/L1_L1M5 chr1 20922312 20922595 - SINE/Alu_AluSp chr1 20922793 20922896 + LINE/L2_L2c chr1 20922959 20923117 - SINE/MIR_MIRb chr1 20923810 20923834 + Simple_repeat_(TA)n chr1 20923835 20923967 + LINE/L2_L2b chr1 20924559 20924861 - SINE/Alu_AluSx chr1 20925396 20925433 + LINE/L2_L2a chr1 20925555 20925831 - SINE/Alu_AluJr chr1 20926162 20926387 + SINE/MIR_MIRb chr1 20926398 20926695 + SINE/Alu_AluJr chr1 20926762 20926809 + Low_complexity_A-rich chr1 20927047 20927210 - SINE/Alu_AluSg4 chr1 20927859 20928085 - SINE/Alu_AluJr chr1 20928409 20928698 - SINE/Alu_AluSx3 chr1 20928725 20929161 - LINE/L1_L1MB7 chr1 20929610 20929856 - SINE/MIR_MIR chr1 20929912 20930872 - DNA/TcMar-Tigger_Tigger1 chr1 20930926 20931001 - SINE/Alu_FLAM_A chr1 20931002 20931034 - DNA/TcMar-Tigger_Tigger1 chr1 20931035 20931096 - LINE/L2_L2 chr1 20931184 20931644 - DNA/TcMar-Tigger_Tigger1 chr1 20931645 20931940 + SINE/Alu_AluSc chr1 20931941 20932965 - DNA/TcMar-Tigger_Tigger1 chr1 20933177 20933374 - LINE/L2_L2 chr1 20933375 20933654 + SINE/Alu_AluSg chr1 20933655 20933941 - LINE/L2_L2 chr1 20933943 20934020 - SINE/Alu_FLAM_C chr1 20934024 20934114 + LINE/L1_L1ME1 chr1 20934528 20934577 - LINE/L2_L2 chr1 20934824 20935055 + LINE/L2_L2a chr1 20935210 20935371 + SINE/MIR_MIRc chr1 20935559 20935718 - LINE/L1_L1MA5 chr1 20937029 20937648 + LINE/L1_L1MEd chr1 20938000 20938294 - SINE/Alu_AluSx chr1 20938827 20939135 + SINE/Alu_AluSg chr1 20939848 20940155 - SINE/Alu_AluSx chr1 20941209 20941241 + Simple_repeat_(CAA)n chr1 20943017 20943180 + SINE/Alu_FRAM chr1 20943975 20944041 + Simple_repeat_(TG)n chr1 20944233 20944537 + SINE/Alu_AluJb chr1 20944543 20944596 + Low_complexity_GA-rich chr1 20944751 20945009 + SINE/Alu_AluJb chr1 20945010 20945036 + Simple_repeat_(AAACA)n chr1 20945214 20945459 + LINE/L1_L1ME4a chr1 20945469 20945783 - SINE/Alu_AluJb chr1 20945789 20945811 + Simple_repeat_(TTTG)n chr1 20946052 20946206 + DNA/hAT-Charlie_MER20B chr1 20946572 20946805 - SINE/MIR_MIRb chr1 20947069 20947369 + SINE/Alu_AluJb chr1 20947531 20947652 + Low_complexity_GA-rich chr1 20948729 20948961 + SINE/Alu_AluSz chr1 20948971 20948997 + Simple_repeat_(A)n chr1 20949400 20949571 - SINE/MIR_MIR chr1 20950292 20950334 + Low_complexity_A-rich chr1 20951060 20951083 + Simple_repeat_(T)n chr1 20952155 20952453 - SINE/Alu_AluSc8 chr1 20952567 20952850 + SINE/Alu_AluSq chr1 20952853 20952885 + Simple_repeat_(AAAC)n chr1 20953096 20953143 + LINE/L1_L1P5 chr1 20953719 20954043 + DNA/hAT-Charlie_MER58B chr1 20954256 20954562 + SINE/Alu_AluSg4 chr1 20954722 20955123 - LINE/L2_L2c chr1 20954981 20955225 - LINE/L2_L2 chr1 20955226 20955533 - SINE/Alu_AluJr chr1 20955534 20955970 - LINE/L2_L2 chr1 20956032 20956083 + Simple_repeat_(AAT)n chr1 20956179 20956234 + LTR/ERVL-MaLR_THE1C chr1 20956302 20956593 - LINE/L2_L2c chr1 20956618 20956640 + Simple_repeat_(A)n chr1 20956670 20956903 - SINE/Alu_AluJo chr1 20957232 20959655 + LINE/L1_L1PB chr1 20959656 20959697 + Simple_repeat_(AAT)n chr1 20959698 20960146 + LINE/L1_L1PB chr1 20960187 20960476 + SINE/Alu_AluSx1 chr1 20960484 20960780 + SINE/Alu_AluJr chr1 20961103 20961388 + SINE/Alu_AluSc5 chr1 20961478 20961619 - LINE/L1_L1MA4 chr1 20962151 20962919 - DNA/TcMar-Tigger_Tigger2b_Pri chr1 20962920 20963049 - SINE/Alu_FLAM_C chr1 20963050 20963375 - DNA/TcMar-Tigger_Tigger2b_Pri chr1 20963672 20963949 + SINE/Alu_AluSz chr1 20964178 20964324 - SINE/MIR_MIR3 chr1 20964619 20965222 + LTR/ERVL_MER73 chr1 20965379 20965491 + DNA/hAT-Blackjack_MER94 chr1 20965612 20965763 + LINE/L2_L2d2 chr1 20965770 20965976 - SINE/MIR_MIRb chr1 20966476 20966769 - SINE/Alu_AluSx1 chr1 20966783 20966882 + DNA/hAT-Charlie_Charlie8 chr1 20967967 20968074 - SINE/Alu_FLAM_A chr1 20968176 20968480 - SINE/Alu_AluSx chr1 20968649 20968944 + DNA/TcMar-Tigger_MER2 chr1 20969967 20970124 - SINE/Alu_AluSz6 chr1 20970332 20970622 + SINE/Alu_AluSc8 chr1 20970629 20970681 + LINE/L1_L1MC4a chr1 20970682 20970971 + SINE/Alu_AluSq2 chr1 20970972 20971064 + LINE/L1_L1MC4a chr1 20971521 20971599 + DNA/hAT-Charlie_Chap1a_Mam chr1 20971639 20971932 - SINE/Alu_AluSz chr1 20972352 20972651 + SINE/Alu_AluSz chr1 20973888 20974898 - LINE/L2_L2 chr1 20975371 20975400 + Simple_repeat_(A)n chr1 20975578 20975631 + Simple_repeat_(CTA)n chr1 20975604 20975654 + Simple_repeat_(ATA)n chr1 20975725 20975779 + SINE/MIR_MIR3 chr1 20975795 20976023 + DNA/hAT-Charlie_MER58A chr1 20976176 20976355 - LINE/L1_L1PA4 chr1 20976756 20976891 + SINE/Alu_AluSx chr1 20977192 20977486 - SINE/Alu_AluSx1 chr1 20978448 20978679 + DNA/TcMar-Tigger_Tigger7 chr1 20978691 20978785 + DNA/TcMar-Tigger_Tigger7 chr1 20979407 20979569 - LINE/L2_L2a chr1 20979748 20980037 - SINE/Alu_AluY chr1 20980055 20980187 + SINE/Alu_FLAM_A chr1 20981463 20981840 + Simple_repeat_(TATA)n chr1 20984163 20984465 - SINE/Alu_AluSx4 chr1 20984545 20984559 + Simple_repeat_(TA)n chr1 20984560 20984584 + Simple_repeat_(AC)n chr1 20984594 20984887 - SINE/Alu_AluSc8 chr1 20984891 20985033 - SINE/MIR_MIR chr1 20985812 20986028 - LINE/CR1_L3 chr1 20986219 20986303 - LINE/CR1_CR1-L3A_Croc chr1 20986304 20986497 - LINE/CR1_L3 chr1 20986546 20986771 + SINE/Alu_AluY chr1 20986787 20986852 - LINE/CR1_L3 chr1 20987361 20988920 + DNA/TcMar-Tigger_Tigger1 chr1 20988921 20989042 + SINE/Alu_FLAM_A chr1 20989043 20989107 + Simple_repeat_(GAGAG)n chr1 20989108 20989273 + DNA/TcMar-Tigger_Tigger1 chr1 20989274 20989583 + SINE/Alu_AluSq2 chr1 20989584 20989894 + DNA/TcMar-Tigger_Tigger1 chr1 20989895 20990203 + SINE/Alu_AluSx chr1 20990204 20990393 + DNA/TcMar-Tigger_Tigger1 chr1 20990394 20990682 + SINE/Alu_AluSg chr1 20990683 20990871 + DNA/TcMar-Tigger_Tigger1 chr1 20991222 20991285 - DNA/hAT-Charlie_MER3 chr1 20991286 20991589 + SINE/Alu_AluSz6 chr1 20991590 20991720 - DNA/hAT-Charlie_MER3 chr1 20991994 20992115 - SINE/MIR_MIR3 chr1 20992534 20992556 + Simple_repeat_(CTC)n chr1 20994594 20994625 + Simple_repeat_(AATA)n chr1 20994629 20994937 - SINE/Alu_AluJb chr1 20995365 20995671 - SINE/Alu_AluSx1 chr1 20996091 20996130 + Simple_repeat_(ACTAAT)n chr1 20996139 20996340 - SINE/MIR_MIRb chr1 20996709 20996827 - SINE/MIR_MIR1_Amn chr1 20997649 20997706 + Low_complexity_A-rich chr1 20998213 20998249 + Simple_repeat_(AC)n chr1 20999829 20999858 + Simple_repeat_(T)n chr1 21000594 21000672 + Low_complexity_A-rich chr1 21001502 21001679 + SINE/tRNA_LFSINE_Vert chr1 21001734 21001766 + Low_complexity_A-rich chr1 21002982 21003271 - SINE/Alu_AluJr chr1 21003272 21003283 + Retroposon/SVA_SVA_D chr1 21003284 21003396 - SINE/Alu_FLAM_A chr1 21003397 21003408 + Retroposon/SVA_SVA_D chr1 21004412 21004574 + SINE/MIR_MIRb chr1 21004593 21004673 - SINE/MIR_MIR chr1 21004911 21005435 - DNA/hAT-Charlie_Charlie25 chr1 21005577 21006611 - DNA/hAT-Charlie_Charlie25 chr1 21006903 21006958 - SINE/MIR_MIR chr1 21007118 21007409 + SINE/Alu_AluSq2 chr1 21007516 21007557 + Low_complexity_A-rich chr1 21008007 21008156 + SINE/Alu_FRAM chr1 21008337 21008634 - SINE/Alu_AluSx chr1 21009179 21009480 - SINE/Alu_AluJr chr1 21009673 21009951 - SINE/Alu_AluSx4 chr1 21009989 21010142 + DNA/hAT-Charlie_MER3 chr1 21010165 21010449 + SINE/Alu_AluSx4 chr1 21010473 21010592 - LINE/L1_L1MB2 chr1 21010916 21011075 + DNA/hAT-Charlie_Charlie1a chr1 21011210 21011238 + Simple_repeat_(A)n chr1 21011338 21011765 - LINE/L1_L1MD2 chr1 21011787 21011982 + DNA/hAT-Charlie_Charlie1a chr1 21012031 21012073 + DNA/hAT-Charlie_Charlie1b chr1 21012697 21013221 + LINE/L1_L1PBa chr1 21013888 21014177 + SINE/Alu_AluSx1 chr1 21014187 21014294 - LTR/ERV1_LTR06 chr1 21014311 21014625 - LINE/L1_L1PA15 chr1 21014636 21014934 - SINE/Alu_AluSz chr1 21014936 21015494 + LINE/L1_L1M4 chr1 21015495 21015601 + SINE/Alu_AluJr chr1 21015606 21016379 + LINE/L1_L1M4 chr1 21016404 21016675 + SINE/Alu_AluJb chr1 21016676 21016993 + SINE/Alu_AluSx chr1 21016995 21017341 + LINE/L1_L1MB3 chr1 21017347 21017657 + SINE/Alu_AluYj4 chr1 21017671 21017868 + DNA/TcMar-Tigger_Tigger4a chr1 21017869 21017943 + LINE/L1_L1MB3 chr1 21017944 21018245 - SINE/Alu_AluJr chr1 21018246 21018299 + LINE/L1_L1MB3 chr1 21018301 21018576 + SINE/Alu_AluSg chr1 21018586 21018976 + LTR/ERVL-MaLR_MSTB1 chr1 21019030 21019185 - SINE/Alu_AluJo chr1 21019642 21019906 + SINE/Alu_AluSc chr1 21020207 21020491 + SINE/Alu_AluJr chr1 21020492 21020522 + Simple_repeat_(AAAT)n chr1 21020968 21020997 + Simple_repeat_(ATTT)n chr1 21020998 21021283 - SINE/Alu_AluSz6 chr1 21021545 21021854 - SINE/Alu_AluSx chr1 21023240 21024912 + Retroposon/SVA_SVA_F chr1 21024917 21025230 - LINE/L2_L2c chr1 21025572 21025727 + LINE/L2_L2 chr1 21025917 21026669 + LINE/L1_L1MB3 chr1 21026670 21026955 + SINE/Alu_AluSz6 chr1 21026956 21027167 + LINE/L1_L1MB3 chr1 21027194 21027353 - SINE/Alu_AluSx3 chr1 21027355 21027643 + SINE/Alu_AluSc8 chr1 21027644 21028309 + LINE/L1_L1MB3 chr1 21028335 21028374 - SINE/MIR_MIR3 chr1 21028444 21028641 + LINE/L1_L1ME3F chr1 21028896 21029046 - SINE/Alu_AluSx chr1 21029049 21029319 - SINE/Alu_AluSz6 chr1 21029488 21029610 + SINE/Alu_AluJr chr1 21029848 21030167 - SINE/Alu_AluJr chr1 21030220 21030586 + LTR/ERVL-MaLR_THE1B chr1 21030631 21030719 - DNA/hAT-Charlie_MER33 chr1 21030719 21030834 - DNA/hAT-Charlie_MER33 chr1 21030905 21031193 + SINE/Alu_AluSz chr1 21031195 21031216 + Simple_repeat_(ACA)n chr1 21031385 21031402 + Simple_repeat_(A)n chr1 21033524 21033661 - SINE/MIR_MIR chr1 21033806 21034108 + SINE/Alu_AluJr4 chr1 21034617 21034681 + LTR/ERV1_MER52D chr1 21035017 21035196 + LTR/ERV1_MER52D chr1 21035286 21036392 + LTR/ERV1_MER52D chr1 21036357 21036408 + LTR/ERV1_LTR1E chr1 21036385 21036454 + LTR/ERV1_MER52D chr1 21036598 21036900 + SINE/Alu_AluSz chr1 21037230 21037252 + Simple_repeat_(A)n chr1 21037332 21037395 - LINE/L2_L2a chr1 21037913 21038010 + DNA/hAT-Charlie_MER5B chr1 21038650 21038679 + Simple_repeat_(TC)n chr1 21038903 21039515 + LINE/L1_L1M4 chr1 21039516 21039683 + SINE/Alu_AluJr chr1 21039692 21039940 + LINE/L1_L1M4 chr1 21040003 21040577 + LTR/ERVL_MER21B chr1 21040705 21041121 + LTR/ERV1_MER57A1 chr1 21041195 21041256 + LTR/ERVL_LTR33A chr1 21041257 21041387 - SINE/Alu_AluJo chr1 21041388 21041495 + LTR/ERVL_LTR33A chr1 21041976 21042277 - SINE/Alu_AluJb chr1 21043346 21043590 + DNA/hAT-Charlie_Charlie16 chr1 21043610 21043883 + SINE/Alu_AluJb chr1 21043893 21043916 + Simple_repeat_(GA)n chr1 21044089 21044320 - DNA/hAT-Charlie_Charlie16 chr1 21044638 21044848 - SINE/Alu_AluJb chr1 21044852 21045144 + SINE/Alu_AluJr chr1 21045235 21045587 + DNA/hAT-Charlie_Charlie16 chr1 21046184 21046261 + DNA/hAT-Tip100_MamTip2b chr1 21046951 21047177 - SINE/MIR_MIRc chr1 21047229 21047774 + LTR/ERV1_LTR48 chr1 21048293 21048828 + LTR/ERVL_LTR41 chr1 21048849 21049112 + LTR/ERVL_ERVL-E-int chr1 21049169 21049363 + LTR/ERVL_LTR41 chr1 21049363 21049402 + LTR/ERVL_LTR41 chr1 21049668 21049914 + SINE/Alu_AluJr4 chr1 21050423 21050448 + Simple_repeat_(AAAC)n chr1 21051254 21051505 + LINE/L1_L1ME1 chr1 21051570 21051879 + SINE/Alu_AluJr4 chr1 21051894 21052029 + Simple_repeat_(AT)n chr1 21052406 21052531 - DNA/hAT-Charlie_Charlie4a chr1 21052544 21054486 + Retroposon/SVA_SVA_D chr1 21057017 21057654 + LINE/L1_L1ME3B chr1 21058207 21058436 - DNA/hAT-Charlie_MER102c chr1 21058525 21058719 - SINE/Alu_AluJb chr1 21058720 21058749 + Simple_repeat_(T)n chr1 21058750 21058868 - SINE/Alu_AluJb chr1 21059962 21060249 - SINE/Alu_AluJb chr1 21060252 21060364 + LTR/ERVL-MaLR_MLT1E1A chr1 21060442 21060626 - LINE/L2_L2 chr1 21060630 21060804 + SINE/Alu_FAM chr1 21060809 21060863 - LINE/L2_L2 chr1 21060916 21061098 - LINE/L2_L2a chr1 21061154 21061364 - DNA/TcMar-Tigger_Tigger4a chr1 21061425 21061532 - LINE/L2_L2 chr1 21061742 21062046 - SINE/Alu_AluSz chr1 21062058 21062359 - SINE/Alu_AluJo chr1 21062567 21062597 + Simple_repeat_(AGATAT)n chr1 21062602 21062890 - SINE/Alu_AluSx chr1 21063052 21063221 - SINE/Alu_AluJb chr1 21063728 21064027 - SINE/Alu_AluSz chr1 21065389 21065559 + SINE/Alu_AluJo chr1 21065567 21065782 + SINE/MIR_MIRb chr1 21065977 21066156 + SINE/Alu_AluJb chr1 21066295 21066339 + Low_complexity_A-rich chr1 21067120 21067431 - SINE/Alu_AluSq2 chr1 21067776 21067821 + LINE/CR1_L3b chr1 21067869 21067942 + SINE/Alu_Alu chr1 21067967 21068044 + DNA/hAT-Charlie_Charlie4z chr1 21068093 21068400 + SINE/Alu_AluSx1 chr1 21068489 21068574 + SINE/MIR_MIRb chr1 21068581 21068768 + SINE/MIR_MIR chr1 21068796 21069019 - DNA/TcMar-Tigger_Tigger4a chr1 21069440 21069541 + LINE/L2_L2d2 chr1 21070485 21070587 - LINE/L1_L1ME3G chr1 21070914 21070941 + Simple_repeat_(TTCAT)n chr1 21071124 21071433 + SINE/Alu_AluJr4 chr1 21071552 21071835 + SINE/Alu_AluSg chr1 21072252 21072379 + SINE/Alu_FLAM_C chr1 21072392 21072701 - SINE/Alu_AluYk3 chr1 21072882 21073113 - SINE/MIR_MIR chr1 21073178 21073277 + LINE/L2_L2b chr1 21073316 21073406 - DNA/hAT-Charlie_MER3 chr1 21073455 21073635 + LINE/L2_L2b chr1 21073809 21073922 - SINE/Alu_AluJb chr1 21074140 21074364 - DNA/hAT-Charlie_MER20 chr1 21074791 21074938 + SINE/Alu_AluJo chr1 21074945 21075286 + LINE/L1_L1MA4A chr1 21075287 21075598 + SINE/Alu_AluSx1 chr1 21075599 21076402 + LINE/L1_L1MA4A chr1 21076403 21076433 + Simple_repeat_(AAAGCA)n chr1 21076435 21076516 + SINE/Alu_AluYk2 chr1 21076517 21077139 + LINE/L1_L1MA4A chr1 21077140 21077424 + SINE/Alu_AluJo chr1 21077425 21077598 + LINE/L1_L1MA4A chr1 21077599 21077906 + SINE/Alu_AluSp chr1 21077907 21078702 + LINE/L1_L1MA4A chr1 21078703 21079000 + SINE/Alu_AluSz chr1 21079001 21079038 + LINE/L1_L1MA4A chr1 21079120 21079330 + LINE/CR1_L3 chr1 21079386 21079703 + SINE/Alu_AluJo chr1 21079725 21079871 + SINE/Alu_AluJo chr1 21079872 21080165 + SINE/Alu_AluSx1 chr1 21080166 21080343 + SINE/Alu_AluJo chr1 21080379 21080459 + SINE/Alu_Alu chr1 21080500 21080806 - SINE/Alu_AluSg chr1 21081151 21081436 + SINE/Alu_AluSx3 chr1 21081637 21081943 - SINE/Alu_AluSz6 chr1 21081950 21082095 - SINE/Alu_FLAM_C chr1 21082328 21082611 + SINE/Alu_AluSz chr1 21082621 21082756 + SINE/Alu_AluJr chr1 21082757 21083067 + SINE/Alu_AluY chr1 21083068 21083259 + SINE/Alu_AluJr chr1 21083350 21083623 - SINE/Alu_AluJb chr1 21083937 21084179 + LINE/L2_L2c chr1 21084180 21084566 - LTR/ERVL-MaLR_THE1D chr1 21084567 21084704 + LINE/L2_L2c chr1 21084597 21085017 + LINE/L2_L2a chr1 21085401 21085696 - SINE/Alu_AluJo chr1 21085705 21085991 - SINE/Alu_AluSc chr1 21086058 21086129 - DNA/hAT-Blackjack_BLACKJACK chr1 21086200 21086369 - SINE/Alu_AluJr chr1 21086662 21086965 + SINE/Alu_AluSz chr1 21087103 21087174 + LINE/L2_L2c chr1 21087341 21087438 + SINE/Alu_AluSz6 chr1 21087513 21087638 + SINE/Alu_AluJo chr1 21087639 21087932 - SINE/Alu_AluY chr1 21087933 21088112 + SINE/Alu_AluJo chr1 21088145 21088435 + SINE/Alu_AluSp chr1 21088488 21088531 + Low_complexity_A-rich chr1 21088603 21088877 + SINE/Alu_AluSx chr1 21089519 21089734 + SINE/Alu_AluJr chr1 21091138 21091460 - SINE/Alu_AluSz6 chr1 21091593 21092497 - LINE/L1_L1PA5 chr1 21092493 21093362 + LINE/L1_L1PA5 chr1 21093213 21094109 + LINE/L1_L1PA5 chr1 21094145 21094765 + LINE/L1_L1PA5 chr1 21094766 21094805 + Simple_repeat_(ATA)n chr1 21094869 21094955 + SINE/Alu_FLAM_A chr1 21095077 21095192 - DNA/TcMar-Tigger_MER46C chr1 21095362 21095612 - SINE/Alu_AluJr chr1 21095785 21095814 + Simple_repeat_(AAAC)n chr1 21096116 21096521 + LINE/L1_L1PB4 chr1 21096956 21097068 + LINE/L1_L1MB3 chr1 21097582 21097958 + LINE/L1_L1MD chr1 21097959 21098085 + SINE/Alu_AluJo chr1 21098086 21098259 + LINE/L1_L1MD chr1 21098260 21098569 + SINE/Alu_AluSz chr1 21098570 21098645 + LINE/L1_L1MD chr1 21098668 21098936 - SINE/Alu_AluJr chr1 21098939 21099511 + LINE/L1_L1ME1 chr1 21099513 21099553 + SINE/Alu_FRAM chr1 21099563 21099805 + LINE/L1_L1ME1 chr1 21099809 21100405 + LINE/L1_L1MB8 chr1 21100582 21100732 + SINE/Alu_FAM chr1 21101192 21101237 - SINE/Alu_Alu chr1 21101286 21101588 + SINE/Alu_AluSx1 chr1 21101591 21101656 + Low_complexity_GA-rich chr1 21101668 21101813 - LINE/CR1_L3 chr1 21101937 21101996 + Low_complexity_A-rich chr1 21102169 21102304 + SINE/Alu_AluSx chr1 21103562 21103857 + SINE/Alu_AluSx chr1 21103936 21104124 + SINE/MIR_MIRb chr1 21104316 21104351 - LINE/L2_L2a chr1 21104362 21105140 + LINE/L1_L1PA15 chr1 21105141 21105451 + SINE/Alu_AluSx chr1 21105452 21105530 + LINE/L1_L1PA15 chr1 21105539 21105571 + Low_complexity_A-rich chr1 21105641 21105762 - LINE/L2_L2c chr1 21105781 21106066 + SINE/Alu_AluJr4 chr1 21106067 21106086 + Simple_repeat_(T)n chr1 21106096 21106856 - LINE/L2_L2c chr1 21107077 21107144 + SINE/MIR_MIRb chr1 21107170 21107471 - SINE/Alu_AluSz chr1 21107676 21107843 - SINE/Alu_AluJb chr1 21107870 21108176 + SINE/Alu_AluJb chr1 21108324 21108377 + Simple_repeat_(TA)n chr1 21108622 21108925 + SINE/Alu_AluSx chr1 21108934 21109224 + SINE/Alu_AluSz6 chr1 21109289 21109362 - LINE/RTE-X_L4_B_Mam chr1 21109363 21109485 + SINE/Alu_AluJo chr1 21109486 21109643 - LINE/RTE-X_L4_B_Mam chr1 21109791 21110087 - SINE/Alu_AluSz chr1 21110294 21110594 - SINE/Alu_AluSx chr1 21110595 21110653 - SINE/Alu_AluSx4 chr1 21110654 21110864 - SINE/Alu_AluSx chr1 21111258 21111427 + SINE/MIR_MIRb chr1 21113013 21113166 + SINE/Alu_FRAM chr1 21113194 21113273 - DNA/hAT-Charlie_Charlie1 chr1 21113353 21113732 - DNA/hAT-Charlie_Charlie1 chr1 21113733 21114017 + SINE/Alu_AluJb chr1 21114018 21114148 - DNA/hAT-Charlie_Charlie1 chr1 21114149 21114428 - SINE/Alu_AluSc chr1 21114429 21114870 - DNA/hAT-Charlie_Charlie1 chr1 21114895 21115005 + SINE/Alu_AluJr chr1 21115095 21115592 - DNA/TcMar-Tigger_Tigger1 chr1 21115706 21116021 - SINE/Alu_AluJr4 chr1 21116556 21116854 + SINE/Alu_AluSg chr1 21116866 21116938 + LINE/L1_L1ME3Cz chr1 21117737 21117767 + Simple_repeat_(A)n chr1 21118505 21118811 + SINE/Alu_AluSx1 chr1 21118820 21118855 + srpRNA_7SLRNA chr1 21118867 21118951 + srpRNA_7SLRNA chr1 21120382 21120638 + SINE/Alu_AluSg chr1 21120933 21121194 - SINE/Alu_AluJb chr1 21121494 21121790 + SINE/Alu_AluSx chr1 21121940 21122084 - SINE/MIR_MIRc chr1 21123281 21123591 + SINE/Alu_AluSz6 chr1 21124009 21124301 + SINE/Alu_AluSq2 chr1 21125393 21125436 + Simple_repeat_(TA)n chr1 21125437 21125731 + SINE/Alu_AluSx1 chr1 21125732 21125771 + Simple_repeat_(TA)n chr1 21125772 21125805 + Simple_repeat_(AC)n chr1 21125808 21125924 - SINE/MIR_MIR chr1 21125925 21126246 + SINE/Alu_AluJb chr1 21126247 21126342 - SINE/MIR_MIR chr1 21126343 21126352 + Simple_repeat_(TA)n chr1 21126411 21126669 + SINE/Alu_AluJr chr1 21126760 21126958 - DNA/TcMar-Tigger_MER2 chr1 21126985 21127276 - SINE/Alu_AluJr chr1 21127311 21127384 - SINE/Alu_AluJo chr1 21127385 21127571 + LINE/L2_L2a chr1 21127923 21128221 + SINE/Alu_AluY chr1 21128227 21128525 + SINE/Alu_AluSq2 chr1 21128550 21128764 + LINE/RTE-X_L4_A_Mam chr1 21128838 21129032 + LINE/L1_L1MCc chr1 21129033 21129328 + SINE/Alu_AluSq4 chr1 21129329 21130029 + LINE/L1_L1MCc chr1 21130038 21130109 + LINE/L1_L1MCc chr1 21130110 21130216 + SINE/Alu_AluJo chr1 21130217 21130516 - SINE/Alu_AluSx1 chr1 21130517 21130950 + LINE/L1_L1MCc chr1 21130951 21131256 + SINE/Alu_AluSz6 chr1 21131257 21131699 + LINE/L1_L1MCc chr1 21131700 21131979 + SINE/Alu_AluJb chr1 21131980 21132074 + LINE/L1_L1MCc chr1 21132448 21132738 - SINE/Alu_AluJr chr1 21132783 21133083 - SINE/Alu_AluSz chr1 21133226 21133524 - SINE/Alu_AluSx4 chr1 21134352 21134386 + LINE/L1_L1ME3C chr1 21134387 21134697 + SINE/Alu_AluSx chr1 21134698 21134829 + LINE/L1_L1ME3C chr1 21135218 21135528 + SINE/Alu_AluSq2 chr1 21135902 21136202 + SINE/Alu_AluYb8 chr1 21136237 21136546 + SINE/Alu_AluSg chr1 21136825 21137124 + DNA/hAT-Charlie_MER33 chr1 21137134 21137439 - SINE/Alu_AluJr chr1 21137527 21137832 + SINE/Alu_AluJr chr1 21138541 21138828 + SINE/Alu_AluSz chr1 21138884 21139049 - SINE/Alu_AluJb chr1 21139309 21139472 + SINE/Alu_FRAM chr1 21139702 21139851 - SINE/Alu_FAM chr1 21140315 21140609 - SINE/Alu_AluSx4 chr1 21141038 21141253 - SINE/MIR_MIR chr1 21141378 21141404 + Simple_repeat_(TG)n chr1 21141405 21141414 + Simple_repeat_(TGTATG)n chr1 21141481 21141499 + Simple_repeat_(A)n chr1 21141647 21141951 + SINE/Alu_AluJb chr1 21142095 21142188 + SINE/Alu_FLAM_C chr1 21142218 21142506 + SINE/Alu_AluJb chr1 21142551 21142601 + Simple_repeat_(AACAA)n chr1 21142954 21142966 + Low_complexity_A-rich chr1 21142967 21143269 + SINE/Alu_AluSx1 chr1 21143270 21143400 + Low_complexity_A-rich chr1 21143671 21143936 + SINE/Alu_AluSz6 chr1 21143939 21144002 - SINE/Alu_FLAM_C chr1 21144252 21144547 - SINE/Alu_AluJr chr1 21144996 21145103 + SINE/Alu_AluJr chr1 21145117 21145313 - SINE/MIR_MIRb chr1 21145314 21145343 + Simple_repeat_(AACAA)n chr1 21145436 21145563 - SINE/Alu_FLAM_A chr1 21145799 21146109 + SINE/Alu_AluJr chr1 21146110 21146367 + SINE/Alu_AluJo chr1 21146479 21146779 + SINE/Alu_AluSq2 chr1 21146982 21147278 + SINE/Alu_AluSx3 chr1 21147331 21147630 - SINE/Alu_AluSg chr1 21147650 21147838 + LINE/L1_L1ME4c chr1 21147948 21148070 + LINE/L1_L1ME4a chr1 21148071 21148371 - SINE/Alu_AluJr chr1 21148372 21148395 + LINE/L1_L1ME4a chr1 21148422 21148726 + SINE/Alu_AluY chr1 21148730 21148839 + SINE/Alu_FLAM_A chr1 21148841 21148949 + LINE/L1_L1ME4a chr1 21148950 21149111 + SINE/Alu_FRAM chr1 21149112 21149223 + LINE/L1_L1ME4a chr1 21149229 21149270 + Simple_repeat_(TATA)n chr1 21150695 21150995 + SINE/Alu_AluSx chr1 21151229 21151536 - SINE/Alu_AluY chr1 21152191 21152362 + SINE/Alu_AluJb chr1 21152604 21152839 + DNA/hAT-Charlie_Charlie1a chr1 21152841 21153002 + SINE/Alu_FRAM chr1 21153391 21153449 + SINE/MIR_MIR3 chr1 21153554 21153864 - SINE/Alu_AluSx chr1 21154742 21154790 + Simple_repeat_(ATCT)n chr1 21154982 21155290 + SINE/Alu_AluSx1 chr1 21155295 21155426 + DNA/hAT-Charlie_Charlie19a chr1 21155550 21155713 + SINE/Alu_FRAM chr1 21155846 21156154 + SINE/Alu_AluSq2 chr1 21156268 21156658 + LINE/L2_L2 chr1 21156793 21157063 + SINE/Alu_AluJb chr1 21157363 21157635 - SINE/Alu_AluJb chr1 21157706 21157757 - LINE/L2_L2a chr1 21158170 21158475 - SINE/Alu_AluJb chr1 21158616 21158703 + SINE/Alu_AluJb chr1 21158719 21158752 + Simple_repeat_(AC)n chr1 21158766 21158798 - LINE/Penelope_Penelope1_Vert chr1 21158977 21159014 + Low_complexity_A-rich chr1 21159151 21159455 + SINE/Alu_AluJr chr1 21159460 21159688 + SINE/Alu_AluSx chr1 21159690 21159823 + SINE/Alu_AluJr4 chr1 21159826 21160123 + SINE/Alu_AluSq2 chr1 21160124 21160822 + LINE/L1_L1MC4a chr1 21160904 21161178 + SINE/Alu_AluSz chr1 21161223 21161529 + SINE/Alu_AluSp chr1 21161682 21161983 + SINE/Alu_AluSq2 chr1 21162214 21162464 + SINE/Alu_AluSx1 chr1 21162469 21162771 + SINE/Alu_AluSz chr1 21163762 21164072 - SINE/Alu_AluSx chr1 21164445 21164580 + SINE/Alu_AluJo chr1 21164582 21164871 + SINE/Alu_AluJb chr1 21164872 21165859 + LINE/L1_L1MB8 chr1 21166146 21166436 + SINE/Alu_AluJb chr1 21166444 21166758 + SINE/Alu_AluSz chr1 21166770 21167082 - SINE/Alu_AluJb chr1 21167388 21167486 + SINE/Alu_AluSx chr1 21167499 21167771 + SINE/Alu_AluSg4 chr1 21168122 21168422 + SINE/Alu_AluSx1 chr1 21168430 21168725 - SINE/Alu_AluJb chr1 21168767 21168893 + LINE/L1_L1ME4a chr1 21168913 21169166 + SINE/Alu_AluJb chr1 21169169 21169441 + SINE/Alu_AluSx chr1 21169454 21169485 + Simple_repeat_(AAAAT)n chr1 21169874 21169894 - LINE/L2_L2a chr1 21169895 21170189 + SINE/Alu_AluSc chr1 21170191 21170217 + Simple_repeat_(AAAC)n chr1 21170218 21170324 - LINE/L2_L2a chr1 21170389 21170685 + SINE/Alu_AluSz chr1 21170711 21171004 + SINE/Alu_AluSz6 chr1 21171747 21171881 - SINE/MIR_MIRc chr1 21172119 21172138 + Simple_repeat_(A)n chr1 21172231 21172487 + LINE/L2_L2c chr1 21172547 21172826 - SINE/Alu_AluY chr1 21172859 21173165 + SINE/Alu_AluSc8 chr1 21173202 21173397 - SINE/Alu_AluSx chr1 21173404 21173705 + SINE/Alu_AluSx chr1 21176227 21176280 + Simple_repeat_(CCG)n chr1 21176298 21176347 + Simple_repeat_(GCCGCT)n chr1 21176422 21176553 + Low_complexity_G-rich chr1 21176568 21176611 + Simple_repeat_(CGC)n chr1 21177346 21177493 - SINE/MIR_MIRb chr1 21177816 21178106 + SINE/Alu_AluSx1 chr1 21178140 21178253 - SINE/MIR_MIRb chr1 21178243 21178400 - SINE/MIR_MIRc chr1 21178401 21178702 - SINE/Alu_AluSp chr1 21178964 21179279 - SINE/Alu_AluY chr1 21179350 21179503 - DNA/hAT-Tip100_MamTip2b chr1 21179528 21179603 - SINE/MIR_MIRb chr1 21179761 21180269 + Simple_repeat_(ATATATA)n chr1 21180272 21180565 - SINE/Alu_AluSz chr1 21180568 21180854 - SINE/Alu_AluJb chr1 21180855 21180955 - SINE/MIR_MIR chr1 21181082 21181354 + LINE/L2_L2c chr1 21181388 21181554 + SINE/MIR_MIR chr1 21181555 21181839 + SINE/Alu_AluSg chr1 21181840 21181923 + SINE/MIR_MIR chr1 21181957 21182268 - SINE/Alu_AluSp chr1 21182828 21182853 + Simple_repeat_(A)n chr1 21183103 21183401 + SINE/Alu_AluSc chr1 21183405 21183524 + Low_complexity_GA-rich chr1 21183526 21183837 + SINE/Alu_AluJr chr1 21183840 21184017 + SINE/Alu_AluJr chr1 21184235 21184415 + SINE/MIR_MIRb chr1 21184884 21185190 + SINE/Alu_AluJb chr1 21185213 21185489 + SINE/Alu_AluJo chr1 21185497 21185639 + SINE/Alu_FRAM chr1 21185656 21185847 - SINE/Alu_AluSq2 chr1 21185848 21185886 + Simple_repeat_(AAAAAT)n chr1 21185887 21186234 - LINE/L1_L1M3 chr1 21186235 21186537 - SINE/Alu_AluSq2 chr1 21186538 21186572 - LINE/L1_L1M3 chr1 21186573 21186860 + SINE/Alu_AluSp chr1 21186861 21186979 - LINE/L1_L1M3 chr1 21186980 21187287 - SINE/Alu_AluSq2 chr1 21187288 21187355 - LINE/L1_L1M3 chr1 21187356 21187420 + SINE/MIR_MIR chr1 21187432 21187718 + SINE/Alu_AluJb chr1 21187819 21187898 - SINE/MIR_MIR chr1 21188431 21188459 + Simple_repeat_(GCCT)n chr1 21188751 21188929 + LINE/L2_L2c chr1 21188930 21189234 + SINE/Alu_AluSx1 chr1 21189235 21189281 + LINE/L2_L2a chr1 21189369 21189430 + SINE/MIR_MIRb chr1 21189678 21189816 + LINE/L2_L2a chr1 21190073 21190260 + SINE/MIR_MIRb chr1 21190270 21190325 + SINE/Alu_Alu chr1 21190326 21190376 + Simple_repeat_(GGAA)n chr1 21190400 21190817 + LTR/ERV1_MER4A1 chr1 21191068 21191175 + SINE/MIR_MIR chr1 21191176 21191484 - SINE/Alu_AluJb chr1 21191485 21191496 + SINE/MIR_MIR chr1 21191497 21191804 - SINE/Alu_AluY chr1 21191805 21191866 + SINE/MIR_MIR chr1 21191867 21192177 - SINE/Alu_AluY chr1 21192178 21192237 + SINE/MIR_MIR chr1 21192445 21192661 + SINE/MIR_MIRb chr1 21193373 21193427 + LINE/L2_L2c chr1 21193475 21193530 - LINE/CR1_L3 chr1 21193537 21193562 + Simple_repeat_(TTCTTT)n chr1 21193565 21193842 - SINE/Alu_AluSc chr1 21193871 21194229 - LINE/L1_L1PB1 chr1 21194230 21194285 + Simple_repeat_(TG)n chr1 21194286 21194313 - LINE/L1_L1PB1 chr1 21194317 21194412 - SINE/MIR_MIR3 chr1 21194468 21194610 - SINE/MIR_MIRb chr1 21194611 21194915 + SINE/Alu_AluJb chr1 21194916 21195013 - SINE/MIR_MIRb chr1 21195039 21195188 - SINE/MIR_MIRc chr1 21195485 21195756 + SINE/Alu_AluJb chr1 21195767 21196074 + SINE/Alu_AluSx1 chr1 21196085 21196436 + SINE/Alu_AluSq2 chr1 21196494 21196882 + LINE/L2_L2b chr1 21196883 21197198 - SINE/Alu_AluSg4 chr1 21197209 21197512 - SINE/Alu_AluJr chr1 21197521 21197813 - SINE/Alu_AluSc chr1 21197814 21197903 + LINE/L2_L2b chr1 21197904 21198029 - SINE/Alu_AluJr4 chr1 21198030 21198141 + LINE/L2_L2b chr1 21198720 21199376 + LINE/L1_L1MCa chr1 21199382 21199569 + SINE/Alu_AluJb chr1 21199575 21199894 + LINE/L1_L1MCa chr1 21199895 21200201 + SINE/Alu_AluSg chr1 21200202 21200488 + LINE/L1_L1MCa chr1 21200489 21200786 + SINE/Alu_AluJb chr1 21200787 21200838 + LINE/L1_L1MCa chr1 21200839 21201092 - LTR/ERVL_MLT2B4 chr1 21201093 21201382 - SINE/Alu_AluSp chr1 21201383 21201693 - LTR/ERVL_MLT2B4 chr1 21201694 21201869 + LINE/L1_L1MCa chr1 21201868 21202165 + LINE/L1_L1MD2 chr1 21202175 21202342 - SINE/Alu_FRAM chr1 21202347 21202365 + Simple_repeat_(AT)n chr1 21202366 21202528 - SINE/Alu_AluJb chr1 21202529 21202824 - SINE/Alu_AluSx1 chr1 21202825 21202968 - SINE/Alu_AluJb chr1 21202994 21203254 + SINE/Alu_AluSc chr1 21203310 21203576 + SINE/Alu_AluJr chr1 21203617 21203870 + LINE/L1_L1MA8 chr1 21203871 21204180 + SINE/Alu_AluSq10 chr1 21204199 21204237 + LINE/L1_L1MA8 chr1 21204238 21204540 + SINE/Alu_AluSz6 chr1 21204541 21204551 + LINE/L1_L1MA8 chr1 21204587 21204887 - SINE/Alu_AluSx1 chr1 21204888 21205107 + DNA/hAT-Charlie_MER58B chr1 21205115 21205180 + LTR/ERVL_ERVL-B4-int chr1 21205181 21205482 + SINE/Alu_AluSq2 chr1 21205493 21205517 + Simple_repeat_(GGTT)n chr1 21205518 21205779 + LTR/ERVL_ERVL-B4-int chr1 21205780 21206094 + SINE/Alu_AluSx chr1 21206095 21206340 + LTR/ERVL_ERVL-B4-int chr1 21206342 21206445 - SINE/Alu_AluJb chr1 21206446 21206755 + LTR/ERVL_ERVL-B4-int chr1 21206780 21206879 + SINE/Alu_AluYj4 chr1 21206880 21206994 + Simple_repeat_(AT)n chr1 21206997 21207272 - SINE/Alu_AluSp chr1 21207278 21207573 - SINE/Alu_AluSp chr1 21207574 21207719 - SINE/Alu_AluJr chr1 21207724 21208699 + LTR/ERVL_ERVL-B4-int chr1 21208730 21209029 + SINE/Alu_AluY chr1 21209030 21209311 - SINE/Alu_AluSx chr1 21209383 21209462 - SINE/Alu_AluJr chr1 21209464 21209707 + LTR/ERVL_ERVL-B4-int chr1 21209708 21209958 - SINE/Alu_AluSz chr1 21209959 21210128 + LTR/ERVL_ERVL-B4-int chr1 21210132 21210155 + Simple_repeat_(TTAT)n chr1 21210178 21210417 + LTR/ERVL_ERVL-B4-int chr1 21210418 21210629 - SINE/Alu_AluSz6 chr1 21210688 21210775 + SINE/Alu_AluYj4 chr1 21210780 21210838 + LTR/ERVL_ERVL-B4-int chr1 21210839 21211133 - SINE/Alu_AluSc5 chr1 21211134 21211462 + LTR/ERVL_ERVL-B4-int chr1 21211463 21211893 + DNA/TcMar-Tigger_Tigger5b chr1 21211894 21212177 + SINE/Alu_AluJb chr1 21212178 21212221 + DNA/TcMar-Tigger_Tigger5b chr1 21212222 21212574 + LTR/ERVL_ERVL-B4-int chr1 21212575 21212881 + SINE/Alu_AluJb chr1 21212882 21213077 + LTR/ERVL_ERVL-B4-int chr1 21213155 21213181 + Simple_repeat_(GATT)n chr1 21213183 21213455 - SINE/Alu_AluJo chr1 21213496 21213779 - SINE/Alu_AluJo chr1 21213781 21213978 - LINE/L1_L1M5 chr1 21213979 21214269 - SINE/Alu_AluY chr1 21214270 21214391 - LINE/L1_L1M5 chr1 21214416 21214443 + Simple_repeat_(AAT)n chr1 21214460 21214734 - SINE/Alu_AluSq2 chr1 21214743 21214806 - LINE/L1_L1M5 chr1 21214807 21215103 - SINE/Alu_AluY chr1 21215104 21215198 - LINE/L1_L1M5 chr1 21215514 21215779 + SINE/Alu_AluSp chr1 21215780 21216107 + LTR/ERVL-MaLR_MLT1C chr1 21216109 21216270 - LINE/L2_L2c chr1 21216300 21216601 - SINE/Alu_AluSz chr1 21216602 21216897 - SINE/Alu_AluSx chr1 21218586 21218890 - SINE/Alu_AluSx1 chr1 21220214 21220512 + SINE/Alu_AluJr chr1 21220513 21220808 + SINE/Alu_AluSz chr1 21221189 21221470 - SINE/Alu_AluSx1 chr1 21221498 21221568 - LINE/CR1_L3 chr1 21221993 21222440 + LINE/L2_L2a chr1 21222441 21222558 - DNA/hAT-Charlie_MER58A chr1 21222559 21222865 + SINE/Alu_AluSx chr1 21222866 21222972 - DNA/hAT-Charlie_MER58A chr1 21222974 21223270 - SINE/Alu_AluSx3 chr1 21223278 21223479 - SINE/Alu_AluSz6 chr1 21223480 21223763 - SINE/Alu_AluSg7 chr1 21223764 21223896 - SINE/Alu_AluSz6 chr1 21223897 21224395 + LINE/L2_L2a chr1 21224435 21224591 + SINE/MIR_MIRb chr1 21224743 21224895 - SINE/MIR_MIRc chr1 21225683 21225993 - SINE/Alu_AluSz chr1 21226306 21226481 - SINE/MIR_MIRb chr1 21226573 21226722 - SINE/Alu_AluJr chr1 21226723 21227023 - SINE/Alu_AluSx1 chr1 21227024 21227146 - SINE/Alu_AluJr chr1 21227433 21227625 - SINE/MIR_MIRb chr1 21228210 21228362 - SINE/Alu_AluJb chr1 21228364 21228484 + LINE/L1_L1ME4b chr1 21228511 21228816 + SINE/Alu_AluSz6 chr1 21228843 21229135 - SINE/Alu_AluYe6 chr1 21229182 21229363 - SINE/Alu_AluJr chr1 21229409 21229888 + DNA/hAT-Charlie_Charlie1a chr1 21229889 21230215 + SINE/Alu_AluJb chr1 21230216 21230381 + DNA/hAT-Charlie_Charlie1a chr1 21230382 21230685 - SINE/Alu_AluSg7 chr1 21230686 21230852 + DNA/hAT-Charlie_Charlie1a chr1 21230881 21231126 - SINE/Alu_AluSp chr1 21231348 21231641 - SINE/Alu_AluSz chr1 21231660 21231913 - SINE/Alu_AluJb chr1 21232289 21232591 - SINE/Alu_AluJr chr1 21232613 21232673 - DNA/hAT-Charlie_MER5B chr1 21232674 21232978 - SINE/Alu_AluSz6 chr1 21232979 21233043 - DNA/hAT-Charlie_MER5B chr1 21233377 21233451 + SINE/MIR_MIR chr1 21233903 21233982 + DNA/hAT-Charlie_MER3 chr1 21233983 21234287 - SINE/Alu_AluSx chr1 21234288 21234396 + DNA/hAT-Charlie_MER3 chr1 21234480 21234777 - SINE/Alu_AluJr chr1 21235070 21235157 + SINE/Alu_AluSx3 chr1 21236030 21236157 + LINE/L2_L2b chr1 21236370 21236667 + SINE/Alu_AluSp chr1 21237049 21237234 + DNA/hAT-Charlie_MER103C chr1 21237564 21237769 - SINE/MIR_MIRb chr1 21238434 21238641 - SINE/MIR_MIRb chr1 21238809 21239131 - SINE/Alu_AluJb chr1 21239188 21239348 - DNA/hAT-Tip100_MER91A chr1 21239424 21239626 + LINE/L1_L1ME2 chr1 21239748 21239799 + Simple_repeat_(T)n chr1 21239820 21239888 + LINE/L1_L1ME2 chr1 21239889 21240198 + SINE/Alu_AluSz chr1 21240206 21240507 + SINE/Alu_AluSx1 chr1 21240508 21240566 + LINE/L1_L1ME2 chr1 21240681 21240708 + Simple_repeat_(TCCC)n chr1 21240906 21241066 - LINE/L2_L2b chr1 21241140 21241335 - SINE/Alu_AluSx1 chr1 21241343 21241423 - SINE/MIR_MIR chr1 21241424 21241724 - SINE/Alu_AluSx chr1 21241725 21241785 - SINE/MIR_MIR chr1 21242086 21242306 - SINE/MIR_MIRc chr1 21242379 21242514 - SINE/MIR_MIRc chr1 21242762 21242783 - LINE/L1_L1MB2 chr1 21242784 21242944 - SINE/Alu_FRAM chr1 21242945 21243037 - LINE/L1_L1MB2 chr1 21243075 21243220 + SINE/MIR_MIRb chr1 21243379 21243416 + Low_complexity_GA-rich chr1 21243418 21243518 - SINE/Alu_AluJo chr1 21243521 21243604 + SINE/MIR_MIR3 chr1 21244712 21244801 + SINE/MIR_MIR chr1 21244829 21244936 + LINE/L2_L2d2 chr1 21245266 21245368 + LINE/L2_L2d2 chr1 21245669 21245822 + LINE/L1_L1ME4a chr1 21245984 21246108 + SINE/Alu_FLAM_C chr1 21246219 21246528 + SINE/Alu_AluSx1 chr1 21246552 21246663 - SINE/MIR_MIR chr1 21246664 21246941 - SINE/Alu_AluJb chr1 21246942 21247067 - SINE/MIR_MIR chr1 21247498 21247546 + LINE/CR1_L3 chr1 21247616 21247759 - SINE/MIR_MIRb chr1 21247769 21247957 + SINE/MIR_MIR chr1 21247959 21248080 + DNA/TcMar-Tigger_Tigger9a chr1 21248174 21248473 - SINE/Alu_AluSx1 chr1 21248618 21248925 - SINE/Alu_AluSg chr1 21249097 21249354 + SINE/Alu_AluSx chr1 21249357 21249413 - LINE/L1_L1MB3 chr1 21249414 21249693 + SINE/Alu_AluJb chr1 21249694 21249766 - LINE/L1_L1MB3 chr1 21249767 21250078 - SINE/Alu_AluSx3 chr1 21250079 21250278 - LINE/L1_L1MB3 chr1 21250280 21250405 + DNA/TcMar-Tigger_Tigger9a chr1 21250406 21250471 + SINE/MIR_MIR chr1 21250667 21250725 - SINE/MIR_MIRb chr1 21250726 21251021 + SINE/Alu_AluY chr1 21251022 21251113 - SINE/MIR_MIRb chr1 21251231 21251535 + SINE/Alu_AluSx chr1 21252849 21252888 + Simple_repeat_(TAA)n chr1 21252918 21252985 - SINE/MIR_MIRc chr1 21252987 21253034 + DNA/hAT-Charlie_Charlie4z chr1 21253035 21253071 + Simple_repeat_(TTATT)n chr1 21253072 21253356 - SINE/Alu_AluSx chr1 21253357 21253479 + DNA/hAT-Charlie_Charlie24 chr1 21253481 21253783 + SINE/Alu_AluYk3 chr1 21253789 21254098 + SINE/Alu_AluSg chr1 21254285 21254350 + SINE/Alu_FLAM_C chr1 21255563 21255636 - LINE/L2_L2b chr1 21255739 21255891 + SINE/MIR_MIRb chr1 21256274 21256575 + SINE/Alu_AluSx1 chr1 21257037 21257202 + SINE/MIR_MIRb chr1 21257907 21258047 + SINE/MIR_MIRb chr1 21258118 21258270 - SINE/MIR_MIRc chr1 21258874 21258962 + SINE/MIR_MIRb chr1 21259083 21259182 + LINE/L1_L1ME4b chr1 21259278 21259540 - SINE/Alu_AluJr chr1 21259563 21259620 - DNA/hAT-Ac_OldhAT1 chr1 21259980 21260084 + Simple_repeat_(CA)n chr1 21260114 21260151 + Simple_repeat_(CTCT)n chr1 21260212 21260252 + Low_complexity_G-rich chr1 21260392 21260603 + SINE/MIR_MIRb chr1 21261272 21261358 + LINE/L2_L2b chr1 21261367 21261466 - LINE/L2_L2d2 chr1 21261574 21261694 + LINE/L2_L2b chr1 21261677 21261974 - LINE/L2_L2c chr1 21263348 21263653 - SINE/Alu_AluSc chr1 21263928 21264233 - LINE/L2_L2c chr1 21264321 21264614 - SINE/Alu_AluSx1 chr1 21264729 21264789 + DNA/hAT-Charlie_MER106A chr1 21264858 21265193 + LINE/L2_L2a chr1 21265261 21265548 + SINE/Alu_AluSx1 chr1 21266522 21266656 - SINE/MIR_MIRc chr1 21266758 21266837 - SINE/MIR_MIRc chr1 21266941 21267256 + LTR/ERVL-MaLR_MLT1A1 chr1 21267257 21267629 - LTR/ERVL-MaLR_THE1B chr1 21267630 21267673 + LTR/ERVL-MaLR_MLT1A1 chr1 21267829 21267927 - LINE/L2_L2d2 chr1 21268530 21268576 - SINE/MIR_MIR3 chr1 21269024 21269052 + Simple_repeat_(CTGGG)n chr1 21269158 21269349 + SINE/MIR_MIRb chr1 21269448 21269592 - SINE/MIR_MIRb chr1 21269614 21269851 - SINE/MIR_MIRb chr1 21270021 21270053 + Simple_repeat_(AGAACC)n chr1 21270167 21270325 + SINE/MIR_MIRb chr1 21270433 21270657 + SINE/MIR_MIRc chr1 21270702 21270879 - SINE/MIR_MIRb chr1 21271834 21271985 + DNA/hAT-Tip100_hAT-N1a_Mam chr1 21272057 21272291 - SINE/MIR_MIRb chr1 21272294 21272591 - SINE/Alu_AluSz6 chr1 21273179 21273248 + Simple_repeat_(ATTC)n chr1 21273339 21273382 + Simple_repeat_(GT)n chr1 21273564 21273615 + Low_complexity_A-rich chr1 21273665 21273730 + SINE/Alu_FLAM_C chr1 21273733 21273845 + SINE/Alu_AluSx chr1 21274053 21274287 - SINE/MIR_MIRb chr1 21274351 21274521 + SINE/MIR_MIRb chr1 21275183 21275204 + Simple_repeat_(TG)n chr1 21275313 21275610 + SINE/Alu_AluSq2 chr1 21275781 21276026 + LINE/L2_L2b chr1 21276027 21276325 - SINE/Alu_AluSx1 chr1 21276326 21276350 + LINE/L2_L2b chr1 21276676 21277017 - SINE/Alu_AluSx chr1 21277566 21277620 + Simple_repeat_(CA)n chr1 21277663 21277692 + Simple_repeat_(TTCA)n chr1 21277760 21277805 - LINE/L2_L2a chr1 21278265 21278331 - LINE/L2_L2b chr1 21278555 21278663 + SINE/MIR_MIRb chr1 21278828 21278994 - SINE/MIR_MIRb chr1 21279778 21279954 - SINE/MIR_MIRb chr1 21280321 21280478 - LINE/L2_L2b chr1 21280907 21281189 + SINE/Alu_AluSx1 chr1 21281192 21281226 + Simple_repeat_(AAAAC)n chr1 21281685 21281714 + Simple_repeat_(TGTTT)n chr1 21281715 21281964 - SINE/Alu_AluSg chr1 21282006 21282305 + SINE/Alu_AluJr chr1 21282306 21282617 + SINE/Alu_AluSx chr1 21282777 21282842 + Low_complexity_GA-rich chr1 21282935 21283244 - SINE/Alu_AluSc8 chr1 21283256 21283570 - SINE/Alu_AluSx1 chr1 21283572 21283614 - SINE/MIR_MIR chr1 21283780 21283814 + Low_complexity_A-rich chr1 21284443 21284559 + Low_complexity_GA-rich chr1 21285327 21285392 + SINE/MIR_MIR3 chr1 21285544 21285709 + SINE/Alu_FRAM chr1 21285710 21286020 + SINE/Alu_AluSx chr1 21286359 21286577 - SINE/MIR_MIRc chr1 21286655 21286945 + SINE/Alu_AluJb chr1 21287244 21287555 + SINE/Alu_AluSx chr1 21288377 21288492 - SINE/MIR_MIRb chr1 21288754 21288963 + SINE/MIR_MIRb chr1 21289609 21289695 - DNA/hAT-Blackjack_MER81 chr1 21289985 21290019 + Simple_repeat_(GGCAGC)n chr1 21290222 21290266 + Simple_repeat_(GCC)n chr1 21290887 21290978 + DNA/hAT-Charlie_Charlie4z chr1 21291308 21291405 - SINE/MIR_MIRc chr1 21291394 21291489 - SINE/MIR_MIR3 chr1 21291447 21291539 + LINE/L2_L2c chr1 21291540 21291837 + SINE/Alu_AluSz chr1 21291838 21291849 + LINE/L2_L2c chr1 21291850 21292180 + SINE/Alu_AluJb chr1 21292181 21292692 + LINE/L2_L2c chr1 21293100 21293211 - SINE/MIR_MIRb chr1 21293942 21294103 - SINE/MIR_MIRb chr1 21294111 21294185 - SINE/MIR_MIRc chr1 21295024 21295228 + SINE/MIR_MIR chr1 21295229 21295361 + SINE/Alu_FLAM_C chr1 21295456 21295800 + LINE/RTE-X_L4_B_Mam chr1 21295822 21295931 + SINE/MIR_MIRb chr1 21296242 21296546 + SINE/Alu_AluJr chr1 21296592 21296738 - SINE/MIR_MIRb chr1 21297520 21297824 - SINE/Alu_AluSx1 chr1 21297829 21298139 - SINE/Alu_AluJo chr1 21298410 21298581 + SINE/MIR_MIR chr1 21298671 21298774 - SINE/MIR_MIRc chr1 21298955 21298988 + Simple_repeat_(TGCTCC)n chr1 21299009 21299299 - DNA/hAT-Tip100?_hAT-N1_Mam chr1 21299322 21299450 - SINE/MIR_MIRb chr1 21299479 21299583 + SINE/MIR_MIR3 chr1 21299663 21299786 + SINE/MIR_MIR chr1 21299872 21299971 - DNA/hAT-Charlie_Charlie15a chr1 21300284 21300418 + SINE/MIR_MIRc chr1 21300441 21300735 - SINE/Alu_AluSz chr1 21300783 21300897 + SINE/MIR_MIRc chr1 21300898 21300951 + SINE/MIR_MIR chr1 21300902 21300964 + LINE/L2_L2d2 chr1 21301213 21301522 + SINE/Alu_AluYe5 chr1 21301535 21301854 + SINE/Alu_AluJb chr1 21301899 21301998 - SINE/MIR_MIRb chr1 21302289 21302357 - SINE/MIR_MIRb chr1 21302336 21302495 + SINE/MIR_MIRb chr1 21302478 21302542 - SINE/MIR_MIR chr1 21303319 21303553 + SINE/MIR_MIR chr1 21303581 21303643 + DNA/hAT-Charlie_Charlie8 chr1 21303644 21303949 - SINE/Alu_AluSz chr1 21303950 21304033 + DNA/hAT-Charlie_Charlie8 chr1 21304034 21304345 + SINE/Alu_AluY chr1 21304346 21304574 + DNA/hAT-Charlie_Charlie8 chr1 21304577 21304818 + LINE/L1_L1ME2 chr1 21304819 21304908 + DNA/hAT-Charlie_Charlie8 chr1 21305041 21305231 - DNA/hAT-Charlie_MER3 chr1 21305311 21305375 + LINE/L2_L2a chr1 21306105 21306319 - LINE/L2_L2a chr1 21306246 21306325 - SINE/MIR_MIR chr1 21306326 21306632 - SINE/Alu_AluSq2 chr1 21306633 21306760 - SINE/MIR_MIR chr1 21306873 21307005 - SINE/MIR_MIR3 chr1 21307330 21307526 + SINE/MIR_MIRb chr1 21308052 21308221 + LINE/L2_L2d2 chr1 21308606 21308692 + SINE/MIR_MIRb chr1 21309254 21309474 + SINE/MIR_MIR chr1 21309487 21309520 + Simple_repeat_(TGAA)n chr1 21309774 21310094 - SINE/Alu_AluY chr1 21310163 21310574 - LTR/ERVL-MaLR_MLT1K chr1 21310884 21310927 + Simple_repeat_(CAACCCT)n chr1 21311076 21311153 + SINE/MIR_MIRb chr1 21311391 21311533 + SINE/Alu_AluJr4 chr1 21311534 21311820 + SINE/Alu_AluJb chr1 21311821 21311841 + SINE/Alu_AluJr4 chr1 21311842 21312141 + SINE/Alu_AluSz chr1 21312142 21312160 + SINE/Alu_AluJr4 chr1 21312161 21312466 + SINE/Alu_AluJr chr1 21312467 21312635 + SINE/Alu_AluJr4 chr1 21312694 21312906 + SINE/MIR_MIR chr1 21312925 21313115 - SINE/MIR_MIRb chr1 21313628 21313834 + SINE/MIR_MIRb chr1 21314039 21314235 - SINE/MIR_MIRb chr1 21314898 21315058 + LINE/L2_L2c chr1 21315065 21315289 - SINE/MIR_MIRb chr1 21315512 21315812 + SINE/Alu_AluSz chr1 21315880 21316063 + SINE/MIR_MIRb chr1 21316086 21316175 - LINE/L2_L2a chr1 21316262 21316573 - SINE/Alu_AluSx chr1 21316613 21316772 + LINE/L1_L1ME3Cz chr1 21316773 21316896 + LINE/L1_L1ME3E chr1 21316907 21316984 + SINE/Alu_AluSp chr1 21317028 21317113 + LINE/L1_L1ME3E chr1 21317255 21317361 + SINE/MIR_MIRb chr1 21318399 21318431 + Simple_repeat_(TCCCTG)n chr1 21318498 21318614 + SINE/MIR_MIR3 chr1 21319063 21319347 + SINE/Alu_AluSx chr1 21319348 21319387 + Simple_repeat_(ATAATC)n chr1 21319931 21320146 - DNA/hAT-Charlie_MER30 chr1 21320235 21320418 + SINE/MIR_MIRb chr1 21320518 21320712 - SINE/MIR_MIRb chr1 21321008 21321227 - SINE/MIR_MIRb chr1 21321523 21321609 - SINE/MIR_MIRb chr1 21321610 21321635 + Simple_repeat_(GATT)n chr1 21321636 21321911 - SINE/Alu_AluSz chr1 21321912 21322026 - SINE/MIR_MIRb chr1 21322117 21322200 - SINE/MIR_MIRb chr1 21322951 21323128 - DNA/hAT-Charlie_Charlie16 chr1 21323401 21323478 - LINE/L2_L2a chr1 21323479 21323629 + SINE/Alu_FRAM chr1 21323633 21323669 + Simple_repeat_(AAAAT)n chr1 21323670 21323807 - LINE/L2_L2a chr1 21323811 21324121 - SINE/Alu_AluSz chr1 21324124 21324299 - SINE/Alu_AluJb chr1 21325591 21325767 + SINE/MIR_MIRb chr1 21325862 21326046 - LINE/CR1_X7A_LINE chr1 21327243 21327349 + SINE/MIR_MIRc chr1 21327634 21327720 + LINE/L2_L2c chr1 21327721 21328108 + DNA/hAT-Charlie_MER1B chr1 21328119 21328256 + LINE/L2_L2b chr1 21328257 21328312 + LINE/L2_L2c chr1 21328338 21328402 + LINE/L2_L2c chr1 21328371 21328411 + SINE/MIR_MIR chr1 21328480 21328789 + SINE/Alu_AluSq chr1 21328810 21328936 + SINE/MIR_MIRb chr1 21329136 21329298 + DNA/hAT-Charlie_MER5A chr1 21329435 21329646 + SINE/MIR_MIRb chr1 21329936 21330043 + SINE/MIR_MIR chr1 21330021 21330079 + LINE/L2_L2c chr1 21330134 21330157 + Simple_repeat_(T)n chr1 21330165 21330226 + LINE/L2_L2c chr1 21330227 21330529 - SINE/Alu_AluSz6 chr1 21330530 21330614 + LINE/L2_L2c chr1 21330996 21331120 + SINE/Alu_AluJb chr1 21331121 21331431 + SINE/Alu_AluSc chr1 21331432 21331609 + SINE/Alu_AluJb chr1 21331610 21331839 + SINE/MIR_MIR chr1 21332336 21332583 - SINE/MIR_MIR chr1 21332590 21332760 + Low_complexity_G-rich chr1 21332928 21333178 - SINE/MIR_MIRb chr1 21333335 21333358 + Simple_repeat_(G)n chr1 21333550 21333846 + SINE/Alu_AluSz chr1 21333864 21334098 - SINE/MIR_MIRb chr1 21334119 21334279 + SINE/MIR_MIR3 chr1 21334877 21335116 + LINE/L2_L2 chr1 21335469 21335574 - SINE/MIR_MIRc chr1 21335828 21335914 - SINE/MIR_MIR3 chr1 21336084 21336106 + LINE/L1_L1ME3 chr1 21336107 21336402 + SINE/Alu_AluJr chr1 21336413 21336558 + SINE/Alu_AluSq2 chr1 21336559 21336837 + SINE/Alu_AluY chr1 21336838 21337062 + SINE/Alu_AluSq2 chr1 21337063 21337541 + LINE/L1_L1ME3 chr1 21337627 21337889 - SINE/MIR_MIRb chr1 21338375 21338565 - SINE/MIR_MIRb chr1 21338595 21338730 - SINE/MIR_MIR3 chr1 21339142 21339204 + SINE/MIR_MIRc chr1 21339487 21339594 + SINE/MIR_MIRb chr1 21339955 21340073 + LINE/L2_L2c chr1 21340403 21340556 + LINE/L2_L2b chr1 21340557 21340845 + SINE/Alu_AluSz chr1 21340846 21341340 + LINE/L2_L2b chr1 21341810 21341935 - SINE/Alu_FLAM_C chr1 21341939 21342139 - SINE/MIR_MIRc chr1 21342289 21342369 + SINE/MIR_MIR3 chr1 21342575 21342647 + Simple_repeat_(AC)n chr1 21343090 21343141 + LINE/L2_L2c chr1 21343561 21343736 - SINE/MIR_MIRb chr1 21345380 21345459 + Simple_repeat_(GCTCCCG)n chr1 21345885 21345920 + Simple_repeat_(TG)n chr1 21345921 21345942 + Simple_repeat_(CG)n chr1 21346056 21346150 + Simple_repeat_(TG)n chr1 21346175 21346214 + Simple_repeat_(GT)n chr1 21346796 21346816 + Simple_repeat_(TG)n chr1 21347027 21347071 + Simple_repeat_(TG)n chr1 21347147 21347213 - LINE/L2_L2b chr1 21347804 21347971 + SINE/MIR_MIRc chr1 21347991 21348227 + LINE/L1_L1ME3A chr1 21348232 21348534 + SINE/Alu_AluSx1 chr1 21348548 21348802 + SINE/Alu_AluSx1 chr1 21349043 21349138 - SINE/MIR_MIR1_Amn chr1 21349291 21349461 - SINE/MIR_MIRb chr1 21349676 21350054 - LTR/ERVL-MaLR_MLT1K chr1 21351045 21351058 + Low_complexity_A-rich chr1 21351059 21351205 + SINE/Alu_AluJr chr1 21351206 21351515 - SINE/Alu_AluSq2 chr1 21351516 21351547 + SINE/Alu_AluJr chr1 21351548 21351854 + SINE/Alu_AluSc8 chr1 21351855 21351999 + SINE/Alu_AluJr chr1 21352561 21352584 + Simple_repeat_(GTTT)n chr1 21352738 21352927 - SINE/MIR_MIR chr1 21353021 21353306 - SINE/Alu_AluSz chr1 21353337 21353464 - SINE/Alu_FLAM_A chr1 21353672 21353705 + Simple_repeat_(T)n chr1 21353773 21353920 - LINE/CR1_L3 chr1 21354177 21354270 + LINE/RTE-X_L4_C_Mam chr1 21354324 21354629 - SINE/Alu_AluSz chr1 21354711 21354882 + LINE/L2_L2c chr1 21354974 21355108 - SINE/MIR_MIRb chr1 21355262 21355564 - SINE/Alu_AluJb chr1 21355569 21355601 + SINE/MIR_MIR3 chr1 21355603 21355716 + DNA/hAT-Charlie_MER5B chr1 21355799 21355860 + SINE/MIR_MIRb chr1 21356489 21356708 + LINE/L2_L2a chr1 21356709 21357018 - SINE/Alu_AluSq2 chr1 21357019 21357121 + LINE/L2_L2a chr1 21357141 21357272 - SINE/Alu_AluJr chr1 21357273 21357612 - SINE/Alu_AluJb chr1 21357613 21357754 - SINE/Alu_AluJr chr1 21357766 21357896 - LINE/L1_L1MB8 chr1 21357897 21358193 + SINE/Alu_AluY chr1 21358194 21358328 - LINE/L1_L1MB8 chr1 21358329 21358458 + SINE/Alu_AluJr chr1 21358459 21358759 + SINE/Alu_AluY chr1 21358760 21358875 + SINE/Alu_AluJr chr1 21359321 21359629 + SINE/Alu_AluSx1 chr1 21359681 21359734 + LINE/L1_L1MD chr1 21359787 21359994 + SINE/Alu_AluJb chr1 21360048 21360164 + LINE/L1_L1ME3Cz chr1 21360177 21360307 + LINE/L1_L1ME3Cz chr1 21360308 21360614 + SINE/Alu_AluY chr1 21360615 21360722 + LINE/L1_L1ME3Cz chr1 21360801 21361110 + SINE/Alu_AluSq2 chr1 21361487 21361727 + DNA/hAT-Charlie_Charlie29b chr1 21361741 21361855 + DNA/hAT-Tip100_ORSL-2b chr1 21361882 21361904 + Simple_repeat_(CA)n chr1 21361951 21362102 + DNA/hAT-Tip100_ORSL-2b chr1 21362313 21362555 + DNA/hAT-Charlie_Charlie29b chr1 21362879 21363184 - SINE/Alu_AluSx chr1 21363227 21363380 + LINE/L2_L2a chr1 21363413 21363715 - SINE/Alu_AluY chr1 21363716 21364126 - LINE/L1_L1MC1 chr1 21364156 21364208 + LINE/L2_L2a chr1 21364209 21364259 + Simple_repeat_(TA)n chr1 21364265 21364576 - SINE/Alu_AluSx1 chr1 21364577 21364769 + LINE/L2_L2a chr1 21364612 21365083 + LINE/L2_L2a chr1 21365086 21365406 + LINE/L1_L1MB7 chr1 21365407 21365726 + SINE/Alu_AluJb chr1 21365730 21365849 + SINE/Alu_AluJr chr1 21365850 21366138 - SINE/Alu_AluY chr1 21366139 21366327 + SINE/Alu_AluJr chr1 21366328 21366696 + LINE/L1_L1MB7 chr1 21366697 21366991 - SINE/Alu_AluSq2 chr1 21366992 21367011 + LINE/L1_L1MB7 chr1 21367018 21367302 + SINE/Alu_AluJo chr1 21367306 21367715 + LINE/L1_L1MB7 chr1 21367716 21368026 + SINE/Alu_AluY chr1 21368027 21368100 + LINE/L1_L1MB7 chr1 21368113 21368409 + SINE/Alu_AluSx chr1 21368546 21368586 + Simple_repeat_(CCTT)n chr1 21368803 21368867 - LINE/CR1_L3b chr1 21368827 21369031 + LINE/L2_L2a chr1 21369031 21369207 + SINE/MIR_MIRb chr1 21369313 21369404 + SINE/MIR_MIR1_Amn chr1 21369405 21369712 + SINE/Alu_AluSz chr1 21369968 21370196 + SINE/Alu_AluJo chr1 21370243 21370372 + SINE/MIR_MIR chr1 21370373 21370636 + SINE/Alu_AluSx1 chr1 21371097 21371262 + SINE/Alu_AluJb chr1 21371272 21371435 + LINE/L1_L1PA5 chr1 21371441 21371612 + LINE/L1_L1PA5 chr1 21371613 21371622 + Simple_repeat_(AAG)n chr1 21372211 21372429 - SINE/Alu_AluJb chr1 21372484 21372593 - SINE/MIR_MIR3 chr1 21372823 21372912 + LINE/L1_L1ME3Cz chr1 21372913 21373197 + SINE/Alu_AluJb chr1 21373202 21373229 + Simple_repeat_(AAAGC)n chr1 21373230 21373319 + LINE/L1_L1ME3Cz chr1 21373320 21373621 - SINE/Alu_AluJo chr1 21373622 21373857 + LINE/L1_L1ME3Cz chr1 21373885 21374197 + SINE/Alu_AluYk3 chr1 21374226 21374522 - SINE/Alu_AluJr chr1 21374526 21374704 + Simple_repeat_(AT)n chr1 21374705 21374958 - SINE/Alu_AluSx1 chr1 21374987 21375052 + LINE/L1_L1ME3Cz chr1 21375053 21375363 - SINE/Alu_AluSz chr1 21375364 21375498 + LINE/L1_L1ME3Cz chr1 21375525 21375728 + SINE/Alu_AluSx chr1 21375729 21376873 + LTR/ERVL-MaLR_THE1A-int chr1 21376874 21377225 + LTR/ERVL-MaLR_THE1A chr1 21377227 21377340 - SINE/Alu_AluJb chr1 21377342 21377430 + LINE/L1_L1MC4 chr1 21377555 21377859 + SINE/Alu_AluSp chr1 21377924 21378079 + LINE/L1_L1ME3Cz chr1 21378112 21378230 + SINE/Alu_FLAM_C chr1 21378276 21378314 - LTR/ERV1_MER4B chr1 21378335 21379194 + LINE/L1_L1ME3Cz chr1 21379259 21379347 + LTR/ERVL-MaLR_MLT1K chr1 21379431 21379644 + LTR/ERVL-MaLR_MLT1K chr1 21379903 21380033 - LINE/L1_L1M5 chr1 21380034 21380297 - SINE/Alu_AluY chr1 21380298 21380366 - LINE/L1_L1M5 chr1 21380367 21380531 - SINE/Alu_FAM chr1 21380535 21380788 - LINE/L1_L1M5 chr1 21380811 21380945 - DNA/hAT-Charlie_MER117 chr1 21380946 21381170 + SINE/MIR_MIR chr1 21381191 21381277 + LINE/L2_L2d2 chr1 21381395 21381511 + LTR/ERVL-MaLR_MLT1N2 chr1 21381526 21381800 + LTR/ERVL-MaLR_MLT1N2 chr1 21381936 21382028 - DNA/TcMar-Tigger_MER46C chr1 21382081 21382405 - SINE/Alu_AluYb9 chr1 21382430 21382515 - DNA/TcMar-Tigger_MER46C chr1 21382603 21382900 + SINE/Alu_AluSx1 chr1 21382912 21382999 - SINE/MIR_MIRb chr1 21383000 21383302 - SINE/Alu_AluSg chr1 21383303 21383355 - SINE/MIR_MIRb chr1 21383396 21383492 - SINE/MIR_MIRb chr1 21384172 21384316 - SINE/MIR_MIRb chr1 21384595 21385246 - LTR/ERV1_MER50 chr1 21385295 21385752 - LTR/ERV1_LTR2 chr1 21385791 21385832 - LINE/L1_L1MC4 chr1 21385833 21385972 + DNA/hAT-Charlie_MER3 chr1 21385973 21386259 - SINE/Alu_AluY chr1 21386260 21386337 + DNA/hAT-Charlie_MER3 chr1 21386338 21386462 - LINE/L1_L1MC4 chr1 21386466 21386626 + LTR/ERV1_MER41B chr1 21386627 21386916 + SINE/Alu_AluSx1 chr1 21386917 21386965 + LTR/ERV1_MER41B chr1 21386966 21387076 + Simple_repeat_(CCTCTC)n chr1 21387077 21388776 + Retroposon/SVA_SVA_F chr1 21388794 21389774 + Retroposon/SVA_SVA_F chr1 21389775 21389816 + Low_complexity_A-rich chr1 21389817 21390167 + LTR/ERV1_MER41B chr1 21390168 21390470 - SINE/Alu_AluSz chr1 21390471 21390541 + LTR/ERV1_MER41B chr1 21390542 21390865 + LTR/ERV1_MER41-int chr1 21390866 21390999 + scRNA_BC200 chr1 21391000 21391824 + LTR/ERV1_MER41-int chr1 21391825 21392106 - SINE/Alu_AluYf1 chr1 21392107 21392490 + LTR/ERV1_MER41-int chr1 21392491 21392881 - LTR/ERVL-MaLR_THE1D chr1 21392882 21393020 + LTR/ERV1_MER41-int chr1 21393021 21393319 + SINE/Alu_AluSx chr1 21393320 21393876 + LTR/ERV1_MER41-int chr1 21393877 21394183 - SINE/Alu_AluSg chr1 21394184 21394695 + LTR/ERV1_MER41-int chr1 21394696 21395007 + SINE/Alu_AluSz6 chr1 21395008 21395046 + LTR/ERV1_MER41-int chr1 21395047 21395353 + SINE/Alu_AluY chr1 21395354 21395943 + LTR/ERV1_MER41-int chr1 21395944 21396254 + SINE/Alu_AluSx chr1 21396255 21396460 + LTR/ERV1_MER41-int chr1 21396461 21397061 + LTR/ERV1_LTR10E chr1 21397071 21397160 + LTR/ERV1_MER41-int chr1 21397161 21397434 - SINE/Alu_AluSz chr1 21397435 21397811 + LTR/ERV1_MER41-int chr1 21399385 21399902 + LTR/ERV1_MER41-int chr1 21399903 21400201 - SINE/Alu_AluSz6 chr1 21400202 21401477 + LTR/ERV1_MER41-int chr1 21401481 21401548 + LTR/ERV1_MER41B chr1 21401549 21401829 + SINE/Alu_AluSg chr1 21401830 21401877 + Simple_repeat_(AAAT)n chr1 21401878 21402446 + LTR/ERV1_MER41B chr1 21402468 21402865 - LINE/L1_L1MB8 chr1 21402867 21402964 - LINE/L1_L1MB5 chr1 21402966 21403204 - LTR/ERVL-MaLR_MLT1A0 chr1 21403205 21403494 - SINE/Alu_AluJb chr1 21403495 21403633 - LTR/ERVL-MaLR_MLT1A0 chr1 21403640 21403782 - LTR/ERVL-MaLR_MLT1A0-int chr1 21403783 21403976 + SINE/Alu_AluSz6 chr1 21403977 21404281 + SINE/Alu_AluY chr1 21404282 21404398 + SINE/Alu_AluSz6 chr1 21404406 21404700 + SINE/Alu_AluSg7 chr1 21404701 21405565 - LTR/ERVL-MaLR_MLT1A0-int chr1 21405566 21405732 - SINE/Alu_AluJb chr1 21405735 21406032 - SINE/Alu_AluSx1 chr1 21406039 21406154 - LTR/ERVL-MaLR_MLT1A0 chr1 21406170 21406936 - LINE/L1_L1MB5 chr1 21406937 21407220 - SINE/Alu_AluJb chr1 21407221 21407565 - LINE/L1_L1MB5 chr1 21407566 21407739 - SINE/Alu_AluJb chr1 21407740 21408007 - LINE/L1_L1MB5 chr1 21408008 21408308 + SINE/Alu_AluSc chr1 21408309 21408526 - LINE/L1_L1MB5 chr1 21408527 21408808 + SINE/Alu_AluJb chr1 21408809 21408995 - LINE/L1_L1MB5 chr1 21408996 21409305 + SINE/Alu_AluSp chr1 21409306 21409609 - SINE/Alu_AluY chr1 21409610 21409927 - LINE/L1_L1MB5 chr1 21409928 21410263 + LTR/ERVL-MaLR_THE1B chr1 21410264 21411102 - LINE/L1_L1MB5 chr1 21411104 21411201 - SINE/MIR_MIR chr1 21412124 21412323 - SINE/MIR_MIRb chr1 21412575 21412774 - SINE/MIR_MIR chr1 21412964 21412986 + Simple_repeat_(TTA)n chr1 21413546 21413632 + LINE/L2_L2c chr1 21413715 21413748 + Low_complexity_A-rich chr1 21414221 21414292 + tRNA_tRNA-Gln-CAG chr1 21415199 21415291 + Low_complexity_G-rich chr1 21416677 21416740 + LINE/L1_HAL1 chr1 21416864 21416907 + Simple_repeat_(AT)n chr1 21416908 21416940 + Simple_repeat_(ATGT)n chr1 21417069 21417335 + LINE/L1_L1ME3Cz chr1 21417431 21417616 - SINE/MIR_MIRb chr1 21418493 21418572 + Simple_repeat_(TATGTA)n chr1 21418667 21418723 + LINE/L1_L1M3 chr1 21418724 21419014 + SINE/Alu_AluJo chr1 21419015 21419070 + LINE/L1_L1M3 chr1 21419090 21419503 - LINE/L1_L1MEc chr1 21419504 21419541 + Simple_repeat_(GT)n chr1 21419542 21419628 - LINE/L1_L1MEc chr1 21419621 21420390 - LINE/L1_L1MEc chr1 21420391 21420656 - SINE/Alu_AluSp chr1 21420951 21421350 + LINE/L1_L1MD chr1 21421351 21421551 + LTR/ERVL_MLT2B4 chr1 21421568 21421641 + LTR/ERVL_MLT2B2 chr1 21421696 21421794 + LINE/L1_L1MC5 chr1 21422232 21422784 + LINE/L1_L1MC4 chr1 21422872 21422903 + Simple_repeat_(CA)n chr1 21423263 21423704 + LINE/L1_L1MC4 chr1 21423775 21423889 - LINE/L1_L1MC4 chr1 21425040 21425092 + Simple_repeat_(GAGA)n chr1 21426592 21426631 + Simple_repeat_(AGACAGAG)n chr1 21432220 21432240 + Simple_repeat_(A)n chr1 21432266 21432288 + Simple_repeat_(GT)n chr1 21432345 21432656 + SINE/Alu_AluSx chr1 21432690 21432744 + Simple_repeat_(A)n chr1 21432745 21432880 + LINE/L1_L1ME4b chr1 21432906 21433205 - SINE/Alu_AluSx chr1 21433206 21433262 - LINE/L1_L1ME4b chr1 21433263 21433292 + Simple_repeat_(AC)n chr1 21433293 21433806 - LINE/L1_L1ME4b chr1 21433807 21433937 - SINE/Alu_FLAM_A chr1 21433947 21434252 - SINE/Alu_AluSx3 chr1 21434744 21435035 + SINE/Alu_AluSx1 chr1 21435090 21435214 - SINE/MIR_MIR3 chr1 21435513 21435745 - SINE/MIR_MIRc chr1 21435812 21435906 + SINE/MIR_MIR3 chr1 21437616 21437657 + DNA/hAT-Charlie_MER3 chr1 21437660 21437821 - SINE/Alu_AluYf1 chr1 21437822 21438117 - SINE/Alu_AluSp chr1 21438122 21438412 - SINE/Alu_AluSx chr1 21438414 21438598 + SINE/MIR_MIR chr1 21438596 21438752 - SINE/MIR_MIR chr1 21438753 21438847 + SINE/MIR_MIRc chr1 21438856 21438929 + DNA/TcMar-Tigger_Tigger15a chr1 21439133 21439268 + SINE/Alu_AluSx3 chr1 21439269 21439514 + SINE/Alu_AluSq2 chr1 21439524 21439647 + LINE/L1_L1ME4b chr1 21439648 21439718 - SINE/Alu_FLAM_A chr1 21439728 21439776 + LINE/L1_L1ME4b chr1 21440113 21440143 + Simple_repeat_(GCAGGC)n chr1 21440449 21440480 + Low_complexity_G-rich chr1 21440973 21441000 + Simple_repeat_(TGTC)n chr1 21441379 21441426 - LINE/L1_L1ME3A chr1 21441427 21441725 + SINE/Alu_AluJb chr1 21441726 21441780 - LINE/L1_L1ME3A chr1 21441782 21442218 - LINE/L1_L1M5 chr1 21442219 21442515 - SINE/Alu_AluJb chr1 21442516 21442669 - LINE/L1_L1M5 chr1 21442670 21442960 - SINE/Alu_AluJb chr1 21442961 21443117 - LINE/L1_L1M5 chr1 21443119 21443172 + SINE/Alu_AluJo chr1 21443174 21443204 + Simple_repeat_(AATA)n chr1 21443201 21443248 + Simple_repeat_(ATAAGTG)n chr1 21443616 21443910 - SINE/Alu_AluJb chr1 21443987 21444158 + LINE/L2_L2a chr1 21444236 21444347 + LTR/ERVL-MaLR_MLT1I chr1 21444500 21444548 + LINE/L2_L2a chr1 21444579 21444778 - SINE/Alu_AluJr4 chr1 21444830 21444970 + LINE/L2_L2 chr1 21445327 21445374 + LINE/L2_L2a chr1 21445858 21445991 - LINE/L2_L2b chr1 21446447 21446494 + Simple_repeat_(TTCC)n chr1 21446498 21446528 + Simple_repeat_(CTTC)n chr1 21446529 21446605 + Simple_repeat_(TCCC)n chr1 21446606 21446646 + Simple_repeat_(CTTC)n chr1 21446657 21446728 + LINE/Penelope_Penelope1_Vert chr1 21447089 21447744 - LINE/L1_L1MC2 chr1 21447745 21448570 - LINE/L1_L1MC2 chr1 21448565 21448951 - LINE/L1_L1MCa chr1 21448969 21449131 - LINE/L1_L1ME2z chr1 21449459 21449762 - SINE/Alu_AluSx1 chr1 21449921 21449955 + Simple_repeat_(ATTG)n chr1 21450021 21450565 - LTR/ERVL-MaLR_MLT1E3 chr1 21450594 21451055 - LTR/ERVL-MaLR_MLT1E3-int chr1 21451093 21451448 - LINE/L1_L1ME2z chr1 21452422 21452493 - LINE/CR1_L3 chr1 21453079 21453372 + LTR/ERVL_LTR33 chr1 21453383 21453605 + LTR/ERVL_ERVL-E-int chr1 21453623 21453988 + LTR/ERVL_ERVL-E-int chr1 21454011 21454095 + LTR/ERVL_LTR16B2 chr1 21454776 21455400 - LTR/ERVL-MaLR_MLT1E3 chr1 21455539 21455804 + LINE/L1_L1ME1 chr1 21456227 21456273 + LINE/L1_L1ME1 chr1 21456274 21456353 + LINE/L1_L1MC chr1 21456560 21457147 + LINE/L1_L1ME1 chr1 21457168 21457219 + Simple_repeat_(GTGT)n chr1 21457349 21457435 + Simple_repeat_(TA)n chr1 21457439 21457800 + LINE/L1_L1ME1 chr1 21457801 21458465 - LTR/ERV1_PABL_A chr1 21458466 21458933 + LINE/L1_L1ME1 chr1 21458942 21459425 + LINE/L1_L1ME3 chr1 21460234 21460470 - LINE/L1_L1PA6 chr1 21460500 21461473 + LINE/L1_L1ME3 chr1 21461474 21461762 - SINE/Alu_AluSx1 chr1 21461763 21461817 + LINE/L1_L1ME3 chr1 21461818 21462247 + LTR/ERVL-MaLR_MSTB1 chr1 21462248 21462451 + LINE/L1_L1ME3 chr1 21462464 21462706 + LINE/L1_L1M8 chr1 21462714 21462900 + SINE/MIR_MIRb chr1 21463521 21464619 + LINE/L1_L1MB7 chr1 21464726 21465028 + SINE/Alu_AluJr chr1 21465136 21465294 + SINE/MIR_MIRc chr1 21465293 21465428 + DNA/hAT-Charlie_MER5A chr1 21466784 21466913 + SINE/MIR_MIRb chr1 21467012 21467100 + SINE/MIR_MIRc chr1 21469445 21469730 + SINE/Alu_AluSg chr1 21469880 21470038 + SINE/MIR_MIR chr1 21470964 21471058 - LINE/L1_L1MB5 chr1 21471265 21471312 - LINE/L2_L2 chr1 21472089 21472209 + SINE/MIR_MIRc chr1 21473782 21473819 - DNA/hAT-Tip100_MamTip2b chr1 21474194 21474466 - SINE/Alu_AluSz chr1 21475187 21477575 - LINE/L1_L1P2 chr1 21479786 21479820 + Simple_repeat_(CT)n chr1 21479821 21479859 + Simple_repeat_(TG)n chr1 21479860 21479874 + Simple_repeat_(CT)n chr1 21479875 21479884 + Simple_repeat_(TCTCTT)n chr1 21481373 21481427 + Simple_repeat_(TCTT)n chr1 21481428 21481434 + Simple_repeat_(CTCTCT)n chr1 21482918 21482960 + Simple_repeat_(TC)n chr1 21484117 21484231 + LINE/L1_L1MC4 chr1 21484303 21484744 - LINE/L1_L1MC4 chr1 21485104 21485135 + Simple_repeat_(TG)n chr1 21485223 21485631 - LINE/L1_L1MC4 chr1 21485666 21485890 - LINE/L1_L1MC4 chr1 21486213 21486311 - LINE/L1_L1MC4 chr1 21486371 21486443 - LTR/ERVL_MLT2B4 chr1 21486460 21486591 - LTR/ERVL_MLT2B4 chr1 21486600 21486684 - LINE/L1_L1MC4 chr1 21486703 21486867 - LINE/L1_L1ME2 chr1 21486988 21487019 + SINE/MIR_MIR chr1 21487020 21487316 - SINE/Alu_AluSz chr1 21487317 21487416 + SINE/MIR_MIR chr1 21487548 21487834 - SINE/Alu_AluJb chr1 21487850 21487910 + LINE/L2_L2c chr1 21487914 21488049 + SINE/Alu_AluJr chr1 21488050 21488346 + SINE/Alu_AluYk3 chr1 21488347 21488499 + SINE/Alu_AluJr chr1 21488512 21488533 + Simple_repeat_(AACA)n chr1 21488594 21489137 - LINE/L2_L2b chr1 21489193 21489256 - SINE/MIR_MIRb chr1 21489516 21489552 + LINE/L2_L2d2 chr1 21489687 21489992 + SINE/Alu_AluY chr1 21490070 21490376 + SINE/Alu_AluSz chr1 21490464 21490538 - SINE/Alu_AluJb chr1 21490542 21491480 + LTR/ERV1_LTR28C chr1 21491484 21491707 - SINE/Alu_AluJb chr1 21491708 21491844 - SINE/MIR_MIRb chr1 21492456 21492563 - LINE/L2_L2b chr1 21492598 21492674 + DNA/hAT-Charlie_MER113 chr1 21492731 21492799 + DNA/hAT-Charlie_MER113 chr1 21492810 21493107 + SINE/Alu_AluJb chr1 21493108 21493139 + Simple_repeat_(GGGAAG)n chr1 21493556 21493853 - SINE/Alu_AluSg chr1 21493913 21493948 + Simple_repeat_(AG)n chr1 21494000 21494194 - DNA/hAT-Tip100_MamTip1b chr1 21494572 21494880 + SINE/Alu_AluSx chr1 21494931 21495033 - SINE/MIR_MIR3 chr1 21496055 21496183 - LTR/ERVL-MaLR_MLT1M chr1 21496184 21496224 + Simple_repeat_(GT)n chr1 21496305 21496512 - LTR/ERVL-MaLR_MSTC chr1 21496513 21496661 - SINE/Alu_FRAM chr1 21496683 21496867 - LTR/ERVL-MaLR_MSTC chr1 21497010 21497489 + LTR/ERVL-MaLR_MLT1O chr1 21497534 21497722 - SINE/MIR_MIR chr1 21498166 21498315 + SINE/MIR_MIRb chr1 21498817 21498864 + Simple_repeat_(T)n chr1 21498907 21499019 + LTR/ERVL-MaLR_MLT1K chr1 21499020 21499370 - SINE/Alu_AluJb chr1 21499371 21499456 + LTR/ERVL-MaLR_MLT1K chr1 21499578 21499773 + LTR/ERVL-MaLR_MLT1K-int chr1 21499929 21500026 + DNA/TcMar-Tigger_Tigger19b chr1 21500240 21500324 + LTR/ERVL-MaLR_MLT1K-int chr1 21500325 21500642 + SINE/Alu_AluSz chr1 21500643 21500812 + LTR/ERVL-MaLR_MLT1K-int chr1 21500823 21501125 + SINE/Alu_AluSz chr1 21501297 21501327 + Simple_repeat_(TGTTT)n chr1 21501352 21501425 + LINE/L1_L1MB3 chr1 21501432 21501563 + LINE/L2_L2d2 chr1 21501690 21501877 + LINE/L2_L2a chr1 21502037 21503512 - Retroposon/SVA_SVA_C chr1 21503529 21503665 - SINE/Alu_FLAM_A chr1 21504289 21504464 + LINE/L2_L2a chr1 21504465 21504778 - SINE/Alu_AluSc8 chr1 21504779 21504804 + Simple_repeat_(TATT)n chr1 21504805 21505099 - SINE/Alu_AluSz chr1 21505100 21505271 + LINE/L2_L2a chr1 21505437 21505484 + SINE/MIR_MIR chr1 21505813 21505855 + Simple_repeat_(TCTCTG)n chr1 21505932 21506241 - SINE/Alu_AluSx chr1 21506283 21506440 - SINE/Alu_FRAM chr1 21506518 21506689 + SINE/MIR_MIRc chr1 21506817 21506986 - SINE/MIR_MIR3 chr1 21507292 21507595 + SINE/Alu_AluJb chr1 21507783 21507841 - SINE/MIR_MIRb chr1 21507851 21508055 - DNA/hAT-Charlie_MER113 chr1 21508057 21508163 + SINE/MIR_MIRc chr1 21508292 21508338 + LINE/CR1_L3 chr1 21508500 21508659 - SINE/MIR_MIRb chr1 21508907 21509042 + Simple_repeat_(GACAGA)n chr1 21509279 21509360 + Low_complexity_G-rich chr1 21509424 21509473 + Simple_repeat_(CCGCGCT)n chr1 21510551 21510612 - SINE/MIR_MIR3 chr1 21510559 21510680 - SINE/MIR_MIRb chr1 21511065 21511173 - LINE/L2_L2a chr1 21511316 21511454 + SINE/MIR_MIR chr1 21511848 21511960 - SINE/MIR_MIR3 chr1 21512257 21512397 + SINE/MIR_MIRb chr1 21512546 21512638 + SINE/MIR_MIR3 chr1 21513060 21513090 + Simple_repeat_(TAT)n chr1 21513133 21513292 - SINE/MIR_MIR3 chr1 21513308 21513377 - LINE/L1_L1ME3C chr1 21513409 21513532 + SINE/MIR_MIRc chr1 21513713 21513956 + DNA/hAT-Charlie_MER102c chr1 21514783 21514891 - SINE/MIR_MIRb chr1 21515245 21515363 - SINE/Alu_FLAM_C chr1 21515402 21515688 - SINE/Alu_AluSx chr1 21515833 21515871 + Simple_repeat_(TTGT)n chr1 21515874 21516179 - SINE/Alu_AluJo chr1 21516443 21516557 - SINE/MIR_MIR chr1 21516863 21517212 - DNA/TcMar-Tigger_MER47A chr1 21517223 21517393 - SINE/MIR_MIRb chr1 21517638 21517719 + SINE/MIR_MIRb chr1 21517786 21517946 - DNA/hAT-Charlie_MER117 chr1 21518291 21518524 - SINE/MIR_MIRb chr1 21519480 21519514 - SINE/MIR_MIRc chr1 21519515 21519814 + SINE/Alu_AluSz chr1 21519815 21519880 - SINE/MIR_MIRc chr1 21520036 21520089 + SINE/MIR_MIR3 chr1 21520115 21520430 - SINE/Alu_AluJo chr1 21520456 21520748 - SINE/Alu_AluSc8 chr1 21520755 21520931 - SINE/MIR_MIRb chr1 21521192 21521461 - SINE/Alu_AluSx1 chr1 21521883 21521939 + LINE/L2_L2a chr1 21522073 21522373 - SINE/Alu_AluY chr1 21522377 21522448 - SINE/MIR_MIR chr1 21522869 21523003 - SINE/MIR_MIR chr1 21523004 21523322 + SINE/Alu_AluSx1 chr1 21523323 21523397 - SINE/MIR_MIR chr1 21523762 21523851 - SINE/MIR_MIR chr1 21524000 21524301 - SINE/Alu_AluSg chr1 21524340 21524523 + SINE/Alu_FAM chr1 21524526 21524642 - SINE/MIR_MIR chr1 21525594 21525705 - LINE/L2_L2b chr1 21525706 21526007 + SINE/Alu_AluSp chr1 21526008 21526132 - LINE/L2_L2b chr1 21526141 21526439 - SINE/Alu_AluSx chr1 21526462 21526755 - LINE/L1_L1MEh chr1 21527010 21527046 + Simple_repeat_(TTCTT)n chr1 21527048 21527331 - SINE/Alu_AluSp chr1 21527496 21527549 - LINE/L1_L1MEh chr1 21527550 21527848 - SINE/Alu_AluSx1 chr1 21527849 21527923 - LINE/L1_L1MEh chr1 21528000 21528039 + Simple_repeat_(TGTTTTTT)n chr1 21528040 21528304 - SINE/Alu_AluJb chr1 21528343 21528649 - SINE/Alu_AluSx3 chr1 21528652 21528787 - LINE/L1_L1M5 chr1 21528788 21529088 + SINE/Alu_AluSq2 chr1 21529089 21529243 - LINE/L1_L1M5 chr1 21529230 21529256 - LINE/L1_L1MEh chr1 21529257 21529515 - SINE/Alu_AluJr chr1 21529516 21529752 - LINE/L1_L1MEh chr1 21529759 21530074 - SINE/Alu_AluJb chr1 21530265 21530777 - LINE/L1_L1MEh chr1 21530778 21530947 - SINE/Alu_AluJo chr1 21530948 21531257 - SINE/Alu_AluSx1 chr1 21531258 21531367 - SINE/Alu_AluJo chr1 21531368 21531428 - LINE/L1_L1MEh chr1 21532185 21532490 - SINE/Alu_AluSx1 chr1 21532707 21532795 + LINE/L2_L2a chr1 21532839 21533018 + SINE/MIR_MIR3 chr1 21533006 21533146 + SINE/MIR_MIRb chr1 21533132 21533298 + SINE/MIR_MIR chr1 21533462 21533623 - SINE/MIR_MIRb chr1 21533641 21533935 + SINE/Alu_AluSq2 chr1 21533936 21533963 + Simple_repeat_(AAG)n chr1 21534021 21534273 - SINE/Alu_AluJb chr1 21534709 21534880 + SINE/MIR_MIRb chr1 21534881 21535122 + LINE/L2_L2a chr1 21535334 21535628 + SINE/Alu_AluJo chr1 21535811 21535995 + SINE/MIR_MIRb chr1 21536402 21536592 - LTR/ERVL-MaLR_MLT1K chr1 21536679 21536817 - LTR/ERVL-MaLR_MLT1K chr1 21536892 21537195 - SINE/Alu_AluSc8 chr1 21537228 21537271 - SINE/MIR_MIRc chr1 21537707 21537873 + SINE/MIR_MIRb chr1 21537933 21538065 + SINE/MIR_MIR chr1 21538077 21538202 - SINE/MIR_MIR chr1 21538234 21538345 + SINE/MIR_MIRc chr1 21538853 21538997 + SINE/MIR_MIRc chr1 21539150 21539386 + SINE/MIR_MIR chr1 21539577 21539641 + SINE/MIR_MIRc chr1 21539645 21539955 - SINE/Alu_AluYm1 chr1 21540429 21540537 + SINE/MIR_MIR chr1 21541339 21541453 - SINE/MIR_MIR chr1 21542440 21542503 + SINE/MIR_MIR3 chr1 21542561 21542835 + SINE/Alu_AluSx1 chr1 21542871 21543158 + SINE/Alu_AluJo chr1 21543159 21543211 - DNA/hAT-Charlie_MER5A chr1 21543328 21543387 - SINE/MIR_MIRc chr1 21543462 21543605 + SINE/Alu_AluSq2 chr1 21543606 21543672 - LINE/L1_L1ME3 chr1 21544016 21544192 + SINE/MIR_MIRb chr1 21544226 21544313 - SINE/MIR_MIRc chr1 21544357 21544461 + LTR/ERV1_MER57-int chr1 21544462 21544758 + SINE/Alu_AluSx chr1 21544759 21545132 + LTR/ERV1_MER57-int chr1 21545133 21545275 + LTR/ERV1_MER57B1 chr1 21545276 21545590 - SINE/Alu_AluSx1 chr1 21545591 21545801 + LTR/ERV1_MER57B1 chr1 21545802 21545834 + Simple_repeat_(TTTAT)n chr1 21545835 21546093 - SINE/Alu_AluSc5 chr1 21546094 21546135 + LTR/ERV1_MER57B1 chr1 21546136 21546187 + SINE/MIR_MIRc chr1 21546438 21546533 + SINE/MIR_MIRc chr1 21546702 21546800 + SINE/MIR_MIRb chr1 21546794 21546838 + LINE/L2_L2b chr1 21546979 21547059 + LINE/L2_L2d2 chr1 21547050 21547169 + LINE/L2_L2b chr1 21547591 21547678 - SINE/MIR_MIRc chr1 21548446 21548592 - LINE/L2_L2c chr1 21548644 21549047 - LINE/L1_L1ME5 chr1 21549066 21549195 - LINE/L1_L1M5 chr1 21549201 21549427 + LINE/L2_L2a chr1 21549463 21549761 - SINE/Alu_AluSz chr1 21549783 21549833 + LINE/L1_L1MC chr1 21549834 21549876 - tRNA_tRNA-Gln-CAG chr1 21549879 21550070 + LINE/L1_L1MD chr1 21550060 21550227 + LINE/L1_L1ME1 chr1 21550304 21550491 + SINE/MIR_MIRc chr1 21550561 21550985 - LINE/L1_L1ME3A chr1 21550999 21551128 - LINE/L1_L1ME3A chr1 21551217 21551436 - LINE/L2_L2c chr1 21551720 21552024 - SINE/Alu_AluYf1 chr1 21552085 21552156 + Simple_repeat_(TCCCT)n chr1 21552206 21552506 + SINE/Alu_AluSp chr1 21552587 21552714 - SINE/Alu_FLAM_A chr1 21553033 21553234 - LINE/L1_L1ME4a chr1 21553204 21553332 - LINE/L1_L1ME4a chr1 21553509 21553669 + DNA/hAT-Charlie_MER5A chr1 21553799 21553900 + LINE/L2_L2a chr1 21554326 21554484 + Simple_repeat_(AT)n chr1 21554485 21554789 - SINE/Alu_AluYk3 chr1 21554790 21554815 + Simple_repeat_(TGTC)n chr1 21554816 21554962 + Simple_repeat_(TCTT)n chr1 21554968 21555417 - LTR/ERV1_LTR7 chr1 21555419 21555717 - SINE/Alu_AluSz chr1 21555768 21556072 - SINE/Alu_AluY chr1 21556359 21556647 + SINE/Alu_AluJr4 chr1 21556657 21556953 + SINE/Alu_AluSz chr1 21557192 21557323 + SINE/MIR_MIR chr1 21557605 21557746 - SINE/Alu_AluJr chr1 21557748 21557996 - LINE/L1_L1ME3 chr1 21557970 21558066 - LINE/L1_L1ME4a chr1 21558154 21558218 - LINE/L2_L2c chr1 21558909 21558983 + SINE/MIR_MIRb chr1 21559365 21559517 - SINE/MIR_MIRb chr1 21559518 21559818 - SINE/Alu_AluSx chr1 21559819 21559856 - SINE/MIR_MIRb chr1 21560112 21560266 - DNA/TcMar-Tigger_MamRep434 chr1 21560299 21560479 + SINE/MIR_MIRb chr1 21561243 21561392 - LINE/L1_L1ME3A chr1 21561404 21561656 - SINE/Alu_AluJr chr1 21561667 21561966 - SINE/Alu_AluSx chr1 21562040 21562253 - DNA/TcMar-Tigger_Tigger1a_Art chr1 21562250 21562368 - DNA/TcMar-Tigger_Tigger1a_Art chr1 21562469 21562573 - LINE/L2_L2c chr1 21562669 21562718 + Simple_repeat_(CCCTTC)n chr1 21563311 21563423 + SINE/MIR_MIR3 chr1 21563494 21563574 + SINE/MIR_MIRb chr1 21563961 21563984 + Simple_repeat_(GGGA)n chr1 21564613 21564867 + SINE/MIR_MIRb chr1 21565296 21565483 + SINE/MIR_MIRc chr1 21565838 21565867 + Simple_repeat_(AAAG)n chr1 21566280 21566590 - SINE/Alu_AluSx chr1 21566593 21566896 - SINE/Alu_AluJr chr1 21566902 21567007 - SINE/MIR_MIRb chr1 21567621 21567812 + SINE/MIR_MIRb chr1 21569473 21569531 + SINE/MIR_MIR chr1 21569858 21570143 + LINE/L2_L2b chr1 21570575 21570680 - SINE/MIR_MIRc chr1 21571112 21571259 + SINE/MIR_MIR chr1 21571335 21571380 - LTR/ERVL-MaLR_MLT1F chr1 21571391 21571703 + SINE/Alu_AluY chr1 21571708 21572013 + SINE/Alu_AluJr chr1 21572015 21572158 + SINE/Alu_AluJr chr1 21572159 21572592 - LTR/ERVL-MaLR_MLT1F chr1 21572956 21573149 + SINE/MIR_MIR chr1 21573152 21573466 + SINE/Alu_AluSx chr1 21574194 21574391 + SINE/MIR_MIRb chr1 21575161 21575362 + SINE/MIR_MIRb chr1 21576192 21576322 + Simple_repeat_(GGAT)n chr1 21576791 21576975 + SINE/MIR_MIR chr1 21576939 21576988 + LINE/L2_L2d chr1 21577183 21577311 + SINE/MIR_MIRb chr1 21578530 21578650 + SINE/MIR_MIRb chr1 21578651 21578964 + SINE/Alu_AluSz chr1 21579092 21579152 - LINE/L2_L2a chr1 21579153 21579444 + SINE/Alu_AluSp chr1 21579445 21579507 - LINE/L2_L2a chr1 21579656 21579900 - SINE/MIR_MIR chr1 21580140 21580439 + SINE/Alu_AluJr chr1 21580443 21580760 + SINE/Alu_AluY chr1 21580767 21581075 + SINE/Alu_AluJb chr1 21581136 21581194 - DNA/hAT-Charlie_MER113A chr1 21581195 21581497 - SINE/Alu_AluYk2 chr1 21581498 21581534 - DNA/hAT-Charlie_MER113A chr1 21581627 21582524 - LINE/L1_L1PB4 chr1 21582525 21582823 - SINE/Alu_AluY chr1 21582824 21582928 - LINE/L1_L1PB4 chr1 21582945 21583283 - LTR/ERVL_LTR16C chr1 21583381 21583488 + SINE/MIR_MIRb chr1 21584082 21584204 - LTR/ERVL-MaLR_MLT1J2 chr1 21584416 21584714 + SINE/Alu_AluSz chr1 21584766 21584897 + SINE/Alu_AluY chr1 21584901 21585098 + DNA/hAT-Charlie_MER102c chr1 21585485 21585745 + SINE/Alu_AluSx chr1 21585999 21586235 - SINE/MIR_MIRb chr1 21586353 21586578 - LINE/RTE-X_L4_C_Mam chr1 21586710 21586945 - SINE/MIR_MIR chr1 21587305 21587340 + Low_complexity_G-rich chr1 21588086 21588122 - SINE/MIR_MIR1_Amn chr1 21588145 21588420 - LINE/L2_L2a chr1 21588421 21588728 + SINE/Alu_AluSg chr1 21588729 21589210 - LINE/L2_L2a chr1 21589197 21589296 - SINE/MIR_MIRb chr1 21589445 21589694 + SINE/Alu_AluJr chr1 21589781 21589855 - SINE/MIR_MIR3 chr1 21589822 21589859 - SINE/MIR_MIRc chr1 21591155 21591160 + Simple_repeat_(AATGAT)n chr1 21591161 21591185 + Simple_repeat_(TAA)n chr1 21591232 21591402 - SINE/MIR_MIRb chr1 21591485 21591508 + Simple_repeat_(TGAA)n chr1 21592089 21592175 - LINE/L2_L2c chr1 21592396 21592584 - DNA/hAT_MER53 chr1 21593517 21593648 - SINE/MIR_MIR chr1 21594420 21594646 - SINE/MIR_MIR chr1 21594668 21594718 + Low_complexity_G-rich chr1 21595773 21596007 - SINE/MIR_MIRb chr1 21596738 21596884 - SINE/MIR_MIR3 chr1 21598713 21598846 - LINE/L2_L2c chr1 21598843 21598963 + LINE/L2_L2b chr1 21598961 21599346 - LINE/L2_L2c chr1 21599778 21599911 + LINE/L2_L2 chr1 21599930 21600142 - SINE/MIR_MIR chr1 21600455 21600604 + LINE/L2_L2b chr1 21600605 21600899 + SINE/Alu_AluY chr1 21600900 21600971 + LINE/L2_L2b chr1 21601021 21601194 - SINE/Alu_FAM chr1 21601278 21601540 + LINE/L2_L2b chr1 21602059 21602124 - SINE/MIR_MIRc chr1 21602203 21602351 - SINE/MIR_MIR chr1 21602485 21602614 + SINE/MIR_MIRb chr1 21603587 21603637 + Low_complexity_G-rich chr1 21604726 21604797 + LINE/L2_L2b chr1 21604802 21605088 + Simple_repeat_(TGGA)n chr1 21605470 21605513 + LINE/L2_L2d chr1 21605534 21605565 + LINE/L2_L2b chr1 21605621 21605652 + LINE/L2_L2b chr1 21606028 21606058 + Low_complexity_G-rich chr1 21606427 21606489 + SINE/MIR_MIR1_Amn chr1 21606639 21606679 + Simple_repeat_(TCCT)n chr1 21606723 21606802 - SINE/MIR_MIRb chr1 21607329 21607477 - SINE/MIR_MIR chr1 21610006 21610049 - SINE/MIR_MIR1_Amn chr1 21610442 21610580 + LINE/L1_L1PB3 chr1 21610610 21610723 - SINE/MIR_MIR3 chr1 21610964 21611164 + LINE/L2_L2b chr1 21611166 21611267 - SINE/MIR_MIR chr1 21612149 21612375 - SINE/MIR_MIRb chr1 21612504 21612606 + SINE/MIR_MIR3 chr1 21612934 21613118 + SINE/MIR_MIRb chr1 21613804 21613934 + SINE/MIR_MIR3 chr1 21614208 21614392 + SINE/MIR_MIRb chr1 21614466 21614518 + LINE/L2_L2b chr1 21615395 21615690 - SINE/Alu_AluSx1 chr1 21615784 21615828 - LINE/L2_L2b chr1 21616137 21616191 + Simple_repeat_(AC)n chr1 21616231 21616374 + SINE/MIR_MIRb chr1 21616375 21616583 - SINE/MIR_MIR chr1 21616584 21616620 + SINE/MIR_MIRb chr1 21616731 21616844 + LINE/L2_L2b chr1 21616849 21617092 - SINE/MIR_MIR chr1 21618223 21618330 - DNA/hAT-Charlie_MER5A chr1 21618834 21618915 + LINE/L2_L2b chr1 21619402 21619488 + Simple_repeat_(AGAGAC)n chr1 21620853 21620885 + Simple_repeat_(CTCT)n chr1 21621892 21621927 + Simple_repeat_(CACAGC)n chr1 21622419 21622505 + Simple_repeat_(GCCCG)n chr1 21622469 21622545 + Simple_repeat_(GGCTCCG)n chr1 21622568 21622633 + Simple_repeat_(GGCGG)n chr1 21622861 21623028 - SINE/MIR_MIR chr1 21623066 21623090 + Simple_repeat_(TCC)n chr1 21623196 21623344 + SINE/MIR_MIR1_Amn chr1 21624318 21624438 - SINE/MIR_MIRc chr1 21624540 21624664 + SINE/MIR_MIR3 chr1 21624812 21624920 - LINE/L2_L2 chr1 21624852 21624942 - SINE/MIR_MIR3 chr1 21625240 21625440 + SINE/MIR_MIRc chr1 21625461 21625693 + SINE/MIR_MIR chr1 21625698 21625861 + LINE/L2_L2b chr1 21625857 21626000 - SINE/MIR_MIRb chr1 21626841 21627059 - SINE/MIR_MIRb chr1 21627404 21627714 - SINE/Alu_AluSp chr1 21627815 21627941 - DNA/hAT-Tip100_MamTip2b chr1 21627934 21627997 + SINE/MIR_MIRc chr1 21628105 21628128 + Simple_repeat_(TGAA)n chr1 21628271 21628437 + SINE/Alu_AluSx1 chr1 21628438 21628738 + SINE/Alu_AluSc chr1 21628739 21628900 + SINE/Alu_AluSx1 chr1 21628901 21628933 + Simple_repeat_(GGAG)n chr1 21628967 21629140 - SINE/MIR_MIRb chr1 21629723 21629891 - SINE/MIR_MIRb chr1 21630031 21630215 - SINE/MIR_MIR chr1 21630253 21630555 - SINE/Alu_AluJo chr1 21630556 21630856 - SINE/Alu_AluJr chr1 21631323 21631403 - SINE/MIR_MIRc chr1 21631404 21631716 + SINE/Alu_AluSp chr1 21631717 21631873 - SINE/MIR_MIRc chr1 21632014 21632094 - SINE/MIR_MIR3 chr1 21632334 21632568 - SINE/MIR_MIRc chr1 21632620 21632898 + SINE/Alu_AluJr chr1 21633048 21633103 + SINE/MIR_MIR chr1 21633247 21633459 + LINE/L2_L2c chr1 21634382 21634524 - SINE/MIR_MIRb chr1 21634966 21635107 - SINE/MIR_MIRc chr1 21635171 21635248 - DNA/hAT-Tip100_MamTip2 chr1 21635402 21635499 - LINE/L2_L2b chr1 21635777 21635874 + SINE/MIR_MIRc chr1 21635975 21636159 + SINE/MIR_MIRb chr1 21636175 21636371 + SINE/MIR_MIRb chr1 21636393 21636419 + Simple_repeat_(CCTT)n chr1 21636420 21636482 + LINE/CR1_L3 chr1 21636525 21636834 + SINE/Alu_AluSp chr1 21636835 21636969 + Low_complexity_GA-rich chr1 21637086 21637136 + LINE/CR1_L3 chr1 21637137 21637453 - SINE/Alu_AluJb chr1 21637525 21637840 + LINE/L1_L1ME4b chr1 21637853 21638175 + SINE/Alu_AluJb chr1 21638176 21638485 + SINE/Alu_AluSz chr1 21638755 21638976 - SINE/MIR_MIRb chr1 21639422 21639653 + SINE/MIR_MIRb chr1 21639939 21640203 + LINE/L2_L2a chr1 21640907 21641219 - SINE/Alu_AluSx chr1 21641336 21641652 + LINE/L2_L2b chr1 21641679 21641799 - SINE/MIR_MIRb chr1 21641813 21641899 - SINE/MIR_MIR3 chr1 21641972 21642138 - SINE/MIR_MIRb chr1 21642316 21642528 - SINE/MIR_MIR chr1 21642814 21642875 + LINE/L2_L2b chr1 21642876 21642915 + Simple_repeat_(AC)n chr1 21642916 21643212 + LINE/L2_L2b chr1 21643278 21643586 + SINE/Alu_AluSg chr1 21643616 21643725 + DNA/hAT-Tip100_MamTip2b chr1 21643762 21643883 - SINE/MIR_MIRb chr1 21644183 21644326 + LINE/L2_L2b chr1 21644342 21644583 + SINE/MIR_MIR chr1 21644618 21644930 + SINE/Alu_AluSg7 chr1 21645004 21645057 - SINE/MIR_MIRc chr1 21645061 21645176 + LINE/L2_L2a chr1 21645334 21645508 + SINE/Alu_AluJb chr1 21645955 21646192 + SINE/MIR_MIRb chr1 21646394 21646567 + SINE/MIR_MIRb chr1 21647218 21647477 + SINE/Alu_AluSz chr1 21647606 21647729 - SINE/MIR_MIRb chr1 21648172 21648294 - SINE/MIR_MIR3 chr1 21648382 21648617 + SINE/MIR_MIRb chr1 21649399 21649586 - SINE/MIR_MIRb chr1 21650023 21650078 + Low_complexity_G-rich chr1 21651413 21651446 + Simple_repeat_(CA)n chr1 21651542 21651569 + Simple_repeat_(CACACAG)n chr1 21651570 21651606 + Simple_repeat_(AC)n chr1 21651607 21651625 + Simple_repeat_(CACACAG)n chr1 21651692 21651768 + Simple_repeat_(CG)n chr1 21651749 21651783 + Simple_repeat_(CGCCC)n chr1 21652001 21652068 + Low_complexity_G-rich chr1 21652095 21652128 + Simple_repeat_(CCCCGC)n chr1 21652274 21652412 + SINE/MIR_MIRb chr1 21653544 21653616 + Simple_repeat_(CTTC)n chr1 21653635 21653667 + Simple_repeat_(CCTC)n chr1 21653670 21653692 + Simple_repeat_(AGC)n chr1 21654083 21654123 + Simple_repeat_(GGGGTG)n chr1 21654326 21654538 + SINE/MIR_MIR chr1 21654551 21654858 + SINE/Alu_AluJr chr1 21654859 21655169 + SINE/Alu_AluSx1 chr1 21655194 21655251 - DNA/TcMar-Mariner_MADE2 chr1 21655260 21655382 + LINE/L1_L1MC4a chr1 21655411 21655480 + SINE/MIR_MIRc chr1 21656008 21656111 + SINE/MIR_MIR3 chr1 21656151 21656448 + SINE/Alu_AluSg chr1 21656931 21656971 - LINE/L2_L2a chr1 21656942 21656981 - SINE/MIR_MIRb chr1 21656972 21657047 - LINE/L2_L2a chr1 21657710 21657835 + SINE/MIR_MIR3 chr1 21658089 21658222 + DNA/hAT-Charlie_MER20B chr1 21658286 21658589 + SINE/Alu_AluSx chr1 21658600 21658716 + SINE/Alu_AluJo chr1 21658942 21659204 - SINE/Alu_AluSp chr1 21659612 21659694 - SINE/MIR_MIRc chr1 21659777 21659916 - SINE/MIR_MIRb chr1 21660025 21660051 + Simple_repeat_(CTGCC)n chr1 21660174 21660210 + LINE/L2_L2d chr1 21660265 21660347 + SINE/MIR_MIRb chr1 21660348 21660633 - SINE/Alu_AluSx1 chr1 21660634 21660774 + SINE/MIR_MIRb chr1 21660971 21661267 + SINE/Alu_AluSz6 chr1 21661385 21661420 + LINE/L2_L2c chr1 21661499 21661726 + SINE/MIR_MIRb chr1 21661748 21661892 + SINE/MIR_MIR3 chr1 21662170 21662308 + SINE/MIR_MIRc chr1 21662339 21662396 + SINE/MIR_MIR3 chr1 21662738 21662935 + SINE/MIR_MIRb chr1 21664002 21664126 - SINE/MIR_MIRc chr1 21664194 21664352 - SINE/MIR_MIRc chr1 21664596 21664625 - LINE/L2_L2b chr1 21664860 21665067 - SINE/MIR_MIRc chr1 21665069 21665273 - SINE/MIR_MIRb chr1 21665326 21665469 + SINE/MIR_MIRb chr1 21665989 21666069 - SINE/MIR_MIRb chr1 21667011 21667073 + Simple_repeat_(GTCTGT)n chr1 21667226 21667264 + Low_complexity_G-rich chr1 21667690 21667831 + SINE/MIR_MIRc chr1 21669299 21669352 + Simple_repeat_(CCG)n chr1 21669458 21669482 + Simple_repeat_(CCCCG)n chr1 21669494 21669634 - SINE/MIR_MIR3 chr1 21670042 21670095 - SINE/MIR_MIRc chr1 21670984 21671039 - DNA/hAT-Charlie_MER5A1 chr1 21672929 21673029 - LINE/L2_L2b chr1 21673416 21673529 - SINE/MIR_MIR3 chr1 21674110 21674243 + SINE/Alu_AluSx1 chr1 21674244 21674534 + SINE/Alu_AluSc chr1 21674535 21674721 + SINE/Alu_AluSx1 chr1 21675530 21675706 - SINE/MIR_MIRb chr1 21675777 21675796 + Simple_repeat_(A)n chr1 21676024 21676048 + Simple_repeat_(GT)n chr1 21676178 21676373 - SINE/Alu_AluJb chr1 21677257 21677357 - DNA/hAT-Charlie_MER5A1 chr1 21679595 21679680 - DNA/TcMar-Tigger_Tigger12c chr1 21679705 21679993 - SINE/Alu_AluSz chr1 21680150 21680259 - DNA/TcMar-Tigger_Tigger12c chr1 21681006 21681076 - LINE/L1_HAL1 chr1 21681143 21681258 - LINE/L1_HAL1 chr1 21681259 21681561 - SINE/Alu_AluSx1 chr1 21681562 21682269 - LINE/L1_HAL1 chr1 21682595 21682891 + SINE/Alu_AluSx4 chr1 21682979 21683271 + SINE/Alu_AluSx chr1 21683277 21683382 - LINE/L1_L1MA4A chr1 21683383 21683643 - SINE/Alu_AluJr chr1 21683644 21685320 - LINE/L1_L1MA4A chr1 21685321 21685614 - SINE/Alu_AluSz chr1 21685615 21685687 - LINE/L1_L1MA4A chr1 21685688 21685724 - tRNA_tRNA-Val-GTY chr1 21685725 21685835 - LINE/L1_L1MA4A chr1 21685836 21686115 + SINE/Alu_AluSz6 chr1 21686116 21686553 - LINE/L1_L1MA4A chr1 21686797 21687000 - LINE/L1_HAL1 chr1 21688219 21688516 - SINE/Alu_AluSx3 chr1 21688561 21688868 + SINE/Alu_AluSx chr1 21690453 21690511 + LINE/L2_L2d chr1 21690512 21690828 - SINE/Alu_AluJr chr1 21690829 21690992 + LINE/L2_L2d chr1 21691034 21691338 + SINE/Alu_AluSp chr1 21691356 21691643 + SINE/Alu_AluSx chr1 21691730 21691911 + LINE/L1_L1ME3F chr1 21692598 21692880 - LINE/L2_L2a chr1 21694291 21694601 + SINE/Alu_AluSg7 chr1 21695506 21695707 + SINE/Alu_AluJo chr1 21695929 21696130 + LINE/L1_L1MC5a chr1 21696158 21696464 + SINE/Alu_AluSp chr1 21696872 21696918 + Simple_repeat_(GGT)n chr1 21697382 21697691 + SINE/Alu_AluY chr1 21698254 21698312 + LINE/CR1_L3 chr1 21698695 21698906 + SINE/Alu_AluY chr1 21699051 21699189 - SINE/Alu_AluSz6 chr1 21699193 21699497 - SINE/Alu_AluY chr1 21699502 21699815 - SINE/Alu_AluY chr1 21699816 21699833 + Simple_repeat_(AC)n chr1 21700792 21701084 + SINE/Alu_AluSx3 chr1 21701085 21701394 + SINE/Alu_AluSx3 chr1 21702115 21702241 - DNA/hAT-Charlie_Charlie22a chr1 21702420 21702719 + LINE/L2_L2a chr1 21702734 21702784 + DNA/hAT-Charlie_MER3 chr1 21702751 21702938 - DNA/hAT-Charlie_MER3 chr1 21702940 21703319 + LINE/L2_L2a chr1 21703738 21704021 - SINE/Alu_AluJr chr1 21704547 21704710 + LINE/L1_L1ME3Cz chr1 21704946 21705048 + Low_complexity_G-rich chr1 21707811 21707878 - SINE/MIR_MIRb chr1 21707882 21708165 + SINE/Alu_AluSz chr1 21708166 21708209 + Simple_repeat_(AAAAC)n chr1 21708219 21708502 + SINE/Alu_AluSg chr1 21708506 21708533 + Simple_repeat_(AAAC)n chr1 21708591 21708601 + Simple_repeat_(AAAAG)n chr1 21708602 21708914 + SINE/Alu_AluSx3 chr1 21708915 21708928 + Simple_repeat_(AAAAG)n chr1 21708929 21709185 - SINE/Alu_AluJr chr1 21709460 21709550 + LINE/L1_L1ME3G chr1 21709964 21710031 + Simple_repeat_(ATTA)n chr1 21710815 21711080 - SINE/Alu_AluJr4 chr1 21711107 21711744 + LINE/L1_HAL1 chr1 21711964 21712080 + SINE/MIR_MIR chr1 21712081 21712386 + SINE/Alu_AluSx chr1 21712387 21712444 + SINE/MIR_MIR chr1 21712629 21712927 - SINE/Alu_AluSz chr1 21713032 21713100 + Simple_repeat_(CTCT)n chr1 21713101 21713409 - SINE/Alu_AluJr chr1 21713846 21713970 + SINE/MIR_MIRb chr1 21714282 21714560 - SINE/Alu_AluJo chr1 21714863 21714920 + SINE/Alu_AluJo chr1 21714922 21715029 + SINE/Alu_AluJb chr1 21715777 21715806 + Simple_repeat_(TCATG)n chr1 21715890 21716589 - DNA/TcMar-Tigger_MER44C chr1 21716747 21717025 + SINE/Alu_AluY chr1 21717121 21717433 + SINE/Alu_AluJb chr1 21717434 21717498 + Simple_repeat_(ACAA)n chr1 21717677 21717936 + SINE/Alu_AluSg chr1 21717937 21717961 + Simple_repeat_(AAAAC)n chr1 21718559 21718818 - SINE/Alu_AluSg chr1 21718986 21719318 + SINE/Alu_AluSz chr1 21719433 21719467 + Low_complexity_A-rich chr1 21719468 21719604 + SINE/Alu_FLAM_C chr1 21719609 21719910 + SINE/Alu_AluSq chr1 21719911 21719949 + Low_complexity_A-rich chr1 21720192 21720366 - DNA/TcMar-Tigger_Tigger19a chr1 21720511 21720828 - SINE/Alu_AluSz chr1 21720839 21720976 - SINE/Alu_FRAM chr1 21721461 21721570 - DNA/TcMar-Tigger_Tigger20a chr1 21722265 21722558 + SINE/Alu_AluJr chr1 21722563 21722853 + SINE/Alu_AluJb chr1 21723237 21723531 + SINE/Alu_AluSz chr1 21723672 21723704 + Low_complexity_A-rich chr1 21724322 21724459 - SINE/MIR_MIRc chr1 21725183 21725392 + SINE/MIR_MIRc chr1 21725470 21725765 + SINE/Alu_AluSp chr1 21729006 21729304 + SINE/Alu_AluSg7 chr1 21730137 21730159 + Low_complexity_A-rich chr1 21730160 21730476 + SINE/Alu_AluSz chr1 21730501 21730703 + SINE/Alu_AluSx chr1 21730759 21731057 - SINE/Alu_AluSc chr1 21731266 21731563 - SINE/Alu_AluSx chr1 21731599 21731884 + SINE/Alu_AluSx chr1 21732050 21732291 + SINE/Alu_AluSz chr1 21732294 21732458 + DNA/hAT-Charlie_Charlie2a chr1 21732466 21732658 + DNA/hAT-Charlie_Charlie2b chr1 21732664 21732973 + DNA/hAT-Charlie_Charlie2b chr1 21733134 21733429 + SINE/Alu_AluSz chr1 21733787 21733951 - SINE/Alu_AluJb chr1 21734117 21734428 + SINE/Alu_AluJr chr1 21735320 21735468 + SINE/Alu_AluJb chr1 21735469 21735606 + SINE/Alu_AluSz6 chr1 21735607 21735907 + SINE/Alu_AluSp chr1 21735908 21736085 + SINE/Alu_AluSz6 chr1 21736086 21736277 + SINE/Alu_AluJb chr1 21737202 21737292 + LINE/L1_L1MA9 chr1 21737293 21737441 + DNA/TcMar-Tigger_Tigger13a chr1 21737465 21737550 + DNA/TcMar-Tigger_Tigger13a chr1 21737900 21738048 - SINE/Alu_AluJr chr1 21738049 21738357 - SINE/Alu_AluSq2 chr1 21738358 21738657 - SINE/Alu_AluSq2 chr1 21738658 21738704 - SINE/Alu_AluJr chr1 21738800 21738906 - LINE/L2_L2a chr1 21739217 21739237 - LINE/L1_L1MA7 chr1 21739238 21739545 + SINE/Alu_AluSx1 chr1 21739546 21739915 - LINE/L1_L1MA7 chr1 21739916 21740211 - SINE/Alu_AluSp chr1 21740212 21740351 - LINE/L1_L1MA7 chr1 21740535 21740600 + LINE/L1_L1ME3B chr1 21740714 21741139 + LINE/L1_L1ME3B chr1 21741140 21741179 + snRNA_U7 chr1 21741180 21741694 + LINE/L1_L1ME3B chr1 21741695 21741977 + SINE/Alu_AluJr chr1 21741978 21742066 + LINE/L1_L1ME3B chr1 21742258 21742573 + SINE/Alu_AluSx chr1 21742574 21742616 + Simple_repeat_(AAAGAA)n chr1 21743449 21743536 - LINE/L2_L2 chr1 21743548 21743837 + LINE/L1_L1ME3B chr1 21743883 21743986 - LINE/L2_L2 chr1 21744001 21744131 + DNA/hAT-Charlie_Charlie4 chr1 21744162 21744470 + SINE/Alu_AluSx chr1 21744494 21744802 + SINE/Alu_AluSx1 chr1 21745280 21745313 + Simple_repeat_(TATT)n chr1 21745334 21745586 + SINE/Alu_AluJb chr1 21746218 21746454 + SINE/Alu_AluSz6 chr1 21746455 21746475 + Simple_repeat_(TACA)n chr1 21747567 21747872 - SINE/Alu_AluSq2 chr1 21748530 21748582 + LINE/L1_L1ME4c chr1 21748629 21748928 + SINE/Alu_AluSz6 chr1 21749123 21749163 - SINE/Alu_AluJr chr1 21749219 21749595 + LINE/L2_L2a chr1 21749596 21749901 - SINE/Alu_AluJr chr1 21749902 21750590 + LINE/L2_L2a chr1 21750591 21750898 + SINE/Alu_AluSp chr1 21750984 21751004 + Simple_repeat_(AGCT)n chr1 21751761 21752046 + SINE/Alu_AluSg chr1 21752095 21752201 - LINE/L1_L1PB4 chr1 21753270 21753511 + SINE/Alu_AluSp chr1 21753545 21753848 + SINE/Alu_AluSz6 chr1 21753881 21754180 + SINE/Alu_AluSg chr1 21754485 21755157 + LTR/ERVL_LTR41 chr1 21755279 21755578 + SINE/Alu_AluJb chr1 21755579 21755629 + LTR/ERVL_LTR41 chr1 21755750 21755873 + SINE/Alu_AluJr chr1 21755884 21756190 + SINE/Alu_AluSp chr1 21756199 21756505 + SINE/Alu_AluSx1 chr1 21757597 21757639 + Low_complexity_A-rich chr1 21757830 21758185 + LINE/L1_L1MC4a chr1 21758186 21758224 + Simple_repeat_(AC)n chr1 21758225 21758481 + LINE/L1_L1MC4a chr1 21758482 21758755 + SINE/Alu_AluSg chr1 21758756 21758793 + Simple_repeat_(AATG)n chr1 21758794 21758889 + LINE/L1_L1MC4 chr1 21758890 21759205 + SINE/Alu_AluSz chr1 21759206 21759456 + LINE/L1_L1MC4 chr1 21759458 21760294 + LINE/L1_L1MC4 chr1 21760434 21760749 + SINE/Alu_AluSq4 chr1 21760813 21761119 + SINE/Alu_AluSp chr1 21761291 21761484 - SINE/Alu_AluJb chr1 21761494 21761554 + LINE/L1_L1MC4a chr1 21761593 21761801 + SINE/Alu_AluJr chr1 21761984 21762292 + SINE/Alu_AluSz6 chr1 21762586 21762888 + SINE/Alu_AluSg chr1 21763533 21763822 + SINE/Alu_AluSp chr1 21763823 21763872 + Low_complexity_GA-rich chr1 21763960 21764154 + DNA/hAT-Charlie_Charlie8 chr1 21764158 21764453 + SINE/Alu_AluSz6 chr1 21764466 21764730 + SINE/Alu_AluSg7 chr1 21764731 21765018 - LTR/ERVL_MER21B chr1 21765334 21765625 + SINE/Alu_AluSz chr1 21765740 21765930 + SINE/Alu_AluSx chr1 21766086 21766357 + SINE/Alu_AluSz6 chr1 21766680 21766796 - SINE/Alu_AluSx3 chr1 21766797 21767093 + SINE/Alu_AluSx1 chr1 21767138 21767284 - SINE/Alu_AluJb chr1 21767316 21767623 - SINE/Alu_AluSg chr1 21767641 21767680 - SINE/Alu_FLAM_A chr1 21767926 21768219 + SINE/Alu_AluY chr1 21768802 21768963 - SINE/Alu_FRAM chr1 21769189 21769291 + LINE/L1_L1ME3A chr1 21769303 21769417 + LINE/L1_L1PA14 chr1 21769421 21769710 - SINE/Alu_AluSz chr1 21769726 21769898 + SINE/Alu_AluJb chr1 21769902 21770039 + LINE/L1_L1ME3A chr1 21770053 21770176 + SINE/Alu_FLAM_C chr1 21770178 21770481 + LINE/L1_L1ME3A chr1 21770482 21770781 - SINE/Alu_AluSx3 chr1 21770782 21770824 + LINE/L1_L1ME3A chr1 21770842 21771132 + SINE/Alu_AluSz6 chr1 21771170 21771386 + SINE/Alu_AluSx chr1 21771389 21771494 + Simple_repeat_(ATTATTA)n chr1 21771527 21771637 + LINE/L1_L1ME4b chr1 21771638 21771930 + SINE/Alu_AluSp chr1 21771931 21772269 + LINE/L1_L1ME4b chr1 21772342 21772636 + SINE/Alu_AluY chr1 21772640 21772953 + SINE/Alu_AluSx chr1 21772978 21773282 + SINE/Alu_AluSg chr1 21773292 21773384 + LINE/L1_L1ME4b chr1 21773385 21773513 + SINE/Alu_FLAM_C chr1 21773514 21773852 + LINE/L1_L1MC5a chr1 21773861 21774180 + SINE/Alu_AluSz6 chr1 21774181 21774224 + Simple_repeat_(AAT)n chr1 21774248 21774354 + LINE/L1_L1MA9 chr1 21774407 21774708 + SINE/Alu_AluSc chr1 21774711 21774990 + SINE/Alu_AluSx chr1 21774991 21775036 + Simple_repeat_(AAAT)n chr1 21775096 21775238 - SINE/Alu_AluSx3 chr1 21775272 21775554 - SINE/Alu_AluSz6 chr1 21775762 21775891 + SINE/Alu_FLAM_A chr1 21775928 21776156 + LINE/L1_L1ME4b chr1 21776455 21776612 + SINE/Alu_FLAM_C chr1 21776672 21776965 + SINE/Alu_AluSq chr1 21776969 21777295 + SINE/Alu_AluJo chr1 21777296 21777315 + Simple_repeat_(TTAA)n chr1 21777390 21777668 + SINE/Alu_AluJr chr1 21777690 21777845 + LINE/L1_L1MB5 chr1 21777846 21778167 + SINE/Alu_AluSx3 chr1 21778168 21778487 + LINE/L1_L1MB5 chr1 21778488 21778618 + SINE/Alu_FLAM_A chr1 21778654 21778752 + SINE/Alu_AluJr chr1 21778753 21779079 - SINE/Alu_AluY chr1 21779080 21779254 + SINE/Alu_AluJr chr1 21779264 21779573 + SINE/Alu_AluSx1 chr1 21779574 21780553 + LINE/L1_L1MB5 chr1 21780568 21780706 + LINE/L1_L1MB8 chr1 21780739 21781039 - SINE/Alu_AluSg chr1 21781135 21781453 + SINE/Alu_AluSp chr1 21781465 21781738 + SINE/Alu_AluSz chr1 21781907 21782013 + LINE/L2_L2d2 chr1 21782991 21783025 + Simple_repeat_(CCG)n chr1 21783997 21784068 + SINE/MIR_MIR1_Amn chr1 21784095 21784392 + SINE/Alu_AluSp chr1 21784434 21784646 - LINE/L2_L2c chr1 21784710 21785020 + SINE/Alu_AluYk2 chr1 21785021 21785070 + Simple_repeat_(CAA)n chr1 21785165 21785463 - SINE/Alu_AluSq2 chr1 21785464 21785750 - LTR/ERV1_MER41B chr1 21785751 21786034 + SINE/Alu_AluSg chr1 21786035 21786132 - LTR/ERV1_MER41B chr1 21786411 21786708 + SINE/Alu_AluSx1 chr1 21786725 21787212 + LINE/L2_L2 chr1 21787213 21787258 - LTR/ERV1_LTR10C chr1 21787259 21787561 + SINE/Alu_AluSc chr1 21787562 21787800 - LTR/ERV1_LTR10C chr1 21787801 21788105 + SINE/Alu_AluSp chr1 21788106 21788334 - LTR/ERV1_LTR10C chr1 21788335 21788614 - SINE/Alu_AluSp chr1 21788615 21788693 - LTR/ERV1_LTR10C chr1 21788694 21788749 + LINE/L2_L2 chr1 21788750 21789029 + LINE/L2_L2 chr1 21789078 21789118 - SINE/Alu_AluSx chr1 21789126 21789382 + SINE/Alu_AluSz chr1 21789394 21789462 + LINE/L2_L2 chr1 21789463 21789492 + LTR/ERV1_MER4B chr1 21789493 21789792 + SINE/Alu_AluSq chr1 21789793 21790371 + LTR/ERV1_MER4B chr1 21790402 21790516 + LINE/L2_L2 chr1 21790517 21790714 + SINE/Alu_AluJr chr1 21790776 21791079 + SINE/Alu_AluSq chr1 21791080 21791391 + SINE/Alu_AluSx1 chr1 21791392 21791926 - LTR/ERV1_MER41B chr1 21791927 21792182 + SINE/Alu_AluYk2 chr1 21792183 21792255 - LTR/ERV1_MER41B chr1 21792257 21792383 - LINE/L2_L2 chr1 21792385 21792690 - SINE/Alu_AluSx chr1 21792751 21792775 + Simple_repeat_(TTTA)n chr1 21792787 21793072 - SINE/Alu_AluSg chr1 21793073 21793368 - SINE/Alu_AluJb chr1 21793371 21793587 - SINE/Alu_AluJb chr1 21793588 21793888 - SINE/Alu_AluY chr1 21793889 21793950 - SINE/Alu_AluJb chr1 21793961 21794034 - DNA/hAT-Charlie_MER33 chr1 21794037 21794208 + SINE/Alu_AluSx4 chr1 21794224 21794534 + SINE/Alu_AluJb chr1 21794540 21794764 - DNA/hAT-Charlie_MER33 chr1 21794769 21794988 + LINE/L2_L2 chr1 21794989 21795452 - LTR/ERV1_LTR27C chr1 21795453 21795752 + SINE/Alu_AluSg chr1 21795753 21796004 - LTR/ERV1_LTR27C chr1 21796005 21796087 + LINE/L2_L2 chr1 21796118 21796410 - SINE/Alu_AluSx1 chr1 21796436 21796735 + SINE/Alu_AluSq2 chr1 21796808 21797130 + SINE/Alu_AluJb chr1 21797302 21797609 - SINE/Alu_AluSz chr1 21797902 21798075 - SINE/Alu_AluJb chr1 21798076 21798383 - SINE/Alu_AluY chr1 21798384 21798519 - SINE/Alu_AluJb chr1 21798560 21798855 - SINE/Alu_AluSz6 chr1 21798914 21799224 - SINE/Alu_AluSq2 chr1 21799470 21799778 - SINE/Alu_AluSx1 chr1 21800056 21800228 - SINE/Alu_AluJb chr1 21800229 21800528 - SINE/Alu_AluY chr1 21800529 21800667 - SINE/Alu_AluJb chr1 21800895 21800921 + Simple_repeat_(TTTA)n chr1 21800924 21801193 - SINE/Alu_AluSq2 chr1 21801507 21801807 - SINE/Alu_AluSx chr1 21801808 21801930 - SINE/Alu_AluJb chr1 21802209 21802495 + SINE/Alu_AluSz chr1 21802569 21802629 + SINE/MIR_MIRb chr1 21802727 21802801 + LINE/L2_L2b chr1 21802770 21802916 + SINE/MIR_MIR chr1 21802926 21803212 + SINE/Alu_AluJb chr1 21803225 21803431 + SINE/Alu_AluSx3 chr1 21803433 21803480 + Simple_repeat_(AC)n chr1 21803509 21803806 - SINE/Alu_AluY chr1 21803810 21804202 + LINE/L2_L2a chr1 21804203 21804361 - SINE/Alu_FRAM chr1 21804362 21804443 + LINE/L2_L2a chr1 21804443 21804568 - SINE/MIR_MIR chr1 21804566 21804744 + SINE/MIR_MIRb chr1 21804748 21804896 + SINE/Alu_AluSx chr1 21804897 21804935 + Simple_repeat_(AAAAC)n chr1 21805046 21805344 - SINE/Alu_AluSg7 chr1 21805345 21805416 - SINE/Alu_Alu chr1 21805446 21805744 + LINE/L2_L2c chr1 21805745 21805826 - DNA/hAT-Tip100_Arthur1B chr1 21805827 21806128 + SINE/Alu_AluSx chr1 21806129 21806474 - DNA/hAT-Tip100_Arthur1B chr1 21806475 21806779 - SINE/Alu_AluJr chr1 21806789 21807090 - SINE/Alu_AluSx3 chr1 21807091 21807170 - DNA/hAT-Tip100_Arthur1B chr1 21807171 21807479 - SINE/Alu_AluY chr1 21807480 21808015 - DNA/hAT-Tip100_Arthur1 chr1 21808018 21808307 - SINE/Alu_AluSx chr1 21808351 21808674 - SINE/Alu_AluSx1 chr1 21808675 21808819 - SINE/Alu_AluSx chr1 21808847 21808912 - LTR/ERV1_MER4D0 chr1 21808913 21808956 - LINE/L1_L1MB2 chr1 21808957 21809170 - SINE/Alu_AluJr4 chr1 21809171 21809432 - SINE/Alu_AluSx3 chr1 21809433 21809524 - SINE/Alu_AluJr4 chr1 21809525 21809635 - LINE/L1_L1MB2 chr1 21809636 21809662 - LTR/ERV1_MER4D0 chr1 21809672 21809931 + SINE/Alu_AluSx chr1 21809932 21810021 + SINE/Alu_AluSx3 chr1 21810022 21810302 + SINE/Alu_AluY chr1 21810303 21810341 + Simple_repeat_(AAAACA)n chr1 21810350 21810405 - DNA/hAT-Tip100_Arthur1B chr1 21810547 21810817 - SINE/Alu_AluSx chr1 21810967 21811040 - SINE/MIR_MIR chr1 21811041 21811345 - SINE/Alu_AluSx3 chr1 21811346 21811438 - SINE/MIR_MIR chr1 21811519 21811591 - SINE/MIR_MIR3 chr1 21811590 21811978 - LINE/L2_L2a chr1 21812801 21813010 + SINE/MIR_MIRb chr1 21813015 21813313 + SINE/Alu_AluSc5 chr1 21813532 21813598 - SINE/MIR_MIR chr1 21813599 21813844 - LTR/ERVL-MaLR_MLT1L chr1 21813877 21814169 - SINE/Alu_AluSx chr1 21814219 21814396 - SINE/MIR_MIRb chr1 21815498 21815625 + SINE/Alu_FLAM_C chr1 21815656 21815776 + SINE/MIR_MIR chr1 21816432 21816546 - SINE/MIR_MIR3 chr1 21816728 21817277 + LINE/L2_L2a chr1 21817315 21817615 - SINE/Alu_AluJb chr1 21817616 21817862 - LINE/L1_L1MC3 chr1 21817863 21818175 - SINE/Alu_AluSz chr1 21818176 21818311 - SINE/Alu_AluSp chr1 21818312 21818712 - LINE/L1_L1MC3 chr1 21818720 21819091 + LINE/L2_L2a chr1 21819092 21819380 + SINE/Alu_AluSp chr1 21819382 21819423 + Simple_repeat_(TTA)n chr1 21819427 21819486 + LTR/ERVL-MaLR_MSTB chr1 21819560 21819750 + LINE/L1_L1MB3 chr1 21819751 21820164 - LTR/ERVL-MaLR_MSTA chr1 21820165 21820244 + LINE/L1_L1MB3 chr1 21820255 21820551 + SINE/Alu_AluYc chr1 21820564 21820772 + LINE/L2_L2a chr1 21820779 21820914 + SINE/MIR_MIRb chr1 21820916 21821198 + SINE/Alu_AluJb chr1 21821246 21821393 + SINE/MIR_MIR chr1 21821963 21822121 + SINE/MIR_MIRb chr1 21822551 21822584 + Low_complexity_G-rich chr1 21823237 21823257 + Simple_repeat_(ATA)n chr1 21824881 21825287 + DNA/hAT-Charlie_Charlie4a chr1 21825319 21825425 + DNA/hAT-Blackjack_MER81 chr1 21825458 21825514 - SINE/MIR_MIRc chr1 21825654 21825769 - SINE/MIR_MIR chr1 21825780 21826082 - SINE/Alu_AluSx chr1 21826083 21826366 - SINE/Alu_AluJo chr1 21826367 21826502 - SINE/Alu_AluJo chr1 21826514 21826808 - SINE/Alu_AluSp chr1 21826825 21826890 - SINE/MIR_MIR chr1 21826893 21826935 + SINE/Alu_AluYj4 chr1 21826936 21827216 + SINE/Alu_AluSx1 chr1 21827217 21827408 - SINE/MIR_MIR chr1 21827403 21827498 + SINE/MIR_MIRb chr1 21827506 21827627 + LINE/L2_L2c chr1 21827631 21827753 - SINE/MIR_MIR3 chr1 21828583 21828761 - SINE/MIR_MIRb chr1 21829179 21829289 - SINE/MIR_MIRb chr1 21830405 21830714 + SINE/Alu_AluSq chr1 21831924 21832253 + LINE/L2_L2a chr1 21832255 21832280 + Simple_repeat_(ATGA)n chr1 21833704 21833783 - LINE/L2_L2d2 chr1 21833997 21834157 - SINE/MIR_MIRb chr1 21835048 21835102 + Simple_repeat_(CACT)n chr1 21835140 21835286 - SINE/Alu_FAM chr1 21835470 21835511 + Simple_repeat_(GCCTCTCT)n chr1 21835702 21836006 + SINE/Alu_AluSz chr1 21836049 21836237 - SINE/MIR_MIRc chr1 21836249 21836324 - SINE/MIR_MIRb chr1 21836325 21836620 - SINE/Alu_AluSq2 chr1 21836621 21836700 - SINE/MIR_MIRb chr1 21837293 21837588 - SINE/Alu_AluSx chr1 21837729 21837853 - SINE/MIR_MIR chr1 21837854 21838165 + SINE/Alu_AluSz6 chr1 21838166 21838288 - SINE/MIR_MIR chr1 21840127 21840304 - SINE/Alu_AluSx chr1 21840305 21840605 - SINE/Alu_AluY chr1 21840606 21840701 - SINE/Alu_AluSx chr1 21840702 21840791 - SINE/Alu_AluJo chr1 21841367 21841498 - SINE/MIR_MIRb chr1 21842534 21842647 - SINE/MIR_MIR3 chr1 21843587 21843649 + DNA/hAT-Tip100_MER91B chr1 21843650 21843935 - SINE/Alu_AluSz chr1 21843936 21843990 + DNA/hAT-Tip100_MER91B chr1 21844608 21844847 + SINE/MIR_MIR chr1 21844854 21844964 - DNA/hAT-Charlie_MER113A chr1 21844968 21845268 + SINE/Alu_AluSg chr1 21845371 21845673 - SINE/Alu_AluSx3 chr1 21845739 21845901 - SINE/MIR_MIRb chr1 21846718 21847012 + SINE/Alu_AluSx chr1 21847017 21847103 - SINE/MIR_MIR chr1 21847112 21847200 + DNA/hAT-Charlie_MER3 chr1 21847504 21847533 + Simple_repeat_(GATGGA)n chr1 21848338 21848459 + LINE/L2_L2b chr1 21848457 21848552 + LINE/L2_L2c chr1 21849574 21849684 - SINE/MIR_MIRb chr1 21849686 21849977 - SINE/Alu_AluSg chr1 21850702 21850856 + DNA/TcMar-Tc2_MER104 chr1 21850880 21850974 - SINE/MIR_MIR chr1 21850975 21851271 - SINE/Alu_AluSx1 chr1 21851272 21851424 - SINE/MIR_MIR chr1 21853374 21853461 - SINE/MIR_MIRc chr1 21853462 21853751 + SINE/Alu_AluY chr1 21853752 21853798 + Simple_repeat_(AAAAT)n chr1 21853807 21853972 - SINE/MIR_MIRc chr1 21855956 21856411 + LINE/L2_L2a chr1 21856412 21856716 - SINE/Alu_AluSx1 chr1 21856717 21856945 + LINE/L2_L2a chr1 21857481 21858140 + LINE/L2_L2b chr1 21858156 21858220 + LINE/L2_L2-3_Crp chr1 21858269 21858636 + LINE/L2_L2b chr1 21858681 21859013 + LTR/ERVL_LTR16C chr1 21859047 21859346 - SINE/Alu_AluSg chr1 21859350 21859502 + LINE/L2_L2b chr1 21860499 21860784 - SINE/Alu_AluY chr1 21860785 21861017 - LINE/L2_L2b chr1 21861036 21861257 + LINE/L2_L2b chr1 21861236 21861375 - LINE/L2_L2c chr1 21861376 21861502 + SINE/Alu_FLAM_C chr1 21861503 21861571 - LINE/L2_L2c chr1 21862144 21862301 + LINE/L1_L1ME3G chr1 21862302 21862613 + SINE/Alu_AluSx chr1 21862614 21862710 + LINE/L1_L1ME3G chr1 21862794 21863090 + SINE/Alu_AluSq2 chr1 21863091 21863400 + SINE/Alu_AluY chr1 21863579 21863717 + SINE/Alu_AluSx chr1 21863725 21863855 + SINE/Alu_FLAM_C chr1 21864534 21864719 + SINE/MIR_MIRb chr1 21866242 21866452 + LTR/ERVL-MaLR_MLT1K chr1 21866537 21866671 + LTR/ERVL-MaLR_MLT1K chr1 21867054 21867084 + Simple_repeat_(T)n chr1 21867121 21867382 - SINE/Alu_AluJr chr1 21867386 21867596 + SINE/MIR_MIR chr1 21867727 21868029 - SINE/Alu_AluSq chr1 21868041 21868337 - SINE/Alu_AluSx1 chr1 21868426 21868470 + Simple_repeat_(ATAATG)n chr1 21868474 21868563 + LINE/L2_L2b chr1 21868661 21868693 + Simple_repeat_(CAGGGGT)n chr1 21868698 21868853 + SINE/MIR_MIR3 chr1 21869008 21869049 + Low_complexity_G-rich chr1 21869479 21869534 + Simple_repeat_(GAG)n chr1 21870785 21870816 + Simple_repeat_(CACCA)n chr1 21871151 21871195 + Simple_repeat_(CCA)n chr1 21871255 21871547 - SINE/Alu_AluJb chr1 21871548 21871768 + SINE/MIR_MIR chr1 21871937 21871978 - SINE/MIR_MIR chr1 21871975 21872086 - SINE/MIR_MIR chr1 21872867 21872902 + Simple_repeat_(CCCTG)n chr1 21873206 21873288 - SINE/MIR_MIRc chr1 21873538 21873635 - SINE/MIR_MIR3 chr1 21874116 21874327 - SINE/MIR_MIRb chr1 21875012 21875192 - SINE/MIR_MIRb chr1 21875201 21875467 + LTR/ERVL_LTR16A1 chr1 21875471 21875616 - SINE/MIR_MIRb chr1 21876096 21876231 + LINE/L2_L2d2 chr1 21876777 21877089 + SINE/Alu_AluSx chr1 21877237 21877482 - SINE/MIR_MIRb chr1 21877494 21877797 - SINE/Alu_AluSq chr1 21877816 21877963 + SINE/MIR_MIRb chr1 21877962 21878028 - SINE/MIR_MIR3 chr1 21878795 21878955 - SINE/MIR_MIR3 chr1 21879170 21879655 - LINE/CR1_L3 chr1 21879656 21879952 - SINE/Alu_AluSz chr1 21879953 21880041 - LINE/CR1_L3 chr1 21880939 21881142 - LINE/L2_L2b chr1 21881662 21881972 + SINE/Alu_AluSz chr1 21882255 21882346 + SINE/MIR_MIR chr1 21882401 21882443 + Simple_repeat_(AC)n chr1 21882494 21882563 + DNA/hAT-Charlie_MER3 chr1 21882564 21882618 - DNA/TcMar-Mariner_MADE1 chr1 21882618 21882736 + DNA/hAT-Charlie_MER3 chr1 21882745 21882919 - LINE/CR1_L3 chr1 21883074 21883205 + SINE/MIR_MIRb chr1 21883320 21883624 - SINE/Alu_AluJr chr1 21883735 21883858 - SINE/MIR_MIR chr1 21883863 21884016 - SINE/MIR_MIR3 chr1 21884074 21884304 - SINE/MIR_MIR chr1 21886019 21886052 + Simple_repeat_(GACAGA)n chr1 21886667 21886801 + Simple_repeat_(GGAT)n chr1 21887120 21887185 + Low_complexity_G-rich chr1 21888473 21888605 - SINE/Alu_FLAM_C chr1 21888880 21888925 + LINE/CR1_L3 chr1 21888926 21889089 - SINE/Alu_FRAM chr1 21889090 21889305 + LINE/CR1_L3 chr1 21889333 21889437 + SINE/MIR_MIR chr1 21889450 21889675 + SINE/MIR_MIRb chr1 21889816 21889937 + LINE/CR1_L3 chr1 21891048 21891216 + LINE/L2_L2c chr1 21891278 21891441 + SINE/MIR_MIRb chr1 21891451 21891576 + LINE/L2_L2c chr1 21891613 21891877 - SINE/Alu_AluJo chr1 21892307 21892344 + Simple_repeat_(CCCCAG)n chr1 21892593 21892890 + SINE/Alu_AluSz chr1 21895988 21896061 - LINE/L2_L2a chr1 21897436 21897586 + LINE/L2_L2c chr1 21897587 21897898 + LINE/L1_L1ME2 chr1 21897935 21898082 + LINE/L2_L2c chr1 21898360 21898390 + Simple_repeat_(TCCCTC)n chr1 21898446 21898519 - DNA/hAT-Charlie_MER5A chr1 21899391 21899592 - SINE/MIR_MIR chr1 21899680 21899776 - SINE/MIR_MIRc chr1 21899988 21900085 - SINE/MIR_MIR3 chr1 21900659 21900892 + SINE/Alu_AluSz chr1 21900901 21901161 + DNA/TcMar-Tigger_MER46C chr1 21901165 21901243 - SINE/MIR_MIRc chr1 21901222 21901302 + SINE/MIR_MIRb chr1 21901345 21901417 + Low_complexity_G-rich chr1 21901805 21901878 + Simple_repeat_(CCTG)n chr1 21902374 21902603 - SINE/MIR_MIR chr1 21902769 21902847 + SINE/MIR_MIR3 chr1 21902940 21903015 + LINE/L2_L2a chr1 21903034 21903170 - SINE/MIR_MIRb chr1 21903319 21903630 + SINE/Alu_AluSq chr1 21903952 21903996 + SINE/MIR_MIR3 chr1 21904014 21904436 - LINE/L2_L2c chr1 21905067 21905149 + LINE/L2_L2a chr1 21905150 21905203 + Simple_repeat_(AC)n chr1 21905204 21905236 + LINE/L2_L2a chr1 21905237 21905280 + Simple_repeat_(AC)n chr1 21905281 21905457 + LINE/L2_L2a chr1 21905469 21905752 + SINE/Alu_AluSq2 chr1 21905759 21905769 + Simple_repeat_(AATT)n chr1 21905770 21906018 + SINE/Alu_AluJr chr1 21906019 21906039 + Simple_repeat_(AATT)n chr1 21906044 21906137 - SINE/MIR_MIRc chr1 21906752 21906840 + DNA/hAT-Charlie_MER103C chr1 21907357 21907445 + SINE/MIR_MIR3 chr1 21907544 21907812 - SINE/Alu_AluJr chr1 21907859 21908056 + SINE/MIR_MIR chr1 21908844 21909128 + SINE/Alu_AluSg chr1 21909203 21909536 - LINE/RTE-X_L4_A_Mam chr1 21910414 21910585 + SINE/MIR_MIR3 chr1 21910563 21910690 + LINE/L2_L2a chr1 21910787 21910989 - SINE/MIR_MIRb chr1 21911024 21911194 + SINE/MIR_MIRc chr1 21911820 21911911 + LINE/L2_L2d chr1 21912164 21912324 + SINE/MIR_MIRb chr1 21912468 21912544 - SINE/MIR_MIR3 chr1 21912545 21912694 + SINE/MIR_MIR1_Amn chr1 21912699 21912994 + SINE/Alu_AluSz chr1 21913841 21914061 + SINE/MIR_MIRb chr1 21914131 21914433 - LINE/L2_L2a chr1 21914395 21914499 - LINE/CR1_L3b chr1 21914598 21914722 + SINE/MIR_MIRc chr1 21915631 21915650 - SINE/MIR_MIRb chr1 21915651 21915768 + DNA/hAT-Charlie_MER3 chr1 21915769 21916080 + SINE/Alu_AluSx chr1 21916085 21916219 + SINE/Alu_AluSx3 chr1 21916220 21916533 + SINE/Alu_AluY chr1 21916534 21916709 + SINE/Alu_AluSx3 chr1 21916710 21916797 + DNA/hAT-Charlie_MER3 chr1 21916798 21916928 - SINE/MIR_MIRb chr1 21916953 21917124 + DNA/hAT-Charlie_Charlie19a chr1 21917150 21917494 + SINE/Alu_AluJo chr1 21917648 21917959 + LINE/L2_L2c chr1 21917649 21918049 + LINE/L2_L2a chr1 21918183 21918481 + SINE/Alu_AluSx chr1 21918522 21918665 - SINE/MIR_MIR chr1 21918666 21918736 + SINE/MIR_MIRb chr1 21918805 21918848 + LINE/L2_L2c chr1 21919144 21919442 + SINE/Alu_AluSz chr1 21919605 21919643 + SINE/MIR_MIR chr1 21919644 21919806 - DNA/hAT-Charlie_MER5A1 chr1 21919807 21919856 + SINE/MIR_MIR chr1 21919912 21920205 - SINE/Alu_AluSz chr1 21920217 21920363 - SINE/MIR_MIRb chr1 21921030 21921061 + Simple_repeat_(AATAAC)n chr1 21921125 21921263 - SINE/MIR_MIRb chr1 21922106 21922303 - SINE/MIR_MIRb chr1 21923126 21923429 + SINE/Alu_AluSx1 chr1 21923752 21923862 + LINE/L2_L2b chr1 21923867 21923915 - SINE/MIR_MIRc chr1 21924266 21924397 - LINE/L2_L2d2 chr1 21924845 21925062 + LINE/L2_L2d chr1 21925124 21925248 + SINE/MIR_MIRb chr1 21925749 21925865 + SINE/MIR_MIR3 chr1 21925866 21925887 + Simple_repeat_(T)n chr1 21925895 21926113 - SINE/Alu_AluSz chr1 21926132 21926247 + SINE/MIR_MIRb chr1 21926472 21926790 + SINE/Alu_AluSx chr1 21928004 21928215 - SINE/MIR_MIRc chr1 21928436 21928737 - SINE/Alu_AluSx3 chr1 21928755 21929060 - SINE/Alu_AluSp chr1 21929072 21929179 - LINE/L2_L2a chr1 21929402 21929571 - SINE/Alu_AluJb chr1 21929583 21929601 + Simple_repeat_(T)n chr1 21929649 21930219 + LINE/L2_L2b chr1 21930056 21930491 + LINE/L2_L2a chr1 21930492 21930789 + SINE/Alu_AluSz chr1 21930790 21930844 + LINE/L2_L2a chr1 21930864 21930892 + Simple_repeat_(CATT)n chr1 21932275 21932434 + SINE/MIR_MIR chr1 21932444 21932775 - LINE/L2_L2a chr1 21932783 21932900 + SINE/Alu_AluJo chr1 21932937 21933248 + SINE/Alu_AluJb chr1 21934369 21934469 - SINE/MIR_MIRc chr1 21934472 21934597 + SINE/Alu_FLAM_C chr1 21934599 21934894 - SINE/Alu_AluSx3 chr1 21934904 21935204 - SINE/Alu_AluSg chr1 21935333 21935511 + LINE/L2_L2b chr1 21935511 21935574 + LINE/L2_L2c chr1 21936531 21936637 - SINE/MIR_MIRb chr1 21937482 21937529 + Simple_repeat_(GGCCCGC)n chr1 21937554 21937613 + Low_complexity_G-rich chr1 21938107 21938410 - SINE/Alu_AluSg chr1 21939390 21939592 + SINE/MIR_MIR chr1 21940639 21940682 + Simple_repeat_(TCAT)n chr1 21940683 21940799 + DNA/hAT-Tip100_MER91A chr1 21940800 21941098 - SINE/Alu_AluSz chr1 21941099 21941156 + DNA/hAT-Tip100_MER91A chr1 21941291 21941354 + Low_complexity_GA-rich chr1 21941438 21941819 + LINE/L2_L2c chr1 21941890 21942075 - SINE/MIR_MIRc chr1 21942253 21942540 - DNA/hAT-Charlie_MER103C chr1 21942554 21942678 + LINE/L2_L2d2 chr1 21942846 21943143 - SINE/Alu_AluY chr1 21943195 21943460 - DNA/hAT-Charlie_Charlie10a chr1 21943472 21943777 + SINE/Alu_AluSq2 chr1 21943798 21944107 - SINE/Alu_AluSp chr1 21944185 21944251 - LINE/L1_L1MC5a chr1 21944273 21944418 - SINE/MIR_MIR3 chr1 21944991 21945301 - SINE/Alu_AluJb chr1 21945666 21945685 + Low_complexity_A-rich chr1 21945686 21946022 + SINE/Alu_AluSx1 chr1 21946037 21946182 + LINE/L1_L1PB1 chr1 21946183 21946478 + SINE/Alu_AluSq2 chr1 21946479 21946702 + LINE/L1_L1PB1 chr1 21946714 21946961 + SINE/MIR_MIRb chr1 21947356 21947414 - SINE/MIR_MIRb chr1 21947556 21947631 + SINE/MIR_MIR chr1 21947633 21947932 + SINE/Alu_AluSx1 chr1 21947935 21948240 + SINE/Alu_AluSg chr1 21949276 21949429 - SINE/MIR_MIR3 chr1 21949769 21949847 + SINE/MIR_MIR chr1 21950029 21950140 + DNA/hAT-Charlie_Charlie8 chr1 21950148 21950444 + SINE/Alu_AluSg chr1 21950494 21950615 + DNA/hAT-Charlie_MER102c chr1 21951952 21952022 - DNA/hAT-Tip100_MamTip2 chr1 21952133 21952336 - SINE/MIR_MIRb chr1 21953252 21953365 - LINE/CR1_Plat_L3 chr1 21953521 21953588 + LINE/L2_L2b chr1 21953681 21953890 - LINE/L1_L1MC4 chr1 21953891 21954192 - SINE/Alu_AluSp chr1 21954193 21954489 - SINE/Alu_AluSx chr1 21954490 21954817 - LINE/L1_L1MC4 chr1 21954827 21955130 - SINE/Alu_AluSg4 chr1 21955931 21956053 - SINE/MIR_MIRb chr1 21957362 21957434 + SINE/MIR_MIRc chr1 21957551 21957699 + DNA/hAT-Blackjack_MER63A chr1 21957704 21957998 - SINE/Alu_AluSz chr1 21957999 21958197 - LTR/Gypsy?_LTR85c chr1 21958595 21958746 + SINE/Alu_AluJb chr1 21958764 21958884 + SINE/MIR_MIRc chr1 21958937 21959080 + SINE/MIR_MIR3 chr1 21959125 21959153 + LTR/ERVL_MER21C chr1 21959154 21959467 - SINE/Alu_AluJr chr1 21959468 21959805 + LTR/ERVL_MER21C chr1 21959828 21959877 + SINE/Alu_AluJo chr1 21959878 21959925 + SINE/Alu_Alu chr1 21959928 21960208 + SINE/Alu_AluJo chr1 21960234 21960534 + SINE/Alu_AluSz6 chr1 21960547 21960580 + Low_complexity_A-rich chr1 21960581 21960842 + SINE/Alu_AluSx chr1 21960843 21960871 + Low_complexity_A-rich chr1 21961032 21961345 + SINE/Alu_AluY chr1 21961399 21961519 + SINE/MIR_MIR3 chr1 21961597 21961712 + LINE/L2_L2b chr1 21962232 21962385 + SINE/MIR_MIR3 chr1 21962409 21962818 - LINE/L1_L1ME3C chr1 21962991 21963131 - SINE/MIR_MIRb chr1 21963136 21963440 + SINE/Alu_AluSz chr1 21963446 21963522 - SINE/MIR_MIRb chr1 21963577 21963744 - LINE/L2_L2c chr1 21963927 21963973 - LINE/L2_L2b chr1 21964333 21964632 - SINE/Alu_AluSz chr1 21964843 21965162 - LTR/ERVL_LTR47A2 chr1 21965163 21965457 - SINE/Alu_AluSc chr1 21965468 21965753 - SINE/Alu_AluY chr1 21965754 21965828 - LTR/ERVL_LTR47A2 chr1 21965908 21966204 - SINE/Alu_AluSp chr1 21966224 21966383 + SINE/Alu_AluJb chr1 21966384 21966487 - LTR/ERVL_LTR47A2 chr1 21966580 21966789 + LTR/ERVL_LTR16C chr1 21966813 21966870 + LTR/ERVL-MaLR_MLT1L chr1 21966871 21966926 + LTR/ERV1_MER41D chr1 21966927 21967238 + SINE/Alu_AluSx1 chr1 21967239 21967488 + LTR/ERV1_MER41D chr1 21967514 21967825 + SINE/Alu_AluSz chr1 21967892 21968178 - SINE/Alu_AluJb chr1 21968230 21968402 + LINE/L2_L2 chr1 21968512 21968893 + LTR/ERVL_LTR33A_ chr1 21969054 21969346 + SINE/Alu_AluJb chr1 21969407 21969456 + LTR/ERVL-MaLR_MLT1J chr1 21969457 21969760 - SINE/Alu_AluSx4 chr1 21969761 21969962 + LTR/ERVL-MaLR_MLT1J chr1 21969980 21970031 + DNA/TcMar-Mariner_MADE1 chr1 21970027 21970256 - LTR/ERVL-MaLR_MLT1B chr1 21970257 21970520 + SINE/Alu_AluSx chr1 21970524 21971019 + LTR/ERV1_MER41A chr1 21971266 21971403 - LTR/ERV1_LTR24C chr1 21971404 21971699 + SINE/Alu_AluSz chr1 21971700 21971713 - LTR/ERV1_LTR24C chr1 21971714 21971880 + SINE/Alu_FRAM chr1 21971881 21971896 - LTR/ERV1_LTR24C chr1 21971897 21972174 + SINE/Alu_AluSg chr1 21972175 21972471 + SINE/Alu_AluSp chr1 21972475 21972778 + SINE/Alu_AluSq10 chr1 21972779 21973226 - LTR/ERV1_LTR24C chr1 21973280 21973380 - DNA/hAT-Charlie_MER103C chr1 21973389 21973488 - LINE/L2_L2a chr1 21973689 21973821 - LINE/L2_L2a chr1 21974339 21974391 + SINE/MIR_MIR3 chr1 21974392 21974696 - SINE/Alu_AluSp chr1 21974697 21974772 + SINE/MIR_MIR3 chr1 21975123 21975367 + SINE/Alu_AluSx chr1 21975374 21975578 - LINE/L1_L1M5 chr1 21975595 21975866 + LINE/L2_L2a chr1 21975867 21976170 - SINE/Alu_AluYe5 chr1 21976171 21976184 + LINE/L2_L2a chr1 21976185 21976498 - SINE/Alu_AluSx1 chr1 21976499 21976607 + LINE/L2_L2a chr1 21976606 21976754 - SINE/MIR_MIRb chr1 21977321 21977402 + SINE/MIR_MIR chr1 21977538 21977727 - SINE/MIR_MIR chr1 21977728 21978008 - SINE/Alu_AluJr chr1 21978009 21978057 - SINE/MIR_MIR chr1 21978063 21978143 - SINE/MIR_MIR chr1 21978200 21978316 - SINE/MIR_MIR3 chr1 21978630 21978753 - DNA/hAT-Tip100_MamTip2 chr1 21978894 21979056 + SINE/Alu_AluSx chr1 21979079 21979370 - SINE/Alu_AluSq2 chr1 21979373 21979426 - DNA/hAT-Charlie_MER3 chr1 21979434 21979753 - SINE/Alu_AluJb chr1 21979754 21979858 - DNA/hAT-Charlie_MER3 chr1 21979861 21980065 - DNA/hAT-Charlie_MER58A chr1 21980198 21980491 + SINE/Alu_AluSq2 chr1 21980643 21980776 + DNA/hAT-Tip100_MER91C chr1 21981330 21981447 - SINE/MIR_MIR3 chr1 21981531 21981699 + SINE/MIR_MIRb chr1 21981726 21982024 - SINE/Alu_AluY chr1 21982251 21982336 + DNA/hAT-Charlie_MER5A chr1 21982337 21982605 + SINE/Alu_AluSz chr1 21982606 21982669 + DNA/hAT-Charlie_MER5A chr1 21982670 21982923 - SINE/Alu_AluY chr1 21982924 21982969 + DNA/hAT-Charlie_MER5A chr1 21983068 21983373 + SINE/Alu_AluSx chr1 21983400 21983549 + SINE/Alu_FRAM chr1 21983550 21983567 + Simple_repeat_(TGA)n chr1 21983998 21984056 - LINE/L2_L2c chr1 21984463 21984661 + SINE/MIR_MIR3 chr1 21984665 21984965 + SINE/Alu_AluSx1 chr1 21985000 21985275 + SINE/Alu_AluJr chr1 21985276 21985566 - SINE/Alu_AluJb chr1 21985567 21985608 + SINE/Alu_AluJr chr1 21985610 21985904 + SINE/Alu_AluY chr1 21985965 21986091 - SINE/MIR_MIRb chr1 21986092 21986407 + SINE/Alu_AluSx1 chr1 21986408 21986439 - SINE/MIR_MIRb chr1 21987137 21987164 + Simple_repeat_(A)n chr1 21987189 21987468 + SINE/Alu_AluSg7 chr1 21987481 21987795 + srpRNA_7SLRNA chr1 21987816 21987913 + snRNA_U6 chr1 21987929 21988241 + SINE/Alu_AluSx3 chr1 21988298 21988309 + Simple_repeat_(AT)n chr1 21988310 21988610 + SINE/Alu_AluSx4 chr1 21988611 21988903 + SINE/Alu_AluY chr1 21988904 21989012 + Simple_repeat_(AT)n chr1 21989062 21989240 - SINE/MIR_MIR chr1 21989505 21989613 - DNA/hAT-Blackjack_MER81 chr1 21989774 21989868 - LINE/L2_L2c chr1 21989881 21990243 - LTR/ERVL-MaLR_THE1C chr1 21990258 21990381 - LINE/L2_L2c chr1 21990466 21990550 + SINE/Alu_AluSp chr1 21990615 21990914 + SINE/Alu_AluJb chr1 21991063 21991139 - LINE/L1_L1ME3Cz chr1 21991207 21991336 - SINE/Alu_AluSg4 chr1 21991337 21991635 - SINE/Alu_AluSz chr1 21991638 21991837 - LTR/ERV1_MER49 chr1 21991838 21992133 + SINE/Alu_AluSq2 chr1 21992134 21992245 - LTR/ERV1_MER49 chr1 21992247 21992403 + SINE/Alu_FAM chr1 21992404 21992694 - LTR/ERV1_MER49 chr1 21992695 21992992 + SINE/Alu_AluJb chr1 21992993 21993016 - LTR/ERV1_MER49 chr1 21993006 21993153 + LTR/ERV1_MER41D chr1 21993179 21993490 + SINE/Alu_AluSz chr1 21993557 21993842 - SINE/Alu_AluJb chr1 21993894 21994066 + LINE/L2_L2 chr1 21994176 21994557 + LTR/ERVL_LTR33A_ chr1 21994718 21995025 + SINE/Alu_AluJb chr1 21995086 21995131 + LTR/ERVL-MaLR_MLT1J chr1 21995132 21995436 - SINE/Alu_AluSx4 chr1 21995437 21995538 + LTR/ERVL-MaLR_MLT1J chr1 21995656 21995707 + DNA/TcMar-Mariner_MADE1 chr1 21995703 21995932 - LTR/ERVL-MaLR_MLT1B chr1 21995933 21996196 + SINE/Alu_AluSz6 chr1 21996200 21996695 + LTR/ERV1_MER41A chr1 21996942 21997078 - LTR/ERV1_LTR24C chr1 21997079 21997364 + SINE/Alu_AluSz chr1 21997365 21997407 - LTR/ERV1_LTR24C chr1 21997408 21997705 + SINE/Alu_AluSq10 chr1 21997706 21998149 - LTR/ERV1_LTR24C chr1 21998203 21998303 - DNA/hAT-Charlie_MER103C chr1 21998312 21998411 - LINE/L2_L2a chr1 21998615 21998717 - LINE/L2_L2a chr1 21999267 21999319 + SINE/MIR_MIR3 chr1 21999320 21999626 - SINE/Alu_AluSp chr1 21999627 21999702 + SINE/MIR_MIR3 chr1 22000049 22000293 + SINE/Alu_AluSx chr1 22000307 22000489 - LINE/L1_L1M5 chr1 22000520 22000816 + LINE/L2_L2b chr1 22000817 22001134 - SINE/Alu_AluSz chr1 22001135 22001237 + LINE/L2_L2b chr1 22001221 22001390 - SINE/MIR_MIRb chr1 22001957 22002038 + SINE/MIR_MIR chr1 22002172 22002346 - SINE/MIR_MIR chr1 22002373 22002412 - SINE/Alu_AluJr chr1 22002413 22002642 - SINE/Alu_AluJr chr1 22002697 22002777 - SINE/MIR_MIR chr1 22002834 22002950 - SINE/MIR_MIR3 chr1 22003264 22003366 - DNA/hAT-Tip100_MamTip2 chr1 22003528 22003692 + SINE/Alu_AluSx chr1 22003715 22004002 - SINE/Alu_AluSq2 chr1 22004005 22004061 - DNA/hAT-Charlie_MER3 chr1 22004062 22004372 - SINE/Alu_AluJb chr1 22004373 22004477 - DNA/hAT-Charlie_MER3 chr1 22004480 22004685 - DNA/hAT-Charlie_MER58A chr1 22004818 22005114 + SINE/Alu_AluSq2 chr1 22005266 22005399 + DNA/hAT-Tip100_MER91C chr1 22006171 22006338 + SINE/MIR_MIRc chr1 22006361 22006552 + SINE/Alu_AluSz chr1 22006578 22006716 + SINE/Alu_FRAM chr1 22006717 22006743 + Simple_repeat_(AAT)n chr1 22007182 22007240 - LINE/L2_L2c chr1 22007647 22007845 + SINE/MIR_MIR3 chr1 22007872 22008144 + SINE/Alu_AluJr chr1 22008146 22008439 - SINE/Alu_AluJb chr1 22008478 22008772 + SINE/Alu_AluY chr1 22008836 22008962 - SINE/MIR_MIRb chr1 22008963 22009083 + SINE/Alu_AluSx1 chr1 22009084 22009393 + SINE/Alu_AluY chr1 22009394 22009581 + SINE/Alu_AluSx1 chr1 22009582 22009613 - SINE/MIR_MIRb chr1 22010353 22010637 + SINE/Alu_AluSg7 chr1 22010650 22010964 + srpRNA_7SLRNA chr1 22010985 22011081 + snRNA_U6 chr1 22011098 22011410 + SINE/Alu_AluSx3 chr1 22011467 22011478 + Simple_repeat_(AT)n chr1 22011479 22011772 + SINE/Alu_AluSx4 chr1 22011773 22012072 + SINE/Alu_AluY chr1 22012073 22012209 + Simple_repeat_(AT)n chr1 22012227 22012428 - SINE/MIR_MIR chr1 22012693 22012801 - DNA/hAT-Blackjack_MER81 chr1 22012961 22013055 - LINE/L2_L2c chr1 22013068 22013430 - LTR/ERVL-MaLR_THE1C chr1 22013445 22013568 - LINE/L2_L2c chr1 22013653 22013737 + SINE/Alu_AluSp chr1 22013802 22014102 + SINE/Alu_AluJb chr1 22014251 22014327 - LINE/L1_L1ME3Cz chr1 22014395 22014524 - SINE/Alu_AluSg4 chr1 22014525 22014822 - SINE/Alu_AluSz chr1 22014825 22015024 - LTR/ERV1_MER49 chr1 22015025 22015318 + SINE/Alu_AluSq2 chr1 22015319 22015430 - LTR/ERV1_MER49 chr1 22015432 22015588 + SINE/Alu_FAM chr1 22015589 22015879 - LTR/ERV1_MER49 chr1 22015880 22016178 + SINE/Alu_AluJb chr1 22016179 22016326 - LTR/ERV1_MER49 chr1 22016327 22016628 - SINE/Alu_AluSx chr1 22016629 22016813 - LTR/ERV1_MER49 chr1 22016895 22016953 + LTR/ERVL-MaLR_MLT1I chr1 22016954 22017274 + SINE/Alu_AluJb chr1 22017290 22017593 + SINE/Alu_AluJr chr1 22017680 22017842 + LTR/ERVL-MaLR_MLT1I chr1 22017846 22018008 - LINE/L1_L1ME3Cz chr1 22018009 22018187 - SINE/Alu_AluSx1 chr1 22018188 22018259 + Simple_repeat_(AT)n chr1 22018260 22018381 - SINE/Alu_AluSx1 chr1 22018382 22018668 - LINE/L1_L1ME3Cz chr1 22018670 22018820 - LTR/ERV1?_LTR103_Mam chr1 22018821 22019063 - SINE/Alu_AluSx chr1 22019064 22019225 - LTR/ERV1?_LTR103_Mam chr1 22019709 22019995 - SINE/Alu_AluSg chr1 22020061 22020363 + SINE/Alu_AluSz chr1 22020369 22020674 + SINE/Alu_AluSp chr1 22020748 22020915 + SINE/Alu_AluSg4 chr1 22020942 22021163 + SINE/Alu_AluSq2 chr1 22021164 22021646 + LTR/ERV1_LTR65 chr1 22021664 22021966 - SINE/Alu_AluSx chr1 22021970 22022270 - SINE/Alu_AluJr chr1 22022293 22022332 - LINE/L2_L2a chr1 22022333 22022639 - SINE/Alu_AluSq2 chr1 22022640 22022652 - LINE/L2_L2a chr1 22022653 22022964 - SINE/Alu_AluY chr1 22022965 22023235 - LINE/L2_L2a chr1 22023236 22023524 + SINE/Alu_AluSz6 chr1 22023525 22023835 + SINE/Alu_AluSp chr1 22023836 22023942 - LINE/L2_L2a chr1 22024094 22024190 - SINE/MIR_MIR3 chr1 22024622 22024905 - SINE/Alu_AluSx1 chr1 22025271 22025366 - SINE/MIR_MIRb chr1 22025945 22026100 + SINE/MIR_MIRb chr1 22026266 22026574 - SINE/Alu_AluSx chr1 22026747 22027052 + SINE/Alu_AluSx1 chr1 22027217 22027329 - LINE/L1_HAL1ME chr1 22027370 22027407 + Simple_repeat_(TTTA)n chr1 22027471 22027622 - LINE/L1_L1ME3G chr1 22028036 22028334 + SINE/Alu_AluSc8 chr1 22028688 22028894 - SINE/Alu_AluJb chr1 22028895 22029195 - SINE/Alu_AluSp chr1 22029197 22029331 - SINE/Alu_AluSx chr1 22029336 22029412 - SINE/Alu_FAM chr1 22029598 22029892 + SINE/Alu_AluSg chr1 22031028 22031050 + Simple_repeat_(T)n chr1 22031957 22032250 + SINE/Alu_AluYk2 chr1 22032336 22032619 + SINE/Alu_AluY chr1 22032799 22033074 - SINE/Alu_AluSx3 chr1 22033340 22033643 - SINE/Alu_AluSz chr1 22033646 22033937 - SINE/Alu_AluSq2 chr1 22033946 22034246 - SINE/Alu_AluJr chr1 22034280 22034506 - DNA/hAT-Charlie_MER20 chr1 22034652 22034949 + SINE/Alu_AluSz chr1 22035007 22035309 + SINE/Alu_AluSp chr1 22035364 22035579 - SINE/MIR_MIRb chr1 22035627 22035939 - SINE/Alu_AluSg4 chr1 22036168 22036457 + SINE/Alu_AluY chr1 22036471 22036738 + SINE/Alu_AluJr chr1 22036739 22036837 + Simple_repeat_(TA)n chr1 22037107 22037410 + SINE/Alu_AluSz6 chr1 22038397 22038454 + LINE/L2_L2c chr1 22038455 22038712 - SINE/Alu_AluJb chr1 22038713 22038827 + LINE/L2_L2c chr1 22038901 22038983 + LINE/L2_L2c chr1 22039045 22039345 - SINE/Alu_AluY chr1 22039346 22039657 - SINE/Alu_AluSc chr1 22039799 22039829 + Simple_repeat_(AACAA)n chr1 22040132 22040199 + SINE/MIR_MIR3 chr1 22040421 22040713 + SINE/Alu_AluJr chr1 22040726 22041069 + SINE/Alu_AluSx chr1 22041072 22041374 + SINE/Alu_AluSg chr1 22041408 22041663 - LINE/L1_L1ME3A chr1 22041819 22042010 + SINE/MIR_MIR chr1 22042075 22042104 + Simple_repeat_(TTA)n chr1 22042105 22042391 - SINE/Alu_AluSx chr1 22042475 22042779 - SINE/Alu_AluY chr1 22042786 22043228 - LINE/L2_L2a chr1 22043245 22043326 - DNA/hAT-Charlie_UCON38 chr1 22043750 22043959 - SINE/Alu_AluJr chr1 22043965 22044252 - SINE/Alu_AluSx1 chr1 22044259 22044565 - SINE/Alu_AluSz chr1 22044568 22044795 - LINE/L1_L1ME3B chr1 22045568 22045726 - LINE/L2_L2d2 chr1 22045729 22046210 + LTR/ERVL_MLT2B4 chr1 22046211 22046220 + Simple_repeat_(AT)n chr1 22046221 22046303 + Simple_repeat_(TATTATA)n chr1 22046328 22046629 - SINE/Alu_AluSz chr1 22046649 22046712 + LTR/ERVL_MLT2B4 chr1 22046784 22047053 - SINE/Alu_AluSg chr1 22047054 22047163 - LINE/L2_L2d2 chr1 22047184 22047485 - SINE/Alu_AluSc chr1 22047491 22047795 - SINE/Alu_AluJb chr1 22048009 22048314 + SINE/Alu_AluSx1 chr1 22048421 22048936 - LTR/ERVL-MaLR_MLT1K chr1 22049148 22049324 + DNA/hAT-Tip100_Arthur2 chr1 22049389 22049543 + LINE/L1_L1ME4a chr1 22049558 22049688 - SINE/Alu_AluJo chr1 22049726 22050015 - SINE/Alu_AluSp chr1 22050016 22050328 - SINE/Alu_AluSq2 chr1 22050393 22050612 + SINE/MIR_MIR chr1 22050640 22050947 + SINE/Alu_AluSg4 chr1 22051042 22051341 - SINE/Alu_AluY chr1 22051543 22051825 + SINE/Alu_AluJb chr1 22051826 22051862 + Simple_repeat_(AAC)n chr1 22052020 22052128 + SINE/MIR_MIRc chr1 22052129 22052433 + SINE/Alu_AluSx1 chr1 22052434 22052487 + SINE/MIR_MIRc chr1 22053256 22053316 + Simple_repeat_(GGCGG)n chr1 22053317 22053344 + Simple_repeat_(GCG)n chr1 22054256 22054452 - SINE/Alu_AluJb chr1 22054885 22054921 + Simple_repeat_(AT)n chr1 22054922 22055252 - SINE/Alu_AluY chr1 22055491 22055782 - SINE/Alu_AluJb chr1 22055868 22055886 + Simple_repeat_(T)n chr1 22056055 22056181 + LINE/L2_L2a chr1 22056631 22056647 + Simple_repeat_(CTG)n chr1 22057026 22057272 + SINE/MIR_MIR chr1 22057385 22057683 - SINE/Alu_AluSp chr1 22057721 22058028 - SINE/Alu_AluSx1 chr1 22058097 22058238 + DNA/hAT-Charlie_MER5A1 chr1 22058486 22058798 - SINE/Alu_AluSp chr1 22058809 22059124 - SINE/Alu_AluJr chr1 22059129 22059289 - SINE/Alu_AluSp chr1 22059547 22059843 - SINE/Alu_AluSx chr1 22060060 22060355 + SINE/Alu_AluSx chr1 22061126 22061430 + SINE/Alu_AluSq2 chr1 22061436 22061524 - DNA/hAT-Charlie_MER103C chr1 22061533 22061860 - SINE/Alu_AluSx1 chr1 22061861 22061893 - DNA/hAT-Charlie_MER103C chr1 22061927 22062234 - SINE/Alu_AluSz chr1 22062412 22062541 - LINE/RTE-BovB_MamRTE1 chr1 22062606 22062765 + SINE/Alu_FLAM_C chr1 22063101 22063124 + Simple_repeat_(T)n chr1 22063728 22064019 - SINE/Alu_AluSz6 chr1 22064190 22064211 + Simple_repeat_(A)n chr1 22064293 22064590 - SINE/Alu_AluSx chr1 22064689 22064979 - SINE/Alu_AluSx1 chr1 22065063 22065215 - SINE/MIR_MIRb chr1 22065541 22065768 - SINE/MIR_MIRb chr1 22065794 22066039 - SINE/Alu_AluSz chr1 22066121 22066225 - LINE/L2_L2b chr1 22066233 22066386 + SINE/Alu_AluSx3 chr1 22066565 22066781 - LINE/L2_L2b chr1 22066782 22067023 + SINE/Alu_AluSp chr1 22067024 22067147 - LINE/L2_L2b chr1 22067164 22067317 - LTR/ERVL-MaLR_MLT1A0 chr1 22067318 22067640 - SINE/Alu_AluSx chr1 22067641 22067845 - LTR/ERVL-MaLR_MLT1A0 chr1 22067954 22068095 + SINE/Alu_AluJb chr1 22068956 22069098 + LINE/L2_L2a chr1 22069171 22069475 - SINE/Alu_AluSz chr1 22069476 22069613 - SINE/Alu_AluJb chr1 22069614 22069639 + Simple_repeat_(T)n chr1 22069640 22069766 - SINE/Alu_AluJb chr1 22069785 22070087 - SINE/Alu_AluSz6 chr1 22070132 22070443 + LTR/ERV1_LTR10C chr1 22070444 22070763 - SINE/Alu_AluY chr1 22070764 22071032 + LTR/ERV1_LTR10C chr1 22071033 22071343 - SINE/Alu_AluY chr1 22071866 22072091 - LTR/ERVL_MER21C chr1 22072092 22072396 - SINE/Alu_AluYm1 chr1 22072397 22072834 - LTR/ERVL_MER21C chr1 22073255 22073564 + SINE/Alu_AluSx chr1 22073677 22073950 - SINE/Alu_AluJr chr1 22074067 22074374 - SINE/Alu_AluSc8 chr1 22074601 22074713 - SINE/Alu_AluJo chr1 22074725 22075231 - LTR/ERV1_LTR54 chr1 22075361 22075456 - DNA/TcMar-Tigger_MER2B chr1 22075626 22075828 - SINE/MIR_MIR chr1 22076178 22076425 + SINE/Alu_AluSz chr1 22076427 22076454 + Simple_repeat_(AC)n chr1 22076455 22076691 + SINE/MIR_MIR chr1 22076797 22076835 + Low_complexity_A-rich chr1 22076859 22077169 + SINE/Alu_AluJb chr1 22077486 22077525 + Low_complexity_A-rich chr1 22077631 22077714 - DNA/TcMar-Tc2_Kanga1c chr1 22078887 22078912 + Simple_repeat_(T)n chr1 22079127 22079438 - SINE/Alu_AluSx1 chr1 22080082 22080264 - SINE/MIR_MIR3 chr1 22080889 22081177 + SINE/Alu_AluSg chr1 22082876 22083179 - SINE/Alu_AluSp chr1 22083276 22083320 - LINE/L1_L1ME1 chr1 22083321 22083618 + SINE/Alu_AluSx chr1 22083619 22083703 - LINE/L1_L1ME1 chr1 22083713 22083792 - LINE/L1_L1M4 chr1 22083804 22084335 - LINE/L1_L1MB5 chr1 22084336 22084360 + Simple_repeat_(T)n chr1 22084361 22084949 - LINE/L1_L1MB5 chr1 22084950 22085254 + SINE/Alu_AluSx chr1 22085255 22085668 - LINE/L1_L1MB5 chr1 22085863 22086124 - SINE/Alu_AluSx chr1 22091778 22091820 + Simple_repeat_(T)n chr1 22093314 22093388 - DNA/hAT-Charlie_MER5B chr1 22094295 22094596 - SINE/Alu_AluY chr1 22095278 22095568 - SINE/Alu_AluY chr1 22095949 22095976 + Simple_repeat_(ATTT)n chr1 22095979 22096258 - SINE/Alu_AluSx3 chr1 22096707 22096772 - DNA/hAT-Tip100_MER91B chr1 22096813 22096935 - DNA/hAT-Tip100_Zaphod2 chr1 22097043 22097211 - SINE/MIR_MIR3 chr1 22097266 22097541 - SINE/Alu_AluSx chr1 22098207 22098265 + SINE/MIR_MIR3 chr1 22098353 22098455 - DNA/hAT-Charlie_MER5A chr1 22098758 22099052 - SINE/Alu_AluJb chr1 22099288 22099506 - SINE/MIR_MIRb chr1 22099780 22099994 - SINE/MIR_MIR chr1 22100003 22100044 + Simple_repeat_(T)n chr1 22100287 22100376 - SINE/MIR_MIRc chr1 22100425 22100618 + SINE/MIR_MIRb chr1 22101235 22101351 + SINE/MIR_MIRc chr1 22101867 22102162 - SINE/Alu_AluSp chr1 22102441 22102609 + SINE/MIR_MIRb chr1 22102800 22103005 + SINE/MIR_MIR chr1 22103283 22103475 - SINE/MIR_MIRc chr1 22103920 22103968 - LINE/L2_L2a chr1 22103969 22104355 - LTR/ERVL-MaLR_THE1C chr1 22104356 22104486 - LINE/L2_L2a chr1 22104860 22105358 - LINE/L1_L1MB8 chr1 22105574 22105723 + SINE/MIR_MIRb chr1 22107030 22107240 - SINE/MIR_MIRc chr1 22107590 22107897 - SINE/Alu_AluSg chr1 22107947 22108132 + SINE/MIR_MIRb chr1 22109419 22109489 - LINE/L2_L2b chr1 22109682 22109869 - SINE/MIR_MIR chr1 22110153 22110206 + SINE/Alu_Alu chr1 22110217 22110284 - SINE/Alu_AluJb chr1 22110287 22110582 - SINE/Alu_AluSx3 chr1 22110819 22111124 + SINE/Alu_AluY chr1 22111886 22111960 + Low_complexity_GA-rich chr1 22112104 22112166 - SINE/MIR_MIRc chr1 22112247 22112357 - SINE/MIR_MIR1_Amn chr1 22113063 22113268 + SINE/MIR_MIRb chr1 22113550 22113844 + SINE/Alu_AluSx1 chr1 22114143 22114228 + LINE/L2_L2b chr1 22116091 22116227 - SINE/MIR_MIRb chr1 22116268 22116465 - SINE/Alu_AluJb chr1 22118911 22119008 + LINE/L2_L2b chr1 22119172 22119229 + Simple_repeat_(GT)n chr1 22121767 22121937 - SINE/MIR_MIR chr1 22121979 22122080 - SINE/MIR_MIR3 chr1 22122279 22122396 - SINE/MIR_MIRb chr1 22123492 22123742 - SINE/MIR_MIR chr1 22123806 22124024 - LINE/CR1_L3b chr1 22124508 22124708 - SINE/MIR_MIRb chr1 22125038 22125185 + SINE/MIR_MIRb chr1 22125620 22125666 - SINE/MIR_MIR3 chr1 22125954 22126058 - LINE/L2_L2a chr1 22126313 22126499 - SINE/MIR_MIRb chr1 22126785 22126909 - SINE/MIR_MIR chr1 22127612 22127775 + LINE/L1_L1MC4 chr1 22128459 22128612 + LINE/L2_L2c chr1 22128645 22128708 + SINE/MIR_MIR chr1 22128709 22129018 - SINE/Alu_AluYj4 chr1 22129019 22129082 + SINE/MIR_MIR chr1 22129174 22129307 + SINE/MIR_MIRc chr1 22130642 22130704 + SINE/MIR_MIR3 chr1 22130802 22130823 + Simple_repeat_(TGAA)n chr1 22130902 22131104 - SINE/MIR_MIRb chr1 22131124 22131281 + LINE/CR1_L3 chr1 22131299 22131374 - DNA/hAT-Charlie_MER5A chr1 22131581 22131713 + SINE/MIR_MIRb chr1 22132386 22132479 + SINE/MIR_MIR3 chr1 22132831 22132871 + Simple_repeat_(AGCCCC)n chr1 22133007 22133141 - SINE/MIR_MIRb chr1 22133180 22133235 + LINE/L2_L2b chr1 22134515 22134669 + SINE/MIR_MIRc chr1 22135266 22135420 - SINE/MIR_MIRb chr1 22136663 22136767 + LINE/L2_L2b chr1 22137977 22138196 - LINE/L2_L2d2 chr1 22138185 22138338 + SINE/MIR_MIR3 chr1 22138390 22138589 + DNA/hAT-Charlie_MER20 chr1 22139282 22139483 - SINE/MIR_MIRb chr1 22139682 22139737 - SINE/MIR_MIR3 chr1 22140291 22140357 + SINE/MIR_MIRc chr1 22141075 22141166 + SINE/MIR_MIR3 chr1 22141255 22141294 + Simple_repeat_(TTTG)n chr1 22142369 22142411 + Simple_repeat_(CAGCG)n chr1 22143044 22143083 + Simple_repeat_(TGCCCGC)n chr1 22143123 22143159 + Simple_repeat_(GCG)n chr1 22143160 22143200 + Simple_repeat_(GCGG)n chr1 22143777 22143911 + SINE/MIR_MIRc chr1 22144080 22144109 + Simple_repeat_(TGGAA)n chr1 22144540 22145177 - DNA/hAT-Tip100_MER115 chr1 22145241 22145264 + Simple_repeat_(AAT)n chr1 22145289 22145407 - DNA/hAT-Blackjack_MER94 chr1 22145439 22145617 - SINE/MIR_MIR chr1 22145673 22145787 + SINE/MIR_MIRc chr1 22145912 22146219 - SINE/Alu_AluSc chr1 22146372 22146484 - SINE/MIR_MIR chr1 22146520 22146657 - SINE/MIR_MIR chr1 22146914 22147058 - DNA/TcMar-Tigger_X11_DNA chr1 22147194 22147364 - SINE/Alu_AluSq2 chr1 22147508 22147597 + SINE/MIR_MIR3 chr1 22147691 22147842 + SINE/MIR_MIRb chr1 22147872 22148044 + DNA/hAT-Charlie_MER102a chr1 22148142 22148384 - SINE/MIR_MIRb chr1 22148452 22148547 + DNA/hAT-Charlie_Charlie23a chr1 22148579 22148706 - LINE/L1_L1MC5a chr1 22148707 22149003 - SINE/Alu_AluSx chr1 22149004 22149104 - LINE/L1_L1MC5a chr1 22149105 22149318 + DNA/hAT-Blackjack_MER63A chr1 22149319 22149563 - LINE/L1_L1MC5a chr1 22149564 22149847 + SINE/Alu_AluSp chr1 22149848 22149904 + Simple_repeat_(AAAT)n chr1 22149912 22149960 - LINE/L1_L1MC5a chr1 22150195 22150315 - LINE/CR1_Plat_L3 chr1 22150340 22150650 - SINE/Alu_AluSx1 chr1 22151157 22151452 - SINE/Alu_AluJo chr1 22152012 22152081 + Simple_repeat_(TCCT)n chr1 22152087 22152245 - SINE/Alu_FRAM chr1 22152246 22152284 + SINE/Alu_AluYj4 chr1 22152291 22152343 + LINE/L2_L2a chr1 22152344 22152649 - SINE/Alu_AluSz chr1 22152650 22152953 + LINE/L2_L2a chr1 22153373 22153655 - LINE/L2_L2c chr1 22153704 22153748 + Simple_repeat_(TG)n chr1 22154381 22154533 + SINE/MIR_MIRb chr1 22154540 22154634 + SINE/MIR_MIRc chr1 22154880 22154929 - SINE/MIR_MIR3 chr1 22154968 22155127 - LINE/L2_L2b chr1 22155287 22155567 + SINE/Alu_AluSg chr1 22155577 22155660 - LINE/L2_L2b chr1 22155741 22155902 - SINE/MIR_MIRc chr1 22156063 22156215 + SINE/MIR_MIR3 chr1 22156514 22156619 + LTR/ERVL-MaLR_MLT1I chr1 22156709 22156811 + LTR/ERVL-MaLR_MLT1I chr1 22156891 22156934 + Simple_repeat_(CCTT)n chr1 22156974 22157041 - SINE/MIR_MIRb chr1 22157051 22157155 + LINE/L2_L2d chr1 22157636 22157873 + SINE/MIR_MIR chr1 22158063 22158364 + SINE/Alu_AluSx4 chr1 22158367 22158699 + SINE/Alu_AluY chr1 22158742 22158962 - SINE/MIR_MIRb chr1 22159380 22159487 - SINE/MIR_MIR3 chr1 22159509 22159616 - Retroposon/SVA_SVA_E chr1 22159843 22159946 - DNA/hAT-Tip100_MamTip2 chr1 22160414 22160660 + LINE/L2_L2b chr1 22160686 22160737 - DNA/hAT-Charlie_MER5A chr1 22161114 22161422 + DNA/hAT-Tip100_MER115 chr1 22161673 22162012 - LTR/ERVL-MaLR_MLT1A1 chr1 22162030 22162211 - SINE/MIR_MIRb chr1 22162383 22162553 - DNA/hAT-Tip100?_MamRep1879 chr1 22163332 22163426 - SINE/MIR_MIRb chr1 22163564 22163610 + Low_complexity_G-rich chr1 22164110 22164258 - LINE/L2_L2a chr1 22164294 22164427 - LTR/ERVL-MaLR_MLT1K chr1 22164578 22164671 - LINE/L2_L2b chr1 22164672 22165001 + SINE/Alu_AluSx1 chr1 22165002 22165074 - LINE/L2_L2b chr1 22165076 22165444 - LTR/ERVL-MaLR_THE1B chr1 22165521 22165909 - LINE/L2_L2b chr1 22165923 22165954 - SINE/MIR_MIR chr1 22165955 22166253 - SINE/Alu_AluSq2 chr1 22166254 22166349 - SINE/MIR_MIR3 chr1 22166881 22166949 + SINE/MIR_MIRb chr1 22167160 22167251 + LINE/L2_L2b chr1 22167362 22167512 + SINE/MIR_MIRb chr1 22167573 22167870 + SINE/Alu_AluJr chr1 22167872 22167963 + LINE/L2_L2b chr1 22168007 22168228 + SINE/MIR_MIRb chr1 22168313 22168497 + LTR/ERVL-MaLR_MLT1L chr1 22168511 22168713 + LTR/ERVL-MaLR_MLT1L chr1 22168865 22168935 + LINE/Dong-R4_Mam_R4 chr1 22169349 22169455 + LTR/ERVL-MaLR_MLT1N2 chr1 22169575 22169796 + DNA/hAT-Charlie_Charlie23a chr1 22169910 22170056 + LTR/ERVL-MaLR_MLT1K chr1 22170211 22170484 - LINE/L2_L2b chr1 22170485 22170783 + SINE/Alu_AluSz chr1 22170784 22170875 - LINE/L2_L2b chr1 22171164 22171306 - DNA/hAT-Charlie_MER5B chr1 22171307 22171474 - SINE/Alu_FRAM chr1 22171543 22171634 - DNA/hAT-Charlie_MER102a chr1 22171635 22171920 - SINE/Alu_AluY chr1 22171921 22171942 - DNA/hAT-Charlie_MER102a chr1 22171995 22172187 - SINE/MIR_MIR chr1 22172815 22173014 - SINE/MIR_MIRb chr1 22173437 22173728 + SINE/Alu_AluSx3 chr1 22174327 22174379 + Simple_repeat_(CCTT)n chr1 22174929 22175217 + LINE/L1_L1MB3 chr1 22175577 22175805 + SINE/MIR_MIRb chr1 22175867 22176069 + SINE/MIR_MIR chr1 22176113 22176269 + SINE/MIR_MIR3 chr1 22176643 22177002 + LTR/Gypsy?_LTR85c chr1 22177145 22177331 - LINE/L2_L2 chr1 22177689 22177790 - SINE/MIR_MIR3 chr1 22177759 22177868 + SINE/MIR_MIRc chr1 22178099 22178390 + LINE/L2_L2d chr1 22179145 22179600 + LTR/ERVL-MaLR_MLT1J chr1 22179627 22180058 + LINE/L2_L2d chr1 22180072 22180457 + LINE/L1_L1MB3 chr1 22180458 22180748 + SINE/Alu_AluSc8 chr1 22180749 22181079 + LINE/L1_L1MB3 chr1 22181090 22181334 - SINE/Alu_AluSx3 chr1 22181335 22181513 - SINE/Alu_AluSx1 chr1 22181514 22181538 + Simple_repeat_(T)n chr1 22181539 22181656 - SINE/Alu_AluSx1 chr1 22181716 22182001 + LINE/L2_L2d chr1 22182277 22182459 + LINE/L2_L2d chr1 22182463 22182566 + SINE/Alu_AluSq2 chr1 22182621 22182833 - SINE/MIR_MIRb chr1 22183035 22183078 + Low_complexity_G-rich chr1 22183218 22183300 + SINE/MIR_MIRc chr1 22183303 22183694 - SINE/Alu_AluJo chr1 22183995 22184278 + SINE/Alu_AluSx chr1 22184280 22184414 + LINE/L1_L1MC4 chr1 22184628 22184703 - SINE/MIR_MIR3 chr1 22184720 22184749 + Simple_repeat_(AAAG)n chr1 22184820 22184979 - SINE/MIR_MIRb chr1 22185643 22185847 + SINE/MIR_MIR chr1 22186057 22186219 + DNA/hAT-Charlie_MER5A chr1 22186380 22186541 - SINE/Alu_AluJb chr1 22186542 22186841 - SINE/Alu_AluSz chr1 22186842 22186993 - SINE/Alu_AluJb chr1 22187131 22187168 + SINE/MIR_MIR chr1 22187237 22187474 - SINE/MIR_MIRb chr1 22187780 22188084 - SINE/Alu_AluSz chr1 22188132 22188216 - LINE/L2_L2b chr1 22188235 22188299 + Simple_repeat_(GCTCT)n chr1 22188573 22188685 + LINE/L2_L2c chr1 22188835 22188929 - SINE/MIR_MIRc chr1 22189495 22189575 + LINE/L2_L2c chr1 22189694 22189923 - SINE/MIR_MIR chr1 22190603 22190841 + SINE/MIR_MIRb chr1 22190902 22191287 + LTR/ERVL-MaLR_MLT1K chr1 22191488 22191865 - LINE/L2_L2b chr1 22192154 22192286 - LINE/L1_L1ME3A chr1 22192287 22192585 - SINE/Alu_AluSz6 chr1 22192586 22193122 - LINE/L1_L1ME3A chr1 22193123 22193426 - SINE/Alu_AluY chr1 22193427 22193491 - LINE/L1_L1ME3A chr1 22193492 22193805 - SINE/Alu_AluSx1 chr1 22193806 22194306 - LINE/L1_L1ME3A chr1 22194307 22194599 - SINE/Alu_AluJb chr1 22194600 22194780 - LINE/L1_L1ME3A chr1 22194781 22194825 + Simple_repeat_(CA)n chr1 22194826 22194913 - LINE/L1_L1ME3A chr1 22195017 22195275 + SINE/Alu_AluYc chr1 22195285 22195406 - LINE/L1_L1ME4a chr1 22195407 22195714 - SINE/Alu_AluSx1 chr1 22195715 22195987 - LINE/L1_L1ME4a chr1 22195999 22196060 - SINE/MIR_MIRc chr1 22196467 22196774 - SINE/Alu_AluJo chr1 22196784 22196955 + SINE/MIR_MIRb chr1 22197858 22197971 + SINE/MIR_MIRb chr1 22198041 22198588 - LINE/L1_L1MC2 chr1 22198589 22198897 - SINE/Alu_AluSg chr1 22198898 22200773 - LINE/L1_L1MC2 chr1 22200774 22200819 + Simple_repeat_(GT)n chr1 22200820 22200865 - LINE/L1_L1MC2 chr1 22200867 22200947 + SINE/MIR_MIR chr1 22200971 22201207 + SINE/MIR_MIR chr1 22201210 22201256 + Simple_repeat_(TAT)n chr1 22201327 22201484 + SINE/MIR_MIR chr1 22201499 22201810 - SINE/Alu_AluSp chr1 22201814 22201844 + Simple_repeat_(TAT)n chr1 22201980 22202057 + LINE/CR1_CR1-3_Croc chr1 22202112 22202155 + DNA/hAT-Charlie_Charlie15a chr1 22202156 22202471 + SINE/Alu_AluSx chr1 22202472 22202625 + DNA/hAT-Charlie_Charlie15a chr1 22202645 22202770 - SINE/MIR_MIR3 chr1 22203049 22203220 - LINE/L2_L2c chr1 22203221 22203614 - LTR/ERVL-MaLR_MLT1J2 chr1 22203615 22203868 - LINE/L2_L2c chr1 22204207 22204399 + DNA/hAT-Charlie_MER58A chr1 22204732 22204865 - SINE/MIR_MIRb chr1 22205740 22205777 + Simple_repeat_(TTCA)n chr1 22205831 22206161 + LINE/L2_L2c chr1 22206162 22206186 + Simple_repeat_(GAAT)n chr1 22206187 22206485 - SINE/Alu_AluY chr1 22206667 22206750 - SINE/MIR_MIR chr1 22208328 22208363 + Low_complexity_G-rich chr1 22208457 22208654 - SINE/MIR_MIRb chr1 22209253 22209698 - LINE/L2_L2a chr1 22209720 22209875 - SINE/MIR_MIR3 chr1 22209912 22210121 - SINE/MIR_MIRb chr1 22210212 22210292 + LINE/L2_L2b chr1 22211421 22211697 - SINE/Alu_AluJr chr1 22211734 22212038 - SINE/Alu_AluSx4 chr1 22212126 22212527 + LINE/L2_L2c chr1 22213120 22213218 + SINE/MIR_MIR3 chr1 22213312 22213654 + LINE/L1_L1M7 chr1 22213772 22214328 + LINE/L1_L1ME4b chr1 22214415 22214584 + LINE/L1_L1ME4b chr1 22214585 22214897 - SINE/Alu_AluSx chr1 22214898 22215601 + LINE/L1_L1ME4b chr1 22215603 22215830 + SINE/Alu_AluSx3 chr1 22216707 22217169 - LTR/ERVL-MaLR_MLT1E2 chr1 22217258 22217467 - SINE/MIR_MIRb chr1 22217470 22217771 - SINE/Alu_AluSq2 chr1 22218487 22218551 + SINE/MIR_MIRb chr1 22218674 22218862 + SINE/MIR_MIR chr1 22218879 22219396 + LINE/L2_L2a chr1 22219382 22219563 + LINE/L2_L2a chr1 22219629 22219831 + SINE/MIR_MIRb chr1 22219909 22220019 - SINE/MIR_MIRb chr1 22220094 22220120 + Simple_repeat_(T)n chr1 22220132 22220435 - SINE/Alu_AluSx chr1 22220438 22220672 - SINE/Alu_AluJb chr1 22220764 22220948 - SINE/MIR_MIRb chr1 22221373 22221567 - DNA/hAT-Blackjack_MER63A chr1 22222028 22222073 + Simple_repeat_(CA)n chr1 22222771 22222964 + SINE/MIR_MIRb chr1 22223018 22223036 + Simple_repeat_(GT)n chr1 22223048 22223242 + SINE/MIR_MIRb chr1 22223562 22223704 - SINE/MIR_MIRb chr1 22223812 22224012 + LINE/L2_L2 chr1 22224538 22224626 - SINE/MIR_MIR chr1 22224762 22225275 + LTR/ERVL-MaLR_MLT1F1 chr1 22225286 22225670 + LINE/L1_L1ME3A chr1 22226390 22226554 + LINE/L2_L2a chr1 22226557 22226678 + SINE/Alu_AluSx chr1 22226680 22226724 + Low_complexity_A-rich chr1 22226732 22226916 + SINE/Alu_AluSx chr1 22226943 22227151 + LINE/L2_L2a chr1 22227223 22227347 - SINE/MIR_MIRb chr1 22227473 22227638 + Simple_repeat_(CTTT)n chr1 22227643 22227918 - SINE/Alu_AluSx1 chr1 22227986 22228117 - SINE/MIR_MIR3 chr1 22228105 22228145 + LINE/L2_L2c chr1 22228264 22228409 - SINE/MIR_MIR chr1 22228457 22228512 + Low_complexity_G-rich chr1 22228527 22228554 + Simple_repeat_(TGTG)n chr1 22229147 22229201 + SINE/MIR_MIR1_Amn chr1 22229239 22229332 - DNA/hAT-Charlie_MER5A chr1 22229627 22229727 + LINE/L2_L2b chr1 22229816 22230055 - SINE/MIR_MIRb chr1 22230064 22230158 + LINE/L2_L2b chr1 22230202 22230289 - SINE/MIR_MIR1_Amn chr1 22230295 22230598 - LTR/ERVL_LTR16C chr1 22230746 22231525 + Simple_repeat_(TCCA)n chr1 22232081 22232308 - SINE/MIR_MIRb chr1 22232522 22232721 + SINE/MIR_MIRb chr1 22233490 22233707 + SINE/MIR_MIRb chr1 22233796 22233932 - SINE/MIR_MIRb chr1 22234567 22234710 + SINE/MIR_MIRb chr1 22234824 22235020 + DNA/hAT-Blackjack_MER63D chr1 22235269 22235450 + DNA/hAT-Blackjack_MER63B chr1 22235451 22235757 - SINE/Alu_AluSx1 chr1 22235791 22235983 - SINE/MIR_MIR chr1 22236725 22236837 + SINE/MIR_MIRb chr1 22236856 22236925 - SINE/MIR_MIR chr1 22237090 22237132 - SINE/MIR_MIRb chr1 22237133 22237253 + SINE/MIR_MIR1_Amn chr1 22237469 22237763 + SINE/Alu_AluSq2 chr1 22237872 22237988 + SINE/MIR_MIRc chr1 22238168 22238443 + LINE/L2_L2b chr1 22238736 22238900 - SINE/MIR_MIR chr1 22239717 22239847 + SINE/MIR_MIRb chr1 22239935 22240245 - SINE/Alu_AluSx chr1 22240251 22240566 - SINE/Alu_AluSz chr1 22240580 22240716 - LINE/L1_L1MB8 chr1 22240717 22241020 - SINE/Alu_AluJr chr1 22241021 22241392 - LINE/L1_L1MB8 chr1 22241676 22241707 - SINE/MIR_MIR1_Amn chr1 22241708 22241759 + Simple_repeat_(AC)n chr1 22241760 22241880 - SINE/MIR_MIRb chr1 22242049 22242334 - LINE/L2_L2b chr1 22242867 22243106 + LINE/L1_L1ME2 chr1 22243159 22243441 + SINE/Alu_AluJb chr1 22243442 22243472 + Simple_repeat_(GAAG)n chr1 22243473 22243513 + Simple_repeat_(AGGC)n chr1 22244360 22244550 + SINE/MIR_MIRb chr1 22245403 22245490 + SINE/MIR_MIR3 chr1 22245603 22245665 + SINE/MIR_MIR3 chr1 22245621 22245706 - SINE/tRNA_LFSINE_Vert chr1 22245744 22245790 + Low_complexity_G-rich chr1 22246301 22246423 - LINE/L2_L2b chr1 22246399 22246561 + LINE/L2_L2b chr1 22246827 22246980 + SINE/MIR_MIRb chr1 22247462 22247546 + LINE/L2_L2a chr1 22247554 22247609 + SINE/MIR_MIR chr1 22247580 22247665 + LINE/L2_L2a chr1 22247649 22247785 - SINE/MIR_MIRb chr1 22248281 22248436 + SINE/MIR_MIR3 chr1 22249096 22249158 + SINE/MIR_MIRc chr1 22249632 22249657 + Simple_repeat_(TG)n chr1 22249768 22249813 + Low_complexity_A-rich chr1 22249926 22250059 + SINE/MIR_MIRb chr1 22250370 22250463 - SINE/MIR_MIRc chr1 22250518 22250746 - SINE/MIR_MIRb chr1 22251020 22251044 + Simple_repeat_(TCC)n chr1 22251205 22251394 + DNA/hAT-Charlie_MER20 chr1 22251468 22251672 - LINE/L2_L2b chr1 22251673 22251866 + SINE/MIR_MIR3 chr1 22251908 22252084 + DNA/hAT-Charlie_MER58A chr1 22252230 22252300 + SINE/MIR_MIRb chr1 22252593 22252827 - SINE/MIR_MIRb chr1 22253107 22253162 + Simple_repeat_(GGCCT)n chr1 22253313 22253560 - DNA/hAT-Tip100_Arthur1 chr1 22253562 22253612 - DNA/hAT-Tip100_Arthur1B chr1 22253634 22253683 + Simple_repeat_(TG)n chr1 22254161 22254209 + Simple_repeat_(GT)n chr1 22254210 22254281 + Simple_repeat_(AG)n chr1 22254311 22254420 - SINE/MIR_MIR3 chr1 22254947 22255064 - LINE/L2_L2d chr1 22255479 22255632 + LTR/ERVL_LTR33 chr1 22255671 22255786 - SINE/Alu_AluJr chr1 22256363 22256510 - LINE/L2_L2d chr1 22256561 22256860 + SINE/Alu_AluSc8 chr1 22257221 22257407 - SINE/MIR_MIRb chr1 22257613 22257763 - SINE/MIR_MIRc chr1 22258141 22258178 + Simple_repeat_(GGGCT)n chr1 22258268 22258568 + SINE/Alu_AluJr chr1 22258613 22258818 - SINE/MIR_MIR chr1 22258984 22259033 + SINE/MIR_MIRc chr1 22259750 22259983 + LINE/CR1_L3 chr1 22259989 22260213 - DNA/hAT-Charlie_MER58A chr1 22260355 22260406 + Simple_repeat_(TCCC)n chr1 22260495 22260565 + Simple_repeat_(CTCA)n chr1 22260772 22260830 - LINE/L2_L2b chr1 22260838 22260955 - LINE/L2_L2b chr1 22261541 22261582 + Low_complexity_G-rich chr1 22261757 22261943 + SINE/MIR_MIR3 chr1 22262128 22262283 + SINE/MIR_MIRb chr1 22262274 22262447 - SINE/MIR_MIR chr1 22263509 22263649 - SINE/MIR_MIRb chr1 22263978 22264001 + Simple_repeat_(GGT)n chr1 22264041 22264064 + Simple_repeat_(GT)n chr1 22264153 22264232 + Simple_repeat_(TGGTGG)n chr1 22264492 22264865 - LINE/L2_L2b chr1 22265336 22265379 + Simple_repeat_(TG)n chr1 22265583 22265610 + Simple_repeat_(TG)n chr1 22265739 22266451 - LINE/L2_L2a chr1 22266647 22266835 + SINE/MIR_MIRb chr1 22266956 22267083 - SINE/MIR_MIRb chr1 22267242 22267353 - SINE/Alu_Alu chr1 22267540 22267653 + Simple_repeat_(TG)n chr1 22269161 22269206 + Simple_repeat_(TTCTC)n chr1 22269221 22269531 - SINE/Alu_AluSg chr1 22269605 22269665 + LINE/L2_L2a chr1 22270243 22270363 - DNA/TcMar-Tigger_Tigger21a chr1 22270364 22270664 + SINE/Alu_AluSx1 chr1 22270665 22270819 - DNA/TcMar-Tigger_Tigger21a chr1 22270912 22270991 - LINE/L2_L2 chr1 22271063 22271208 + SINE/MIR_MIRb chr1 22271832 22272298 + Simple_repeat_(TCAT)n chr1 22272320 22272373 + Low_complexity_A-rich chr1 22273225 22273253 + Low_complexity_A-rich chr1 22273336 22273372 + Simple_repeat_(CCTGTCC)n chr1 22273734 22273967 + SINE/MIR_MIR chr1 22274024 22274183 + SINE/MIR_MIR chr1 22274349 22274434 - SINE/MIR_MIR3 chr1 22274502 22274541 + Simple_repeat_(GAT)n chr1 22274551 22274663 - SINE/MIR_MIRb chr1 22274831 22274904 - SINE/MIR_MIR3 chr1 22275231 22275368 + SINE/MIR_MIRc chr1 22275476 22275602 + SINE/MIR_MIR3 chr1 22276198 22276410 + SINE/MIR_MIRb chr1 22276457 22276561 - LINE/L2_L2d chr1 22276559 22276665 + LINE/L2_L2c chr1 22276996 22277041 + Simple_repeat_(GT)n chr1 22277188 22277299 - LINE/L2_L2a chr1 22277411 22277633 + SINE/MIR_MIR chr1 22277793 22277959 - SINE/MIR_MIRb chr1 22278177 22278215 + Low_complexity_A-rich chr1 22278416 22278575 + SINE/MIR_MIR3 chr1 22278590 22278785 - LINE/L2_L2a chr1 22278786 22279085 - SINE/Alu_AluJb chr1 22279086 22279447 - LINE/L2_L2a chr1 22279501 22279594 + DNA/hAT-Charlie_MER20B chr1 22279626 22279943 + SINE/Alu_AluSz6 chr1 22279944 22280021 + Low_complexity_A-rich chr1 22280027 22280260 + LINE/L1_L1MB2 chr1 22280261 22280565 - SINE/Alu_AluSx1 chr1 22280566 22281071 + LINE/L1_L1MB2 chr1 22281081 22281394 + SINE/Alu_AluSx1 chr1 22281440 22281732 - LINE/L2_L2a chr1 22282758 22282975 - LINE/CR1_L3b chr1 22282976 22283469 + LTR/ERVL-MaLR_MLT1K chr1 22283485 22283670 - LINE/CR1_L3b chr1 22283675 22284217 - LTR/ERVL-MaLR_MLT1K chr1 22284301 22284574 + Simple_repeat_(ATCC)n chr1 22284576 22284878 - LINE/L2_L2b chr1 22285460 22285576 + SINE/MIR_MIR3 chr1 22286133 22286263 - SINE/MIR_MIRb chr1 22286369 22286476 - SINE/MIR_MIRb chr1 22286512 22286566 + LINE/L2_L2d2 chr1 22286795 22286859 + SINE/MIR_MIRc chr1 22286972 22287288 + LINE/RTE-X_L4_A_Mam chr1 22287474 22287545 + DNA/hAT-Charlie_MER117 chr1 22287576 22287652 + LINE/RTE-X_L4_A_Mam chr1 22287744 22287896 - DNA/hAT-Tip100_MER91A chr1 22288551 22288723 - SINE/MIR_MIR3 chr1 22288793 22288897 - SINE/MIR_MIRc chr1 22288899 22288954 - DNA/hAT-Tip100_MER91B chr1 22288984 22289028 - SINE/MIR_MIR chr1 22289035 22289275 + SINE/MIR_MIRb chr1 22289594 22289618 + Simple_repeat_(TTG)n chr1 22289663 22289776 - LINE/L2_L2b chr1 22290305 22290534 + SINE/MIR_MIR chr1 22291015 22291047 + Simple_repeat_(CCTCC)n chr1 22291148 22291354 + SINE/MIR_MIRb chr1 22291382 22291505 - SINE/Alu_AluJo chr1 22291538 22291935 - LINE/L2_L2b chr1 22292295 22292418 - LINE/CR1_CR1-3_Croc chr1 22292528 22292774 + SINE/MIR_MIRb chr1 22292910 22293236 + SINE/Alu_AluJr chr1 22293280 22293555 + LTR/ERVL-MaLR_MLT1F2 chr1 22293556 22294440 + LTR/ERVL_MER21C chr1 22294457 22294618 + LTR/ERVL-MaLR_MLT1F2 chr1 22294620 22294650 + Simple_repeat_(TTAA)n chr1 22294651 22294945 - SINE/Alu_AluSz chr1 22294973 22295263 + SINE/Alu_AluSz6 chr1 22295274 22295321 + LTR/ERVL-MaLR_MLT1F2 chr1 22295432 22295554 - SINE/MIR_MIRb chr1 22295679 22295942 - LINE/L1_L1MD3 chr1 22295950 22296090 - LTR?_Eutr5 chr1 22296248 22296349 - LTR/ERVL-MaLR_MLT1C chr1 22296547 22296865 - LTR/ERVL-MaLR_MLT1C chr1 22296866 22297099 - LTR/ERVL-MaLR_MLT1G3 chr1 22297729 22297755 + Simple_repeat_(GCTCT)n chr1 22297904 22298157 - LTR/ERVL-MaLR_MLT1K chr1 22298374 22298551 + LINE/L2_L2c chr1 22298553 22298766 - SINE/MIR_MIRb chr1 22298795 22298879 + Low_complexity_GA-rich chr1 22298916 22299063 - SINE/Alu_FLAM_A chr1 22299071 22299385 - SINE/Alu_AluSx chr1 22299993 22302514 + LINE/L1_L1PA13 chr1 22302626 22303364 + LINE/L1_L1ME1 chr1 22303365 22303671 + SINE/Alu_AluSz6 chr1 22303672 22304180 + LINE/L1_L1ME1 chr1 22304181 22304459 + SINE/Alu_AluJb chr1 22304460 22304691 + LINE/L1_L1ME1 chr1 22304692 22305540 + LINE/L1_L1MA7 chr1 22305541 22305660 + LINE/L1_L1ME1 chr1 22305661 22305956 - SINE/Alu_AluY chr1 22305957 22307931 + LINE/L1_L1ME1 chr1 22307929 22309252 + LINE/L1_L1ME1 chr1 22309270 22309447 + LINE/L1_L1PA11 chr1 22309459 22309676 + SINE/Alu_AluJb chr1 22309680 22310330 + LINE/L1_L1ME1 chr1 22310381 22310674 - SINE/Alu_AluSx1 chr1 22310687 22310806 - SINE/Alu_AluJr chr1 22310910 22311082 - LINE/L2_L2a chr1 22311083 22311367 + SINE/Alu_AluSz chr1 22311370 22311415 + Simple_repeat_(ATA)n chr1 22311416 22311792 - LINE/L2_L2a chr1 22311920 22311994 + SINE/MIR_MIR chr1 22312126 22312227 + LINE/L2_L2c chr1 22312288 22312623 - SINE/Alu_AluSz chr1 22312678 22312882 + SINE/MIR_MIRb chr1 22312901 22313136 - SINE/MIR_MIR chr1 22313866 22313964 - LINE/CR1_L3 chr1 22313965 22314001 + Simple_repeat_(CTTT)n chr1 22314002 22314287 - SINE/Alu_AluSx1 chr1 22314288 22314337 - LINE/CR1_L3 chr1 22314358 22314449 + SINE/MIR_MIRb chr1 22314494 22314606 - LTR/ERVL_LTR16C chr1 22314607 22314740 + LTR/ERVL-MaLR_MLT1C2 chr1 22314737 22315147 + LTR/ERVL-MaLR_MLT1C2 chr1 22315150 22315514 - LTR/ERVL_LTR16C chr1 22315516 22315577 - LINE/L2_L2a chr1 22315651 22315921 - LINE/L2_L2a chr1 22316040 22316199 + SINE/MIR_MIRc chr1 22316575 22316622 - LINE/L1_L1ME4a chr1 22316623 22316928 + SINE/Alu_AluSg chr1 22316929 22317033 - LINE/L1_L1ME4a chr1 22317130 22317272 - SINE/MIR_MIRb chr1 22317572 22317722 - SINE/Alu_AluJr chr1 22317901 22318038 - LINE/CR1_L3 chr1 22318201 22318361 + DNA/hAT-Charlie_MER112 chr1 22319047 22319136 + SINE/MIR_MIRb chr1 22319130 22319207 + LINE/L2_L2a chr1 22319250 22319473 + LTR/ERVL-MaLR_MLT1H1 chr1 22319474 22319756 + SINE/Alu_AluSp chr1 22319757 22319803 + LTR/ERVL-MaLR_MLT1H1 chr1 22319837 22320002 + LTR/ERVL-MaLR_MLT1H1 chr1 22320007 22320059 + SINE/MIR_MIRb chr1 22320108 22320253 + SINE/MIR_MIR chr1 22320254 22320542 + SINE/Alu_AluJb chr1 22320543 22320633 + SINE/MIR_MIR chr1 22321070 22321223 - SINE/MIR_MIR3 chr1 22321431 22321479 - SINE/MIR_MIRc chr1 22321536 22321682 + DNA/hAT-Tip100_MamTip2 chr1 22321746 22321896 + SINE/MIR_MIRc chr1 22321909 22322296 - LTR/ERVL-MaLR_MLT1G1 chr1 22322327 22322566 - DNA/TcMar-Tigger_Tigger3c chr1 22322589 22322637 - DNA/TcMar-Tigger_Tigger3c chr1 22323378 22323458 - DNA/TcMar-Tigger_Tigger3d chr1 22323505 22323608 + LINE/L2_L2a chr1 22323665 22323777 + SINE/MIR_MIRb chr1 22323706 22323779 + SINE/MIR_MIR1_Amn chr1 22324171 22324300 - SINE/MIR_MIR chr1 22324411 22324710 - SINE/Alu_AluSx3 chr1 22324763 22324843 - SINE/MIR_MIR chr1 22325324 22325378 + SINE/MIR_MIRb chr1 22325513 22325567 - LTR/Gypsy?_LTR88a chr1 22326257 22326341 + SINE/MIR_MIR3 chr1 22326446 22326543 - DNA/hAT?_MER121B chr1 22326838 22327039 + LINE/L2_L2d chr1 22327490 22327618 - SINE/MIR_MIR chr1 22328083 22328187 + LINE/CR1_L3 chr1 22328441 22328506 - SINE/MIR_MIRc chr1 22329258 22329554 + LINE/L2_L2b chr1 22329609 22329666 + Simple_repeat_(GATTAAT)n chr1 22329804 22329897 - SINE/MIR_MIRc chr1 22330792 22330864 + LINE/L1_L1MC5 chr1 22330868 22330962 + Low_complexity_GA-rich chr1 22330963 22330993 + Simple_repeat_(GGAG)n chr1 22330994 22331005 + Low_complexity_GA-rich chr1 22331013 22331166 + LINE/L1_L1ME4a chr1 22331203 22331509 + SINE/Alu_AluSz chr1 22332133 22332160 + Simple_repeat_(TCTG)n chr1 22333094 22333266 - SINE/MIR_MIRc chr1 22333591 22334043 + Simple_repeat_(TGGA)n chr1 22334071 22334137 + Simple_repeat_(TGGA)n chr1 22335032 22335102 - SINE/MIR_MIR1_Amn chr1 22335702 22335734 + Simple_repeat_(CCT)n chr1 22336131 22336169 + Simple_repeat_(GATG)n chr1 22336175 22337516 + Simple_repeat_(CATC)n chr1 22337857 22337998 + LINE/L2_L2d chr1 22338002 22338280 + SINE/Alu_AluJr chr1 22338628 22338762 + SINE/MIR_MIRb chr1 22339761 22339997 + LINE/L2_L2a chr1 22340003 22340167 - SINE/MIR_MIRb chr1 22340474 22340513 + Simple_repeat_(TGGACTG)n chr1 22340516 22340740 - SINE/MIR_MIR chr1 22340780 22340966 + SINE/MIR_MIRb chr1 22341110 22341195 + DNA/hAT-Tip100_MamTip2 chr1 22341241 22341531 - SINE/Alu_AluSx chr1 22341843 22341896 - SINE/MIR_MIRc chr1 22342488 22342722 + Simple_repeat_(AGAGAC)n chr1 22342914 22342958 + Low_complexity_G-rich chr1 22343370 22344741 - LINE/L2_L2a chr1 22344747 22344879 - LINE/L1_L1MD3 chr1 22345892 22345924 + Simple_repeat_(CA)n chr1 22345985 22346293 - DNA/TcMar-Tigger_MER46C chr1 22346419 22346457 + Simple_repeat_(TGCGTG)n chr1 22346555 22346601 - LINE/CR1_L3 chr1 22346619 22346758 + Simple_repeat_(CTC)n chr1 22346949 22347069 + SINE/Alu_AluJb chr1 22347070 22347099 + Simple_repeat_(GAA)n chr1 22347100 22347410 + SINE/Alu_AluY chr1 22347592 22347904 - LINE/L1_L1MC4 chr1 22348181 22348281 - SINE/MIR_MIR chr1 22348917 22349216 + SINE/Alu_AluSz chr1 22349237 22349293 + LINE/L1_L1M5 chr1 22349855 22349883 + Simple_repeat_(CA)n chr1 22349950 22350066 + SINE/MIR_MIRc chr1 22350732 22350770 + Low_complexity_G-rich chr1 22351152 22351300 + DNA/TcMar-Tigger_X6a_DNA chr1 22352289 22352458 - SINE/MIR_MIRb chr1 22352495 22352590 + LINE/L2_L2c chr1 22352653 22352692 - DNA/hAT-Tip100_MER96B chr1 22352954 22353254 - SINE/Alu_AluJb chr1 22353505 22353730 + DNA/hAT-Tip100_MER96B chr1 22353749 22354298 + LINE/L1_L1ME4a chr1 22354390 22354698 - SINE/Alu_AluSz chr1 22354706 22354838 - SINE/Alu_FLAM_C chr1 22354874 22355178 - SINE/Alu_AluSz chr1 22355237 22355338 + LINE/L2_L2a chr1 22355340 22355640 - SINE/Alu_AluJr chr1 22355682 22355755 - LTR/ERVL-MaLR_MLT1K chr1 22356061 22356328 + SINE/Alu_AluSx1 chr1 22356804 22357006 - SINE/MIR_MIRc chr1 22357007 22357195 - DNA/hAT-Charlie_MER103C chr1 22357196 22357567 - LTR/ERVL-MaLR_THE1D chr1 22357568 22357655 - DNA/hAT-Charlie_MER103C chr1 22357770 22357876 + DNA/hAT-Charlie_Chap1a_Mam chr1 22358271 22358520 + LTR/ERVL-MaLR_MLT1K chr1 22358534 22358834 + LTR/ERVL-MaLR_MLT1K-int chr1 22359084 22359498 + LTR/ERVL-MaLR_MSTB chr1 22359529 22360095 - LINE/L2_L2a chr1 22360096 22360391 + SINE/Alu_AluSx1 chr1 22360392 22360425 - LINE/L2_L2a chr1 22360426 22360570 + Simple_repeat_(AGGA)n chr1 22360571 22360996 - LINE/L2_L2a chr1 22361472 22361615 + SINE/MIR_MIR3 chr1 22362105 22362335 + SINE/Alu_AluSx3 chr1 22362578 22362696 - DNA/hAT-Charlie_MER5B chr1 22362733 22362937 + SINE/MIR_MIRb chr1 22364034 22364274 - SINE/MIR_MIR chr1 22364608 22364689 + LINE/L1_L1MC chr1 22364741 22364815 - SINE/MIR_MIRb chr1 22364984 22365128 - SINE/MIR_MIR chr1 22365133 22365366 + LINE/L1_L1ME4a chr1 22365641 22366009 + LTR/ERVL-MaLR_THE1C chr1 22366790 22366918 + SINE/MIR_MIR chr1 22367026 22367145 - SINE/Alu_FLAM_C chr1 22367146 22367244 - SINE/MIR_MIR3 chr1 22367485 22367532 - LINE/RTE-BovB_MamRTE1 chr1 22367549 22368057 + LINE/L1_L1ME4a chr1 22368179 22368354 + SINE/MIR_MIR chr1 22368682 22368972 + SINE/Alu_AluSz chr1 22368973 22369007 + Simple_repeat_(ACAA)n chr1 22369008 22369020 + SINE/Alu_AluSz chr1 22369028 22369110 + SINE/Alu_FLAM_A chr1 22369140 22369205 + Simple_repeat_(TGAA)n chr1 22369215 22369360 - SINE/MIR_MIRb chr1 22369499 22369726 + DNA_X9a_DNA chr1 22370108 22370215 + Simple_repeat_(TTCC)n chr1 22370224 22370385 - LINE/L2_L2a chr1 22370386 22370602 - DNA/hAT-Charlie_MER30 chr1 22370603 22370885 - LINE/L2_L2a chr1 22370907 22371165 + SINE/MIR_MIRb chr1 22371182 22371492 + SINE/Alu_AluJo chr1 22371518 22371678 + SINE/Alu_FRAM chr1 22372211 22372309 - LINE/L2_L2a chr1 22372313 22372495 + DNA/hAT-Charlie_MER5A chr1 22372496 22372630 + SINE/Alu_AluSz6 chr1 22372634 22372884 + SINE/Alu_AluSx chr1 22372899 22372918 + Low_complexity_GA-rich chr1 22372919 22372982 + Simple_repeat_(GGAAT)n chr1 22372983 22373007 + Low_complexity_GA-rich chr1 22373046 22373564 + LTR/ERVL-MaLR_MLT1H1 chr1 22373675 22373836 + SINE/MIR_MIR3 chr1 22373984 22374187 + SINE/MIR_MIR chr1 22374400 22374581 + SINE/MIR_MIRc chr1 22374641 22374711 + SINE/MIR_MIR1_Amn chr1 22374713 22374767 + DNA/hAT-Charlie_Charlie8 chr1 22374768 22375081 - SINE/Alu_AluJb chr1 22375090 22375403 - SINE/Alu_AluSq2 chr1 22375404 22375654 + DNA/hAT-Charlie_MER102c chr1 22375712 22375797 + LINE/L2_L2d2 chr1 22376134 22376361 + SINE/MIR_MIRb chr1 22376631 22377045 + LTR/ERVL-MaLR_MSTB chr1 22377046 22377559 + LTR/ERVL-MaLR_MLT1K chr1 22377604 22377696 + Simple_repeat_(AATG)n chr1 22377765 22377867 - LTR_EUTREP8 chr1 22377868 22377999 + SINE/MIR_MIRb chr1 22378188 22378358 - DNA/hAT-Charlie_MER5A chr1 22378378 22378460 - SINE/MIR_MIR3 chr1 22378908 22378969 + LINE/L2_L2b chr1 22380083 22380335 + SINE/MIR_MIR chr1 22380867 22381057 + LINE/L1_L1ME1 chr1 22380891 22381099 + LINE/L1_L1MEf chr1 22381100 22381234 + SINE/Alu_FLAM_C chr1 22381235 22381296 + LINE/L1_L1MEf chr1 22381297 22381597 + SINE/Alu_AluSq2 chr1 22381602 22381625 + LINE/L1_L1ME1 chr1 22381626 22381704 + LINE/L1_L1M4 chr1 22381731 22382143 + LINE/L1_L1ME1 chr1 22382007 22382162 + LINE/L1_L1ME1 chr1 22382163 22382517 - DNA/TcMar-Tigger_Tigger3a chr1 22382518 22383188 + LINE/L1_L1ME1 chr1 22383640 22383917 - LTR/ERVL_LTR16E2 chr1 22383930 22384103 - LINE/L2_L2c chr1 22384155 22384524 + LTR/ERVL-MaLR_MLT1K chr1 22384695 22384910 + SINE/MIR_MIR chr1 22385029 22385185 + LINE/L2_L2 chr1 22385198 22385242 + DNA/hAT-Charlie_MER5A1 chr1 22385335 22385447 - DNA/hAT-Charlie_MER5B chr1 22385587 22385679 - LTR/Gypsy_LTR81AB chr1 22385688 22385839 - SINE/MIR_MIR chr1 22386166 22386331 - LTR/Gypsy_LTR81AB chr1 22386486 22386838 - LTR/Gypsy_LTR81AB chr1 22386873 22387294 + LINE/L1_L1ME1 chr1 22387415 22387725 + LINE/L2_L2c chr1 22387831 22387968 - LINE/L2_L2d chr1 22388294 22388373 - LINE/L2_L2c chr1 22388458 22388696 - SINE/MIR_MIR chr1 22388720 22388790 - LINE/L2_L2a chr1 22388828 22388928 + LINE/L2_L2b chr1 22388929 22388980 + SINE/MIR_MIRc chr1 22389030 22389195 - DNA/hAT-Tip100_MamTip2 chr1 22389686 22389798 + DNA/hAT-Charlie_MER5A chr1 22389799 22390099 - SINE/Alu_AluY chr1 22390324 22390504 - SINE/MIR_MIRb chr1 22391058 22391202 - SINE/MIR_MIR3 chr1 22391999 22392031 + SINE/MIR_MIR chr1 22392032 22392307 + SINE/Alu_AluYc chr1 22392308 22392520 + SINE/MIR_MIR chr1 22392570 22392705 + SINE/Alu_FRAM chr1 22392706 22392972 - SINE/Alu_AluSx3 chr1 22392973 22392995 + SINE/Alu_FRAM chr1 22393817 22393880 + LTR/ERVL-MaLR_MLT1D chr1 22393883 22394172 + SINE/Alu_AluSp chr1 22394173 22394243 + Low_complexity_G-rich chr1 22394244 22394323 + Simple_repeat_(GAAA)n chr1 22394324 22394388 + Low_complexity_G-rich chr1 22394391 22394864 + LTR/ERVL-MaLR_MLT1D chr1 22394930 22395084 - SINE/Alu_AluJr chr1 22395087 22395324 - SINE/MIR_MIRb chr1 22395641 22395686 + Simple_repeat_(TG)n chr1 22395687 22395840 - SINE/Alu_FRAM chr1 22395880 22395947 - DNA/hAT-Charlie_MER5A1 chr1 22396115 22396599 + LINE/L2_L2a chr1 22396450 22396924 + LINE/L2_L2a chr1 22397260 22397359 - DNA/hAT-Ac_OldhAT1 chr1 22397575 22397761 - DNA/hAT-Charlie_MER20 chr1 22397777 22397995 + LINE/L1_L1ME4a chr1 22397996 22398130 - LINE/L2_L2a chr1 22398218 22398507 + SINE/Alu_AluJb chr1 22398581 22398635 + LINE/L2_L2a chr1 22398624 22398694 - LINE/L2_L2 chr1 22398949 22399036 + LINE/L1_L1ME4a chr1 22399066 22399179 + LINE/CR1_Plat_L3 chr1 22399351 22399534 - SINE/MIR_MIR chr1 22399540 22399597 - LINE/L2_L2b chr1 22399709 22399935 - LINE/L2_L2b chr1 22400061 22401073 - LINE/L2_L2b chr1 22401686 22401726 + Simple_repeat_(GAT)n chr1 22401851 22401960 - SINE/MIR_MIRb chr1 22401961 22402247 - SINE/Alu_AluJb chr1 22402248 22402326 - SINE/MIR_MIRb chr1 22402683 22402720 + Low_complexity_A-rich chr1 22403126 22403431 - SINE/Alu_AluJo chr1 22403590 22403801 + SINE/MIR_MIR chr1 22403832 22404270 - LINE/L2_L2c chr1 22404337 22404474 + SINE/MIR_MIRb chr1 22404484 22404539 + SINE/MIR_MIRb chr1 22404547 22404759 + SINE/MIR_MIR chr1 22404845 22405364 - LINE/L1_L1MA7 chr1 22405365 22405415 + Simple_repeat_(TTTTG)n chr1 22405416 22405439 - LINE/L1_L1MA7 chr1 22405440 22405680 - SINE/Alu_AluSx1 chr1 22405681 22405988 - LINE/L1_L1MA7 chr1 22406332 22406642 + SINE/Alu_AluSq2 chr1 22407113 22407215 + SINE/MIR_MIRb chr1 22407419 22407460 + Low_complexity_A-rich chr1 22408020 22408179 - SINE/MIR_MIR3 chr1 22408292 22408362 + LINE/L2_L2a chr1 22409380 22409526 + SINE/MIR_MIRb chr1 22409597 22409756 + SINE/MIR_MIRb chr1 22409758 22409885 - LTR/ERVL-MaLR_MLT1N2 chr1 22410416 22410619 + SINE/MIR_MIRc chr1 22410621 22410903 + SINE/Alu_AluJb chr1 22410983 22411360 - LINE/L1_L1MB7 chr1 22411700 22411769 + Simple_repeat_(TGGA)n chr1 22411770 22411938 + Simple_repeat_(ATGG)n chr1 22411939 22411968 + Simple_repeat_(TGGA)n chr1 22412013 22412319 - SINE/Alu_AluJb chr1 22412527 22412567 + Simple_repeat_(GAAT)n chr1 22412583 22412731 - SINE/MIR_MIR1_Amn chr1 22412869 22412908 + Simple_repeat_(GT)n chr1 22412917 22412958 + Low_complexity_GA-rich chr1 22413165 22413229 + SINE/MIR_MIRb chr1 22413282 22413477 + SINE/MIR_MIRc chr1 22413478 22413689 + DNA/hAT-Charlie_MER58A chr1 22413831 22414092 + SINE/MIR_MIRb chr1 22414536 22414678 + SINE/MIR_MIR3 chr1 22414811 22414913 + Unknown_MamRep564 chr1 22415050 22415356 + SINE/Alu_AluSx chr1 22415641 22415817 + SINE/MIR_MIR chr1 22415944 22416076 + SINE/Alu_AluSx chr1 22416077 22416381 + SINE/Alu_AluSz chr1 22416416 22416593 - SINE/MIR_MIRc chr1 22416934 22417098 - SINE/MIR_MIR3 chr1 22417242 22417424 + SINE/MIR_MIR chr1 22417901 22418027 + SINE/MIR_MIRb chr1 22418911 22418980 + SINE/MIR_MIRb chr1 22418985 22419123 + LINE/L2_L2c chr1 22419124 22419169 + Simple_repeat_(GGAT)n chr1 22419200 22419451 - SINE/MIR_MIRb chr1 22419529 22419755 + SINE/Alu_AluSx chr1 22419776 22419875 - SINE/MIR_MIRb chr1 22420151 22420389 - SINE/MIR_MIRc chr1 22420889 22421180 - SINE/Alu_AluSc chr1 22421272 22421323 - SINE/MIR_MIRb chr1 22421324 22421513 - DNA/hAT_MER53 chr1 22421514 22421689 - SINE/MIR_MIRb chr1 22421879 22422101 + SINE/MIR_MIR chr1 22422072 22422114 + LINE/L2_L2c chr1 22422129 22422239 - SINE/MIR_MIR3 chr1 22422431 22422573 - LINE/L2_L2c chr1 22422574 22422886 + SINE/Alu_AluSz chr1 22422888 22423216 + SINE/Alu_AluSp chr1 22423217 22423448 - LINE/L2_L2c chr1 22424325 22424681 - LTR/ERVL-MaLR_THE1B chr1 22424756 22424829 + LINE/L2_L2 chr1 22424842 22424953 + SINE/MIR_MIRb chr1 22424869 22424974 + SINE/MIR_MIR3 chr1 22425068 22425374 + SINE/Alu_AluSx1 chr1 22425694 22425795 + SINE/MIR_MIRb chr1 22425866 22425953 + LINE/L2_L2a chr1 22426103 22426408 + SINE/Alu_AluSx chr1 22426410 22426727 - LINE/L2_L2a chr1 22427158 22427287 - LINE/L2_L2c chr1 22427307 22427617 - SINE/Alu_AluSx4 chr1 22427621 22428142 - LINE/L1_L1ME3Cz chr1 22428143 22428320 + LINE/L1_L1PA17 chr1 22428321 22428643 - LINE/L1_L1ME3Cz chr1 22428700 22428944 + LTR/ERVL-MaLR_MLT1J chr1 22429081 22429100 - LINE/L1_L1ME3Cz chr1 22429101 22429399 + SINE/Alu_AluYk3 chr1 22429400 22429597 - LINE/L1_L1M5 chr1 22429603 22429710 - LINE/L1_L1PREC2 chr1 22429711 22430012 + SINE/Alu_AluSp chr1 22430210 22430301 - LINE/L1_L1M5 chr1 22430308 22430533 + SINE/Alu_AluJr chr1 22430534 22430770 - SINE/Alu_AluJb chr1 22430775 22431153 - LINE/L1_L1ME3Cz chr1 22431229 22431360 + Simple_repeat_(AT)n chr1 22431363 22431384 + Simple_repeat_(TG)n chr1 22431385 22431418 + Simple_repeat_(TA)n chr1 22431457 22431748 + SINE/Alu_AluSq2 chr1 22431848 22431933 - LINE/L1_L1ME4b chr1 22431997 22432034 - LTR/ERVL-MaLR_MLT1C2 chr1 22432036 22432193 + LTR/ERVL_LTR41C chr1 22432266 22432432 + LTR/ERVL_LTR41 chr1 22432433 22432630 - DNA/hAT-Charlie_MER3 chr1 22432631 22432690 + LTR/ERVL_LTR41 chr1 22432861 22433008 + DNA/hAT-Charlie_MER58B chr1 22433036 22433151 + DNA/TcMar-Tigger_MER2 chr1 22433152 22433457 + SINE/Alu_AluY chr1 22433458 22433632 + DNA/TcMar-Tigger_MER2 chr1 22433764 22433799 + SINE/Alu_Alu chr1 22433800 22433823 + Simple_repeat_(TTAA)n chr1 22433825 22434351 - LINE/L1_L1ME4b chr1 22434200 22434930 - LINE/L1_L1M5 chr1 22435019 22435299 - SINE/Alu_AluJb chr1 22435471 22435489 + Simple_repeat_(TC)n chr1 22435496 22435674 - LINE/L1_L1ME4b chr1 22435687 22435838 + LINE/L1_L1ME1 chr1 22435846 22435991 + SINE/Alu_AluSx1 chr1 22435995 22436230 + LINE/L1_L1ME2 chr1 22436231 22436540 + SINE/Alu_AluSc8 chr1 22436541 22436782 + LINE/L1_L1ME2 chr1 22436845 22437047 + LINE/L1_L1ME1 chr1 22437044 22437374 + LINE/L1_L1MCa chr1 22437384 22437424 - LINE/L1_L1MB3 chr1 22437425 22437719 - SINE/Alu_AluSx chr1 22437720 22437843 - LINE/L1_L1MB3 chr1 22437844 22438152 + SINE/Alu_AluSp chr1 22438153 22438858 - LINE/L1_L1MB3 chr1 22438859 22439165 - SINE/Alu_AluSx4 chr1 22439166 22440357 - LINE/L1_L1MB3 chr1 22440358 22440487 + SINE/Alu_FLAM_A chr1 22440488 22441469 - LINE/L1_L1MB3 chr1 22441470 22441782 - SINE/Alu_AluSx chr1 22441783 22442278 - LINE/L1_L1MB3 chr1 22442279 22442764 - LTR/ERV1_LTR21A chr1 22442765 22442963 - LINE/L1_L1MB3 chr1 22442970 22443172 + LINE/L1_L1MCa chr1 22443173 22444008 - LTR/ERV1_MER4D chr1 22444009 22444131 + LTR/ERV1_MER50 chr1 22444132 22444428 + SINE/Alu_AluSx1 chr1 22444429 22445027 + LTR/ERV1_MER50 chr1 22445066 22445114 + LTR/ERV1_MER50-int chr1 22445116 22445183 - LTR/ERV1_MER50 chr1 22445184 22445228 - LTR/ERV1_MER4D chr1 22445229 22445295 + LINE/L1_L1MCa chr1 22445299 22445575 + LINE/L1_L1MCa chr1 22445576 22445877 + SINE/Alu_AluSx3 chr1 22445878 22446820 + LINE/L1_L1MCa chr1 22446821 22447118 + SINE/Alu_AluJb chr1 22447119 22447235 + LINE/L1_L1ME1 chr1 22447243 22447594 - LINE/L2_L2 chr1 22448256 22448334 + SINE/MIR_MIR chr1 22448361 22448527 + LINE/L1_HAL1b chr1 22448539 22448772 + SINE/MIR_MIR chr1 22448801 22449113 - SINE/Alu_AluSx3 chr1 22449941 22450212 - SINE/Alu_AluSx1 chr1 22450246 22450305 - LINE/L2_L2a chr1 22451392 22451668 + SINE/Alu_AluSq chr1 22452359 22452678 + DNA/hAT-Charlie_MER58B chr1 22453341 22453508 - LINE/L2_L2a chr1 22453558 22453725 - SINE/MIR_MIR chr1 22453768 22453909 + LINE/L2_L2d chr1 22453938 22454252 - SINE/Alu_AluSz chr1 22454256 22454658 - LINE/L2_L2a chr1 22454742 22454811 + SINE/MIR_MIRb chr1 22456413 22456575 + DNA/hAT-Charlie_Charlie4z chr1 22456987 22457122 + SINE/Alu_FRAM chr1 22458114 22458290 + LINE/L2_L2d chr1 22458516 22458689 - LINE/L1_L1MC5a chr1 22458749 22458889 - SINE/Alu_AluJr chr1 22458980 22459094 - LINE/L1_L1MC5a chr1 22459110 22459950 - LINE/L1_L1MC5a chr1 22460447 22460582 - SINE/MIR_MIRb chr1 22460629 22460731 - SINE/MIR_MIRc chr1 22461327 22461346 + Simple_repeat_(T)n chr1 22461764 22461833 + LINE/L2_L2d chr1 22462221 22462477 + SINE/MIR_MIR chr1 22462574 22462719 + SINE/MIR_MIR3 chr1 22462984 22463223 - SINE/MIR_MIR chr1 22464064 22464303 - SINE/MIR_MIR chr1 22464427 22464526 + LINE/L2_L2c chr1 22464589 22464842 - LINE/L1_L1ME4a chr1 22464876 22464930 - LINE/L1_L1MB7 chr1 22464945 22465945 - LTR/ERV1_MER52C chr1 22465949 22466968 - LINE/L1_L1MB7 chr1 22466970 22467179 - LINE/L1_L1ME4a chr1 22467476 22467774 - SINE/Alu_AluSz chr1 22467777 22467795 + Simple_repeat_(T)n chr1 22467812 22468111 + SINE/Alu_AluJb chr1 22468112 22468286 - SINE/MIR_MIRb chr1 22468466 22468643 - SINE/Alu_AluJo chr1 22468644 22468672 + Simple_repeat_(T)n chr1 22468673 22468796 - SINE/Alu_AluJo chr1 22468822 22469127 - SINE/Alu_AluJb chr1 22469160 22469223 + Simple_repeat_(TGTTTTA)n chr1 22469313 22469483 - SINE/Alu_FAM chr1 22469545 22469836 - SINE/Alu_AluSz chr1 22470079 22470255 + SINE/MIR_MIR3 chr1 22470986 22471218 + LTR?_MamRep605 chr1 22471390 22471594 + SINE/MIR_MIR chr1 22471693 22472185 - LTR/ERVL_LTR53 chr1 22472186 22472485 - SINE/Alu_AluJb chr1 22472486 22472520 - LTR/ERVL_LTR53 chr1 22473136 22473295 + DNA/hAT-Charlie_MER5A1 chr1 22473301 22473522 + SINE/MIR_MIR chr1 22473812 22473986 + SINE/MIR_MIRc chr1 22474708 22474764 + SINE/MIR_MIR3 chr1 22475736 22475817 + LINE/L2_L2d2 chr1 22476236 22476519 - SINE/Alu_AluSz6 chr1 22476578 22476808 - LINE/CR1_Plat_L3 chr1 22477297 22477350 - SINE/MIR_MIR chr1 22477406 22477677 + SINE/Alu_AluSc chr1 22477685 22477756 + LINE/L1_L1MEi chr1 22477757 22477848 - SINE/Alu_AluJr chr1 22478120 22478241 + SINE/MIR_MIR chr1 22478242 22478544 - SINE/Alu_AluY chr1 22478545 22478692 + SINE/MIR_MIR chr1 22478785 22479136 - LINE/L1_L1ME2 chr1 22479109 22479132 - LINE/L1_L1ME2 chr1 22479133 22479302 - LINE/L1_L1M5 chr1 22479279 22479866 - LINE/L1_L1MEi chr1 22479907 22480205 - SINE/Alu_AluSx1 chr1 22480206 22480458 - LINE/L1_L1ME2 chr1 22480495 22480515 + Simple_repeat_(TGT)n chr1 22480518 22480649 - SINE/Alu_AluY chr1 22480705 22481218 - LINE/L1_L1ME2 chr1 22481788 22481815 + Simple_repeat_(A)n chr1 22482307 22482789 - LINE/L1_L1MC1 chr1 22482790 22483097 + SINE/Alu_AluY chr1 22483098 22485524 - LINE/L1_L1MC1 chr1 22485524 22486713 - LINE/L1_L1MC1 chr1 22486714 22487033 - SINE/Alu_AluSx1 chr1 22487034 22487233 - LINE/L1_L1MC1 chr1 22487448 22488115 + LINE/L2_L2c chr1 22488181 22489442 - LINE/L2_L2a chr1 22489444 22489515 + LINE/L2_L2c chr1 22489546 22489713 + SINE/MIR_MIRb chr1 22490890 22491193 - SINE/Alu_AluSx3 chr1 22491878 22491913 + Low_complexity_A-rich chr1 22491997 22492241 - SINE/MIR_MIR chr1 22493425 22493455 + Simple_repeat_(CT)n chr1 22493471 22493931 - LINE/L1_L1ME1 chr1 22493932 22494109 + SINE/MIR_MIRb chr1 22494421 22494576 - DNA/hAT-Charlie_MER5A chr1 22494703 22494802 - LINE/L2_L2c chr1 22495493 22495526 + Simple_repeat_(GTTTT)n chr1 22495527 22495815 - SINE/Alu_AluSx chr1 22495864 22495947 + SINE/MIR_MIR chr1 22496578 22496786 - LTR/ERVL-MaLR_MLT1K chr1 22496840 22497094 - LTR/ERVL_MLT2B3 chr1 22497564 22497615 - LINE/L2_L2c chr1 22497737 22497865 + SINE/MIR_MIRb chr1 22498929 22499446 - LTR/ERVL-MaLR_MLT1F2 chr1 22501043 22501320 - LINE/L2_L2d2 chr1 22502546 22502633 + LINE/L2_L2d2 chr1 22502647 22502756 + Simple_repeat_(ATGG)n chr1 22503030 22503312 - LINE/L1_L1ME4a chr1 22503608 22503631 + Simple_repeat_(CA)n chr1 22504350 22504599 - SINE/MIR_MIRb chr1 22504610 22504867 - SINE/MIR_MIRb chr1 22505170 22505267 - DNA/TcMar-Tigger_Tigger23a chr1 22506908 22507097 - LINE/L1_L1ME4a chr1 22507098 22507336 + SINE/MIR_MIR chr1 22507337 22507475 - LINE/L1_L1ME4a chr1 22508127 22508164 + Low_complexity_GA-rich chr1 22509339 22509648 - SINE/Alu_AluSz chr1 22509672 22509867 - SINE/MIR_MIRb chr1 22511094 22511110 + Simple_repeat_(T)n chr1 22512306 22512450 + SINE/MIR_MIRb chr1 22513226 22513444 + LINE/L1_L1MA1 chr1 22513462 22513726 + SINE/Alu_AluY chr1 22513847 22513895 - DNA/TcMar-Mariner_MADE1 chr1 22514241 22514884 + LINE/L2_L2a chr1 22514856 22515355 - LINE/L2_L2a chr1 22515356 22515814 - LTR/ERVL-MaLR_MLT1D chr1 22515815 22516264 - LINE/L2_L2a chr1 22516265 22516314 - LTR/ERVL-MaLR_MLT1D chr1 22516315 22516511 - LINE/L2_L2a chr1 22517623 22517691 + DNA/TcMar_X4a_DNA chr1 22518190 22518454 + SINE/MIR_MIR chr1 22518636 22518749 - LINE/L2_L2d2 chr1 22519360 22519595 + SINE/MIR_MIR chr1 22521073 22521323 + SINE/MIR_MIRb chr1 22521802 22521826 + Simple_repeat_(AGC)n chr1 22521910 22522060 + SINE/MIR_MIRb chr1 22522083 22522176 - SINE/MIR_MIRb chr1 22522178 22522227 - SINE/MIR_MIR chr1 22522487 22522633 + SINE/MIR_MIR chr1 22522764 22522933 - SINE/Alu_AluSg4 chr1 22522946 22523250 - SINE/Alu_AluY chr1 22523251 22523895 - DNA/TcMar-Tc2_Kanga1b chr1 22523942 22524105 + DNA/hAT-Tip100_Arthur1A chr1 22524110 22524164 + Simple_repeat_(CCTCATT)n chr1 22525125 22525261 + DNA/hAT-Charlie_Charlie15a chr1 22528426 22528456 + Low_complexity_A-rich chr1 22528535 22528835 - SINE/Alu_AluJo chr1 22530901 22531109 + SINE/MIR_MIR chr1 22531851 22531928 + SINE/MIR_MIRc chr1 22533028 22533196 + LINE/L2_L2d chr1 22533816 22533889 + LINE/L2_L2c chr1 22534130 22534154 + Simple_repeat_(TTGG)n chr1 22534206 22534508 + SINE/Alu_AluSx chr1 22534518 22534570 - SINE/MIR_MIR chr1 22534571 22534879 - SINE/Alu_AluSx chr1 22534880 22535064 - SINE/MIR_MIR chr1 22535713 22536067 - LINE/L1_L1PA13 chr1 22536258 22536503 - SINE/MIR_MIRc chr1 22536519 22536678 - SINE/MIR_MIRc chr1 22536856 22537036 + DNA/hAT-Charlie_MER3 chr1 22537047 22537138 - SINE/MIR_MIRb chr1 22537393 22537481 - SINE/MIR_MIRc chr1 22537894 22537933 + SINE/MIR_MIR3 chr1 22538008 22538093 + SINE/MIR_MIRc chr1 22538605 22538740 + SINE/MIR_MIRb chr1 22539476 22539636 - LINE/L1_L1MB3 chr1 22539633 22539812 - LINE/L1_L1MB7 chr1 22539820 22540123 - SINE/Alu_AluJb chr1 22540277 22540871 - LINE/L2_L2a chr1 22540872 22540970 + DNA/hAT-Charlie_Cheshire chr1 22541003 22541223 - LINE/L2_L2a chr1 22541386 22541493 - LINE/L2_L2c chr1 22541535 22541622 + SINE/MIR_MIR3 chr1 22541874 22541990 + DNA/hAT-Charlie_MER106B chr1 22541991 22542302 + SINE/Alu_AluSz chr1 22542303 22542372 + DNA/hAT-Charlie_MER106B chr1 22542431 22542540 - LINE/L2_L2c chr1 22542864 22543001 + SINE/MIR_MIR3 chr1 22543130 22543214 - SINE/MIR_MIR chr1 22543733 22543870 + LINE/L2_L2a chr1 22544002 22544140 - SINE/MIR_MIRc chr1 22544215 22544349 + LINE/L2_L2a chr1 22544350 22544646 + SINE/Alu_AluSz6 chr1 22544647 22544789 + LINE/L2_L2a chr1 22544837 22544984 - SINE/MIR_MIRb chr1 22545520 22545611 + SINE/MIR_MIR3 chr1 22545741 22545813 - DNA/hAT-Charlie_MER3 chr1 22546158 22546237 - SINE/MIR_MIRb chr1 22546242 22546405 + SINE/Alu_FRAM chr1 22546427 22546449 + Simple_repeat_(AAAT)n chr1 22546452 22546512 - SINE/MIR_MIRc chr1 22546574 22546619 + Simple_repeat_(GT)n chr1 22546932 22546993 + Simple_repeat_(TGCTCC)n chr1 22547048 22548201 + Simple_repeat_(TC)n chr1 22548663 22548879 + SINE/MIR_MIRb chr1 22548967 22548995 + Simple_repeat_(TTTG)n chr1 22549007 22549292 - SINE/Alu_AluSz chr1 22549293 22549355 - SINE/MIR_MIR chr1 22549358 22549492 - SINE/Alu_AluSz6 chr1 22549494 22549568 - SINE/MIR_MIR chr1 22551128 22551282 - SINE/MIR_MIR3 chr1 22552662 22552901 - LINE/L2_L2c chr1 22553809 22553880 + SINE/MIR_MIRb chr1 22554136 22554236 + SINE/MIR_MIR3 chr1 22554339 22554467 + LINE/L2_L2b chr1 22554952 22554997 + Simple_repeat_(CCAC)n chr1 22555026 22555051 + Simple_repeat_(CT)n chr1 22555052 22555087 + Simple_repeat_(AC)n chr1 22555088 22555140 + Simple_repeat_(CT)n chr1 22555381 22555550 - SINE/MIR_MIRb chr1 22555927 22556105 + SINE/MIR_MIRb chr1 22556172 22556242 - SINE/MIR_MIR3 chr1 22556526 22556575 + Low_complexity_GA-rich chr1 22556687 22557101 - LINE/L2_L2 chr1 22557585 22557931 + DNA/TcMar-Tigger_Tigger15a chr1 22558074 22558372 + SINE/Alu_AluSz chr1 22558463 22558603 + LINE/L2_L2b chr1 22558648 22558684 + DNA/hAT-Charlie_MER33 chr1 22558685 22558995 - SINE/Alu_AluSq2 chr1 22558996 22559075 + DNA/hAT-Charlie_MER33 chr1 22559744 22559893 - SINE/MIR_MIRc chr1 22559865 22559970 + DNA/hAT-Ac_OldhAT1 chr1 22560028 22560068 + Simple_repeat_(CTGCC)n chr1 22560275 22560402 - LINE/L2_L2c chr1 22560465 22560540 + SINE/MIR_MIRb chr1 22561254 22561568 - SINE/Alu_AluJb chr1 22561664 22561862 + DNA/hAT-Charlie_MER102b chr1 22561932 22562051 - LINE/CR1_L3 chr1 22562118 22562343 + SINE/MIR_MIRb chr1 22562743 22562885 - SINE/MIR_MIRb chr1 22563452 22563480 + Simple_repeat_(CGG)n chr1 22563607 22563673 + Simple_repeat_(CGCCC)n chr1 22563682 22563712 + Simple_repeat_(CGG)n chr1 22563713 22563788 + Low_complexity_G-rich chr1 22563789 22563799 + Simple_repeat_(CGG)n chr1 22564405 22564451 + Low_complexity_G-rich chr1 22566220 22566461 + DNA/TcMar-Tigger_Tigger19a chr1 22566480 22566608 - SINE/MIR_MIRc chr1 22566950 22567042 + DNA/TcMar-Tigger_Tigger19a chr1 22567864 22568027 - SINE/MIR_MIR3 chr1 22568149 22568311 - SINE/MIR_MIRb chr1 22568463 22568660 + SINE/MIR_MIRb chr1 22568830 22569011 - SINE/MIR_MIRb chr1 22569033 22569181 - LINE/L2_L2d chr1 22569430 22569497 - SINE/MIR_MIR3 chr1 22569915 22570141 + SINE/MIR_MIRb chr1 22570238 22570412 + Simple_repeat_(AC)n chr1 22570493 22570524 + Simple_repeat_(AAT)n chr1 22571285 22571492 - SINE/MIR_MIRb chr1 22571701 22571766 - DNA/hAT-Charlie_MER5A chr1 22571769 22572050 + SINE/Alu_AluJr chr1 22572258 22572386 + SINE/MIR_MIR chr1 22572591 22572728 - SINE/MIR_MIRc chr1 22573280 22573438 - SINE/MIR_MIRb chr1 22573984 22574278 - SINE/Alu_AluY chr1 22574296 22574959 - LINE/L1_L1MB3 chr1 22574960 22575250 - SINE/Alu_AluSx1 chr1 22575251 22575559 - LINE/L1_L1MB3 chr1 22575747 22575839 - LINE/L2_L2 chr1 22575892 22576083 + SINE/MIR_MIRb chr1 22577230 22577436 - SINE/MIR_MIR chr1 22580113 22580434 - SINE/Alu_AluSq2 chr1 22580652 22580863 - SINE/MIR_MIRc chr1 22580975 22581074 - LINE/L2_L2c chr1 22581074 22581233 - SINE/MIR_MIRb chr1 22581497 22581610 - SINE/MIR_MIR1_Amn chr1 22581613 22581997 + DNA/hAT-Tip100_MamRep4096 chr1 22582519 22582703 - SINE/MIR_MIR3 chr1 22583312 22583404 - LINE/Dong-R4_Mam_R4 chr1 22583457 22583605 + SINE/MIR_MIRb chr1 22583612 22583812 - SINE/MIR_MIR chr1 22583912 22584021 + LINE/L2_L2c chr1 22584824 22585263 - LINE/L2_L2c chr1 22585383 22585637 + LINE/L2_L2a chr1 22585720 22585777 + Simple_repeat_(GTGACA)n chr1 22585816 22585947 + DNA/hAT-Charlie_MER5A chr1 22586226 22586348 + SINE/MIR_MIR chr1 22587074 22587103 + Simple_repeat_(TG)n chr1 22587822 22587848 + Simple_repeat_(CACA)n chr1 22588110 22588171 + Simple_repeat_(TTCAAGA)n chr1 22589465 22589605 + SINE/MIR_MIRb chr1 22589570 22589620 + LINE/L2_L2c chr1 22589874 22590052 - SINE/MIR_MIRc chr1 22590105 22590200 - LINE/L2_L2b chr1 22590318 22590856 + LINE/L2_L2a chr1 22591108 22591232 + SINE/Alu_AluJr chr1 22591235 22591519 - SINE/Alu_AluJb chr1 22591649 22592064 + LINE/L2_L2a chr1 22592295 22592488 - SINE/MIR_MIRb chr1 22592539 22592571 - LINE/L2_L2d2 chr1 22592570 22592711 - LINE/L2_L2b chr1 22593837 22594130 - SINE/Alu_AluSg4 chr1 22594136 22594267 - SINE/Alu_AluSx chr1 22594393 22594547 + DNA/hAT-Charlie_MER5A chr1 22594725 22594893 + SINE/MIR_MIRb chr1 22595411 22595634 - SINE/MIR_MIRb chr1 22595707 22595783 - SINE/MIR_MIR3 chr1 22596509 22596622 - DNA/hAT-Charlie_MER117 chr1 22596651 22596957 - SINE/Alu_AluSz chr1 22598509 22598731 + SINE/MIR_MIRc chr1 22599168 22599258 + SINE/MIR_MIRc chr1 22599378 22599573 + DNA/hAT-Charlie_Charlie15a chr1 22599574 22600382 + Low_complexity_GA-rich chr1 22602041 22602075 + Simple_repeat_(TG)n chr1 22602746 22602861 - SINE/MIR_MIR3 chr1 22603664 22603858 - SINE/MIR_MIRb chr1 22604360 22604529 - SINE/MIR_MIRb chr1 22604656 22604950 - LINE/L2_L2c chr1 22605014 22606097 + LINE/L1_L1ME3Cz chr1 22606098 22606515 - LTR/ERVL_LTR16A1 chr1 22606516 22607040 + LINE/L1_L1ME3Cz chr1 22607070 22607157 + LINE/L1_L1MC2 chr1 22607347 22607684 + LINE/L2_L2 chr1 22607697 22607865 - SINE/Alu_AluJr chr1 22607866 22607904 + Simple_repeat_(TTTG)n chr1 22607905 22608024 - SINE/Alu_AluJr chr1 22608055 22608479 + LINE/L2_L2b chr1 22608563 22608704 + SINE/MIR_MIRb chr1 22609540 22609770 - SINE/MIR_MIRb chr1 22609963 22609976 + Simple_repeat_(TCCC)n chr1 22609977 22610022 + Simple_repeat_(TCTT)n chr1 22610023 22610065 + Simple_repeat_(TCCC)n chr1 22610068 22610102 + Simple_repeat_(TTCT)n chr1 22610104 22610400 - SINE/Alu_AluJr chr1 22610682 22610812 + SINE/Alu_FLAM_A chr1 22610839 22610864 + Simple_repeat_(A)n chr1 22610868 22610953 + Simple_repeat_(GT)n chr1 22611353 22611408 - SINE/MIR_MIRc chr1 22611972 22612264 + LINE/CR1_L3 chr1 22612323 22612541 - SINE/MIR_MIRb chr1 22612823 22612872 + Simple_repeat_(CCTCCC)n chr1 22612924 22613150 + SINE/MIR_MIRb chr1 22613673 22613783 + SINE/MIR_MIR chr1 22614020 22614045 + Simple_repeat_(GAG)n chr1 22615139 22615438 - SINE/Alu_AluSc chr1 22615507 22615579 + LINE/L2_L2b chr1 22615599 22615661 + LINE/L2_L2b chr1 22615668 22615694 + Simple_repeat_(ATGA)n chr1 22616063 22616200 - SINE/MIR_MIRc chr1 22616269 22616403 + LINE/L2_L2a chr1 22616458 22616697 - SINE/MIR_MIRc chr1 22617158 22617398 - SINE/MIR_MIRb chr1 22617476 22617658 + SINE/MIR_MIRb chr1 22617799 22617851 - SINE/MIR_MIR3 chr1 22617946 22617978 + Simple_repeat_(T)n chr1 22619021 22619133 + SINE/MIR_MIRb chr1 22619223 22619270 + Simple_repeat_(CTCCCCT)n chr1 22619271 22619276 + Simple_repeat_(CCCCT)n chr1 22619293 22619375 + SINE/MIR_MIRb chr1 22619511 22619682 - LINE/L1_L1ME3 chr1 22620577 22620902 - SINE/Alu_AluSx chr1 22620903 22620916 + Simple_repeat_(TTTTA)n chr1 22620917 22621027 - SINE/MIR_MIRb chr1 22621154 22621259 - SINE/MIR_MIR3 chr1 22621397 22621707 - SINE/Alu_AluSz chr1 22622450 22622672 - SINE/MIR_MIR chr1 22622890 22623047 - SINE/Alu_AluJr4 chr1 22623048 22623350 - SINE/Alu_AluSg chr1 22623351 22623482 - SINE/Alu_AluJr4 chr1 22623581 22623695 + SINE/Alu_AluSx chr1 22623696 22623740 + Simple_repeat_(CTA)n chr1 22623741 22623752 + SINE/Alu_AluSx chr1 22623829 22623965 - LINE/L2_L2d2 chr1 22624085 22624327 + LINE/L1_L1MB3 chr1 22624328 22624573 + SINE/Alu_AluSz chr1 22624574 22624872 + LINE/L1_L1MB3 chr1 22625005 22625181 + SINE/MIR_MIRc chr1 22625180 22625391 - SINE/MIR_MIR chr1 22625882 22626062 - LTR/ERVL-MaLR_MLT1I chr1 22627384 22627517 + DNA/hAT-Charlie_MER5A chr1 22627534 22627594 - SINE/MIR_MIR3 chr1 22627887 22627911 + SINE/MIR_MIRb chr1 22627912 22628040 - DNA/hAT_MER53 chr1 22628041 22628162 + SINE/MIR_MIRb chr1 22628347 22628638 + SINE/Alu_AluSc chr1 22628639 22628815 + SINE/Alu_AluSz6 chr1 22628823 22629004 + SINE/MIR_MIRb chr1 22629049 22629136 - DNA/hAT-Tip100_MamTip2 chr1 22629501 22629535 + Simple_repeat_(T)n chr1 22629668 22629779 - SINE/MIR_MIRb chr1 22630077 22630139 + Simple_repeat_(GAGCAGG)n chr1 22630258 22630752 + LINE/L1_L1ME3 chr1 22630779 22630871 + DNA/TcMar-Tigger_MamRep137 chr1 22630872 22631166 - SINE/Alu_AluSp chr1 22631167 22631561 + DNA/TcMar-Tigger_MamRep137 chr1 22631566 22631664 + LTR/ERV1?_LTR103_Mam chr1 22631888 22632096 + LTR/ERV1_LTR37B chr1 22632124 22632421 + LTR/ERV1_LTR37B chr1 22632455 22632534 + SINE/MIR_MIR3 chr1 22632634 22632934 + SINE/Alu_AluSx chr1 22632935 22632959 + Simple_repeat_(AAG)n chr1 22632969 22633267 + SINE/Alu_AluSx chr1 22633268 22633286 + Simple_repeat_(GGA)n chr1 22633288 22633456 - SINE/MIR_MIR3 chr1 22633485 22633586 + DNA/hAT-Charlie_Charlie15a chr1 22633652 22633688 + Simple_repeat_(CTC)n chr1 22633793 22633915 + LINE/CR1_L3 chr1 22634005 22635725 - LTR/ERV1_MER52A chr1 22635726 22636021 - SINE/Alu_AluJr chr1 22636068 22636278 - SINE/MIR_MIR chr1 22637188 22637219 + LINE/L2_L2a chr1 22637335 22637448 + Simple_repeat_(TG)n chr1 22638098 22638248 + SINE/MIR_MIRc chr1 22638633 22638735 - SINE/MIR_MIRc chr1 22639700 22640004 - SINE/Alu_AluSq2 chr1 22640054 22640260 + SINE/MIR_MIR chr1 22640207 22640346 - SINE/MIR_MIRc chr1 22640262 22640388 - SINE/MIR_MIRb chr1 22640389 22640699 + SINE/Alu_AluSz chr1 22640700 22640795 - SINE/MIR_MIRb chr1 22640847 22640950 - SINE/MIR_MIR1_Amn chr1 22641205 22641259 + Simple_repeat_(CA)n chr1 22641852 22641954 - SINE/MIR_MIRb chr1 22643189 22643293 + SINE/MIR_MIRb chr1 22643381 22643412 + Simple_repeat_(AC)n chr1 22644059 22644090 + Simple_repeat_(GCT)n chr1 22644228 22644271 + Low_complexity_G-rich chr1 22644399 22644519 + SINE/MIR_MIRb chr1 22644587 22644791 + SINE/MIR_MIR chr1 22645386 22645533 - SINE/MIR_MIRb chr1 22645725 22645791 - SINE/MIR_MIRc chr1 22645847 22646040 - LINE/L2_L2d2 chr1 22646054 22646188 + SINE/MIR_MIR3 chr1 22646207 22646299 - DNA/hAT-Charlie_MER102b chr1 22646320 22646396 - DNA/hAT-Tip100_Arthur1A chr1 22646422 22646641 + LINE/L1_L1MD2 chr1 22646643 22646738 - DNA/hAT-Tip100_Arthur1A chr1 22646750 22646905 - DNA/hAT-Charlie_MER102b chr1 22646953 22647092 - SINE/MIR_MIR3 chr1 22647902 22648064 + LINE/L2_L2d2 chr1 22648489 22648589 + Simple_repeat_(AATG)n chr1 22648594 22648677 - SINE/MIR_MIR chr1 22648703 22648804 + LINE/L2_L2a chr1 22649241 22649442 + LINE/L2_L2c chr1 22649651 22649766 + LINE/L2_L2c chr1 22650650 22650797 + LTR?_Eutr18 chr1 22651117 22651382 - SINE/MIR_MIR chr1 22651446 22651755 + SINE/Alu_AluSz chr1 22652218 22652337 + LINE/L2_L2a chr1 22652343 22652441 + SINE/MIR_MIR3 chr1 22652648 22652887 - SINE/MIR_MIRc chr1 22653361 22653471 + DNA/hAT-Charlie_MER5A chr1 22653546 22653728 + SINE/MIR_MIRb chr1 22653986 22654120 - SINE/MIR_MIRb chr1 22654542 22654719 - SINE/MIR_MIR chr1 22654821 22654896 - SINE/MIR_MIR chr1 22654936 22655177 + SINE/MIR_MIRb chr1 22655541 22655686 + SINE/MIR_MIRb chr1 22655745 22655866 - SINE/MIR_MIRc chr1 22655835 22656020 + LINE/CR1_L3 chr1 22656195 22656390 + LINE/L1_L1MB5 chr1 22656393 22656522 + SINE/Alu_FLAM_A chr1 22656566 22657367 - LTR/ERVL_MER21B chr1 22657368 22657764 + LINE/L1_L1MB5 chr1 22657997 22658089 + SINE/MIR_MIRb chr1 22658189 22658697 + LINE/L2_L2a chr1 22658698 22659401 + Simple_repeat_(GGAT)n chr1 22659736 22659897 - SINE/MIR_MIRb chr1 22659966 22660060 + SINE/MIR_MIRb chr1 22660109 22660333 - SINE/MIR_MIRb chr1 22660342 22660508 + DNA/hAT-Charlie_MER117 chr1 22660632 22660761 - SINE/MIR_MIR3 chr1 22661471 22661511 + LINE/L2_L2b chr1 22661515 22661618 + LINE/L2_L2c chr1 22661690 22661871 - SINE/MIR_MIRb chr1 22662030 22662135 + LINE/L2_L2b chr1 22662175 22662500 + LINE/L2_L2b chr1 22662906 22663123 - SINE/MIR_MIRb chr1 22663630 22663935 - SINE/Alu_AluJb chr1 22664268 22664641 - LTR/ERVL-MaLR_MLT1O chr1 22664659 22664712 - DNA/hAT-Charlie_MER5A chr1 22664709 22664760 - DNA/hAT-Charlie_MER5A chr1 22664761 22664894 - SINE/Alu_FLAM_C chr1 22664895 22664934 - DNA/hAT-Charlie_MER5A chr1 22664938 22665081 - SINE/MIR_MIRb chr1 22665086 22665200 - SINE/MIR_MIR chr1 22665426 22665602 - SINE/MIR_MIR chr1 22665627 22665731 + SINE/MIR_MIRb chr1 22665738 22665887 - LINE/L1_L1ME3G chr1 22666128 22666288 + DNA/hAT-Charlie_MER5A chr1 22666297 22666619 - DNA/hAT-Charlie_MER58B chr1 22666659 22666728 - SINE/MIR_MIRb chr1 22666729 22666829 + SINE/MIR_MIRc chr1 22667128 22667377 + SINE/MIR_MIRb chr1 22667418 22667581 - DNA/hAT-Charlie_MER5A1 chr1 22667614 22667688 + SINE/MIR_MIRb chr1 22667917 22667943 + Simple_repeat_(AC)n chr1 22668105 22668252 + SINE/MIR_MIR3 chr1 22668406 22668470 + DNA/TcMar-Tigger_Tigger15a chr1 22668814 22668917 - SINE/MIR_MIRb chr1 22668973 22668982 + Simple_repeat_(CTTT)n chr1 22668983 22669055 + Simple_repeat_(TTCC)n chr1 22669056 22669094 + Simple_repeat_(CTTT)n chr1 22669095 22669406 - SINE/Alu_AluSz chr1 22669407 22669418 + Simple_repeat_(CTTT)n chr1 22669517 22669809 - SINE/Alu_AluJb chr1 22669934 22669970 + Simple_repeat_(GTGCCAG)n chr1 22669986 22670190 + SINE/MIR_MIR chr1 22670203 22670506 + SINE/Alu_AluJr chr1 22670519 22670549 + LINE/L2_L2c chr1 22671106 22671257 + SINE/MIR_MIRb chr1 22671437 22671515 + SINE/MIR_MIR3 chr1 22671569 22671639 + SINE/MIR_MIR3 chr1 22672043 22672257 - SINE/MIR_MIR chr1 22672264 22672508 - SINE/MIR_MIRb chr1 22672763 22672985 - SINE/MIR_MIRb chr1 22673605 22673632 + Simple_repeat_(CTCTC)n chr1 22673928 22674110 + SINE/tRNA-RTE_MamSINE1 chr1 22674505 22674756 + SINE/MIR_MIR chr1 22674911 22675348 + LINE/L2_L2c chr1 22675813 22676007 + DNA/hAT-Charlie_MER20 chr1 22676019 22676127 + LINE/L2_L2c chr1 22676128 22676424 - SINE/Alu_AluSx chr1 22676674 22676821 + SINE/MIR_MIR3 chr1 22677406 22677435 + Low_complexity_G-rich chr1 22677492 22677586 - SINE/MIR_MIRb chr1 22677690 22677749 - SINE/MIR_MIR3 chr1 22677812 22678005 + SINE/MIR_MIRb chr1 22678310 22679474 + Simple_repeat_(AC)n chr1 22679495 22679560 - SINE/MIR_MIRc chr1 22679667 22679705 + Simple_repeat_(CAAGGGA)n chr1 22679820 22679940 + Simple_repeat_(TTCTCTC)n chr1 22680501 22680644 + SINE/MIR_MIR3 chr1 22680730 22680853 - SINE/MIR_MIR3 chr1 22681198 22681492 - SINE/Alu_AluSz chr1 22681496 22681597 - SINE/MIR_MIRb chr1 22681877 22682055 - LINE/L2_L2a chr1 22682056 22682330 - LTR/ERVL-MaLR_MLT1C2 chr1 22682331 22682448 - SINE/MIR_MIRb chr1 22682536 22683033 + LTR/ERVL-MaLR_MLT1K chr1 22683214 22683505 + SINE/Alu_AluSx chr1 22683746 22683780 + Simple_repeat_(TCAT)n chr1 22684328 22684543 - LINE/L2_L2 chr1 22684628 22684878 + LINE/L2_L2b chr1 22684915 22685119 + SINE/MIR_MIR chr1 22686121 22686194 + DNA/hAT-Charlie_Charlie1b chr1 22686193 22686379 + DNA/hAT-Charlie_Charlie1b chr1 22686380 22686568 - SINE/Alu_AluSx chr1 22686600 22686684 + DNA/hAT-Charlie_Charlie1b chr1 22686698 22686824 + LINE/L2_L2b chr1 22686837 22687267 + LINE/L2_L2c chr1 22687297 22687391 + DNA/hAT-Tip100?_MamRep1879 chr1 22687406 22687545 + SINE/MIR_MIRc chr1 22687571 22687618 - SINE/MIR_MIR3 chr1 22687619 22687925 + SINE/Alu_AluSx3 chr1 22687926 22688023 - SINE/MIR_MIR3 chr1 22688124 22688256 + SINE/MIR_MIR3 chr1 22688258 22688780 + LINE/L1_L1ME4c chr1 22688804 22688876 - DNA/TcMar-Tigger_Tigger4b chr1 22688922 22689214 + SINE/Alu_AluY chr1 22689215 22689392 + SINE/Alu_AluSz6 chr1 22689427 22689450 + Simple_repeat_(GA)n chr1 22689875 22689974 - SINE/MIR_MIR3 chr1 22689975 22690033 + DNA/hAT-Tip100_Zaphod chr1 22690034 22690133 - DNA/hAT-Charlie_MER5C1 chr1 22690135 22690445 + SINE/Alu_AluSp chr1 22690453 22690700 + LINE/L1_L1MB2 chr1 22690836 22690994 - DNA/hAT-Charlie_MER5C1 chr1 22690995 22691594 + DNA/hAT-Tip100_Zaphod chr1 22691576 22692213 + DNA/hAT-Tip100_Zaphod chr1 22692214 22692378 - SINE/Alu_AluJr chr1 22692379 22692415 + Simple_repeat_(AC)n chr1 22692416 22692571 - SINE/Alu_AluJr chr1 22692572 22692652 + DNA/hAT-Tip100_Zaphod chr1 22692695 22692839 - LINE/L2_L2c chr1 22692848 22692932 + SINE/Alu_AluSx chr1 22692933 22693013 - DNA/hAT-Charlie_MER3 chr1 22693025 22693172 - DNA/hAT-Charlie_MER33 chr1 22693191 22693431 + SINE/Alu_AluSx chr1 22693475 22693642 + SINE/MIR_MIRb chr1 22693650 22694184 - LTR/ERVL-MaLR_MLT1F1 chr1 22694242 22694440 - SINE/MIR_MIRc chr1 22694531 22694820 + SINE/Alu_AluSg chr1 22695192 22695312 + SINE/MIR_MIRb chr1 22695434 22695646 - DNA/hAT-Charlie_MER3 chr1 22695658 22695738 - DNA/hAT-Charlie_MER33 chr1 22695739 22696047 + SINE/Alu_AluSx1 chr1 22696048 22696149 - DNA/hAT-Charlie_MER33 chr1 22696150 22696290 + SINE/Alu_FLAM_C chr1 22696291 22696441 - DNA/hAT-Charlie_MER33 chr1 22696460 22696593 + SINE/MIR_MIRb chr1 22696482 22696702 + SINE/MIR_MIRc chr1 22696762 22696930 - SINE/Alu_AluJo chr1 22696931 22697200 - SINE/Alu_AluSp chr1 22697201 22697218 - SINE/Alu_AluJo chr1 22697219 22697513 - SINE/Alu_AluSz chr1 22697514 22697663 - SINE/Alu_AluJo chr1 22697699 22697793 + LINE/L2_L2c chr1 22698014 22698163 + SINE/MIR_MIR3 chr1 22698260 22698390 + SINE/MIR_MIR chr1 22698410 22698449 + Simple_repeat_(GT)n chr1 22698605 22698822 - SINE/MIR_MIRb chr1 22698937 22699620 + LINE/L1_L1PA8 chr1 22700446 22700578 + SINE/MIR_MIR3 chr1 22700579 22700872 + SINE/Alu_AluSq2 chr1 22701090 22701186 + SINE/MIR_MIR3 chr1 22701401 22701454 + SINE/MIR_MIRb chr1 22701455 22701771 - SINE/Alu_AluJr chr1 22701772 22701793 + SINE/MIR_MIRb chr1 22702040 22702080 + Simple_repeat_(TCCA)n chr1 22702537 22702754 - SINE/MIR_MIRc chr1 22702767 22702959 + SINE/MIR_MIRc chr1 22703175 22703253 - LINE/L2_L2b chr1 22703254 22703275 - LINE/L2_L2d2 chr1 22703429 22703592 + SINE/Alu_FRAM chr1 22703595 22703708 - SINE/MIR_MIRb chr1 22703784 22704111 - LINE/L2_L2d2 chr1 22704229 22704440 - SINE/MIR_MIR chr1 22704892 22705157 + LINE/L1_L1ME4a chr1 22705257 22705328 + LINE/L2_L2b chr1 22705404 22705714 + DNA/hAT-Charlie_MER33 chr1 22705751 22705832 - SINE/MIR_MIR3 chr1 22705836 22706621 + LINE/L1_L1PA5 chr1 22706970 22707114 - SINE/MIR_MIR3 chr1 22707337 22707765 + LINE/L2_L2a chr1 22707860 22708042 + SINE/MIR_MIRc chr1 22708122 22708223 - LINE/L2_L2b chr1 22708596 22708650 + SINE/MIR_MIR3 chr1 22708695 22708896 + SINE/MIR_MIRb chr1 22709006 22709046 + LINE/L2_L2b chr1 22710894 22710972 + Low_complexity_G-rich chr1 22711009 22711029 + Simple_repeat_(GCT)n chr1 22711048 22711104 + Simple_repeat_(GCGG)n chr1 22711400 22711459 + Low_complexity_G-rich chr1 22712098 22712342 - SINE/MIR_MIRc chr1 22713300 22713401 + SINE/MIR_MIR3 chr1 22713416 22713530 + SINE/MIR_MIRc chr1 22713720 22713742 + Simple_repeat_(CTTC)n chr1 22714493 22714685 - LINE/L2_L2 chr1 22714752 22714829 + LINE/L2_L2c chr1 22714835 22715088 + LINE/L2_L2a chr1 22715118 22715159 + Simple_repeat_(GAAT)n chr1 22715152 22715202 + Simple_repeat_(ATATGTG)n chr1 22715779 22715892 + SINE/MIR_MIRb chr1 22716335 22716638 - SINE/Alu_AluSz chr1 22717515 22717669 + SINE/MIR_MIRb chr1 22717809 22718049 + SINE/MIR_MIR chr1 22718377 22718686 - SINE/Alu_AluSq chr1 22718700 22718927 - SINE/MIR_MIRb chr1 22719172 22719358 - SINE/MIR_MIRc chr1 22719360 22719523 + DNA/hAT-Charlie_MER102a chr1 22719613 22719759 + SINE/MIR_MIR3 chr1 22719854 22720261 - LTR/ERVL-MaLR_MLT1H1 chr1 22720724 22720809 + LINE/L2_L2b chr1 22720936 22721046 + SINE/MIR_MIR chr1 22721505 22721607 - DNA/hAT-Charlie_MER102b chr1 22721608 22721915 - SINE/Alu_AluJb chr1 22721916 22721951 - DNA/hAT-Charlie_MER102b chr1 22721952 22722296 - SINE/Alu_AluSz6 chr1 22722297 22722406 - DNA/hAT-Charlie_MER102b chr1 22722435 22722539 - SINE/MIR_MIRc chr1 22722519 22722685 - LINE/CR1_L3 chr1 22722690 22722855 + SINE/MIR_MIRc chr1 22722831 22723046 - LINE/L2_L2c chr1 22723620 22723798 + SINE/MIR_MIRb chr1 22723861 22723882 + Simple_repeat_(AAT)n chr1 22723914 22724061 - SINE/MIR_MIR chr1 22724099 22724224 - LINE/L1_L1MC4 chr1 22724237 22724259 + Simple_repeat_(TG)n chr1 22724458 22724629 - SINE/MIR_MIRb chr1 22724903 22725014 + LINE/L2_L2a chr1 22725654 22725744 - DNA/TcMar-Tigger_Tigger23a chr1 22726003 22726081 + Simple_repeat_(CATC)n chr1 22726185 22726290 + Simple_repeat_(CTCCTT)n chr1 22726304 22726408 - SINE/MIR_MIRb chr1 22726409 22726721 - SINE/Alu_AluSz chr1 22726722 22726798 - SINE/MIR_MIRb chr1 22726889 22727001 - LINE/L2_L2d2 chr1 22727568 22727658 - SINE/MIR_MIR3 chr1 22727729 22727783 + Simple_repeat_(TTTTC)n chr1 22727784 22727810 + Simple_repeat_(A)n chr1 22727812 22727941 - SINE/Alu_FLAM_C chr1 22728010 22728078 + SINE/MIR_MIRc chr1 22728091 22728239 + LINE/L2_L2a chr1 22728252 22728489 - SINE/MIR_MIRb chr1 22729390 22729914 + LINE/L2_L2b chr1 22730152 22730318 - SINE/MIR_MIRc chr1 22730442 22730992 - LINE/L2_L2a chr1 22731376 22731526 + SINE/MIR_MIR chr1 22731558 22731866 + SINE/Alu_AluSg chr1 22732072 22732218 + SINE/MIR_MIRb chr1 22732927 22732973 - SINE/MIR_MIR3 chr1 22733042 22733348 - SINE/Alu_AluSq2 chr1 22733360 22733443 - SINE/MIR_MIR3 chr1 22733594 22733766 - SINE/MIR_MIRb chr1 22734256 22734382 - SINE/MIR_MIRc chr1 22734420 22734721 - SINE/Alu_AluSx chr1 22734744 22734868 - DNA/hAT-Charlie_Charlie4z chr1 22734912 22735004 - SINE/MIR_MIRb chr1 22735142 22735466 + SINE/Alu_AluJr chr1 22735927 22736061 + SINE/MIR_MIRb chr1 22737470 22737794 + LINE/L2_L2b chr1 22738116 22738186 - SINE/MIR_MIR3 chr1 22738394 22738620 - SINE/MIR_MIR chr1 22738622 22738653 + Simple_repeat_(ATTC)n chr1 22738707 22738771 - LINE/L2_L2b chr1 22739049 22739207 - SINE/Alu_AluJr chr1 22739208 22739515 - SINE/Alu_AluSz chr1 22740310 22740429 - DNA/hAT-Charlie_MER20B chr1 22740634 22740735 + LINE/L2_L2a chr1 22740793 22740903 + SINE/MIR_MIR chr1 22741502 22741571 + LINE/L2_L2a chr1 22741680 22741703 + Simple_repeat_(A)n chr1 22741837 22741985 - SINE/MIR_MIRb chr1 22742010 22742128 - SINE/MIR_MIR chr1 22742482 22742764 - SINE/Alu_AluJr chr1 22742894 22743191 - SINE/Alu_AluJr chr1 22743192 22743274 - SINE/MIR_MIR chr1 22743342 22743463 + SINE/MIR_MIRb chr1 22743464 22743772 - SINE/Alu_AluSz6 chr1 22743773 22743835 + SINE/MIR_MIRb chr1 22744181 22744267 + LINE/L1_L1ME3 chr1 22744268 22744533 - DNA/TcMar-Tigger_Tigger2a chr1 22744534 22744882 + SINE/Alu_AluJo chr1 22744883 22745079 - DNA/TcMar-Tigger_Tigger2a chr1 22745080 22745582 + LINE/L1_L1ME3 chr1 22745601 22745764 + SINE/MIR_MIRb chr1 22746069 22746611 - LINE/L2_L2b chr1 22746606 22746864 - SINE/MIR_MIRb chr1 22746847 22746981 + SINE/MIR_MIRc chr1 22747011 22747096 - SINE/MIR_MIR3 chr1 22747277 22747380 - SINE/MIR_MIR chr1 22747838 22747966 + SINE/MIR_MIRb chr1 22747988 22748237 + SINE/MIR_MIR chr1 22748269 22748315 + LINE/RTE-X_L4_B_Mam chr1 22748378 22748678 - SINE/Alu_AluSg chr1 22748722 22748785 + Simple_repeat_(AT)n chr1 22748827 22748905 - DNA/TcMar-Mariner_MADE1 chr1 22749023 22749319 - SINE/Alu_AluSz chr1 22749720 22749749 + Simple_repeat_(CATT)n chr1 22749759 22749840 - LINE/L2_L2b chr1 22749955 22750178 - SINE/MIR_MIRb chr1 22750470 22750697 + DNA/hAT-Charlie_MER113 chr1 22750734 22750961 - SINE/MIR_MIR chr1 22750972 22751149 + DNA/hAT-Charlie_MER113 chr1 22751153 22751237 - SINE/MIR_MIR3 chr1 22751622 22751722 + SINE/MIR_MIRc chr1 22752010 22752160 + SINE/MIR_MIRb chr1 22752229 22752501 + SINE/Alu_AluJb chr1 22752503 22752566 + Simple_repeat_(ATAAA)n chr1 22752660 22752759 + Simple_repeat_(AT)n chr1 22752756 22752762 + Simple_repeat_(TACACA)n chr1 22752763 22752793 + Simple_repeat_(AC)n chr1 22752798 22753080 - SINE/Alu_AluJo chr1 22753147 22753388 + SINE/MIR_MIRb chr1 22753730 22753875 - LINE/CR1_X7B_LINE chr1 22754292 22754440 - DNA/hAT-Charlie_MER113 chr1 22754507 22754663 - LTR/ERVL_LTR16E2 chr1 22754987 22755214 - DNA/hAT-Charlie_MER113 chr1 22755466 22755494 + Simple_repeat_(CCTG)n chr1 22755593 22755791 + SINE/MIR_MIR chr1 22755812 22755831 + Simple_repeat_(TAT)n chr1 22755854 22755934 - SINE/MIR_MIRb chr1 22757419 22757555 + SINE/MIR_MIRb chr1 22757580 22757877 + SINE/Alu_AluJr chr1 22757909 22758217 - SINE/Alu_AluY chr1 22758615 22758774 + SINE/MIR_MIR3 chr1 22758804 22758849 + LINE/L2_L2b chr1 22758861 22759040 + Simple_repeat_(TGGA)n chr1 22759099 22759179 + LINE/L2_L2a chr1 22759180 22759312 + SINE/MIR_MIRc chr1 22759996 22760123 - SINE/MIR_MIRc chr1 22760156 22760347 - LINE/L2_L2b chr1 22760867 22761026 - SINE/MIR_MIRb chr1 22761044 22761108 + DNA/hAT-Charlie_MER5A1 chr1 22761268 22761444 + LINE/L1_L1ME4a chr1 22761515 22761639 + SINE/MIR_MIRb chr1 22761826 22761874 - SINE/MIR_MIR1_Amn chr1 22762410 22762522 + SINE/MIR_MIR3 chr1 22762578 22762762 + SINE/MIR_MIRb chr1 22763104 22763291 + SINE/MIR_MIR chr1 22763666 22764155 - LTR/ERVL-MaLR_MLT1D chr1 22764287 22764443 - SINE/MIR_MIRb chr1 22764465 22764745 + SINE/Alu_AluSz chr1 22765266 22765563 + SINE/Alu_AluSx1 chr1 22766184 22766367 + SINE/MIR_MIR chr1 22766547 22766600 - SINE/MIR_MIR chr1 22767374 22767616 + SINE/MIR_MIRb chr1 22768145 22768290 + SINE/MIR_MIRb chr1 22768405 22768711 + SINE/Alu_AluJr chr1 22768758 22768977 + LINE/L2_L2b chr1 22769402 22769700 - SINE/Alu_AluSz6 chr1 22769707 22769775 + LINE/L2_L2b chr1 22769943 22770310 + Simple_repeat_(TGGA)n chr1 22770652 22770754 + SINE/MIR_MIRc chr1 22770838 22770909 + Low_complexity_G-rich chr1 22770910 22770988 + SINE/MIR_MIRb chr1 22771146 22771468 - LINE/L2_L2c chr1 22771544 22771743 + SINE/MIR_MIRb chr1 22771841 22771990 + SINE/MIR_MIRc chr1 22772647 22772857 + SINE/MIR_MIRb chr1 22772881 22773000 + SINE/MIR_MIRb chr1 22773081 22773241 - SINE/MIR_MIRc chr1 22773821 22774023 + SINE/MIR_MIRb chr1 22774070 22774389 + LINE/L2_L2b chr1 22774780 22774858 - LINE/L2_L2b chr1 22774889 22775067 - SINE/MIR_MIRb chr1 22775172 22775391 + SINE/MIR_MIR chr1 22776105 22776336 + LINE/L2_L2c chr1 22776564 22776829 + DNA/hAT-Charlie_MER113 chr1 22776883 22777215 - LINE/L2_L2b chr1 22777273 22777385 + SINE/MIR_MIRb chr1 22777942 22777995 - SINE/MIR_MIRc chr1 22778015 22778277 - SINE/Alu_AluSz chr1 22778337 22778426 - SINE/MIR_MIR3 chr1 22778819 22778917 + SINE/MIR_MIRc chr1 22779632 22779771 + SINE/MIR_MIR3 chr1 22779941 22780121 + LINE/L2_L2b chr1 22780628 22780738 - DNA/hAT-Charlie_Charlie4z chr1 22781039 22781345 + SINE/Alu_AluY chr1 22781658 22781786 - SINE/MIR_MIRb chr1 22781785 22782031 - SINE/MIR_MIRb chr1 22782097 22782315 + SINE/MIR_MIRb chr1 22783006 22783128 - SINE/MIR_MIR3 chr1 22783173 22783283 + SINE/MIR_MIRb chr1 22783958 22784034 + LINE/L2_L2c chr1 22784274 22784329 + LINE/L2_L2c chr1 22785505 22785796 - LTR/ERVL-MaLR_MLT1I chr1 22785819 22785983 - SINE/MIR_MIR chr1 22785988 22786074 + DNA/hAT-Charlie_MER113A chr1 22786087 22786286 + SINE/MIR_MIRb chr1 22787014 22787413 - LINE/L2_L2b chr1 22787429 22787470 - LTR/ERVL-MaLR_MLT1F2 chr1 22787471 22787756 + SINE/Alu_AluJr chr1 22787757 22788201 - LTR/ERVL-MaLR_MLT1F2 chr1 22788591 22788715 - DNA/hAT-Charlie_MER5A chr1 22788743 22789052 - SINE/Alu_AluSz6 chr1 22789584 22789907 - LINE/L2_L2a chr1 22789984 22790239 - LINE/L2_L2d2 chr1 22790961 22791157 + SINE/MIR_MIRb chr1 22791226 22791443 - LINE/L1_L1ME4a chr1 22791444 22791613 - SINE/Alu_FLAM_C chr1 22791614 22792017 - LINE/L1_L1ME4a chr1 22792572 22792624 + Simple_repeat_(TCCA)n chr1 22792708 22793025 - LINE/L2_L2c chr1 22793216 22793459 - LINE/L2_L2b chr1 22793715 22793886 - SINE/MIR_MIRb chr1 22794066 22794173 + SINE/MIR_MIRc chr1 22794197 22794301 - LINE/L2_L2b chr1 22794526 22794570 - SINE/MIR_MIRc chr1 22794606 22794792 - SINE/MIR_MIRb chr1 22795141 22795241 - SINE/MIR_MIR3 chr1 22795342 22795423 - SINE/MIR_MIR3 chr1 22795466 22795496 + Simple_repeat_(GTTTT)n chr1 22795558 22795606 - LINE/RTE-X_L4_A_Mam chr1 22795640 22795836 - SINE/MIR_MIRc chr1 22796054 22796264 - LINE/L2_L2a chr1 22796576 22796706 - SINE/MIR_MIR chr1 22796762 22796951 - SINE/MIR_MIRb chr1 22797006 22797186 - SINE/MIR_MIRb chr1 22797231 22797638 + LINE/L2_L2a chr1 22797689 22798178 + LINE/L2_L2a chr1 22798678 22798764 + SINE/MIR_MIR3 chr1 22799356 22799579 + SINE/MIR_MIRb chr1 22799619 22799720 - SINE/MIR_MIRc chr1 22800691 22800724 + Simple_repeat_(TG)n chr1 22800772 22800797 + Simple_repeat_(AC)n chr1 22801050 22801181 + SINE/MIR_MIRb chr1 22801336 22801548 - SINE/MIR_MIRb chr1 22802297 22802491 - DNA/hAT-Tip100_MamRep488 chr1 22802542 22802629 - SINE/MIR_MIR3 chr1 22802836 22803108 - LINE/L2_L2b chr1 22803325 22803617 + SINE/Alu_AluJb chr1 22803618 22803754 + Simple_repeat_(TA)n chr1 22803755 22803929 + SINE/Alu_AluJr chr1 22804001 22804155 - SINE/MIR_MIRb chr1 22804192 22804294 + LINE/L2_L2b chr1 22804301 22804323 + Simple_repeat_(AATC)n chr1 22804826 22804910 + Simple_repeat_(TC)n chr1 22805732 22805764 + Low_complexity_GA-rich chr1 22805991 22806105 - SINE/MIR_MIR3 chr1 22806200 22806361 - SINE/MIR_MIR chr1 22806354 22806462 + LINE/L2_L2b chr1 22806464 22806534 + Simple_repeat_(TGGA)n chr1 22807293 22807473 - SINE/MIR_MIRb chr1 22807575 22807823 - LINE/L2_L2c chr1 22807852 22808161 + SINE/Alu_AluSx chr1 22808275 22808387 + LINE/L2_L2b chr1 22808394 22808500 - DNA/hAT-Charlie_MER117 chr1 22808636 22808889 - SINE/MIR_MIR chr1 22809514 22809721 - SINE/MIR_MIRb chr1 22809870 22810062 - LINE/L2_L2c chr1 22810457 22810575 + SINE/MIR_MIR3 chr1 22810979 22811131 + SINE/MIR_MIR3 chr1 22811388 22811517 + LINE/RTE-X_L4_A_Mam chr1 22811707 22811756 + SINE/MIR_MIRb chr1 22811757 22812055 + SINE/Alu_AluJr chr1 22812056 22812193 + SINE/MIR_MIRb chr1 22812656 22812711 + SINE/MIR_MIRb chr1 22813101 22813290 - SINE/MIR_MIRc chr1 22813661 22813746 + SINE/MIR_MIRc chr1 22813893 22814194 + SINE/Alu_AluSq2 chr1 22814491 22814530 - LINE/L2_L2b chr1 22814497 22814696 - SINE/MIR_MIRb chr1 22815345 22815487 - SINE/MIR_MIR1_Amn chr1 22815941 22816094 + SINE/MIR_MIRb chr1 22816720 22816792 + SINE/MIR_MIRc chr1 22816794 22816880 - SINE/MIR_MIRc chr1 22817277 22817438 - SINE/MIR_MIR chr1 22817996 22818107 + SINE/MIR_MIRb chr1 22818253 22818913 + LINE/L2_L2c chr1 22819040 22819183 + SINE/MIR_MIR3 chr1 22819206 22819250 + Simple_repeat_(TC)n chr1 22820054 22820223 - SINE/MIR_MIRb chr1 22820380 22820688 + SINE/Alu_AluJb chr1 22820694 22820928 - SINE/MIR_MIRb chr1 22820936 22821389 - LTR/ERVL-MaLR_MLT1J chr1 22821443 22821591 - SINE/MIR_MIR3 chr1 22822044 22822167 + LINE/L1_L1M6 chr1 22822202 22822318 + SINE/Alu_AluJr chr1 22822408 22822550 - SINE/MIR_MIRc chr1 22823083 22823117 + SINE/MIR_MIR3 chr1 22823247 22823332 + LINE/L2_L2c chr1 22823419 22823531 + DNA/hAT-Charlie_Charlie4z chr1 22823667 22823841 + SINE/MIR_MIR chr1 22824166 22824339 + Low_complexity_GA-rich chr1 22825139 22825235 + SINE/MIR_MIRc chr1 22825441 22825604 + SINE/MIR_MIR3 chr1 22825948 22826109 - LINE/L2_L2 chr1 22826807 22826923 + LINE/L2_L2b chr1 22827131 22827258 + LINE/L2_L2c chr1 22827458 22827676 + SINE/MIR_MIRb chr1 22829686 22829994 - LINE/L2_L2b chr1 22830375 22830510 + SINE/MIR_MIR chr1 22830564 22830854 - SINE/Alu_AluSg4 chr1 22830855 22830949 - SINE/MIR_MIR3 chr1 22831203 22831281 + SINE/MIR_MIRb chr1 22831435 22831654 - SINE/MIR_MIR chr1 22832153 22832288 + SINE/MIR_MIR3 chr1 22832193 22832325 + SINE/MIR_MIRb chr1 22832391 22832519 - SINE/MIR_MIRc chr1 22832582 22832737 + LINE/L2_L2c chr1 22833107 22833417 - SINE/Alu_AluSx4 chr1 22833443 22834156 + LINE/L1_L1MEd chr1 22834386 22834463 + SINE/MIR_MIRb chr1 22834464 22834642 - DNA/hAT-Charlie_MER58A chr1 22834643 22834954 + SINE/Alu_AluSz chr1 22834955 22834996 - DNA/hAT-Charlie_MER58A chr1 22834997 22835070 + SINE/MIR_MIRb chr1 22835097 22835391 + SINE/Alu_AluJb chr1 22835540 22835577 + Simple_repeat_(CATG)n chr1 22835580 22835691 + SINE/MIR_MIRb chr1 22836263 22836333 - LINE/L2_L2c chr1 22836794 22836913 - LINE/L2_L2b chr1 22837183 22837254 - SINE/MIR_MIR3 chr1 22837446 22837569 - SINE/MIR_MIR chr1 22838077 22838311 + SINE/MIR_MIRc chr1 22838361 22838580 + SINE/MIR_MIRc chr1 22838648 22838941 + SINE/Alu_AluY chr1 22838943 22838976 + Simple_repeat_(AAAAGAAA)n chr1 22839001 22839093 - SINE/MIR_MIR3 chr1 22839094 22839289 - DNA/hAT-Charlie_MER117 chr1 22839418 22839628 - LINE/L2_L2c chr1 22839944 22840066 + LINE/L2_L2b chr1 22840070 22840109 + Simple_repeat_(TGGA)n chr1 22840131 22840253 + SINE/MIR_MIRb chr1 22840457 22840759 - SINE/Alu_AluSz6 chr1 22840795 22840958 - SINE/MIR_MIR chr1 22841046 22841116 + LINE/L2_L2b chr1 22841421 22841576 + SINE/MIR_MIR3 chr1 22841694 22841854 + DNA/hAT-Tip100_MamTip2b chr1 22842412 22843098 + LINE/L2_L2b chr1 22843131 22843339 - SINE/MIR_MIRb chr1 22843342 22843536 - LINE/L1_L1PA15 chr1 22843537 22843840 - SINE/Alu_AluSx1 chr1 22843841 22844261 - LINE/L1_L1PA15 chr1 22844280 22844401 - SINE/MIR_MIRb chr1 22844521 22844712 - SINE/MIR_MIR chr1 22844875 22845047 + SINE/MIR_MIRb chr1 22845106 22845320 + SINE/MIR_MIR chr1 22845710 22845749 + Simple_repeat_(AC)n chr1 22846137 22846445 + SINE/Alu_AluSz chr1 22846587 22846933 + LINE/L2_L2c chr1 22846985 22847153 + SINE/MIR_MIR chr1 22847167 22847343 - SINE/MIR_MIR chr1 22847376 22847420 + SINE/MIR_MIRb chr1 22847677 22847843 + LINE/L2_L2c chr1 22847789 22848084 + LINE/L2_L2b chr1 22848465 22848584 - LINE/CR1_Plat_L3 chr1 22848932 22849014 + SINE/MIR_MIRc chr1 22849214 22849483 + LINE/L2_L2c chr1 22849625 22849711 + SINE/MIR_MIR1_Amn chr1 22850811 22850978 - LINE/L2_L2 chr1 22850979 22851295 - SINE/Alu_AluJb chr1 22851296 22851453 - LINE/L2_L2 chr1 22851517 22851687 + SINE/MIR_MIR3 chr1 22851677 22851938 + LINE/L2_L2c chr1 22851976 22852163 + SINE/MIR_MIRb chr1 22852784 22852961 - LINE/L2_L2b chr1 22853069 22853337 + SINE/Alu_AluY chr1 22853347 22853856 - LINE/L2_L2c chr1 22854111 22854159 + Low_complexity_GA-rich chr1 22854210 22854228 + SINE/MIR_MIRb chr1 22854229 22854528 + SINE/Alu_AluJb chr1 22854529 22854726 + SINE/MIR_MIRb chr1 22855429 22855674 + LINE/L2_L2c chr1 22855677 22855936 - SINE/MIR_MIR chr1 22856595 22856825 + SINE/MIR_MIRb chr1 22856844 22856957 - LINE/L2_L2c chr1 22856963 22857111 - LINE/L2_L2b chr1 22857112 22857299 + DNA/hAT-Blackjack_MER63A chr1 22857300 22857466 - LINE/L2_L2b chr1 22857527 22857604 - DNA/hAT-Blackjack_MER81 chr1 22857804 22858476 - LINE/L2_L2a chr1 22858466 22858597 - SINE/MIR_MIRc chr1 22858752 22858872 + SINE/MIR_MIR3 chr1 22858984 22859152 + SINE/Alu_AluSx chr1 22859153 22859363 - LINE/L2_L2b chr1 22859442 22859513 - LINE/L1_L1MB7 chr1 22859514 22859808 + SINE/Alu_AluSz chr1 22859809 22860149 - LINE/L1_L1MB7 chr1 22860971 22861067 - SINE/MIR_MIR chr1 22861085 22861227 - SINE/MIR_MIR chr1 22861356 22861488 + SINE/Alu_AluJo chr1 22861495 22861604 - LINE/L2_L2d2 chr1 22861821 22861880 - SINE/MIR_MIRc chr1 22862345 22862590 - SINE/MIR_MIRb chr1 22863609 22863774 - LTR/ERVL-MaLR_MLT1J chr1 22863775 22864022 - SINE/Alu_AluJo chr1 22864032 22864336 - SINE/Alu_AluSx1 chr1 22864337 22864615 - LTR/ERVL-MaLR_MLT1J chr1 22865419 22865793 - LTR/ERVL-MaLR_MLT1J2 chr1 22865882 22866104 + LINE/L2_L2b chr1 22866376 22866437 - LINE/L2_L2b chr1 22866438 22866563 - SINE/Alu_FLAM_C chr1 22866564 22866649 - LINE/L2_L2b chr1 22866652 22866951 + SINE/Alu_AluSq2 chr1 22866987 22867168 - SINE/MIR_MIRb chr1 22867591 22867890 + SINE/Alu_AluSx1 chr1 22868288 22868368 + SINE/MIR_MIRc chr1 22868414 22868497 - LTR/ERVL-MaLR_MLT1F chr1 22868660 22868829 - LTR/ERVL-MaLR_MLT1H chr1 22868925 22869028 + DNA/hAT-Charlie_MER5A chr1 22869189 22869320 - SINE/MIR_MIRc chr1 22869326 22869624 - LINE/L2_L2d chr1 22869643 22869988 + LINE/L2_L2b chr1 22870347 22870440 - SINE/MIR_MIRb chr1 22870471 22870636 - LINE/L2_L2b chr1 22870763 22870987 + SINE/MIR_MIRb chr1 22871033 22871213 - SINE/MIR_MIR chr1 22871406 22871629 + SINE/MIR_MIRb chr1 22871634 22871735 - LTR/ERVL-MaLR_MLT1E2 chr1 22871736 22872032 + SINE/Alu_AluSg chr1 22872033 22872516 - LTR/ERVL-MaLR_MLT1E2 chr1 22872530 22872655 - SINE/MIR_MIR3 chr1 22872950 22873015 - SINE/MIR_MIRb chr1 22873194 22873570 - LTR/ERVL-MaLR_MLT1H chr1 22873586 22873964 - LTR/ERVL-MaLR_MLT1J chr1 22873984 22874074 - SINE/MIR_MIR3 chr1 22874182 22874318 - SINE/MIR_MIRb chr1 22874508 22874672 + SINE/MIR_MIRb chr1 22874837 22875057 + DNA/hAT-Charlie_MER103C chr1 22875608 22875656 + Simple_repeat_(CCTT)n chr1 22875664 22876118 - LINE/L2_L2b chr1 22876359 22876443 - SINE/MIR_MIR3 chr1 22877164 22877354 + SINE/MIR_MIR chr1 22877676 22877795 + SINE/MIR_MIR3 chr1 22877797 22877995 + DNA/hAT-Charlie_MER5A chr1 22878397 22878530 + LINE/L2_L2c chr1 22878566 22878788 - SINE/MIR_MIRb chr1 22879551 22879745 + SINE/MIR_MIRb chr1 22880084 22880210 - SINE/MIR_MIR3 chr1 22880608 22880801 + SINE/MIR_MIRb chr1 22880897 22880986 + SINE/MIR_MIR3 chr1 22880993 22881120 + SINE/Alu_AluJb chr1 22881121 22881407 + SINE/Alu_AluSx chr1 22881408 22881558 + SINE/Alu_AluJb chr1 22881607 22881892 - SINE/Alu_AluSq2 chr1 22882032 22882069 - LINE/L1_L1MB5 chr1 22882585 22882716 + SINE/MIR_MIRb chr1 22882747 22882900 - DNA/hAT-Charlie_Charlie19a chr1 22883029 22883245 + SINE/MIR_MIRb chr1 22883716 22883907 + LINE/L2_L2b chr1 22883970 22883999 + Simple_repeat_(CCAC)n chr1 22884156 22884196 + Simple_repeat_(TGAA)n chr1 22884224 22884359 + SINE/Alu_AluSx chr1 22884361 22884644 + SINE/Alu_AluSg chr1 22884653 22884689 + Simple_repeat_(ATGG)n chr1 22884692 22885029 + LINE/L2_L2c chr1 22885492 22885684 - SINE/MIR_MIR chr1 22885918 22886606 - LINE/L2_L2b chr1 22886619 22886925 - SINE/Alu_AluSp chr1 22887801 22887853 - LINE/L2_L2d2 chr1 22887950 22887985 + Simple_repeat_(CATT)n chr1 22888020 22888340 - SINE/Alu_AluSp chr1 22888375 22888629 - SINE/MIR_MIRb chr1 22888680 22888863 - SINE/MIR_MIRb chr1 22888874 22889159 + SINE/Alu_AluJb chr1 22889472 22889791 - LINE/L2_L2c chr1 22889792 22889980 - DNA/hAT_MER53 chr1 22889981 22890140 - LINE/L2_L2c chr1 22890254 22890354 - LINE/L2_L2d chr1 22890444 22890606 + LINE/L2_L2b chr1 22890620 22890993 + LTR/ERVL-MaLR_THE1C chr1 22891049 22891246 - SINE/MIR_MIR chr1 22891783 22892096 - SINE/Alu_AluJb chr1 22892242 22892304 - LINE/L2_L2b chr1 22892559 22892772 - LTR/ERVL-MaLR_MLT1H2 chr1 22893105 22893172 - DNA/hAT-Tip100_MER91B chr1 22893277 22893357 + SINE/MIR_MIR3 chr1 22893420 22893535 + DNA/hAT-Charlie_MER117 chr1 22893591 22893809 + SINE/MIR_MIRb chr1 22894184 22894316 - LINE/L2_L2c chr1 22894317 22894624 + SINE/Alu_AluSq2 chr1 22894625 22894654 - LINE/L2_L2c chr1 22894726 22894925 + SINE/MIR_MIRb chr1 22894910 22895174 - SINE/MIR_MIR chr1 22895202 22895314 - SINE/MIR_MIRb chr1 22896512 22896947 + LINE/L2_L2b chr1 22897431 22897505 - LINE/L2_L2a chr1 22897506 22897810 + SINE/Alu_AluSz chr1 22897818 22898148 + SINE/Alu_AluSz chr1 22898149 22898863 - LINE/L2_L2a chr1 22898930 22899211 + SINE/Alu_AluSp chr1 22899220 22899516 + SINE/Alu_AluSp chr1 22899711 22900011 + SINE/Alu_AluJr chr1 22900012 22900314 + SINE/Alu_AluSx chr1 22900383 22900584 - SINE/MIR_MIR chr1 22900814 22900902 - SINE/MIR_MIR3 chr1 22900963 22901176 + SINE/MIR_MIRc chr1 22901184 22901398 - SINE/MIR_MIR chr1 22901713 22901804 + SINE/MIR_MIR chr1 22901805 22901846 + Simple_repeat_(GT)n chr1 22901848 22902121 - SINE/Alu_AluJb chr1 22902122 22902178 + SINE/MIR_MIR chr1 22902179 22902256 - DNA/TcMar-Mariner_MADE2 chr1 22902257 22902363 + SINE/MIR_MIR chr1 22902361 22902397 - LINE/L2_L2b chr1 22902434 22902580 + DNA/hAT-Charlie_Charlie4z chr1 22902595 22902696 - LINE/L2_L2b chr1 22902819 22903106 - LINE/L2_L2b chr1 22903485 22903740 + SINE/MIR_MIRb chr1 22903783 22903884 - LINE/L2_L2d2 chr1 22903891 22904002 - DNA/hAT-Charlie_Charlie4z chr1 22904006 22904321 + SINE/Alu_AluSz chr1 22904599 22904769 - DNA/hAT-Charlie_MER58A chr1 22904824 22904871 - SINE/MIR_MIRc chr1 22905707 22905777 - DNA/hAT-Charlie_MER117 chr1 22906243 22906407 - SINE/MIR_MIR3 chr1 22907072 22907249 - DNA/hAT-Charlie_MER117 chr1 22907251 22907383 - SINE/MIR_MIR3 chr1 22907536 22907781 - SINE/MIR_MIRb chr1 22908239 22908480 - LINE/L2_L2b chr1 22908755 22908980 - LINE/L2_L2b chr1 22909455 22909550 - LINE/L2_L2d chr1 22910581 22910618 + Simple_repeat_(GCCCT)n chr1 22910856 22911181 + SINE/Alu_AluSq2 chr1 22911372 22911435 + Simple_repeat_(CA)n chr1 22911985 22912075 - LTR/ERVL_LTR33 chr1 22912721 22912854 + SINE/MIR_MIRb chr1 22912914 22913207 + SINE/Alu_AluSx3 chr1 22916945 22917030 + DNA/hAT_MamRep38 chr1 22917024 22917106 + DNA/hAT_MamRep38 chr1 22917503 22917591 + SINE/MIR_MIR3 chr1 22917622 22917784 - SINE/MIR_MIRc chr1 22917823 22917880 - LINE/L2_L2d chr1 22918838 22918944 - SINE/MIR_MIRb chr1 22918980 22919138 + DNA/hAT-Charlie_MER5A chr1 22919688 22919723 + Simple_repeat_(GAGGGAG)n chr1 22919973 22920011 + Low_complexity_A-rich chr1 22920785 22921015 - SINE/MIR_MIRb chr1 22921605 22921681 + Low_complexity_G-rich chr1 22921727 22921922 + SINE/MIR_MIRb chr1 22921963 22922030 + LINE/L2_L2a chr1 22922031 22922456 - LTR/ERVL-MaLR_MSTA chr1 22922457 22922499 + LINE/L2_L2a chr1 22922517 22922717 + DNA/hAT-Charlie_MER103C chr1 22922814 22922914 + LTR/ERVL-MaLR_MSTA chr1 22922942 22923042 - LINE/L2_L2c chr1 22923108 22923391 - LINE/L2_L2c chr1 22923426 22923834 + LTR/ERVL-MaLR_MLT1A1 chr1 22924542 22924573 + Simple_repeat_(CCCCT)n chr1 22924765 22925075 + SINE/Alu_AluSx1 chr1 22925291 22925314 + Simple_repeat_(CA)n chr1 22925449 22925525 - SINE/MIR_MIR chr1 22925587 22925748 - SINE/Alu_FRAM chr1 22925801 22925993 - DNA/hAT-Charlie_Charlie10 chr1 22926003 22926065 - SINE/MIR_MIR chr1 22926066 22926376 - SINE/Alu_AluSx3 chr1 22926377 22926479 - SINE/MIR_MIR chr1 22926649 22926738 + LINE/L2_L2a chr1 22926848 22926975 + SINE/MIR_MIRb chr1 22926979 22927280 - SINE/Alu_AluSz chr1 22927283 22927523 - LINE/L2_L2a chr1 22927536 22927720 + LINE/L1_L1ME3 chr1 22927790 22927854 - LINE/L2_L2a chr1 22927855 22928160 + SINE/Alu_AluSz chr1 22928161 22928475 - LINE/L2_L2a chr1 22928476 22928864 - LTR/ERVL_MLT2C1 chr1 22928865 22929261 - LINE/L2_L2a chr1 22929262 22929329 - LINE/L2_L2a chr1 22929330 22929623 + SINE/Alu_AluSx chr1 22929624 22929975 - LINE/L2_L2a chr1 22929979 22930198 + SINE/Alu_AluJo chr1 22930313 22930396 + SINE/MIR_MIRb chr1 22930767 22930890 + SINE/Alu_AluJb chr1 22930945 22931103 + DNA/hAT-Tip100?_MamRep1879 chr1 22931211 22931271 - DNA/hAT-Blackjack_MER63C chr1 22931272 22931577 + SINE/Alu_AluSc chr1 22931586 22931911 + SINE/Alu_AluJr chr1 22931912 22931968 - DNA/hAT-Blackjack_MER63C chr1 22931942 22931972 - DNA/hAT-Blackjack_MER63C chr1 22931973 22932281 - SINE/Alu_AluSg chr1 22932282 22932388 - DNA/hAT-Blackjack_MER63C chr1 22932396 22932527 - SINE/MIR_MIR3 chr1 22932873 22932991 + SINE/MIR_MIRb chr1 22933025 22933115 + DNA/hAT-Tip100_MamTip2 chr1 22933437 22933608 - SINE/Alu_AluSx chr1 22933616 22933879 + SINE/Alu_AluJr chr1 22934062 22934366 + SINE/Alu_AluSq2 chr1 22934510 22934563 + SINE/MIR_MIR chr1 22934580 22934966 - LTR/ERVL-MaLR_MLT1D chr1 22934967 22935273 + SINE/Alu_AluJr chr1 22935274 22935393 - LTR/ERVL-MaLR_MLT1D chr1 22935456 22935899 + LTR/ERV1_MER51E chr1 22936108 22936320 - SINE/MIR_MIRb chr1 22936350 22936646 - LTR/Gypsy_LTR104_Mam chr1 22936653 22936839 - LTR/Gypsy_MamGypLTR1a chr1 22936863 22936944 - DNA/hAT-Tip100_MER91A chr1 22936954 22937198 - LTR/Gypsy_MamGypLTR1a chr1 22937199 22937507 + SINE/Alu_AluJr chr1 22937508 22937551 - LTR/Gypsy_MamGypLTR1a chr1 22937552 22937832 - SINE/Alu_AluJr chr1 22937833 22937859 - LTR/Gypsy_MamGypLTR1a chr1 22937937 22938011 - LTR/ERVL-MaLR_MLT1H1 chr1 22938078 22939704 - LINE/L1_L1PA4 chr1 22939694 22940173 + LINE/L1_L1MD chr1 22940174 22940359 - SINE/Alu_AluJr chr1 22940464 22940538 - LTR/ERVL-MaLR_MLT1G chr1 22940539 22941276 + LINE/L1_L1MD chr1 22941279 22941674 + LTR/ERV1_LOR1b chr1 22941789 22942047 + LTR/ERV1_LOR1-int chr1 22942049 22942350 - SINE/Alu_AluSz chr1 22942369 22942403 + Simple_repeat_(GT)n chr1 22943307 22943371 - LTR/ERV1_LOR1-int chr1 22943572 22943884 + SINE/Alu_AluSz chr1 22943885 22943908 + Simple_repeat_(AAGAA)n chr1 22944027 22944895 + LTR/ERV1_PABL_B-int chr1 22944908 22945028 + LTR/ERV1_LOR1-int chr1 22945029 22945321 - SINE/Alu_AluJo chr1 22945332 22945631 - SINE/Alu_AluSg chr1 22945632 22945824 + LTR/ERV1_LOR1-int chr1 22945825 22946072 + LINE/L1_L1MD1 chr1 22946073 22946377 + SINE/Alu_AluSg chr1 22946378 22946461 + LINE/L1_L1MD1 chr1 22946462 22946769 + SINE/Alu_AluY chr1 22946770 22947577 + LINE/L1_L1MD1 chr1 22947578 22947918 - SINE/Alu_AluJb chr1 22947919 22947960 + LINE/L1_L1MD1 chr1 22947961 22948380 + LTR/ERVL-MaLR_MLT1A1 chr1 22948381 22948637 + LINE/L1_L1MD1 chr1 22948638 22949022 - LTR/ERVL-MaLR_MLT1D chr1 22949023 22949319 + SINE/Alu_AluJr chr1 22949320 22949446 - LTR/ERVL-MaLR_MLT1D chr1 22949447 22950367 + LINE/L1_L1MD1 chr1 22950368 22950406 + Simple_repeat_(TAT)n chr1 22950407 22950557 - SINE/Alu_FRAM chr1 22950558 22950684 + LINE/L1_L1MD1 chr1 22950689 22950768 - DNA/hAT-Charlie_Charlie10 chr1 22950769 22951031 + LINE/L1_L1ME1 chr1 22951032 22951345 + SINE/Alu_AluY chr1 22951346 22951552 + LINE/L1_L1ME1 chr1 22951553 22951857 + SINE/Alu_AluSg chr1 22951858 22952195 + LINE/L1_L1ME1 chr1 22952196 22952405 - SINE/Alu_AluJb chr1 22952414 22952706 - SINE/Alu_AluSx1 chr1 22952730 22952918 - DNA/hAT-Charlie_MER5C chr1 22953522 22953562 + Simple_repeat_(CCAGCA)n chr1 22954150 22954271 - SINE/MIR_MIR3 chr1 22954283 22954349 + DNA/hAT-Tip100_MamTip2b chr1 22954568 22954723 - SINE/MIR_MIR3 chr1 22954954 22955107 + LINE/L2_L2c chr1 22955556 22955757 + SINE/MIR_MIRb chr1 22955781 22956083 + SINE/Alu_AluSx chr1 22956093 22956394 + SINE/Alu_AluJr chr1 22956831 22957640 - LINE/L1_L1ME4b chr1 22957641 22957951 - SINE/Alu_AluSq2 chr1 22957952 22958077 - LINE/L1_L1ME4b chr1 22958150 22958384 - SINE/Alu_AluJr chr1 22959170 22959367 + SINE/MIR_MIRb chr1 22959745 22959807 + SINE/MIR_MIR3 chr1 22960334 22960642 + SINE/Alu_AluSq chr1 22960774 22961079 - SINE/Alu_AluJb chr1 22961135 22961346 - SINE/MIR_MIRb chr1 22961351 22961663 - SINE/Alu_AluSx chr1 22961775 22962044 - SINE/Alu_AluSg chr1 22962049 22962092 + SINE/MIR_MIRc chr1 22962206 22962420 - LINE/CR1_L3 chr1 22962558 22963012 + LINE/L2_L2c chr1 22963934 22964236 - SINE/Alu_AluSx1 chr1 22964257 22964431 + LTR/ERVL-MaLR_MLT1H2 chr1 22964473 22964611 + LTR/ERVL-MaLR_MLT1H2 chr1 22964849 22965056 + LTR/Gypsy?_LTR88c chr1 22965063 22965207 - SINE/MIR_MIRc chr1 22965676 22965724 + LINE/L2_L2a chr1 22965725 22965851 - LTR/ERVL-MaLR_THE1C chr1 22965852 22966111 + LINE/L1_L1MB7 chr1 22966121 22966225 + SINE/MIR_MIR chr1 22966221 22966350 - LINE/L2_L2a chr1 22966476 22966595 + LINE/L1_L1M5 chr1 22966596 22966929 + LINE/L2_L2 chr1 22966954 22967086 + SINE/Alu_AluJb chr1 22967087 22967394 + SINE/Alu_AluSz6 chr1 22967395 22967546 + SINE/Alu_AluJb chr1 22967559 22967645 + Simple_repeat_(CATACA)n chr1 22967645 22967675 + Simple_repeat_(AG)n chr1 22967742 22967916 + LINE/L2_L2a chr1 22967925 22968230 - LINE/L1_L1ME3C chr1 22968311 22968597 - LINE/L1_L1ME3C chr1 22968645 22968875 + SINE/MIR_MIR chr1 22968889 22968943 - SINE/Alu_AluJb chr1 22968945 22969077 - SINE/Alu_AluJb chr1 22969254 22969420 - LINE/L1_L1ME3C chr1 22969884 22969944 + LINE/L2_L2b chr1 22969957 22970102 + SINE/MIR_MIR3 chr1 22970165 22970557 + LINE/L1_L1ME1 chr1 22970558 22970830 + SINE/Alu_AluJr chr1 22970831 22970876 + LINE/L1_L1ME1 chr1 22971115 22971203 - SINE/MIR_MIRb chr1 22971674 22971879 + SINE/MIR_MIRb chr1 22972140 22972192 + Simple_repeat_(GAT)n chr1 22972571 22972678 + DNA/hAT-Blackjack_MER81 chr1 22973288 22973486 + SINE/MIR_MIRb chr1 22973505 22973529 - SINE/Alu_AluJr chr1 22973530 22973834 + SINE/Alu_AluJb chr1 22973835 22974106 - SINE/Alu_AluJr chr1 22974107 22974730 + LTR/ERV1_MER4E chr1 22974731 22975035 + SINE/Alu_AluSx chr1 22975036 22975165 + LTR/ERV1_MER4E chr1 22975315 22975479 - DNA/hAT-Charlie_MER5B chr1 22975523 22975649 - DNA/hAT-Charlie_MER5A1 chr1 22975806 22975922 - SINE/MIR_MIR3 chr1 22975999 22976168 - SINE/MIR_MIR chr1 22976201 22976282 + SINE/MIR_MIRb chr1 22976307 22976338 + Simple_repeat_(GTTT)n chr1 22976384 22976430 + LINE/L2_L2c chr1 22976689 22976782 - SINE/MIR_MIRb chr1 22976784 22976851 + LINE/L2_L2 chr1 22976883 22977117 + SINE/MIR_MIR chr1 22977190 22977205 + Low_complexity_polypurine chr1 22977206 22977513 + SINE/Alu_AluSx1 chr1 22977520 22977578 + Simple_repeat_(AAGG)n chr1 22977588 22977860 + SINE/Alu_AluSz6 chr1 22977861 22978046 + Low_complexity_GA-rich chr1 22978091 22978388 + SINE/Alu_AluSc8 chr1 22978456 22978487 + Simple_repeat_(TTCA)n chr1 22978501 22978569 - LINE/L2_L2b chr1 22978584 22978751 - LINE/L2_L2a chr1 22978752 22978898 + SINE/Alu_AluJb chr1 22978899 22979215 + SINE/Alu_AluY chr1 22979216 22979413 + SINE/Alu_AluJb chr1 22979414 22979821 - LINE/L2_L2a chr1 22979824 22979985 - SINE/Alu_AluJo chr1 22979987 22980289 - SINE/Alu_AluJo chr1 22980296 22980310 - SINE/Alu_AluSx chr1 22980311 22980356 + Simple_repeat_(TTAT)n chr1 22980357 22980637 - SINE/Alu_AluSx chr1 22980664 22980749 + SINE/Alu_AluJo chr1 22980783 22980935 - LINE/L2_L2a chr1 22980936 22981158 - DNA/TcMar-Tigger_MER2 chr1 22981159 22981461 + SINE/Alu_AluSx1 chr1 22981462 22981548 - LINE/L2_L2a chr1 22982843 22983338 - LTR/ERV1_MER4A1_ chr1 22983339 22983637 - SINE/Alu_AluSx1 chr1 22983638 22983759 - LTR/ERV1_MER4A1_ chr1 22984342 22984667 + SINE/Alu_AluSq10 chr1 22985050 22985355 + SINE/Alu_AluSq4 chr1 22985935 22986137 - LTR/ERVL_LTR33 chr1 22986139 22986219 - LINE/L2_L2a chr1 22986234 22986613 + LTR/ERVL-MaLR_MLT1A1 chr1 22986596 22986607 - Retroposon/SVA_SVA_B chr1 22986608 22986676 + SINE/Alu_FLAM_A chr1 22986678 22986723 + LTR/ERVL-MaLR_MLT1A1 chr1 22986729 22986839 + LTR/ERVL-MaLR_MLT1A1-int chr1 22986840 22987005 - SINE/Alu_AluSz6 chr1 22987006 22987038 + Simple_repeat_(T)n chr1 22987039 22987158 - SINE/Alu_AluSz6 chr1 22987159 22987182 + LTR/ERVL-MaLR_MLT1A1-int chr1 22987183 22987486 - SINE/Alu_AluSx1 chr1 22987487 22987774 + LTR/ERVL-MaLR_MLT1A1-int chr1 22987775 22988400 + LTR/ERV1_MER4A1_ chr1 22988404 22988539 + LTR/ERV1_MER4-int chr1 22988540 22988614 + LINE/L2_L2 chr1 22988615 22988806 + LTR/ERV1_MER4-int chr1 22988807 22988832 + Simple_repeat_(T)n chr1 22988833 22988875 + LTR/ERV1_MER4-int chr1 22988876 22989181 + SINE/Alu_AluJb chr1 22989182 22989388 + LTR/ERV1_MER4-int chr1 22989410 22989766 + LTR/ERV1_MER4-int chr1 22989788 22990127 + LTR/ERV1_MER4-int chr1 22990128 22990434 + SINE/Alu_AluSz chr1 22990435 22990649 + LTR/ERV1_MER4-int chr1 22990650 22990948 + SINE/Alu_AluSx chr1 22990949 22991587 + LTR/ERV1_MER4-int chr1 22991588 22991870 + SINE/Alu_AluSz chr1 22991871 22991922 + Simple_repeat_(AAATA)n chr1 22991923 22991971 + LTR/ERV1_MER4-int chr1 22991972 22992277 + SINE/Alu_AluYk4 chr1 22992278 22992637 + LTR/ERV1_MER4-int chr1 22992638 22992940 + SINE/Alu_AluSq2 chr1 22992941 22993474 + LTR/ERV1_MER4-int chr1 22993475 22993730 - SINE/Alu_AluSx1 chr1 22993731 22994471 + LTR/ERV1_MER4-int chr1 22994472 22994765 - SINE/Alu_AluSx3 chr1 22994766 22996309 + LTR/ERV1_MER4-int chr1 22996310 22996619 + SINE/Alu_AluSg chr1 22996620 22997502 + LTR/ERV1_MER4-int chr1 22997505 22997912 - LTR/ERV1_LTR7C chr1 22997913 22998471 - LTR/ERV1_HERVH-int chr1 22998465 23000667 - LTR/ERV1_HERVH-int chr1 23000670 23003523 - LTR/ERV1_HERVH-int chr1 23003541 23003991 - LTR/ERV1_HERVH-int chr1 23003996 23004403 - LTR/ERV1_LTR7C chr1 23004404 23004476 + LTR/ERV1_MER4-int chr1 23004477 23004609 + SINE/Alu_FLAM_C chr1 23004610 23004673 + LTR/ERV1_MER4-int chr1 23004680 23005121 + LTR/ERV1_MER4A1_ chr1 23005122 23005381 - SINE/Alu_AluSx1 chr1 23005382 23005576 + LTR/ERV1_MER4A1_ chr1 23005582 23005723 + LTR/ERVL-MaLR_MLT1A1-int chr1 23005724 23006017 + SINE/Alu_AluSq4 chr1 23006018 23006898 + LTR/ERVL-MaLR_MLT1A1-int chr1 23006899 23007194 + SINE/Alu_AluSx3 chr1 23007195 23007509 + LTR/ERVL-MaLR_MLT1A1-int chr1 23007510 23007660 + LTR/ERVL-MaLR_MLT1A1 chr1 23007661 23007806 + SINE/Alu_AluY chr1 23007807 23008120 + SINE/Alu_AluY chr1 23008121 23008286 + SINE/Alu_AluY chr1 23008287 23008510 + LTR/ERVL-MaLR_MLT1A1 chr1 23008524 23008672 - SINE/Alu_AluJb chr1 23008673 23008976 - SINE/Alu_AluJb chr1 23008981 23009013 + LTR/ERVL-MaLR_MLT1A1 chr1 23009023 23009183 - SINE/MIR_MIR chr1 23009269 23009570 - SINE/Alu_AluSx chr1 23009571 23009622 - SINE/MIR_MIRb chr1 23009580 23009864 - LINE/L2_L2d2 chr1 23009985 23010201 + DNA/hAT-Charlie_MER113 chr1 23010205 23010509 + SINE/Alu_AluSq2 chr1 23011242 23011388 - SINE/MIR_MIRb chr1 23011389 23011723 - SINE/Alu_AluSq2 chr1 23011724 23011735 - SINE/MIR_MIRb chr1 23012069 23012371 + SINE/Alu_AluJb chr1 23012415 23012589 - LINE/L1_L1ME3C chr1 23012590 23012810 + DNA/hAT-Charlie_MER20 chr1 23012811 23012869 - LINE/L1_L1ME3C chr1 23012870 23013166 - SINE/Alu_AluJb chr1 23013169 23013472 - SINE/Alu_AluSz chr1 23013473 23013576 - LINE/L1_L1ME3C chr1 23013664 23013816 - SINE/MIR_MIR3 chr1 23014196 23014317 - SINE/MIR_MIRb chr1 23014542 23014811 - SINE/Alu_AluJr chr1 23014832 23015140 - SINE/Alu_AluSz chr1 23015169 23015466 + SINE/Alu_AluSx1 chr1 23015483 23016007 + LINE/L1_L1MB2 chr1 23016050 23016203 - SINE/Alu_AluSx chr1 23016277 23016594 - DNA/TcMar-Tigger_Tigger3a chr1 23016632 23016791 - SINE/Alu_FRAM chr1 23016794 23016946 - LINE/L1_L1M5 chr1 23016961 23017176 - LINE/L1_HAL1 chr1 23017178 23017239 + Simple_repeat_(TA)n chr1 23017241 23017528 - SINE/Alu_AluY chr1 23017711 23018022 + SINE/Alu_AluSz chr1 23018040 23018131 - LINE/L1_HAL1 chr1 23018133 23018292 - SINE/Alu_FLAM_C chr1 23018346 23018612 - LINE/L1_L1ME2 chr1 23018805 23018868 + Low_complexity_G-rich chr1 23019076 23019192 + LINE/L2_L2c chr1 23019449 23019522 + Simple_repeat_(GCGCGGC)n chr1 23019614 23019668 + Simple_repeat_(GGC)n chr1 23020651 23020821 - SINE/MIR_MIRb chr1 23021380 23021679 + SINE/Alu_AluSq chr1 23021680 23022324 - LINE/L1_L1MB7 chr1 23022325 23022639 - SINE/Alu_AluSp chr1 23022646 23022947 - SINE/Alu_AluSx1 chr1 23022948 23023784 - LINE/L1_L1MB7 chr1 23023861 23024148 - SINE/Alu_AluJo chr1 23024452 23024597 - DNA/hAT-Tip100_Arthur1A chr1 23024766 23024890 + SINE/MIR_MIRb chr1 23025088 23025268 - DNA/hAT-Charlie_MER3 chr1 23025275 23025329 - LINE/L2_L2d2 chr1 23025330 23025628 - SINE/Alu_AluSq10 chr1 23025629 23025763 - LINE/L2_L2d2 chr1 23025810 23026114 + SINE/Alu_AluSz6 chr1 23026265 23026344 - LINE/L1_L1MA1 chr1 23026378 23026681 - SINE/Alu_AluJb chr1 23027073 23027284 - DNA/hAT-Charlie_Charlie15b chr1 23027309 23027403 - DNA/hAT-Charlie_Charlie15b chr1 23027404 23027721 - SINE/Alu_AluJb chr1 23027722 23027778 - DNA/hAT-Charlie_Charlie15b chr1 23027771 23027894 - DNA/hAT-Charlie_Charlie15b chr1 23028152 23028328 - SINE/Alu_AluJb chr1 23028329 23028465 + DNA/hAT-Charlie_MER103C chr1 23028866 23028958 + LINE/L2_L2d chr1 23029629 23029939 - SINE/Alu_AluSx1 chr1 23030768 23030816 + Simple_repeat_(TG)n chr1 23032912 23033159 - SINE/Alu_AluSg chr1 23033252 23033552 + SINE/Alu_AluSx chr1 23033667 23033949 - LINE/L2_L2d chr1 23034898 23034927 + Simple_repeat_(GT)n chr1 23035198 23035501 - SINE/Alu_AluSp chr1 23036589 23036721 - LINE/L2_L2c chr1 23036998 23037322 + SINE/Alu_AluSz6 chr1 23037423 23037639 - LINE/L1_L1MA8 chr1 23038255 23038291 + Simple_repeat_(TG)n chr1 23038909 23039000 - SINE/Alu_AluJr chr1 23039345 23039485 + LINE/L2_L2 chr1 23039813 23039912 + LINE/L2_L2d2 chr1 23040515 23040811 + SINE/Alu_AluJr4 chr1 23041436 23041740 - SINE/Alu_AluSg chr1 23042075 23042114 + Low_complexity_A-rich chr1 23042251 23042392 - DNA/hAT-Charlie_Charlie30b chr1 23042441 23042751 - SINE/Alu_AluYa5 chr1 23042763 23043047 - SINE/Alu_AluSz chr1 23045651 23045681 + Simple_repeat_(T)n chr1 23045748 23045909 - SINE/tRNA-RTE_MamSINE1 chr1 23046029 23046087 - LINE/CR1_Plat_L3 chr1 23046088 23046225 - SINE/MIR_MIR3 chr1 23046740 23047007 + DNA/TcMar-Tigger_Tigger19b chr1 23047620 23047921 + SINE/Alu_AluJb chr1 23048225 23048281 + Simple_repeat_(GTTTT)n chr1 23048411 23048441 + Simple_repeat_(T)n chr1 23048867 23049176 + SINE/Alu_AluSz chr1 23049439 23049707 + SINE/Alu_AluSq chr1 23049726 23049792 + Simple_repeat_(TA)n chr1 23050803 23051102 + SINE/Alu_AluSx1 chr1 23051218 23051474 - LINE/L1_L1M5 chr1 23052097 23052209 - SINE/tRNA-RTE_MamSINE1 chr1 23052326 23052344 + Simple_repeat_(AGG)n chr1 23052649 23052972 + LINE/L2_L2 chr1 23053149 23053219 + SINE/MIR_MIR chr1 23053225 23053286 - LINE/CR1_L3 chr1 23053377 23053684 - SINE/Alu_AluJb chr1 23054279 23054408 + SINE/Alu_FLAM_A chr1 23054506 23054582 - SINE/Alu_AluJb chr1 23054639 23054926 - SINE/Alu_AluJr chr1 23055312 23055340 + Simple_repeat_(TAAA)n chr1 23056792 23056825 + Simple_repeat_(TTTG)n chr1 23057801 23058017 - SINE/Alu_AluYh7 chr1 23058031 23058288 - SINE/Alu_AluJb chr1 23058291 23058377 - SINE/Alu_AluSp chr1 23058500 23058647 - SINE/Alu_AluJr chr1 23059247 23059285 + Simple_repeat_(AT)n chr1 23060183 23060275 - LINE/L2_L2c chr1 23060287 23060468 - LINE/L1_L1MC5a chr1 23061153 23061345 - DNA/hAT-Charlie_Charlie10b chr1 23061661 23061958 - SINE/Alu_AluSz6 chr1 23062004 23062215 - SINE/MIR_MIRb chr1 23063129 23063147 + Simple_repeat_(T)n chr1 23063222 23063255 + Simple_repeat_(GT)n chr1 23063706 23063763 - LINE/L2_L2a chr1 23063895 23064189 - SINE/Alu_AluJb chr1 23065692 23065717 + Simple_repeat_(TTTG)n chr1 23065718 23065743 + Simple_repeat_(TTG)n chr1 23065744 23065759 + Simple_repeat_(TTTG)n chr1 23066053 23066153 - Unknown_UCON4 chr1 23068835 23068888 + SINE/MIR_MIR3 chr1 23069400 23069602 - DNA/hAT-Charlie_MER30 chr1 23069945 23070077 + SINE/MIR_MIR chr1 23070190 23070488 + SINE/Alu_AluSz chr1 23070527 23070823 + SINE/Alu_AluY chr1 23071629 23071848 + SINE/MIR_MIRb chr1 23072652 23072939 - SINE/Alu_AluSq2 chr1 23073095 23073150 - LINE/L2_L2a chr1 23073484 23074321 - LINE/L1_L1ME1 chr1 23074193 23074435 - LINE/L1_L1MC chr1 23074436 23074562 - SINE/Alu_AluJb chr1 23074569 23075212 - LINE/L1_L1ME1 chr1 23075302 23075584 + SINE/Alu_AluSx1 chr1 23075666 23075957 + SINE/Alu_AluSq chr1 23076690 23076979 + SINE/Alu_AluSg chr1 23077601 23077703 - DNA/TcMar-Tigger_Tigger4a chr1 23078350 23078394 + Simple_repeat_(AT)n chr1 23078732 23078879 + SINE/MIR_MIR chr1 23079766 23079861 - LINE/L2_L2c chr1 23080337 23080927 + LINE/L2_L2b chr1 23084159 23084545 - LTR/ERVL-MaLR_MLT1D chr1 23087167 23087234 - SINE/MIR_MIR3 chr1 23087387 23087565 - SINE/MIR_MIR3 chr1 23088847 23088882 + Simple_repeat_(AGAGCAG)n chr1 23089602 23089756 - SINE/Alu_AluSx chr1 23089757 23090057 - SINE/Alu_AluSg chr1 23090144 23090365 + SINE/MIR_MIRc chr1 23091120 23091172 + Low_complexity_A-rich chr1 23093624 23093661 + Simple_repeat_(TCATTTC)n chr1 23094465 23094515 + LINE/L2_L2a chr1 23094667 23094813 + SINE/MIR_MIRc chr1 23095299 23096529 + LINE/L1_L1MEd chr1 23096626 23096690 - LTR/ERVL-MaLR_MLT1A1 chr1 23096691 23096987 + SINE/Alu_AluSz chr1 23096988 23097272 - LTR/ERVL-MaLR_MLT1A1 chr1 23097335 23097569 - LINE/L2_L2 chr1 23097570 23097849 + SINE/Alu_AluJr chr1 23097850 23098024 - LINE/L2_L2 chr1 23098029 23098697 - LINE/L2_L2 chr1 23099381 23099473 + LINE/L1_L1MB7 chr1 23099800 23100216 + LINE/L2_L2a chr1 23100653 23100851 + DNA/hAT-Charlie_Charlie15a chr1 23100872 23101001 - SINE/MIR_MIRc chr1 23101267 23101330 + SINE/MIR_MIRb chr1 23101377 23101465 - SINE/MIR_MIRb chr1 23101467 23101538 + SINE/MIR_MIRb chr1 23101839 23101936 - LINE/L2_L2c chr1 23101968 23102070 + LINE/L2_L2b chr1 23102523 23102677 - DNA/hAT-Charlie_MER58A chr1 23102678 23102795 + SINE/Alu_AluJr chr1 23102887 23102902 - SINE/Alu_AluJr chr1 23102903 23102949 + Simple_repeat_(ATTTC)n chr1 23102950 23103244 - SINE/Alu_AluJr chr1 23103450 23103486 - LINE/CR1_CR1-3_Croc chr1 23103506 23103605 - LINE/CR1_L3 chr1 23103613 23103805 + LINE/L2_L2a chr1 23103810 23103944 + Simple_repeat_(GGGA)n chr1 23104171 23104483 - SINE/Alu_AluSz chr1 23104608 23104734 + LINE/L2_L2b chr1 23104735 23104818 + LINE/CR1_L3 chr1 23105131 23105225 - LINE/L2_L2 chr1 23105253 23105360 - LINE/CR1_L3 chr1 23105393 23105622 + SINE/MIR_MIRb chr1 23105708 23105988 - LINE/CR1_L3 chr1 23106133 23106258 - LINE/CR1_L3 chr1 23106325 23106639 + SINE/Alu_AluJb chr1 23106676 23107430 + LINE/L2_L2a chr1 23107519 23107824 + SINE/Alu_AluSx chr1 23108335 23108619 + SINE/Alu_AluJr chr1 23108627 23108798 + SINE/MIR_MIRb chr1 23109443 23109483 - SINE/MIR_MIR3 chr1 23109484 23109838 + DNA/TcMar-Tigger_Tigger4b chr1 23109839 23109923 - SINE/MIR_MIR3 chr1 23110024 23110166 - SINE/MIR_MIRb chr1 23110629 23110665 + Simple_repeat_(CA)n chr1 23110808 23110896 - SINE/Alu_AluJr chr1 23110918 23111220 + SINE/Alu_AluSx chr1 23111269 23111566 + SINE/Alu_AluJo chr1 23111779 23111827 + Simple_repeat_(AC)n chr1 23112391 23112506 - SINE/MIR_MIR3 chr1 23112576 23113112 + LINE/L1_L1MEb chr1 23113113 23113434 + SINE/Alu_AluJo chr1 23113435 23113599 + LINE/L1_L1MEb chr1 23113600 23113903 + SINE/Alu_AluSc chr1 23113904 23114100 + LINE/L1_L1MEb chr1 23114761 23114907 + SINE/MIR_MIR3 chr1 23115549 23115841 - SINE/Alu_AluSz chr1 23115967 23116098 + SINE/Alu_AluJr4 chr1 23116099 23116371 + SINE/Alu_AluJr chr1 23116372 23116523 + SINE/Alu_AluJr4 chr1 23116586 23116651 + Low_complexity_A-rich chr1 23117177 23117384 + SINE/MIR_MIRb chr1 23117455 23117477 + Simple_repeat_(CT)n chr1 23117508 23117529 + Simple_repeat_(G)n chr1 23117810 23118118 + SINE/Alu_AluSg chr1 23118130 23118406 + SINE/Alu_AluSz chr1 23118506 23118606 + SINE/MIR_MIRc chr1 23119252 23119396 - SINE/Alu_AluJr4 chr1 23119425 23119505 + SINE/MIR_MIR3 chr1 23119587 23119755 + LINE/L2_L2c chr1 23119926 23120237 - SINE/Alu_AluJb chr1 23120357 23120380 + Simple_repeat_(AATA)n chr1 23120383 23120656 - SINE/Alu_AluJr chr1 23120657 23120802 - SINE/MIR_MIRc chr1 23121437 23121717 + SINE/Alu_AluJr chr1 23121732 23122024 + SINE/Alu_AluSx chr1 23122025 23122046 + Simple_repeat_(TTAA)n chr1 23122133 23122201 + LINE/L2_L2c chr1 23123212 23123510 + SINE/Alu_AluSx4 chr1 23124194 23124384 - SINE/MIR_MIR3 chr1 23125415 23125451 + Simple_repeat_(CTTTCT)n chr1 23125832 23125856 + Simple_repeat_(TGCC)n chr1 23125901 23126058 - DNA/hAT-Charlie_MER5A chr1 23126337 23126445 + SINE/Alu_AluJo chr1 23126456 23126640 + LINE/L2_L2a chr1 23127161 23127350 - SINE/MIR_MIRb chr1 23127518 23127822 - SINE/Alu_AluYj4 chr1 23127851 23128156 + SINE/Alu_AluSg chr1 23128383 23128486 + SINE/MIR_MIRc chr1 23129300 23129598 + SINE/Alu_AluSp chr1 23130531 23130663 - SINE/Alu_FLAM_C chr1 23130778 23130935 + LINE/L2_L2a chr1 23130936 23131247 + SINE/Alu_AluSx1 chr1 23131248 23131585 + LINE/L2_L2a chr1 23131675 23131985 - SINE/Alu_AluSx chr1 23131986 23132051 - SINE/Alu_AluJr4 chr1 23132178 23132232 - LINE/L2_L2d2 chr1 23132527 23132828 + LINE/L1_L1MC5a chr1 23133243 23133285 + Simple_repeat_(ATT)n chr1 23133310 23133480 + LINE/L1_L1MC4 chr1 23133481 23133764 + SINE/Alu_AluSz chr1 23133765 23133803 + Simple_repeat_(AAAT)n chr1 23133804 23133989 + LINE/L1_L1MC4 chr1 23134031 23134175 + DNA/hAT-Charlie_MER58A chr1 23134199 23134493 + SINE/Alu_AluJo chr1 23134497 23134795 + SINE/Alu_AluJb chr1 23134821 23134991 - LINE/L1_L1PA11 chr1 23135103 23135294 - DNA/TcMar-Tigger_MER46C chr1 23135922 23136014 + SINE/MIR_MIR3 chr1 23136225 23136285 + SINE/Alu_Alu chr1 23136296 23136342 + Simple_repeat_(AC)n chr1 23136502 23136629 - SINE/MIR_MIR3 chr1 23136630 23136934 + SINE/Alu_AluY chr1 23136935 23136946 - SINE/MIR_MIR3 chr1 23137118 23137358 + LINE/L1_L1MB7 chr1 23137359 23137652 + SINE/Alu_AluSq2 chr1 23137653 23137980 + LINE/L1_L1MB7 chr1 23137981 23138273 - SINE/Alu_AluSx chr1 23138274 23138657 + LINE/L1_L1MB7 chr1 23138658 23138698 + Simple_repeat_(TG)n chr1 23138699 23138720 + Simple_repeat_(AT)n chr1 23138721 23139019 + SINE/Alu_AluSx chr1 23139020 23139115 + Simple_repeat_(AT)n chr1 23139389 23139676 + DNA/TcMar-Tigger_Tigger2a chr1 23139677 23139966 + SINE/Alu_AluJr chr1 23139967 23140033 + DNA/TcMar-Tigger_Tigger2a chr1 23141684 23141846 - SINE/Alu_AluJr chr1 23141847 23142144 - SINE/Alu_AluSp chr1 23142145 23142293 - SINE/Alu_AluJr chr1 23142701 23142751 + Simple_repeat_(AC)n chr1 23142752 23142812 + DNA/TcMar-Tigger_Tigger2b_Pri chr1 23142884 23143004 + SINE/Alu_AluJo chr1 23143005 23143306 - SINE/Alu_AluSg4 chr1 23143608 23143735 - SINE/MIR_MIRb chr1 23143737 23143958 - SINE/MIR_MIRb chr1 23144893 23145200 - SINE/Alu_AluJb chr1 23145363 23145468 - SINE/MIR_MIR chr1 23145469 23145772 - SINE/Alu_AluSp chr1 23145773 23145884 - SINE/MIR_MIR chr1 23146041 23146315 - SINE/Alu_AluSc chr1 23146373 23146516 + SINE/MIR_MIRb chr1 23146524 23146804 + SINE/Alu_AluJb chr1 23146816 23147099 + SINE/Alu_AluY chr1 23147100 23147128 + Simple_repeat_(AAT)n chr1 23147159 23147465 + SINE/Alu_AluSx1 chr1 23147482 23147632 + SINE/Alu_FLAM_C chr1 23147634 23147714 + SINE/MIR_MIR chr1 23148080 23148238 - SINE/MIR_MIRb chr1 23148823 23149106 + SINE/Alu_AluJb chr1 23149226 23149264 + Simple_repeat_(ACAGAGG)n chr1 23149693 23149984 + SINE/Alu_AluSc chr1 23150220 23150261 + Low_complexity_A-rich chr1 23151041 23151343 - SINE/Alu_AluSx1 chr1 23151516 23151717 + SINE/MIR_MIRb chr1 23151723 23152026 + SINE/Alu_AluSx4 chr1 23152532 23152830 - SINE/Alu_AluSq chr1 23153335 23153378 + Low_complexity_A-rich chr1 23153379 23153488 + LINE/L2_L2d chr1 23153584 23153653 + DNA/hAT-Charlie_Charlie18a chr1 23153663 23153691 + Simple_repeat_(TTTTG)n chr1 23153725 23153914 + SINE/Alu_AluSx3 chr1 23153922 23154277 + LINE/L1_L1ME1 chr1 23154278 23154537 + SINE/Alu_AluJr chr1 23154538 23154827 - SINE/Alu_AluY chr1 23155400 23155616 + SINE/MIR_MIRb chr1 23155664 23155811 + SINE/Alu_AluSq2 chr1 23155938 23155973 + Simple_repeat_(TTAACG)n chr1 23156099 23156400 + SINE/Alu_AluSz chr1 23156718 23156899 + DNA/TcMar-Mariner_MARNA chr1 23157052 23157344 + SINE/Alu_AluJb chr1 23157395 23157509 + SINE/Alu_AluJr chr1 23157510 23157819 + SINE/Alu_AluSx1 chr1 23157820 23158023 + SINE/Alu_AluJr chr1 23158369 23158677 + SINE/Alu_AluSq2 chr1 23158687 23158953 + SINE/Alu_AluSx1 chr1 23158954 23158966 + Simple_repeat_(TGGGT)n chr1 23159059 23159351 + SINE/Alu_AluYm1 chr1 23159439 23159617 + SINE/MIR_MIR chr1 23159613 23159691 + SINE/MIR_MIRb chr1 23159704 23160008 + SINE/Alu_AluSq chr1 23160011 23160154 + DNA/hAT-Tag1_hAT-5_Mam chr1 23160826 23161104 - LINE/L2_L2d2 chr1 23161107 23161454 - LINE/L2_L2a chr1 23161472 23161609 + SINE/Alu_FLAM_C chr1 23161742 23162049 + SINE/Alu_AluSg7 chr1 23162110 23162420 + LINE/L1_L1ME3A chr1 23162438 23162648 - SINE/Alu_AluY chr1 23162704 23162810 + snRNA_U6 chr1 23162960 23163258 + SINE/Alu_AluSg chr1 23163280 23163575 + SINE/Alu_AluJo chr1 23163784 23163933 + SINE/MIR_MIRb chr1 23163957 23164025 + snRNA_U6 chr1 23164214 23164503 + SINE/Alu_AluY chr1 23165259 23165366 + LINE/L1_L1ME1 chr1 23165390 23165646 + SINE/Alu_AluJb chr1 23165676 23165718 - LINE/L2_L2b chr1 23166049 23166076 + Simple_repeat_(A)n chr1 23166143 23166227 - SINE/MIR_MIR3 chr1 23166221 23166372 + SINE/MIR_MIRb chr1 23166373 23166677 + SINE/Alu_AluSz chr1 23166678 23166783 + SINE/MIR_MIRb chr1 23168101 23168210 + Simple_repeat_(GCCCGCG)n chr1 23168708 23168751 + Simple_repeat_(CCG)n chr1 23168979 23169051 - LINE/L2_L2d2 chr1 23169083 23169359 + SINE/Alu_AluJb chr1 23169368 23169470 + SINE/MIR_MIRb chr1 23169479 23169658 + SINE/MIR_MIR3 chr1 23169972 23170000 + Simple_repeat_(AC)n chr1 23170298 23170446 + SINE/MIR_MIRb chr1 23170454 23170766 - SINE/Alu_AluSq2 chr1 23170799 23171100 + SINE/Alu_AluJb chr1 23171101 23171163 + Simple_repeat_(TA)n chr1 23171164 23171186 + Simple_repeat_(TATTA)n chr1 23171930 23172159 + SINE/MIR_MIRb chr1 23172377 23172505 + SINE/Alu_AluJr chr1 23172506 23172814 - SINE/Alu_AluSz chr1 23172824 23173112 - SINE/Alu_AluSg chr1 23173113 23173279 + SINE/Alu_AluJr chr1 23173298 23173656 - SINE/Alu_AluSx1 chr1 23174006 23174524 - LTR/ERVL_MLT2B1 chr1 23174930 23174960 + Simple_repeat_(TTCA)n chr1 23174967 23175203 - SINE/MIR_MIRb chr1 23175259 23175402 + SINE/MIR_MIRb chr1 23175529 23175621 + LINE/L2_L2d2 chr1 23175647 23175901 - LINE/L2_L2c chr1 23175903 23176023 - SINE/MIR_MIRb chr1 23176063 23176364 - SINE/Alu_AluSz chr1 23176438 23176578 - DNA/hAT-Charlie_Charlie4z chr1 23176879 23177189 - SINE/Alu_AluJr chr1 23177199 23177333 - DNA/hAT-Charlie_Charlie4z chr1 23177413 23177535 + SINE/MIR_MIRb chr1 23178155 23178237 - SINE/MIR_MIRb chr1 23178270 23178491 - SINE/MIR_MIRb chr1 23178518 23178580 + LINE/L2_L2b chr1 23178601 23178758 - LINE/CR1_L3 chr1 23178788 23179078 - SINE/Alu_AluSp chr1 23179229 23179513 + LTR/ERVL-MaLR_MLT1J1 chr1 23179550 23179611 + LTR/ERVL-MaLR_MLT1J1 chr1 23179618 23179699 + LINE/L2_L2d2 chr1 23179966 23180025 - LINE/RTE-X_L4_B_Mam chr1 23180041 23180245 + SINE/MIR_MIR chr1 23180246 23180553 - SINE/Alu_AluSx1 chr1 23180554 23180592 + SINE/MIR_MIR chr1 23180632 23180722 - LINE/RTE-X_L4_C_Mam chr1 23180771 23181046 - SINE/Alu_AluSg chr1 23181219 23181322 + SINE/MIR_MIRb chr1 23181323 23181634 + SINE/Alu_AluSp chr1 23181635 23181685 + SINE/MIR_MIRb chr1 23181767 23181795 + DNA/hAT-Charlie_Charlie19a chr1 23181796 23182061 + SINE/Alu_AluJr chr1 23182063 23182371 + SINE/Alu_AluY chr1 23182372 23182479 + DNA/hAT-Charlie_Charlie19a chr1 23182588 23182743 - SINE/MIR_MIRb chr1 23182744 23183060 + SINE/Alu_AluSz6 chr1 23183061 23183183 - SINE/MIR_MIRb chr1 23183453 23183495 + LINE/L2_L2a chr1 23183496 23183798 - SINE/Alu_AluSz chr1 23183799 23184067 + LINE/L2_L2a chr1 23184470 23184509 + Simple_repeat_(TGTATA)n chr1 23184527 23184746 - SINE/Alu_AluJb chr1 23185037 23185114 + SINE/MIR_MIR3 chr1 23185115 23185406 + SINE/Alu_AluSx1 chr1 23185407 23185453 + SINE/MIR_MIR3 chr1 23185421 23185589 + LINE/L2_L2a chr1 23185592 23185799 + LINE/CR1_CR1-3_Croc chr1 23185971 23186075 + SINE/MIR_MIRb chr1 23186097 23186151 - SINE/Alu_FLAM_C chr1 23186227 23186533 + SINE/Alu_AluSq2 chr1 23186609 23186909 + SINE/Alu_AluSc chr1 23187027 23187331 - SINE/Alu_AluJb chr1 23187332 23187437 + SINE/MIR_MIR chr1 23187484 23187545 + SINE/MIR_MIR3 chr1 23187905 23187972 + DNA/hAT-Charlie_MER102c chr1 23187987 23188025 + Simple_repeat_(TG)n chr1 23188114 23188259 - SINE/MIR_MIRb chr1 23188300 23188525 + SINE/MIR_MIR chr1 23188505 23188679 - LINE/L2_L2c chr1 23188747 23188927 + SINE/Alu_FAM chr1 23189174 23189469 - SINE/Alu_AluSp chr1 23189498 23189792 - SINE/Alu_AluJb chr1 23190211 23190489 - SINE/Alu_AluJo chr1 23190653 23190735 - SINE/MIR_MIR chr1 23190736 23191016 + SINE/Alu_AluJb chr1 23191018 23191328 + SINE/Alu_AluSq2 chr1 23191329 23191410 - SINE/MIR_MIR chr1 23191411 23191705 - SINE/Alu_AluJb chr1 23191706 23191742 - SINE/MIR_MIR chr1 23191787 23191837 + LINE/L2_L2c chr1 23192560 23192859 + SINE/Alu_AluSc chr1 23194391 23194431 + Simple_repeat_(AAT)n chr1 23195077 23195127 + LINE/L1_L1ME4c chr1 23195146 23195297 + Unknown_MamRep564 chr1 23195445 23195735 - SINE/Alu_AluSg chr1 23195750 23195820 + LTR/ERVL_MER21C chr1 23195827 23196126 - SINE/Alu_AluJr chr1 23196164 23196460 + SINE/Alu_AluSp chr1 23196624 23196848 - SINE/MIR_MIRb chr1 23197433 23197448 + LINE/L1_L1ME3B chr1 23197449 23197744 + SINE/Alu_AluSx1 chr1 23197755 23198084 + SINE/Alu_AluJo chr1 23198090 23198387 + SINE/Alu_AluYc chr1 23198388 23198915 + LINE/L1_L1ME3B chr1 23198916 23199126 - SINE/Alu_AluSg chr1 23199127 23199405 - SINE/Alu_AluJr chr1 23199416 23199615 - SINE/Alu_AluJr chr1 23199693 23200003 - SINE/Alu_AluSx chr1 23200110 23200308 + SINE/MIR_MIRc chr1 23200324 23200517 + SINE/MIR_MIRb chr1 23200542 23200673 - SINE/Alu_FLAM_C chr1 23201045 23201348 + LTR/ERVL-MaLR_MLT1O chr1 23201542 23201852 - SINE/Alu_AluJr chr1 23201857 23202064 + SINE/MIR_MIRb chr1 23202086 23202392 - SINE/Alu_AluSz chr1 23202736 23202790 - SINE/MIR_MIRc chr1 23202804 23202911 + SINE/MIR_MIRb chr1 23202912 23203198 - SINE/Alu_AluSx chr1 23203199 23203228 + SINE/MIR_MIRc chr1 23203379 23203674 + SINE/Alu_AluJr4 chr1 23203691 23203862 + LINE/L1_L1M5 chr1 23203863 23203994 + SINE/Alu_AluJr4 chr1 23204005 23204130 + LTR/ERVL-MaLR_THE1B chr1 23204131 23204424 - SINE/Alu_AluY chr1 23204428 23204484 + SINE/MIR_MIR chr1 23204494 23204535 - SINE/MIR_MIR3 chr1 23204584 23204891 + LTR/ERVL-MaLR_MLT1H chr1 23204892 23205398 + LINE/L1_L1PB1 chr1 23205399 23205520 + LTR/ERVL-MaLR_MLT1H chr1 23205547 23205841 - SINE/Alu_AluSp chr1 23205919 23206045 + LINE/L2_L2c chr1 23206046 23206329 - SINE/Alu_AluYc chr1 23206330 23206881 + LINE/L2_L2c chr1 23206901 23207054 + SINE/MIR_MIR3 chr1 23207055 23207114 - DNA/hAT-Charlie_MER5B chr1 23207117 23207423 + SINE/Alu_AluSz chr1 23207465 23207540 + DNA/hAT-Charlie_MER5B chr1 23207760 23208062 - SINE/Alu_AluSx chr1 23208148 23208288 + SINE/Alu_AluJo chr1 23208290 23208601 + SINE/Alu_AluSp chr1 23208689 23208804 - SINE/MIR_MIR3 chr1 23208810 23208990 + DNA/hAT-Charlie_MER33 chr1 23208991 23209299 - SINE/Alu_AluSx chr1 23209300 23209393 + DNA/hAT-Charlie_MER33 chr1 23209557 23209794 - SINE/MIR_MIR chr1 23209863 23209908 - LINE/L2_L2b chr1 23209998 23210080 - DNA/hAT-Charlie_MER5A1 chr1 23210081 23210377 - SINE/Alu_AluSz chr1 23210378 23210411 - DNA/hAT-Charlie_MER5A1 chr1 23210476 23210636 - SINE/MIR_MIR3 chr1 23211071 23211268 + SINE/MIR_MIRb chr1 23211208 23211477 - LINE/L2_L2c chr1 23211610 23211660 + Simple_repeat_(TATG)n chr1 23211661 23211962 - SINE/Alu_AluJb chr1 23212102 23212172 + LINE/L2_L2c chr1 23212362 23212678 + LINE/L2_L2c chr1 23212827 23213125 - SINE/Alu_AluSz chr1 23213241 23213505 + SINE/Alu_AluSc chr1 23213565 23213725 + LINE/L2_L2b chr1 23213726 23213857 - SINE/Alu_FLAM_C chr1 23213858 23214069 + LINE/L2_L2b chr1 23214145 23214424 + SINE/Alu_AluJb chr1 23214426 23214462 + Simple_repeat_(TCAT)n chr1 23214464 23214573 + LINE/L2_L2a chr1 23214559 23214817 + LINE/L2_L2a chr1 23214846 23215134 - LINE/L2_L2 chr1 23215135 23215441 + SINE/Alu_AluJb chr1 23215442 23215490 - LINE/L2_L2 chr1 23215694 23215772 - LINE/L2_L2b chr1 23216270 23216500 - SINE/MIR_MIRb chr1 23216517 23216655 + SINE/MIR_MIR3 chr1 23216653 23216867 - SINE/MIR_MIRc chr1 23217346 23217427 + Simple_repeat_(CGC)n chr1 23217493 23217556 + Simple_repeat_(CTC)n chr1 23217888 23218174 - LINE/L2_L2a chr1 23218509 23218542 + Simple_repeat_(TTGG)n chr1 23218546 23218823 - SINE/Alu_AluSc8 chr1 23218837 23218893 - LINE/L1_L1M4 chr1 23218894 23218942 + Simple_repeat_(TTGT)n chr1 23218943 23219062 - SINE/Alu_FLAM_C chr1 23219063 23219168 - LINE/L1_L1M4 chr1 23219837 23220146 + SINE/Alu_AluYa8 chr1 23220316 23220799 - LINE/L2_L2c chr1 23221183 23221386 + SINE/MIR_MIRb chr1 23221396 23221554 + LINE/L2_L2b chr1 23221559 23221598 + Simple_repeat_(GGAT)n chr1 23222017 23222325 - SINE/Alu_AluSx chr1 23222328 23222363 + Simple_repeat_(TG)n chr1 23222364 23222536 - SINE/MIR_MIRb chr1 23222555 23222685 - LINE/L2_L2d2 chr1 23222664 23222741 + LINE/L2_L2c chr1 23222740 23222912 - SINE/MIR_MIR3 chr1 23222849 23223032 + SINE/MIR_MIRb chr1 23223042 23223342 - SINE/Alu_AluSz chr1 23223344 23223376 + Simple_repeat_(ATT)n chr1 23223386 23224529 - LTR/ERVK_MER11A chr1 23224533 23224803 - SINE/Alu_AluJr chr1 23224873 23224947 - SINE/MIR_MIRc chr1 23225727 23225836 + SINE/Alu_AluSz chr1 23225837 23225893 - SINE/Alu_AluJo chr1 23225895 23225963 + LINE/L2_L2b chr1 23226048 23226160 + SINE/MIR_MIRb chr1 23226383 23226723 - SINE/Alu_AluSz chr1 23226968 23227104 - SINE/MIR_MIR chr1 23227121 23227149 + Simple_repeat_(AAC)n chr1 23227171 23227367 - SINE/MIR_MIR chr1 23227379 23227416 + SINE/MIR_MIR chr1 23227417 23227708 + SINE/Alu_AluJb chr1 23227709 23227783 + SINE/MIR_MIR chr1 23228027 23228323 + DNA/hAT-Charlie_Charlie30b chr1 23228329 23228670 + SINE/Alu_AluJr chr1 23228690 23228989 + SINE/Alu_AluSx chr1 23229007 23229376 + LINE/L1_L1ME3G chr1 23229589 23229679 + SINE/Alu_AluJo chr1 23229680 23229972 - SINE/Alu_AluSq2 chr1 23229973 23230167 + SINE/Alu_AluJo chr1 23230306 23230492 - SINE/Alu_AluJb chr1 23230513 23230526 + Simple_repeat_(A)n chr1 23230527 23230830 - SINE/Alu_AluSz6 chr1 23230964 23231045 - SINE/MIR_MIRc chr1 23231093 23231118 + Simple_repeat_(A)n chr1 23231633 23231927 + SINE/Alu_AluY chr1 23231994 23232212 + SINE/MIR_MIRb chr1 23232225 23232493 - SINE/Alu_AluSx1 chr1 23232666 23232730 + SINE/MIR_MIR3 chr1 23232769 23232923 - SINE/Alu_FRAM chr1 23233173 23233397 - SINE/MIR_MIRb chr1 23233449 23233572 - SINE/MIR_MIRb chr1 23233606 23233697 - DNA/hAT-Charlie_Charlie4a chr1 23233698 23234006 - SINE/Alu_AluSz chr1 23234009 23234306 - SINE/Alu_AluJb chr1 23234312 23234653 - LINE/L1_L1PA16 chr1 23234654 23234956 - SINE/Alu_AluSx chr1 23234957 23235121 - LINE/L1_L1PA16 chr1 23235122 23235335 - SINE/Alu_AluSz chr1 23235336 23235350 - LINE/L1_L1PA16 chr1 23235351 23235795 - DNA/hAT-Charlie_Charlie4a chr1 23235874 23236188 - SINE/Alu_AluSq10 chr1 23236191 23236368 - SINE/Alu_AluSx chr1 23236369 23236675 - SINE/Alu_AluSx1 chr1 23236676 23236810 - SINE/Alu_AluSx chr1 23237086 23237245 - SINE/Alu_AluJr chr1 23237246 23237526 - SINE/Alu_AluSz chr1 23237527 23237654 - SINE/Alu_AluJr chr1 23237907 23237978 - LTR/ERVL-MaLR_MLT1K chr1 23237992 23238116 - LTR/ERVL-MaLR_MLT1K chr1 23238116 23238228 - LTR/ERVL_LTR53B chr1 23238229 23238394 + SINE/Alu_FRAM chr1 23238395 23238756 - LTR/ERVL_LTR53B chr1 23238845 23238885 - LINE/RTE-BovB_MamRTE1 chr1 23238923 23239001 - LINE/CR1_L3 chr1 23239027 23239043 + Simple_repeat_(A)n chr1 23239057 23239102 + DNA/hAT-Charlie_MER58B chr1 23239169 23239273 + SINE/MIR_MIRb chr1 23239274 23239375 + LINE/L1_L1MB7 chr1 23239376 23239688 - SINE/Alu_AluSx chr1 23239689 23239741 + LINE/L1_L1MB7 chr1 23239754 23239978 + SINE/Alu_AluJr chr1 23240004 23240087 + SINE/MIR_MIRb chr1 23240089 23240114 + Simple_repeat_(TCA)n chr1 23240427 23240712 + SINE/Alu_AluY chr1 23240713 23240747 + Simple_repeat_(AAAAC)n chr1 23240786 23241088 + SINE/Alu_AluSx1 chr1 23241093 23241273 + SINE/Alu_AluSx chr1 23241280 23241339 + LINE/L1_L1M5 chr1 23241340 23241590 + SINE/Alu_AluYc chr1 23241591 23241774 + LINE/L1_L1ME3G chr1 23241791 23241948 - SINE/Alu_FAM chr1 23242096 23242381 + LINE/L1_L1ME4b chr1 23242382 23242716 + SINE/Alu_AluJb chr1 23242717 23242959 + LINE/L1_L1ME4b chr1 23243079 23243104 + Simple_repeat_(AAAGG)n chr1 23243175 23243460 + SINE/Alu_AluJr chr1 23243461 23243488 + Simple_repeat_(CTAAA)n chr1 23244194 23244385 + SINE/MIR_MIRb chr1 23245122 23245161 + Simple_repeat_(GGCCAA)n chr1 23245156 23245194 + Simple_repeat_(CCAGCT)n chr1 23245722 23245767 - LINE/L2_L2d2 chr1 23245738 23245850 + SINE/MIR_MIRc chr1 23245885 23246191 + SINE/Alu_AluSz chr1 23246198 23246352 + LINE/L1_L1PB1 chr1 23246400 23246520 - SINE/MIR_MIRb chr1 23246951 23247070 + SINE/MIR_MIR chr1 23247071 23247379 + SINE/Alu_AluSx1 chr1 23247380 23247546 + SINE/MIR_MIR chr1 23247539 23247627 + LINE/L2_L2d2 chr1 23247658 23247954 + SINE/Alu_AluJb chr1 23247955 23248256 + SINE/Alu_AluSz chr1 23248433 23248591 + LINE/L2_L2a chr1 23248700 23248813 + SINE/Alu_AluYc chr1 23248823 23248952 + SINE/Alu_FLAM_A chr1 23248970 23249288 + SINE/Alu_AluJr chr1 23249718 23249750 + Low_complexity_GA-rich chr1 23250248 23250329 - SINE/MIR_MIRb chr1 23250474 23250678 - SINE/MIR_MIRb chr1 624434 624526 + Low_complexity_G-rich chr1 827542 827629 + Low_complexity_G-rich chr1 832657 832701 + Low_complexity_G-rich chr1 910107 910140 + Low_complexity_G-rich chr1 913581 913633 + Low_complexity_G-rich chr1 942055 942105 + Low_complexity_G-rich chr1 961038 961080 + Low_complexity_G-rich chr1 1000024 1000057 + Low_complexity_G-rich chr1 1000441 1000482 + Low_complexity_G-rich chr1 1001404 1001460 + Low_complexity_G-rich chr1 1019467 1019507 + Low_complexity_G-rich chr1 1019613 1019662 + Low_complexity_G-rich chr1 1019934 1020044 + Low_complexity_G-rich chr1 1020686 1020724 + Low_complexity_G-rich chr1 1036570 1036635 + Low_complexity_G-rich chr1 1040871 1041142 + Low_complexity_G-rich chr1 1049062 1049205 + Low_complexity_G-rich chr1 1058524 1058566 + Low_complexity_G-rich chr1 1064784 1064831 + Low_complexity_G-rich chr1 1116237 1116279 + Low_complexity_G-rich chr1 1137821 1137864 + Low_complexity_G-rich chr1 1160230 1160279 + Low_complexity_G-rich chr1 1168000 1168144 + Low_complexity_G-rich chr1 1182524 1182612 + Low_complexity_G-rich chr1 1200941 1200977 + Low_complexity_G-rich chr1 1214190 1214235 + Low_complexity_G-rich chr1 1228780 1228853 + Low_complexity_G-rich chr1 1234293 1234336 + Low_complexity_G-rich chr1 1239944 1239988 + Low_complexity_G-rich chr1 1273983 1274021 + Low_complexity_G-rich chr1 1274489 1274525 + Low_complexity_G-rich chr1 1284293 1284344 + Low_complexity_G-rich chr1 1293938 1294075 + Low_complexity_G-rich chr1 1299703 1299755 + Low_complexity_G-rich chr1 1302400 1302437 + Low_complexity_G-rich chr1 1304981 1305040 + Low_complexity_G-rich chr1 1322536 1322742 + Low_complexity_G-rich chr1 1344055 1344109 + Low_complexity_G-rich chr1 1349764 1349810 + Low_complexity_G-rich chr1 1355136 1355232 + Low_complexity_G-rich chr1 1398839 1398943 + Low_complexity_G-rich chr1 1406683 1406728 + Low_complexity_G-rich chr1 1407559 1407625 + Low_complexity_G-rich chr1 1421877 1421915 + Low_complexity_G-rich chr1 1421992 1422029 + Low_complexity_G-rich chr1 1435597 1435630 + Low_complexity_G-rich chr1 1435912 1435968 + Low_complexity_G-rich chr1 1439368 1439403 + Low_complexity_G-rich chr1 1456579 1456617 + Low_complexity_G-rich chr1 1486898 1486936 + Low_complexity_G-rich chr1 1547093 1547158 + Low_complexity_G-rich chr1 1599236 1599275 + Low_complexity_G-rich chr1 1600232 1600338 + Low_complexity_G-rich chr1 1615445 1615487 + Low_complexity_G-rich chr1 1624918 1624946 + Low_complexity_G-rich chr1 1778558 1778602 + Low_complexity_G-rich chr1 1891294 1891328 + Low_complexity_G-rich chr1 1891399 1891496 + Low_complexity_G-rich chr1 1909165 1909300 + Low_complexity_G-rich chr1 1920053 1920094 + Low_complexity_G-rich chr1 1922001 1922050 + Low_complexity_G-rich chr1 1927229 1927263 + Low_complexity_G-rich chr1 1970454 1970500 + Low_complexity_G-rich chr1 2003926 2004016 + Low_complexity_G-rich chr1 2019496 2019542 + Low_complexity_G-rich chr1 2060794 2060807 + Low_complexity_G-rich chr1 2060849 2060864 + Low_complexity_G-rich chr1 2169791 2169831 + Low_complexity_G-rich chr1 2197584 2197638 + Low_complexity_G-rich chr1 2224835 2224864 + Low_complexity_G-rich chr1 2227254 2227297 + Low_complexity_G-rich chr1 2279575 2279647 + Low_complexity_G-rich chr1 2317438 2317471 + Low_complexity_G-rich chr1 2324860 2324904 + Low_complexity_G-rich chr1 2343942 2344010 + Low_complexity_G-rich chr1 2392124 2392164 + Low_complexity_G-rich chr1 2392206 2392239 + Low_complexity_G-rich chr1 2396665 2396735 + Low_complexity_G-rich chr1 2406008 2406057 + Low_complexity_G-rich chr1 2412623 2412808 + Low_complexity_G-rich chr1 2429045 2429096 + Low_complexity_G-rich chr1 2431791 2431837 + Low_complexity_G-rich chr1 2476233 2476286 + Low_complexity_G-rich chr1 2477677 2477735 + Low_complexity_G-rich chr1 2486428 2486468 + Low_complexity_G-rich chr1 2506883 2506925 + Low_complexity_G-rich chr1 2509792 2509858 + Low_complexity_G-rich chr1 2546203 2546298 + Low_complexity_G-rich chr1 2555526 2555570 + Low_complexity_G-rich chr1 2580706 2580740 + Low_complexity_G-rich chr1 2587017 2587065 + Low_complexity_G-rich chr1 2628952 2628996 + Low_complexity_G-rich chr1 2808950 2809026 + Low_complexity_G-rich chr1 2811550 2811590 + Low_complexity_G-rich chr1 2815576 2815648 + Low_complexity_G-rich chr1 2827714 2827887 + Low_complexity_G-rich chr1 2835050 2835095 + Low_complexity_G-rich chr1 2932280 2932701 + Low_complexity_G-rich chr1 3016313 3016710 + Low_complexity_G-rich chr1 3017314 3017352 + Low_complexity_G-rich chr1 3061434 3061463 + Low_complexity_G-rich chr1 3065512 3065553 + Low_complexity_G-rich chr1 3069458 3069503 + Low_complexity_G-rich chr1 3069593 3069641 + Low_complexity_G-rich chr1 3111516 3111585 + Low_complexity_G-rich chr1 3113276 3113332 + Low_complexity_G-rich chr1 3113464 3113507 + Low_complexity_G-rich chr1 3193800 3193835 + Low_complexity_G-rich chr1 3236181 3236216 + Low_complexity_G-rich chr1 3249209 3249272 + Low_complexity_G-rich chr1 3284821 3284855 + Low_complexity_G-rich chr1 3394473 3394511 + Low_complexity_G-rich chr1 3412052 3412118 + Low_complexity_G-rich chr1 3425806 3425854 + Low_complexity_G-rich chr1 3436803 3436851 + Low_complexity_G-rich chr1 3471528 3471560 + Low_complexity_G-rich chr1 3491753 3491792 + Low_complexity_G-rich chr1 3502203 3502241 + Low_complexity_G-rich chr1 3503242 3503278 + Low_complexity_G-rich chr1 3503749 3503807 + Low_complexity_G-rich chr1 3522584 3522630 + Low_complexity_G-rich chr1 3531801 3531850 + Low_complexity_G-rich chr1 3580212 3580282 + Low_complexity_G-rich chr1 3580734 3580777 + Low_complexity_G-rich chr1 3674927 3674964 + Low_complexity_G-rich chr1 3717314 3717377 + Low_complexity_G-rich chr1 3725366 3726967 + Low_complexity_G-rich chr1 4047254 4047297 + Low_complexity_G-rich chr1 4058672 4058714 + Low_complexity_G-rich chr1 4489676 4489731 + Low_complexity_G-rich chr1 4617611 4617639 + Low_complexity_G-rich chr1 4654351 4654384 + Low_complexity_G-rich chr1 4655793 4655857 + Low_complexity_G-rich chr1 4969138 4969253 + Low_complexity_G-rich chr1 5005919 5005955 + Low_complexity_G-rich chr1 5112289 5112530 + Low_complexity_G-rich chr1 5593623 5593673 + Low_complexity_G-rich chr1 5726357 5726388 + Low_complexity_G-rich chr1 5841036 5841071 + Low_complexity_G-rich chr1 6162952 6162985 + Low_complexity_G-rich chr1 6179534 6179591 + Low_complexity_G-rich chr1 6199328 6199386 + Low_complexity_G-rich chr1 6209039 6209074 + Low_complexity_G-rich chr1 6241774 6241810 + Low_complexity_G-rich chr1 6392868 6392937 + Low_complexity_G-rich chr1 6418908 6418974 + Low_complexity_G-rich chr1 6424672 6424738 + Low_complexity_G-rich chr1 6440450 6440569 + Low_complexity_G-rich chr1 6495388 6495440 + Low_complexity_G-rich chr1 6509187 6509226 + Low_complexity_G-rich chr1 6513171 6513210 + Low_complexity_G-rich chr1 6554193 6554272 + Low_complexity_G-rich chr1 6581962 6581993 + Low_complexity_G-rich chr1 6614872 6614923 + Low_complexity_G-rich chr1 7061680 7061728 + Low_complexity_G-rich chr1 7065224 7065270 + Low_complexity_G-rich chr1 7229406 7229663 + Low_complexity_G-rich chr1 7447419 7447449 + Low_complexity_G-rich chr1 7531154 7531207 + Low_complexity_G-rich chr1 7534934 7534999 + Low_complexity_G-rich chr1 7550061 7550117 + Low_complexity_G-rich chr1 7557987 7558023 + Low_complexity_G-rich chr1 7705483 7705571 + Low_complexity_G-rich chr1 8217064 8217105 + Low_complexity_G-rich chr1 8238915 8238971 + Low_complexity_G-rich chr1 8405021 8405052 + Low_complexity_G-rich chr1 8873709 8873742 + Low_complexity_G-rich chr1 9026012 9026050 + Low_complexity_G-rich chr1 9039489 9039533 + Low_complexity_G-rich chr1 9077738 9077827 + Low_complexity_G-rich chr1 9290069 9290106 + Low_complexity_G-rich chr1 9293332 9293381 + Low_complexity_G-rich chr1 9337495 9337535 + Low_complexity_G-rich chr1 9424537 9424582 + Low_complexity_G-rich chr1 9687551 9687594 + Low_complexity_G-rich chr1 9823796 9823840 + Low_complexity_G-rich chr1 9850576 9850627 + Low_complexity_G-rich chr1 10033001 10033043 + Low_complexity_G-rich chr1 10649961 10650019 + Low_complexity_G-rich chr1 10653688 10653731 + Low_complexity_G-rich chr1 10659174 10659209 + Low_complexity_G-rich chr1 10664203 10664257 + Low_complexity_G-rich chr1 10680265 10680313 + Low_complexity_G-rich chr1 10684176 10684207 + Low_complexity_G-rich chr1 10708926 10709028 + Low_complexity_G-rich chr1 10743851 10743922 + Low_complexity_G-rich chr1 10773375 10773445 + Low_complexity_G-rich chr1 10792367 10792393 + Low_complexity_G-rich chr1 10820755 10820795 + Low_complexity_G-rich chr1 10867222 10867259 + Low_complexity_G-rich chr1 10872882 10872918 + Low_complexity_G-rich chr1 10905357 10905383 + Low_complexity_G-rich chr1 10980159 10980213 + Low_complexity_G-rich chr1 11060049 11060084 + Low_complexity_G-rich chr1 11060218 11060270 + Low_complexity_G-rich chr1 11329774 11329819 + Low_complexity_G-rich chr1 11330119 11330130 + Low_complexity_G-rich chr1 11361914 11361971 + Low_complexity_G-rich chr1 11396372 11396417 + Low_complexity_G-rich chr1 11414290 11414345 + Low_complexity_G-rich chr1 11448053 11448105 + Low_complexity_G-rich chr1 11490100 11490138 + Low_complexity_G-rich chr1 11728966 11729017 + Low_complexity_G-rich chr1 11776656 11776705 + Low_complexity_G-rich chr1 11926473 11926538 + Low_complexity_G-rich chr1 11939347 11939383 + Low_complexity_G-rich chr1 12006715 12006755 + Low_complexity_G-rich chr1 12054141 12054194 + Low_complexity_G-rich chr1 12064123 12064180 + Low_complexity_G-rich chr1 12595391 12595442 + Low_complexity_G-rich chr1 12623125 12623159 + Low_complexity_G-rich chr1 12636835 12636876 + Low_complexity_G-rich chr1 12715492 12715561 + Low_complexity_G-rich chr1 13336767 13336823 + Low_complexity_G-rich chr1 13635553 13635597 + Low_complexity_G-rich chr1 13811892 13811929 + Low_complexity_G-rich chr1 13907521 13907532 + Low_complexity_G-rich chr1 13963146 13963196 + Low_complexity_G-rich chr1 14757890 14757924 + Low_complexity_G-rich chr1 14924045 14924073 + Low_complexity_G-rich chr1 14924583 14924651 + Low_complexity_G-rich chr1 15524014 15524066 + Low_complexity_G-rich chr1 15617497 15617555 + Low_complexity_G-rich chr1 15617628 15617670 + Low_complexity_G-rich chr1 15735917 15735950 + Low_complexity_G-rich chr1 15989425 15989466 + Low_complexity_G-rich chr1 16019750 16019785 + Low_complexity_G-rich chr1 16026924 16026972 + Low_complexity_G-rich chr1 16032313 16032355 + Low_complexity_G-rich chr1 16073508 16073564 + Low_complexity_G-rich chr1 16073681 16073729 + Low_complexity_G-rich chr1 16076354 16076384 + Low_complexity_G-rich chr1 16127726 16127763 + Low_complexity_G-rich chr1 16152477 16152530 + Low_complexity_G-rich chr1 16175531 16175560 + Low_complexity_G-rich chr1 16191298 16191452 + Low_complexity_G-rich chr1 16199287 16199334 + Low_complexity_G-rich chr1 16395375 16395408 + Low_complexity_G-rich chr1 16409502 16409547 + Low_complexity_G-rich chr1 16409672 16409709 + Low_complexity_G-rich chr1 16648016 16648054 + Low_complexity_G-rich chr1 16939206 16939243 + Low_complexity_G-rich chr1 17157987 17158015 + Low_complexity_G-rich chr1 17197765 17197791 + Low_complexity_G-rich chr1 17205569 17205607 + Low_complexity_G-rich chr1 17235147 17235310 + Low_complexity_G-rich chr1 17427805 17427845 + Low_complexity_G-rich chr1 17539389 17539429 + Low_complexity_G-rich chr1 17551993 17552028 + Low_complexity_G-rich chr1 17608030 17608065 + Low_complexity_G-rich chr1 17633129 17633182 + Low_complexity_G-rich chr1 17709264 17709306 + Low_complexity_G-rich chr1 17743257 17743317 + Low_complexity_G-rich chr1 17780729 17780762 + Low_complexity_G-rich chr1 17794853 17794888 + Low_complexity_G-rich chr1 17826593 17826629 + Low_complexity_G-rich chr1 17856862 17856900 + Low_complexity_G-rich chr1 17859923 17859983 + Low_complexity_G-rich chr1 18047026 18047100 + Low_complexity_G-rich chr1 18079299 18079349 + Low_complexity_G-rich chr1 18098310 18098339 + Low_complexity_G-rich chr1 18163382 18163428 + Low_complexity_G-rich chr1 18408849 18408885 + Low_complexity_G-rich chr1 18599463 18599550 + Low_complexity_G-rich chr1 18631441 18631491 + Low_complexity_G-rich chr1 18636753 18636791 + Low_complexity_G-rich chr1 18638243 18638275 + Low_complexity_G-rich chr1 18642913 18643012 + Low_complexity_G-rich chr1 18646969 18647048 + Low_complexity_G-rich chr1 18716912 18717007 + Low_complexity_G-rich chr1 18730887 18730966 + Low_complexity_G-rich chr1 19030690 19030724 + Low_complexity_G-rich chr1 19058154 19058221 + Low_complexity_G-rich chr1 19070513 19070554 + Low_complexity_G-rich chr1 19086332 19086360 + Low_complexity_G-rich chr1 19328466 19328493 + Low_complexity_G-rich chr1 19657803 19657835 + Low_complexity_G-rich chr1 19678217 19678263 + Low_complexity_G-rich chr1 19799720 19799795 + Low_complexity_G-rich chr1 19975631 19975660 + Low_complexity_G-rich chr1 20153095 20153141 + Low_complexity_G-rich chr1 20350094 20350127 + Low_complexity_G-rich chr1 20354273 20354320 + Low_complexity_G-rich chr1 20430627 20430752 + Low_complexity_G-rich chr1 20485449 20485525 + Low_complexity_G-rich chr1 20563599 20563631 + Low_complexity_G-rich chr1 20587140 20587184 + Low_complexity_G-rich chr1 20587466 20587505 + Low_complexity_G-rich chr1 20704141 20704176 + Low_complexity_G-rich chr1 20717769 20717850 + Low_complexity_G-rich chr1 21176422 21176553 + Low_complexity_G-rich chr1 21260212 21260252 + Low_complexity_G-rich chr1 21332590 21332760 + Low_complexity_G-rich chr1 21415199 21415291 + Low_complexity_G-rich chr1 21440449 21440480 + Low_complexity_G-rich chr1 21509279 21509360 + Low_complexity_G-rich chr1 21587305 21587340 + Low_complexity_G-rich chr1 21594668 21594718 + Low_complexity_G-rich chr1 21603587 21603637 + Low_complexity_G-rich chr1 21606028 21606058 + Low_complexity_G-rich chr1 21650023 21650078 + Low_complexity_G-rich chr1 21652001 21652068 + Low_complexity_G-rich chr1 21667226 21667264 + Low_complexity_G-rich chr1 21704946 21705048 + Low_complexity_G-rich chr1 21822551 21822584 + Low_complexity_G-rich chr1 21869008 21869049 + Low_complexity_G-rich chr1 21887120 21887185 + Low_complexity_G-rich chr1 21901345 21901417 + Low_complexity_G-rich chr1 21937554 21937613 + Low_complexity_G-rich chr1 22163564 22163610 + Low_complexity_G-rich chr1 22183035 22183078 + Low_complexity_G-rich chr1 22208328 22208363 + Low_complexity_G-rich chr1 22228457 22228512 + Low_complexity_G-rich chr1 22245744 22245790 + Low_complexity_G-rich chr1 22261541 22261582 + Low_complexity_G-rich chr1 22342914 22342958 + Low_complexity_G-rich chr1 22350732 22350770 + Low_complexity_G-rich chr1 22394173 22394243 + Low_complexity_G-rich chr1 22394324 22394388 + Low_complexity_G-rich chr1 22563713 22563788 + Low_complexity_G-rich chr1 22564405 22564451 + Low_complexity_G-rich chr1 22644228 22644271 + Low_complexity_G-rich chr1 22677406 22677435 + Low_complexity_G-rich chr1 22710894 22710972 + Low_complexity_G-rich chr1 22711400 22711459 + Low_complexity_G-rich chr1 22770838 22770909 + Low_complexity_G-rich chr1 22921605 22921681 + Low_complexity_G-rich chr1 23018805 23018868 + Low_complexity_G-rich chr1 23332129 23332171 + Low_complexity_G-rich chr1 23434493 23434525 + Low_complexity_G-rich chr1 23484137 23484176 + Low_complexity_G-rich chr1 23598756 23598808 + Low_complexity_G-rich chr1 23652376 23652430 + Low_complexity_G-rich chr1 23690154 23690201 + Low_complexity_G-rich chr1 23743733 23743772 + Low_complexity_G-rich chr1 23791313 23791372 + Low_complexity_G-rich chr1 23796464 23796503 + Low_complexity_G-rich chr1 24067217 24067249 + Low_complexity_G-rich chr1 24585981 24586029 + Low_complexity_G-rich chr1 24707821 24707864 + Low_complexity_G-rich chr1 24732327 24732358 + Low_complexity_G-rich chr1 24929984 24930021 + Low_complexity_G-rich chr1 25200369 25200408 + Low_complexity_G-rich chr1 25246598 25246649 + Low_complexity_G-rich chr1 25369484 25369517 + Low_complexity_G-rich chr1 25431353 25431401 + Low_complexity_G-rich chr1 25499682 25499713 + Low_complexity_G-rich chr1 25505475 25505530 + Low_complexity_G-rich chr1 25551808 25551845 + Low_complexity_G-rich chr1 25653751 25653806 + Low_complexity_G-rich chr1 26045553 26045593 + Low_complexity_G-rich chr1 26067095 26067135 + Low_complexity_G-rich chr1 26300703 26300757 + Low_complexity_G-rich chr1 26367797 26367838 + Low_complexity_G-rich chr1 26410210 26410258 + Low_complexity_G-rich chr1 26410838 26410882 + Low_complexity_G-rich chr1 26697554 26697604 + Low_complexity_G-rich chr1 27122850 27122883 + Low_complexity_G-rich chr1 27235124 27235167 + Low_complexity_G-rich chr1 27383355 27383443 + Low_complexity_G-rich chr1 27518612 27518671 + Low_complexity_G-rich chr1 27575934 27575973 + Low_complexity_G-rich chr1 27604230 27604269 + Low_complexity_G-rich chr1 27607474 27607522 + Low_complexity_G-rich chr1 27662454 27662554 + Low_complexity_G-rich chr1 27726039 27726078 + Low_complexity_G-rich chr1 27816587 27816637 + Low_complexity_G-rich chr1 27816945 27817131 + Low_complexity_G-rich chr1 27872407 27872509 + Low_complexity_G-rich chr1 27968469 27968509 + Low_complexity_G-rich chr1 28176933 28177063 + Low_complexity_G-rich chr1 28592397 28592472 + Low_complexity_G-rich chr1 28668844 28668954 + Low_complexity_G-rich chr1 28812179 28812284 + Low_complexity_G-rich chr1 28839864 28839940 + Low_complexity_G-rich chr1 29121678 29121775 + Low_complexity_G-rich chr1 29134321 29134365 + Low_complexity_G-rich chr1 29260088 29260150 + Low_complexity_G-rich chr1 29453046 29453090 + Low_complexity_G-rich chr1 29541357 29541409 + Low_complexity_G-rich chr1 29550139 29550173 + Low_complexity_G-rich chr1 29621204 29621259 + Low_complexity_G-rich chr1 29646650 29646696 + Low_complexity_G-rich chr1 29698208 29698253 + Low_complexity_G-rich chr1 29707258 29707649 + Low_complexity_G-rich chr1 29749502 29749634 + Low_complexity_G-rich chr1 29810522 29810557 + Low_complexity_G-rich chr1 29991639 29991690 + Low_complexity_G-rich chr1 30129407 30129442 + Low_complexity_G-rich chr1 30267146 30267181 + Low_complexity_G-rich chr1 30301380 30301416 + Low_complexity_G-rich chr1 30314562 30314595 + Low_complexity_G-rich chr1 30360400 30360432 + Low_complexity_G-rich chr1 30686641 30686686 + Low_complexity_G-rich chr1 30775813 30775872 + Low_complexity_G-rich chr1 30777505 30777534 + Low_complexity_G-rich chr1 30805456 30805488 + Low_complexity_G-rich chr1 30880342 30880433 + Low_complexity_G-rich chr1 30908637 30908685 + Low_complexity_G-rich chr1 30908815 30908853 + Low_complexity_G-rich chr1 30952673 30952707 + Low_complexity_G-rich chr1 31154953 31155209 + Low_complexity_G-rich chr1 31239969 31240050 + Low_complexity_G-rich chr1 31436042 31436110 + Low_complexity_G-rich chr1 31584627 31584662 + Low_complexity_G-rich chr1 31688677 31688739 + Low_complexity_G-rich chr1 31704372 31704420 + Low_complexity_G-rich chr1 31760579 31760622 + Low_complexity_G-rich chr1 31761018 31761054 + Low_complexity_G-rich chr1 31762062 31762091 + Low_complexity_G-rich chr1 31763900 31763941 + Low_complexity_G-rich chr1 31775214 31775256 + Low_complexity_G-rich chr1 31782315 31782360 + Low_complexity_G-rich chr1 32254040 32254077 + Low_complexity_G-rich chr1 32334305 32334344 + Low_complexity_G-rich chr1 32361673 32361723 + Low_complexity_G-rich chr1 32361906 32361949 + Low_complexity_G-rich chr1 32500510 32500535 + Low_complexity_G-rich chr1 32539961 32539991 + Low_complexity_G-rich chr1 32760900 32760911 + Low_complexity_G-rich chr1 32884600 32884656 + Low_complexity_G-rich chr1 32893628 32893666 + Low_complexity_G-rich chr1 32973206 32973232 + Low_complexity_G-rich chr1 33181241 33181328 + Low_complexity_G-rich chr1 33257557 33257593 + Low_complexity_G-rich chr1 33265308 33265352 + Low_complexity_G-rich chr1 33374383 33374427 + Low_complexity_G-rich chr1 33516620 33516649 + Low_complexity_G-rich chr1 34164682 34164700 + Low_complexity_G-rich chr1 34177086 34177132 + Low_complexity_G-rich chr1 34177887 34177926 + Low_complexity_G-rich chr1 34379903 34379971 + Low_complexity_G-rich chr1 34422973 34423009 + Low_complexity_G-rich chr1 34474584 34474612 + Low_complexity_G-rich chr1 34547449 34547495 + Low_complexity_G-rich chr1 34760266 34760278 + Low_complexity_G-rich chr1 34866050 34866101 + Low_complexity_G-rich chr1 34874812 34874854 + Low_complexity_G-rich chr1 34885272 34885316 + Low_complexity_G-rich chr1 34925543 34925594 + Low_complexity_G-rich chr1 34929483 34929546 + Low_complexity_G-rich chr1 35007815 35007851 + Low_complexity_G-rich chr1 35565690 35565719 + Low_complexity_G-rich chr1 35690886 35690918 + Low_complexity_G-rich chr1 35919680 35919723 + Low_complexity_G-rich chr1 36156163 36156220 + Low_complexity_G-rich chr1 36157095 36157152 + Low_complexity_G-rich chr1 36179052 36179089 + Low_complexity_G-rich chr1 36303836 36303879 + Low_complexity_G-rich chr1 36319168 36319207 + Low_complexity_G-rich chr1 36323654 36323682 + Low_complexity_G-rich chr1 36410713 36410806 + Low_complexity_G-rich chr1 36450159 36450190 + Low_complexity_G-rich chr1 36586286 36586324 + Low_complexity_G-rich chr1 36765060 36765092 + Low_complexity_G-rich chr1 36824682 36824722 + Low_complexity_G-rich chr1 36911118 36911149 + Low_complexity_G-rich chr1 37064468 37064547 + Low_complexity_G-rich chr1 37275184 37275247 + Low_complexity_G-rich chr1 37480126 37480164 + Low_complexity_G-rich chr1 37701211 37701269 + Low_complexity_G-rich chr1 37716526 37716569 + Low_complexity_G-rich chr1 37735439 37735483 + Low_complexity_G-rich chr1 37753405 37753443 + Low_complexity_G-rich chr1 37808027 37808070 + Low_complexity_G-rich chr1 38006211 38006275 + Low_complexity_G-rich chr1 38045080 38045133 + Low_complexity_G-rich chr1 38125359 38125411 + Low_complexity_G-rich chr1 38128102 38128160 + Low_complexity_G-rich chr1 38346733 38346817 + Low_complexity_G-rich chr1 38508526 38508555 + Low_complexity_G-rich chr1 38559816 38559867 + Low_complexity_G-rich chr1 38564129 38564229 + Low_complexity_G-rich chr1 38578416 38578482 + Low_complexity_G-rich chr1 38586771 38586812 + Low_complexity_G-rich chr1 38730309 38730370 + Low_complexity_G-rich chr1 38819192 38819222 + Low_complexity_G-rich chr1 38927024 38927083 + Low_complexity_G-rich chr1 38991486 38991524 + Low_complexity_G-rich chr1 39408603 39408678 + Low_complexity_G-rich chr1 39492829 39492883 + Low_complexity_G-rich chr1 39495007 39495037 + Low_complexity_G-rich chr1 39522775 39522826 + Low_complexity_G-rich chr1 39576441 39576547 + Low_complexity_G-rich chr1 39633022 39633077 + Low_complexity_G-rich chr1 39691218 39691248 + Low_complexity_G-rich chr1 39788263 39788317 + Low_complexity_G-rich chr1 39788923 39789000 + Low_complexity_G-rich chr1 39789020 39789070 + Low_complexity_G-rich chr1 39901704 39901738 + Low_complexity_G-rich chr1 39962645 39962683 + Low_complexity_G-rich chr1 40161427 40161479 + Low_complexity_G-rich chr1 40299750 40299809 + Low_complexity_G-rich chr1 40373648 40373703 + Low_complexity_G-rich chr1 40776546 40776592 + Low_complexity_G-rich chr1 40784271 40784302 + Low_complexity_G-rich chr1 40818985 40819030 + Low_complexity_G-rich chr1 40819127 40819179 + Low_complexity_G-rich chr1 40949438 40949480 + Low_complexity_G-rich chr1 40980273 40980323 + Low_complexity_G-rich chr1 41222151 41222212 + Low_complexity_G-rich chr1 41262027 41262037 + Low_complexity_G-rich chr1 41262249 41262275 + Low_complexity_G-rich chr1 41286551 41286595 + Low_complexity_G-rich chr1 41382191 41382239 + Low_complexity_G-rich chr1 41405371 41405418 + Low_complexity_G-rich chr1 41488440 41488493 + Low_complexity_G-rich chr1 41501745 41501789 + Low_complexity_G-rich chr1 41509624 41509675 + Low_complexity_G-rich chr1 41605130 41605226 + Low_complexity_G-rich chr1 41668127 41668171 + Low_complexity_G-rich chr1 42167707 42167778 + Low_complexity_G-rich chr1 42171716 42171750 + Low_complexity_G-rich chr1 42817424 42817469 + Low_complexity_G-rich chr1 43057261 43057300 + Low_complexity_G-rich chr1 43068386 43068429 + Low_complexity_G-rich chr1 43164570 43164603 + Low_complexity_G-rich chr1 43308350 43308424 + Low_complexity_G-rich chr1 43349055 43349122 + Low_complexity_G-rich chr1 43530684 43530738 + Low_complexity_G-rich chr1 43561218 43561248 + Low_complexity_G-rich chr1 43707772 43707848 + Low_complexity_G-rich chr1 43791919 43791981 + Low_complexity_G-rich chr1 43939232 43939367 + Low_complexity_G-rich chr1 43968350 43968393 + Low_complexity_G-rich chr1 43983817 43983875 + Low_complexity_G-rich chr1 44138102 44138170 + Low_complexity_G-rich chr1 44406210 44406259 + Low_complexity_G-rich chr1 44421625 44421654 + Low_complexity_G-rich chr1 44516616 44516670 + Low_complexity_G-rich chr1 44622602 44622640 + Low_complexity_G-rich chr1 44775763 44775817 + Low_complexity_G-rich chr1 44813962 44813992 + Low_complexity_G-rich chr1 44826775 44826810 + Low_complexity_G-rich chr1 44829190 44829230 + Low_complexity_G-rich chr1 44850427 44850467 + Low_complexity_G-rich chr1 46400178 46400227 + Low_complexity_G-rich chr1 46440143 46440335 + Low_complexity_G-rich chr1 46453232 46453350 + Low_complexity_G-rich chr1 46527588 46527674 + Low_complexity_G-rich chr1 46533437 46533504 + Low_complexity_G-rich chr1 46547677 46547713 + Low_complexity_G-rich chr1 46548552 46548638 + Low_complexity_G-rich chr1 46550523 46550568 + Low_complexity_G-rich chr1 46668451 46668493 + Low_complexity_G-rich chr1 47175072 47175107 + Low_complexity_G-rich chr1 47225567 47225600 + Low_complexity_G-rich chr1 47230473 47230499 + Low_complexity_G-rich chr1 47231965 47231998 + Low_complexity_G-rich chr1 47508214 47508309 + Low_complexity_G-rich chr1 47651879 47651914 + Low_complexity_G-rich chr1 47693187 47693227 + Low_complexity_G-rich chr1 47904478 47904519 + Low_complexity_G-rich chr1 47965192 47965250 + Low_complexity_G-rich chr1 48040051 48040108 + Low_complexity_G-rich chr1 48617030 48617094 + Low_complexity_G-rich chr1 50023967 50023987 + Low_complexity_G-rich chr1 50110357 50110400 + Low_complexity_G-rich chr1 50200500 50200543 + Low_complexity_G-rich chr1 50326230 50326277 + Low_complexity_G-rich chr1 50968529 50968662 + Low_complexity_G-rich chr1 50978056 50978101 + Low_complexity_G-rich chr1 51519284 51519341 + Low_complexity_G-rich chr1 51729791 51729831 + Low_complexity_G-rich chr1 51935711 51935844 + Low_complexity_G-rich chr1 51990620 51990657 + Low_complexity_G-rich chr1 52184582 52184620 + Low_complexity_G-rich chr1 52455636 52455701 + Low_complexity_G-rich chr1 52553102 52553219 + Low_complexity_G-rich chr1 53061936 53061992 + Low_complexity_G-rich chr1 53075993 53076034 + Low_complexity_G-rich chr1 53089970 53090024 + Low_complexity_G-rich chr1 53310180 53310224 + Low_complexity_G-rich chr1 53317152 53317181 + Low_complexity_G-rich chr1 53326264 53326308 + Low_complexity_G-rich chr1 53348614 53348650 + Low_complexity_G-rich chr1 53458002 53458062 + Low_complexity_G-rich chr1 53486735 53486782 + Low_complexity_G-rich chr1 53585556 53585591 + Low_complexity_G-rich chr1 53595097 53595132 + Low_complexity_G-rich chr1 53678311 53678362 + Low_complexity_G-rich chr1 53704549 53704592 + Low_complexity_G-rich chr1 53719656 53719699 + Low_complexity_G-rich chr1 53725054 53725089 + Low_complexity_G-rich chr1 54117713 54117753 + Low_complexity_G-rich chr1 54247008 54247042 + Low_complexity_G-rich chr1 54255173 54255189 + Low_complexity_G-rich chr1 54258243 54258295 + Low_complexity_G-rich chr1 54463719 54463753 + Low_complexity_G-rich chr1 54488682 54488726 + Low_complexity_G-rich chr1 54550285 54550319 + Low_complexity_G-rich chr1 54642047 54642076 + Low_complexity_G-rich chr1 54841007 54841043 + Low_complexity_G-rich chr1 54972867 54972915 + Low_complexity_G-rich chr1 55022190 55022228 + Low_complexity_G-rich chr1 55616049 55616082 + Low_complexity_G-rich chr1 56327336 56327555 + Low_complexity_G-rich chr1 56410563 56410620 + Low_complexity_G-rich chr1 56645567 56645637 + Low_complexity_G-rich chr1 56742042 56742085 + Low_complexity_G-rich chr1 56771607 56771649 + Low_complexity_G-rich chr1 57315433 57315464 + Low_complexity_G-rich chr1 57999797 58000072 + Low_complexity_G-rich chr1 58389764 58389798 + Low_complexity_G-rich chr1 58396106 58396142 + Low_complexity_G-rich chr1 58396772 58396782 + Low_complexity_G-rich chr1 58397093 58397157 + Low_complexity_G-rich chr1 58717595 58717627 + Low_complexity_G-rich chr1 58852332 58852397 + Low_complexity_G-rich chr1 59426363 59426396 + Low_complexity_G-rich chr1 59546415 59546441 + Low_complexity_G-rich chr1 59858718 59858853 + Low_complexity_G-rich chr1 60598309 60598804 + Low_complexity_G-rich chr1 60683160 60683205 + Low_complexity_G-rich chr1 61082849 61082923 + Low_complexity_G-rich chr1 61150865 61150903 + Low_complexity_G-rich chr1 61316225 61316258 + Low_complexity_G-rich chr1 61912650 61912736 + Low_complexity_G-rich chr1 62195023 62195074 + Low_complexity_G-rich chr1 62287046 62287091 + Low_complexity_G-rich chr1 63084434 63084477 + Low_complexity_G-rich chr1 63306216 63306364 + Low_complexity_G-rich chr1 63322195 63322231 + Low_complexity_G-rich chr1 63516318 63516399 + Low_complexity_G-rich chr1 64321275 64321307 + Low_complexity_G-rich chr1 64494020 64494052 + Low_complexity_G-rich chr1 64744885 64744930 + Low_complexity_G-rich chr1 65310202 65310263 + Low_complexity_G-rich chr1 65420513 65420554 + Low_complexity_G-rich chr1 66687797 66687840 + Low_complexity_G-rich chr1 67175107 67175146 + Low_complexity_G-rich chr1 67542662 67542691 + Low_complexity_G-rich chr1 67801913 67801958 + Low_complexity_G-rich chr1 68448842 68448910 + Low_complexity_G-rich chr1 70494368 70494403 + Low_complexity_G-rich chr1 72471242 72471410 + Low_complexity_G-rich chr1 73109106 73109142 + Low_complexity_G-rich chr1 73826864 73827023 + Low_complexity_G-rich chr1 74646859 74646875 + Low_complexity_G-rich chr1 74732595 74732668 + Low_complexity_G-rich chr1 75136980 75137025 + Low_complexity_G-rich chr1 76074604 76074720 + Low_complexity_G-rich chr1 76336773 76336810 + Low_complexity_G-rich chr1 76614874 76614923 + Low_complexity_G-rich chr1 77536620 77536666 + Low_complexity_G-rich chr1 77589816 77589850 + Low_complexity_G-rich chr1 79618629 79618677 + Low_complexity_G-rich chr1 80520936 80521123 + Low_complexity_G-rich chr1 83893846 83893875 + Low_complexity_G-rich chr1 84600887 84600920 + Low_complexity_G-rich chr1 84621094 84621143 + Low_complexity_G-rich chr1 84690818 84690870 + Low_complexity_G-rich chr1 84701859 84701889 + Low_complexity_G-rich chr1 84938727 84938764 + Low_complexity_G-rich chr1 85201785 85201837 + Low_complexity_G-rich chr1 85577104 85577156 + Low_complexity_G-rich chr1 86139164 86139246 + Low_complexity_G-rich chr1 86155970 86156016 + Low_complexity_G-rich chr1 86868551 86868588 + Low_complexity_G-rich chr1 87328818 87328847 + Low_complexity_G-rich chr1 87331556 87331595 + Low_complexity_G-rich chr1 87335560 87335597 + Low_complexity_G-rich chr1 87670097 87670127 + Low_complexity_G-rich chr1 88891121 88891159 + Low_complexity_G-rich chr1 89696463 89696521 + Low_complexity_G-rich chr1 90301064 90301098 + Low_complexity_G-rich chr1 90717314 90717356 + Low_complexity_G-rich chr1 90718250 90718292 + Low_complexity_G-rich chr1 90728736 90728793 + Low_complexity_G-rich chr1 91162223 91162274 + Low_complexity_G-rich chr1 91566256 91566292 + Low_complexity_G-rich chr1 91637959 91638000 + Low_complexity_G-rich chr1 91876098 91876127 + Low_complexity_G-rich chr1 94419001 94419036 + Low_complexity_G-rich chr1 94843845 94843889 + Low_complexity_G-rich chr1 96722436 96722479 + Low_complexity_G-rich chr1 97449355 97449457 + Low_complexity_G-rich chr1 99577163 99577209 + Low_complexity_G-rich chr1 101237990 101238030 + Low_complexity_G-rich chr1 104757999 104758289 + Low_complexity_G-rich chr1 105736341 105736509 + Low_complexity_G-rich chr1 107140412 107140450 + Low_complexity_G-rich chr1 107968304 107968352 + Low_complexity_G-rich chr1 108200192 108200244 + Low_complexity_G-rich chr1 108595255 108595594 + Low_complexity_G-rich chr1 108828624 108828657 + Low_complexity_G-rich chr1 109108575 109108610 + Low_complexity_G-rich chr1 109262464 109262520 + Low_complexity_G-rich chr1 109269300 109269333 + Low_complexity_G-rich chr1 109335350 109335378 + Low_complexity_G-rich chr1 109485229 109485287 + Low_complexity_G-rich chr1 109498241 109498268 + Low_complexity_G-rich chr1 109716386 109716426 + Low_complexity_G-rich chr1 109878988 109879030 + Low_complexity_G-rich chr1 109895452 109895492 + Low_complexity_G-rich chr1 109985178 109985220 + Low_complexity_G-rich chr1 110070686 110070776 + Low_complexity_G-rich chr1 110152583 110152611 + Low_complexity_G-rich chr1 110197961 110198031 + Low_complexity_G-rich chr1 110457242 110457277 + Low_complexity_G-rich chr1 110488404 110488438 + Low_complexity_G-rich chr1 110637577 110637613 + Low_complexity_G-rich chr1 110674722 110674802 + Low_complexity_G-rich chr1 110738490 110738528 + Low_complexity_G-rich chr1 111775351 111775391 + Low_complexity_G-rich chr1 111989950 111990014 + Low_complexity_G-rich chr1 111990232 111990262 + Low_complexity_G-rich chr1 113043990 113044061 + Low_complexity_G-rich chr1 113531711 113531752 + Low_complexity_G-rich chr1 113929782 113929832 + Low_complexity_G-rich chr1 114156882 114156922 + Low_complexity_G-rich chr1 114344616 114344652 + Low_complexity_G-rich chr1 115160024 115160123 + Low_complexity_G-rich chr1 115310843 115310925 + Low_complexity_G-rich chr1 116590576 116590614 + Low_complexity_G-rich chr1 116826141 116826178 + Low_complexity_G-rich chr1 116871649 116871691 + Low_complexity_G-rich chr1 117136704 117136732 + Low_complexity_G-rich chr1 117760555 117760582 + Low_complexity_G-rich chr1 117813508 117813566 + Low_complexity_G-rich chr1 117964564 117964597 + Low_complexity_G-rich chr1 118348142 118348162 + Low_complexity_G-rich chr1 118564621 118564652 + Low_complexity_G-rich chr1 119003362 119003413 + Low_complexity_G-rich chr1 119712665 119712700 + Low_complexity_G-rich chr1 120102815 120102857 + Low_complexity_G-rich chr1 120626103 120626152 + Low_complexity_G-rich chr1 121257958 121258003 + Low_complexity_G-rich chr1 143320107 143323677 + Low_complexity_G-rich chr1 143353735 143353824 + Low_complexity_G-rich chr1 143659465 143659531 + Low_complexity_G-rich chr1 144083755 144083786 + Low_complexity_G-rich chr1 144145471 144149057 + Low_complexity_G-rich chr1 144179320 144179409 + Low_complexity_G-rich chr1 144412465 144412510 + Low_complexity_G-rich chr1 144652338 144652378 + Low_complexity_G-rich chr1 144783197 144783280 + Low_complexity_G-rich chr1 144882690 144882723 + Low_complexity_G-rich chr1 144884037 144884079 + Low_complexity_G-rich chr1 144973427 144973463 + Low_complexity_G-rich chr1 145074152 145074186 + Low_complexity_G-rich chr1 145454408 145454476 + Low_complexity_G-rich chr1 146263806 146263848 + Low_complexity_G-rich chr1 146376697 146376741 + Low_complexity_G-rich chr1 146389349 146389386 + Low_complexity_G-rich chr1 146489312 146489355 + Low_complexity_G-rich chr1 146670426 146670453 + Low_complexity_G-rich chr1 147523504 147523554 + Low_complexity_G-rich chr1 147541889 147542004 + Low_complexity_G-rich chr1 148278390 148278420 + Low_complexity_G-rich chr1 148300844 148300909 + Low_complexity_G-rich chr1 148522481 148522534 + Low_complexity_G-rich chr1 148714589 148714631 + Low_complexity_G-rich chr1 148890239 148890276 + Low_complexity_G-rich chr1 149246122 149246171 + Low_complexity_G-rich chr1 149914073 149914116 + Low_complexity_G-rich chr1 149996259 149996297 + Low_complexity_G-rich chr1 150808375 150808408 + Low_complexity_G-rich chr1 150965457 150965494 + Low_complexity_G-rich chr1 151071222 151071262 + Low_complexity_G-rich chr1 151132749 151132793 + Low_complexity_G-rich chr1 151254877 151254915 + Low_complexity_G-rich chr1 151406270 151406311 + Low_complexity_G-rich chr1 151428197 151428260 + Low_complexity_G-rich chr1 151709964 151709997 + Low_complexity_G-rich chr1 151710464 151710539 + Low_complexity_G-rich chr1 151721457 151721514 + Low_complexity_G-rich chr1 151728473 151728506 + Low_complexity_G-rich chr1 151824285 151824316 + Low_complexity_G-rich chr1 151831807 151831861 + Low_complexity_G-rich chr1 151847683 151847727 + Low_complexity_G-rich chr1 151861355 151861407 + Low_complexity_G-rich chr1 152628989 152629023 + Low_complexity_G-rich chr1 152897048 152897082 + Low_complexity_G-rich chr1 152918737 152918783 + Low_complexity_G-rich chr1 153149111 153149146 + Low_complexity_G-rich chr1 153367192 153367241 + Low_complexity_G-rich chr1 153467247 153467278 + Low_complexity_G-rich chr1 153777580 153777614 + Low_complexity_G-rich chr1 153806273 153806303 + Low_complexity_G-rich chr1 153891630 153891682 + Low_complexity_G-rich chr1 153922834 153922892 + Low_complexity_G-rich chr1 153948042 153948079 + Low_complexity_G-rich chr1 154220842 154220916 + Low_complexity_G-rich chr1 154428807 154428838 + Low_complexity_G-rich chr1 154729133 154729176 + Low_complexity_G-rich chr1 154970969 154971019 + Low_complexity_G-rich chr1 154992784 154992819 + Low_complexity_G-rich chr1 155002310 155002409 + Low_complexity_G-rich chr1 155004065 155004115 + Low_complexity_G-rich chr1 155013677 155013721 + Low_complexity_G-rich chr1 155051198 155051301 + Low_complexity_G-rich chr1 155127801 155127843 + Low_complexity_G-rich chr1 155182167 155182201 + Low_complexity_G-rich chr1 155209171 155209207 + Low_complexity_G-rich chr1 155255459 155255491 + Low_complexity_G-rich chr1 155284247 155284287 + Low_complexity_G-rich chr1 156070721 156070760 + Low_complexity_G-rich chr1 156149983 156150028 + Low_complexity_G-rich chr1 156216586 156216630 + Low_complexity_G-rich chr1 156390880 156390920 + Low_complexity_G-rich chr1 156500883 156500931 + Low_complexity_G-rich chr1 156501041 156501087 + Low_complexity_G-rich chr1 156591735 156591867 + Low_complexity_G-rich chr1 156642033 156642075 + Low_complexity_G-rich chr1 156684889 156684940 + Low_complexity_G-rich chr1 156690111 156690150 + Low_complexity_G-rich chr1 156708337 156708401 + Low_complexity_G-rich chr1 156840109 156840165 + Low_complexity_G-rich chr1 156907851 156907891 + Low_complexity_G-rich chr1 157045603 157045640 + 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Low_complexity_G-rich chr1 161141130 161141170 + Low_complexity_G-rich chr1 161197366 161197431 + Low_complexity_G-rich chr1 161218788 161218830 + Low_complexity_G-rich chr1 161305571 161305601 + Low_complexity_G-rich chr1 161555913 161555950 + Low_complexity_G-rich chr1 161722286 161722323 + Low_complexity_G-rich chr1 161776452 161776515 + Low_complexity_G-rich chr1 162357361 162357387 + Low_complexity_G-rich chr1 162416464 162416503 + Low_complexity_G-rich chr1 164559329 164559359 + Low_complexity_G-rich chr1 165221594 165221636 + Low_complexity_G-rich chr1 165336190 165336223 + Low_complexity_G-rich chr1 165439256 165439339 + Low_complexity_G-rich chr1 165580195 165580305 + Low_complexity_G-rich chr1 165622754 165622784 + Low_complexity_G-rich chr1 165698592 165698636 + Low_complexity_G-rich chr1 165827486 165827523 + Low_complexity_G-rich chr1 166839901 166839945 + Low_complexity_G-rich chr1 167898691 167898749 + Low_complexity_G-rich chr1 168376332 168376367 + 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Background letter frequencies (from uniform background): A 0.25000 C 0.25000 G 0.25000 T 0.25000 MOTIF AC0001:GATA/PROP:GATA AC0001:GATA/PROP:GATA letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.676859 0.103120 0.104093 0.115927 0.023893 0.035573 0.009097 0.931437 0.934550 0.021763 0.019600 0.024087 0.917327 0.041030 0.018193 0.023450 0.038483 0.317794 0.210558 0.433165 0.190786 0.100360 0.553218 0.155636 0.303647 0.276917 0.157037 0.262400 0.193229 0.356892 0.190209 0.259669 0.486882 0.219741 0.239644 0.053733 0.021013 0.020403 0.043023 0.915561 0.026120 0.019233 0.026153 0.928494 0.917833 0.008397 0.043430 0.030340 0.112230 0.107057 0.106463 0.674251 MOTIF AC0002:PROP/ALX:Homeodomain AC0002:PROP/ALX:Homeodomain letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.070020 0.039968 0.011852 0.878160 0.737015 0.006383 0.210664 0.045937 0.991269 0.002983 0.002646 0.003102 0.012670 0.044178 0.013890 0.929262 0.024947 0.111357 0.062462 0.801234 0.491825 0.106176 0.245316 0.156683 0.388738 0.163204 0.414210 0.033848 0.696075 0.035808 0.214818 0.053300 0.005811 0.009027 0.005716 0.979446 0.039457 0.104083 0.010127 0.846333 0.813842 0.023174 0.061061 0.101923 MOTIF AC0003:HNF1A/HNF1B:Homeodomain AC0003:HNF1A/HNF1B:Homeodomain letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.043390 0.101613 0.085711 0.769286 0.121847 0.146660 0.046618 0.684875 0.949010 0.011809 0.030862 0.008319 0.842458 0.063088 0.020894 0.073560 0.098126 0.017778 0.012976 0.871120 0.272067 0.213304 0.348447 0.166182 0.828818 0.002082 0.032306 0.136794 0.050111 0.018913 0.059806 0.871170 0.008976 0.027477 0.001556 0.961991 0.799415 0.018407 0.092076 0.090102 0.858469 0.055420 0.065752 0.020359 0.051574 0.853694 0.024065 0.070667 MOTIF AC0004:ZSCAN:C2H2_ZF AC0004:ZSCAN:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.045050 0.830650 0.027755 0.096545 0.946450 0.017965 0.023565 0.012020 0.003235 0.000000 0.066100 0.930665 0.037160 0.015575 0.947150 0.000115 0.000395 0.026730 0.000500 0.972375 0.000640 0.000060 0.998795 0.000505 0.027480 0.972465 0.000000 0.000055 0.000000 0.000290 0.000000 0.999710 0.981575 0.017090 0.000055 0.001280 0.990570 0.000000 0.009370 0.000060 0.000115 0.060000 0.000000 0.939885 0.009440 0.048450 0.002060 0.940050 0.935975 0.013145 0.050880 0.000000 0.000445 0.994320 0.000000 0.005235 MOTIF AC0005:POU3F/POU1F:Homeodomain,POU AC0005:POU3F/POU1F:Homeodomain,POU letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.028714 0.009164 0.001321 0.960801 0.989711 0.001851 0.005834 0.002604 0.997031 0.001043 0.001073 0.000853 0.000439 0.010054 0.002120 0.987387 0.014909 0.040320 0.779626 0.165145 0.932318 0.005882 0.032534 0.029266 0.010606 0.100105 0.504943 0.384346 0.966061 0.006411 0.017753 0.009775 0.001870 0.003164 0.003369 0.991597 0.022515 0.002735 0.919093 0.055657 0.004801 0.944495 0.012714 0.037990 MOTIF AC0006:MEOX:Homeodomain AC0006:MEOX:Homeodomain letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.043350 0.027290 0.014150 0.915211 0.807730 0.003370 0.158280 0.030620 0.982520 0.003860 0.003740 0.009880 0.006090 0.019110 0.001430 0.973370 0.011510 0.001450 0.788510 0.198530 0.984070 0.002290 0.012070 0.001570 0.000850 0.006300 0.004970 0.987880 0.142141 0.011030 0.817538 0.029290 0.983820 0.007850 0.003260 0.005070 0.003980 0.009160 0.003980 0.982880 0.037890 0.014990 0.407900 0.539220 0.809812 0.011530 0.133049 0.045610 MOTIF AC0007:BARX/NKX:Homeodomain AC0007:BARX/NKX:Homeodomain letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.145858 0.018726 0.028750 0.806666 0.876838 0.012454 0.095078 0.015630 0.993778 0.001590 0.002484 0.002148 0.023046 0.192731 0.056790 0.727433 0.049498 0.165006 0.490491 0.295005 0.326739 0.052214 0.527317 0.093730 0.103697 0.160891 0.193445 0.541967 0.228792 0.067248 0.046560 0.657401 0.411065 0.022434 0.028780 0.537721 0.320795 0.085868 0.082252 0.511086 0.233325 0.167171 0.157287 0.442217 0.184934 0.172452 0.161226 0.481387 0.462162 0.172134 0.206680 0.159024 0.544199 0.150360 0.153610 0.151830 MOTIF AC0008:VENTX:Homeodomain AC0008:VENTX:Homeodomain letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.005550 0.000910 0.002040 0.991500 0.989340 0.002680 0.006980 0.001000 0.985940 0.006400 0.006590 0.001070 0.000970 0.002560 0.004610 0.991860 0.005800 0.504525 0.373766 0.115909 0.037120 0.041020 0.904819 0.017040 0.288787 0.067159 0.622404 0.021650 0.709157 0.002540 0.012970 0.275333 0.885691 0.002650 0.003100 0.108559 0.879121 0.017480 0.002900 0.100499 0.597340 0.231000 0.153650 0.018010 0.005560 0.988270 0.003250 0.002920 0.065780 0.059470 0.873630 0.001120 0.989600 0.007100 0.000750 0.002550 0.000910 0.000460 0.001540 0.997090 0.000660 0.001650 0.000660 0.997030 0.990370 0.000820 0.001450 0.007360 MOTIF AC0009:PAX/VSX:Homeodomain AC0009:PAX/VSX:Homeodomain letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.113577 0.102567 0.041327 0.742529 0.717911 0.065410 0.145784 0.070895 0.881651 0.032571 0.035830 0.049948 0.064193 0.091716 0.089219 0.754872 0.128381 0.115381 0.228460 0.527778 0.432561 0.100562 0.334065 0.132812 MOTIF AC0010:DLX/LHX:Homeodomain AC0010:DLX/LHX:Homeodomain letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.026034 0.054165 0.010981 0.908820 0.879332 0.056719 0.043934 0.020015 0.989605 0.002987 0.003573 0.003835 0.012267 0.013605 0.015924 0.958204 0.024895 0.058160 0.147516 0.769429 0.580228 0.016996 0.369075 0.033701 MOTIF AC0011:POU6F/NKX:Homeodomain AC0011:POU6F/NKX:Homeodomain letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.044177 0.072814 0.012083 0.870926 0.876841 0.036015 0.069213 0.017931 0.968518 0.002620 0.018381 0.010481 0.007593 0.074367 0.032635 0.885405 0.044785 0.050171 0.724959 0.180085 0.721706 0.033125 0.184251 0.060918 0.033585 0.182298 0.594503 0.189614 MOTIF AC0012:SATB:['Homeo_domain_factors'] AC0012:SATB:['Homeo_domain_factors'] letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.007660 0.967800 0.004090 0.020450 0.014380 0.005700 0.001080 0.978840 0.983810 0.004790 0.006460 0.004940 0.955700 0.003360 0.003510 0.037430 0.008420 0.003740 0.003590 0.984250 0.935280 0.014170 0.025980 0.024570 0.895869 0.025740 0.031290 0.047100 MOTIF AC0013:SIX:Homeodomain AC0013:SIX:Homeodomain letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.072298 0.348402 0.082952 0.496348 0.097836 0.052625 0.777248 0.072291 0.832046 0.046690 0.054733 0.066531 0.017476 0.119738 0.037424 0.825362 0.789445 0.149143 0.046696 0.014716 0.038211 0.608273 0.144775 0.208741 MOTIF AC0014:BATF/FOSL:bZIP AC0014:BATF/FOSL:bZIP letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.697226 0.095478 0.166726 0.040570 0.005532 0.181452 0.043140 0.769876 0.024542 0.080414 0.868788 0.026256 0.765018 0.078694 0.079220 0.077068 0.006342 0.754758 0.005466 0.233434 0.781634 0.018140 0.190598 0.009628 0.031990 0.848256 0.054260 0.065494 0.258706 0.097074 0.504834 0.139386 MOTIF AC0015:RUNX:Runt AC0015:RUNX:Runt letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.645073 0.088063 0.175039 0.091825 0.885905 0.045588 0.061582 0.006925 0.005321 0.986259 0.004212 0.004208 0.006229 0.986071 0.005244 0.002456 0.552380 0.011252 0.370584 0.065784 0.011736 0.968522 0.008086 0.011656 0.898678 0.032965 0.025265 0.043092 0.524085 0.107529 0.256301 0.112085 MOTIF AC0016:KLF:C2H2_ZF AC0016:KLF:C2H2_ZF letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.582700 0.005630 0.409880 0.001790 0.007740 0.718590 0.272640 0.001030 0.000250 0.997760 0.000870 0.001120 0.024350 0.005560 0.970090 0.000000 0.009880 0.985270 0.000740 0.004110 0.977890 0.013520 0.000490 0.008100 0.243670 0.028620 0.724690 0.003020 0.001240 0.997770 0.000990 0.000000 0.000190 0.999000 0.000750 0.000060 0.001730 0.990430 0.001360 0.006480 MOTIF AC0017:RUNX:Runt AC0017:RUNX:Runt letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.906356 0.037622 0.049465 0.006557 0.002547 0.993754 0.002282 0.001417 0.006730 0.989045 0.002402 0.001823 0.428941 0.003792 0.550169 0.017098 0.002400 0.990820 0.002683 0.004097 0.741622 0.031832 0.194863 0.031683 0.635882 0.048562 0.182679 0.132877 0.694597 0.034155 0.052443 0.218805 0.845604 0.022340 0.053343 0.078713 0.912793 0.033800 0.047287 0.006120 0.009920 0.974112 0.010063 0.005905 0.006713 0.982592 0.007925 0.002770 0.459768 0.012542 0.505682 0.022008 0.011010 0.961213 0.006610 0.021167 0.693738 0.030830 0.240570 0.034862 MOTIF AC0018:ZBTB:C2H2_ZF AC0018:ZBTB:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.614884 0.067089 0.289857 0.028170 0.024970 0.904570 0.037560 0.032900 0.010570 0.679187 0.303863 0.006380 0.241220 0.030370 0.722060 0.006350 0.129170 0.565680 0.147120 0.158030 0.285190 0.115760 0.541930 0.057120 0.304580 0.321730 0.130280 0.243410 0.164360 0.183310 0.091910 0.560420 0.139579 0.562054 0.041520 0.256847 0.731000 0.030160 0.224970 0.013870 0.028630 0.928431 0.021230 0.021710 0.017780 0.875110 0.084090 0.023020 0.230990 0.023120 0.729260 0.016630 0.080760 0.639310 0.103400 0.176530 MOTIF AC0019:AIRE:SAND AC0019:AIRE:SAND letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.363640 0.159090 0.022730 0.454540 0.568180 0.181820 0.181820 0.068180 0.431820 0.250000 0.090910 0.227270 0.022730 0.977270 0.000000 0.000000 0.090910 0.886360 0.022730 0.000000 0.568186 0.068181 0.136361 0.227272 0.500000 0.068180 0.045460 0.386360 0.250000 0.136360 0.090910 0.522730 0.454545 0.136361 0.068181 0.340913 0.363640 0.136360 0.136360 0.363640 0.500000 0.022730 0.090910 0.386360 0.613634 0.022730 0.045460 0.318177 0.181820 0.818180 0.000000 0.000000 0.068180 0.886360 0.000000 0.045460 0.522730 0.204550 0.136360 0.136360 MOTIF AC0020:CENPB:CENPB AC0020:CENPB:CENPB letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.097829 0.796912 0.000430 0.104829 0.017160 0.957440 0.000690 0.024710 0.000360 0.996960 0.000180 0.002500 0.028140 0.030250 0.907460 0.034150 0.001250 0.582956 0.148661 0.267133 0.395880 0.112720 0.205560 0.285840 0.047070 0.068620 0.000000 0.884310 0.305730 0.293550 0.207350 0.193370 0.231720 0.316670 0.265050 0.186560 0.388530 0.147310 0.136200 0.327960 0.536535 0.101019 0.275547 0.086899 0.047530 0.933890 0.001840 0.016740 0.133400 0.013000 0.853600 0.000000 0.686680 0.071620 0.007510 0.234190 0.910870 0.046690 0.009630 0.032810 MOTIF AC0021:ZNF:C2H2_ZF AC0021:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.066023 0.774787 0.098317 0.060873 0.073220 0.198787 0.073920 0.654073 0.056003 0.270588 0.153622 0.519787 0.515416 0.133017 0.242687 0.108880 0.494404 0.343831 0.141477 0.020287 0.018593 0.890933 0.029667 0.060807 0.030840 0.810109 0.074897 0.084153 0.014703 0.019350 0.010060 0.955887 0.001387 0.961007 0.016293 0.021313 0.003730 0.010017 0.021860 0.964393 0.000600 0.988013 0.008430 0.002957 0.004530 0.001443 0.006130 0.987897 0.002230 0.012317 0.980136 0.005317 0.409314 0.018593 0.291508 0.280585 0.096826 0.022227 0.712478 0.168469 MOTIF AC0022:ZNF:C2H2_ZF AC0022:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.016980 0.005270 0.967800 0.009950 0.002930 0.986530 0.000590 0.009950 0.000590 0.002930 0.000590 0.995890 0.024000 0.002930 0.956090 0.016980 0.087239 0.000590 0.909241 0.002930 0.009950 0.009950 0.970150 0.009950 0.998230 0.000590 0.000590 0.000590 0.007610 0.000590 0.988870 0.002930 0.021660 0.002930 0.972480 0.002930 0.000590 0.998230 0.000590 0.000590 0.000590 0.000590 0.000590 0.998230 0.965460 0.005270 0.019320 0.009950 MOTIF AC0023:ZNF:C2H2_ZF AC0023:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.170650 0.070210 0.652095 0.107045 0.216591 0.770159 0.010415 0.002835 0.004505 0.988430 0.006945 0.000120 0.000000 0.005880 0.002520 0.991600 0.000000 0.990780 0.005990 0.003230 0.816325 0.052395 0.104165 0.027115 0.136180 0.000000 0.847460 0.016360 0.006105 0.054516 0.003815 0.935564 0.026780 0.021585 0.073915 0.877721 0.016405 0.014980 0.067985 0.900630 0.538853 0.418022 0.030880 0.012245 0.019540 0.902705 0.031345 0.046410 0.096250 0.800649 0.067940 0.035160 MOTIF AC0024:ZNF:C2H2_ZF AC0024:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.002480 0.035675 0.008280 0.953565 0.004060 0.823640 0.005645 0.166655 0.126786 0.015035 0.857284 0.000895 0.914370 0.001950 0.079620 0.004060 0.019700 0.953695 0.021490 0.005115 0.000895 0.994145 0.001425 0.003535 0.000895 0.005015 0.001425 0.992665 0.001950 0.992565 0.003535 0.001950 0.041325 0.816190 0.004060 0.138425 MOTIF AC0025:ZNF:C2H2_ZF AC0025:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.100000 0.050000 0.750000 0.100000 0.050000 0.950000 0.000000 0.000000 0.000000 0.050000 0.000000 0.950000 0.000000 0.750000 0.000000 0.250000 0.350000 0.150000 0.500000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.950000 0.050000 0.000000 0.000000 0.800000 0.100000 0.100000 0.000000 0.150000 0.000000 0.850000 0.000000 0.900000 0.100000 0.000000 0.900000 0.000000 0.000000 0.100000 0.700000 0.050000 0.200000 0.050000 0.450000 0.000000 0.300000 0.250000 MOTIF AC0026:ZKSCAN:C2H2_ZF AC0026:ZKSCAN:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.021803 0.960837 0.010320 0.007040 0.075183 0.886670 0.009160 0.028987 0.006330 0.130263 0.026603 0.836804 0.965000 0.015333 0.010667 0.009000 0.023970 0.945453 0.018270 0.012307 0.018667 0.015753 0.005883 0.959697 0.567967 0.042470 0.374390 0.015173 0.145290 0.162660 0.083783 0.608268 0.113000 0.134333 0.625000 0.127667 MOTIF AC0027:ZNF:C2H2_ZF AC0027:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 18 nsites= 20 E= 0 0.000570 0.002860 0.000570 0.996000 0.002860 0.000570 0.002860 0.993710 0.018880 0.018880 0.012010 0.950230 0.929630 0.034900 0.016590 0.018880 0.934210 0.000570 0.037190 0.028030 0.039470 0.005150 0.927350 0.028030 0.030320 0.826660 0.053200 0.089820 0.021170 0.927341 0.005150 0.046340 0.016590 0.842680 0.000570 0.140160 0.890730 0.016590 0.069220 0.023460 0.012010 0.856410 0.028030 0.103550 0.032610 0.023460 0.014300 0.929630 0.071510 0.014300 0.881580 0.032610 0.000570 0.002860 0.000570 0.996000 0.002860 0.014300 0.002860 0.979980 0.039470 0.009720 0.945659 0.005150 0.984560 0.000570 0.012010 0.002860 0.000570 0.000570 0.998290 0.000570 MOTIF AC0028:KLF:C2H2_ZF AC0028:KLF:C2H2_ZF letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.181818 0.545455 0.090909 0.181818 0.181820 0.636360 0.090910 0.090910 0.090910 0.636360 0.090910 0.181820 0.818180 0.000000 0.181820 0.000000 0.000000 0.818180 0.090910 0.090910 0.181820 0.000000 0.181820 0.636360 0.090910 0.000000 0.727270 0.181820 MOTIF AC0029:ZNF:C2H2_ZF AC0029:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.018060 0.018060 0.876450 0.087430 0.685690 0.075870 0.217490 0.020950 0.055640 0.515170 0.084540 0.344650 0.778180 0.038300 0.133670 0.049850 0.000720 0.992060 0.000720 0.006500 0.408240 0.015170 0.171240 0.405350 0.009390 0.000720 0.986280 0.003610 0.003610 0.003610 0.983390 0.009390 0.217488 0.000720 0.737713 0.044080 0.041180 0.006500 0.942929 0.009390 0.000720 0.951590 0.009390 0.038300 0.341763 0.483385 0.026730 0.148121 0.604770 0.041180 0.220380 0.133670 0.015170 0.792630 0.061420 0.130780 MOTIF AC0030:ZNF:C2H2_ZF AC0030:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.750000 0.250000 0.000000 0.000000 1.000000 0.000000 0.375000 0.000000 0.500000 0.125000 0.000000 1.000000 0.000000 0.000000 MOTIF AC0031:POU3F/POU2F:Homeodomain,POU AC0031:POU3F/POU2F:Homeodomain,POU letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.147089 0.144498 0.083819 0.624593 0.914071 0.010013 0.048710 0.027206 0.019874 0.010260 0.010630 0.959236 0.056265 0.018255 0.879315 0.046165 0.023276 0.863034 0.014881 0.098809 0.106156 0.014468 0.869642 0.009734 0.045110 0.878095 0.033798 0.042997 0.897881 0.025381 0.062956 0.013782 0.045486 0.068322 0.012688 0.873504 0.666256 0.098159 0.117755 0.117830 MOTIF AC0032:SIX/ZNF:Homeodomain AC0032:SIX/ZNF:Homeodomain letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.031667 0.011828 0.929438 0.027067 0.394814 0.065697 0.051620 0.487869 0.877375 0.056165 0.010942 0.055518 0.978477 0.006873 0.006965 0.007685 0.035823 0.664605 0.040837 0.258735 0.189248 0.532475 0.085762 0.192516 0.066195 0.206785 0.063108 0.663912 0.104097 0.015478 0.831537 0.048888 0.977988 0.004795 0.007525 0.009692 0.071583 0.236792 0.220217 0.471408 0.595963 0.293457 0.049375 0.061205 MOTIF AC0033:PRDM:C2H2_ZF AC0033:PRDM:C2H2_ZF letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.052515 0.078510 0.845470 0.023505 0.020000 0.049500 0.823000 0.107500 0.133500 0.023000 0.144500 0.699000 0.014000 0.881000 0.042000 0.063000 0.051030 0.320635 0.005500 0.622835 0.001500 0.844419 0.070540 0.083540 0.004500 0.003500 0.002500 0.989500 0.982500 0.005500 0.005500 0.006500 0.767500 0.037000 0.022500 0.173000 0.060500 0.649500 0.191000 0.099000 MOTIF AC0034:NR2F/NR1H:Nuclear_receptor AC0034:NR2F/NR1H:Nuclear_receptor letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.628943 0.047025 0.305531 0.018501 0.022237 0.011416 0.910878 0.055469 0.022392 0.009595 0.900393 0.067620 0.017057 0.059634 0.071608 0.851701 0.004342 0.885228 0.035261 0.075169 0.742190 0.029232 0.211198 0.017380 0.220952 0.242260 0.232604 0.304185 0.008491 0.247528 0.020918 0.723063 0.064397 0.058760 0.868128 0.008715 0.865927 0.088271 0.034285 0.011517 0.059208 0.903734 0.005700 0.031358 0.053648 0.911515 0.012414 0.022423 0.011024 0.343516 0.019563 0.625897 MOTIF AC0035:ZNF:C2H2_ZF AC0035:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.732960 0.070155 0.106332 0.090553 0.126736 0.022032 0.773058 0.078174 0.017388 0.005237 0.898980 0.078395 0.159017 0.131455 0.671469 0.038058 0.058162 0.926671 0.006737 0.008430 0.714000 0.036893 0.192107 0.057000 0.001738 0.946879 0.043145 0.008238 0.087230 0.065510 0.099278 0.747981 0.784860 0.037753 0.125702 0.051685 0.938125 0.029810 0.014040 0.018025 0.005630 0.268332 0.012578 0.713460 0.064525 0.641896 0.061435 0.232144 0.124620 0.594141 0.117918 0.163320 0.139233 0.555924 0.094438 0.210405 0.456350 0.194275 0.175200 0.174175 MOTIF AC0036:BCL11A/BCL11B:C2H2_ZF AC0036:BCL11A/BCL11B:C2H2_ZF letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.053297 0.149155 0.066050 0.731498 0.017350 0.038497 0.922961 0.021192 0.470449 0.057718 0.083238 0.388594 0.017668 0.946560 0.017617 0.018155 0.009890 0.955528 0.032777 0.001805 0.544708 0.139100 0.225875 0.090317 MOTIF AC0037:ZNF:C2H2_ZF AC0037:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.001560 0.314063 0.589066 0.095311 0.082811 0.489065 0.426564 0.001560 0.182812 0.389064 0.370314 0.057811 0.001560 0.764068 0.232812 0.001560 0.257813 0.001560 0.001560 0.739067 0.001560 0.001560 0.995320 0.001560 0.001560 0.001560 0.995320 0.001560 0.201562 0.282813 0.445314 0.070311 0.001560 0.995320 0.001560 0.001560 0.620316 0.170312 0.082811 0.126561 0.001560 0.207812 0.770318 0.020310 MOTIF AC0038:THAP:['C2CH_THAP-type_zinc_finger_factors'] AC0038:THAP:['C2CH_THAP-type_zinc_finger_factors'] letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.092120 0.186560 0.615300 0.106020 0.106799 0.647154 0.180188 0.065859 0.913679 0.042290 0.042490 0.001540 0.004250 0.024530 0.957510 0.013710 0.007730 0.035340 0.944960 0.011970 0.017570 0.012940 0.954430 0.015060 0.018930 0.938780 0.020080 0.022210 0.946310 0.018350 0.033220 0.002120 MOTIF AC0039:TFAP2A/TFAP2C:AP-2 AC0039:TFAP2A/TFAP2C:AP-2 letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.000000 0.206338 0.793662 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.030703 0.785807 0.056131 0.127359 0.047290 0.371259 0.140534 0.440917 0.131595 0.328376 0.327360 0.212669 0.411594 0.173465 0.360890 0.054051 0.172746 0.055890 0.742455 0.028910 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.811913 0.188087 0.000000 MOTIF AC0040:ZNF:C2H2_ZF AC0040:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.000740 0.218010 0.176340 0.604910 0.012650 0.935270 0.018600 0.033480 0.003720 0.991820 0.000740 0.003720 0.087051 0.628726 0.015620 0.268603 0.048360 0.712050 0.045390 0.194200 0.447174 0.500745 0.012650 0.039430 0.372770 0.057290 0.551340 0.018600 0.578120 0.006700 0.381700 0.033480 0.241820 0.000740 0.756700 0.000740 0.000740 0.015620 0.167412 0.816228 0.000740 0.997780 0.000740 0.000740 0.527530 0.209080 0.063240 0.200150 0.006700 0.069200 0.015620 0.908480 0.009670 0.962060 0.012650 0.015620 MOTIF AC0041:ZNF:C2H2_ZF AC0041:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 20 nsites= 20 E= 0 0.209644 0.404872 0.164574 0.220911 0.136461 0.612925 0.154981 0.095633 0.094157 0.621823 0.122837 0.161183 0.363387 0.363637 0.175713 0.097263 0.059577 0.431308 0.186876 0.322239 0.204661 0.645532 0.073877 0.075930 0.460203 0.081393 0.398340 0.060063 0.058147 0.014660 0.833430 0.093763 0.096997 0.015953 0.734677 0.152373 0.140840 0.146377 0.596711 0.116073 0.005400 0.967513 0.019680 0.007407 0.013430 0.866883 0.026220 0.093467 0.145190 0.373902 0.246739 0.234169 0.258001 0.172751 0.433901 0.135347 0.033737 0.010420 0.939400 0.016443 0.743360 0.006403 0.160500 0.089737 0.100010 0.030500 0.836983 0.032507 0.006403 0.915031 0.053353 0.025213 0.019680 0.842294 0.020487 0.117540 0.269783 0.146177 0.120047 0.463993 MOTIF AC0042:ZSCAN:C2H2_ZF AC0042:ZSCAN:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.134190 0.744490 0.119480 0.001840 0.045960 0.950360 0.001840 0.001840 0.443010 0.053310 0.075370 0.428310 0.009190 0.001840 0.987130 0.001840 0.001840 0.619486 0.318013 0.060661 0.531250 0.023900 0.443010 0.001840 0.031250 0.001840 0.965070 0.001840 0.001840 0.876831 0.045960 0.075369 0.001840 0.979780 0.009190 0.009190 0.354780 0.251840 0.053310 0.340070 0.031250 0.273900 0.332720 0.362130 0.023900 0.597424 0.325367 0.053309 MOTIF AC0043:TFAP2A/TFAP2C:AP-2 AC0043:TFAP2A/TFAP2C:AP-2 letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.033038 0.285696 0.654657 0.026609 0.004021 0.956888 0.032205 0.006886 0.005208 0.917354 0.008923 0.068515 0.030976 0.315453 0.109384 0.544187 0.110623 0.356065 0.380872 0.152441 0.543818 0.132279 0.290526 0.033377 0.130525 0.027752 0.831336 0.010387 0.010234 0.019082 0.962713 0.007971 0.025955 0.550833 0.400606 0.022606 MOTIF AC0044:ZNF:C2H2_ZF AC0044:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 19 nsites= 20 E= 0 0.025000 0.748810 0.139290 0.086900 0.201190 0.520240 0.067860 0.210710 0.067860 0.205950 0.272620 0.453570 0.010710 0.977390 0.001190 0.010710 0.029760 0.396434 0.086901 0.486905 0.029760 0.358334 0.425004 0.186902 0.001190 0.982140 0.001190 0.015480 0.596430 0.158330 0.177380 0.067860 0.010710 0.810718 0.144051 0.034520 0.510715 0.348813 0.029760 0.110711 0.144050 0.158330 0.382140 0.315480 0.096430 0.091670 0.810710 0.001190 0.001190 0.958330 0.039290 0.001190 0.010710 0.982150 0.001190 0.005950 0.082140 0.225000 0.439290 0.253570 0.044050 0.239290 0.386900 0.329760 0.148811 0.482145 0.158332 0.210712 0.482145 0.110711 0.320243 0.086901 0.010710 0.215480 0.629760 0.144050 MOTIF AC0045:ZNF:C2H2_ZF AC0045:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.032295 0.701769 0.099280 0.166656 0.103676 0.049330 0.030060 0.816934 0.044710 0.035070 0.514450 0.405770 0.027055 0.839595 0.068990 0.064360 0.001000 0.815075 0.030295 0.153630 0.038310 0.900565 0.004010 0.057115 0.020040 0.035690 0.008015 0.936255 0.030060 0.006010 0.942270 0.021660 0.245746 0.055110 0.646038 0.053105 0.074380 0.051100 0.810774 0.063745 0.745640 0.159170 0.043085 0.052105 0.078005 0.392895 0.209205 0.319895 0.159490 0.151385 0.057115 0.632010 0.099670 0.162260 0.062125 0.675945 MOTIF AC0046:EBF:['Rel_homology_region_(RHR)_factors'] AC0046:EBF:['Rel_homology_region_(RHR)_factors'] letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.036455 0.230159 0.048070 0.685316 0.010608 0.971035 0.006227 0.012130 0.010157 0.863773 0.005495 0.120575 0.009485 0.954753 0.006225 0.029537 0.254321 0.078717 0.033908 0.633055 0.163015 0.034272 0.471245 0.331468 0.018732 0.004407 0.970946 0.005915 0.092758 0.004183 0.890757 0.012302 0.030203 0.010752 0.952210 0.006835 0.706707 0.060207 0.188783 0.044303 MOTIF AC0047:ZNF:C2H2_ZF AC0047:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.041560 0.082480 0.013430 0.862530 0.049230 0.005750 0.885559 0.059461 0.031330 0.000640 0.941820 0.026210 0.033890 0.000640 0.946930 0.018540 0.036440 0.003200 0.952050 0.008310 0.008310 0.005750 0.985300 0.000640 0.000640 0.995520 0.003200 0.000640 0.000640 0.000640 0.000640 0.998080 0.102940 0.793480 0.031330 0.072250 0.322890 0.264070 0.374040 0.039000 0.039000 0.253840 0.220590 0.486570 0.212920 0.568410 0.087600 0.131070 0.105500 0.788360 0.059460 0.046680 MOTIF AC0048:ZNF:C2H2_ZF AC0048:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.023890 0.000580 0.974950 0.000580 0.988930 0.000580 0.009910 0.000580 0.256993 0.000580 0.741847 0.000580 0.014570 0.748837 0.002910 0.233682 0.026220 0.807110 0.079840 0.086830 0.044870 0.900350 0.026220 0.028560 0.000580 0.981940 0.000580 0.016900 0.709203 0.110139 0.126459 0.054199 0.930659 0.000580 0.065851 0.002910 0.065850 0.030890 0.902680 0.000580 MOTIF AC0049:PLAGL:C2H2_ZF AC0049:PLAGL:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.039410 0.039410 0.813890 0.107290 0.000025 0.000025 0.999925 0.000025 0.000025 0.151184 0.848766 0.000025 0.000025 0.799610 0.200340 0.000025 0.000025 0.999925 0.000025 0.000025 0.000025 0.999925 0.000025 0.000025 0.000025 0.925730 0.000025 0.074220 0.090464 0.685513 0.025480 0.198543 MOTIF AC0050:ZNF:C2H2_ZF AC0050:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 19 nsites= 20 E= 0 0.085135 0.092012 0.447119 0.375734 0.115043 0.057673 0.719831 0.107453 0.011100 0.010287 0.966328 0.012285 0.011905 0.008955 0.974782 0.004358 0.310660 0.026865 0.442327 0.220147 0.114850 0.584333 0.137045 0.163772 0.104593 0.778300 0.041705 0.075402 0.399442 0.411800 0.052692 0.136067 0.285845 0.039227 0.552746 0.122182 0.076105 0.009035 0.776538 0.138322 0.010262 0.000992 0.983734 0.005012 0.023338 0.003295 0.972917 0.000450 0.026787 0.017683 0.939490 0.016040 0.274689 0.711900 0.008578 0.004833 0.275941 0.581273 0.036913 0.105873 0.344147 0.057815 0.178110 0.419928 0.314622 0.084710 0.514820 0.085848 0.077035 0.159595 0.148433 0.614937 0.064430 0.082367 0.750696 0.102508 MOTIF AC0051:PLAG:C2H2_ZF AC0051:PLAG:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.000000 0.888890 0.000000 0.111110 0.111110 0.888890 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.111110 0.777780 0.000000 0.111110 0.000000 0.111110 0.277780 0.611110 0.333330 0.000000 0.000000 0.666670 0.166668 0.055559 0.555554 0.222218 0.111110 0.055560 0.833330 0.000000 0.000000 0.222220 0.777780 0.000000 0.055560 0.944440 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.055559 0.777772 0.000000 0.166668 0.000000 1.000000 0.000000 0.000000 MOTIF AC0052:ZNF:C2H2_ZF AC0052:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.606774 0.010870 0.348467 0.033890 0.023660 0.916231 0.046680 0.013430 0.056901 0.036440 0.008310 0.898349 0.085039 0.803702 0.023660 0.087599 0.995520 0.000640 0.003200 0.000640 0.036440 0.923919 0.010870 0.028770 0.880430 0.064580 0.010870 0.044120 0.013430 0.003200 0.931581 0.051789 0.110610 0.177110 0.576090 0.136190 0.062020 0.427750 0.128520 0.381710 0.033890 0.898340 0.041560 0.026210 0.044120 0.747447 0.026210 0.182222 MOTIF AC0053:ZNF:C2H2_ZF AC0053:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.086080 0.222375 0.090945 0.600600 0.733170 0.070920 0.122360 0.073550 0.065660 0.121145 0.726935 0.086260 0.004315 0.029485 0.958715 0.007485 0.000000 0.968985 0.002765 0.028250 0.087525 0.716354 0.078220 0.117901 0.006895 0.303088 0.030410 0.659607 0.726575 0.071900 0.129935 0.071590 MOTIF AC0054:ZFX/ZNF:C2H2_ZF AC0054:ZFX/ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.093080 0.367863 0.186949 0.352108 0.887425 0.001520 0.087000 0.024055 0.003555 0.001520 0.992390 0.002535 0.003555 0.013590 0.980320 0.002535 0.001520 0.976445 0.020035 0.002000 0.005075 0.984760 0.001520 0.008645 MOTIF AC0055:ZNF:C2H2_ZF AC0055:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.002610 0.987990 0.002610 0.006790 0.002610 0.992170 0.000520 0.004700 0.006790 0.013050 0.002610 0.977550 0.006790 0.960850 0.013050 0.019310 0.015140 0.482775 0.004700 0.497385 0.453550 0.004700 0.503650 0.038100 0.038100 0.000520 0.956680 0.004700 0.017220 0.002610 0.969210 0.010960 0.000520 0.960860 0.002610 0.036010 0.004700 0.975470 0.000520 0.019310 0.002610 0.000520 0.000520 0.996350 0.050630 0.004700 0.927450 0.017220 MOTIF AC0056:ZNF/SP:C2H2_ZF AC0056:ZNF/SP:C2H2_ZF letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.110040 0.854713 0.034840 0.000407 0.023358 0.210354 0.010273 0.756015 0.998779 0.000407 0.000407 0.000407 0.000407 0.019552 0.979634 0.000407 0.988162 0.007323 0.004108 0.000407 0.059545 0.009125 0.927068 0.004262 0.310676 0.505694 0.116555 0.067075 0.037672 0.016005 0.925993 0.020330 0.055477 0.109753 0.656232 0.178538 0.145333 0.780735 0.008932 0.065000 MOTIF AC0057:ZBTB/ZNF:C2H2_ZF AC0057:ZBTB/ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.159110 0.093173 0.659034 0.088683 0.031222 0.045132 0.819886 0.103760 0.002272 0.990063 0.002228 0.005437 0.006698 0.001862 0.003192 0.988248 0.000888 0.993724 0.004415 0.000973 0.202017 0.557293 0.014927 0.225762 0.848957 0.023433 0.102602 0.025008 0.008413 0.040188 0.609576 0.341823 0.120717 0.598812 0.225173 0.055298 0.449007 0.079462 0.260205 0.211327 0.065372 0.538169 0.060622 0.335837 MOTIF AC0058:ZNF:C2H2_ZF AC0058:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.000000 1.000000 0.000000 0.000000 0.956520 0.043480 0.000000 0.000000 0.000000 0.000000 0.347830 0.652170 0.000000 1.000000 0.000000 0.000000 0.000000 0.043480 0.000000 0.956520 0.000000 1.000000 0.000000 0.000000 0.086960 0.913040 0.000000 0.000000 0.869570 0.130430 0.000000 0.000000 0.173910 0.000000 0.826090 0.000000 0.217390 0.217390 0.565220 0.000000 MOTIF AC0059:YY/ZFP:C2H2_ZF AC0059:YY/ZFP:C2H2_ZF letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.766021 0.056720 0.112765 0.064494 0.756111 0.074685 0.057149 0.112055 0.385402 0.154513 0.355090 0.104995 0.991521 0.001201 0.005212 0.002066 0.003235 0.004218 0.004832 0.987715 0.001709 0.001186 0.996015 0.001090 0.006055 0.005792 0.963153 0.025000 0.048831 0.891032 0.010113 0.050024 0.045139 0.204675 0.566077 0.184109 0.085583 0.114257 0.710475 0.089685 MOTIF AC0060:YY/MZF:C2H2_ZF AC0060:YY/MZF:C2H2_ZF letter-probability matrix: alength= 4 w= 19 nsites= 20 E= 0 0.099753 0.794547 0.094047 0.011653 0.000000 0.999937 0.000000 0.000063 0.994590 0.004260 0.000607 0.000543 0.000063 0.000743 0.000063 0.999131 0.066897 0.276114 0.067117 0.589872 0.003757 0.000060 0.059227 0.936956 0.000000 0.000000 0.000870 0.999130 0.106944 0.084271 0.332645 0.476140 0.217224 0.098850 0.536045 0.147881 0.341733 0.106553 0.392123 0.159590 0.092090 0.525141 0.174319 0.208449 0.332291 0.167054 0.252831 0.247824 0.355708 0.151984 0.179214 0.313094 0.337418 0.147106 0.140382 0.375094 0.283088 0.162047 0.180727 0.374138 0.232598 0.326278 0.137653 0.303471 0.172510 0.088397 0.643180 0.095913 0.088600 0.088780 0.087567 0.735052 0.087154 0.088228 0.740748 0.083871 MOTIF AC0061:YY:C2H2_ZF AC0061:YY:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.166668 0.388886 0.277777 0.166668 0.333333 0.111111 0.222222 0.333333 0.055559 0.722213 0.055559 0.166668 0.000000 1.000000 0.000000 0.000000 0.944440 0.055560 0.000000 0.000000 0.000000 0.000000 0.111110 0.888890 0.277777 0.166668 0.166668 0.388886 0.055560 0.111110 0.111110 0.722220 0.333330 0.111110 0.055560 0.500000 0.166670 0.222220 0.277780 0.333330 0.222222 0.222222 0.333333 0.222222 0.333333 0.222222 0.333333 0.111111 MOTIF AC0062:ZNF:C2H2_ZF AC0062:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.038880 0.004158 0.943536 0.013426 0.725979 0.152668 0.114786 0.006566 0.853519 0.022492 0.118870 0.005118 0.841600 0.022048 0.119670 0.016682 0.005118 0.053998 0.012648 0.928236 0.007494 0.003526 0.894546 0.094434 0.353578 0.106674 0.481624 0.058124 0.005444 0.988178 0.001884 0.004494 0.005356 0.074644 0.010558 0.909442 0.002210 0.003074 0.004152 0.990564 0.001132 0.941894 0.002654 0.054320 0.003952 0.961024 0.002644 0.032380 MOTIF AC0063:ZNF:C2H2_ZF AC0063:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.141089 0.002480 0.754942 0.101489 0.002480 0.002480 0.992560 0.002480 0.002480 0.002480 0.992560 0.002480 0.507425 0.358906 0.131189 0.002480 0.586630 0.061880 0.141090 0.210400 0.002480 0.002480 0.992560 0.002480 0.150990 0.695540 0.150990 0.002480 0.071780 0.358910 0.002480 0.566830 0.992560 0.002480 0.002480 0.002480 MOTIF AC0064:RFX/MXI:RFX AC0064:RFX/MXI:RFX letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.080687 0.058286 0.805908 0.055119 0.054742 0.061243 0.064385 0.819630 0.141871 0.143893 0.046297 0.667939 0.247433 0.068647 0.560874 0.123046 0.034873 0.759717 0.044107 0.161303 0.029694 0.721891 0.033973 0.214441 0.801258 0.057088 0.080305 0.061349 0.052831 0.067682 0.034017 0.845470 0.235153 0.011095 0.751067 0.002685 0.154427 0.023225 0.817753 0.004595 0.110555 0.581837 0.039573 0.268035 0.762539 0.008094 0.129650 0.099717 0.935943 0.020490 0.030121 0.013446 0.011621 0.940767 0.011340 0.036272 MOTIF AC0065:RFX:RFX AC0065:RFX:RFX letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.201247 0.104465 0.115477 0.578811 0.121827 0.122695 0.051948 0.703531 0.342169 0.031380 0.572063 0.054388 0.038352 0.409396 0.397814 0.154438 0.031405 0.475437 0.045963 0.447196 0.680876 0.020310 0.205052 0.093762 0.508028 0.108110 0.175870 0.207992 0.039567 0.452632 0.476648 0.031152 0.053513 0.449996 0.354776 0.141715 0.051717 0.072550 0.864778 0.010955 0.036410 0.033385 0.035570 0.894636 0.096323 0.153310 0.021057 0.729310 0.220837 0.025485 0.690586 0.063092 0.050147 0.625459 0.098795 0.225600 0.033883 0.438690 0.068343 0.459085 MOTIF AC0066:RFX:RFX AC0066:RFX:RFX letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.077943 0.554803 0.116111 0.251143 0.268122 0.047489 0.189588 0.494801 0.012683 0.005114 0.976036 0.006167 0.002204 0.025164 0.023030 0.949602 0.052967 0.123939 0.030949 0.792145 0.193381 0.031443 0.669615 0.105561 0.011083 0.934037 0.003290 0.051590 0.003741 0.431700 0.007940 0.556619 0.699944 0.008290 0.276025 0.015741 0.149200 0.017601 0.619136 0.214063 0.049603 0.042814 0.874086 0.033497 MOTIF AC0067:RFX:RFX AC0067:RFX:RFX letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.003671 0.000207 0.983939 0.012183 0.000509 0.004316 0.000854 0.994321 0.013730 0.034059 0.000940 0.951271 0.070891 0.026660 0.884608 0.017841 0.000090 0.998717 0.000719 0.000474 0.007880 0.212297 0.016790 0.763033 0.778689 0.036304 0.070736 0.114271 0.352396 0.049027 0.567116 0.031461 0.015027 0.005030 0.966433 0.013510 0.034231 0.836327 0.022013 0.107429 0.825962 0.010407 0.075043 0.088587 0.936905 0.033959 0.015283 0.013853 0.010704 0.938943 0.020346 0.030007 MOTIF AC0068:HIC:C2H2_ZF AC0068:HIC:C2H2_ZF letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.975340 0.024660 0.000000 0.975340 0.000000 0.024660 1.000000 0.000000 0.000000 0.000000 0.630140 0.345200 0.000000 0.024660 0.073970 0.926030 0.000000 0.000000 0.123289 0.753422 0.098629 0.024660 MOTIF AC0069:ZNF:C2H2_ZF AC0069:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 20 nsites= 20 E= 0 0.117980 0.272550 0.006925 0.602545 0.018580 0.000145 0.981015 0.000260 0.065270 0.928930 0.005545 0.000255 0.975510 0.001380 0.019195 0.003915 0.995285 0.000550 0.004165 0.000000 0.000105 0.999585 0.000205 0.000105 0.463410 0.287940 0.192715 0.055935 0.078065 0.430585 0.113450 0.377900 0.278515 0.208530 0.390085 0.122870 0.306875 0.215560 0.264320 0.213245 0.484817 0.058450 0.380952 0.075780 0.007715 0.909825 0.037590 0.044870 0.833940 0.061825 0.025575 0.078660 0.978310 0.004295 0.014090 0.003305 0.000055 0.999425 0.000310 0.000210 0.072090 0.295796 0.074350 0.557763 0.001450 0.005605 0.987625 0.005320 0.054960 0.103631 0.305917 0.535493 0.657328 0.106681 0.137506 0.098485 0.034375 0.851199 0.007255 0.107171 MOTIF AC0070:MYB/MYBL:Myb/SANT AC0070:MYB/MYBL:Myb/SANT letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.156335 0.505053 0.050835 0.287777 0.982360 0.015640 0.000250 0.001750 0.942250 0.010750 0.031250 0.015750 0.002500 0.996000 0.001250 0.000250 0.091840 0.193920 0.267770 0.446470 0.063250 0.024473 0.836805 0.075472 0.132037 0.429381 0.119625 0.318957 0.100150 0.571056 0.063775 0.265019 MOTIF AC0071:MYB:Myb/SANT AC0071:MYB:Myb/SANT letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.000000 0.990840 0.003270 0.005890 0.967910 0.001970 0.025540 0.004580 0.994760 0.001310 0.000000 0.003930 0.000000 1.000000 0.000000 0.000000 0.000650 0.039290 0.044530 0.915529 0.005240 0.001970 0.992790 0.000000 MOTIF AC0072:MYBL:Myb/SANT AC0072:MYBL:Myb/SANT letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.083643 0.841820 0.040970 0.033567 0.119481 0.724398 0.143294 0.012827 0.009190 0.000000 0.971497 0.019313 0.010100 0.003147 0.012587 0.974166 0.013870 0.023403 0.000000 0.962727 0.633613 0.076509 0.057413 0.232465 0.921240 0.033893 0.025880 0.018987 0.872804 0.030273 0.030610 0.066313 0.059273 0.904317 0.014140 0.022270 0.036337 0.336270 0.369840 0.257553 0.088392 0.099546 0.735119 0.076943 MOTIF AC0073:MYBL:Myb/SANT AC0073:MYBL:Myb/SANT letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.748945 0.015363 0.140388 0.095304 0.092913 0.848507 0.031153 0.027427 0.067843 0.677786 0.230398 0.023973 0.017120 0.002740 0.976923 0.003217 0.040017 0.033537 0.006040 0.920406 0.002993 0.011167 0.000000 0.985840 0.980867 0.000000 0.013620 0.005513 0.907520 0.043247 0.029223 0.020010 0.004173 0.979924 0.002303 0.013600 0.045457 0.359103 0.431223 0.164217 0.112092 0.093109 0.586699 0.208099 MOTIF AC0074:MYBL:Myb/SANT AC0074:MYBL:Myb/SANT letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.179583 0.154557 0.059397 0.606463 0.809415 0.010307 0.158265 0.022013 0.881229 0.021598 0.069665 0.027508 0.000707 0.991249 0.001982 0.006062 0.016393 0.242120 0.704571 0.036917 0.012137 0.009823 0.947853 0.030187 0.088317 0.165918 0.018088 0.727676 0.085605 0.287431 0.010200 0.616764 0.332801 0.154800 0.201567 0.310832 0.267472 0.188865 0.119158 0.424505 0.482349 0.169902 0.175063 0.172685 0.477557 0.180767 0.169728 0.171948 MOTIF AC0075:OVOL:C2H2_ZF AC0075:OVOL:C2H2_ZF letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.597620 0.107177 0.064554 0.230650 0.034134 0.939066 0.013838 0.012962 0.029592 0.932942 0.009094 0.028372 0.015911 0.002505 0.967805 0.013779 0.003573 0.155339 0.005764 0.835324 0.015802 0.000151 0.000381 0.983666 0.913888 0.015942 0.039242 0.030928 MOTIF AC0076:OVOL:C2H2_ZF AC0076:OVOL:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.714290 0.000000 0.000000 0.285710 0.000000 0.857140 0.000000 0.142860 0.571430 0.285710 0.000000 0.142860 0.000000 0.000000 0.857140 0.142860 0.000000 0.000000 0.142860 0.857140 0.000000 0.000000 0.000000 1.000000 0.857140 0.142860 0.000000 0.000000 0.000000 0.857140 0.000000 0.142860 0.571430 0.285710 0.142860 0.000000 MOTIF AC0077:ZIC/GLIS:C2H2_ZF AC0077:ZIC/GLIS:C2H2_ZF letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.614452 0.134995 0.234197 0.016355 0.058047 0.881761 0.022834 0.037358 0.046887 0.881218 0.029271 0.042624 0.126564 0.746923 0.026078 0.100435 0.002585 0.992185 0.005053 0.000177 0.048366 0.944186 0.003047 0.004401 0.019362 0.535105 0.001680 0.443853 0.134350 0.008135 0.853042 0.004473 0.004330 0.807057 0.049623 0.138990 0.083357 0.099648 0.142092 0.674902 0.024644 0.011107 0.955502 0.008747 0.146878 0.238415 0.073775 0.540933 0.007189 0.050057 0.839220 0.103534 MOTIF AC0078:CTCF/CTCFL:C2H2_ZF AC0078:CTCF/CTCFL:C2H2_ZF letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.036609 0.576729 0.032043 0.354619 0.081579 0.023185 0.799122 0.096114 0.054816 0.818625 0.039634 0.086925 0.004737 0.982003 0.002393 0.010867 0.336382 0.592947 0.034155 0.036516 0.006422 0.593501 0.007549 0.392528 0.004117 0.987708 0.001873 0.006302 0.031729 0.055670 0.022278 0.890323 0.304343 0.077714 0.494273 0.123670 0.018072 0.325949 0.618554 0.037425 0.088867 0.172486 0.028103 0.710544 0.008761 0.009301 0.968005 0.013933 0.058971 0.060564 0.795785 0.084680 MOTIF AC0079:ZNF:C2H2_ZF AC0079:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.062500 0.937500 0.000000 0.000000 0.687500 0.125000 0.187500 0.000000 0.187500 0.000000 0.625000 0.187500 0.125000 0.125000 0.125000 0.625000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.937500 0.000000 0.062500 0.000000 0.437500 0.000000 0.562500 0.000000 0.125000 0.062500 0.812500 0.062500 0.625000 0.187500 0.125000 0.375000 0.062500 0.000000 0.562500 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 MOTIF AC0080:ZNF:C2H2_ZF AC0080:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.476062 0.085170 0.171344 0.267424 0.071950 0.062483 0.733019 0.132547 0.180507 0.615773 0.049230 0.154490 0.029343 0.945930 0.008137 0.016590 0.071503 0.078620 0.035583 0.814294 0.218679 0.057337 0.687207 0.036777 0.076253 0.416667 0.413590 0.093490 0.071153 0.094107 0.104067 0.730674 0.746052 0.173316 0.037900 0.042733 0.007987 0.978290 0.005170 0.008553 0.352090 0.085973 0.213527 0.348410 0.016223 0.407978 0.551539 0.024260 0.486727 0.089543 0.119517 0.304213 0.048880 0.023907 0.867680 0.059533 0.066383 0.787214 0.043563 0.102840 0.115207 0.698797 0.066153 0.119843 MOTIF AC0081:NFIA/NFIC:SMAD AC0081:NFIA/NFIC:SMAD letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.012613 0.010545 0.008800 0.968042 0.011665 0.002145 0.980252 0.005938 0.006497 0.895093 0.093357 0.005053 0.001995 0.988050 0.003027 0.006928 0.980682 0.004063 0.007423 0.007832 0.887460 0.008197 0.100775 0.003568 MOTIF AC0082:NFIX/NFIC:SMAD AC0082:NFIX/NFIC:SMAD letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.058500 0.053058 0.110018 0.778424 0.050630 0.048597 0.848785 0.051988 0.055743 0.051829 0.840239 0.052189 0.137231 0.746440 0.061696 0.054633 0.421169 0.152252 0.214691 0.211889 0.213765 0.318330 0.212841 0.255064 0.245687 0.228352 0.316767 0.209195 0.234723 0.182950 0.370118 0.212210 0.145688 0.108766 0.089816 0.655730 0.009137 0.011030 0.903795 0.076038 0.006305 0.963980 0.022528 0.007187 0.006539 0.986078 0.003774 0.003609 0.912784 0.063408 0.007827 0.015981 MOTIF AC0083:ZBTB:C2H2_ZF AC0083:ZBTB:C2H2_ZF letter-probability matrix: alength= 4 w= 18 nsites= 20 E= 0 0.019260 0.009330 0.931365 0.040045 0.000490 0.000985 0.000510 0.998015 0.000000 0.001015 0.998475 0.000510 0.000000 0.995160 0.001995 0.002845 0.001800 0.994570 0.000100 0.003530 0.553995 0.189125 0.062290 0.194590 0.204304 0.291624 0.065345 0.438728 0.463047 0.151611 0.186576 0.198766 0.319425 0.209420 0.183960 0.287195 0.313030 0.216595 0.184610 0.285765 0.270171 0.161261 0.346237 0.222331 0.228230 0.182000 0.404730 0.185040 0.074385 0.568572 0.034420 0.322623 0.933510 0.032230 0.002350 0.031910 0.001180 0.004815 0.991100 0.002905 0.001355 0.737571 0.005965 0.255109 0.995080 0.000785 0.004135 0.000000 0.013475 0.921776 0.036275 0.028475 MOTIF AC0084:HIC:C2H2_ZF AC0084:HIC:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.455093 0.058122 0.442806 0.043978 0.000000 0.011680 0.000000 0.988320 0.041935 0.030050 0.915195 0.012820 0.002335 0.986850 0.010785 0.000030 0.043335 0.951185 0.000000 0.005480 0.451361 0.541037 0.000000 0.007602 0.487035 0.225123 0.189927 0.097915 0.116677 0.611583 0.157750 0.113990 MOTIF AC0085:ZNF:C2H2_ZF AC0085:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.448080 0.000000 0.544030 0.007890 0.063855 0.006755 0.928980 0.000410 0.000000 0.996760 0.001765 0.001475 0.001760 0.001760 0.993850 0.002630 0.002750 0.992425 0.001155 0.003670 0.054180 0.941155 0.004665 0.000000 0.448697 0.046585 0.048770 0.455947 0.001595 0.004510 0.937500 0.056395 0.004950 0.001280 0.989895 0.003875 0.001475 0.995875 0.001765 0.000885 0.003525 0.001755 0.993705 0.001015 0.000520 0.913775 0.000000 0.085705 0.007820 0.576793 0.004055 0.411332 MOTIF AC0086:ZNF:C2H2_ZF AC0086:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.027987 0.035070 0.060670 0.876273 0.069307 0.013710 0.823113 0.093870 0.028497 0.037670 0.912960 0.020873 0.013437 0.946529 0.004467 0.035567 0.091066 0.006777 0.884457 0.017700 0.001837 0.994490 0.001000 0.002673 0.001000 0.974677 0.001000 0.023323 0.147140 0.635529 0.120460 0.096870 0.719535 0.063717 0.083420 0.133327 0.510012 0.159281 0.209964 0.120743 0.131290 0.470673 0.156897 0.241140 0.419843 0.187590 0.310293 0.082273 0.139650 0.233696 0.099303 0.527351 0.109178 0.171551 0.415859 0.303412 MOTIF AC0087:E2F:E2F AC0087:E2F:E2F letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.506196 0.051526 0.040613 0.401665 0.434486 0.023443 0.059151 0.482920 0.132082 0.021107 0.175435 0.671376 0.028309 0.096306 0.851544 0.023841 0.000210 0.011275 0.987949 0.000566 0.004497 0.994474 0.000169 0.000860 0.001076 0.000367 0.996433 0.002124 0.000081 0.989255 0.010498 0.000166 0.010223 0.878259 0.090506 0.021012 0.773677 0.109313 0.018210 0.098800 0.702831 0.049110 0.021946 0.226113 0.640968 0.046671 0.041338 0.271023 MOTIF AC0088:ZNF:C2H2_ZF AC0088:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.121643 0.083303 0.693831 0.101223 0.218347 0.067347 0.641793 0.072513 0.167558 0.562852 0.113240 0.156350 0.108353 0.454135 0.116643 0.320868 0.695797 0.189803 0.065150 0.049250 0.884670 0.048797 0.050023 0.016510 0.008767 0.017480 0.955806 0.017947 0.009517 0.926473 0.022460 0.041550 0.005127 0.970797 0.007053 0.017023 0.009487 0.976660 0.006030 0.007823 MOTIF AC0089:ZNF:C2H2_ZF AC0089:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.035230 0.787900 0.072900 0.103970 0.035230 0.083269 0.114249 0.767252 0.086760 0.255130 0.485330 0.172780 0.073009 0.000860 0.890901 0.035230 0.000860 0.000860 0.980240 0.018040 0.066130 0.846150 0.024890 0.062830 0.238000 0.289560 0.069550 0.402890 0.488960 0.182930 0.244790 0.083320 0.189832 0.255273 0.382224 0.172672 0.024920 0.004300 0.963050 0.007730 0.138420 0.736220 0.083320 0.042040 0.361680 0.440830 0.169140 0.028350 0.035230 0.856590 0.093570 0.014610 0.231180 0.062670 0.148570 0.557580 0.007730 0.000860 0.921840 0.069570 0.134870 0.371960 0.392660 0.100510 MOTIF AC0090:ZNF:C2H2_ZF AC0090:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 24 nsites= 20 E= 0 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.250000 0.750000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.250000 0.750000 0.000000 1.000000 0.000000 0.000000 0.000000 0.250000 0.750000 0.000000 0.000000 0.000000 0.750000 0.250000 0.000000 0.000000 0.750000 0.000000 0.250000 0.000000 1.000000 0.000000 0.000000 0.250000 0.000000 0.500000 0.250000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.750000 0.250000 0.000000 0.000000 0.500000 0.000000 0.500000 0.000000 0.250000 0.250000 0.500000 0.000000 0.000000 0.000000 1.000000 0.250000 0.750000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.750000 0.000000 0.250000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 MOTIF AC0091:ZNF:C2H2_ZF AC0091:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.008690 0.919880 0.012550 0.058880 0.000970 0.112930 0.668920 0.217180 0.024130 0.765440 0.166990 0.043440 0.132241 0.688227 0.039570 0.139961 0.012550 0.000970 0.962350 0.024130 0.024130 0.935330 0.035710 0.004830 0.000970 0.993230 0.000970 0.004830 0.012550 0.363900 0.047300 0.576250 0.128379 0.120659 0.668913 0.082049 0.398650 0.325290 0.228760 0.047300 0.008690 0.124519 0.506755 0.360036 0.000970 0.024130 0.329150 0.645750 0.000970 0.047300 0.004830 0.946901 0.000970 0.854241 0.000970 0.143819 MOTIF AC0092:ZNF:C2H2_ZF AC0092:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.052495 0.732689 0.167152 0.047663 0.333447 0.411949 0.142005 0.112600 0.087600 0.040165 0.825511 0.046723 0.023053 0.918827 0.043537 0.014583 0.036057 0.803110 0.074788 0.086045 0.070862 0.045567 0.796382 0.087188 0.001312 0.959116 0.026912 0.012660 0.168370 0.670259 0.082960 0.078410 0.029833 0.248007 0.659371 0.062790 0.004602 0.982903 0.007893 0.004602 0.027123 0.689273 0.227304 0.056300 0.079700 0.091182 0.428835 0.400283 0.009668 0.767124 0.070222 0.152986 0.063755 0.692845 0.195218 0.048182 MOTIF AC0093:ZNF:C2H2_ZF AC0093:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 21 nsites= 20 E= 0 0.019070 0.747873 0.163138 0.069919 0.002120 0.993640 0.002120 0.002120 0.078390 0.078390 0.078390 0.764830 0.027540 0.154662 0.807208 0.010590 0.002120 0.993640 0.002120 0.002120 0.332630 0.290250 0.112290 0.264830 0.154660 0.010590 0.832630 0.002120 0.205510 0.510590 0.281780 0.002120 0.086860 0.781780 0.002120 0.129240 0.459750 0.154660 0.290250 0.095340 0.002120 0.866529 0.120761 0.010590 0.069920 0.722460 0.010590 0.197030 0.036020 0.315680 0.281780 0.366520 0.044490 0.298730 0.205510 0.451270 0.044490 0.230932 0.620766 0.103811 0.307200 0.273310 0.315680 0.103810 0.095340 0.493640 0.061440 0.349580 0.078390 0.637710 0.112290 0.171610 0.044490 0.349577 0.163138 0.442796 0.019070 0.273307 0.256357 0.451265 0.002120 0.188558 0.739403 0.069919 MOTIF AC0094:ZNF:C2H2_ZF AC0094:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.248370 0.170200 0.544790 0.036640 0.069220 0.307000 0.124590 0.499190 0.017100 0.010590 0.082250 0.890060 0.000810 0.997570 0.000810 0.000810 0.000810 0.144141 0.000810 0.854239 0.000810 0.645770 0.000810 0.352610 0.004070 0.000810 0.535020 0.460100 0.004070 0.991050 0.000810 0.004070 0.000810 0.873789 0.000810 0.124591 0.000810 0.994310 0.000810 0.004070 0.150648 0.694623 0.072479 0.082249 MOTIF AC0095:E2F:E2F AC0095:E2F:E2F letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.302000 0.108000 0.560000 0.030000 0.288000 0.134000 0.552000 0.026000 0.288000 0.342000 0.370000 0.000000 0.406000 0.154000 0.276000 0.164000 0.224000 0.078000 0.628000 0.070000 0.226000 0.368000 0.404000 0.002000 0.006000 0.000000 0.994000 0.000000 0.036000 0.964000 0.000000 0.000000 0.018000 0.000000 0.982000 0.000000 0.006000 0.684000 0.310000 0.000000 0.000000 0.198000 0.802000 0.000000 0.252000 0.616000 0.104000 0.028000 0.274000 0.106000 0.458000 0.162000 MOTIF AC0096:ZNF:C2H2_ZF AC0096:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.037790 0.700580 0.095930 0.165700 0.130811 0.526165 0.154072 0.188952 0.002910 0.026160 0.933140 0.037790 0.154070 0.642440 0.200580 0.002910 0.084300 0.584300 0.095930 0.235470 0.014530 0.002910 0.072671 0.909889 0.014530 0.909889 0.002910 0.072671 0.037790 0.723840 0.026160 0.212210 0.014530 0.595936 0.014530 0.375004 0.002910 0.142440 0.340120 0.514530 0.002910 0.084300 0.037790 0.875000 0.061050 0.363370 0.386630 0.188950 0.095929 0.723833 0.177328 0.002910 0.270350 0.584300 0.095930 0.049420 0.002910 0.002910 0.979650 0.014530 0.002910 0.956400 0.026160 0.014530 MOTIF AC0097:DNMT/PHF:CxxC AC0097:DNMT/PHF:CxxC letter-probability matrix: alength= 4 w= 4 nsites= 20 E= 0 0.131264 0.115534 0.349113 0.404088 0.009965 0.986565 0.000885 0.002585 0.008195 0.109814 0.880561 0.001430 0.035600 0.140389 0.796176 0.027835 MOTIF AC0098:ZNF:C2H2_ZF AC0098:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.004055 0.967570 0.008115 0.020260 0.526195 0.141585 0.187955 0.144265 0.113961 0.754474 0.046140 0.085425 0.003075 0.983765 0.005990 0.007170 0.033115 0.954350 0.004425 0.008110 0.874754 0.099095 0.020950 0.005200 0.005545 0.069870 0.003455 0.921130 0.004865 0.113115 0.781530 0.100490 0.002080 0.087515 0.775330 0.135075 0.005555 0.982590 0.004770 0.007085 0.003005 0.994910 0.000000 0.002085 0.067925 0.133694 0.784921 0.013460 0.010785 0.955710 0.005170 0.028335 0.053340 0.107571 0.753704 0.085385 0.439028 0.384333 0.012820 0.163819 0.115216 0.707649 0.096105 0.081030 MOTIF AC0099:ZNF:C2H2_ZF AC0099:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.023580 0.117930 0.136790 0.721700 0.325473 0.589626 0.061321 0.023580 0.117930 0.797170 0.061320 0.023580 0.929240 0.061320 0.004720 0.004720 0.438680 0.155660 0.268870 0.136790 0.080189 0.004720 0.910371 0.004720 0.004720 0.004720 0.985840 0.004720 0.325470 0.363210 0.042450 0.268870 0.117929 0.042450 0.834902 0.004720 0.004720 0.080189 0.834902 0.080189 0.193402 0.759438 0.023580 0.023580 0.117929 0.419806 0.099059 0.363206 0.457550 0.004720 0.306600 0.231130 0.080190 0.155660 0.608490 0.155660 0.099060 0.665090 0.099060 0.136790 MOTIF AC0100:ZNF:C2H2_ZF AC0100:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 27 nsites= 20 E= 0 0.000000 0.333330 0.666670 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.111110 0.111110 0.777780 0.000000 0.000000 0.111110 0.000000 0.888890 0.000000 0.000000 0.666670 0.333330 0.000000 0.222220 0.222220 0.555560 0.000000 0.222220 0.777780 0.000000 0.111111 0.000000 0.444444 0.444444 0.111110 0.111110 0.555560 0.222220 0.000000 0.444444 0.222222 0.333333 0.555560 0.111110 0.333330 0.000000 0.222220 0.000000 0.111110 0.666670 0.000000 0.333330 0.666670 0.000000 0.111110 0.111110 0.111110 0.666670 0.000000 0.000000 0.888890 0.111110 0.000000 0.000000 0.111110 0.888890 0.444440 0.000000 0.555560 0.000000 0.000000 0.000000 0.111110 0.888890 0.000000 0.000000 1.000000 0.000000 0.111110 0.222220 0.111110 0.555560 0.333333 0.222222 0.444444 0.000000 0.000000 0.555560 0.000000 0.444440 0.222222 0.333333 0.444444 0.000000 0.444444 0.000000 0.111111 0.444444 0.222220 0.000000 0.777780 0.000000 MOTIF AC0101:ZBTB:C2H2_ZF AC0101:ZBTB:C2H2_ZF letter-probability matrix: alength= 4 w= 20 nsites= 20 E= 0 0.087910 0.769230 0.000000 0.142860 0.296700 0.043960 0.659340 0.000000 0.021980 0.736260 0.153850 0.087910 0.098900 0.043960 0.747250 0.109890 0.032970 0.857140 0.109890 0.000000 0.450550 0.000000 0.516480 0.032970 0.000000 0.857140 0.043960 0.098900 0.538460 0.043960 0.417580 0.000000 0.000000 0.780220 0.219780 0.000000 0.153850 0.043960 0.626370 0.175820 0.000000 0.912081 0.054949 0.032970 0.505500 0.109890 0.384610 0.000000 0.010990 0.835162 0.021980 0.131869 0.285710 0.021980 0.681320 0.010990 0.186812 0.637366 0.087911 0.087911 0.395604 0.043960 0.494505 0.065931 0.054950 0.945050 0.000000 0.000000 0.296700 0.021980 0.538460 0.142860 0.043960 0.912081 0.043960 0.000000 0.439560 0.076920 0.450550 0.032970 MOTIF AC0102:ZNF:C2H2_ZF AC0102:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.187486 0.178091 0.539848 0.094575 0.089025 0.684545 0.132850 0.093580 0.134626 0.216541 0.586988 0.061845 0.796075 0.111345 0.030015 0.062565 0.029805 0.858365 0.029900 0.081930 0.141145 0.133320 0.692895 0.032640 0.032695 0.881819 0.015650 0.069835 0.054175 0.073840 0.814340 0.057645 0.727930 0.121420 0.022115 0.128535 0.646530 0.234810 0.055125 0.063535 0.101570 0.808090 0.048850 0.041490 0.559157 0.207569 0.128809 0.104464 MOTIF AC0103:ZBTB:['C2H2_zinc_finger_factors'] AC0103:ZBTB:['C2H2_zinc_finger_factors'] letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.073680 0.775240 0.135030 0.016050 0.009020 0.943911 0.034090 0.012980 0.020450 0.064661 0.901259 0.013630 0.014950 0.896859 0.070161 0.018030 0.029030 0.067080 0.889160 0.014730 0.012320 0.918841 0.057399 0.011440 0.989660 0.008140 0.002200 0.000000 0.021770 0.808008 0.143171 0.027050 MOTIF AC0104:ZNF:C2H2_ZF AC0104:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.000000 0.001340 0.000000 0.998660 0.001240 0.001150 0.997610 0.000000 0.952435 0.038310 0.001320 0.007935 0.000095 0.001110 0.000000 0.998795 0.236515 0.094105 0.305575 0.363805 0.374107 0.271341 0.251841 0.102711 0.008870 0.741115 0.018205 0.231810 0.000980 0.991210 0.001600 0.006210 0.004835 0.994435 0.000350 0.000380 0.798865 0.066550 0.096545 0.038040 0.169900 0.471345 0.080455 0.278300 0.094375 0.701764 0.055075 0.148786 0.001325 0.864389 0.000200 0.134086 0.050755 0.047985 0.035635 0.865624 MOTIF AC0105:EGR/ZNF:C2H2_ZF AC0105:EGR/ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.149307 0.668790 0.052947 0.128956 0.274140 0.051879 0.376755 0.297225 0.047939 0.905151 0.016404 0.030506 0.025283 0.941765 0.017853 0.015099 0.036384 0.805250 0.017020 0.141346 0.516748 0.346048 0.073317 0.063887 0.024489 0.897297 0.025973 0.052241 0.297096 0.193701 0.328486 0.180716 0.073604 0.704806 0.072473 0.149117 MOTIF AC0106:EGR/WT:C2H2_ZF AC0106:EGR/WT:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.017501 0.939408 0.016443 0.026648 0.122991 0.025853 0.753341 0.097815 0.013145 0.977288 0.003832 0.005735 0.005774 0.986005 0.003927 0.004294 0.001183 0.887975 0.006176 0.104666 0.707734 0.276691 0.009722 0.005853 0.001275 0.989142 0.003392 0.006191 0.092396 0.026449 0.808783 0.072372 0.008121 0.907400 0.034895 0.049584 MOTIF AC0107:KLF:C2H2_ZF AC0107:KLF:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.025243 0.949664 0.012693 0.012400 0.014733 0.959650 0.015100 0.010517 0.865613 0.026330 0.014040 0.094017 0.011610 0.953476 0.007337 0.027577 0.199640 0.219677 0.540066 0.040617 0.147920 0.821852 0.010437 0.019790 0.544193 0.062017 0.144068 0.249721 0.035043 0.904511 0.047253 0.013193 0.023183 0.950774 0.016630 0.009413 0.017090 0.955857 0.009510 0.017543 0.359320 0.412554 0.118896 0.109229 0.124677 0.159143 0.073340 0.642839 MOTIF AC0108:KLF/SP:C2H2_ZF AC0108:KLF/SP:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.139271 0.695979 0.064871 0.099879 0.068375 0.820317 0.034651 0.076657 0.569100 0.375323 0.039118 0.016459 0.012964 0.967202 0.003716 0.016118 0.223146 0.027059 0.682699 0.067096 0.005283 0.982715 0.005689 0.006313 0.010955 0.977604 0.007631 0.003810 0.009810 0.904943 0.007968 0.077279 0.342938 0.463470 0.034865 0.158727 MOTIF AC0109:SP/KLF:C2H2_ZF AC0109:SP/KLF:C2H2_ZF letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.111439 0.552388 0.156911 0.179261 0.243618 0.401818 0.163361 0.191202 0.201107 0.232267 0.436217 0.130410 0.135288 0.177621 0.556009 0.131081 0.043115 0.805985 0.050119 0.100781 0.048733 0.649675 0.045545 0.256047 0.117615 0.831955 0.032506 0.017925 0.018139 0.939715 0.010942 0.031204 0.140757 0.014759 0.684859 0.159625 0.005347 0.941951 0.023197 0.029505 0.009566 0.940669 0.017148 0.032617 0.027433 0.762079 0.022638 0.187850 0.144710 0.595961 0.075416 0.183914 MOTIF AC0110:ZNF/MAZ:C2H2_ZF AC0110:ZNF/MAZ:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.029922 0.885435 0.029983 0.054660 0.042080 0.825016 0.063074 0.069830 0.007126 0.973584 0.013417 0.005873 0.055423 0.886335 0.014658 0.043584 0.071038 0.010079 0.180008 0.738875 0.004031 0.975035 0.007111 0.013823 0.015323 0.956903 0.018282 0.009492 0.035807 0.834367 0.040254 0.089572 0.096739 0.678756 0.107217 0.117288 MOTIF AC0111:CTCF/KLF:C2H2_ZF AC0111:CTCF/KLF:C2H2_ZF letter-probability matrix: alength= 4 w= 18 nsites= 20 E= 0 0.187500 0.203125 0.390625 0.218750 0.203000 0.208750 0.260875 0.327375 0.368896 0.072490 0.379981 0.178633 0.151168 0.163332 0.423917 0.261582 0.204682 0.141320 0.518838 0.135160 0.121015 0.199470 0.610578 0.068937 0.005312 0.037218 0.803300 0.154170 0.111423 0.078630 0.675373 0.134575 0.089362 0.304339 0.442106 0.164193 0.088350 0.143830 0.617242 0.150578 0.101713 0.261749 0.331839 0.304699 0.268488 0.131160 0.171031 0.429321 0.368800 0.132021 0.195903 0.303277 0.150122 0.158900 0.357021 0.333956 0.183653 0.139593 0.410175 0.266580 0.175090 0.246932 0.395022 0.182955 0.232126 0.230838 0.368649 0.168388 0.184750 0.164750 0.455750 0.194750 MOTIF AC0112:PAX:Homeodomain,Paired_box AC0112:PAX:Homeodomain,Paired_box letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.235292 0.411764 0.058821 0.294123 0.117650 0.529410 0.000000 0.352940 0.058821 0.882359 0.058821 0.000000 0.941180 0.000000 0.000000 0.058820 0.058820 0.823530 0.117650 0.000000 0.176470 0.705880 0.000000 0.117650 0.058821 0.705887 0.176472 0.058821 0.000000 0.375000 0.375000 0.250000 MOTIF AC0113:ZNF:C2H2_ZF AC0113:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.125270 0.125270 0.530440 0.219020 0.059620 0.030205 0.792636 0.117539 0.036510 0.026495 0.922520 0.014475 0.359352 0.224001 0.408422 0.008225 0.000795 0.996540 0.000795 0.001870 0.595473 0.001870 0.394432 0.008225 0.001870 0.988035 0.004025 0.006070 0.072775 0.742449 0.001870 0.182906 0.017980 0.942990 0.001870 0.037160 0.046425 0.787335 0.050145 0.116095 0.214601 0.478257 0.168926 0.138216 0.246910 0.476645 0.141775 0.134670 0.195432 0.572561 0.108651 0.123356 0.150341 0.432072 0.115701 0.301887 0.193174 0.283309 0.370418 0.153099 0.088776 0.536855 0.218782 0.155587 MOTIF AC0114:ZNF/VEZF:C2H2_ZF AC0114:ZNF/VEZF:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.083389 0.806751 0.082816 0.027044 0.032842 0.943725 0.005785 0.017648 0.016507 0.961388 0.007815 0.014290 0.007798 0.971758 0.007318 0.013126 0.024813 0.922007 0.015578 0.037602 0.013271 0.953271 0.010323 0.023135 0.074146 0.878368 0.020622 0.026864 0.099024 0.776308 0.047432 0.077236 MOTIF AC0115:ZNF:C2H2_ZF AC0115:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 21 nsites= 20 E= 0 0.076920 0.538460 0.269230 0.115390 0.153848 0.461535 0.192308 0.192308 0.038460 0.384614 0.384614 0.192312 0.076920 0.730770 0.153850 0.038460 0.076921 0.769238 0.076921 0.076921 0.000000 1.000000 0.000000 0.000000 0.038460 0.884620 0.000000 0.076920 0.115389 0.461535 0.153848 0.269227 0.230770 0.576920 0.038460 0.153850 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.115390 0.230770 0.269230 0.384610 0.076921 0.615386 0.038460 0.269233 0.615380 0.192310 0.000000 0.192310 0.000000 0.000000 1.000000 0.000000 0.000000 0.076920 0.923080 0.000000 1.000000 0.000000 0.000000 0.000000 0.923080 0.000000 0.076920 0.000000 0.000000 0.192310 0.000000 0.807690 0.000000 0.192310 0.538460 0.269230 0.076921 0.576926 0.076921 0.269233 MOTIF AC0116:GLIS:C2H2_ZF AC0116:GLIS:C2H2_ZF letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.847110 0.065776 0.085789 0.001325 0.002692 0.991701 0.001487 0.004120 0.001979 0.996750 0.000883 0.000388 0.020302 0.948881 0.000792 0.030025 0.001051 0.996353 0.001315 0.001281 0.047415 0.950041 0.000374 0.002170 0.034700 0.888429 0.001105 0.075766 0.701196 0.005071 0.274604 0.019129 0.004236 0.916617 0.021246 0.057901 0.215844 0.054429 0.673946 0.055781 0.598054 0.018897 0.156759 0.226290 0.467274 0.213997 0.052270 0.266459 0.008607 0.091924 0.869712 0.029757 MOTIF AC0117:GLI/ZIC:C2H2_ZF AC0117:GLI/ZIC:C2H2_ZF letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.044269 0.054675 0.833193 0.067863 0.774293 0.118160 0.092339 0.015208 0.029082 0.906242 0.037870 0.026806 0.026232 0.921785 0.020708 0.031275 0.811958 0.096616 0.023151 0.068275 0.019734 0.947991 0.018810 0.013465 0.157149 0.813378 0.017775 0.011699 0.034472 0.886958 0.020088 0.058482 0.806543 0.089196 0.072319 0.031941 0.191858 0.544654 0.135635 0.127854 MOTIF AC0118:ZBTB7A:C2H2_ZF AC0118:ZBTB7A:C2H2_ZF letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.441000 0.335000 0.193000 0.031000 0.014000 0.001000 0.974000 0.011000 0.575000 0.419000 0.000000 0.006000 0.001000 0.989000 0.001000 0.009000 0.002000 0.995000 0.000000 0.003000 0.047000 0.930000 0.000000 0.023000 0.003000 0.632000 0.005000 0.360000 MOTIF AC0119:ZBTB7A/ZBTB7B:C2H2_ZF AC0119:ZBTB7A/ZBTB7B:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.066845 0.817880 0.083573 0.031702 0.091622 0.027370 0.850600 0.030408 0.830697 0.143932 0.004955 0.020416 0.007285 0.985668 0.000981 0.006066 0.006517 0.979272 0.007138 0.007073 0.808657 0.181173 0.001823 0.008347 0.005627 0.986748 0.002811 0.004814 0.135038 0.765400 0.053345 0.046217 MOTIF AC0120:RREB:C2H2_ZF AC0120:RREB:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.272730 0.727270 0.000000 0.000000 0.045455 0.954545 0.000000 0.000000 0.181820 0.818180 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.818180 0.181820 0.000000 0.000000 0.818180 0.181820 0.000000 0.000000 0.727270 0.272730 0.000000 0.000000 0.272729 0.636362 0.000000 0.090910 0.181820 0.818180 0.000000 0.000000 0.818180 0.181820 0.000000 0.000000 0.318185 0.681815 0.000000 0.000000 0.136365 0.863635 0.000000 0.000000 0.272730 0.727270 0.000000 0.000000 0.272729 0.590907 0.090910 0.045455 MOTIF AC0121:ZNF:C2H2_ZF AC0121:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.759320 0.001550 0.144410 0.094720 0.641306 0.131991 0.212732 0.013970 0.001550 0.970500 0.001550 0.026400 0.001550 0.995350 0.001550 0.001550 0.001550 0.995350 0.001550 0.001550 0.094720 0.802800 0.013970 0.088510 0.982930 0.001550 0.001550 0.013970 0.684780 0.045030 0.250000 0.020190 0.063661 0.895969 0.038820 0.001550 MOTIF AC0122:XPA:Unknown AC0122:XPA:Unknown letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.110130 0.709450 0.125655 0.054765 0.700840 0.139205 0.105130 0.054825 0.003105 0.960245 0.000000 0.036650 0.022245 0.899955 0.062475 0.015325 0.065210 0.107229 0.112174 0.715386 0.030480 0.895141 0.000000 0.074380 0.834774 0.054205 0.075975 0.035045 0.000000 0.984930 0.000000 0.015070 0.653200 0.201095 0.113090 0.032615 MOTIF AC0123:FOXO:Forkhead AC0123:FOXO:Forkhead letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.161795 0.107732 0.075982 0.654491 0.070773 0.078080 0.055945 0.795202 0.054805 0.888846 0.029832 0.026517 0.048612 0.897991 0.030240 0.023157 0.013672 0.942561 0.010157 0.033610 0.013832 0.918550 0.027180 0.040438 0.927698 0.027597 0.014623 0.030082 0.009402 0.978515 0.001690 0.010393 0.883245 0.071217 0.020625 0.024913 0.099438 0.795357 0.044213 0.060992 MOTIF AC0124:ZNF:C2H2_ZF AC0124:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.043523 0.040763 0.878644 0.037070 0.000000 0.002793 0.995987 0.001220 0.080697 0.153991 0.700334 0.064977 0.005777 0.961197 0.001863 0.031163 0.001273 0.987430 0.010770 0.000527 0.000093 0.993797 0.005460 0.000650 0.957283 0.000000 0.042717 0.000000 0.026430 0.779795 0.136243 0.057533 MOTIF AC0125:ZNF:C2H2_ZF AC0125:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.800623 0.059310 0.031923 0.108143 0.011557 0.006860 0.966533 0.015050 0.018140 0.865068 0.092632 0.024160 0.004973 0.988214 0.002073 0.004740 0.004483 0.989554 0.002540 0.003423 0.892653 0.047507 0.013633 0.046207 0.758843 0.007127 0.214743 0.019287 0.033423 0.925917 0.020307 0.020353 0.098190 0.812394 0.024107 0.065310 MOTIF AC0126:ZNF:C2H2_ZF AC0126:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.235105 0.698905 0.033165 0.032825 0.000000 0.055305 0.922275 0.022420 0.032655 0.889871 0.054005 0.023470 0.004690 0.891225 0.096620 0.007465 0.019325 0.959110 0.011905 0.009660 0.796061 0.145794 0.001180 0.056965 0.869510 0.118245 0.000000 0.012245 0.000000 0.998720 0.001280 0.000000 0.063210 0.810455 0.099450 0.026885 MOTIF AC0127:ZFP:C2H2_ZF AC0127:ZFP:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.155560 0.755560 0.044440 0.044440 0.088890 0.755550 0.000000 0.155560 0.511105 0.066669 0.066669 0.355556 0.311110 0.066670 0.600000 0.022220 0.000000 1.000000 0.000000 0.000000 0.400000 0.266670 0.022220 0.311110 0.311110 0.688890 0.000000 0.000000 0.866670 0.066670 0.022220 0.044440 0.977780 0.022220 0.000000 0.000000 0.022220 0.911119 0.022220 0.044440 0.177780 0.133330 0.000000 0.688890 0.844450 0.044440 0.066670 0.044440 0.400000 0.088890 0.111110 0.400000 0.066669 0.866661 0.000000 0.066669 0.177780 0.711110 0.000000 0.111110 MOTIF AC0128:TFCP:Grainyhead AC0128:TFCP:Grainyhead letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.666670 0.333330 0.000000 0.000000 1.000000 0.000000 0.000000 0.166670 0.500000 0.333330 0.000000 0.000000 0.166670 0.833330 0.333333 0.333333 0.000000 0.333333 0.000000 0.000000 0.666670 0.333330 0.333333 0.000000 0.333333 0.333333 0.000000 0.166670 0.833330 0.000000 0.000000 0.833330 0.166670 0.000000 MOTIF AC0129:THAP:THAP_finger AC0129:THAP:THAP_finger letter-probability matrix: alength= 4 w= 18 nsites= 20 E= 0 0.138159 0.603947 0.124999 0.132894 0.125000 0.625000 0.125000 0.125000 0.614475 0.135525 0.125000 0.125000 0.125000 0.135525 0.125000 0.614475 0.146051 0.409212 0.235526 0.209211 0.146050 0.138160 0.132895 0.582895 0.127630 0.153950 0.125000 0.593420 0.190790 0.151315 0.330265 0.327630 0.290790 0.172370 0.385525 0.151315 0.290789 0.214474 0.206579 0.288159 0.169875 0.071665 0.145950 0.612510 0.314268 0.197752 0.362604 0.125376 0.327310 0.356625 0.087450 0.228615 0.017945 0.002510 0.712765 0.266780 0.002510 0.000000 0.987325 0.010165 0.035650 0.000000 0.923075 0.041275 0.134476 0.781064 0.000000 0.084460 0.661437 0.043310 0.225984 0.069270 MOTIF AC0130:GCM:GCM AC0130:GCM:GCM letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.701864 0.080371 0.200073 0.017691 0.016090 0.029529 0.024432 0.929949 0.223133 0.025362 0.747000 0.004504 0.102384 0.704619 0.098781 0.094215 0.046930 0.009501 0.785373 0.158196 0.006287 0.008571 0.972515 0.012627 0.004732 0.002867 0.986670 0.005731 0.044597 0.262035 0.039191 0.654177 MOTIF AC0131:ZNF:C2H2_ZF AC0131:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.771649 0.072237 0.090783 0.065332 0.055120 0.806489 0.065217 0.073175 0.735807 0.067052 0.093232 0.103908 0.070823 0.126588 0.727506 0.075083 0.044352 0.045688 0.062930 0.847030 0.047932 0.853119 0.052560 0.046390 0.016503 0.939177 0.001230 0.043090 0.752140 0.002845 0.073900 0.171115 0.002343 0.930404 0.036038 0.031215 0.027230 0.953553 0.007305 0.011912 0.002942 0.905580 0.002725 0.088753 0.000200 0.995250 0.000292 0.004258 0.129975 0.069470 0.015560 0.784995 0.027085 0.050793 0.017157 0.904965 MOTIF AC0132:ZSCAN/KLF:C2H2_ZF AC0132:ZSCAN/KLF:C2H2_ZF letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.222955 0.042197 0.703507 0.031341 0.004079 0.981605 0.013634 0.000682 0.985896 0.000000 0.013620 0.000484 0.005995 0.993688 0.000111 0.000206 0.911870 0.086805 0.000826 0.000499 0.005131 0.924917 0.001988 0.067964 0.417169 0.402568 0.162091 0.018172 0.019051 0.974676 0.000734 0.005539 0.025593 0.006281 0.084352 0.883774 0.034886 0.058531 0.802395 0.104188 0.485540 0.171500 0.154895 0.188064 0.567748 0.344529 0.039482 0.048241 0.857014 0.033125 0.075228 0.034633 MOTIF AC0133:ZNF:C2H2_ZF AC0133:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.060042 0.751528 0.083610 0.104820 0.018734 0.031320 0.007734 0.942212 0.010624 0.842516 0.017474 0.129386 0.024682 0.013700 0.955496 0.006122 0.623498 0.114726 0.201838 0.059938 0.987174 0.007296 0.002012 0.003518 0.001618 0.995684 0.000288 0.002410 0.003392 0.986146 0.006052 0.004410 0.005800 0.981210 0.004840 0.008150 MOTIF AC0134:HSF:HSF AC0134:HSF:HSF letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.162438 0.055839 0.172588 0.609134 0.010150 0.802030 0.005080 0.182740 0.005080 0.598980 0.390860 0.005080 0.106600 0.538070 0.055840 0.299490 0.030460 0.086290 0.766500 0.116750 0.106599 0.000000 0.873091 0.020310 0.000000 0.005080 0.994920 0.000000 0.055839 0.000000 0.005080 0.939081 0.000000 0.000000 0.000000 1.000000 0.000000 0.989850 0.000000 0.010150 0.299490 0.000000 0.700510 0.000000 1.000000 0.000000 0.000000 0.000000 0.426396 0.055839 0.203048 0.314717 0.091370 0.279190 0.010150 0.619290 0.000000 0.994920 0.000000 0.005080 0.020310 0.868011 0.005080 0.106599 MOTIF AC0135:ZBTB:C2H2_ZF AC0135:ZBTB:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.209541 0.077915 0.568543 0.144001 0.180710 0.227790 0.451960 0.139540 0.016415 0.164751 0.046040 0.772794 0.000000 0.975500 0.019500 0.005000 0.000000 0.995000 0.001000 0.004000 0.000000 1.000000 0.000000 0.000000 0.007040 0.030960 0.000000 0.962000 0.412790 0.034960 0.264585 0.287665 MOTIF AC0136:INSM:C2H2_ZF AC0136:INSM:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.041665 0.439510 0.000000 0.518825 0.104165 0.000000 0.804645 0.091190 0.139345 0.790300 0.070355 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.979165 0.020835 0.000000 0.000000 1.000000 0.000000 0.000000 0.181010 0.000000 0.000000 0.818990 0.076845 0.000000 0.622990 0.300165 0.631595 0.123355 0.245050 0.000000 0.211705 0.788295 0.000000 0.000000 0.749641 0.197359 0.000000 0.053000 MOTIF AC0137:ZNF:C2H2_ZF AC0137:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.000000 0.500000 0.500000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 1.000000 0.000000 0.000000 MOTIF AC0138:GTF3A:C2H2_ZF AC0138:GTF3A:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.018000 0.890000 0.050000 0.042000 0.026000 0.002000 0.002000 0.970000 0.002000 0.938000 0.042000 0.018000 0.002000 0.962000 0.002000 0.034000 0.010000 0.978000 0.002000 0.010000 0.858000 0.018000 0.002000 0.122000 0.002000 0.010000 0.002000 0.986000 0.002000 0.962000 0.002000 0.034000 0.002000 0.954000 0.002000 0.042000 MOTIF AC0139:ZNF:C2H2_ZF AC0139:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.135430 0.075272 0.035395 0.753903 0.042045 0.064437 0.041502 0.852015 0.008318 0.969309 0.017420 0.004953 0.003445 0.993458 0.000000 0.003097 0.002747 0.955806 0.008245 0.033202 0.577752 0.329850 0.002045 0.090353 0.030652 0.550478 0.015547 0.403323 0.610361 0.080905 0.161900 0.146835 0.917932 0.071293 0.005515 0.005260 0.015547 0.953373 0.009130 0.021950 0.708662 0.238668 0.014430 0.038240 0.020695 0.939635 0.025845 0.013825 MOTIF AC0140:ZNF75A/ZNF75D:C2H2_ZF AC0140:ZNF75A/ZNF75D:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.076916 0.058760 0.748264 0.116060 0.131214 0.498046 0.325292 0.045448 0.091700 0.189439 0.080504 0.638357 0.010570 0.053443 0.007141 0.928846 0.023201 0.072954 0.036514 0.867331 0.001259 0.003200 0.006803 0.988738 0.007203 0.948388 0.005370 0.039039 0.040843 0.934644 0.001948 0.022565 0.000418 0.991082 0.001481 0.007019 0.971535 0.001975 0.013962 0.012528 0.003604 0.995399 0.000247 0.000750 MOTIF AC0141:IKZF:C2H2_ZF AC0141:IKZF:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.088285 0.411480 0.019525 0.480710 0.103180 0.584735 0.015280 0.296805 0.000000 0.165615 0.000000 0.834385 0.000000 0.946055 0.053945 0.000000 0.000000 0.929925 0.015280 0.054795 0.000000 0.881075 0.000000 0.118925 0.836085 0.000000 0.089600 0.074315 0.580530 0.118845 0.279405 0.021220 MOTIF AC0142:IKZF:C2H2_ZF AC0142:IKZF:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.163333 0.242592 0.013890 0.580185 0.150000 0.000000 0.850000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.805556 0.046297 0.009260 0.138887 0.134260 0.213703 0.083333 0.568704 0.477223 0.101851 0.261112 0.159814 0.138333 0.587224 0.074073 0.200370 MOTIF AC0143:RBPJ:CSL AC0143:RBPJ:CSL letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.095056 0.172676 0.106610 0.625657 0.055060 0.039760 0.009893 0.895287 0.024594 0.917630 0.008544 0.049232 0.138830 0.613215 0.054249 0.193705 0.008396 0.948992 0.014149 0.028463 0.797226 0.032456 0.088999 0.081319 MOTIF AC0144:IKZF/NFKB:C2H2_ZF AC0144:IKZF/NFKB:C2H2_ZF letter-probability matrix: alength= 4 w= 5 nsites= 20 E= 0 0.017490 0.074310 0.006125 0.902075 0.006125 0.000445 0.002340 0.991090 0.004235 0.987300 0.006125 0.002340 0.000445 0.902075 0.000445 0.097035 0.019385 0.893535 0.000445 0.086635 MOTIF AC0145:ZNF:C2H2_ZF AC0145:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.098590 0.084133 0.731944 0.085333 0.092967 0.084933 0.725111 0.096990 0.003253 0.012833 0.983197 0.000717 0.993760 0.003940 0.001983 0.000317 0.015540 0.000717 0.981020 0.002723 0.005590 0.953743 0.006870 0.033797 0.013013 0.874658 0.000317 0.112012 0.961087 0.023300 0.011280 0.004333 0.033550 0.249664 0.039827 0.676959 0.271111 0.028107 0.505562 0.195221 0.017167 0.030203 0.943083 0.009547 0.661011 0.182433 0.096293 0.060263 0.567930 0.024447 0.295603 0.112020 0.099413 0.011760 0.824534 0.064293 0.111153 0.016760 0.839620 0.032467 MOTIF AC0146:ZNF/ZNF324B:['C2H2_zinc_finger_factors'] AC0146:ZNF/ZNF324B:['C2H2_zinc_finger_factors'] letter-probability matrix: alength= 4 w= 18 nsites= 20 E= 0 0.830066 0.083525 0.076615 0.009795 0.486750 0.000575 0.502880 0.009795 0.000575 0.998275 0.000575 0.000575 0.000575 0.917630 0.002880 0.078915 0.846195 0.021315 0.019010 0.113480 0.161869 0.002880 0.099655 0.735596 0.000575 0.993665 0.005185 0.000575 0.000575 0.959105 0.000575 0.039745 0.025920 0.247116 0.088135 0.638828 0.028225 0.005185 0.076615 0.889975 0.279375 0.078920 0.327765 0.313940 0.182604 0.074310 0.507487 0.235599 0.030530 0.931455 0.014400 0.023615 0.127305 0.067395 0.005185 0.800115 0.051270 0.120394 0.516702 0.311633 0.235597 0.413019 0.207947 0.143436 0.161869 0.592742 0.042050 0.203339 0.440668 0.138824 0.157259 0.263249 MOTIF AC0147:ZNF:C2H2_ZF AC0147:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.089973 0.084390 0.035960 0.789678 0.013907 0.627691 0.060923 0.297479 0.506425 0.156509 0.247369 0.089697 0.734597 0.188830 0.032103 0.044470 0.630221 0.067320 0.273199 0.029260 0.021547 0.938026 0.019547 0.020880 0.014233 0.847572 0.020213 0.117982 0.738194 0.080640 0.048970 0.132196 0.115680 0.042467 0.093527 0.748325 0.010880 0.939597 0.029290 0.020233 0.010213 0.923143 0.011857 0.054787 0.072437 0.234057 0.027317 0.666189 0.113980 0.153900 0.121313 0.610807 MOTIF AC0148:ZNF:C2H2_ZF AC0148:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.210481 0.030692 0.730585 0.028242 0.289761 0.198749 0.203584 0.307906 0.095520 0.793888 0.024332 0.086260 0.223480 0.679971 0.016238 0.080310 0.845611 0.008643 0.118813 0.026933 0.707133 0.005913 0.253334 0.033620 0.003802 0.003355 0.990933 0.001910 0.989693 0.002935 0.003037 0.004335 0.943073 0.000885 0.047387 0.008655 0.110247 0.004482 0.873327 0.011945 0.984177 0.007758 0.005850 0.002215 0.562764 0.207943 0.043622 0.185670 0.056365 0.122520 0.045863 0.775252 0.262334 0.117502 0.350475 0.269689 0.664722 0.094450 0.066520 0.174309 MOTIF AC0149:ZNF:C2H2_ZF AC0149:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 19 nsites= 20 E= 0 0.022042 0.941166 0.004197 0.032595 0.713274 0.042450 0.218866 0.025410 0.002595 0.028645 0.006678 0.962082 0.000465 0.005298 0.002330 0.991907 0.036230 0.188570 0.083980 0.691220 0.084102 0.039015 0.068615 0.808267 0.151403 0.035532 0.521303 0.291762 0.026385 0.120320 0.253171 0.600124 0.083088 0.883874 0.005618 0.027420 0.078773 0.889270 0.006770 0.025187 0.033577 0.198302 0.018925 0.749197 0.010945 0.938698 0.012717 0.037640 0.017253 0.724116 0.024872 0.233759 0.106974 0.138744 0.192949 0.561333 0.498347 0.079042 0.318615 0.103995 0.240774 0.112365 0.502418 0.144443 0.095725 0.215707 0.416287 0.272281 0.057200 0.840883 0.030560 0.071357 0.113965 0.736460 0.037270 0.112305 MOTIF AC0150:ZNF:C2H2_ZF AC0150:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.022215 0.145175 0.800500 0.032110 0.081675 0.716720 0.119215 0.082390 0.581115 0.178450 0.162900 0.077535 0.000750 0.002495 0.975575 0.021180 0.002495 0.994740 0.002015 0.000750 0.002015 0.002015 0.023400 0.972570 0.002015 0.941090 0.003275 0.053620 0.003275 0.319940 0.063470 0.613315 0.014210 0.959465 0.007240 0.019085 0.321467 0.122126 0.037540 0.518868 0.047390 0.454395 0.365910 0.132305 MOTIF AC0151:ZNF:C2H2_ZF AC0151:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.147656 0.169531 0.544533 0.138281 0.125781 0.619533 0.125781 0.128906 0.000780 0.000780 0.000780 0.997660 0.000780 0.994535 0.000780 0.003905 0.000780 0.997660 0.000780 0.000780 0.000780 0.997660 0.000780 0.000780 0.264266 0.007030 0.035155 0.693548 0.063280 0.057030 0.853909 0.025780 MOTIF AC0152:ZNF:C2H2_ZF AC0152:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.000890 0.997330 0.000890 0.000890 0.000890 0.000890 0.000890 0.997330 0.000890 0.997330 0.000890 0.000890 0.000890 0.997330 0.000890 0.000890 0.000890 0.419326 0.000890 0.578894 0.000890 0.000890 0.000890 0.997330 MOTIF AC0153:ZNF:C2H2_ZF AC0153:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 21 nsites= 20 E= 0 0.106440 0.125210 0.084088 0.684262 0.323623 0.059300 0.339396 0.277681 0.145323 0.047370 0.662821 0.144486 0.064345 0.053015 0.610069 0.272571 0.341530 0.068502 0.499213 0.090755 0.095640 0.170709 0.065168 0.668483 0.043772 0.807666 0.021317 0.127245 0.067655 0.067375 0.196278 0.668692 0.033057 0.048755 0.905140 0.013048 0.002962 0.010217 0.008358 0.978463 0.641108 0.010630 0.216892 0.131370 0.050363 0.237478 0.640804 0.071355 0.015485 0.020073 0.006535 0.957907 0.381363 0.014680 0.535802 0.068155 0.503366 0.155535 0.194430 0.146670 0.021855 0.050530 0.020002 0.907613 0.168457 0.035342 0.602619 0.193582 0.039582 0.190375 0.194662 0.575382 0.049212 0.861747 0.015883 0.073158 0.125330 0.271801 0.015428 0.587441 0.031710 0.827435 0.038900 0.101955 MOTIF AC0154:ZNF:C2H2_ZF AC0154:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 24 nsites= 20 E= 0 0.097119 0.020190 0.797112 0.085579 0.212498 0.097119 0.585574 0.104809 0.854801 0.031730 0.097119 0.016350 0.035580 0.254807 0.024040 0.685573 0.189420 0.404810 0.104810 0.300960 0.016350 0.031730 0.000960 0.950960 0.074040 0.000960 0.908650 0.016350 0.047110 0.120190 0.135580 0.697120 0.362500 0.089420 0.520190 0.027890 0.070190 0.024040 0.766350 0.139420 0.531730 0.120190 0.250960 0.097120 0.074040 0.077880 0.081730 0.766350 0.081730 0.089420 0.243270 0.585580 0.077880 0.531730 0.193270 0.197120 0.781738 0.031730 0.177882 0.008650 0.066350 0.043270 0.131730 0.758650 0.185580 0.124040 0.577880 0.112500 0.085580 0.343270 0.020190 0.550960 0.020190 0.908659 0.020190 0.050961 0.008650 0.239422 0.004810 0.747117 0.062500 0.104810 0.162500 0.670190 0.097119 0.185578 0.097119 0.620184 0.174040 0.743270 0.050960 0.031730 0.850969 0.039420 0.058651 0.050961 MOTIF AC0155:ETS/SIX:Ets AC0155:ETS/SIX:Ets letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.114876 0.222596 0.339016 0.323512 0.125264 0.632631 0.123224 0.118881 0.072971 0.192551 0.067579 0.666899 0.069524 0.064859 0.797723 0.067894 0.039940 0.033269 0.886392 0.040399 0.035659 0.007240 0.934822 0.022279 0.888502 0.022299 0.057280 0.031919 0.393993 0.108874 0.412291 0.084842 0.265128 0.097409 0.096780 0.540683 0.019365 0.084218 0.044846 0.851572 0.010675 0.023995 0.947724 0.017606 0.028930 0.037573 0.062320 0.871177 0.841387 0.035824 0.082628 0.040161 0.039350 0.036340 0.889404 0.034906 0.037566 0.037359 0.056979 0.868096 MOTIF AC0156:ZNF:C2H2_ZF AC0156:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.377321 0.052402 0.028627 0.541650 0.020512 0.961632 0.000699 0.017157 0.002716 0.995447 0.001062 0.000775 0.000852 0.995068 0.001263 0.002817 0.908494 0.002062 0.081007 0.008437 0.042519 0.330224 0.303314 0.323943 0.593041 0.244207 0.155065 0.007687 0.847758 0.030041 0.107346 0.014855 0.026800 0.014992 0.145921 0.812287 0.033897 0.152945 0.736965 0.076193 0.006360 0.984329 0.006268 0.003043 0.606464 0.244715 0.015923 0.132898 0.201262 0.365312 0.082159 0.351267 0.097391 0.417114 0.060450 0.425045 0.208158 0.072958 0.594863 0.124020 MOTIF AC0157:ZFP:['C2H2_zinc_finger_factors'] AC0157:ZFP:['C2H2_zinc_finger_factors'] letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.000945 0.002920 0.011055 0.985080 0.001895 0.005605 0.990605 0.001895 0.002055 0.993840 0.001265 0.002840 0.000870 0.941800 0.056775 0.000555 0.007185 0.001265 0.986890 0.004660 0.003000 0.960205 0.022030 0.014765 MOTIF AC0158:E2F/TFDP:E2F AC0158:E2F/TFDP:E2F letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.128870 0.206901 0.638066 0.026163 0.010950 0.041025 0.944352 0.003673 0.071072 0.889621 0.005567 0.033740 0.029266 0.019581 0.930727 0.020426 0.013835 0.095295 0.880555 0.010315 0.037590 0.050263 0.902852 0.009295 0.900747 0.023111 0.063806 0.012336 0.648242 0.062280 0.257084 0.032394 MOTIF AC0159:E2F:E2F AC0159:E2F:E2F letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.109342 0.073675 0.093443 0.723539 0.097137 0.086154 0.072196 0.744512 0.077249 0.157845 0.719657 0.045249 0.007979 0.045431 0.945864 0.000726 0.006099 0.958374 0.024186 0.011341 0.001759 0.002033 0.994137 0.002071 0.004854 0.314304 0.672836 0.008006 0.005769 0.119797 0.854557 0.019877 0.897035 0.041969 0.033582 0.027414 0.888538 0.019744 0.058672 0.033046 0.780260 0.052794 0.074967 0.091979 MOTIF AC0160:BCL11B:C2H2_ZF AC0160:BCL11B:C2H2_ZF letter-probability matrix: alength= 4 w= 19 nsites= 20 E= 0 0.000000 0.005630 0.994370 0.000000 0.001155 0.014375 0.000580 0.983890 0.000000 0.001145 0.986240 0.012615 0.993960 0.002340 0.001170 0.002530 0.998235 0.001175 0.000590 0.000000 0.000590 0.999410 0.000000 0.000000 0.172315 0.001860 0.824065 0.001760 0.094035 0.475473 0.166024 0.264469 0.243759 0.221159 0.153484 0.381598 0.353545 0.083290 0.344735 0.218430 0.313378 0.253534 0.185444 0.247644 0.305357 0.285666 0.139796 0.269181 0.256649 0.227579 0.402213 0.113559 0.002350 0.844315 0.000000 0.153335 0.032145 0.005250 0.000000 0.962605 0.998235 0.001175 0.000590 0.000000 0.000000 0.991640 0.001750 0.006610 0.997555 0.001175 0.001270 0.000000 0.013395 0.978340 0.003470 0.004795 MOTIF AC0161:E2F/ZBTB:E2F AC0161:E2F/ZBTB:E2F letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.037830 0.186842 0.741571 0.033757 0.004358 0.976015 0.010675 0.008952 0.028068 0.028583 0.903021 0.040328 0.003377 0.962551 0.019820 0.014252 0.036383 0.037505 0.899442 0.026670 0.003910 0.975392 0.003523 0.017175 0.145216 0.167969 0.505548 0.181266 0.013057 0.900430 0.051713 0.034800 MOTIF AC0162:ZNF:C2H2_ZF AC0162:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 18 nsites= 20 E= 0 0.006070 0.150170 0.070170 0.773590 0.428460 0.299745 0.235810 0.035985 0.047435 0.787776 0.006070 0.158719 0.819230 0.010340 0.006070 0.164360 0.051710 0.023165 0.833586 0.091540 0.014615 0.849319 0.010340 0.125726 0.048805 0.833586 0.006070 0.111539 0.010340 0.911880 0.006070 0.071710 0.111539 0.075985 0.026070 0.786406 0.715296 0.038890 0.124359 0.121454 0.097350 0.068805 0.691025 0.142820 0.750855 0.152905 0.018890 0.077350 0.027435 0.800770 0.047435 0.124360 0.107264 0.078890 0.038890 0.774956 0.006070 0.749315 0.006070 0.238545 0.054615 0.224359 0.026070 0.694957 0.080255 0.840600 0.034615 0.044530 0.082990 0.842139 0.010340 0.064530 MOTIF AC0163:ZNF:C2H2_ZF AC0163:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.071430 0.800000 0.000000 0.128570 0.771430 0.000000 0.000000 0.228570 0.064290 0.000000 0.935710 0.000000 0.500000 0.071430 0.321430 0.107140 0.000000 0.928570 0.000000 0.071430 0.000000 0.064290 0.000000 0.935710 0.000000 0.028570 0.000000 0.971430 0.035710 0.164290 0.414290 0.385710 0.064289 0.071429 0.121429 0.742853 0.042860 0.928570 0.021430 0.007140 0.078570 0.514290 0.021430 0.385710 0.064290 0.121430 0.107140 0.707140 MOTIF AC0164:ZNF:C2H2_ZF AC0164:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.069436 0.815513 0.025595 0.089456 0.059885 0.055705 0.012110 0.872300 0.937245 0.031045 0.008920 0.022790 0.016845 0.960090 0.006265 0.016800 0.016760 0.962785 0.009140 0.011315 0.169800 0.569140 0.089805 0.171255 0.136095 0.262300 0.041790 0.559815 0.297546 0.344597 0.119161 0.238696 0.206195 0.445370 0.145505 0.202930 0.279576 0.278141 0.228006 0.214276 0.356015 0.437790 0.153405 0.052790 0.083815 0.811001 0.026225 0.078960 0.062730 0.059045 0.016590 0.861636 0.934155 0.026490 0.013085 0.026270 0.021220 0.962160 0.004190 0.012430 0.015975 0.970165 0.009960 0.003900 MOTIF AC0165:ZNF:C2H2_ZF AC0165:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.089760 0.881499 0.003845 0.024895 0.006030 0.127059 0.009435 0.857476 0.865574 0.084870 0.001105 0.048450 0.039290 0.950365 0.004890 0.005455 0.022555 0.953185 0.015605 0.008655 0.064720 0.835435 0.018770 0.081075 0.117289 0.223409 0.012405 0.646897 0.182510 0.470635 0.049760 0.297095 0.324050 0.122725 0.392615 0.160610 0.223070 0.639170 0.109790 0.027970 0.035215 0.905035 0.020265 0.039485 0.015900 0.086075 0.013710 0.884316 0.853105 0.094670 0.022370 0.029855 0.027395 0.964230 0.000565 0.007810 0.023820 0.962855 0.008315 0.005010 0.090980 0.756630 0.033690 0.118700 MOTIF AC0166:ZFP:C2H2_ZF AC0166:ZFP:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.158596 0.021010 0.792034 0.028360 0.138920 0.000867 0.857213 0.003000 0.988260 0.003000 0.007207 0.001533 0.001533 0.004533 0.983341 0.010593 0.085250 0.285738 0.620145 0.008867 0.241603 0.520271 0.142663 0.095463 0.346790 0.336160 0.059390 0.257660 0.157801 0.641407 0.084184 0.116608 0.208393 0.253060 0.162133 0.376413 0.154009 0.022890 0.687919 0.135182 0.029803 0.000867 0.968463 0.000867 0.981600 0.000867 0.014333 0.003200 0.619590 0.108480 0.262603 0.009327 0.157587 0.304881 0.204138 0.333394 0.056780 0.070370 0.736990 0.135860 MOTIF AC0167:ZNF:C2H2_ZF AC0167:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.101450 0.579710 0.159420 0.159420 0.000000 0.434780 0.144930 0.420290 0.144930 0.492750 0.188410 0.173910 0.086959 0.826082 0.043480 0.043480 0.681167 0.072461 0.014490 0.231882 0.000000 0.101450 0.898550 0.000000 0.086960 0.869560 0.028990 0.014490 0.000000 0.014490 0.028990 0.956520 0.434780 0.202900 0.188410 0.173910 0.028990 0.971010 0.000000 0.000000 0.000000 0.000000 0.014490 0.985510 0.028990 0.971010 0.000000 0.000000 0.000000 0.840580 0.000000 0.159420 MOTIF AC0168:ZNF:C2H2_ZF AC0168:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.081017 0.030723 0.836863 0.051397 0.399531 0.255108 0.283658 0.061703 0.671927 0.095963 0.050600 0.181510 0.598267 0.039787 0.224603 0.137343 0.031550 0.005127 0.962186 0.001137 0.010107 0.096803 0.885040 0.008050 0.493280 0.358460 0.070517 0.077743 0.395775 0.208482 0.276816 0.118927 0.681993 0.066907 0.198363 0.052737 0.021503 0.002630 0.965760 0.010107 0.096634 0.010733 0.828715 0.063918 0.034950 0.001137 0.957920 0.005993 0.097428 0.396452 0.458263 0.047857 0.107323 0.571187 0.059427 0.262063 0.504492 0.117543 0.271762 0.106203 0.065647 0.097728 0.766925 0.069700 MOTIF AC0169:ZNF/PRDM:C2H2_ZF AC0169:ZNF/PRDM:C2H2_ZF letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.110124 0.513246 0.119333 0.257297 0.105264 0.378344 0.144808 0.371584 0.077387 0.508774 0.116298 0.297541 0.097652 0.441451 0.117643 0.343254 0.083643 0.513750 0.104763 0.297844 0.063180 0.630863 0.096434 0.209523 0.132257 0.522573 0.105493 0.239677 0.078250 0.339611 0.156703 0.425435 0.078007 0.581348 0.159914 0.180731 0.057172 0.640112 0.091668 0.211048 0.037945 0.520046 0.046750 0.395259 0.060127 0.661063 0.100431 0.178379 0.055428 0.663098 0.049980 0.231494 0.101836 0.151956 0.145357 0.600851 0.051347 0.661215 0.134037 0.153400 0.050688 0.785883 0.056371 0.107058 0.095519 0.548795 0.067338 0.288348 MOTIF AC0170:FLI/ZNF:Ets AC0170:FLI/ZNF:Ets letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.027600 0.001625 0.956165 0.014610 0.063095 0.001625 0.933655 0.001625 0.841020 0.040585 0.103785 0.014610 0.685172 0.092535 0.175214 0.047080 0.285605 0.008115 0.685175 0.021105 0.043615 0.118505 0.831495 0.006385 0.654435 0.066560 0.128030 0.150975 0.584743 0.008115 0.373052 0.034090 0.008115 0.008115 0.970895 0.012875 0.008115 0.011150 0.979110 0.001625 0.940580 0.046645 0.011150 0.001625 0.923700 0.001625 0.060065 0.014610 0.001625 0.001625 0.995125 0.001625 0.040585 0.024135 0.920670 0.014610 0.718071 0.119804 0.092535 0.069590 0.610715 0.125000 0.134525 0.129760 MOTIF AC0171:RBAK/ZNF:C2H2_ZF AC0171:RBAK/ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.055605 0.786636 0.021120 0.136639 0.028020 0.209049 0.050685 0.712246 0.064350 0.438068 0.350598 0.146984 0.000430 0.506785 0.014350 0.478435 0.000430 0.292736 0.043910 0.662923 0.000430 0.346265 0.003880 0.649425 0.024820 0.815570 0.159180 0.000430 0.000430 0.447195 0.037015 0.515360 0.024820 0.664310 0.000430 0.310440 0.116326 0.138361 0.031465 0.713849 0.000430 0.910400 0.000430 0.088740 0.073095 0.621710 0.112500 0.192695 0.118050 0.684285 0.093535 0.104130 MOTIF AC0172:ZNF:C2H2_ZF AC0172:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 27 nsites= 20 E= 0 0.129545 0.134090 0.125000 0.611365 0.125000 0.588635 0.147730 0.138635 0.125000 0.125000 0.165910 0.584090 0.124999 0.556817 0.161364 0.156819 0.152275 0.129545 0.129545 0.588635 0.138636 0.579548 0.143181 0.138636 0.156820 0.129545 0.125000 0.588635 0.125000 0.547725 0.152275 0.175000 0.138635 0.125000 0.161365 0.575000 0.134090 0.584090 0.152275 0.129545 0.134090 0.156820 0.125000 0.584090 0.134089 0.552272 0.156819 0.156819 0.088670 0.186875 0.070515 0.653940 0.094300 0.654787 0.059935 0.190979 0.044125 0.173445 0.043480 0.738950 0.009520 0.466175 0.047180 0.477125 0.004975 0.053860 0.023160 0.918005 0.003895 0.954335 0.023160 0.018610 0.000430 0.022720 0.014065 0.962785 0.009520 0.918410 0.027705 0.044365 0.076795 0.105325 0.018610 0.799270 0.078725 0.590540 0.162245 0.168490 0.045210 0.128901 0.012545 0.813344 0.021195 0.726161 0.074010 0.178634 0.035915 0.083475 0.152036 0.728574 0.024655 0.840145 0.033545 0.101655 0.068145 0.290884 0.003895 0.637077 MOTIF AC0173:ZNF:C2H2_ZF AC0173:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 18 nsites= 20 E= 0 0.084078 0.305652 0.118074 0.492196 0.056697 0.224327 0.326360 0.392617 0.037337 0.860473 0.017200 0.084990 0.079653 0.478399 0.056940 0.385009 0.063497 0.118170 0.341563 0.476770 0.031657 0.131033 0.477152 0.360159 0.001043 0.227173 0.019310 0.752474 0.074437 0.042057 0.382875 0.500630 0.011107 0.951239 0.008417 0.029237 0.191544 0.321154 0.053673 0.433629 0.051297 0.201240 0.262300 0.485163 0.165306 0.430228 0.294349 0.110117 0.160997 0.504391 0.096600 0.238012 0.224809 0.319819 0.195272 0.260099 0.176149 0.284626 0.116477 0.422748 0.058653 0.599120 0.060120 0.282107 0.047677 0.604646 0.046987 0.300691 0.167726 0.412245 0.087073 0.332956 MOTIF AC0174:ZSCAN:C2H2_ZF AC0174:ZSCAN:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.101090 0.042085 0.675300 0.181525 0.094565 0.042630 0.076980 0.785825 0.072475 0.753010 0.167535 0.006980 0.037700 0.957160 0.004145 0.000995 0.055025 0.859545 0.012905 0.072525 0.120626 0.478577 0.056465 0.344332 0.262337 0.519350 0.106959 0.111354 0.139080 0.814205 0.014980 0.031735 0.194385 0.550915 0.112610 0.142090 0.018580 0.956395 0.000560 0.024465 0.869176 0.018455 0.102144 0.010225 0.996335 0.001795 0.000060 0.001810 0.789420 0.012565 0.192860 0.005155 0.215995 0.065955 0.310810 0.407240 MOTIF AC0175:MZF:C2H2_ZF AC0175:MZF:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.156250 0.187500 0.000000 0.656250 0.187500 0.468750 0.156250 0.187500 0.125000 0.843750 0.000000 0.031250 0.000000 0.875000 0.093750 0.031250 0.062500 0.937500 0.000000 0.000000 0.250000 0.718750 0.000000 0.031250 0.000000 0.093750 0.031250 0.875000 0.406250 0.531250 0.000000 0.062500 MOTIF AC0176:MZF:C2H2_ZF AC0176:MZF:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.627192 0.191634 0.132604 0.048570 0.672692 0.078845 0.028355 0.220109 0.008430 0.005395 0.007945 0.978230 0.008065 0.974230 0.009065 0.008640 0.010190 0.968620 0.003275 0.017915 0.071735 0.907400 0.005520 0.015345 0.010100 0.770480 0.005035 0.214385 0.502175 0.160065 0.047360 0.290400 MOTIF AC0177:ZNF/MZF:C2H2_ZF AC0177:ZNF/MZF:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.303464 0.114990 0.409920 0.171626 0.122474 0.627517 0.103243 0.146767 0.606995 0.174147 0.118776 0.100081 0.113750 0.170767 0.171543 0.543940 0.140903 0.111507 0.614648 0.132941 0.001421 0.030033 0.008714 0.959832 0.001164 0.985933 0.002303 0.010600 0.016279 0.958210 0.001497 0.024014 0.011359 0.959517 0.001164 0.027960 0.014127 0.935273 0.002149 0.048451 0.457993 0.295448 0.094943 0.151616 0.131539 0.278693 0.127147 0.462620 MOTIF AC0178:ZNF:C2H2_ZF AC0178:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.132440 0.561010 0.126490 0.180060 0.156249 0.501487 0.126489 0.215774 0.132440 0.584820 0.150300 0.132440 0.002055 0.978885 0.006555 0.012505 0.013960 0.883161 0.002055 0.100824 0.095520 0.368203 0.090210 0.446068 0.185771 0.226311 0.102116 0.485802 0.231620 0.146835 0.174030 0.447515 0.234355 0.127530 0.562865 0.075250 0.172419 0.264439 0.537277 0.025865 0.008005 0.101310 0.012505 0.878180 0.022325 0.057230 0.618682 0.301763 0.004305 0.958770 0.020715 0.016210 0.004305 0.039375 0.002055 0.954265 0.002055 0.969225 0.008005 0.020715 MOTIF AC0179:ZNF:C2H2_ZF AC0179:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.073470 0.296341 0.551272 0.078917 0.043662 0.060693 0.048518 0.847127 0.053863 0.490696 0.407009 0.048433 0.017543 0.963168 0.006717 0.012572 0.008740 0.014847 0.005152 0.971261 0.004263 0.980310 0.006982 0.008445 0.018743 0.965129 0.008723 0.007405 0.065450 0.814563 0.020738 0.099248 MOTIF AC0180:ZSCAN/ZNF:C2H2_ZF AC0180:ZSCAN/ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.025943 0.884896 0.028933 0.060228 0.010160 0.940615 0.007080 0.042145 0.023367 0.026278 0.271635 0.678720 0.012895 0.972655 0.006055 0.008395 0.004817 0.758730 0.129606 0.106847 0.059757 0.085148 0.240670 0.614424 0.005768 0.975810 0.002767 0.015655 0.004605 0.934775 0.009565 0.051055 0.316305 0.421162 0.141030 0.121502 0.082135 0.103724 0.066845 0.747296 0.067217 0.765092 0.064768 0.102923 0.378440 0.108482 0.227727 0.285350 0.101955 0.178468 0.606889 0.112688 MOTIF AC0181:ZNF:C2H2_ZF AC0181:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.075543 0.623881 0.135927 0.164650 0.030900 0.738082 0.098637 0.132380 0.048040 0.281640 0.111396 0.558924 0.016357 0.805361 0.075160 0.103123 0.508504 0.117323 0.288273 0.085900 0.025480 0.023803 0.929204 0.021513 0.005323 0.977350 0.005530 0.011797 0.003607 0.981740 0.002530 0.012123 0.006087 0.016767 0.005557 0.971589 0.001300 0.969060 0.011927 0.017713 0.006457 0.887939 0.011937 0.093667 MOTIF AC0182:ZNF:C2H2_ZF AC0182:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.001230 0.045570 0.626840 0.326360 0.001230 0.996310 0.001230 0.001230 0.001230 0.089910 0.020930 0.887930 0.001230 0.986460 0.011080 0.001230 0.099749 0.030790 0.424886 0.444576 0.001230 0.282023 0.690887 0.025860 0.075120 0.326360 0.296800 0.301720 0.001230 0.996310 0.001230 0.001230 0.001230 0.006160 0.001230 0.991380 0.001230 0.996310 0.001230 0.001230 0.144090 0.474140 0.040640 0.341130 0.025870 0.508620 0.149010 0.316500 0.035710 0.282023 0.208132 0.474135 0.055420 0.676100 0.114540 0.153940 MOTIF AC0183:ZNF:C2H2_ZF AC0183:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.088711 0.787638 0.110211 0.013440 0.088710 0.314520 0.293010 0.303760 0.131720 0.282260 0.443550 0.142470 0.002690 0.948921 0.002690 0.045700 0.217740 0.142470 0.604840 0.034950 0.002690 0.002690 0.002690 0.991930 0.002690 0.970430 0.002690 0.024190 0.002690 0.991930 0.002690 0.002690 0.002690 0.056450 0.002690 0.938170 0.002690 0.110210 0.013440 0.873660 MOTIF AC0184:ZNF:C2H2_ZF AC0184:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.046903 0.017397 0.882537 0.053163 0.050933 0.869687 0.076093 0.003287 0.003007 0.988183 0.006220 0.002590 0.003007 0.006780 0.988183 0.002030 0.001053 0.994467 0.003427 0.001053 0.003007 0.005243 0.006780 0.984970 0.001330 0.988600 0.009157 0.000913 0.043593 0.859919 0.044350 0.052137 MOTIF AC0185:ZNF:C2H2_ZF AC0185:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.067120 0.032100 0.888130 0.012650 0.078791 0.028210 0.888139 0.004860 0.880341 0.008760 0.102139 0.008760 0.000970 0.008760 0.989300 0.000970 0.218870 0.650780 0.071010 0.059340 0.016540 0.012650 0.919251 0.051559 0.028210 0.880350 0.078790 0.012650 0.000970 0.907590 0.020430 0.071010 0.000970 0.386190 0.226650 0.386190 0.156620 0.518480 0.323930 0.000970 0.164400 0.316150 0.183850 0.335600 0.117700 0.063230 0.751950 0.067120 0.207200 0.514590 0.234440 0.043770 0.176070 0.592410 0.168290 0.063230 0.133270 0.393970 0.343380 0.129380 0.144940 0.195530 0.518480 0.141050 0.055449 0.425096 0.506805 0.012650 MOTIF AC0186:ZNF:C2H2_ZF AC0186:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.023630 0.356010 0.312470 0.307890 0.001110 0.247288 0.001110 0.750492 0.135450 0.001110 0.822450 0.040990 0.001110 0.001110 0.868000 0.129780 0.005540 0.342520 0.154210 0.497730 0.001110 0.001110 0.983180 0.014600 0.138640 0.686300 0.129650 0.045410 0.055329 0.379346 0.001110 0.564214 0.005540 0.974520 0.001110 0.018830 0.001110 0.996670 0.001110 0.001110 MOTIF AC0187:IRF:IRF AC0187:IRF:IRF letter-probability matrix: alength= 4 w= 18 nsites= 20 E= 0 0.001995 0.038260 0.958015 0.001730 0.000265 0.000420 0.000610 0.998705 0.522480 0.005565 0.000045 0.471910 0.000425 0.002730 0.000960 0.995885 0.000340 0.987350 0.001435 0.010875 0.045095 0.004595 0.850156 0.100154 0.270499 0.220719 0.351478 0.157304 0.238570 0.189905 0.130960 0.440565 0.301863 0.156429 0.224299 0.317408 0.369292 0.200881 0.169736 0.260091 0.334445 0.132505 0.194560 0.338490 0.213974 0.260149 0.348548 0.177329 0.052055 0.509393 0.002465 0.436087 0.005680 0.001715 0.992105 0.000500 0.994715 0.002435 0.002045 0.000805 0.468245 0.000535 0.008085 0.523135 0.997635 0.000900 0.001130 0.000335 0.001395 0.959395 0.037325 0.001885 MOTIF AC0188:IRF/STAT:IRF AC0188:IRF/STAT:IRF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.138476 0.029665 0.806544 0.025315 0.895105 0.024846 0.064135 0.015914 0.932424 0.005721 0.014608 0.047247 0.961473 0.007201 0.004754 0.026572 0.136238 0.490011 0.315726 0.058025 0.057401 0.524973 0.056867 0.360759 0.057263 0.006147 0.929026 0.007564 0.940378 0.023505 0.027206 0.008911 0.907172 0.014382 0.020253 0.058193 0.949992 0.009479 0.011528 0.029001 0.065391 0.628330 0.261178 0.045101 MOTIF AC0189:IRF:IRF AC0189:IRF:IRF letter-probability matrix: alength= 4 w= 20 nsites= 20 E= 0 0.026245 0.841014 0.017270 0.115471 0.334075 0.095890 0.300065 0.269970 0.036530 0.129650 0.798945 0.034875 0.020270 0.000160 0.000400 0.979170 0.027310 0.002620 0.000040 0.970030 0.003600 0.029105 0.001825 0.965470 0.000175 0.988675 0.000150 0.011000 0.086014 0.613229 0.265272 0.035485 0.321070 0.257990 0.145455 0.275485 0.037515 0.289091 0.599038 0.074355 0.030555 0.000065 0.968850 0.000530 0.930630 0.000065 0.067765 0.001540 0.955485 0.000025 0.000355 0.044135 0.956835 0.001335 0.000235 0.041595 0.094095 0.786500 0.111665 0.007740 0.117870 0.527585 0.139735 0.214810 0.191160 0.000170 0.806805 0.001865 0.680820 0.203925 0.096340 0.018915 0.721580 0.095930 0.009975 0.172515 0.776334 0.128866 0.013365 0.081435 MOTIF AC0190:IRF/ZBED:IRF AC0190:IRF/ZBED:IRF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.733856 0.097975 0.110848 0.057321 0.115725 0.704742 0.075790 0.103743 0.011727 0.844640 0.016530 0.127103 0.068195 0.027444 0.895112 0.009249 0.960700 0.020891 0.010393 0.008016 0.768023 0.013176 0.029851 0.188950 0.881779 0.081365 0.017454 0.019402 0.036095 0.863263 0.069130 0.031512 0.062555 0.414983 0.034015 0.488447 MOTIF AC0191:IRF:IRF AC0191:IRF:IRF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.811670 0.011380 0.068960 0.107990 0.749050 0.086770 0.039190 0.124990 0.334313 0.236512 0.170162 0.259013 0.105860 0.781830 0.074440 0.037870 0.047930 0.000340 0.950390 0.001340 0.997060 0.000120 0.002700 0.000120 0.990200 0.000470 0.000350 0.008980 0.994270 0.000230 0.000350 0.005150 0.553160 0.089840 0.001500 0.355500 0.001950 0.018580 0.006310 0.973160 0.010170 0.959090 0.001020 0.029720 0.064660 0.067710 0.809270 0.058360 0.311450 0.210350 0.218010 0.260190 0.118111 0.045850 0.068551 0.767488 0.118250 0.076030 0.015000 0.790720 MOTIF AC0192:PRDM:C2H2_ZF AC0192:PRDM:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.746150 0.110918 0.095237 0.047694 0.027296 0.921145 0.023593 0.027966 0.017856 0.013322 0.006992 0.961830 0.009834 0.087426 0.011442 0.891298 0.012719 0.006336 0.013348 0.967597 0.006025 0.958879 0.004737 0.030359 0.520209 0.188735 0.064592 0.226463 0.057657 0.835658 0.033994 0.072691 0.081689 0.169221 0.059062 0.690028 MOTIF AC0193:ZNF:C2H2_ZF AC0193:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.000870 0.990420 0.000870 0.007840 0.436414 0.189022 0.000870 0.373694 0.000870 0.997390 0.000870 0.000870 0.631530 0.223870 0.049650 0.094950 0.018290 0.192510 0.000870 0.788330 0.000870 0.000870 0.000870 0.997390 0.000870 0.997390 0.000870 0.000870 0.178572 0.582756 0.000870 0.237802 0.056620 0.401570 0.067070 0.474740 MOTIF AC0194:ZNF:C2H2_ZF AC0194:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.077941 0.730887 0.060291 0.130881 0.472060 0.030880 0.448530 0.048530 0.025000 0.595590 0.048530 0.330880 0.213240 0.125000 0.625000 0.036760 0.001470 0.960300 0.001470 0.036760 0.007350 0.001470 0.001470 0.989710 0.001470 0.830880 0.001470 0.166180 0.801470 0.001470 0.048530 0.148530 0.001470 0.936760 0.019120 0.042650 0.001470 0.854410 0.001470 0.142650 0.036760 0.489710 0.054410 0.419120 MOTIF AC0195:ZNF:C2H2_ZF AC0195:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.000860 0.997420 0.000860 0.000860 0.000860 0.234538 0.000860 0.763742 0.000860 0.000860 0.000860 0.997420 0.000860 0.619414 0.000860 0.378866 0.000860 0.000860 0.000860 0.997420 0.000860 0.275770 0.000860 0.722510 0.000860 0.997420 0.000860 0.000860 0.000860 0.784360 0.000860 0.213920 0.000860 0.258590 0.000860 0.739690 0.000860 0.000860 0.000860 0.997420 0.000860 0.227660 0.595360 0.176120 MOTIF AC0196:NFKB/RELA:Rel AC0196:NFKB/RELA:Rel letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.019680 0.041812 0.875113 0.063395 0.004452 0.003774 0.983326 0.008448 0.017210 0.000641 0.896667 0.085482 0.399219 0.010132 0.550170 0.040479 0.531612 0.249256 0.110130 0.109002 0.246907 0.021749 0.032185 0.699159 0.023949 0.075431 0.039152 0.861468 0.031264 0.354943 0.020717 0.593076 0.037014 0.907412 0.011109 0.044465 0.028447 0.925140 0.015514 0.030899 MOTIF AC0197:ZBTB:C2H2_ZF AC0197:ZBTB:C2H2_ZF letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.119530 0.081713 0.054667 0.744089 0.002867 0.001843 0.011437 0.983853 0.003277 0.977717 0.000613 0.018393 0.020750 0.037870 0.014133 0.927247 0.368390 0.012130 0.618090 0.001390 0.000613 0.004353 0.989674 0.005360 0.975541 0.011523 0.001023 0.011913 MOTIF AC0198:ZBTB:C2H2_ZF AC0198:ZBTB:C2H2_ZF letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.011963 0.940230 0.013840 0.033967 0.011017 0.048137 0.016040 0.924806 0.699743 0.007887 0.287500 0.004870 0.007333 0.007150 0.975280 0.010237 0.963807 0.011743 0.008567 0.015883 0.931033 0.007367 0.044123 0.017477 0.004890 0.969160 0.006687 0.019263 MOTIF AC0199:HSF:HSF AC0199:HSF:HSF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.035580 0.031485 0.903057 0.029878 0.892820 0.036749 0.026148 0.044283 0.838537 0.027257 0.048145 0.086061 0.133509 0.394162 0.191657 0.280671 0.299027 0.163210 0.441308 0.096455 0.105334 0.038826 0.034711 0.821129 0.070680 0.011589 0.088804 0.828927 0.009530 0.975601 0.009658 0.005211 0.015434 0.242383 0.155160 0.587023 0.584186 0.119828 0.264043 0.031943 0.026580 0.040052 0.895441 0.037927 0.784225 0.091933 0.043407 0.080435 MOTIF AC0200:ZNF:C2H2_ZF AC0200:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.089770 0.035710 0.004830 0.869690 0.035710 0.000970 0.916020 0.047300 0.000970 0.035710 0.024130 0.939190 0.000970 0.927610 0.031850 0.039570 0.008690 0.170848 0.000970 0.819492 0.811772 0.000970 0.166988 0.020270 0.000970 0.000970 0.993230 0.004830 0.035710 0.000970 0.197880 0.765440 0.904431 0.000970 0.004830 0.089769 0.518335 0.008690 0.352316 0.120659 0.244208 0.000970 0.668913 0.085909 0.004830 0.000970 0.043440 0.950760 0.070461 0.800198 0.012550 0.116791 0.012550 0.390926 0.000970 0.595554 MOTIF AC0201:TEAD:TEA AC0201:TEAD:TEA letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.325128 0.135978 0.047882 0.491012 0.111237 0.003729 0.875046 0.009988 0.017811 0.019420 0.953046 0.009723 0.796531 0.034959 0.008138 0.160372 0.987072 0.003131 0.004936 0.004861 0.009798 0.010759 0.010284 0.969159 0.007143 0.020025 0.839902 0.132930 0.033795 0.356994 0.023068 0.586143 MOTIF AC0202:ZNF:C2H2_ZF AC0202:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.139170 0.048462 0.069433 0.742934 0.065302 0.007528 0.919833 0.007337 0.410739 0.526311 0.023135 0.039815 0.372939 0.039743 0.059560 0.527758 0.008462 0.003320 0.984325 0.003893 0.059237 0.082325 0.421017 0.437422 0.924307 0.032775 0.037885 0.005033 0.903812 0.067165 0.009288 0.019735 0.004747 0.041210 0.039947 0.914096 0.015880 0.931951 0.018202 0.033967 0.110565 0.157952 0.014175 0.717309 MOTIF AC0203:ZNF:C2H2_ZF AC0203:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 19 nsites= 20 E= 0 0.155821 0.211591 0.598293 0.034295 0.691397 0.186219 0.021955 0.100429 0.007315 0.039635 0.061540 0.891510 0.025050 0.001925 0.009240 0.963785 0.005395 0.715330 0.268485 0.010790 0.005395 0.917895 0.000000 0.076710 0.901765 0.020405 0.057425 0.020405 0.009240 0.040865 0.180396 0.769499 0.037020 0.184240 0.010790 0.767950 0.026975 0.863249 0.017735 0.092040 0.183175 0.357210 0.410740 0.048875 0.893700 0.021155 0.034665 0.050480 0.151924 0.103099 0.194179 0.550797 0.104275 0.031945 0.021155 0.842625 0.030395 0.749094 0.191611 0.028900 0.080395 0.889959 0.007690 0.021955 0.832160 0.107315 0.033175 0.027350 0.115064 0.044660 0.016560 0.823716 0.014635 0.070780 0.107375 0.807210 MOTIF AC0204:ZNF33B:C2H2_ZF AC0204:ZNF33B:C2H2_ZF letter-probability matrix: alength= 4 w= 20 nsites= 20 E= 0 0.873550 0.013080 0.053780 0.059590 0.111920 0.024710 0.088660 0.774710 0.007270 0.117730 0.111920 0.763080 0.501450 0.408430 0.077040 0.013080 0.809598 0.170062 0.018890 0.001450 0.989830 0.001450 0.001450 0.007270 0.007270 0.059591 0.018890 0.914249 0.170060 0.164240 0.036340 0.629360 0.024710 0.838668 0.001450 0.135171 0.001450 0.827038 0.001450 0.170062 0.199130 0.425870 0.001450 0.373550 0.047970 0.111920 0.001450 0.838660 0.059590 0.536340 0.094480 0.309590 0.036340 0.414240 0.042150 0.507270 0.187500 0.251450 0.390990 0.170060 0.292150 0.152620 0.059590 0.495640 0.047970 0.082849 0.053779 0.815402 0.257270 0.088660 0.222380 0.431690 0.501450 0.216570 0.239830 0.042150 0.652613 0.210758 0.100289 0.036340 MOTIF AC0205:NFATC/STAT:Rel AC0205:NFATC/STAT:Rel letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.080933 0.052543 0.155542 0.710981 0.007891 0.013885 0.012035 0.966189 0.017630 0.033767 0.019762 0.928841 0.005977 0.977647 0.010175 0.006201 0.022416 0.928129 0.010084 0.039371 0.652602 0.004566 0.286376 0.056456 0.016285 0.299030 0.048778 0.635907 0.060334 0.016335 0.886934 0.036397 0.010833 0.012999 0.960367 0.015801 0.920130 0.025981 0.034335 0.019554 0.935394 0.023027 0.025057 0.016522 MOTIF AC0206:NFATC:Rel AC0206:NFATC:Rel letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.068394 0.009620 0.125564 0.796422 0.000282 0.001216 0.000676 0.997826 0.000466 0.017000 0.000956 0.981578 0.000214 0.997936 0.000824 0.001026 0.001214 0.992014 0.004422 0.002350 0.565263 0.009260 0.396605 0.028872 0.082880 0.295355 0.052184 0.569581 0.240398 0.192294 0.147958 0.419351 0.437073 0.144018 0.245662 0.173246 0.479322 0.105638 0.205939 0.209101 0.028896 0.386120 0.186813 0.398172 0.000406 0.000228 0.999186 0.000180 0.181696 0.000418 0.817674 0.000212 0.997774 0.000964 0.000916 0.000346 0.977534 0.011730 0.002066 0.008670 MOTIF AC0207:NFAT/NFATC:Rel AC0207:NFAT/NFATC:Rel letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.020470 0.041040 0.927378 0.011112 0.035930 0.012065 0.291114 0.660891 0.784503 0.008845 0.142315 0.064337 0.502747 0.246088 0.105167 0.145998 0.200993 0.232438 0.125832 0.440738 0.157400 0.074958 0.088822 0.678820 0.004655 0.012162 0.015700 0.967483 0.015790 0.038465 0.025397 0.920348 0.004002 0.986570 0.004575 0.004853 0.000087 0.992033 0.002835 0.005045 0.547460 0.094635 0.248923 0.108982 MOTIF AC0208:NFATC/ZNF:Rel AC0208:NFATC/ZNF:Rel letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.128467 0.145587 0.227170 0.498777 0.063165 0.026877 0.094990 0.814968 0.009075 0.016513 0.003353 0.971059 0.005071 0.016957 0.006816 0.971156 0.010379 0.979733 0.003883 0.006005 0.005631 0.985886 0.003749 0.004734 0.575162 0.041537 0.305450 0.077851 MOTIF AC0209:ZNF:C2H2_ZF AC0209:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.025710 0.357144 0.257143 0.360004 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.588570 0.411430 0.000000 MOTIF AC0210:BCL/BCL6B:C2H2_ZF AC0210:BCL/BCL6B:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.045238 0.016692 0.009852 0.928218 0.029964 0.041933 0.015235 0.912868 0.030732 0.946640 0.015866 0.006762 0.016062 0.916301 0.003312 0.064325 0.011457 0.004612 0.007684 0.976247 0.557236 0.318974 0.109151 0.014639 0.032975 0.002272 0.964104 0.000649 0.986444 0.005018 0.004514 0.004024 0.617342 0.046986 0.084124 0.251548 0.653842 0.009767 0.051767 0.284624 0.233358 0.018518 0.721253 0.026872 0.048244 0.857189 0.016771 0.077796 MOTIF AC0211:ZNF:C2H2_ZF AC0211:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.046530 0.021410 0.038090 0.893970 0.003435 0.986640 0.002365 0.007560 0.001335 0.881675 0.001335 0.115655 0.036105 0.025605 0.005530 0.932760 0.027595 0.930035 0.012825 0.029545 0.016910 0.937590 0.000265 0.045235 0.854574 0.017060 0.092370 0.035995 0.069435 0.010765 0.843921 0.075880 0.018130 0.003435 0.977100 0.001335 0.802146 0.120529 0.068700 0.008625 0.937175 0.012900 0.025460 0.024465 0.000265 0.000265 0.999205 0.000265 0.023435 0.738700 0.065420 0.172445 0.010875 0.942435 0.009775 0.036915 0.024575 0.488750 0.019275 0.467400 0.142524 0.157989 0.149744 0.549742 0.127061 0.609538 0.126031 0.137371 MOTIF AC0212:STAT/STAT5A:STAT AC0212:STAT/STAT5A:STAT letter-probability matrix: alength= 4 w= 5 nsites= 20 E= 0 0.212000 0.270000 0.116000 0.402000 0.004000 0.008000 0.020000 0.968000 0.022080 0.000000 0.024202 0.953718 0.118162 0.861718 0.012040 0.008080 0.086040 0.516706 0.080122 0.317132 MOTIF AC0213:STAT/STAT5A:STAT AC0213:STAT/STAT5A:STAT letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.012451 0.006513 0.006634 0.974402 0.020509 0.006483 0.016590 0.956418 0.055647 0.923818 0.002065 0.018470 0.013584 0.798788 0.014837 0.172791 0.191195 0.406255 0.140452 0.262098 0.309505 0.027661 0.629139 0.033695 0.029362 0.014850 0.851504 0.104284 0.879125 0.081413 0.009161 0.030301 0.956538 0.010107 0.013581 0.019774 MOTIF AC0214:OSR:C2H2_ZF AC0214:OSR:C2H2_ZF letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.082494 0.053141 0.854648 0.009717 0.008479 0.974009 0.005034 0.012478 0.007056 0.058259 0.002695 0.931990 0.609559 0.090770 0.073376 0.226295 0.013875 0.975946 0.006279 0.003900 0.005101 0.273996 0.004347 0.716556 0.030934 0.030134 0.892632 0.046300 MOTIF AC0215:ZNF:C2H2_ZF AC0215:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.275897 0.618524 0.032870 0.072709 0.419320 0.172310 0.383470 0.024900 0.001000 0.001000 0.554774 0.443226 0.001000 0.001000 0.020920 0.977080 0.040840 0.224100 0.694220 0.040840 0.001000 0.997000 0.001000 0.001000 0.001000 0.001000 0.001000 0.997000 0.012950 0.001000 0.925291 0.060759 0.001000 0.993020 0.001000 0.004980 0.001000 0.124499 0.020920 0.853581 MOTIF AC0216:ZNF:C2H2_ZF AC0216:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.051200 0.001817 0.941958 0.005025 0.000180 0.999300 0.000180 0.000340 0.000375 0.031633 0.000367 0.967625 0.000610 0.000960 0.000220 0.998210 0.000180 0.999297 0.000180 0.000343 0.526759 0.366745 0.037670 0.068826 0.008777 0.857085 0.027990 0.106148 0.042055 0.616358 0.183376 0.158211 0.329742 0.104995 0.375352 0.189910 0.088447 0.683930 0.068507 0.159115 0.095995 0.076527 0.753921 0.073557 0.112270 0.110625 0.678705 0.098400 MOTIF AC0217:ZNF:C2H2_ZF AC0217:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.500000 0.000000 0.500000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 MOTIF AC0218:ZSCAN:C2H2_ZF AC0218:ZSCAN:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.758576 0.189194 0.026578 0.025652 0.016112 0.033922 0.028834 0.921132 0.754898 0.056804 0.026596 0.161702 0.902498 0.053348 0.027462 0.016692 0.043064 0.552980 0.022376 0.381580 0.027504 0.063354 0.088053 0.821089 0.003524 0.011986 0.969000 0.015490 0.010372 0.956606 0.005660 0.027362 0.028098 0.927064 0.006424 0.038414 0.004452 0.976672 0.009850 0.009026 0.015940 0.216200 0.012650 0.755210 0.016548 0.007174 0.964456 0.011822 0.011600 0.946402 0.012124 0.029874 0.181494 0.031626 0.287218 0.499662 MOTIF AC0219:HMBOX:Homeodomain AC0219:HMBOX:Homeodomain letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.050825 0.019790 0.002597 0.926788 0.808533 0.012410 0.048900 0.130157 0.978843 0.010015 0.000847 0.010295 0.001940 0.977500 0.019785 0.000775 0.013400 0.215654 0.016962 0.753984 0.924819 0.023258 0.011020 0.040903 0.231884 0.101508 0.620673 0.045935 MOTIF AC0220:ZNF:C2H2_ZF AC0220:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.982780 0.000640 0.008290 0.008290 0.000640 0.980230 0.008290 0.010840 0.013390 0.408800 0.000640 0.577170 0.640946 0.041450 0.286353 0.031250 0.115431 0.434314 0.339923 0.110331 0.194512 0.033800 0.054211 0.717477 0.143499 0.000640 0.822062 0.033800 0.031250 0.255740 0.031250 0.681760 0.419006 0.000640 0.559304 0.021050 0.390944 0.003190 0.592476 0.013390 0.008290 0.000640 0.003190 0.987880 0.544000 0.347580 0.026150 0.082270 0.852680 0.028700 0.087370 0.031250 0.003190 0.304210 0.000640 0.691960 0.000640 0.161350 0.005740 0.832270 MOTIF AC0221:EN:Homeodomain AC0221:EN:Homeodomain letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.300000 0.000000 0.700000 0.000000 0.200000 0.000000 0.000000 0.800000 0.600000 0.000000 0.200000 0.200000 0.400000 0.100000 0.300000 0.200000 0.100000 0.000000 0.000000 0.900000 0.100000 0.200000 0.300000 0.400000 MOTIF AC0222:MYNN:C2H2_ZF AC0222:MYNN:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.142299 0.044810 0.730343 0.082549 0.588834 0.041670 0.346697 0.022800 0.010220 0.981920 0.007070 0.000790 0.000790 0.013370 0.000790 0.985050 0.044810 0.308960 0.010220 0.636010 0.013370 0.079400 0.016510 0.890720 0.003930 0.000790 0.000790 0.994490 0.937899 0.032230 0.019650 0.010220 0.261790 0.066820 0.013370 0.658020 0.044810 0.132860 0.085690 0.736640 0.104560 0.120280 0.101420 0.673740 0.085690 0.123430 0.069970 0.720910 MOTIF AC0223:ZNF:C2H2_ZF AC0223:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 18 nsites= 20 E= 0 0.786110 0.002780 0.186110 0.025000 0.027780 0.941661 0.005560 0.025000 0.000000 0.983330 0.002780 0.013890 0.947220 0.002780 0.027780 0.022220 0.005560 0.222220 0.011110 0.761110 0.419444 0.002780 0.569446 0.008330 0.002780 0.941661 0.002780 0.052779 0.022220 0.013890 0.013890 0.950000 0.038890 0.008330 0.950000 0.002780 0.002780 0.027780 0.002780 0.966660 0.005560 0.027780 0.008330 0.958330 0.005560 0.027780 0.000000 0.966660 0.005560 0.008330 0.027780 0.958330 0.066670 0.013890 0.911110 0.008330 0.522220 0.016670 0.413890 0.047220 0.005560 0.027780 0.000000 0.966660 0.030560 0.033330 0.063890 0.872220 0.955560 0.019440 0.011110 0.013890 MOTIF AC0224:ZNF:C2H2_ZF AC0224:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 20 nsites= 20 E= 0 0.164730 0.157820 0.105490 0.571959 0.133743 0.531061 0.180846 0.154350 0.575777 0.165910 0.120613 0.137700 0.019343 0.114033 0.012313 0.854311 0.053483 0.010743 0.701019 0.234756 0.002953 0.030720 0.940420 0.025907 0.004740 0.008943 0.003783 0.982534 0.069267 0.129627 0.713659 0.087447 0.118500 0.433007 0.201327 0.247167 0.095593 0.514702 0.033753 0.355951 0.115010 0.236120 0.153647 0.495223 0.164531 0.225044 0.177531 0.432895 0.440263 0.118350 0.188420 0.252967 0.013490 0.461444 0.076232 0.448834 0.017153 0.039550 0.005577 0.937720 0.012720 0.586795 0.009520 0.390965 0.066920 0.744933 0.050137 0.138010 0.037143 0.854620 0.015950 0.092287 0.119047 0.284137 0.096407 0.500409 0.108233 0.185340 0.138720 0.567707 MOTIF AC0225:ZNF33A:C2H2_ZF AC0225:ZNF33A:C2H2_ZF letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.000750 0.000750 0.997750 0.000750 0.000750 0.000750 0.000750 0.997750 0.000750 0.000750 0.997750 0.000750 0.116930 0.287240 0.379600 0.216230 0.000750 0.705793 0.000750 0.292707 0.000750 0.101029 0.000750 0.897471 MOTIF AC0226:ZKSCAN:['C2H2_zinc_finger_factors'] AC0226:ZKSCAN:['C2H2_zinc_finger_factors'] letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.062230 0.004880 0.908735 0.024155 0.006080 0.004430 0.985210 0.004280 0.979655 0.009685 0.007435 0.003225 0.263542 0.004430 0.727598 0.004430 0.014195 0.005930 0.941075 0.038800 0.045365 0.042880 0.011790 0.899965 0.015710 0.003975 0.978595 0.001720 0.979660 0.006530 0.009685 0.004125 MOTIF AC0227:SPI/BCL11A:Ets AC0227:SPI/BCL11A:Ets letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.651877 0.073366 0.152859 0.121897 0.624002 0.020423 0.154835 0.200740 0.151520 0.080241 0.736071 0.032169 0.681318 0.063740 0.235271 0.019671 0.087079 0.002268 0.898009 0.012644 0.037489 0.005044 0.954086 0.003381 0.982185 0.007063 0.005581 0.005171 0.973557 0.003046 0.007083 0.016314 0.084988 0.235152 0.652974 0.026886 0.071819 0.056891 0.039269 0.832021 0.048091 0.072076 0.846909 0.032924 0.598539 0.107162 0.233034 0.061265 MOTIF AC0228:CTCF:C2H2_ZF AC0228:CTCF:C2H2_ZF letter-probability matrix: alength= 4 w= 20 nsites= 20 E= 0 0.051050 0.010010 0.033030 0.905910 0.003000 0.005000 0.991000 0.001000 0.015000 0.930000 0.001000 0.054000 0.935000 0.008000 0.001000 0.056000 0.203000 0.064000 0.707000 0.026000 0.023000 0.023000 0.018000 0.936000 0.244000 0.041000 0.165000 0.550000 0.049000 0.905000 0.013000 0.033000 0.090000 0.738000 0.005000 0.167000 0.373000 0.329000 0.136000 0.162000 0.380380 0.254250 0.260260 0.105110 0.303000 0.314000 0.208000 0.175000 0.398000 0.149000 0.152000 0.301000 0.293000 0.157000 0.378000 0.172000 0.051000 0.319000 0.044000 0.586000 0.274723 0.056941 0.516485 0.151852 0.244000 0.041000 0.638000 0.077000 0.041000 0.951000 0.003000 0.005000 0.001000 0.995000 0.001000 0.003000 0.928000 0.003000 0.015000 0.054000 MOTIF AC0229:ZFP:C2H2_ZF AC0229:ZFP:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.066669 0.148149 0.155558 0.629624 0.000000 0.948150 0.000000 0.051850 0.000000 1.000000 0.000000 0.000000 0.000000 0.848150 0.085180 0.066670 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.018520 0.000000 0.974070 0.007410 0.014820 0.518515 0.325927 0.140739 0.114820 0.585180 0.111110 0.188890 0.025930 0.800000 0.174070 0.000000 0.074070 0.718520 0.114820 0.092590 MOTIF AC0230:MECP:MBD AC0230:MECP:MBD letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.025000 0.591696 0.358304 0.025000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.575070 0.000000 0.424930 0.000000 0.441710 0.000000 0.558290 0.000000 MOTIF AC0231:PAX:Homeodomain,Paired_box AC0231:PAX:Homeodomain,Paired_box letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.000440 0.000460 0.000190 0.998910 0.038480 0.639074 0.298877 0.023570 0.012800 0.933869 0.007760 0.045570 0.014570 0.952320 0.030230 0.002880 0.006430 0.992860 0.000010 0.000700 0.059221 0.233072 0.707227 0.000480 0.033180 0.058080 0.908500 0.000240 0.069421 0.383844 0.497815 0.048920 0.021410 0.883460 0.005660 0.089470 MOTIF AC0232:SMAD:['SMAD/NF-1_DNA-binding_domain_factors'] AC0232:SMAD:['SMAD/NF-1_DNA-binding_domain_factors'] letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.000500 0.000500 0.998365 0.000635 0.001965 0.000500 0.000500 0.997035 0.002895 0.991185 0.004490 0.001430 0.001565 0.020990 0.020325 0.957120 0.092905 0.023250 0.882545 0.001300 0.003160 0.190440 0.739810 0.066590 MOTIF AC0233:HINFP:['C2H2_zinc_finger_factors'] AC0233:HINFP:['C2H2_zinc_finger_factors'] letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.658858 0.083440 0.011530 0.246171 0.988470 0.000000 0.000000 0.011530 0.086165 0.853456 0.060380 0.000000 0.000000 0.023845 0.951155 0.025000 0.000000 0.025000 0.012315 0.962685 0.000000 0.939625 0.011530 0.048845 0.000000 0.950000 0.050000 0.000000 0.000000 0.000000 0.939620 0.060380 0.120755 0.611950 0.084225 0.183070 MOTIF AC0234:HINFP:C2H2_ZF AC0234:HINFP:C2H2_ZF letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.166752 0.168149 0.496426 0.168672 0.498173 0.168493 0.166667 0.166667 0.166667 0.499493 0.166920 0.166920 0.166666 0.260839 0.405525 0.166969 0.176817 0.241377 0.232703 0.349103 0.179120 0.214847 0.244827 0.361207 0.030183 0.563140 0.279267 0.127410 0.511256 0.212117 0.187260 0.089367 0.511123 0.088150 0.375260 0.025467 0.002087 0.791980 0.196466 0.009467 0.000000 0.018373 0.968500 0.013127 0.000000 0.000827 0.002480 0.996693 0.000550 0.978663 0.020787 0.000000 0.000000 0.994913 0.000000 0.005087 0.000113 0.039667 0.933500 0.026720 0.002853 0.949860 0.027990 0.019297 MOTIF AC0235:HINFP:C2H2_ZF AC0235:HINFP:C2H2_ZF letter-probability matrix: alength= 4 w= 20 nsites= 20 E= 0 0.009330 0.007130 0.981890 0.001650 0.000980 0.997060 0.001960 0.000000 0.000000 0.000000 0.998890 0.001110 0.000000 0.000000 1.000000 0.000000 0.993870 0.002190 0.000440 0.003500 0.000940 0.999060 0.000000 0.000000 0.000000 0.161010 0.838990 0.000000 0.006990 0.297200 0.161840 0.533970 0.039120 0.221700 0.014610 0.724570 0.240940 0.050020 0.699400 0.009640 0.009680 0.632160 0.260940 0.097220 0.476035 0.121261 0.329373 0.073331 0.562860 0.132750 0.299010 0.005380 0.004290 0.881480 0.114230 0.000000 0.000000 0.001020 0.998980 0.000000 0.002660 0.000000 0.000530 0.996810 0.003070 0.996930 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.007160 0.974620 0.000650 0.017570 MOTIF AC0236:BATF/IRF:bZIP AC0236:BATF/IRF:bZIP letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.060800 0.194380 0.559831 0.184990 0.773516 0.036842 0.032800 0.156842 0.041452 0.365369 0.461685 0.131494 0.027726 0.018400 0.003200 0.950674 0.087136 0.882232 0.026168 0.004464 0.987600 0.006800 0.001600 0.004000 0.204822 0.074190 0.160084 0.560905 0.518378 0.083348 0.016948 0.381326 0.290336 0.185684 0.110864 0.413116 0.134274 0.417895 0.102694 0.345137 0.215790 0.142948 0.490799 0.150464 0.539537 0.079558 0.245894 0.135010 0.472968 0.114464 0.292948 0.119620 0.797074 0.097094 0.027264 0.078568 MOTIF AC0237:ZNF:C2H2_ZF AC0237:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.462880 0.045040 0.141610 0.350470 0.088341 0.911239 0.000210 0.000210 0.091741 0.000210 0.000210 0.907839 0.088341 0.819708 0.000210 0.091741 0.999370 0.000210 0.000210 0.000210 0.633026 0.000210 0.000210 0.366554 0.129350 0.028470 0.120000 0.722180 MOTIF AC0238:ZNF:C2H2_ZF AC0238:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 21 nsites= 20 E= 0 0.011500 0.053375 0.041775 0.893349 0.164916 0.321312 0.123856 0.389917 0.075505 0.001350 0.898166 0.024980 0.688065 0.000485 0.311290 0.000160 0.000550 0.000390 0.000725 0.998335 0.000205 0.990545 0.000250 0.009000 0.006430 0.026845 0.427910 0.538815 0.614297 0.214029 0.095330 0.076345 0.453893 0.018210 0.467593 0.060305 0.272306 0.296321 0.312252 0.119121 0.096144 0.099609 0.557229 0.247018 0.056519 0.084979 0.575329 0.283172 0.314165 0.361545 0.158745 0.165545 0.391740 0.243965 0.253060 0.111235 0.342890 0.440110 0.147885 0.069115 0.877801 0.005430 0.112734 0.004035 0.011960 0.078335 0.903330 0.006375 0.002790 0.050785 0.001010 0.945415 0.000265 0.990135 0.009420 0.000180 0.988455 0.000300 0.000740 0.010505 0.000170 0.003030 0.000905 0.995895 MOTIF AC0239:BACH/NFE:bZIP AC0239:BACH/NFE:bZIP letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.006548 0.010394 0.018552 0.964506 0.010941 0.007671 0.960019 0.021369 0.965577 0.021203 0.008251 0.004969 0.021624 0.764642 0.179164 0.034570 0.075143 0.025497 0.017214 0.882146 0.058717 0.886126 0.022079 0.033078 0.916573 0.010984 0.035440 0.037003 0.012320 0.011960 0.908579 0.067141 0.014335 0.923782 0.036352 0.025531 0.730891 0.066555 0.092223 0.110331 MOTIF AC0240:ZNF:C2H2_ZF AC0240:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.092489 0.902011 0.004580 0.000920 0.173078 0.000920 0.000920 0.825082 0.011910 0.125459 0.861711 0.000920 0.707873 0.242668 0.048540 0.000920 0.000920 0.165748 0.000920 0.832412 0.004580 0.000920 0.293957 0.700543 0.766472 0.231688 0.000920 0.000920 0.088830 0.239010 0.000920 0.671240 0.180400 0.217030 0.000920 0.601650 0.000920 0.989920 0.000920 0.008240 0.817762 0.008240 0.173078 0.000920 0.004580 0.748170 0.081500 0.165750 0.008240 0.953290 0.000920 0.037550 0.088829 0.905671 0.004580 0.000920 MOTIF AC0241:NFE2L/JUN:bZIP AC0241:NFE2L/JUN:bZIP letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.251017 0.212000 0.203607 0.333377 0.281889 0.238358 0.285555 0.194198 0.280192 0.264652 0.192774 0.262382 0.308510 0.187687 0.258083 0.245720 0.239207 0.234637 0.303940 0.222217 0.264754 0.239081 0.344664 0.151501 0.686942 0.083847 0.208924 0.020287 0.011110 0.005747 0.008757 0.974386 0.026373 0.000590 0.941994 0.031043 0.978140 0.002353 0.010177 0.009330 0.022720 0.673149 0.179528 0.124603 MOTIF AC0242:FOSL/JUND:bZIP AC0242:FOSL/JUND:bZIP letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.007153 0.013237 0.006115 0.973495 0.009304 0.011406 0.917689 0.061601 0.891738 0.073405 0.007546 0.027311 0.049937 0.266479 0.647045 0.036539 0.013907 0.007181 0.021715 0.957197 0.033283 0.948847 0.011759 0.006111 0.980056 0.005094 0.010745 0.004105 0.031559 0.276435 0.087059 0.604947 MOTIF AC0243:ZSCAN5C:C2H2_ZF AC0243:ZSCAN5C:C2H2_ZF letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.067980 0.234650 0.138160 0.559210 0.111840 0.179090 0.108920 0.600150 0.067980 0.208330 0.065060 0.658630 0.471490 0.035820 0.483190 0.009500 0.076750 0.067980 0.129390 0.725880 0.202482 0.009500 0.243422 0.544595 0.234650 0.155700 0.480260 0.129390 0.945170 0.012430 0.000730 0.041670 0.000730 0.804830 0.076750 0.117690 0.000730 0.021200 0.041670 0.936400 0.018270 0.778518 0.202482 0.000730 0.722950 0.085530 0.076750 0.114770 0.000730 0.898390 0.050440 0.050440 MOTIF AC0244:MAFG/MAFF:bZIP AC0244:MAFG/MAFF:bZIP letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.075670 0.180888 0.036590 0.706852 0.023621 0.031941 0.925852 0.018586 0.105255 0.843939 0.018589 0.032217 0.049288 0.060513 0.020647 0.869552 0.043484 0.026788 0.829443 0.100285 0.876614 0.046051 0.022660 0.054675 0.058952 0.552791 0.268978 0.119279 0.159612 0.070858 0.162696 0.606834 0.166266 0.556700 0.114595 0.162439 0.575956 0.101551 0.157439 0.165054 0.124843 0.094615 0.624353 0.156189 0.103125 0.668701 0.117831 0.110343 MOTIF AC0245:ZBTB:C2H2_ZF AC0245:ZBTB:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.162442 0.073221 0.016930 0.747407 0.002360 0.968400 0.002000 0.027240 0.004650 0.003820 0.973280 0.018250 0.002410 0.007950 0.985310 0.004330 0.000360 0.972570 0.006620 0.020450 0.006400 0.389920 0.000970 0.602710 0.902370 0.035730 0.030890 0.031010 0.834058 0.122511 0.021400 0.022030 0.001700 0.303393 0.298043 0.396864 0.490220 0.042540 0.187430 0.279810 0.001760 0.960180 0.003980 0.034080 0.002170 0.000000 0.934800 0.063030 0.000000 0.013920 0.985360 0.000720 0.048270 0.888830 0.024900 0.038000 MOTIF AC0246:MAFF/MAF:bZIP AC0246:MAFF/MAF:bZIP letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.061501 0.202513 0.022225 0.713761 0.019206 0.036031 0.929989 0.014774 0.098704 0.846677 0.019566 0.035053 0.078892 0.085583 0.030983 0.804542 0.049438 0.053861 0.676305 0.220396 0.873476 0.031304 0.059516 0.035704 0.027727 0.774038 0.098762 0.099473 0.276317 0.082227 0.617569 0.023887 0.039596 0.046900 0.032388 0.881116 0.205614 0.677919 0.054968 0.061499 0.795517 0.025275 0.110894 0.068314 0.062775 0.028742 0.799666 0.108817 0.017222 0.929823 0.027983 0.024972 0.696871 0.027555 0.205304 0.070269 MOTIF AC0247:ZNF:C2H2_ZF AC0247:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.001230 0.001230 0.996310 0.001230 0.001230 0.996310 0.001230 0.001230 0.001230 0.001230 0.001230 0.996310 0.001230 0.216540 0.781010 0.001220 0.683750 0.313790 0.001230 0.001230 0.027970 0.566570 0.316000 0.089460 0.695640 0.125780 0.001230 0.177350 0.001230 0.311993 0.001230 0.685547 MOTIF AC0248:ZNF:C2H2_ZF AC0248:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.566387 0.129060 0.132005 0.172548 0.630002 0.050625 0.081107 0.238266 0.296537 0.041853 0.606913 0.054697 0.012525 0.011255 0.009900 0.966320 0.028442 0.918192 0.006093 0.047272 0.416621 0.025112 0.053718 0.504549 0.028992 0.019860 0.826598 0.124550 0.027922 0.859971 0.026815 0.085292 0.110060 0.068507 0.014880 0.806552 0.038942 0.007675 0.942690 0.010693 0.087350 0.062050 0.032673 0.817927 0.101375 0.480306 0.077765 0.340554 0.538385 0.156409 0.115849 0.189357 0.273146 0.172651 0.242486 0.311718 0.150672 0.196663 0.140838 0.511827 MOTIF AC0249:ZNF:C2H2_ZF AC0249:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 20 nsites= 20 E= 0 0.185182 0.092591 0.000000 0.722227 0.185182 0.074071 0.000000 0.740747 0.314820 0.203700 0.074070 0.407410 0.222222 0.222222 0.185182 0.370374 0.129630 0.203700 0.037040 0.629630 0.333333 0.074071 0.388894 0.203702 0.222220 0.129630 0.111110 0.537040 0.037040 0.833330 0.111110 0.018520 0.166668 0.555554 0.000000 0.277777 0.000000 0.018520 0.574070 0.407410 0.000000 1.000000 0.000000 0.000000 0.092590 0.907410 0.000000 0.000000 0.000000 0.018520 0.944440 0.037040 0.870370 0.000000 0.037040 0.092590 0.000000 0.833330 0.000000 0.166670 0.518515 0.148149 0.037040 0.296297 0.240740 0.277780 0.129630 0.351850 0.259257 0.166668 0.055559 0.518515 0.500000 0.018520 0.074070 0.407410 0.777772 0.055559 0.037040 0.129629 MOTIF AC0250:MECOM/ZSCAN:C2H2_ZF AC0250:MECOM/ZSCAN:C2H2_ZF letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.059064 0.130017 0.030146 0.780772 0.037010 0.000086 0.022595 0.940309 0.931160 0.010269 0.051981 0.006590 0.010664 0.000021 0.000000 0.989315 0.000136 0.988738 0.000735 0.010391 0.028051 0.008569 0.000032 0.963348 0.046696 0.002237 0.001011 0.950056 0.677437 0.001000 0.310923 0.010640 0.001879 0.000756 0.007314 0.990051 0.045482 0.879129 0.034947 0.040442 MOTIF AC0251:MECOM:C2H2_ZF AC0251:MECOM:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.125000 0.000000 0.125000 0.750000 0.750000 0.000000 0.000000 0.250000 0.250000 0.000000 0.000000 0.750000 0.000000 0.750000 0.000000 0.250000 0.125000 0.250000 0.000000 0.625000 0.375000 0.000000 0.000000 0.625000 0.500000 0.000000 0.250000 0.250000 0.250000 0.000000 0.000000 0.750000 0.000000 0.750000 0.000000 0.250000 0.375000 0.250000 0.000000 0.375000 0.125000 0.000000 0.000000 0.875000 0.750000 0.000000 0.125000 0.125000 0.125000 0.000000 0.125000 0.750000 0.000000 0.750000 0.000000 0.250000 MOTIF AC0252:GATA/GATA1::TAL:GATA AC0252:GATA/GATA1::TAL:GATA letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.484875 0.158841 0.071680 0.284604 0.002773 0.008291 0.988506 0.000430 0.986772 0.002160 0.002731 0.008337 0.004311 0.002557 0.011087 0.982045 0.765989 0.043664 0.063407 0.126940 0.590410 0.090959 0.219582 0.099049 MOTIF AC0253:GATA:GATA AC0253:GATA:GATA letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.007019 0.008238 0.977857 0.006886 0.981281 0.004013 0.004774 0.009932 0.001970 0.010122 0.002858 0.985050 0.834498 0.013511 0.003694 0.148297 0.774113 0.025492 0.135524 0.064871 0.107226 0.527927 0.246467 0.118381 0.263942 0.100301 0.615247 0.020510 0.900683 0.033222 0.030196 0.035899 0.045241 0.039266 0.036424 0.879069 0.061051 0.852456 0.049379 0.037114 MOTIF AC0254:GATA/TRPS:GATA AC0254:GATA/TRPS:GATA letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.629561 0.083302 0.019810 0.267327 0.006606 0.003023 0.986135 0.004236 0.988269 0.001937 0.004359 0.005435 0.009506 0.004535 0.004903 0.981056 0.886970 0.037119 0.009028 0.066883 0.889250 0.008411 0.050530 0.051809 0.106767 0.233015 0.587028 0.073190 MOTIF AC0255:GATA:GATA AC0255:GATA:GATA letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.660380 0.047170 0.000000 0.292450 0.000000 0.000000 1.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.367926 0.169808 0.254717 0.207548 0.292450 0.160380 0.216980 0.330190 0.179240 0.254720 0.141510 0.424530 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 1.000000 0.000000 0.000000 MOTIF AC0256:GFI:C2H2_ZF AC0256:GFI:C2H2_ZF letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.994060 0.001980 0.001980 0.001980 0.001420 0.001420 0.001420 0.995740 0.995740 0.001420 0.001420 0.001420 0.001420 0.001420 0.001420 0.995740 0.001420 0.995740 0.001420 0.001420 0.853690 0.001420 0.001420 0.143470 0.167218 0.663903 0.167218 0.001660 MOTIF AC0257:FOXG/FOXK:Forkhead AC0257:FOXG/FOXK:Forkhead letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.025330 0.858571 0.015200 0.100899 0.010355 0.003025 0.981540 0.005080 0.004250 0.006145 0.973285 0.016320 0.986675 0.005705 0.007620 0.000000 0.000000 0.988395 0.000000 0.011605 0.994665 0.002865 0.002470 0.000000 0.002110 0.976990 0.010770 0.010130 0.967775 0.011135 0.013025 0.008065 0.981110 0.000000 0.005860 0.013030 0.021520 0.001330 0.046610 0.930540 MOTIF AC0258:SOX:Sox AC0258:SOX:Sox letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.688087 0.038550 0.163552 0.109811 0.000000 1.000000 0.000000 0.000000 0.606590 0.143150 0.222450 0.027810 0.760980 0.134370 0.086560 0.018090 0.000000 0.388370 0.000000 0.611630 0.241960 0.000000 0.758040 0.000000 0.195620 0.483980 0.320400 0.000000 MOTIF AC0259:SOX:Sox AC0259:SOX:Sox letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.973125 0.005039 0.003145 0.018691 0.004389 0.940352 0.037086 0.018173 0.993806 0.002574 0.002655 0.000965 0.931865 0.034746 0.029821 0.003568 0.524745 0.016909 0.028995 0.429351 0.181959 0.033335 0.755252 0.029454 0.270925 0.207187 0.451531 0.070357 0.299217 0.270674 0.273928 0.156180 0.233579 0.261115 0.249667 0.255639 0.181671 0.330838 0.236577 0.250914 0.179400 0.378805 0.179841 0.261955 0.395401 0.195660 0.157507 0.251432 0.159247 0.172172 0.158953 0.509628 0.152074 0.155167 0.163479 0.529280 MOTIF AC0260:SOX:Sox AC0260:SOX:Sox letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.865633 0.023900 0.072572 0.037895 0.010470 0.022638 0.050837 0.916055 0.008862 0.001253 0.004610 0.985275 0.016160 0.006597 0.973846 0.003397 0.019833 0.008840 0.017417 0.953910 0.043890 0.029837 0.024180 0.902093 0.085607 0.787071 0.042580 0.084742 0.090125 0.078103 0.734557 0.097215 MOTIF AC0261:SOX/SRY:Sox AC0261:SOX/SRY:Sox letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.593620 0.133653 0.148187 0.124539 0.922322 0.014696 0.011916 0.051066 0.021054 0.923290 0.020671 0.034985 0.960651 0.010824 0.013737 0.014788 0.940884 0.017351 0.016124 0.025641 0.117453 0.014082 0.010358 0.858107 0.337755 0.065593 0.495351 0.101301 MOTIF AC0262:SOX/SRY:Sox AC0262:SOX/SRY:Sox letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.855734 0.046864 0.050323 0.047079 0.904145 0.024869 0.044493 0.026493 0.000666 0.997028 0.000535 0.001771 0.993032 0.003225 0.000531 0.003212 0.981552 0.011356 0.005721 0.001371 0.000609 0.000211 0.000601 0.998579 0.051112 0.011188 0.474252 0.463448 0.141935 0.113938 0.419978 0.324149 0.053607 0.564475 0.011691 0.370227 0.997740 0.000432 0.001045 0.000783 0.002132 0.002936 0.527084 0.467848 0.007321 0.000370 0.005069 0.987240 0.001401 0.000948 0.997443 0.000208 0.015398 0.007361 0.008756 0.968485 0.051416 0.056999 0.061092 0.830493 MOTIF AC0263:SOX/SRY:Sox AC0263:SOX/SRY:Sox letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.035643 0.006399 0.012216 0.945742 0.002429 0.992901 0.002868 0.001802 0.997260 0.001755 0.000634 0.000351 0.974093 0.023171 0.001979 0.000757 0.000205 0.000000 0.001802 0.997993 0.023074 0.000000 0.665436 0.311490 0.118955 0.124385 0.385335 0.371326 0.106460 0.372050 0.006900 0.514590 0.996800 0.001295 0.001865 0.000040 0.000302 0.000975 0.427689 0.571034 0.000119 0.001122 0.000505 0.998254 0.000865 0.002714 0.992214 0.004207 0.926943 0.001757 0.020379 0.050921 MOTIF AC0264:FOXD:Forkhead AC0264:FOXD:Forkhead letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.613560 0.059505 0.002680 0.324255 0.886450 0.000865 0.104325 0.008360 0.028490 0.768665 0.195595 0.007250 0.860186 0.055440 0.083775 0.000600 0.851520 0.122110 0.024455 0.001915 0.003650 0.098155 0.001250 0.896946 0.980195 0.009235 0.000410 0.010160 0.959725 0.010990 0.009815 0.019470 0.001165 0.703370 0.001165 0.294300 0.784195 0.004410 0.206520 0.004875 0.295755 0.105130 0.035995 0.563120 MOTIF AC0265:FOXJ:Forkhead AC0265:FOXJ:Forkhead letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.166670 0.333330 0.000000 0.500000 0.500000 0.500000 0.000000 0.000000 0.833330 0.000000 0.000000 0.166670 0.000000 0.333330 0.000000 0.666670 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.166670 0.000000 0.833330 0.833330 0.000000 0.166670 0.000000 0.000000 0.166668 0.166668 0.666663 0.000000 0.166670 0.000000 0.833330 0.000000 0.000000 0.166670 0.833330 MOTIF AC0266:ZNF:C2H2_ZF AC0266:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.017692 0.943465 0.015370 0.023473 0.099188 0.062507 0.761632 0.076673 0.026953 0.969117 0.002557 0.001373 0.021187 0.033118 0.001755 0.943940 0.875458 0.014987 0.101992 0.007563 0.911765 0.070267 0.010965 0.007003 0.000630 0.993639 0.003523 0.002208 0.000990 0.988612 0.001610 0.008788 0.951993 0.012540 0.034077 0.001390 0.010427 0.391618 0.010642 0.587313 0.004770 0.064940 0.002270 0.928020 MOTIF AC0267:SOX/SRY:Sox AC0267:SOX/SRY:Sox letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.981448 0.003625 0.005441 0.009486 0.850383 0.001959 0.019450 0.128208 0.002064 0.997101 0.000696 0.000139 0.997335 0.000000 0.001744 0.000921 0.989856 0.003415 0.005765 0.000964 0.000000 0.001625 0.000543 0.997832 0.334911 0.043474 0.530786 0.090829 0.090634 0.033151 0.079751 0.796464 0.087421 0.118918 0.497781 0.295880 0.046379 0.844677 0.011245 0.097699 0.996915 0.001822 0.000822 0.000441 0.001503 0.000421 0.754255 0.243821 0.001702 0.000402 0.000543 0.997353 0.001466 0.003871 0.994142 0.000521 0.126809 0.000852 0.001818 0.870521 0.008252 0.016304 0.007480 0.967964 MOTIF AC0268:ZNF:C2H2_ZF AC0268:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.045330 0.045330 0.023350 0.885990 0.028850 0.583790 0.001370 0.385990 0.144230 0.567310 0.204670 0.083790 0.688190 0.287090 0.012360 0.012360 0.017860 0.001370 0.034340 0.946430 0.017860 0.001370 0.918960 0.061810 0.655220 0.050820 0.265110 0.028850 0.858520 0.023350 0.061810 0.056320 0.028850 0.633240 0.127750 0.210160 0.902470 0.034340 0.056320 0.006870 0.012360 0.067310 0.001370 0.918960 0.265110 0.017860 0.325550 0.391480 0.023350 0.001370 0.940939 0.034340 0.100279 0.028850 0.199178 0.671693 0.468405 0.309067 0.078299 0.144229 0.155222 0.259613 0.083791 0.501375 MOTIF AC0269:CUX:CUT,Homeodomain AC0269:CUX:CUT,Homeodomain letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.000000 1.000000 0.000000 0.000000 0.291670 0.375000 0.333330 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.958330 0.000000 0.041670 0.000000 0.125000 0.458330 0.166670 0.250000 0.333330 0.166670 0.291670 0.208330 0.041670 0.166670 0.083330 0.708330 0.708330 0.125000 0.125000 0.041670 0.000000 0.125000 0.000000 0.875000 0.041670 0.375000 0.083330 0.500000 0.000000 0.166670 0.583330 0.250000 0.000000 0.086959 0.869561 0.043480 0.000000 0.043480 0.086959 0.869561 0.000000 0.043480 0.913040 0.043480 MOTIF AC0270:BBX:Sox AC0270:BBX:Sox letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.033990 0.003045 0.031900 0.931065 0.015590 0.000715 0.981520 0.002175 0.968550 0.015170 0.004820 0.011460 0.805760 0.002090 0.011700 0.180450 0.019140 0.896734 0.040750 0.043375 0.363400 0.101410 0.387215 0.147975 0.251386 0.243471 0.262511 0.242631 0.127400 0.391500 0.102245 0.378855 0.041115 0.042485 0.891360 0.025040 0.190695 0.011840 0.002150 0.795315 0.011175 0.002000 0.023635 0.963190 0.000650 0.986545 0.000000 0.012805 0.922830 0.031340 0.004285 0.041545 MOTIF AC0271:SOX/SRY:Sox AC0271:SOX/SRY:Sox letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.011317 0.001186 0.003628 0.983869 0.015575 0.000670 0.983079 0.000676 0.998308 0.000211 0.000437 0.001044 0.975991 0.020406 0.003332 0.000271 0.000913 0.000900 0.000704 0.997483 0.141923 0.001642 0.390557 0.465878 0.154084 0.151090 0.330906 0.363920 0.007084 0.669895 0.002912 0.320109 0.997544 0.000739 0.001487 0.000230 0.000996 0.000872 0.431075 0.567057 0.000808 0.000223 0.000265 0.998704 0.000633 0.996936 0.000704 0.001727 0.861875 0.044613 0.046828 0.046684 MOTIF AC0272:HSFY:HSF AC0272:HSFY:HSF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.061593 0.045475 0.029963 0.862969 0.173078 0.628960 0.195692 0.002269 0.002812 0.028424 0.926643 0.042121 0.968653 0.007665 0.009187 0.014495 0.856262 0.018675 0.034295 0.090768 0.116107 0.446504 0.046134 0.391255 0.066602 0.260250 0.587254 0.085894 0.240696 0.083211 0.262891 0.413202 0.164195 0.045222 0.039183 0.751400 0.041597 0.031180 0.032227 0.894996 0.172114 0.660876 0.142816 0.024194 0.024066 0.068950 0.833602 0.073382 0.878345 0.039995 0.032375 0.049285 0.744562 0.078642 0.051544 0.125252 MOTIF AC0273:MTF:C2H2_ZF AC0273:MTF:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.179870 0.043676 0.727568 0.048886 0.153202 0.648031 0.032510 0.166258 0.023170 0.931262 0.031032 0.014536 0.000082 0.000360 0.999042 0.000516 0.027126 0.012492 0.051046 0.909335 0.000276 0.016836 0.970864 0.012024 0.007272 0.082588 0.003030 0.907110 0.000370 0.000244 0.993484 0.005902 0.000154 0.977018 0.005982 0.016846 0.984954 0.000224 0.007550 0.007272 0.767130 0.098498 0.075636 0.058736 0.883860 0.087628 0.011018 0.017494 MOTIF AC0274:PAX:Homeodomain,Paired_box AC0274:PAX:Homeodomain,Paired_box letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.307510 0.000770 0.690950 0.000770 0.000670 0.000670 0.000670 0.997990 0.000670 0.000670 0.997990 0.000670 0.000670 0.997990 0.000670 0.000670 0.931511 0.000670 0.067149 0.000670 0.000670 0.997990 0.000670 0.000670 0.798540 0.133640 0.000670 0.067150 0.001110 0.775440 0.001110 0.222340 MOTIF AC0275:ZNF33A:C2H2_ZF AC0275:ZNF33A:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.006010 0.015620 0.001200 0.977170 0.001200 0.943510 0.049280 0.006010 0.957930 0.001200 0.006010 0.034860 0.006010 0.001200 0.991590 0.001200 0.001200 0.001200 0.087740 0.909860 0.001200 0.001200 0.996400 0.001200 0.001200 0.981980 0.001200 0.015620 0.933899 0.001200 0.001200 0.063701 0.010820 0.957930 0.001200 0.030050 MOTIF AC0276:ZNF:C2H2_ZF AC0276:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.022135 0.017145 0.931980 0.028740 0.812427 0.056263 0.002737 0.128573 0.006210 0.000423 0.991787 0.001580 0.001580 0.000423 0.997574 0.000423 0.175534 0.741853 0.001580 0.081033 0.723417 0.018462 0.257121 0.001000 0.953495 0.001000 0.043925 0.001580 0.065632 0.065632 0.742438 0.126298 MOTIF AC0277:ZNF:C2H2_ZF AC0277:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.000610 0.976220 0.003050 0.020120 0.000610 0.995730 0.000610 0.003050 0.012810 0.859141 0.000610 0.127439 0.020120 0.005490 0.005490 0.968900 0.771340 0.025000 0.059150 0.144510 0.005490 0.971340 0.000610 0.022560 0.000610 0.998170 0.000610 0.000610 0.000610 0.015240 0.000610 0.983540 0.000610 0.993290 0.000610 0.005490 MOTIF AC0278:ZFP:C2H2_ZF AC0278:ZFP:C2H2_ZF letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.049245 0.084530 0.016788 0.849437 0.314775 0.061165 0.100955 0.523105 0.057208 0.218271 0.023463 0.701058 0.061330 0.145905 0.126862 0.665904 0.947853 0.012387 0.020455 0.019305 0.000032 0.052000 0.003272 0.944696 0.961367 0.016015 0.006533 0.016085 0.022670 0.915498 0.048117 0.013715 0.001333 0.987870 0.007537 0.003260 0.012017 0.798662 0.014115 0.175206 0.757339 0.008627 0.011367 0.222667 0.274597 0.057957 0.550844 0.116602 0.084630 0.825925 0.017792 0.071652 MOTIF AC0279:ZNF:C2H2_ZF AC0279:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 20 nsites= 20 E= 0 0.158372 0.101959 0.606413 0.133256 0.025893 0.864440 0.024537 0.085130 0.017677 0.049840 0.004833 0.927650 0.077220 0.004333 0.878504 0.039943 0.124933 0.040030 0.809471 0.025567 0.538054 0.019003 0.415050 0.027893 0.012520 0.114010 0.014643 0.858827 0.921590 0.011603 0.038090 0.028717 0.080713 0.248427 0.011317 0.659543 0.933399 0.007367 0.051577 0.007657 0.159127 0.042060 0.785085 0.013727 0.414960 0.049957 0.075503 0.459580 0.953517 0.013640 0.026893 0.005950 0.018087 0.018797 0.012520 0.950596 0.015967 0.039737 0.029220 0.915076 0.019913 0.855483 0.015053 0.109550 0.026980 0.049927 0.026687 0.896407 0.137254 0.064890 0.136364 0.661493 0.143119 0.003920 0.804411 0.048550 0.100840 0.088077 0.718351 0.092733 MOTIF AC0280:ZNF:['C2H2_zinc_finger_factors'] AC0280:ZNF:['C2H2_zinc_finger_factors'] letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.508590 0.047170 0.089350 0.354890 0.961570 0.009060 0.021560 0.007810 0.011870 0.010620 0.010620 0.966890 0.008430 0.021560 0.008430 0.961580 0.017810 0.909090 0.009680 0.063420 0.012180 0.025930 0.014370 0.947520 0.135270 0.029050 0.159640 0.676040 0.089661 0.005310 0.874419 0.030610 0.034361 0.014990 0.935659 0.014990 0.049050 0.209930 0.010310 0.730710 0.025930 0.018740 0.031860 0.923469 0.051549 0.012500 0.907211 0.028740 0.590124 0.037490 0.327397 0.044990 MOTIF AC0281:ZKSCAN:C2H2_ZF AC0281:ZKSCAN:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.161340 0.278853 0.441287 0.118520 0.710509 0.089117 0.115127 0.085247 0.013217 0.003287 0.978373 0.005123 0.001700 0.010390 0.982533 0.005377 0.011387 0.818773 0.008050 0.161790 0.004873 0.011223 0.004873 0.979031 0.949704 0.008380 0.037043 0.004873 0.007150 0.024423 0.885750 0.082677 0.327390 0.651590 0.015500 0.005520 0.008803 0.940710 0.003887 0.046600 0.003287 0.963440 0.006480 0.026793 MOTIF AC0282:SMAD:SMAD AC0282:SMAD:SMAD letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.004988 0.003442 0.988608 0.002962 0.048306 0.013436 0.052564 0.885694 0.008636 0.985912 0.004368 0.001084 0.001896 0.029728 0.046266 0.922110 0.942996 0.042112 0.013970 0.000922 0.003224 0.008236 0.986518 0.002022 0.878973 0.078946 0.006090 0.035990 0.004028 0.988318 0.001884 0.005770 MOTIF AC0283:ZSCAN:C2H2_ZF,MADF AC0283:ZSCAN:C2H2_ZF,MADF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.293403 0.133502 0.435943 0.137153 0.132273 0.125425 0.130582 0.611720 0.598425 0.125030 0.151425 0.125120 0.242245 0.153525 0.473560 0.130670 0.410635 0.335900 0.127355 0.126110 0.035068 0.737558 0.065287 0.162087 0.014225 0.000338 0.984887 0.000550 0.003247 0.011032 0.161993 0.823728 0.007302 0.884459 0.027417 0.080822 0.000555 0.062277 0.000320 0.936848 0.984453 0.009827 0.002143 0.003577 0.011380 0.780569 0.009285 0.198766 0.656125 0.001747 0.338215 0.003913 0.031558 0.875545 0.034132 0.058765 MOTIF AC0284:ZNF:C2H2_ZF AC0284:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.888890 0.000000 0.111110 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.111110 0.000000 0.851850 0.037040 0.000000 0.000000 0.000000 1.000000 0.037040 0.777780 0.000000 0.185180 0.000000 0.629630 0.000000 0.370370 0.962960 0.037040 0.000000 0.000000 0.000000 0.925930 0.000000 0.074070 MOTIF AC0285:ZFP:C2H2_ZF AC0285:ZFP:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.807125 0.051370 0.120630 0.020875 0.806265 0.028560 0.153985 0.011190 0.000355 0.994945 0.000355 0.004345 0.001780 0.000355 0.000355 0.997510 0.000355 0.969860 0.000355 0.029430 0.007480 0.927090 0.010905 0.054525 0.006055 0.003780 0.000355 0.989810 0.882949 0.065901 0.049370 0.001780 0.035990 0.406690 0.058505 0.498815 0.215045 0.096705 0.180830 0.507420 0.033410 0.808533 0.033985 0.124071 0.797140 0.045395 0.077025 0.080440 0.173960 0.153440 0.119780 0.552820 0.102140 0.745760 0.059085 0.093015 0.035685 0.878950 0.009765 0.075600 MOTIF AC0286:ZNF33A:C2H2_ZF AC0286:ZNF33A:C2H2_ZF letter-probability matrix: alength= 4 w= 20 nsites= 20 E= 0 0.000000 0.273150 0.000000 0.726850 0.000000 1.000000 0.000000 0.000000 0.000000 0.055560 0.000000 0.944440 0.000000 0.662040 0.310180 0.027780 0.018520 0.013890 0.967590 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.990740 0.000000 0.009260 0.944440 0.004630 0.013890 0.037040 0.000000 0.990740 0.004630 0.004630 0.000000 0.324070 0.000000 0.675930 0.273150 0.004630 0.000000 0.722220 0.004630 0.078700 0.000000 0.916670 0.013890 0.004630 0.717590 0.263890 0.000000 0.009260 0.990740 0.000000 0.277780 0.000000 0.722220 0.000000 0.675930 0.000000 0.324070 0.000000 0.004630 0.277780 0.717590 0.000000 0.268520 0.004630 0.726850 0.000000 0.000000 0.995370 0.004630 0.000000 0.004630 0.712960 0.000000 0.282410 MOTIF AC0287:ZNF:['C2H2_zinc_finger_factors'] AC0287:ZNF:['C2H2_zinc_finger_factors'] letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.571016 0.072461 0.223192 0.133331 0.139130 0.081160 0.043480 0.736230 0.002900 0.968110 0.020290 0.008700 0.005800 0.017390 0.002900 0.973910 0.002900 0.020290 0.971010 0.005800 0.005800 0.014490 0.971010 0.008700 0.000000 0.002900 0.988400 0.008700 0.011590 0.927540 0.008700 0.052170 MOTIF AC0288:ZNF:C2H2_ZF AC0288:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.498100 0.066445 0.387315 0.048140 0.011260 0.702151 0.002250 0.284339 0.342360 0.030420 0.586640 0.040580 0.005735 0.002250 0.002250 0.989765 0.891285 0.002250 0.104215 0.002250 0.003995 0.991505 0.002250 0.002250 0.069340 0.917450 0.002250 0.010960 0.650285 0.083280 0.238050 0.028385 0.045805 0.532410 0.225310 0.196475 0.583493 0.102146 0.179646 0.134716 MOTIF AC0289:ZNF:['C2H2_zinc_finger_factors'] AC0289:ZNF:['C2H2_zinc_finger_factors'] letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.181125 0.186255 0.138918 0.493702 0.513799 0.147840 0.200521 0.137840 0.165258 0.127412 0.090097 0.617234 0.317742 0.085727 0.458492 0.138038 0.086495 0.162010 0.478832 0.272663 0.921192 0.034412 0.032883 0.011513 0.002282 0.076690 0.008030 0.912998 0.835273 0.007033 0.148699 0.008995 0.006638 0.006792 0.005475 0.981095 0.961517 0.003720 0.026700 0.008063 0.002325 0.985548 0.003120 0.009007 0.009310 0.949102 0.003193 0.038395 0.941167 0.016528 0.024410 0.017895 0.034745 0.557182 0.050025 0.358048 MOTIF AC0290:FOXP:Forkhead AC0290:FOXP:Forkhead letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.909475 0.002910 0.003040 0.084575 0.200279 0.000000 0.000210 0.799511 0.010805 0.001800 0.001010 0.986385 0.000000 0.202005 0.000000 0.797995 0.213950 0.000785 0.784650 0.000615 0.000000 0.000000 0.000000 1.000000 0.002310 0.994020 0.002585 0.001085 0.917870 0.009330 0.071245 0.001555 0.001275 0.007030 0.014845 0.976850 0.995205 0.000215 0.003790 0.000790 0.000000 0.998995 0.000000 0.001005 1.000000 0.000000 0.000000 0.000000 0.948690 0.000000 0.000430 0.050880 0.693160 0.000425 0.000000 0.306415 0.072200 0.004265 0.000760 0.922775 MOTIF AC0291:ZFP/ZNF:C2H2_ZF AC0291:ZFP/ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.131887 0.652563 0.095353 0.120197 0.010597 0.373144 0.016680 0.599579 0.939070 0.044383 0.002127 0.014420 0.999617 0.000103 0.000167 0.000113 0.000043 0.999657 0.000000 0.000300 0.000560 0.006750 0.000167 0.992523 0.000167 0.999653 0.000137 0.000043 0.000043 0.000337 0.000183 0.999437 0.000167 0.999256 0.000257 0.000320 0.011223 0.988341 0.000253 0.000183 MOTIF AC0292:ZBTB:C2H2_ZF AC0292:ZBTB:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.020630 0.001615 0.328000 0.649755 0.000350 0.998400 0.000180 0.001070 0.990030 0.008865 0.000335 0.000770 0.001025 0.998430 0.000050 0.000495 0.225726 0.134926 0.043240 0.596108 0.991755 0.001760 0.002095 0.004390 0.002500 0.429448 0.070355 0.497698 0.393600 0.097460 0.099705 0.409235 0.498590 0.068205 0.429110 0.004095 0.002960 0.000900 0.002240 0.993900 0.594768 0.044985 0.129401 0.230846 0.001645 0.000060 0.997550 0.000745 0.000455 0.001620 0.006930 0.990995 0.000070 0.000365 0.998575 0.000990 0.649787 0.324838 0.002000 0.023375 MOTIF AC0293:ZNF:C2H2_ZF AC0293:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.029495 0.000000 0.000255 0.970250 0.036100 0.000100 0.000355 0.963445 0.089985 0.024230 0.004735 0.881049 0.102425 0.632735 0.008145 0.256695 0.000925 0.024135 0.972365 0.002575 0.004870 0.014205 0.675977 0.304948 0.989445 0.000000 0.006905 0.003650 0.000025 0.999080 0.000895 0.000000 0.905120 0.000385 0.094030 0.000465 MOTIF AC0294:ZNF:C2H2_ZF AC0294:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.000000 1.000000 0.000000 0.000000 0.000000 0.467815 0.429184 0.103001 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.500000 0.500000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 MOTIF AC0295:ZNF:C2H2_ZF AC0295:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.002450 0.041670 0.002450 0.953430 0.218140 0.002450 0.776960 0.002450 0.002450 0.973040 0.002450 0.022060 0.012260 0.973030 0.002450 0.012260 0.002450 0.669120 0.022060 0.306370 0.012260 0.100489 0.012260 0.874991 0.796570 0.002450 0.178920 0.022060 0.012260 0.031860 0.953430 0.002450 0.002450 0.835788 0.002450 0.159312 0.404410 0.473040 0.090690 0.031860 0.071080 0.188730 0.384800 0.355390 0.394610 0.022060 0.129900 0.453430 0.100490 0.335780 0.551470 0.012260 0.374996 0.061279 0.492645 0.071079 0.041670 0.522060 0.031860 0.404410 0.002450 0.120100 0.051470 0.825980 0.031860 0.463240 0.002450 0.502450 MOTIF AC0296:ZNF:C2H2_ZF AC0296:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 20 nsites= 20 E= 0 0.148649 0.108109 0.716213 0.027030 0.000000 0.000000 0.013510 0.986490 0.864860 0.040540 0.027030 0.067570 0.013510 0.081081 0.013510 0.891899 0.000000 0.000000 0.000000 1.000000 0.000000 0.959460 0.000000 0.040540 0.175680 0.540540 0.000000 0.283780 0.067570 0.067570 0.040540 0.824320 0.013510 0.094591 0.000000 0.891899 0.351350 0.027030 0.594590 0.027030 0.000000 0.000000 0.000000 1.000000 0.000000 0.567570 0.000000 0.432430 0.391890 0.054050 0.256760 0.297300 0.081080 0.027030 0.054050 0.837840 0.121619 0.067569 0.675673 0.135139 0.000000 0.067570 0.040540 0.891890 0.256760 0.000000 0.729730 0.013510 0.040540 0.054051 0.054051 0.851359 0.243240 0.027030 0.621620 0.108110 0.121620 0.000000 0.837840 0.040540 MOTIF AC0297:ZNF:C2H2_ZF AC0297:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.001150 0.001150 0.001150 0.996550 0.618666 0.001150 0.240782 0.139401 0.001150 0.213132 0.001150 0.784568 0.512670 0.194700 0.153230 0.139400 0.001150 0.125580 0.001150 0.872120 0.001150 0.001150 0.042630 0.955070 0.001150 0.996550 0.001150 0.001150 0.001150 0.756918 0.001150 0.240782 0.001150 0.001150 0.001150 0.996550 MOTIF AC0298:PRDM:C2H2_ZF AC0298:PRDM:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.555560 0.000000 0.444440 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 MOTIF AC0299:HMX:Homeodomain AC0299:HMX:Homeodomain letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.090910 0.909090 0.000000 0.000000 0.909090 0.090910 0.000000 0.000000 0.000000 1.000000 0.000000 0.090910 0.000000 0.000000 0.909090 0.090910 0.000000 0.909090 0.000000 MOTIF AC0300:ZNF:C2H2_ZF AC0300:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.000000 0.000000 0.000000 1.000000 0.066300 0.027620 0.033150 0.872930 0.795460 0.127270 0.068180 0.009090 0.823530 0.000000 0.176470 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.948720 0.000000 0.051280 0.095240 0.290040 0.000000 0.614720 0.977780 0.000000 0.011110 0.011110 0.000000 0.959800 0.000000 0.040200 0.522470 0.061800 0.353930 0.061800 0.012900 0.000000 0.025810 0.961290 0.062110 0.000000 0.037270 0.900620 0.000000 0.000000 0.013510 0.986490 0.720000 0.000000 0.151110 0.128890 0.934670 0.000000 0.045230 0.020100 0.000000 1.000000 0.000000 0.000000 MOTIF AC0301:ZNF:C2H2_ZF AC0301:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.011170 0.055870 0.011170 0.921790 0.000000 0.899440 0.027930 0.072630 0.949720 0.033520 0.011170 0.005590 0.893850 0.050280 0.055870 0.000000 0.005590 0.905021 0.016760 0.072629 0.011170 0.022350 0.050280 0.916200 0.000000 0.000000 0.011170 0.988830 0.016760 0.000000 0.983240 0.000000 0.860330 0.100560 0.033520 0.005590 0.027930 0.832400 0.005590 0.134080 0.000000 0.000000 0.000000 1.000000 0.033520 0.000000 0.955310 0.011170 0.039110 0.000000 0.960890 0.000000 0.072630 0.000000 0.927370 0.000000 0.000000 0.949720 0.005590 0.044690 0.005590 0.715080 0.000000 0.279330 0.921790 0.016760 0.027930 0.033520 MOTIF AC0302:ZNF:C2H2_ZF AC0302:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.914069 0.070311 0.007810 0.007810 0.914069 0.007810 0.070311 0.007810 0.007810 0.195312 0.789068 0.007810 0.414064 0.507815 0.039060 0.039060 0.945319 0.007810 0.039060 0.007810 0.007810 0.914069 0.007810 0.070311 0.070311 0.039060 0.070311 0.820318 0.007810 0.007810 0.789068 0.195312 0.007810 0.007810 0.007810 0.976570 0.757818 0.007810 0.132811 0.101561 0.007810 0.789068 0.007810 0.195312 MOTIF AC0303:ZNF:C2H2_ZF AC0303:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 21 nsites= 20 E= 0 0.115390 0.038460 0.846150 0.000000 0.038460 0.038460 0.307693 0.615386 0.000000 0.807690 0.038460 0.153850 0.000000 0.230770 0.000000 0.769230 0.499995 0.230768 0.115389 0.153848 0.538465 0.076921 0.346153 0.038460 0.000000 0.000000 0.076920 0.923080 0.884611 0.000000 0.115389 0.000000 0.115390 0.076920 0.730770 0.076920 0.884611 0.000000 0.115389 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.961540 0.038460 0.000000 0.115389 0.153848 0.000000 0.730763 0.192308 0.038460 0.653843 0.115389 0.230770 0.000000 0.730770 0.038460 0.076919 0.115389 0.692303 0.115389 0.076920 0.500000 0.115390 0.307690 0.000000 0.000000 0.000000 1.000000 0.038460 0.000000 0.961540 0.000000 0.038460 0.846150 0.000000 0.115390 0.846150 0.115390 0.000000 0.038460 MOTIF AC0304:ZFP:C2H2_ZF AC0304:ZFP:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.001940 0.986430 0.001940 0.009690 0.040700 0.273257 0.001940 0.684103 0.001940 0.001940 0.017440 0.978680 0.001940 0.001940 0.001940 0.994180 0.668600 0.009690 0.164730 0.156980 0.094961 0.063951 0.815898 0.025190 0.032950 0.901160 0.001940 0.063950 0.001940 0.017440 0.001940 0.978680 0.040700 0.001940 0.877901 0.079459 0.025190 0.498065 0.017440 0.459305 0.629840 0.040700 0.327520 0.001940 0.001940 0.071709 0.017440 0.908911 MOTIF AC0305:ZNF:C2H2_ZF AC0305:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.000000 0.000000 1.000000 0.000000 0.429580 0.000000 0.000000 0.570420 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.535210 0.000000 0.464790 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 MOTIF AC0306:TOPORS:Unknown AC0306:TOPORS:Unknown letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.000000 0.000000 0.000000 1.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.875000 0.000000 0.125000 0.875000 0.000000 0.083330 0.041670 1.000000 0.000000 0.000000 0.000000 0.941180 0.000000 0.058820 0.000000 0.000000 0.000000 0.958330 0.041670 0.041670 0.000000 0.000000 0.958330 1.000000 0.000000 0.000000 0.000000 0.000000 0.041670 0.916661 0.041670 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 1.000000 0.000000 0.000000 0.000000 MOTIF AC0307:ZNF:C2H2_ZF AC0307:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.312500 0.000000 0.000000 0.687500 0.000000 0.000000 0.125000 0.875000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.125000 0.062500 0.000000 0.812500 0.000000 0.000000 0.000000 1.000000 0.187500 0.000000 0.812500 0.000000 0.125000 0.687500 0.187500 0.000000 0.000000 0.187500 0.000000 0.812500 0.000000 0.000000 1.000000 0.000000 0.312500 0.000000 0.687500 0.000000 0.375000 0.000000 0.562500 0.062500 MOTIF AC0308:ZNF:C2H2_ZF AC0308:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.000000 0.027030 0.162160 0.810810 0.027030 0.000000 0.972970 0.000000 0.000000 0.810810 0.189190 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.918920 0.000000 0.081080 0.000000 0.000000 1.000000 0.000000 0.000000 MOTIF AC0309:ZNF:C2H2_ZF AC0309:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.000000 0.928570 0.071430 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.357140 0.000000 0.000000 0.642860 0.285710 0.000000 0.000000 0.714290 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 MOTIF AC0310:ZNF:C2H2_ZF AC0310:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.994150 0.000530 0.004790 0.000530 0.998410 0.000530 0.000530 0.000530 0.998410 0.000530 0.000530 0.000530 0.892020 0.002660 0.089890 0.015430 0.526065 0.034570 0.396284 0.043080 0.000530 0.983510 0.002660 0.013300 0.015420 0.964370 0.000530 0.019680 0.009040 0.930320 0.002660 0.057980 0.028190 0.664360 0.002660 0.304790 0.664360 0.000530 0.321810 0.013300 0.019680 0.004790 0.972870 0.002660 0.698400 0.011170 0.285640 0.004790 MOTIF AC0311:CTCF:C2H2_ZF AC0311:CTCF:C2H2_ZF letter-probability matrix: alength= 4 w= 19 nsites= 20 E= 0 1.000000 0.000000 0.000000 0.000000 0.500000 0.300000 0.200000 0.000000 0.700000 0.100000 0.100000 0.100000 0.000000 0.800000 0.000000 0.200000 0.800000 0.100000 0.000000 0.100000 0.000000 0.200000 0.800000 0.000000 1.000000 0.000000 0.000000 0.000000 0.500000 0.000000 0.500000 0.000000 0.400000 0.100000 0.200000 0.300000 0.000000 0.000000 0.100000 0.900000 0.000000 0.800000 0.000000 0.200000 0.000000 0.900000 0.000000 0.100000 0.300000 0.400000 0.100000 0.200000 0.000000 0.700000 0.000000 0.300000 0.000000 0.000000 0.000000 1.000000 0.000000 0.400000 0.600000 0.000000 0.000000 0.000000 0.100000 0.900000 0.100000 0.000000 0.800000 0.100000 0.000000 0.200000 0.000000 0.800000 MOTIF AC0312:ZNF:C2H2_ZF AC0312:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 27 nsites= 20 E= 0 0.857140 0.000000 0.000000 0.142860 0.714290 0.285710 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.714290 0.000000 0.285710 0.000000 0.857140 0.142860 0.000000 0.000000 0.285710 0.142860 0.571430 0.142860 0.285710 0.000000 0.571430 0.142859 0.000000 0.714283 0.142859 1.000000 0.000000 0.000000 0.000000 0.000000 0.714290 0.285710 0.000000 0.428570 0.142860 0.285710 0.142860 0.714290 0.000000 0.285710 0.000000 0.142860 0.285710 0.142860 0.428570 0.285710 0.714290 0.000000 0.000000 0.571430 0.428570 0.000000 0.000000 0.857140 0.000000 0.000000 0.142860 0.000000 0.000000 0.857140 0.142860 0.000000 0.000000 1.000000 0.000000 0.285710 0.000000 0.571430 0.142860 0.000000 0.000000 0.285710 0.714290 0.142860 0.000000 0.000000 0.857140 0.285710 0.714290 0.000000 0.000000 0.142860 0.571430 0.000000 0.285710 0.142860 0.428570 0.000000 0.428570 0.000000 0.285710 0.142860 0.571430 0.142860 0.857140 0.000000 0.000000 0.428574 0.285713 0.000000 0.285713 MOTIF AC0313:ZNF:C2H2_ZF AC0313:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 20 nsites= 20 E= 0 0.035710 0.011910 0.952380 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.964290 0.000000 0.035710 0.642860 0.202380 0.107140 0.047620 0.916670 0.059520 0.023810 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.047620 0.130950 0.821430 0.071431 0.285713 0.083331 0.559526 0.000000 0.988090 0.000000 0.011910 0.035710 0.500000 0.000000 0.464290 0.297620 0.119050 0.547620 0.035710 0.000000 0.023810 0.023810 0.952380 0.083330 0.000000 0.904760 0.011910 0.000000 0.000000 1.000000 0.000000 0.000000 0.011910 0.000000 0.988090 0.059520 0.000000 0.940480 0.000000 0.000000 0.011910 0.000000 0.988090 0.988090 0.011910 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.988090 0.000000 0.000000 0.011910 MOTIF AC0314:ZNF:C2H2_ZF AC0314:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 21 nsites= 20 E= 0 0.976740 0.023260 0.000000 0.000000 0.930230 0.000000 0.046510 0.023260 0.046510 0.651160 0.000000 0.302330 0.953480 0.023260 0.000000 0.023260 0.906980 0.093020 0.000000 0.000000 0.000000 0.534880 0.000000 0.465120 0.534880 0.000000 0.465120 0.000000 0.000000 0.976740 0.000000 0.023260 0.046510 0.023260 0.000000 0.930230 0.000000 0.023260 0.976740 0.000000 0.069770 0.651160 0.046510 0.232560 0.674420 0.023260 0.255810 0.046510 0.534885 0.069769 0.325577 0.069769 0.046510 0.069770 0.069770 0.813950 0.000000 0.000000 1.000000 0.000000 0.767440 0.000000 0.232560 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.953490 0.000000 0.046510 MOTIF AC0315:ZNF:C2H2_ZF AC0315:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.500000 0.500000 MOTIF AC0316:ZNF:C2H2_ZF AC0316:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 18 nsites= 20 E= 0 0.142860 0.000000 0.857140 0.000000 0.142859 0.214288 0.071429 0.571424 0.214290 0.785710 0.000000 0.000000 0.000000 0.928570 0.000000 0.071430 0.000000 0.285710 0.714290 0.000000 0.071430 0.500000 0.000000 0.428570 0.000000 0.928570 0.071430 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.928570 0.071430 0.000000 0.000000 0.000000 1.000000 0.000000 0.928570 0.071430 0.000000 0.000000 0.285710 0.571430 0.142860 0.928570 0.071430 0.000000 0.000000 0.642854 0.000000 0.214288 0.142859 0.000000 0.944440 0.000000 0.055560 0.944440 0.055560 0.000000 0.000000 0.833330 0.000000 0.000000 0.166670 MOTIF AC0317:ZNF:C2H2_ZF AC0317:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.015580 0.000830 0.982760 0.000830 0.004880 0.052455 0.000830 0.941835 0.942840 0.000830 0.047400 0.008930 0.008930 0.002135 0.975380 0.013555 0.000830 0.979575 0.000830 0.018765 0.004880 0.006185 0.002855 0.986080 0.002855 0.924755 0.025560 0.046830 0.006185 0.940375 0.003435 0.050005 0.014140 0.034530 0.007485 0.943845 0.177409 0.449543 0.133139 0.239909 MOTIF AC0318:ZBTB:C2H2_ZF AC0318:ZBTB:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.118130 0.015985 0.785065 0.080820 0.936265 0.052185 0.010700 0.000850 0.002475 0.953710 0.021835 0.021980 0.129880 0.012580 0.841530 0.016010 0.131925 0.053900 0.662705 0.151470 0.000000 0.997605 0.002395 0.000000 0.992600 0.000145 0.002370 0.004885 0.000000 0.000145 0.990580 0.009275 0.000435 0.000000 0.994805 0.004760 0.002325 0.982995 0.000580 0.014100 0.000000 0.952025 0.015365 0.032610 MOTIF AC0319:ZBTB:C2H2_ZF AC0319:ZBTB:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.011360 0.001625 0.978695 0.008320 0.000000 1.000000 0.000000 0.000000 0.000650 0.001140 0.005895 0.992315 0.000535 0.000000 0.996815 0.002650 0.387603 0.006570 0.458523 0.147304 0.534655 0.000970 0.448945 0.015430 0.000595 0.997765 0.000120 0.001520 0.000300 0.999345 0.000000 0.000355 0.002130 0.000120 0.997750 0.000000 0.000715 0.999225 0.000000 0.000060 0.051600 0.131795 0.807520 0.009085 MOTIF AC0320:ZNF:C2H2_ZF AC0320:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.000000 0.000000 0.900000 0.100000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.300000 0.700000 0.000000 0.000000 0.800000 0.000000 0.000000 0.200000 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.500000 0.500000 0.000000 0.400000 0.300000 0.300000 0.100000 0.700000 0.100000 0.100000 0.300000 0.700000 0.000000 0.000000 MOTIF AC0321:LEF/TCF:Sox AC0321:LEF/TCF:Sox letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.043019 0.870235 0.017580 0.069166 0.894919 0.046196 0.037828 0.021057 0.175185 0.004506 0.004524 0.815785 0.000623 0.991293 0.003301 0.004783 0.003947 0.064782 0.931051 0.000220 0.411599 0.004429 0.550408 0.033564 0.000888 0.002026 0.995201 0.001885 0.330308 0.076949 0.563260 0.029483 0.003499 0.934785 0.058781 0.002935 0.006752 0.000491 0.989692 0.003065 0.095274 0.825334 0.064022 0.015370 0.116482 0.035329 0.118973 0.729216 0.059429 0.015773 0.877972 0.046826 MOTIF AC0322:ZNF:C2H2_ZF AC0322:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.990594 0.004698 0.003452 0.001256 0.008418 0.981102 0.002886 0.007594 0.009052 0.722839 0.023628 0.244481 0.029182 0.049434 0.024460 0.896925 0.911384 0.018826 0.011580 0.058210 0.004836 0.952924 0.001860 0.040380 0.011352 0.936434 0.035450 0.016764 MOTIF AC0323:ZNF:C2H2_ZF AC0323:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.001115 0.968125 0.028085 0.002675 0.001760 0.997125 0.000710 0.000405 0.002660 0.000975 0.994675 0.001690 0.005860 0.036595 0.957300 0.000245 0.000595 0.963050 0.028140 0.008215 0.002900 0.995420 0.001100 0.000580 0.000435 0.001215 0.996815 0.001535 0.000740 0.024145 0.973160 0.001955 0.024745 0.550852 0.030715 0.393688 MOTIF AC0324:MBD:MBD AC0324:MBD:MBD letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.053569 0.392856 0.464285 0.089289 0.000000 0.250000 0.750000 0.000000 0.000000 0.000000 0.517860 0.482140 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.535715 0.357144 0.107141 0.000000 0.107140 0.107140 0.589290 0.196430 MOTIF AC0325:ZNF705G/ZNF:C2H2_ZF AC0325:ZNF705G/ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.575000 0.139285 0.139285 0.146430 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.014285 0.000000 0.985715 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.450000 0.550000 0.000000 0.000000 0.042855 0.028570 0.007145 0.921430 MOTIF AC0326:ZNF:C2H2_ZF AC0326:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.125331 0.624008 0.125331 0.125331 0.539398 0.209941 0.125331 0.125331 0.001130 0.025005 0.001130 0.972735 0.001130 0.906865 0.017155 0.074850 0.424594 0.438529 0.017155 0.119721 0.001130 0.067886 0.001130 0.929854 0.001130 0.993405 0.001130 0.004335 0.756383 0.007540 0.010745 0.225332 0.004335 0.665513 0.001130 0.329022 0.007755 0.989985 0.001130 0.001130 0.864254 0.098231 0.001130 0.036385 0.125330 0.131965 0.125330 0.617375 MOTIF AC0327:ZBTB:C2H2_ZF AC0327:ZBTB:C2H2_ZF letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.074938 0.001053 0.883916 0.040093 0.000703 0.001220 0.000490 0.997587 0.998880 0.000840 0.000187 0.000093 0.000000 0.999153 0.000330 0.000517 0.992509 0.000887 0.004697 0.001907 0.000610 0.001173 0.997284 0.000933 0.003707 0.000840 0.000863 0.994590 0.712861 0.030533 0.165523 0.091083 0.406614 0.090206 0.091502 0.411677 0.141570 0.181632 0.102490 0.574308 0.745553 0.084380 0.083823 0.086243 0.084010 0.748926 0.083613 0.083450 0.084777 0.087077 0.083963 0.744184 0.084040 0.083427 0.748847 0.083687 0.083687 0.083333 0.083800 0.749180 0.746813 0.084317 0.085070 0.083800 MOTIF AC0328:ATF/ATF6B:bZIP AC0328:ATF/ATF6B:bZIP letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.051060 0.935155 0.005655 0.008130 0.010815 0.977600 0.010440 0.001145 0.998080 0.000000 0.000960 0.000960 0.001695 0.977305 0.009085 0.011915 0.018965 0.039635 0.911280 0.030120 0.835171 0.026880 0.033275 0.104674 0.043920 0.059020 0.002145 0.894915 0.013395 0.002315 0.981975 0.002315 0.145481 0.045785 0.049225 0.759509 MOTIF AC0329:ZNF/ZNF354C:C2H2_ZF AC0329:ZNF/ZNF354C:C2H2_ZF letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.355675 0.500410 0.143505 0.000410 0.094160 0.062910 0.000410 0.842519 0.000410 0.998770 0.000410 0.000410 0.000410 0.998770 0.000410 0.000410 0.998770 0.000410 0.000410 0.000410 0.000410 0.829360 0.169820 0.000410 MOTIF AC0330:LIN28B:CSD AC0330:LIN28B:CSD letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.009715 0.986145 0.004140 0.000000 0.089415 0.009560 0.897684 0.003340 0.027300 0.960820 0.011880 0.000000 0.013230 0.000000 0.920550 0.066220 0.684093 0.009845 0.256216 0.049845 0.236320 0.012260 0.027125 0.724295 0.722294 0.012630 0.256846 0.008230 0.212781 0.145401 0.144106 0.497712 0.457375 0.110240 0.311125 0.121260 0.555985 0.130050 0.242505 0.071460 0.009670 0.936340 0.005805 0.048185 0.503280 0.095295 0.257275 0.144150 0.187406 0.022890 0.768304 0.021400 0.003785 0.959805 0.022355 0.014055 0.094150 0.037295 0.817786 0.050770 MOTIF AC0331:YBX:CSD AC0331:YBX:CSD letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.000000 0.000000 0.998775 0.001225 0.652452 0.000000 0.334698 0.012850 0.066521 0.006135 0.000940 0.926404 0.174760 0.000910 0.824330 0.000000 0.053950 0.002365 0.727879 0.215806 0.330425 0.033985 0.145390 0.490200 0.699895 0.023290 0.225510 0.051305 0.095175 0.731095 0.067515 0.106215 MOTIF AC0332:ZNF:C2H2_ZF AC0332:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.017085 0.048503 0.005727 0.928685 0.998621 0.000003 0.000848 0.000528 0.000420 0.004765 0.000278 0.994537 0.000013 0.000347 0.001380 0.998260 0.994552 0.003880 0.001400 0.000168 0.000407 0.013055 0.001410 0.985128 0.005163 0.000300 0.985777 0.008760 0.000023 0.000003 0.998449 0.001525 0.006420 0.002848 0.880077 0.110655 0.739354 0.101935 0.066107 0.092605 0.006408 0.002608 0.000353 0.990631 0.188157 0.001162 0.795969 0.014712 MOTIF AC0333:ZNF:C2H2_ZF AC0333:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.003810 0.016983 0.009487 0.969720 0.000197 0.000470 0.001040 0.998293 0.000763 0.932990 0.001807 0.064440 0.008990 0.034900 0.943880 0.012230 0.013250 0.000547 0.001517 0.984686 0.004863 0.000483 0.992821 0.001833 0.000210 0.000560 0.996433 0.002797 0.000890 0.997826 0.000907 0.000377 0.859593 0.001793 0.049030 0.089583 0.005130 0.089077 0.634314 0.271479 0.000470 0.000637 0.002413 0.996480 0.000633 0.735167 0.001757 0.262443 0.065060 0.025593 0.640607 0.268739 0.000093 0.000360 0.001450 0.998097 0.000277 0.959113 0.000860 0.039750 MOTIF AC0334:ZNF:C2H2_ZF AC0334:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.389068 0.009460 0.590187 0.011285 0.142161 0.003905 0.851079 0.002855 0.002495 0.991435 0.001280 0.004790 0.000400 0.001235 0.998100 0.000265 0.000135 0.998765 0.000030 0.001070 0.007550 0.985685 0.000425 0.006340 0.004210 0.000210 0.986830 0.008750 0.000890 0.000835 0.998010 0.000265 0.001900 0.995545 0.001225 0.001330 0.002085 0.001090 0.988060 0.008765 0.002385 0.843745 0.005110 0.148760 MOTIF AC0335:NRF:Unknown AC0335:NRF:Unknown letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.138120 0.026880 0.830930 0.004070 0.074627 0.904213 0.018905 0.002255 0.038752 0.012062 0.942503 0.006683 0.003428 0.954347 0.031405 0.010820 0.675035 0.187229 0.098823 0.038913 0.017433 0.056180 0.108640 0.817748 0.002585 0.002247 0.993331 0.001837 0.001595 0.969675 0.001430 0.027300 0.001672 0.025153 0.953132 0.020043 0.004750 0.896829 0.002393 0.096028 0.644842 0.083685 0.240948 0.030525 MOTIF AC0336:ZNF:C2H2_ZF AC0336:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.043660 0.003045 0.936130 0.017165 0.001510 0.002195 0.013645 0.982650 0.096965 0.822311 0.027530 0.053195 0.083940 0.515455 0.066460 0.334145 0.089055 0.005170 0.888325 0.017450 0.000000 0.002705 0.005830 0.991465 0.063460 0.776360 0.042100 0.118080 0.068451 0.179177 0.037845 0.714527 0.053260 0.031835 0.885030 0.029875 0.017360 0.067035 0.023490 0.892114 0.078720 0.862090 0.031275 0.027915 MOTIF AC0337:ZNF:C2H2_ZF AC0337:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 22 nsites= 20 E= 0 0.271556 0.041210 0.037835 0.649398 0.911300 0.010085 0.012510 0.066105 0.891619 0.030900 0.075975 0.001505 0.055710 0.003330 0.936290 0.004670 0.128945 0.021705 0.825020 0.024330 0.124126 0.153621 0.154441 0.567813 0.098805 0.232211 0.623408 0.045575 0.001355 0.005090 0.973310 0.020245 0.497837 0.043925 0.015800 0.442437 0.253659 0.493468 0.240479 0.012395 0.240625 0.098730 0.156255 0.504390 0.676060 0.162345 0.057720 0.103875 0.002695 0.099740 0.202554 0.695012 0.801785 0.017090 0.059295 0.121830 0.002695 0.992525 0.003100 0.001680 0.119755 0.412925 0.177975 0.289345 0.542905 0.168070 0.097025 0.192000 0.043530 0.763272 0.060854 0.132344 0.003370 0.933180 0.003035 0.060415 0.005625 0.067595 0.026950 0.899829 0.029665 0.012500 0.007385 0.950450 0.663415 0.029970 0.017690 0.288925 MOTIF AC0338:ARID5B:ARID/BRIGHT AC0338:ARID5B:ARID/BRIGHT letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.000000 0.200000 0.233335 0.566665 0.000000 0.200000 0.300000 0.500000 0.266664 0.066670 0.399998 0.266669 0.199999 0.066670 0.699997 0.033335 0.000000 0.000000 0.033335 0.966665 0.866666 0.033335 0.100000 0.000000 0.000000 0.066665 0.000000 0.933335 0.000000 0.033335 0.033335 0.933330 0.133330 0.200000 0.600000 0.066670 0.133330 0.066670 0.200000 0.600000 0.225000 0.125000 0.525000 0.125000 MOTIF AC0339:ZNF:['C2H2_zinc_finger_factors'] AC0339:ZNF:['C2H2_zinc_finger_factors'] letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.922811 0.030070 0.025620 0.021500 0.906819 0.007190 0.053341 0.032650 0.021580 0.046310 0.888870 0.043240 0.614730 0.008570 0.345830 0.030870 0.014390 0.008810 0.965000 0.011800 0.017780 0.012530 0.014710 0.954980 0.019400 0.022150 0.020120 0.938330 0.946250 0.017380 0.016570 0.019800 0.742663 0.098279 0.083409 0.075649 MOTIF AC0340:ZNF:C2H2_ZF AC0340:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.070610 0.017180 0.009540 0.902670 0.040080 0.017180 0.024810 0.917931 0.017180 0.032440 0.009540 0.940840 0.200380 0.643130 0.040080 0.116410 0.696567 0.146951 0.101141 0.055341 0.704193 0.001910 0.208018 0.085879 0.917931 0.047710 0.017180 0.017180 0.788170 0.009540 0.001910 0.200380 0.314890 0.276720 0.101140 0.307250 0.124049 0.017180 0.093509 0.765262 0.101140 0.001910 0.895040 0.001910 0.001910 0.627860 0.009540 0.360690 0.085880 0.154580 0.055340 0.704200 0.582066 0.055341 0.139311 0.223282 0.177480 0.062980 0.047710 0.711830 0.009540 0.001910 0.986640 0.001910 0.070610 0.001910 0.864500 0.062980 MOTIF AC0341:ZNF:C2H2_ZF AC0341:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.003320 0.015010 0.963615 0.018055 0.120240 0.866400 0.002870 0.010490 0.178176 0.010250 0.153001 0.658573 0.655048 0.002805 0.308127 0.034020 0.091415 0.086420 0.000995 0.821169 0.910290 0.000000 0.089630 0.000080 0.999820 0.000030 0.000030 0.000120 0.002160 0.984095 0.007545 0.006200 0.504560 0.000000 0.495220 0.000220 0.973560 0.004340 0.007475 0.014625 0.195371 0.133666 0.669528 0.001435 0.032785 0.529523 0.039545 0.398147 0.989310 0.001680 0.007800 0.001210 0.069045 0.113104 0.463918 0.353933 0.022105 0.687117 0.211824 0.078955 0.290209 0.604537 0.009515 0.095740 0.006205 0.984085 0.000470 0.009240 MOTIF AC0342:ZNF:C2H2_ZF AC0342:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 27 nsites= 20 E= 0 0.000000 0.555560 0.000000 0.444440 0.000000 0.666670 0.000000 0.333330 0.000000 0.000000 0.000000 1.000000 0.111111 0.222222 0.444444 0.222222 0.888890 0.000000 0.000000 0.111110 0.222220 0.000000 0.111110 0.666670 0.000000 0.111110 0.000000 0.888890 0.333333 0.444444 0.000000 0.222222 0.333330 0.555560 0.000000 0.111110 0.555560 0.000000 0.000000 0.444440 0.000000 0.444444 0.222222 0.333333 0.555560 0.111110 0.000000 0.333330 0.222220 0.666670 0.111110 0.000000 0.111110 0.000000 0.111110 0.777780 0.222222 0.444444 0.333333 0.000000 0.888890 0.000000 0.000000 0.111110 1.000000 0.000000 0.000000 0.000000 0.777780 0.000000 0.000000 0.222220 0.222222 0.333333 0.111111 0.333333 0.444444 0.444444 0.111111 0.000000 0.111110 0.111110 0.000000 0.777780 0.555560 0.333330 0.111110 0.000000 0.777780 0.000000 0.000000 0.222220 0.222220 0.555560 0.111110 0.111110 0.000000 0.222220 0.000000 0.777780 0.111110 0.000000 0.000000 0.888890 0.000000 0.111110 0.000000 0.888890 MOTIF AC0343:ZNF:C2H2_ZF AC0343:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 21 nsites= 20 E= 0 0.023530 0.073530 0.026470 0.876470 0.050000 0.750000 0.032350 0.167650 0.870590 0.032350 0.050000 0.047060 0.029410 0.079410 0.067650 0.823530 0.088230 0.041180 0.082350 0.788240 0.020590 0.094119 0.020590 0.864701 0.152940 0.311770 0.032350 0.502940 0.008820 0.029410 0.020590 0.941180 0.132350 0.017650 0.832350 0.017650 0.947060 0.026470 0.011760 0.014710 0.908829 0.000000 0.055881 0.035290 0.179410 0.017650 0.744120 0.058820 0.126470 0.094120 0.735290 0.044120 0.820590 0.041180 0.050000 0.088230 0.041180 0.267647 0.023530 0.667643 0.850000 0.020590 0.097060 0.032350 0.170590 0.029410 0.629410 0.170590 0.044120 0.385290 0.055880 0.514710 0.064709 0.076469 0.041180 0.817642 0.005880 0.029410 0.005880 0.958830 0.020590 0.067650 0.038230 0.873530 MOTIF AC0344:ZNF:C2H2_ZF AC0344:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 27 nsites= 20 E= 0 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.166670 0.000000 0.833330 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.166670 0.833330 0.000000 0.166670 0.833330 0.000000 0.000000 0.666663 0.166668 0.166668 0.666670 0.000000 0.000000 0.333330 0.166668 0.000000 0.666663 0.166668 0.166668 0.666663 0.166668 0.000000 0.666670 0.000000 0.333330 0.000000 1.000000 0.000000 0.000000 0.000000 0.166668 0.666663 0.000000 0.166668 0.000000 0.666670 0.000000 0.333330 0.000000 0.000000 0.000000 1.000000 0.000000 0.333333 0.333333 0.333333 0.000000 0.000000 0.166670 0.833330 0.166668 0.166668 0.499995 0.166668 0.000000 0.000000 0.166670 0.833330 0.333330 0.000000 0.000000 0.666670 0.166670 0.500000 0.000000 0.333330 0.000000 0.166670 0.000000 0.833330 0.000000 0.000000 0.000000 1.000000 MOTIF AC0345:ZNF:C2H2_ZF AC0345:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 21 nsites= 20 E= 0 0.016340 0.042480 0.006540 0.934640 0.006540 0.035950 0.029410 0.928100 0.143791 0.140521 0.604576 0.111111 0.029410 0.143790 0.016340 0.810460 0.715690 0.062090 0.156860 0.065360 0.516340 0.006540 0.447710 0.029410 0.183010 0.081700 0.676470 0.058820 0.705880 0.052290 0.039220 0.202610 0.258170 0.009800 0.725490 0.006540 0.006540 0.833330 0.003270 0.156860 0.009800 0.003270 0.003270 0.983660 0.009800 0.934640 0.000000 0.055560 0.006540 0.006540 0.000000 0.986920 0.000000 0.000000 0.003270 0.996730 0.000000 0.016340 0.009800 0.973860 0.820260 0.006540 0.163400 0.009800 0.003270 0.254900 0.000000 0.741830 0.934640 0.003270 0.019610 0.042480 0.000000 0.000000 0.000000 1.000000 0.918300 0.006540 0.062090 0.013070 0.009800 0.026140 0.058821 0.905239 MOTIF AC0346:ZNF:C2H2_ZF AC0346:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 27 nsites= 20 E= 0 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.600000 0.400000 0.000000 0.200000 0.000000 0.800000 0.200000 0.200000 0.200000 0.400000 0.000000 1.000000 0.000000 0.000000 0.200000 0.200000 0.200000 0.400000 0.000000 1.000000 0.000000 0.000000 0.800000 0.200000 0.000000 0.000000 0.000000 0.200000 0.600000 0.200000 0.200000 0.600000 0.200000 0.000000 0.000000 0.800000 0.000000 0.200000 0.000000 0.600000 0.400000 0.000000 0.400000 0.400000 0.000000 0.200000 0.600000 0.400000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.400000 0.000000 0.600000 0.000000 0.400000 0.600000 0.000000 0.000000 0.800000 0.000000 0.200000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.600000 0.400000 0.000000 0.600000 0.000000 0.400000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.200000 0.000000 0.800000 0.000000 0.800000 0.000000 0.200000 0.000000 0.000000 0.200000 0.800000 0.000000 MOTIF AC0347:ZNF:C2H2_ZF AC0347:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.140241 0.034550 0.774398 0.050811 0.026420 0.432930 0.180890 0.359760 0.376020 0.083330 0.319110 0.221540 0.018290 0.075201 0.205282 0.701227 0.099591 0.132111 0.205282 0.563016 0.010160 0.571146 0.189022 0.229672 0.115851 0.619926 0.140241 0.123981 0.026420 0.018290 0.205282 0.750008 0.042680 0.026420 0.254063 0.676837 0.018290 0.010160 0.790658 0.180892 0.002030 0.002030 0.993910 0.002030 0.042680 0.611790 0.042680 0.302850 0.026420 0.042680 0.286583 0.644316 0.026420 0.123981 0.058941 0.790658 0.050811 0.099591 0.839438 0.010160 0.010160 0.091461 0.042680 0.855699 0.083331 0.002030 0.880089 0.034550 MOTIF AC0348:TP:p53 AC0348:TP:p53 letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.595306 0.024921 0.317308 0.062465 0.004047 0.961001 0.015036 0.019916 0.807315 0.068451 0.042376 0.081858 0.231484 0.016504 0.072249 0.679762 0.004986 0.002886 0.989217 0.002911 0.037046 0.443796 0.008119 0.511039 0.098989 0.603718 0.049025 0.248268 0.093161 0.604831 0.180792 0.121216 0.360893 0.152362 0.304302 0.182442 0.211533 0.057436 0.648401 0.082630 0.539109 0.024804 0.369398 0.066689 0.022562 0.928978 0.031727 0.016733 0.695216 0.095278 0.023384 0.186122 0.100519 0.104811 0.079314 0.715356 0.045450 0.029546 0.898941 0.026063 MOTIF AC0349:IRX:Homeodomain AC0349:IRX:Homeodomain letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.960970 0.008860 0.017720 0.012450 0.004925 0.975215 0.002960 0.016900 0.586780 0.002325 0.405375 0.005520 0.234891 0.429742 0.004540 0.330827 0.205545 0.070555 0.689725 0.034175 0.405670 0.183115 0.113145 0.298070 0.377623 0.098835 0.131594 0.391948 0.436403 0.060445 0.073795 0.429358 0.457577 0.082655 0.059280 0.400487 0.383483 0.132294 0.119134 0.365088 0.273185 0.127115 0.185990 0.413710 0.055265 0.647545 0.092095 0.205095 0.558121 0.003940 0.266373 0.171567 0.003395 0.338447 0.003615 0.654543 0.019925 0.003035 0.975185 0.001855 0.012430 0.022355 0.007970 0.957245 MOTIF AC0350:TFCP/TFCP2L:Grainyhead AC0350:TFCP/TFCP2L:Grainyhead letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.624814 0.006022 0.128437 0.240727 0.007988 0.873532 0.116399 0.002081 0.059312 0.664204 0.017460 0.259024 0.261257 0.025788 0.657253 0.055702 0.001339 0.092986 0.905195 0.000480 0.230479 0.148040 0.004478 0.617002 0.181045 0.163228 0.072012 0.583715 0.164586 0.323163 0.147492 0.364760 0.374868 0.139180 0.300141 0.185811 0.618799 0.081537 0.165627 0.134038 0.624831 0.008508 0.144964 0.221698 0.005310 0.879576 0.104843 0.010271 0.056610 0.618284 0.022191 0.302916 0.260561 0.022159 0.633306 0.083974 0.008204 0.113221 0.872973 0.005602 0.281889 0.133719 0.016243 0.568149 MOTIF AC0351:GRHL/TFCP:Grainyhead AC0351:GRHL/TFCP:Grainyhead letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.758930 0.020463 0.138586 0.082021 0.822863 0.006748 0.067325 0.103064 0.002322 0.990356 0.005470 0.001852 0.208514 0.565652 0.028994 0.196840 0.137886 0.018299 0.759512 0.084303 0.002067 0.012537 0.982948 0.002448 0.074350 0.034990 0.006974 0.883686 0.060430 0.091140 0.018485 0.829945 MOTIF AC0352:ZNF:C2H2_ZF AC0352:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.010420 0.010420 0.010420 0.968740 0.052080 0.718750 0.010420 0.218750 0.093749 0.260417 0.135419 0.510415 0.177080 0.343750 0.218750 0.260420 0.427080 0.093750 0.260420 0.218750 0.593756 0.052081 0.302083 0.052081 0.052080 0.468750 0.468750 0.010420 0.010420 0.010420 0.010420 0.968740 0.260420 0.052080 0.677080 0.010420 0.010420 0.010420 0.968740 0.010420 0.010420 0.010420 0.010420 0.968740 0.093750 0.135420 0.468750 0.302080 0.052080 0.468750 0.218750 0.260420 0.052080 0.510420 0.218750 0.218750 0.927080 0.052080 0.010420 0.010420 0.010420 0.927080 0.010420 0.052080 0.010420 0.885411 0.010420 0.093749 MOTIF AC0353:PRDM:C2H2_ZF AC0353:PRDM:C2H2_ZF letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.298711 0.067913 0.551166 0.082210 0.069498 0.792233 0.065625 0.072645 0.325603 0.113855 0.380040 0.180503 0.062540 0.062515 0.810853 0.064092 0.000132 0.001223 0.000128 0.998517 0.000045 0.006497 0.010772 0.982686 0.002470 0.000452 0.000160 0.996918 0.000235 0.977630 0.000045 0.022090 0.669494 0.031088 0.256893 0.042525 0.987780 0.006723 0.001430 0.004067 0.001948 0.009020 0.978975 0.010057 0.050990 0.024162 0.760936 0.163912 0.066690 0.655976 0.214768 0.062565 0.051060 0.415431 0.045105 0.488404 0.100650 0.546579 0.110817 0.241955 0.138428 0.764320 0.075160 0.022092 0.000150 0.990501 0.000132 0.009217 MOTIF AC0354:PRDM/ZEB:['C2H2_zinc_finger_factors'] AC0354:PRDM/ZEB:['C2H2_zinc_finger_factors'] letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.020125 0.217225 0.729500 0.033150 0.969400 0.007650 0.009510 0.013440 0.933360 0.027085 0.027910 0.011645 0.974780 0.005650 0.009440 0.010130 0.002690 0.969535 0.007580 0.020195 0.590345 0.205675 0.144710 0.059270 0.052280 0.105115 0.698240 0.144365 MOTIF AC0355:DMRTC:DM AC0355:DMRTC:DM letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.008123 0.213066 0.105300 0.673511 0.000000 0.000327 0.993240 0.006433 0.001633 0.001307 0.000000 0.997060 0.986337 0.000000 0.002910 0.010753 0.087127 0.001917 0.034950 0.876006 0.000980 0.983967 0.001303 0.013750 0.251640 0.005400 0.731493 0.011467 0.035927 0.003033 0.011050 0.949990 0.049220 0.033920 0.015693 0.901167 MOTIF AC0356:DMRT/DMRTA:DM AC0356:DMRT/DMRTA:DM letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.189736 0.098723 0.075328 0.636213 0.075871 0.182731 0.126515 0.614883 0.007481 0.002031 0.984590 0.005898 0.466807 0.251721 0.001625 0.279848 0.141761 0.064191 0.007729 0.786319 0.971672 0.007865 0.014378 0.006085 0.000971 0.987395 0.003233 0.008401 0.850549 0.006594 0.047010 0.095847 0.215698 0.118427 0.111127 0.554747 MOTIF AC0357:FOXP:Forkhead AC0357:FOXP:Forkhead letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.101329 0.796666 0.086260 0.015745 0.375760 0.008250 0.513590 0.102400 0.871966 0.058285 0.069475 0.000275 0.170024 0.050175 0.000275 0.779526 0.999175 0.000275 0.000275 0.000275 0.000275 0.999175 0.000275 0.000275 0.999175 0.000275 0.000275 0.000275 MOTIF AC0358:ZNF:C2H2_ZF AC0358:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 28 nsites= 20 E= 0 0.923080 0.076920 0.000000 0.000000 0.076921 0.076921 0.769238 0.076921 0.000000 0.153850 0.000000 0.846150 0.000000 0.076920 0.923080 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.076920 0.923080 0.000000 0.000000 0.153850 0.846150 0.000000 0.000000 0.153850 0.846150 0.076921 0.076921 0.000000 0.846158 0.000000 0.000000 0.153850 0.846150 0.000000 1.000000 0.000000 0.000000 0.076921 0.000000 0.076921 0.846158 0.846158 0.076921 0.076921 0.000000 0.076920 0.923080 0.000000 0.000000 0.000000 0.923080 0.076920 0.000000 0.000000 0.000000 0.076920 0.923080 0.000000 1.000000 0.000000 0.000000 0.846150 0.000000 0.153850 0.000000 0.000000 1.000000 0.000000 0.000000 0.923080 0.000000 0.076920 0.000000 0.846150 0.000000 0.153850 0.000000 0.000000 0.076921 0.076921 0.846158 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.846150 0.000000 0.153850 0.000000 0.769230 0.076920 0.153850 0.000000 0.076920 0.923080 0.000000 0.000000 MOTIF AC0359:SRF/MEF2B:MADS_box AC0359:SRF/MEF2B:MADS_box letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.022981 0.966917 0.004075 0.006027 0.001476 0.844444 0.004827 0.149253 0.689653 0.056983 0.056738 0.196626 0.325277 0.026393 0.040206 0.608124 0.821671 0.010613 0.069503 0.098213 0.138579 0.026460 0.006334 0.828627 0.754419 0.026257 0.025737 0.193587 0.362974 0.060183 0.065615 0.511228 0.036454 0.000954 0.962592 0.000000 0.006012 0.003698 0.963885 0.026405 MOTIF AC0360:MYRF:Ndt80/PhoG AC0360:MYRF:Ndt80/PhoG letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.009620 0.009620 0.009620 0.971140 0.009620 0.009620 0.971140 0.009620 0.009620 0.009620 0.971140 0.009620 0.009620 0.009620 0.009620 0.971140 0.971140 0.009620 0.009620 0.009620 0.009620 0.971140 0.009620 0.009620 0.009620 0.971140 0.009620 0.009620 0.971140 0.009620 0.009620 0.009620 MOTIF AC0361:MEF2A/MEF2C:MADS_box AC0361:MEF2A/MEF2C:MADS_box letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.071591 0.804965 0.032708 0.090736 0.016314 0.184433 0.011068 0.788185 0.841436 0.053222 0.026350 0.078992 0.616961 0.009671 0.044301 0.329067 0.887822 0.003508 0.020850 0.087820 0.909031 0.004193 0.005451 0.081325 0.939221 0.003449 0.005716 0.051614 0.018920 0.009196 0.006730 0.965154 0.973036 0.001099 0.021672 0.004193 0.151007 0.025456 0.802410 0.021127 MOTIF AC0362:DPF/ZBTB:C2H2_ZF AC0362:DPF/ZBTB:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.080085 0.699972 0.039178 0.180765 0.486647 0.255315 0.236098 0.021940 0.009385 0.955506 0.009912 0.025197 0.093217 0.739427 0.126445 0.040910 0.002867 0.018943 0.002085 0.976105 0.998230 0.000000 0.000990 0.000780 0.010788 0.014480 0.001747 0.972985 0.799395 0.000000 0.198572 0.002033 0.003395 0.159937 0.546817 0.289851 MOTIF AC0363:ZBTB:C2H2_ZF AC0363:ZBTB:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.023750 0.366163 0.005725 0.604362 0.792840 0.002740 0.102170 0.102250 0.007660 0.016460 0.000155 0.975725 0.907580 0.002215 0.089335 0.000870 0.018865 0.714435 0.180330 0.086370 0.539308 0.043295 0.416252 0.001145 0.065060 0.037920 0.025030 0.871990 0.090765 0.221521 0.033605 0.654108 MOTIF AC0364:ZSCAN:C2H2_ZF AC0364:ZSCAN:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.027158 0.032326 0.939740 0.000776 0.721246 0.000136 0.278354 0.000264 0.000140 0.000180 0.727651 0.272029 0.000020 0.000016 0.999824 0.000140 0.000204 0.840160 0.000008 0.159628 0.000074 0.000078 0.000082 0.999766 0.000028 0.989844 0.000032 0.010096 0.104948 0.000242 0.020438 0.874372 0.000140 0.000148 0.999638 0.000074 0.032790 0.003454 0.000092 0.963664 0.017852 0.124204 0.000184 0.857760 0.995192 0.002588 0.000192 0.002028 0.999556 0.000000 0.000272 0.000172 0.000052 0.999884 0.000024 0.000040 0.931676 0.007008 0.053220 0.008096 MOTIF AC0365:ZIM/ZNF:C2H2_ZF AC0365:ZIM/ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.067450 0.052806 0.779920 0.099824 0.214526 0.233802 0.379903 0.171770 0.116982 0.032020 0.017288 0.833710 0.001658 0.001926 0.001270 0.995146 0.011476 0.011698 0.045306 0.931520 0.001118 0.995344 0.001060 0.002478 0.001106 0.000678 0.000666 0.997550 0.001530 0.016662 0.952226 0.029582 0.021694 0.148887 0.001466 0.827953 0.051386 0.052720 0.051594 0.844301 MOTIF AC0366:ZNF:C2H2_ZF AC0366:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.020620 0.876290 0.036080 0.067010 0.015460 0.020620 0.000000 0.963920 0.144330 0.000000 0.835050 0.020620 0.015460 0.015460 0.927839 0.041240 0.835058 0.025770 0.134021 0.005150 0.025770 0.000000 0.005150 0.969080 0.010310 0.005150 0.000000 0.984540 0.046390 0.432990 0.051550 0.469070 0.087630 0.010310 0.036080 0.865980 0.015460 0.000000 0.969080 0.015460 0.061860 0.041240 0.015460 0.881440 0.000000 0.020620 0.010310 0.969070 0.731953 0.020620 0.206188 0.041240 0.000000 0.103090 0.020620 0.876290 MOTIF AC0367:ZNF:C2H2_ZF AC0367:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 22 nsites= 20 E= 0 0.094807 0.097723 0.103690 0.703779 0.091799 0.092539 0.104576 0.711085 0.200111 0.104590 0.132404 0.562895 0.114923 0.386556 0.138143 0.360379 0.688968 0.107360 0.097713 0.105960 0.106487 0.196660 0.098383 0.598470 0.166090 0.088037 0.568156 0.177717 0.059577 0.050587 0.866349 0.023487 0.011750 0.789699 0.048733 0.149818 0.058783 0.066190 0.014010 0.861017 0.067913 0.076917 0.824939 0.030230 0.216566 0.534885 0.012210 0.236339 0.667354 0.083338 0.237838 0.011470 0.022727 0.014480 0.011470 0.951323 0.873533 0.023257 0.037723 0.065487 0.278712 0.128957 0.567008 0.025323 0.008947 0.042883 0.043887 0.904284 0.875056 0.013157 0.060920 0.050867 0.005093 0.143077 0.034997 0.816832 0.004250 0.009877 0.007530 0.978343 0.000400 0.923470 0.000400 0.075730 0.010983 0.933544 0.001153 0.054320 MOTIF AC0368:ZNF:C2H2_ZF AC0368:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.076948 0.206018 0.077800 0.639234 0.005500 0.078745 0.014930 0.900825 0.005322 0.057125 0.021165 0.916388 0.018583 0.738179 0.019760 0.223478 0.056112 0.018470 0.009930 0.915488 0.005000 0.882825 0.028602 0.083573 0.144895 0.120270 0.024030 0.710806 0.019707 0.900198 0.023875 0.056220 0.017900 0.930638 0.022715 0.028747 0.743554 0.025080 0.048230 0.183136 0.019213 0.048745 0.873310 0.058732 0.028080 0.037373 0.038215 0.896332 0.616883 0.032450 0.342167 0.008500 0.076500 0.071000 0.075500 0.777000 0.079293 0.080765 0.762942 0.077000 MOTIF AC0369:ZNF:C2H2_ZF AC0369:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.103033 0.000000 0.896860 0.000107 0.999993 0.000000 0.000007 0.000000 0.000233 0.000127 0.999167 0.000473 0.688485 0.000000 0.308422 0.003093 0.992494 0.003993 0.000473 0.003040 0.004967 0.854837 0.084363 0.055833 0.040490 0.043333 0.000663 0.915514 0.000000 0.999607 0.000160 0.000233 0.863597 0.125503 0.006030 0.004870 0.083117 0.378407 0.000943 0.537533 0.672243 0.054240 0.249187 0.024330 MOTIF AC0370:ZNF/ZNF280A:C2H2_ZF AC0370:ZNF/ZNF280A:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.171927 0.015810 0.775426 0.036837 0.936370 0.037247 0.019033 0.007350 0.058457 0.006100 0.905133 0.030310 0.798295 0.014150 0.177708 0.009847 0.909633 0.024633 0.054277 0.011457 0.010927 0.471788 0.047520 0.469765 0.059247 0.118587 0.022977 0.799190 0.001470 0.985564 0.005213 0.007753 0.952523 0.041520 0.003240 0.002717 0.020400 0.419144 0.004487 0.555969 0.892617 0.005007 0.059393 0.042983 0.017170 0.894600 0.034810 0.053420 0.200031 0.064047 0.045653 0.690269 0.049677 0.011927 0.900893 0.037503 0.118460 0.100853 0.672299 0.108387 MOTIF AC0371:REST:C2H2_ZF AC0371:REST:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.059573 0.812838 0.061318 0.066271 0.028391 0.035219 0.027117 0.909273 0.047749 0.133061 0.794736 0.024454 0.041045 0.023214 0.067820 0.867921 0.004232 0.959145 0.011856 0.024767 0.002776 0.957543 0.011719 0.027962 0.443550 0.099494 0.342654 0.114302 0.174448 0.149432 0.102301 0.573819 0.041051 0.007636 0.939597 0.011716 0.001294 0.006783 0.990718 0.001205 0.009934 0.003272 0.005060 0.981734 0.067082 0.144609 0.714648 0.073661 0.019764 0.922950 0.026817 0.030469 0.023490 0.037413 0.022484 0.916613 0.083953 0.065791 0.788099 0.062157 MOTIF AC0372:ZNF:C2H2_ZF AC0372:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 18 nsites= 20 E= 0 0.001630 0.000000 0.000000 0.998370 0.000130 0.000000 0.000000 0.999870 0.999870 0.000000 0.000000 0.000130 0.000000 0.013540 0.000000 0.986460 0.000000 0.000130 0.999870 0.000000 0.000000 0.000000 0.018530 0.981470 0.006365 0.000385 0.000255 0.992995 0.905550 0.052595 0.007115 0.034740 0.999615 0.000000 0.000255 0.000130 0.994985 0.000130 0.003475 0.001410 0.147094 0.003355 0.016145 0.833406 0.464752 0.014255 0.041650 0.479342 0.563217 0.058585 0.280794 0.097405 0.057090 0.198331 0.014600 0.729979 0.018240 0.000000 0.000000 0.981760 0.999025 0.000195 0.000520 0.000260 0.000000 0.971010 0.000065 0.028925 0.000130 0.999870 0.000000 0.000000 MOTIF AC0373:ZBTB:C2H2_ZF AC0373:ZBTB:C2H2_ZF letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.003780 0.000120 0.000040 0.996060 0.000040 0.000360 0.000040 0.999560 0.999880 0.000000 0.000120 0.000000 0.000270 0.058739 0.000000 0.940991 0.000040 0.000000 0.999880 0.000080 0.000110 0.000110 0.080110 0.919670 0.041690 0.012330 0.001110 0.944871 0.884380 0.087500 0.003820 0.024300 0.998390 0.000000 0.001610 0.000000 0.990080 0.000000 0.007560 0.002360 0.547170 0.010320 0.032710 0.409800 0.133289 0.012200 0.198338 0.656173 0.244760 0.000160 0.394970 0.360110 0.003050 0.098491 0.888729 0.009730 0.000120 0.999480 0.000080 0.000320 0.000120 0.000000 0.999760 0.000120 0.000040 0.539730 0.000040 0.460190 MOTIF AC0374:CDC5L:Myb/SANT AC0374:CDC5L:Myb/SANT letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.000000 0.100000 0.900000 0.000000 0.600000 0.100000 0.100000 0.200000 0.100000 0.000000 0.300000 0.600000 0.100000 0.000000 0.100000 0.800000 0.100000 0.000000 0.000000 0.900000 1.000000 0.000000 0.000000 0.000000 0.800000 0.100000 0.000000 0.100000 0.100000 0.700000 0.000000 0.200000 0.600000 0.000000 0.300000 0.100000 0.100000 0.100000 0.000000 0.800000 0.800000 0.100000 0.100000 0.000000 MOTIF AC0375:ZBTB/BANP:C2H2_ZF AC0375:ZBTB/BANP:C2H2_ZF letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.018855 0.103755 0.043855 0.833535 0.086122 0.868178 0.029850 0.015850 0.017765 0.117673 0.029797 0.834765 0.004015 0.986213 0.003047 0.006725 0.027790 0.003217 0.956366 0.012627 0.008768 0.957977 0.002715 0.030540 0.005723 0.007065 0.983197 0.004015 0.740165 0.084750 0.123970 0.051115 0.013195 0.066475 0.723541 0.196790 0.707892 0.083700 0.161967 0.046440 MOTIF AC0376:ZBED:BED_ZF AC0376:ZBED:BED_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.072785 0.029640 0.006295 0.891280 0.581349 0.003475 0.407276 0.007900 0.012967 0.004803 0.002770 0.979460 0.003450 0.991143 0.002190 0.003217 0.031950 0.002802 0.961806 0.003442 0.006335 0.968238 0.000710 0.024717 0.006235 0.011485 0.982280 0.000000 0.987702 0.002950 0.001240 0.008108 0.000612 0.697485 0.006332 0.295571 0.958264 0.011325 0.014348 0.016063 0.030222 0.059495 0.156038 0.754245 MOTIF AC0377:ZNF385D:C2H2_ZF AC0377:ZNF385D:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.056425 0.663085 0.072915 0.207575 0.021915 0.037250 0.872576 0.068260 0.027080 0.039730 0.001805 0.931385 0.003225 0.888279 0.026710 0.081785 0.011435 0.001210 0.969700 0.017655 0.007075 0.938195 0.039675 0.015055 0.071235 0.016405 0.912360 0.000000 0.928595 0.010230 0.028550 0.032625 0.098980 0.865931 0.017190 0.017900 MOTIF AC0378:FOXH:Forkhead AC0378:FOXH:Forkhead letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.946743 0.008547 0.036163 0.008547 0.843743 0.031640 0.041793 0.082823 0.017443 0.005123 0.005810 0.971624 0.282651 0.703819 0.007177 0.006353 0.159417 0.829650 0.005123 0.005810 0.931901 0.012653 0.047583 0.007863 0.003757 0.979149 0.008547 0.008547 0.764095 0.039130 0.050743 0.146033 MOTIF AC0379:FOXK/FOXC:Forkhead AC0379:FOXK/FOXC:Forkhead letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.469095 0.138534 0.227334 0.165038 0.169557 0.403966 0.155238 0.271238 0.282264 0.066224 0.630458 0.021054 0.088276 0.196001 0.033565 0.682158 0.689093 0.254089 0.022538 0.034280 0.787022 0.147031 0.012818 0.053129 0.833813 0.037298 0.077060 0.051829 0.008014 0.836138 0.010680 0.145168 0.790479 0.029327 0.101792 0.078402 MOTIF AC0380:PAX:Homeodomain,Paired_box AC0380:PAX:Homeodomain,Paired_box letter-probability matrix: alength= 4 w= 4 nsites= 20 E= 0 0.135140 0.324320 0.540540 0.000000 0.027030 0.000000 0.000000 0.972970 0.000000 0.000000 0.000000 1.000000 0.027030 0.054050 0.000000 0.918920 MOTIF AC0381:SRY:Sox AC0381:SRY:Sox letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.826090 0.000000 0.000000 0.173910 0.956520 0.000000 0.000000 0.043480 0.695650 0.000000 0.043480 0.260870 0.000000 0.739130 0.260870 0.000000 0.608694 0.000000 0.043480 0.347827 0.565220 0.217390 0.086960 0.130430 0.521735 0.347827 0.086959 0.043480 MOTIF AC0382:FOXA/FOXB:Forkhead AC0382:FOXA/FOXB:Forkhead letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.249569 0.047589 0.665235 0.037607 0.091159 0.376328 0.013555 0.518958 0.640522 0.242364 0.004453 0.112661 0.959542 0.017944 0.006424 0.016090 0.954602 0.009128 0.005589 0.030681 0.001368 0.086552 0.001868 0.910212 0.905288 0.002176 0.092131 0.000405 0.016858 0.004318 0.007777 0.971047 0.011749 0.005116 0.018820 0.964315 0.052024 0.004159 0.455260 0.488556 0.772616 0.013576 0.158012 0.055797 0.024433 0.786969 0.033439 0.155159 0.477502 0.100673 0.090828 0.330997 MOTIF AC0383:FOXJ:Forkhead AC0383:FOXJ:Forkhead letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.023480 0.082185 0.016380 0.877954 0.447677 0.034865 0.511283 0.006175 0.000000 0.010415 0.005955 0.983630 0.002900 0.000000 0.062301 0.934799 0.000665 0.000395 0.124756 0.874184 0.963720 0.005770 0.012480 0.018030 0.003610 0.990040 0.000000 0.006350 0.995135 0.003000 0.000910 0.000955 0.995325 0.000520 0.000000 0.004155 0.010095 0.703205 0.000000 0.286700 0.901650 0.010050 0.077495 0.010805 MOTIF AC0384:FOXO/FOXJ:Forkhead AC0384:FOXO/FOXJ:Forkhead letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.548375 0.113884 0.128211 0.209531 0.644275 0.111812 0.112892 0.131021 0.122319 0.525058 0.123426 0.229198 0.476178 0.201863 0.143288 0.178672 0.296007 0.003610 0.698586 0.001797 0.041652 0.015253 0.004480 0.938615 0.796737 0.187183 0.006512 0.009568 0.943826 0.048024 0.003807 0.004343 0.980460 0.011574 0.002819 0.005147 0.012088 0.955583 0.000951 0.031378 0.985180 0.003810 0.002518 0.008492 0.128776 0.151064 0.116283 0.603877 0.153199 0.139386 0.568204 0.139211 0.140560 0.142148 0.139684 0.577607 0.142583 0.140006 0.147552 0.569859 MOTIF AC0385:FOXC/TBP:Forkhead AC0385:FOXC/TBP:Forkhead letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.765226 0.000145 0.160014 0.074615 0.000145 0.000145 0.000145 0.999565 0.947190 0.000145 0.000145 0.052520 0.949345 0.050365 0.000145 0.000145 0.722536 0.010785 0.090000 0.176679 0.154455 0.210205 0.089135 0.546205 0.612102 0.079655 0.079655 0.228589 MOTIF AC0386:FOXR:Forkhead AC0386:FOXR:Forkhead letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.066364 0.418286 0.031050 0.484300 0.594200 0.055255 0.345110 0.005435 0.011750 0.125455 0.000540 0.862255 0.999005 0.000665 0.000330 0.000000 0.574035 0.014745 0.148920 0.262300 0.925115 0.000300 0.073095 0.001490 0.000300 0.928795 0.000000 0.070905 0.986515 0.005605 0.006240 0.001640 0.002470 0.000330 0.000660 0.996540 0.995035 0.000640 0.002340 0.001985 0.765175 0.051600 0.035325 0.147900 0.996040 0.001320 0.001320 0.001320 0.186570 0.091995 0.065805 0.655630 0.518140 0.070750 0.404675 0.006435 MOTIF AC0387:FOXA/FOXO:Forkhead AC0387:FOXA/FOXO:Forkhead letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.305158 0.022560 0.656992 0.015290 0.056761 0.141101 0.016335 0.785803 0.883245 0.095741 0.005839 0.015175 0.949124 0.028614 0.006606 0.015656 0.966660 0.007844 0.014717 0.010779 0.011669 0.794333 0.009746 0.184252 0.959396 0.007719 0.016659 0.016226 MOTIF AC0388:ZKSCAN:C2H2_ZF,MADF AC0388:ZKSCAN:C2H2_ZF,MADF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.424396 0.001010 0.472775 0.101819 0.589714 0.001010 0.408266 0.001010 0.621974 0.053429 0.255037 0.069559 0.605850 0.093750 0.259070 0.041330 0.682453 0.001010 0.210688 0.105849 0.706650 0.206650 0.001010 0.085690 0.617940 0.291330 0.001010 0.089720 0.996970 0.001010 0.001010 0.001010 0.001010 0.996970 0.001010 0.001010 MOTIF AC0389:FOXN:Forkhead AC0389:FOXN:Forkhead letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.040720 0.063050 0.852444 0.043785 0.024450 0.849280 0.012620 0.113650 0.458825 0.033975 0.438830 0.068370 0.103606 0.044795 0.031790 0.819809 0.173879 0.602127 0.142949 0.081045 0.262235 0.162965 0.440950 0.133850 0.160525 0.258225 0.176235 0.405015 0.290420 0.224480 0.193915 0.291185 0.385917 0.187941 0.242896 0.183246 0.115005 0.422620 0.184365 0.278010 0.110949 0.147929 0.599112 0.142009 0.855099 0.049355 0.057585 0.037960 0.097355 0.424955 0.028945 0.448745 0.119991 0.042660 0.801053 0.036295 0.021525 0.874475 0.056860 0.047140 MOTIF AC0390:CREB/JUND:bZIP AC0390:CREB/JUND:bZIP letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.462207 0.030065 0.473747 0.033980 0.014810 0.040340 0.032105 0.912745 0.034075 0.122999 0.799216 0.043710 0.894355 0.032070 0.019365 0.054210 0.008965 0.706755 0.029435 0.254845 0.143434 0.000905 0.850916 0.004745 0.018130 0.759642 0.012830 0.209398 0.440337 0.010795 0.529718 0.019150 MOTIF AC0391:FOXN:Forkhead AC0391:FOXN:Forkhead letter-probability matrix: alength= 4 w= 5 nsites= 20 E= 0 0.125950 0.062540 0.769590 0.041920 0.996920 0.000130 0.000530 0.002420 0.034250 0.874450 0.000050 0.091250 0.015780 0.000120 0.981160 0.002940 0.000100 0.991940 0.000040 0.007920 MOTIF AC0392:CENPBD/GMEB:CxxC AC0392:CENPBD/GMEB:CxxC letter-probability matrix: alength= 4 w= 4 nsites= 20 E= 0 0.427377 0.140118 0.315640 0.116865 0.016270 0.934262 0.025323 0.024145 0.095648 0.011653 0.869759 0.022940 0.164233 0.243507 0.168703 0.423556 MOTIF AC0393:IRX:Homeodomain AC0393:IRX:Homeodomain letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.070035 0.594600 0.137820 0.197545 0.428830 0.034675 0.432515 0.103980 0.043865 0.337330 0.016715 0.602090 0.039865 0.082205 0.810840 0.067090 0.187495 0.161050 0.056470 0.594985 0.426951 0.167703 0.092084 0.313262 0.264914 0.260624 0.241714 0.232749 0.356420 0.116895 0.143400 0.383285 0.698185 0.084370 0.108990 0.108455 0.012715 0.790870 0.027970 0.168445 0.581365 0.014035 0.333000 0.071600 0.141326 0.420472 0.032095 0.406107 0.192795 0.114880 0.605810 0.086515 MOTIF AC0394:PAX:Homeodomain,Paired_box AC0394:PAX:Homeodomain,Paired_box letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.126561 0.024110 0.228872 0.620456 0.699110 0.000320 0.095490 0.205080 0.000320 0.805600 0.000320 0.193760 0.900870 0.000320 0.098490 0.000320 0.000320 0.405796 0.094569 0.499315 0.201160 0.000320 0.605690 0.192830 0.239900 0.050470 0.547990 0.161640 0.210410 0.099250 0.099250 0.591090 MOTIF AC0395:SOX:Sox AC0395:SOX:Sox letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.000000 0.000000 0.001330 0.998670 0.001330 0.003810 0.994527 0.000333 0.999001 0.000333 0.000333 0.000333 0.999333 0.000000 0.000667 0.000000 0.000333 0.000000 0.000000 0.999667 0.279679 0.005340 0.699021 0.015960 0.002390 0.000000 0.003960 0.993650 0.057087 0.044950 0.472970 0.424993 0.001867 0.997466 0.000667 0.000000 0.996947 0.000333 0.001527 0.001193 0.000000 0.000000 0.992853 0.007147 0.000333 0.000667 0.000667 0.998333 0.000000 0.998007 0.000333 0.001660 0.996974 0.002360 0.000333 0.000333 MOTIF AC0396:POU2F:Homeodomain,POU AC0396:POU2F:Homeodomain,POU letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.075510 0.095875 0.078470 0.750146 0.012150 0.000000 0.986280 0.001570 0.649375 0.339260 0.011365 0.000000 0.980875 0.003880 0.011365 0.003880 0.000000 0.000000 0.000790 0.999210 0.952105 0.007670 0.014430 0.025795 0.045455 0.034945 0.026545 0.893056 0.022730 0.049090 0.168940 0.759240 0.073170 0.817715 0.060975 0.048140 MOTIF AC0397:POU2F/POU3F:Homeodomain,POU AC0397:POU2F/POU3F:Homeodomain,POU letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.150714 0.068997 0.066346 0.713943 0.250084 0.058498 0.620378 0.071040 0.497871 0.418474 0.049955 0.033699 0.772401 0.028753 0.056681 0.142165 0.139095 0.032155 0.019894 0.808856 0.859861 0.019839 0.016861 0.103439 0.165288 0.064584 0.053850 0.716278 0.050296 0.026050 0.530285 0.393369 0.112736 0.660466 0.041917 0.184880 0.797620 0.034870 0.048087 0.119423 MOTIF AC0398:POU6F:Homeodomain AC0398:POU6F:Homeodomain letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.125000 0.125000 0.125000 0.625000 0.078675 0.043340 0.053085 0.824899 0.954315 0.013770 0.018615 0.013300 0.001380 0.051695 0.054770 0.892156 0.078760 0.109844 0.753501 0.057895 0.081250 0.611020 0.133200 0.174530 0.278960 0.202600 0.250260 0.268180 0.298295 0.240665 0.196425 0.264615 0.260455 0.265325 0.219870 0.254350 0.170972 0.111251 0.588026 0.129751 0.081680 0.726130 0.109005 0.083185 0.811532 0.067054 0.063379 0.058034 0.011050 0.003205 0.008730 0.977015 0.734436 0.088215 0.081220 0.096130 0.587463 0.125001 0.146021 0.141516 MOTIF AC0399:FOXL:['Fork_head/winged_helix_factors'] AC0399:FOXL:['Fork_head/winged_helix_factors'] letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.304350 0.043480 0.173910 0.478260 0.434780 0.173910 0.086960 0.304350 0.260870 0.130430 0.260870 0.347830 0.565220 0.173910 0.173910 0.086960 0.173910 0.434780 0.086960 0.304350 0.913040 0.000000 0.086960 0.000000 0.000000 0.086960 0.000000 0.913040 0.956520 0.043480 0.000000 0.000000 MOTIF AC0400:GMEB:SAND AC0400:GMEB:SAND letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.866060 0.016630 0.106432 0.010878 0.004235 0.978400 0.003570 0.013795 0.004045 0.003993 0.985539 0.006423 0.032228 0.119310 0.006828 0.841634 0.679014 0.110690 0.116960 0.093335 0.487608 0.317451 0.162389 0.032552 0.123739 0.469168 0.272696 0.134397 0.047945 0.165391 0.357043 0.429621 0.040453 0.126208 0.167435 0.665904 0.849292 0.009030 0.132803 0.008875 0.003550 0.979453 0.011475 0.005522 0.013685 0.004827 0.973418 0.008070 0.022452 0.141466 0.020745 0.815337 MOTIF AC0401:GMEB:SAND AC0401:GMEB:SAND letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.325650 0.003840 0.000000 0.670510 0.829500 0.000960 0.168580 0.000960 0.000910 0.947270 0.051820 0.000000 0.000000 0.000000 1.000000 0.000000 0.001850 0.028730 0.003710 0.965710 0.115160 0.009110 0.863300 0.012430 0.030470 0.218282 0.648016 0.103231 0.019610 0.004460 0.047240 0.928691 0.000700 0.249820 0.018250 0.731230 0.969300 0.000000 0.030700 0.000000 0.000960 0.998080 0.000000 0.000960 0.000000 0.000960 0.996170 0.002870 0.000960 0.311900 0.000000 0.687140 MOTIF AC0402:CEBPB/CEBPE:bZIP AC0402:CEBPB/CEBPE:bZIP letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.016170 0.016260 0.016428 0.951142 0.012274 0.008273 0.178203 0.801250 0.411927 0.011937 0.459477 0.116659 0.057155 0.584966 0.057127 0.300752 0.081368 0.027836 0.871681 0.019115 0.099416 0.771522 0.004105 0.124957 0.960977 0.033912 0.002087 0.003024 0.983359 0.004266 0.007577 0.004798 MOTIF AC0403:DBP/HLF:bZIP AC0403:DBP/HLF:bZIP letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.017083 0.007407 0.003733 0.971777 0.006995 0.012325 0.289426 0.691254 0.867191 0.004213 0.119285 0.009311 0.004913 0.663720 0.012056 0.319311 0.230704 0.012864 0.754002 0.002430 0.024595 0.245861 0.008412 0.721132 0.931940 0.054722 0.005122 0.008216 0.983002 0.004250 0.004007 0.008741 MOTIF AC0404:TIGD:CENPB AC0404:TIGD:CENPB letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.182070 0.055950 0.008710 0.753270 0.817980 0.019630 0.127340 0.035050 0.011470 0.006680 0.009770 0.972080 0.031610 0.004150 0.026660 0.937580 0.026630 0.015680 0.890860 0.066830 0.016080 0.941670 0.003090 0.039160 0.043120 0.045790 0.760570 0.150520 MOTIF AC0405:ZNF:C2H2_ZF AC0405:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 28 nsites= 20 E= 0 0.000000 0.000000 0.000000 1.000000 0.000000 0.333330 0.000000 0.666670 0.333330 0.000000 0.500000 0.166670 0.000000 0.666670 0.000000 0.333330 0.166670 0.000000 0.833330 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.833330 0.000000 0.166670 0.000000 0.000000 0.000000 1.000000 0.166670 0.000000 0.833330 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.833330 0.166670 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.166668 0.000000 0.666663 0.166668 0.166670 0.833330 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.833330 0.166670 0.000000 0.000000 1.000000 0.000000 0.000000 0.833330 0.000000 0.166670 0.500000 0.000000 0.500000 0.000000 1.000000 0.000000 0.000000 0.000000 MOTIF AC0406:ZNF:C2H2_ZF AC0406:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.033543 0.003647 0.953607 0.009203 0.027483 0.012907 0.955963 0.003647 0.144824 0.166338 0.567855 0.120983 0.042567 0.583541 0.003647 0.370246 0.003647 0.003647 0.968186 0.024520 0.878493 0.087280 0.030580 0.003647 0.003647 0.425427 0.014757 0.556169 0.003647 0.009203 0.977947 0.009203 0.749774 0.167953 0.021930 0.060343 0.003647 0.883673 0.009707 0.102973 0.033543 0.773513 0.079130 0.113813 0.489235 0.066403 0.360784 0.083577 0.121997 0.455900 0.199707 0.222397 0.051087 0.088157 0.076033 0.784723 MOTIF AC0407:RXRG/RARB:Nuclear_receptor AC0407:RXRG/RARB:Nuclear_receptor letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.618649 0.039780 0.327881 0.013690 0.017160 0.019538 0.924340 0.038962 0.013066 0.011619 0.861094 0.114221 0.031993 0.046097 0.055936 0.865974 0.008176 0.931010 0.018008 0.042806 0.852480 0.003531 0.136329 0.007660 0.004541 0.400456 0.003088 0.591915 0.039539 0.021681 0.926932 0.011848 0.798044 0.092679 0.065738 0.043539 0.113190 0.852209 0.018974 0.015627 0.056789 0.895384 0.031902 0.015925 0.016986 0.347775 0.039939 0.595300 MOTIF AC0408:RORC/RORA:Nuclear_receptor AC0408:RORC/RORA:Nuclear_receptor letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.803777 0.078723 0.029250 0.088250 0.592870 0.011992 0.027245 0.367893 0.148948 0.281873 0.320328 0.248850 0.042760 0.070773 0.105280 0.781187 0.586039 0.010745 0.389719 0.013497 0.052750 0.009500 0.877250 0.060500 0.022510 0.013000 0.948488 0.016002 0.042500 0.093500 0.094250 0.769750 0.030000 0.853250 0.025750 0.091000 0.902250 0.012000 0.060500 0.025250 MOTIF AC0409:NR2E:Nuclear_receptor AC0409:NR2E:Nuclear_receptor letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.662397 0.085471 0.162392 0.089741 0.242290 0.039650 0.682820 0.035240 0.000000 0.150000 0.075000 0.775000 0.021500 0.833338 0.053761 0.091401 0.945120 0.000000 0.054880 0.000000 0.637860 0.102880 0.102880 0.156380 0.339740 0.057690 0.198720 0.403850 0.828872 0.000000 0.032090 0.139039 0.820110 0.042330 0.111110 0.026450 0.126370 0.021980 0.851650 0.000000 0.025510 0.183670 0.000000 0.790820 0.000000 0.803110 0.046630 0.150260 0.901160 0.000000 0.098840 0.000000 MOTIF AC0410:RARA/RARG:Nuclear_receptor AC0410:RARA/RARG:Nuclear_receptor letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.634045 0.044499 0.309337 0.012119 0.007515 0.004467 0.949249 0.038769 0.002267 0.012648 0.870558 0.114527 0.022586 0.032876 0.063942 0.880596 0.006661 0.919788 0.041292 0.032259 0.923602 0.001842 0.071113 0.003443 0.716457 0.065845 0.075909 0.141789 0.677963 0.029005 0.200689 0.092343 0.764893 0.026495 0.201109 0.007503 0.013565 0.012741 0.962167 0.011527 0.001309 0.034283 0.781611 0.182797 0.020631 0.016951 0.060840 0.901578 0.005045 0.906981 0.046073 0.041901 0.902559 0.002451 0.091891 0.003099 MOTIF AC0411:NR1I/VDR:Nuclear_receptor AC0411:NR1I/VDR:Nuclear_receptor letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.507551 0.085675 0.327322 0.079452 0.093992 0.080264 0.745130 0.080614 0.082423 0.079368 0.362568 0.475641 0.087522 0.105075 0.188728 0.618675 0.090025 0.663203 0.103457 0.143315 0.617816 0.081152 0.216109 0.084923 0.216799 0.257288 0.180245 0.345667 0.263422 0.251935 0.158338 0.326305 0.255790 0.133637 0.521819 0.088754 0.622450 0.028930 0.315855 0.032765 0.023672 0.025522 0.920951 0.029855 0.023158 0.023478 0.186600 0.766764 0.066874 0.062195 0.133604 0.737328 0.033900 0.810066 0.033478 0.122556 0.653501 0.053627 0.248602 0.044271 MOTIF AC0412:NR1I/NR1H:Nuclear_receptor AC0412:NR1I/NR1H:Nuclear_receptor letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.500139 0.070370 0.383627 0.045864 0.136358 0.028972 0.766354 0.068316 0.046306 0.091826 0.604029 0.257839 0.076828 0.083550 0.133206 0.706417 0.062104 0.608213 0.112028 0.217656 0.717287 0.096584 0.121856 0.064274 0.273357 0.300823 0.219386 0.206434 0.239500 0.214898 0.256298 0.289304 0.270679 0.213602 0.228932 0.286787 0.382292 0.215694 0.292360 0.109654 0.800601 0.030366 0.142187 0.026846 0.026378 0.026322 0.920594 0.026706 0.027998 0.060742 0.455573 0.455687 0.167746 0.086190 0.114322 0.631742 0.037948 0.709810 0.109951 0.142291 0.745545 0.069014 0.141874 0.043566 MOTIF AC0413:THRB/RARA:Nuclear_receptor AC0413:THRB/RARA:Nuclear_receptor letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.794859 0.008330 0.154084 0.042727 0.022767 0.132853 0.833427 0.010953 0.015721 0.133319 0.812913 0.038047 0.071789 0.058757 0.035504 0.833950 0.026846 0.852998 0.048678 0.071478 0.911065 0.018609 0.057659 0.012667 0.188746 0.351326 0.137302 0.322626 0.188565 0.233062 0.415581 0.162792 0.143310 0.176656 0.089739 0.590295 0.340559 0.162306 0.468651 0.028484 0.799183 0.028461 0.165547 0.006809 0.015039 0.046060 0.926911 0.011990 0.012189 0.008019 0.923859 0.055933 0.165645 0.025693 0.024287 0.784375 0.031347 0.870422 0.064490 0.033741 0.943242 0.020886 0.032974 0.002898 MOTIF AC0414:NR4A/NR2C:Nuclear_receptor AC0414:NR4A/NR2C:Nuclear_receptor letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.566712 0.077124 0.258841 0.097323 0.783432 0.029069 0.177817 0.009682 0.009347 0.007683 0.916045 0.066925 0.032426 0.006996 0.902353 0.058225 0.017233 0.023119 0.048652 0.910996 0.006101 0.918590 0.018399 0.056910 0.887404 0.007047 0.094085 0.011464 MOTIF AC0415:RARA/ESRRA:Nuclear_receptor AC0415:RARA/ESRRA:Nuclear_receptor letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.811146 0.014027 0.173058 0.001769 0.003905 0.000723 0.971235 0.024137 0.006013 0.001203 0.916459 0.076325 0.028165 0.005504 0.042025 0.924306 0.003526 0.912186 0.032849 0.051439 0.921279 0.004021 0.070176 0.004524 0.382372 0.217628 0.107462 0.292539 0.214349 0.293505 0.303460 0.188686 0.113341 0.293290 0.163229 0.430139 0.427621 0.215184 0.196000 0.161195 0.574358 0.075026 0.313941 0.036675 0.809206 0.032075 0.155754 0.002965 0.011556 0.005531 0.960398 0.022515 0.002019 0.006837 0.912605 0.078539 0.037834 0.020303 0.064905 0.876958 0.014184 0.895085 0.033600 0.057131 0.902667 0.007146 0.077035 0.013152 MOTIF AC0416:RXRA/NR2C:Nuclear_receptor AC0416:RXRA/NR2C:Nuclear_receptor letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.170700 0.185052 0.415749 0.228498 0.203037 0.214835 0.263087 0.319040 0.234148 0.349743 0.192732 0.223378 0.461477 0.235023 0.200118 0.103383 0.352788 0.131350 0.409850 0.106012 0.649603 0.006002 0.337723 0.006672 0.043347 0.003333 0.927317 0.026003 0.022007 0.013003 0.819837 0.145153 0.033338 0.074682 0.211022 0.680958 0.028667 0.863980 0.073013 0.034340 0.904329 0.015000 0.059668 0.021003 0.317188 0.165037 0.384752 0.133023 MOTIF AC0417:NR2E:Nuclear_receptor AC0417:NR2E:Nuclear_receptor letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.820977 0.040657 0.124317 0.014050 0.777843 0.062980 0.121113 0.038063 0.014377 0.000000 0.983336 0.002287 0.000000 0.083063 0.019957 0.896980 0.002927 0.947447 0.003193 0.046433 0.895670 0.000000 0.104330 0.000000 MOTIF AC0418:ZNF:C2H2_ZF AC0418:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.001020 0.553860 0.102640 0.342480 0.001020 0.444110 0.175810 0.379060 0.005080 0.204270 0.240850 0.549800 0.053860 0.244920 0.468500 0.232720 0.017280 0.346540 0.139230 0.496950 0.074189 0.253047 0.114839 0.557924 0.041670 0.533535 0.074189 0.350606 0.001020 0.334350 0.005080 0.659550 0.001020 0.009150 0.037600 0.952230 0.001020 0.045730 0.891260 0.061990 0.415650 0.131100 0.041670 0.411580 0.001020 0.988810 0.009150 0.001020 0.001020 0.383126 0.001020 0.614834 0.001020 0.309957 0.001020 0.688003 0.013210 0.248980 0.001020 0.736790 MOTIF AC0419:ESR/ESRRA:Nuclear_receptor AC0419:ESR/ESRRA:Nuclear_receptor letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.054426 0.006092 0.854529 0.084953 0.022804 0.014241 0.952266 0.010689 0.026504 0.025469 0.093242 0.854785 0.007980 0.912390 0.050902 0.028728 0.901334 0.007305 0.062140 0.029221 0.127200 0.474721 0.223206 0.174874 0.202514 0.386706 0.272297 0.138482 0.178397 0.204870 0.467257 0.149477 0.065654 0.135746 0.035361 0.763239 0.072567 0.056373 0.854764 0.016296 0.699175 0.151135 0.076386 0.073303 0.044742 0.877539 0.020391 0.057328 0.079696 0.850178 0.006933 0.063193 MOTIF AC0420:THRA/THRB:Nuclear_receptor AC0420:THRA/THRB:Nuclear_receptor letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.001920 0.048895 0.007165 0.942020 0.025000 0.071790 0.888010 0.015200 0.751444 0.019055 0.020060 0.209441 0.003975 0.995715 0.000140 0.000170 0.000365 0.998355 0.000720 0.000560 0.000260 0.129861 0.001880 0.867999 0.020105 0.397670 0.124315 0.457910 0.863590 0.018165 0.114875 0.003370 0.001575 0.165581 0.015420 0.817424 0.438770 0.255690 0.288930 0.016610 0.831290 0.006255 0.161915 0.000540 0.000300 0.002070 0.996240 0.001390 0.000965 0.002640 0.993785 0.002610 0.185229 0.015675 0.011465 0.787631 0.005880 0.958140 0.017780 0.018200 0.948190 0.002590 0.045735 0.003485 MOTIF AC0421:THRB:Nuclear_receptor AC0421:THRB:Nuclear_receptor letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.000915 0.128550 0.001057 0.869478 0.043923 0.106780 0.819554 0.029743 0.691164 0.035620 0.048360 0.224856 0.008247 0.991616 0.000037 0.000100 0.001420 0.997631 0.000747 0.000202 0.000345 0.168154 0.010270 0.821231 0.044602 0.351061 0.178381 0.425956 0.652186 0.049665 0.287347 0.010802 0.277852 0.238805 0.230985 0.252358 0.013092 0.314278 0.040067 0.632563 0.391460 0.201734 0.352308 0.054498 0.807555 0.008117 0.184170 0.000158 0.001352 0.001517 0.996706 0.000425 0.000092 0.000775 0.993443 0.005690 0.250392 0.048225 0.039530 0.661853 0.034775 0.825185 0.095627 0.044413 0.868765 0.002642 0.124896 0.003697 MOTIF AC0422:ESRRA/NR5A:Nuclear_receptor AC0422:ESRRA/NR5A:Nuclear_receptor letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.058163 0.714401 0.193448 0.033988 0.936926 0.007796 0.046755 0.008523 0.961190 0.008286 0.021964 0.008560 0.006321 0.004598 0.957276 0.031805 0.003350 0.006014 0.985536 0.005100 0.074437 0.071967 0.070363 0.783233 0.007740 0.869350 0.042720 0.080190 0.784815 0.013093 0.186198 0.015894 MOTIF AC0423:NR2E:Nuclear_receptor AC0423:NR2E:Nuclear_receptor letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.971140 0.009620 0.009620 0.009620 0.971140 0.009620 0.009620 0.009620 0.971140 0.009620 0.009620 0.009620 0.009620 0.009620 0.971140 0.009620 0.971140 0.009620 0.009620 0.009620 0.009620 0.009620 0.009620 0.971140 0.009620 0.971140 0.009620 0.009620 0.971140 0.009620 0.009620 0.009620 MOTIF AC0424:NR4A:Nuclear_receptor AC0424:NR4A:Nuclear_receptor letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.065190 0.013040 0.921770 0.000000 0.896910 0.039360 0.027180 0.036550 0.006020 0.982950 0.000000 0.011030 0.162290 0.828650 0.001810 0.007250 0.000000 0.003040 0.000000 0.996960 0.015270 0.000000 0.000000 0.984730 0.004570 0.050310 0.000000 0.945120 0.969510 0.000000 0.030490 0.000000 0.988580 0.000000 0.004150 0.007270 0.990520 0.005270 0.004210 0.000000 0.002470 0.000000 0.828188 0.169342 0.006580 0.000000 0.977630 0.015790 0.000000 0.050800 0.046440 0.902760 0.000960 0.957740 0.004800 0.036500 0.941500 0.009140 0.045700 0.003660 MOTIF AC0425:NR2E:Nuclear_receptor AC0425:NR2E:Nuclear_receptor letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.142810 0.046620 0.763950 0.046620 0.000080 0.000080 0.905430 0.094410 0.000080 0.000080 0.999760 0.000080 0.694310 0.000080 0.305530 0.000080 0.000080 0.094410 0.000080 0.905430 0.000080 0.999760 0.000080 0.000080 0.905430 0.094410 0.000080 0.000080 MOTIF AC0426:HNF4A/ZNF:Nuclear_receptor AC0426:HNF4A/ZNF:Nuclear_receptor letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.125331 0.127976 0.125331 0.621363 0.037900 0.013845 0.021350 0.926905 0.012340 0.003335 0.968980 0.015345 0.062820 0.027750 0.898589 0.010840 0.884145 0.057385 0.035615 0.022855 0.024355 0.937230 0.019850 0.018565 0.022100 0.034115 0.010090 0.933695 MOTIF AC0427:NR6A:Nuclear_receptor AC0427:NR6A:Nuclear_receptor letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.892174 0.021600 0.065064 0.021162 0.886253 0.040308 0.041534 0.031906 0.020330 0.003880 0.941136 0.034654 0.009854 0.019222 0.432544 0.538380 0.000856 0.042600 0.013420 0.943124 0.007030 0.949560 0.017772 0.025638 0.986060 0.004000 0.001620 0.008320 0.878782 0.053456 0.022628 0.045134 0.009794 0.010500 0.914788 0.064918 0.024636 0.011686 0.559751 0.403927 0.043924 0.089452 0.156219 0.710405 0.022452 0.781346 0.049222 0.146980 0.737813 0.022704 0.206724 0.032760 MOTIF AC0428:NKX:Homeodomain AC0428:NKX:Homeodomain letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.000000 0.000000 0.000000 1.000000 0.000000 0.774510 0.225490 0.000000 0.950980 0.000000 0.049020 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.598040 0.401960 0.598040 0.000000 0.401960 0.000000 MOTIF AC0429:GLI:C2H2_ZF AC0429:GLI:C2H2_ZF letter-probability matrix: alength= 4 w= 19 nsites= 20 E= 0 0.073681 0.105261 0.421054 0.400004 0.326317 0.031580 0.494735 0.147369 0.168418 0.126319 0.557894 0.147369 0.073680 0.652630 0.147370 0.126320 0.168418 0.347367 0.157898 0.326317 0.031580 0.189470 0.010530 0.768420 0.042100 0.000000 0.936849 0.021050 0.126320 0.063160 0.736840 0.073680 0.000000 1.000000 0.000000 0.000000 0.757890 0.178950 0.063160 0.000000 0.000000 0.031580 0.000000 0.968420 0.000000 0.000000 0.000000 1.000000 0.000000 1.000000 0.000000 0.000000 0.810522 0.031580 0.157898 0.000000 0.905260 0.000000 0.063160 0.031580 0.010530 0.000000 0.947370 0.042100 0.010530 0.000000 0.989470 0.000000 0.073680 0.926320 0.000000 0.000000 0.000000 0.747370 0.000000 0.252630 MOTIF AC0430:TCF7L/LEF:Sox AC0430:TCF7L/LEF:Sox letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.720000 0.053745 0.122298 0.103957 0.029703 0.445639 0.484552 0.040107 0.777863 0.013829 0.014319 0.193989 0.025313 0.007593 0.008730 0.958364 0.023164 0.865810 0.099540 0.011486 0.983865 0.004201 0.002650 0.009284 0.951047 0.017004 0.016390 0.015559 0.910598 0.010192 0.034553 0.044657 0.063225 0.098126 0.816534 0.022115 MOTIF AC0431:LEF:Sox AC0431:LEF:Sox letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.032900 0.635964 0.184208 0.146929 0.017540 0.708330 0.013160 0.260970 0.122810 0.046050 0.019740 0.811400 0.002190 0.138160 0.035090 0.824560 0.002190 0.013160 0.032900 0.951750 0.043860 0.425436 0.519735 0.010970 0.456140 0.138160 0.015350 0.390350 0.278507 0.166668 0.157898 0.396926 0.285090 0.190790 0.201750 0.322370 0.041670 0.129389 0.149119 0.679823 0.039470 0.311403 0.383774 0.265353 MOTIF AC0432:BCL11A/BCL11B:C2H2_ZF AC0432:BCL11A/BCL11B:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.066315 0.148455 0.025045 0.760185 0.205838 0.392976 0.112284 0.288902 0.193909 0.524712 0.072760 0.208619 0.146820 0.100245 0.727665 0.025270 0.157295 0.026405 0.728455 0.087845 0.356425 0.589300 0.019730 0.034545 0.000970 0.993875 0.004895 0.000260 0.565715 0.169605 0.156565 0.108115 MOTIF AC0433:HNF4A/HNF4G:Nuclear_receptor AC0433:HNF4A/HNF4G:Nuclear_receptor letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.068325 0.038414 0.836526 0.056735 0.106701 0.048456 0.420136 0.424707 0.108099 0.322755 0.207822 0.361325 0.009797 0.922380 0.008248 0.059575 0.953573 0.009813 0.027314 0.009300 0.916722 0.007438 0.069135 0.006705 0.929919 0.003098 0.058799 0.008184 0.008366 0.004373 0.975947 0.011314 0.017798 0.010491 0.192080 0.779631 0.033445 0.618929 0.070928 0.276698 0.011576 0.920903 0.006327 0.061194 0.763733 0.044835 0.129672 0.061760 MOTIF AC0434:NR2F/RXRG:Nuclear_receptor AC0434:NR2F/RXRG:Nuclear_receptor letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.472517 0.034709 0.463084 0.029689 0.039966 0.014164 0.911276 0.034594 0.026625 0.011125 0.802367 0.159883 0.026220 0.083174 0.089327 0.801279 0.021198 0.827020 0.063686 0.088096 0.944691 0.004713 0.043716 0.006880 0.826748 0.016793 0.103482 0.052977 0.896977 0.005320 0.092733 0.004970 0.029178 0.002564 0.957997 0.010261 0.015544 0.005025 0.873356 0.106075 0.006363 0.024617 0.071793 0.897227 0.020331 0.817999 0.063753 0.097917 0.784326 0.018283 0.173888 0.023503 MOTIF AC0435:ZNF:C2H2_ZF AC0435:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.942030 0.001300 0.055370 0.001300 0.721190 0.016930 0.225180 0.036700 0.027350 0.859709 0.006510 0.106431 0.015850 0.929440 0.001300 0.053410 0.046020 0.157452 0.016930 0.779598 0.014760 0.827408 0.016930 0.140901 0.006510 0.021070 0.011720 0.960700 0.914900 0.010640 0.052320 0.022140 0.774380 0.010640 0.138770 0.076210 0.001300 0.964840 0.001300 0.032560 0.917940 0.006510 0.053410 0.022140 MOTIF AC0436:BPTF/FOXC:Unknown AC0436:BPTF/FOXC:Unknown letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.685744 0.121953 0.097423 0.094880 0.344657 0.294577 0.033437 0.327330 0.830544 0.065417 0.041417 0.062623 0.787336 0.056230 0.031297 0.125137 0.112067 0.627664 0.094470 0.165799 0.800291 0.090393 0.032243 0.077073 0.434195 0.310986 0.097273 0.157546 MOTIF AC0437:CEBPA:bZIP AC0437:CEBPA:bZIP letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.272730 0.000000 0.000000 0.727270 0.272730 0.181820 0.090910 0.454540 0.499995 0.090909 0.045460 0.363636 0.136360 0.545460 0.227270 0.090910 0.000000 1.000000 0.000000 0.000000 0.818180 0.090910 0.000000 0.090910 0.500000 0.363640 0.000000 0.136360 0.636366 0.136361 0.000000 0.227272 MOTIF AC0438:HSF:HSF AC0438:HSF:HSF letter-probability matrix: alength= 4 w= 25 nsites= 20 E= 0 0.328858 0.208604 0.461028 0.001510 0.001830 0.006470 0.989090 0.002610 0.884815 0.006235 0.105690 0.003260 0.942690 0.005550 0.028930 0.022830 0.000350 0.573180 0.012070 0.414400 0.179876 0.111461 0.578698 0.129966 0.088635 0.396098 0.076990 0.438278 0.189330 0.214860 0.176040 0.419770 0.286501 0.216816 0.379262 0.117421 0.249276 0.285496 0.255471 0.209756 0.173965 0.261080 0.374800 0.190155 0.258876 0.203956 0.309062 0.228106 0.267021 0.244096 0.248991 0.239891 0.243774 0.362778 0.173459 0.219989 0.213686 0.379847 0.258076 0.148391 0.199100 0.297920 0.253405 0.249575 0.079865 0.478530 0.180015 0.261590 0.691627 0.092620 0.106129 0.109624 0.850699 0.033750 0.086135 0.029415 0.029925 0.888310 0.052570 0.029195 0.470507 0.004300 0.525163 0.000030 0.021145 0.034790 0.002045 0.942020 0.002075 0.000155 0.001040 0.996730 0.003890 0.992735 0.003115 0.000260 0.004275 0.485630 0.168738 0.341357 MOTIF AC0439:ZNF/DMRT:C2H2_ZF AC0439:ZNF/DMRT:C2H2_ZF letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.171074 0.099680 0.606462 0.122784 0.271117 0.428051 0.118728 0.182104 0.214560 0.127492 0.040770 0.617178 0.852651 0.030298 0.062582 0.054470 0.311049 0.475049 0.067712 0.146190 0.374412 0.216718 0.141468 0.267402 MOTIF AC0440:POU3F/GFI:Homeodomain,POU AC0440:POU3F/GFI:Homeodomain,POU letter-probability matrix: alength= 4 w= 24 nsites= 20 E= 0 0.307203 0.228021 0.256362 0.208413 0.281768 0.222302 0.278656 0.217273 0.232439 0.215677 0.320722 0.231163 0.257983 0.218231 0.291467 0.232320 0.237750 0.217696 0.236258 0.308297 0.239872 0.217262 0.328721 0.214146 0.267327 0.286176 0.226581 0.219917 0.318646 0.186586 0.279473 0.215296 0.221969 0.294990 0.213961 0.269080 0.314509 0.233302 0.217781 0.234409 0.248392 0.230514 0.198678 0.322416 0.221248 0.189518 0.306571 0.282662 0.273412 0.319675 0.238609 0.168303 0.435132 0.164291 0.189507 0.211070 0.225454 0.151013 0.167140 0.456392 0.377693 0.150191 0.188367 0.283749 0.291116 0.210799 0.205234 0.292851 0.254995 0.226434 0.252677 0.265895 0.222447 0.278951 0.260478 0.238124 0.245901 0.245418 0.284415 0.224266 0.226621 0.233967 0.254787 0.284625 0.234268 0.227107 0.263257 0.275367 0.230854 0.226640 0.252833 0.289673 0.337776 0.209763 0.208639 0.243822 MOTIF AC0441:WT/HOXB:Homeodomain AC0441:WT/HOXB:Homeodomain letter-probability matrix: alength= 4 w= 20 nsites= 20 E= 0 0.241412 0.214856 0.219800 0.323932 0.231977 0.215268 0.205680 0.347074 0.245260 0.184508 0.214537 0.355694 0.181326 0.327078 0.133789 0.357807 0.225813 0.193188 0.314905 0.266094 0.319850 0.215796 0.203295 0.261058 0.254409 0.250475 0.226890 0.268225 0.227301 0.254919 0.247118 0.270661 0.285042 0.209860 0.262004 0.243093 0.262050 0.242908 0.238439 0.256603 0.250821 0.249866 0.252039 0.247273 0.248922 0.247128 0.250916 0.253034 0.249968 0.248679 0.252682 0.248671 0.250177 0.248096 0.251463 0.250264 0.259631 0.240362 0.251969 0.248037 0.263303 0.239998 0.253871 0.242827 0.277610 0.238873 0.251293 0.232224 0.311946 0.231676 0.228566 0.227813 0.316249 0.230582 0.228169 0.225000 0.310149 0.233018 0.231687 0.225145 MOTIF AC0442:ZNF:C2H2_ZF AC0442:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.060105 0.043118 0.024602 0.872175 0.011543 0.003175 0.000765 0.984517 0.002285 0.000830 0.055025 0.941860 0.001972 0.005755 0.000822 0.991451 0.012313 0.014863 0.000888 0.971936 0.011277 0.000870 0.001002 0.986851 0.001200 0.000822 0.000773 0.997205 0.104050 0.039497 0.063895 0.792558 MOTIF AC0443:CTCF/ZNF:C2H2_ZF AC0443:CTCF/ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.000490 0.463068 0.000490 0.535952 0.036570 0.111816 0.070695 0.780919 0.000490 0.012205 0.008300 0.979005 0.079535 0.033690 0.079440 0.807335 0.029785 0.012205 0.018065 0.939945 0.279269 0.030710 0.444568 0.245454 0.051270 0.040475 0.129395 0.778860 0.046335 0.103901 0.096085 0.753679 0.000490 0.000490 0.000490 0.998530 0.000490 0.000490 0.000490 0.998530 0.000490 0.031740 0.000490 0.967280 MOTIF AC0444:ZNF:C2H2_ZF AC0444:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.009045 0.007800 0.004075 0.979080 0.000570 0.000345 0.000460 0.998625 0.000450 0.983565 0.000455 0.015530 0.114046 0.455292 0.004170 0.426492 0.469850 0.000685 0.000685 0.528780 0.001365 0.000340 0.995105 0.003190 0.000000 0.000115 0.010420 0.989465 0.000460 0.998965 0.000115 0.000460 0.038085 0.000680 0.000225 0.961010 0.000000 0.000460 0.000115 0.999425 0.000000 0.000230 0.000115 0.999655 0.000000 0.109975 0.000000 0.890025 0.000110 0.000220 0.023260 0.976410 0.000000 0.000230 0.000000 0.999770 0.000420 0.870194 0.000410 0.128976 MOTIF AC0445:ZNF:C2H2_ZF AC0445:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.006250 0.106250 0.006250 0.881250 0.006250 0.006250 0.006250 0.981250 0.006250 0.006250 0.056250 0.931250 0.006250 0.006250 0.031250 0.956250 0.006250 0.981250 0.006250 0.006250 0.156250 0.031250 0.081250 0.731250 0.106250 0.056250 0.156250 0.681250 0.081250 0.006250 0.606250 0.306250 0.206250 0.106250 0.206250 0.481250 0.481250 0.156250 0.156250 0.206250 0.281250 0.231250 0.081250 0.406250 0.156250 0.056250 0.331250 0.456250 0.506250 0.106250 0.106250 0.281250 0.006250 0.056250 0.106250 0.831250 0.556250 0.031250 0.281250 0.131250 0.006250 0.006250 0.031250 0.956250 0.006250 0.556250 0.006250 0.431250 MOTIF AC0446:ZNF:C2H2_ZF AC0446:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.633254 0.133259 0.043630 0.189858 0.227592 0.015330 0.274763 0.482315 0.218162 0.194572 0.288913 0.298353 0.067221 0.288913 0.038910 0.604956 0.010610 0.298350 0.034200 0.656840 0.246462 0.288913 0.199292 0.265333 0.359670 0.378540 0.218160 0.043630 0.137970 0.053070 0.001180 0.807780 0.642690 0.321930 0.029480 0.005900 0.020050 0.001180 0.001180 0.977590 0.001180 0.001180 0.001180 0.996460 0.001180 0.020050 0.029480 0.949290 0.005900 0.935140 0.001180 0.057780 0.090800 0.614390 0.001180 0.293630 MOTIF AC0447:ZFP:C2H2_ZF AC0447:ZFP:C2H2_ZF letter-probability matrix: alength= 4 w= 20 nsites= 20 E= 0 0.888970 0.009310 0.086680 0.015040 0.152580 0.037970 0.794410 0.015040 0.866050 0.020770 0.092410 0.020770 0.894700 0.029370 0.052290 0.023640 0.049430 0.043700 0.006450 0.900421 0.040830 0.172640 0.020770 0.765760 0.780090 0.029370 0.106730 0.083810 0.192690 0.060890 0.696990 0.049430 0.058020 0.078080 0.046560 0.817340 0.284383 0.032230 0.645416 0.037970 0.920490 0.029370 0.032230 0.017910 0.825930 0.046560 0.078080 0.049430 0.121060 0.582380 0.058020 0.238540 0.046560 0.035100 0.015040 0.903299 0.115330 0.003580 0.771490 0.109600 0.123929 0.000720 0.868901 0.006450 0.963470 0.000720 0.032230 0.003580 0.909021 0.006450 0.046560 0.037970 0.255733 0.015040 0.688397 0.040830 0.751430 0.035100 0.115330 0.098140 MOTIF AC0448:ZNF:C2H2_ZF AC0448:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.066690 0.813055 0.024330 0.095925 0.005635 0.930565 0.003300 0.060500 0.056530 0.206790 0.014975 0.721705 0.000965 0.000965 0.003300 0.994770 0.000965 0.003300 0.000965 0.994770 0.003995 0.088725 0.038785 0.868495 0.056340 0.039040 0.020220 0.884400 0.226676 0.209011 0.395017 0.169296 0.000965 0.108420 0.222260 0.668355 0.004245 0.021610 0.003995 0.970150 0.030135 0.085875 0.665510 0.218480 0.000965 0.329447 0.147141 0.522448 0.137860 0.310070 0.012390 0.539680 0.010310 0.664418 0.007975 0.317297 0.129641 0.182176 0.036320 0.651863 0.170305 0.094220 0.023815 0.711660 MOTIF AC0449:ZNF:C2H2_ZF AC0449:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.061950 0.115379 0.633882 0.188789 0.061085 0.499947 0.114771 0.324197 0.078420 0.504442 0.041265 0.375873 0.045300 0.555185 0.033835 0.365680 0.043225 0.112200 0.015835 0.828740 0.001400 0.009400 0.008400 0.980800 0.003400 0.024050 0.030300 0.942250 0.072025 0.061225 0.084455 0.782294 0.504775 0.138810 0.115670 0.240745 0.188749 0.042520 0.107534 0.661197 0.267095 0.072595 0.428015 0.232295 0.528790 0.231275 0.064300 0.175635 0.034050 0.638827 0.119319 0.207804 0.050950 0.621575 0.107180 0.220295 MOTIF AC0450:FEZF:C2H2_ZF AC0450:FEZF:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.080275 0.018005 0.021015 0.880706 0.006400 0.010400 0.973800 0.009400 0.007400 0.619748 0.110696 0.262156 0.013400 0.486630 0.034415 0.465555 0.071465 0.648655 0.013400 0.266480 0.186106 0.078475 0.007400 0.728019 0.005005 0.067400 0.025400 0.902195 0.010400 0.016620 0.009400 0.963580 0.040030 0.135324 0.099505 0.725141 MOTIF AC0451:ZNF:C2H2_ZF AC0451:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 18 nsites= 20 E= 0 0.167110 0.739300 0.022730 0.070860 0.177812 0.028070 0.028070 0.766048 0.135030 0.054810 0.707220 0.102940 0.167110 0.097590 0.092250 0.643050 0.001340 0.001340 0.065509 0.931811 0.001340 0.958550 0.001340 0.038770 0.022730 0.028070 0.001340 0.947860 0.006680 0.017380 0.001340 0.974600 0.295460 0.124330 0.386360 0.193850 0.070859 0.113639 0.049470 0.766032 0.006680 0.514710 0.001340 0.477270 0.193848 0.022730 0.402406 0.381016 0.145720 0.022730 0.733960 0.097590 0.145720 0.033420 0.450540 0.370320 0.049470 0.145719 0.493315 0.311497 0.054811 0.033420 0.017380 0.894389 0.006680 0.557490 0.012030 0.423800 0.038770 0.723260 0.022730 0.215240 MOTIF AC0452:ZNF354A/ZNF:C2H2_ZF AC0452:ZNF354A/ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 28 nsites= 20 E= 0 0.205760 0.146602 0.138080 0.509559 0.144788 0.141105 0.102210 0.611898 0.149075 0.137882 0.183730 0.529313 0.352175 0.148165 0.289033 0.210627 0.213330 0.096255 0.517260 0.173155 0.234196 0.102084 0.125497 0.538222 0.129672 0.291145 0.368985 0.210197 0.023610 0.703669 0.092558 0.180163 0.748465 0.088250 0.031395 0.131890 0.185842 0.078437 0.065120 0.670601 0.256591 0.032217 0.107253 0.603939 0.119002 0.076772 0.016415 0.787810 0.618799 0.143170 0.127125 0.110905 0.062210 0.297986 0.046328 0.593476 0.413625 0.110282 0.218715 0.257377 0.082632 0.138063 0.089720 0.689586 0.110556 0.077893 0.077742 0.733809 0.126945 0.185802 0.044702 0.642552 0.569072 0.043833 0.070955 0.316140 0.277222 0.089762 0.190348 0.442667 0.324412 0.085625 0.115822 0.474140 0.297971 0.138981 0.339215 0.223833 0.301867 0.113140 0.098645 0.486347 0.205649 0.128798 0.141230 0.524323 0.223257 0.147822 0.333820 0.295102 0.282918 0.181700 0.194468 0.340914 0.275577 0.134220 0.134637 0.455565 0.431700 0.129965 0.148657 0.289678 MOTIF AC0453:ZNF:C2H2_ZF AC0453:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 22 nsites= 20 E= 0 0.043100 0.725100 0.135060 0.096740 0.039270 0.928160 0.012450 0.020120 0.694447 0.092911 0.066091 0.146551 0.518200 0.338120 0.089080 0.054600 0.050769 0.023950 0.004790 0.920491 0.012450 0.000960 0.023950 0.962640 0.514370 0.112070 0.204020 0.169540 0.023950 0.104410 0.069920 0.801720 0.813220 0.066090 0.081420 0.039270 0.050770 0.801720 0.050770 0.096740 0.357280 0.211690 0.250000 0.181030 0.077590 0.089080 0.138890 0.694440 0.272987 0.223178 0.238508 0.265327 0.242340 0.123560 0.108240 0.525860 0.115901 0.123561 0.556516 0.204022 0.020120 0.158048 0.062259 0.759572 0.058430 0.058430 0.066090 0.817050 0.104409 0.077589 0.644634 0.173368 0.058430 0.138890 0.418580 0.384100 0.441570 0.138890 0.211690 0.207850 0.069920 0.579500 0.050770 0.299810 0.104409 0.644634 0.058429 0.192528 MOTIF AC0454:ZNF:C2H2_ZF AC0454:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.231190 0.034380 0.283130 0.451300 0.041400 0.090669 0.112189 0.755742 0.048430 0.043400 0.864810 0.043360 0.063430 0.844510 0.040120 0.051940 0.806670 0.032180 0.131470 0.029680 0.490870 0.256040 0.077340 0.175750 0.097770 0.030190 0.052100 0.819940 0.092489 0.014350 0.018090 0.875071 0.060680 0.069300 0.013470 0.856550 0.047970 0.117030 0.031770 0.803230 MOTIF AC0455:NFATC/PRDM:Rel AC0455:NFATC/PRDM:Rel letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.259000 0.123000 0.122000 0.496000 0.125000 0.236000 0.215000 0.424000 0.181000 0.127000 0.143000 0.549000 0.053000 0.168000 0.149000 0.630000 0.036000 0.046000 0.038000 0.880000 0.015000 0.052000 0.036000 0.897000 0.006000 0.018000 0.014000 0.962000 0.010000 0.936000 0.046000 0.008000 0.035000 0.678000 0.009000 0.278000 0.320000 0.131000 0.044000 0.505000 MOTIF AC0456:ZNF/PRDM:C2H2_ZF AC0456:ZNF/PRDM:C2H2_ZF letter-probability matrix: alength= 4 w= 24 nsites= 20 E= 0 0.677040 0.063606 0.197292 0.062062 0.728087 0.078200 0.119532 0.074182 0.666918 0.001692 0.193528 0.137862 0.689130 0.013606 0.275668 0.021596 0.633149 0.125678 0.119970 0.121204 0.686726 0.024066 0.166090 0.123118 0.851249 0.041214 0.096381 0.011156 0.817492 0.047186 0.092606 0.042716 0.686857 0.046226 0.105578 0.161339 0.587751 0.021010 0.280337 0.110902 0.701081 0.086968 0.108918 0.103032 0.844048 0.032492 0.073376 0.050084 0.711737 0.175885 0.088614 0.023764 0.519815 0.067070 0.184568 0.228546 0.611518 0.033272 0.287591 0.067620 0.811374 0.018998 0.145842 0.023786 0.463411 0.168140 0.176274 0.192174 0.574639 0.119462 0.101698 0.204200 0.475218 0.133616 0.048960 0.342206 0.462150 0.104512 0.244564 0.188774 0.417897 0.289565 0.193174 0.099364 0.648990 0.114524 0.082462 0.154024 0.474712 0.203525 0.218367 0.103396 0.433543 0.105494 0.243036 0.217928 MOTIF AC0457:ZNF/ZFP:C2H2_ZF AC0457:ZNF/ZFP:C2H2_ZF letter-probability matrix: alength= 4 w= 27 nsites= 20 E= 0 0.122492 0.132682 0.227040 0.517786 0.130156 0.291158 0.244526 0.334160 0.173821 0.343451 0.173913 0.308815 0.308765 0.336581 0.103482 0.251171 0.137486 0.114532 0.121880 0.626101 0.104028 0.086052 0.244020 0.565901 0.156107 0.300759 0.202301 0.340833 0.149248 0.323325 0.103644 0.423783 0.291205 0.178230 0.042220 0.488345 0.125787 0.248991 0.171505 0.453716 0.191342 0.138604 0.155822 0.514233 0.344119 0.236641 0.175591 0.243649 0.273295 0.193300 0.195850 0.337555 0.274849 0.151358 0.157736 0.416057 0.215581 0.118154 0.229347 0.436918 0.197388 0.157208 0.179968 0.465436 0.114784 0.288716 0.264680 0.331820 0.206983 0.446828 0.090500 0.255689 0.179049 0.158613 0.161745 0.500593 0.076820 0.338677 0.107646 0.476856 0.039708 0.056550 0.020906 0.882836 0.054944 0.307642 0.079256 0.558158 0.140048 0.306847 0.143738 0.409367 0.317236 0.191374 0.233856 0.257534 0.157640 0.288461 0.155410 0.398489 0.225809 0.200705 0.163423 0.410062 0.173132 0.168912 0.169756 0.488201 MOTIF AC0458:ZFP:C2H2_ZF AC0458:ZFP:C2H2_ZF letter-probability matrix: alength= 4 w= 19 nsites= 20 E= 0 0.011990 0.009590 0.002400 0.976020 0.002400 0.870500 0.000000 0.127100 0.088730 0.160670 0.021580 0.729020 0.637896 0.071941 0.208632 0.081531 0.009590 0.105520 0.002400 0.882490 0.011990 0.079140 0.011990 0.896880 0.002400 0.023980 0.002400 0.971220 0.007190 0.901680 0.011990 0.079140 0.103119 0.189448 0.002400 0.705033 0.033570 0.023980 0.004800 0.937650 0.007190 0.805758 0.014390 0.172662 0.119901 0.095921 0.033570 0.750608 0.062350 0.050360 0.263790 0.623500 0.175060 0.050360 0.640290 0.134290 0.362114 0.021580 0.158272 0.458035 0.083930 0.043170 0.786570 0.086330 0.047960 0.040770 0.052760 0.858510 0.083930 0.678660 0.155880 0.081530 0.860910 0.031180 0.033570 0.074340 MOTIF AC0459:ZNF:C2H2_ZF AC0459:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.004500 0.011690 0.011690 0.972120 0.000900 0.008090 0.015290 0.975720 0.803060 0.101620 0.000900 0.094420 0.051259 0.076439 0.828232 0.044070 0.745500 0.022480 0.126800 0.105220 0.148381 0.180752 0.450545 0.220322 0.810250 0.062050 0.044070 0.083630 0.044070 0.065649 0.137589 0.752692 0.018880 0.072840 0.105220 0.803060 0.382190 0.403780 0.047660 0.166370 0.490110 0.083630 0.011690 0.414570 0.008090 0.213130 0.040470 0.738310 0.008090 0.018880 0.022480 0.950550 0.022480 0.267090 0.040470 0.669960 0.029680 0.605214 0.166368 0.198738 0.126801 0.033270 0.008090 0.831838 0.044070 0.029680 0.036870 0.889381 MOTIF AC0460:ZNF:C2H2_ZF AC0460:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.036640 0.048785 0.888820 0.025755 0.154495 0.424505 0.154180 0.266820 0.584647 0.237059 0.158819 0.019475 0.012770 0.023870 0.810514 0.152846 0.017955 0.906970 0.034605 0.040470 0.319590 0.149265 0.344445 0.186700 0.003770 0.055445 0.938690 0.002095 0.084815 0.827175 0.078795 0.009215 0.647360 0.100100 0.130605 0.121935 0.006280 0.004190 0.987435 0.002095 0.123979 0.859896 0.006910 0.009215 0.191190 0.198895 0.483535 0.126380 0.025075 0.198056 0.769124 0.007745 0.078725 0.661365 0.104180 0.155730 0.117620 0.127110 0.450460 0.304810 0.125000 0.225000 0.515000 0.135000 0.145000 0.365000 0.325000 0.165000 MOTIF AC0461:ZNF:C2H2_ZF AC0461:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.014060 0.055225 0.114219 0.816496 0.003425 0.941945 0.004430 0.050200 0.313822 0.521688 0.026590 0.137901 0.104395 0.189930 0.175900 0.529775 0.141871 0.288046 0.316827 0.253256 0.354541 0.143634 0.297867 0.203958 0.104354 0.619762 0.195019 0.080865 0.255580 0.321440 0.031600 0.391380 0.199029 0.030280 0.664217 0.106474 0.029240 0.099945 0.727626 0.143189 0.031600 0.833924 0.123311 0.011165 0.699207 0.237344 0.004430 0.059020 0.314825 0.079380 0.532585 0.073210 0.009440 0.946630 0.009440 0.034490 0.171045 0.195020 0.067355 0.566580 0.051045 0.127639 0.749911 0.071405 MOTIF AC0462:ZNF:C2H2_ZF AC0462:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.001850 0.416670 0.157410 0.424070 0.112961 0.224072 0.135181 0.527785 0.009260 0.424070 0.061110 0.505560 0.001850 0.431484 0.542595 0.024070 0.001850 0.209260 0.016670 0.772220 0.001850 0.461110 0.001850 0.535190 0.268520 0.179630 0.312960 0.238890 0.053701 0.624076 0.098151 0.224072 0.438890 0.150000 0.187040 0.224070 0.001850 0.024070 0.920379 0.053701 0.031480 0.964820 0.001850 0.001850 0.498150 0.231480 0.127780 0.142590 0.031480 0.083330 0.838890 0.046300 0.001850 0.979630 0.009260 0.009260 MOTIF AC0463:ZNF:C2H2_ZF AC0463:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.385735 0.092245 0.137085 0.384935 0.026095 0.010725 0.935590 0.027590 0.005410 0.185695 0.747690 0.061205 0.093915 0.176561 0.208036 0.521488 0.141994 0.126844 0.068605 0.662557 0.024715 0.252280 0.528195 0.194810 0.066710 0.270650 0.121165 0.541475 0.401805 0.287400 0.090125 0.220670 0.149821 0.094215 0.747644 0.008320 0.025620 0.000600 0.954385 0.019395 0.612440 0.071515 0.310635 0.005410 0.282435 0.586620 0.029520 0.101425 0.036260 0.017975 0.266670 0.679095 0.019360 0.009740 0.962580 0.008320 0.219935 0.483245 0.016480 0.280340 0.083390 0.059350 0.320360 0.536900 0.041500 0.058515 0.806940 0.093045 MOTIF AC0464:ZNF:C2H2_ZF AC0464:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.145610 0.580620 0.111500 0.162270 0.285423 0.312683 0.205912 0.195982 0.218002 0.474785 0.151662 0.155552 0.139570 0.653680 0.006210 0.200540 0.126140 0.106530 0.675300 0.092030 0.001240 0.005140 0.010110 0.983510 0.001240 0.991310 0.001240 0.006210 0.518175 0.245262 0.001240 0.235322 0.259100 0.017890 0.558590 0.164420 0.277637 0.093769 0.413146 0.215448 0.305970 0.151250 0.447930 0.094850 0.090281 0.534415 0.137831 0.237472 0.001240 0.979630 0.001240 0.017890 0.009030 0.897030 0.005140 0.088800 0.050530 0.556440 0.030650 0.362380 MOTIF AC0465:ZIK:C2H2_ZF AC0465:ZIK:C2H2_ZF letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.172008 0.112299 0.060029 0.655663 0.103040 0.573090 0.085800 0.238070 0.120280 0.203580 0.393240 0.282900 0.137521 0.359304 0.046230 0.456945 0.242060 0.136980 0.193240 0.427720 0.032990 0.079990 0.592150 0.294870 0.074910 0.000860 0.066740 0.857490 0.066921 0.000860 0.927909 0.004310 0.091610 0.837880 0.008850 0.061660 0.016290 0.528270 0.000860 0.454580 0.276000 0.038790 0.119190 0.566020 0.000860 0.008300 0.026090 0.964750 0.000860 0.000860 0.000860 0.997420 0.008300 0.000860 0.989430 0.001410 0.000860 0.714840 0.049140 0.235160 0.075451 0.679817 0.000860 0.243872 0.417380 0.202500 0.085250 0.294870 MOTIF AC0466:ZNF:C2H2_ZF AC0466:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.268780 0.001170 0.433100 0.296950 0.198358 0.080989 0.649054 0.071599 0.095069 0.771122 0.080989 0.052819 0.010560 0.095070 0.066900 0.827470 0.700707 0.076291 0.118541 0.104461 0.001170 0.940140 0.005870 0.052820 0.019950 0.001170 0.001170 0.977710 0.052820 0.010560 0.930750 0.005870 0.001170 0.977710 0.001170 0.019950 0.827462 0.034040 0.005870 0.132629 0.264087 0.470655 0.207748 0.057509 0.207748 0.357976 0.080989 0.353286 0.414320 0.043430 0.475350 0.066900 0.095070 0.024650 0.879110 0.001170 0.038730 0.127931 0.038730 0.794608 0.071600 0.902580 0.001170 0.024650 0.010560 0.789910 0.010560 0.188970 MOTIF AC0467:PAX:Homeodomain,Paired_box AC0467:PAX:Homeodomain,Paired_box letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.540665 0.011280 0.009800 0.438254 0.105530 0.571900 0.304590 0.017980 0.090369 0.123209 0.752842 0.033580 0.596800 0.187090 0.177960 0.038150 0.529700 0.144360 0.279310 0.046630 0.501870 0.140940 0.251730 0.105460 0.341573 0.119531 0.392904 0.145991 0.458550 0.118880 0.301290 0.121280 MOTIF AC0468:ZNF780A:C2H2_ZF AC0468:ZNF780A:C2H2_ZF letter-probability matrix: alength= 4 w= 19 nsites= 20 E= 0 0.155402 0.634526 0.052211 0.157862 0.366710 0.189800 0.096440 0.347050 0.312653 0.123461 0.460075 0.103811 0.462530 0.177520 0.234030 0.125920 0.189800 0.523960 0.221740 0.064500 0.130840 0.312650 0.177520 0.378990 0.288083 0.093981 0.494475 0.123461 0.012900 0.816340 0.170150 0.000610 0.000610 0.990790 0.000610 0.007990 0.000610 0.000610 0.000610 0.998170 0.000610 0.003070 0.897429 0.098891 0.000610 0.998170 0.000610 0.000610 0.000610 0.985880 0.000610 0.012900 0.410930 0.170150 0.057130 0.361790 0.437960 0.118550 0.290540 0.152950 0.484645 0.199632 0.140661 0.175062 0.349507 0.327397 0.135749 0.187348 0.187350 0.519040 0.123460 0.170150 0.199632 0.435504 0.197172 0.167692 MOTIF AC0469:ZNF:C2H2_ZF AC0469:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 20 nsites= 20 E= 0 0.058250 0.077670 0.757280 0.106800 0.077670 0.621360 0.165050 0.135920 0.019420 0.213590 0.019420 0.747570 0.009710 0.038840 0.844652 0.106799 0.048540 0.339810 0.097090 0.514560 0.009710 0.912620 0.029130 0.048540 0.009710 0.582520 0.019420 0.388350 0.194170 0.184470 0.291260 0.330100 0.145630 0.349520 0.223300 0.281550 0.271847 0.213588 0.349517 0.165048 0.106799 0.271847 0.495145 0.126209 0.233010 0.543690 0.058250 0.165050 0.184468 0.077669 0.475725 0.262137 0.271850 0.446600 0.155340 0.126210 0.029130 0.135920 0.000000 0.834950 0.019420 0.038840 0.815530 0.126210 0.019420 0.873781 0.009710 0.097089 0.038840 0.922321 0.000000 0.038840 0.038840 0.048540 0.048540 0.864080 0.097089 0.106799 0.699023 0.097089 MOTIF AC0470:ZNF:C2H2_ZF AC0470:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 22 nsites= 20 E= 0 0.371386 0.088149 0.492775 0.047690 0.036130 0.255780 0.088150 0.619940 0.712423 0.088149 0.197978 0.001450 0.030350 0.608374 0.001450 0.359826 0.053469 0.001450 0.053469 0.891611 0.140170 0.013010 0.839600 0.007220 0.117051 0.614166 0.122831 0.145951 0.429190 0.059250 0.076590 0.434970 0.186418 0.082369 0.388726 0.342487 0.226880 0.122830 0.319360 0.330930 0.036130 0.752882 0.169078 0.041910 0.088150 0.752890 0.099710 0.059250 0.053469 0.585254 0.013010 0.348267 0.151732 0.163292 0.065031 0.619946 0.284683 0.307803 0.284683 0.122831 0.070809 0.758662 0.001450 0.169078 0.018790 0.943631 0.018790 0.018790 0.810690 0.041910 0.105490 0.041910 0.313580 0.047690 0.411850 0.226880 0.036130 0.059249 0.885831 0.018790 0.475435 0.157512 0.088151 0.278903 0.197978 0.747103 0.053469 0.001450 MOTIF AC0471:ZNF:C2H2_ZF AC0471:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 26 nsites= 20 E= 0 0.037500 0.323863 0.042040 0.596596 0.078410 0.269320 0.173860 0.478410 0.019320 0.092050 0.082950 0.805680 0.201140 0.037500 0.705680 0.055680 0.842050 0.032950 0.110230 0.014770 0.173860 0.060230 0.714770 0.051140 0.528410 0.019320 0.428410 0.023860 0.305680 0.182960 0.355680 0.155680 0.246590 0.032950 0.519320 0.201140 0.096591 0.205682 0.664777 0.032950 0.037500 0.551140 0.073860 0.337500 0.060229 0.732953 0.019320 0.187498 0.019320 0.514770 0.078410 0.387500 0.469315 0.182958 0.037500 0.310227 0.096590 0.142050 0.523860 0.237500 0.542045 0.073861 0.173862 0.210232 0.001140 0.014770 0.010230 0.973860 0.073860 0.114770 0.692050 0.119320 0.032950 0.046590 0.032950 0.887509 0.201138 0.142049 0.042050 0.614764 0.078410 0.014770 0.832960 0.073860 0.151138 0.010230 0.696583 0.142049 0.560224 0.001140 0.210228 0.228408 0.132950 0.442050 0.082960 0.342040 0.042050 0.255677 0.051139 0.651133 0.046590 0.087500 0.028410 0.837500 MOTIF AC0472:ZNF:C2H2_ZF AC0472:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 18 nsites= 20 E= 0 0.155402 0.336873 0.128381 0.379344 0.170850 0.151540 0.321430 0.356180 0.213320 0.360040 0.224900 0.201740 0.329150 0.166990 0.205600 0.298260 0.194010 0.240350 0.290540 0.275100 0.012550 0.325290 0.638030 0.024130 0.035710 0.004830 0.012550 0.946910 0.008690 0.000970 0.707523 0.282817 0.043440 0.611000 0.128380 0.217180 0.305990 0.533780 0.066600 0.093630 0.097490 0.074320 0.163130 0.665060 0.004830 0.008690 0.985510 0.000970 0.024130 0.549225 0.000970 0.425676 0.047300 0.008690 0.082049 0.861961 0.058880 0.746140 0.101350 0.093630 0.410234 0.344593 0.035710 0.209462 0.128380 0.093630 0.417950 0.360040 0.082049 0.047300 0.788602 0.082049 MOTIF AC0473:ZNF:C2H2_ZF AC0473:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 25 nsites= 20 E= 0 0.131620 0.826320 0.010130 0.031930 0.284273 0.661217 0.003890 0.050621 0.885519 0.003890 0.013240 0.097351 0.748433 0.016360 0.047510 0.187698 0.193922 0.243772 0.031930 0.530375 0.144080 0.723520 0.022590 0.109810 0.654980 0.221960 0.047510 0.075550 0.072430 0.739100 0.053740 0.134730 0.081779 0.187698 0.125389 0.605134 0.237540 0.047510 0.611370 0.103580 0.748440 0.022590 0.162770 0.066200 0.259347 0.007010 0.704823 0.028820 0.745330 0.016360 0.234420 0.003890 0.059970 0.848130 0.044390 0.047510 0.913550 0.016360 0.063080 0.007010 0.617600 0.025700 0.355920 0.000780 0.038160 0.745330 0.084890 0.131620 0.711060 0.097350 0.140970 0.050620 0.542830 0.053740 0.355920 0.047510 0.053740 0.028820 0.767130 0.150310 0.116040 0.041280 0.156540 0.686140 0.483640 0.013240 0.433800 0.069320 0.047510 0.075550 0.056850 0.820090 0.050620 0.035050 0.134730 0.779600 0.088009 0.075549 0.804512 0.031930 MOTIF AC0474:ZNF:C2H2_ZF AC0474:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 19 nsites= 20 E= 0 0.086666 0.200702 0.085961 0.626671 0.440119 0.164562 0.138126 0.257193 0.217075 0.142455 0.259650 0.380820 0.021055 0.047840 0.764907 0.166198 0.062925 0.042220 0.773219 0.121636 0.027600 0.378365 0.109940 0.484095 0.061755 0.199534 0.181519 0.557192 0.138125 0.411110 0.113570 0.337195 0.118245 0.497775 0.010060 0.373920 0.045145 0.065615 0.120586 0.768654 0.372165 0.216725 0.279765 0.131345 0.196375 0.227015 0.018950 0.557660 0.107839 0.127839 0.649704 0.114619 0.144330 0.376140 0.244910 0.234620 0.171231 0.555673 0.027950 0.245146 0.036375 0.821520 0.073100 0.069005 0.030760 0.912050 0.001285 0.055905 0.104915 0.579180 0.010875 0.305030 0.140350 0.134505 0.201755 0.523390 MOTIF AC0475:ZNF:C2H2_ZF AC0475:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 19 nsites= 20 E= 0 0.031490 0.000950 0.855920 0.111640 0.039120 0.100190 0.855920 0.004770 0.111641 0.111641 0.062021 0.714697 0.027670 0.943709 0.000950 0.027670 0.042940 0.111640 0.000950 0.844470 0.359730 0.329200 0.294850 0.016220 0.081109 0.439886 0.020040 0.458965 0.775768 0.065841 0.073471 0.084921 0.848280 0.065840 0.081110 0.004770 0.852100 0.008590 0.077290 0.062020 0.199430 0.145990 0.508590 0.145990 0.218510 0.134540 0.397900 0.249050 0.355916 0.149809 0.451335 0.042940 0.138360 0.634540 0.184160 0.042940 0.203240 0.504770 0.046760 0.245230 0.115460 0.375000 0.107820 0.401720 0.142180 0.073470 0.436070 0.348280 0.126910 0.271950 0.336830 0.264310 0.115459 0.126909 0.104009 0.653623 MOTIF AC0476:THAP:THAP_finger AC0476:THAP:THAP_finger letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.022660 0.334580 0.124910 0.517850 0.470670 0.528890 0.000220 0.000220 0.000220 0.332127 0.494215 0.173438 0.173440 0.216970 0.226030 0.383560 0.188380 0.226030 0.216970 0.368620 0.111590 0.223250 0.270280 0.394880 0.047260 0.134370 0.771110 0.047260 0.274433 0.072821 0.579926 0.072821 MOTIF AC0477:ZNF:C2H2_ZF AC0477:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.040030 0.899580 0.009130 0.051260 0.739463 0.129919 0.025980 0.104639 0.784410 0.062500 0.143960 0.009130 0.000700 0.082160 0.009130 0.908010 0.000700 0.000700 0.000700 0.997900 0.025980 0.967000 0.000700 0.006320 0.143961 0.579356 0.000700 0.275983 0.132730 0.051260 0.778790 0.037220 0.045650 0.860250 0.082160 0.011940 0.017560 0.247890 0.000700 0.733850 0.000700 0.865870 0.000700 0.132730 0.315310 0.438900 0.037220 0.208570 0.202950 0.315310 0.219800 0.261940 0.183288 0.295647 0.211378 0.309687 MOTIF AC0478:ZNF/PAX:C2H2_ZF AC0478:ZNF/PAX:C2H2_ZF letter-probability matrix: alength= 4 w= 21 nsites= 20 E= 0 0.209108 0.390606 0.202097 0.198188 0.182844 0.237199 0.398749 0.181207 0.110365 0.730743 0.107000 0.051892 0.248440 0.425894 0.209300 0.116365 0.083077 0.220807 0.630042 0.066075 0.080290 0.756874 0.136620 0.026215 0.286903 0.474818 0.085663 0.152615 0.360784 0.257854 0.306547 0.074815 0.062925 0.321609 0.296624 0.318841 0.274977 0.292859 0.281052 0.151112 0.167519 0.249181 0.231239 0.352061 0.114838 0.525077 0.139747 0.220338 0.288226 0.182918 0.264751 0.264106 0.171487 0.210513 0.480493 0.137507 0.158477 0.511016 0.179854 0.150652 0.163035 0.329023 0.189050 0.318892 0.197143 0.242425 0.356016 0.204415 0.231857 0.389092 0.211587 0.167463 0.136803 0.265229 0.319191 0.278776 0.201430 0.268469 0.349128 0.180973 0.136755 0.394400 0.293168 0.175677 MOTIF AC0479:PAX:Homeodomain,Paired_box AC0479:PAX:Homeodomain,Paired_box letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.641310 0.221420 0.114400 0.022870 0.115201 0.216572 0.645356 0.022870 0.708323 0.000240 0.183098 0.108339 0.094589 0.211178 0.594634 0.099599 0.000240 0.198980 0.709010 0.091770 0.000240 0.539900 0.185230 0.274630 0.114470 0.135580 0.115360 0.634590 MOTIF AC0480:ZFP:C2H2_ZF AC0480:ZFP:C2H2_ZF letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.077921 0.675327 0.100901 0.145851 0.119000 0.466000 0.107000 0.308000 0.355354 0.220222 0.222222 0.202202 0.031030 0.704710 0.024020 0.240240 0.190000 0.383000 0.094000 0.333000 0.108000 0.185000 0.085000 0.622000 0.296000 0.239000 0.126000 0.339000 0.008010 0.268270 0.004000 0.719720 0.743000 0.010000 0.186000 0.061000 0.455000 0.027000 0.490000 0.028000 0.000000 0.999000 0.000000 0.001000 0.080920 0.450550 0.023980 0.444550 0.315000 0.263000 0.257000 0.165000 0.113000 0.345000 0.099000 0.443000 0.398600 0.082920 0.210790 0.307690 0.108890 0.100900 0.059940 0.730270 0.064060 0.681680 0.017020 0.237240 MOTIF AC0481:ZNF:C2H2_ZF AC0481:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 19 nsites= 20 E= 0 0.346090 0.013840 0.593650 0.046420 0.023620 0.854230 0.118080 0.004070 0.004070 0.961730 0.030130 0.004070 0.010590 0.932410 0.000810 0.056190 0.779320 0.013840 0.196250 0.010590 0.017100 0.101790 0.046420 0.834690 0.000810 0.189740 0.007330 0.802120 0.131110 0.046420 0.505700 0.316770 0.163680 0.287460 0.290720 0.258140 0.134369 0.241858 0.398206 0.225568 0.170200 0.137620 0.394950 0.297230 0.092020 0.665310 0.157170 0.085500 0.046420 0.805370 0.052930 0.095280 0.153910 0.176710 0.078990 0.590390 0.525240 0.280950 0.111560 0.082250 0.030130 0.215800 0.075730 0.678340 0.085500 0.092020 0.580620 0.241860 0.105050 0.626220 0.144140 0.124590 0.095280 0.710910 0.078990 0.114820 MOTIF AC0482:ZNF:C2H2_ZF AC0482:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 18 nsites= 20 E= 0 0.058440 0.843534 0.018800 0.079225 0.377405 0.137410 0.120890 0.364295 0.043420 0.075890 0.833465 0.047225 0.617227 0.012375 0.350213 0.020185 0.103655 0.031990 0.859195 0.005160 0.016780 0.591530 0.047225 0.344465 0.007375 0.979305 0.000350 0.012970 0.740475 0.079510 0.112765 0.067250 0.153365 0.276800 0.297315 0.272520 0.175025 0.205285 0.427025 0.192665 0.087035 0.177260 0.147555 0.588150 0.426450 0.020350 0.448690 0.104510 0.092825 0.074800 0.792360 0.040015 0.202049 0.056375 0.470653 0.270924 0.140980 0.089845 0.439025 0.330150 0.149171 0.118531 0.276396 0.455902 0.386575 0.088465 0.434260 0.090700 0.091440 0.723416 0.081870 0.103274 MOTIF AC0483:ZNF:C2H2_ZF AC0483:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 28 nsites= 20 E= 0 0.494530 0.266727 0.143909 0.094834 0.171834 0.700416 0.015485 0.112264 0.082710 0.438875 0.029620 0.448795 0.111514 0.491668 0.023505 0.373313 0.156776 0.273186 0.171361 0.398677 0.120174 0.113164 0.613542 0.153119 0.182696 0.032570 0.681563 0.103171 0.244511 0.051555 0.406182 0.297751 0.105590 0.018435 0.705530 0.170445 0.119746 0.568578 0.130501 0.181176 0.210299 0.545442 0.059575 0.184684 0.603367 0.202514 0.056540 0.137579 0.115255 0.482450 0.055280 0.347015 0.534537 0.041445 0.171899 0.252119 0.040290 0.129519 0.293389 0.536802 0.028145 0.140189 0.743438 0.088229 0.784754 0.012000 0.126331 0.076915 0.253059 0.515592 0.039030 0.192319 0.058335 0.060325 0.042195 0.839145 0.009160 0.655727 0.017710 0.317403 0.259890 0.351660 0.228245 0.160205 0.277295 0.433940 0.155460 0.133305 0.174445 0.126980 0.134890 0.563685 0.142800 0.141220 0.357990 0.357990 0.251980 0.250395 0.346915 0.150710 0.161789 0.266219 0.429193 0.142799 0.282039 0.411788 0.147549 0.158624 0.150710 0.179195 0.125395 0.544700 MOTIF AC0484:ZNF:C2H2_ZF AC0484:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.029840 0.006420 0.028087 0.935653 0.045147 0.006177 0.924329 0.024347 0.097957 0.713379 0.010200 0.178464 0.016230 0.074680 0.003510 0.905580 0.001510 0.022447 0.009753 0.966290 0.036827 0.184957 0.135463 0.642753 0.050233 0.149970 0.026563 0.773234 0.670873 0.010200 0.268824 0.050103 0.006843 0.022260 0.830998 0.139900 0.020983 0.811163 0.012200 0.155654 0.007533 0.652525 0.006447 0.333495 0.236739 0.074060 0.107827 0.581375 0.074290 0.817786 0.043533 0.064390 0.208477 0.305277 0.013143 0.473103 MOTIF AC0485:ZNF:C2H2_ZF AC0485:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.239855 0.026190 0.680325 0.053630 0.183575 0.115420 0.550800 0.150205 0.010925 0.890734 0.007810 0.090530 0.218643 0.049810 0.098314 0.633234 0.000600 0.105525 0.202665 0.691210 0.014600 0.800800 0.163920 0.020680 0.003005 0.422549 0.022515 0.551931 0.976565 0.000600 0.015025 0.007810 0.013470 0.003005 0.982925 0.000600 0.015730 0.952245 0.010215 0.021810 0.039345 0.904445 0.015025 0.041185 MOTIF AC0486:ZFP/ZFP69B:C2H2_ZF AC0486:ZFP/ZFP69B:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.133859 0.185039 0.145669 0.535432 0.153545 0.133860 0.169290 0.543305 0.093070 0.208149 0.518067 0.180714 0.005240 0.191369 0.115209 0.688182 0.104931 0.168941 0.023565 0.702564 0.005240 0.001310 0.002620 0.990830 0.001310 0.937025 0.001310 0.060355 0.013120 0.804478 0.005245 0.177157 0.850409 0.044615 0.040680 0.064295 0.073485 0.001310 0.877969 0.047235 0.032725 0.699605 0.013100 0.254570 0.121965 0.807215 0.005245 0.065575 0.629050 0.095625 0.056365 0.218960 0.003930 0.930460 0.009185 0.056425 MOTIF AC0487:ZNF:C2H2_ZF AC0487:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.144929 0.018430 0.825621 0.011020 0.029415 0.481115 0.093440 0.396030 0.024060 0.615745 0.035990 0.324205 0.132166 0.012520 0.749009 0.106306 0.012505 0.016920 0.634775 0.335800 0.053190 0.564480 0.054230 0.328100 0.446305 0.001075 0.544395 0.008225 0.003950 0.005335 0.015280 0.975435 0.002495 0.986730 0.009700 0.001075 0.015485 0.056365 0.015570 0.912580 0.048600 0.011135 0.798620 0.141645 MOTIF AC0488:ZNF:C2H2_ZF AC0488:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 20 nsites= 20 E= 0 0.004960 0.874010 0.088290 0.032740 0.639880 0.120040 0.199400 0.040680 0.016870 0.100199 0.850191 0.032740 0.711310 0.211310 0.064480 0.012900 0.012900 0.155750 0.076390 0.754960 0.072420 0.092260 0.826390 0.008930 0.044640 0.889880 0.052580 0.012900 0.008930 0.885910 0.000990 0.104170 0.437496 0.040680 0.469245 0.052579 0.100200 0.088290 0.802580 0.008930 0.000990 0.965280 0.004960 0.028770 0.469245 0.104169 0.350196 0.076389 0.024800 0.485125 0.131941 0.358134 0.028770 0.576390 0.112100 0.282740 0.457340 0.068450 0.322420 0.151790 0.036710 0.187500 0.139880 0.635910 0.108130 0.116070 0.695440 0.080360 0.016870 0.965270 0.004960 0.012900 0.008930 0.131940 0.056550 0.802580 0.008930 0.171628 0.076389 0.743053 MOTIF AC0489:ZNF:C2H2_ZF AC0489:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 20 nsites= 20 E= 0 0.004860 0.277243 0.004860 0.713037 0.039880 0.892029 0.000970 0.067121 0.074900 0.086580 0.000970 0.837550 0.008760 0.203310 0.343380 0.444550 0.004860 0.849220 0.008760 0.137160 0.004860 0.534050 0.012650 0.448440 0.425100 0.417310 0.148830 0.008760 0.899800 0.008760 0.012650 0.078790 0.074901 0.004860 0.857009 0.063231 0.390080 0.016540 0.078790 0.514590 0.432876 0.273347 0.024320 0.269457 0.043770 0.055450 0.868680 0.032100 0.090470 0.790860 0.004860 0.113810 0.067120 0.032100 0.285020 0.615760 0.016540 0.000970 0.825872 0.156618 0.024320 0.102140 0.857000 0.016540 0.004860 0.378404 0.168292 0.448444 0.347280 0.012650 0.635210 0.004860 0.016540 0.086580 0.004860 0.892020 0.004860 0.133271 0.000970 0.860899 MOTIF AC0490:ZNF:C2H2_ZF AC0490:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.060942 0.022677 0.229570 0.686811 0.044003 0.045570 0.890554 0.019873 0.782026 0.012590 0.142105 0.063280 0.034463 0.017090 0.927857 0.020590 0.040985 0.912814 0.020678 0.025523 0.043915 0.067475 0.018285 0.870325 0.007090 0.558929 0.030090 0.403891 0.131363 0.095743 0.137351 0.635543 0.076093 0.047488 0.094050 0.782370 0.701095 0.091752 0.144730 0.062423 0.727360 0.177594 0.067583 0.027463 0.301375 0.075495 0.472740 0.150390 0.078800 0.083280 0.761728 0.076192 0.092582 0.550617 0.249928 0.106873 MOTIF AC0491:ZNF:C2H2_ZF AC0491:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.179133 0.551186 0.112607 0.157073 0.062935 0.626292 0.051120 0.259653 0.312004 0.040270 0.080322 0.567404 0.035758 0.010640 0.941637 0.011965 0.006320 0.985818 0.003497 0.004365 0.470564 0.023380 0.023768 0.482289 0.028528 0.008743 0.953177 0.009552 0.961180 0.005440 0.026103 0.007277 0.080510 0.003403 0.903865 0.012222 0.009705 0.976352 0.004835 0.009108 0.160586 0.747910 0.010105 0.081399 0.086095 0.751061 0.048520 0.114325 0.442038 0.118070 0.049380 0.390511 MOTIF AC0492:ZNF:C2H2_ZF AC0492:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.301380 0.055980 0.519170 0.123470 0.025310 0.086660 0.065180 0.822850 0.049850 0.059049 0.448616 0.442486 0.025310 0.000770 0.970090 0.003830 0.000770 0.111199 0.009970 0.878061 0.000770 0.178680 0.031440 0.789110 0.031440 0.847398 0.077451 0.043710 0.114260 0.000770 0.000770 0.884200 0.046780 0.006900 0.936350 0.009970 0.019170 0.905671 0.000770 0.074389 0.203220 0.144940 0.009970 0.641870 MOTIF AC0493:ZNF:C2H2_ZF AC0493:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 18 nsites= 20 E= 0 0.172679 0.642381 0.109525 0.075415 0.857875 0.113270 0.009973 0.018882 0.072498 0.026627 0.072013 0.828862 0.019520 0.658794 0.175710 0.145975 0.705353 0.014063 0.276291 0.004293 0.928676 0.022412 0.034162 0.014750 0.199417 0.222370 0.437157 0.141055 0.204034 0.018060 0.628065 0.149841 0.124313 0.095683 0.286221 0.493782 0.045040 0.667457 0.274001 0.013503 0.078457 0.904050 0.008422 0.009070 0.075825 0.163316 0.026373 0.734486 0.096040 0.568245 0.083473 0.252242 0.705763 0.127938 0.089758 0.076542 0.550383 0.130217 0.153245 0.166155 0.363075 0.235964 0.311076 0.089885 0.697153 0.079643 0.153635 0.069570 0.631345 0.102453 0.113215 0.152987 MOTIF AC0494:ZNF:C2H2_ZF AC0494:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.022305 0.002073 0.972540 0.003082 0.922431 0.009052 0.063052 0.005465 0.022540 0.013280 0.947717 0.016463 0.003580 0.737206 0.010590 0.248624 0.149670 0.004260 0.810112 0.035958 0.003778 0.001305 0.989302 0.005615 0.993085 0.001500 0.002060 0.003355 0.989984 0.001778 0.006720 0.001518 0.003922 0.037545 0.008315 0.950218 0.036490 0.012318 0.031423 0.919769 0.008188 0.004180 0.984624 0.003008 0.033953 0.739493 0.016038 0.210517 MOTIF AC0495:ZNF:C2H2_ZF AC0495:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.341600 0.303440 0.336510 0.018450 0.344150 0.440840 0.008270 0.206740 0.000640 0.000640 0.000640 0.998080 0.000640 0.998080 0.000640 0.000640 0.000640 0.998080 0.000640 0.000640 0.850510 0.000640 0.110050 0.038800 0.280530 0.580790 0.110050 0.028630 0.000640 0.028630 0.000640 0.970090 0.000640 0.799612 0.000640 0.199108 MOTIF AC0496:ZNF:C2H2_ZF AC0496:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.051650 0.026510 0.844265 0.077575 0.077730 0.656515 0.056435 0.209320 0.051689 0.561064 0.075109 0.312137 0.067479 0.014090 0.096814 0.821617 0.066955 0.011465 0.913850 0.007730 0.040125 0.697632 0.030440 0.231804 0.370095 0.061680 0.136505 0.431720 0.004025 0.001555 0.992865 0.001555 0.367288 0.585902 0.044020 0.002790 0.632455 0.031600 0.201795 0.134150 0.007730 0.037805 0.018950 0.935515 0.035340 0.065045 0.135194 0.764421 0.621458 0.067775 0.083045 0.227721 0.104255 0.001555 0.870265 0.023925 0.057710 0.799140 0.082780 0.060370 MOTIF AC0497:ZNF:C2H2_ZF AC0497:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.782610 0.000000 0.000000 0.217390 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 MOTIF AC0498:ZNF:C2H2_ZF AC0498:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 18 nsites= 20 E= 0 0.820055 0.112335 0.039540 0.028070 0.017820 0.087440 0.042475 0.852265 0.039910 0.024145 0.920520 0.015425 0.027955 0.861131 0.061089 0.049825 0.753480 0.143750 0.038970 0.063800 0.090460 0.593297 0.149264 0.166979 0.206926 0.163226 0.322827 0.307022 0.192090 0.192860 0.234445 0.380605 0.355855 0.226060 0.159275 0.258810 0.232185 0.325100 0.264865 0.177850 0.157660 0.435015 0.177880 0.229445 0.190020 0.252160 0.445710 0.112110 0.058845 0.069990 0.066725 0.804439 0.058565 0.003835 0.905885 0.031715 0.042545 0.951930 0.005525 0.000000 0.003020 0.991750 0.000550 0.004680 0.001920 0.003015 0.001095 0.993970 0.009165 0.902065 0.007460 0.081310 MOTIF AC0499:ZBTB:C2H2_ZF AC0499:ZBTB:C2H2_ZF letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.680553 0.248678 0.000090 0.070679 0.000090 0.000090 0.000090 0.999730 0.070150 0.000090 0.929670 0.000090 0.000090 0.929670 0.000090 0.070150 0.999730 0.000090 0.000090 0.000090 0.100349 0.029760 0.278347 0.591544 0.069841 0.535275 0.069841 0.325043 MOTIF AC0500:ZNF:C2H2_ZF AC0500:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 28 nsites= 20 E= 0 0.857140 0.142860 0.000000 0.000000 0.000000 0.857140 0.142860 0.000000 0.928570 0.000000 0.000000 0.071430 0.142859 0.714283 0.142859 0.000000 0.000000 0.500000 0.000000 0.500000 0.142860 0.571430 0.285710 0.000000 0.142860 0.000000 0.071430 0.785710 0.000000 0.642860 0.357140 0.000000 0.928570 0.000000 0.071430 0.000000 0.000000 0.500000 0.500000 0.000000 0.000000 1.000000 0.000000 0.000000 0.642860 0.000000 0.000000 0.357140 0.142860 0.000000 0.357140 0.500000 0.428570 0.428570 0.071430 0.071430 0.428570 0.142860 0.142860 0.285710 0.000000 0.071430 0.928570 0.000000 0.857140 0.071430 0.000000 0.071430 0.000000 0.000000 1.000000 0.000000 0.428570 0.500000 0.071430 0.000000 0.071430 0.428570 0.000000 0.500000 0.000000 0.071430 0.928570 0.000000 0.285710 0.071430 0.500000 0.142860 0.071430 0.714280 0.071430 0.142860 0.357140 0.000000 0.642860 0.000000 0.000000 0.857140 0.142860 0.000000 0.000000 0.785710 0.214290 0.000000 0.000000 0.785710 0.142860 0.071430 0.071430 0.000000 0.928570 0.000000 MOTIF AC0501:ZNF:C2H2_ZF AC0501:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 27 nsites= 20 E= 0 0.171430 0.771430 0.000000 0.057140 0.142860 0.028570 0.057140 0.771430 0.028570 0.885720 0.057140 0.028570 0.057141 0.057141 0.000000 0.885719 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 1.000000 0.000000 0.000000 0.028570 0.085710 0.000000 0.885720 0.000000 0.714290 0.000000 0.285710 0.000000 0.028570 0.000000 0.971430 0.314290 0.028570 0.657140 0.000000 0.000000 0.942860 0.000000 0.057140 0.028570 0.028570 0.000000 0.942860 0.028570 0.000000 0.971430 0.000000 0.000000 0.942860 0.000000 0.057140 1.000000 0.000000 0.000000 0.000000 0.914290 0.000000 0.085710 0.000000 0.028570 0.600000 0.000000 0.371430 0.800000 0.000000 0.028570 0.171430 0.000000 0.942860 0.000000 0.057140 0.028570 0.828570 0.000000 0.142860 0.085710 0.057140 0.114290 0.742860 0.000000 0.200000 0.714290 0.085710 0.000000 0.742860 0.028570 0.228570 0.114289 0.028570 0.142859 0.714283 0.114290 0.000000 0.657140 0.228570 0.000000 0.142860 0.000000 0.857140 MOTIF AC0502:ZNF:C2H2_ZF AC0502:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.001790 0.994630 0.001790 0.001790 0.044640 0.644640 0.001790 0.308930 0.001790 0.337497 0.108929 0.551784 0.001790 0.001790 0.201788 0.794632 0.001790 0.001790 0.994630 0.001790 0.001790 0.894631 0.001790 0.101789 0.687500 0.058930 0.166070 0.087500 0.001790 0.001790 0.994630 0.001790 MOTIF AC0503:ZNF:C2H2_ZF AC0503:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.012000 0.094000 0.114000 0.780000 0.140000 0.198000 0.168000 0.494000 0.014000 0.002000 0.004000 0.980000 0.000000 0.000000 0.934000 0.066000 0.000000 0.842000 0.004000 0.154000 0.008000 0.066000 0.004000 0.922000 0.000000 0.006000 0.982000 0.012000 0.030000 0.774000 0.054000 0.142000 0.042000 0.320000 0.016000 0.622000 0.146000 0.050000 0.228000 0.576000 0.308000 0.250000 0.250000 0.192000 0.090000 0.710000 0.084000 0.116000 MOTIF AC0504:ZNF:C2H2_ZF AC0504:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 21 nsites= 20 E= 0 0.151160 0.018610 0.774420 0.055810 0.339530 0.011630 0.404650 0.244190 0.041860 0.011630 0.937210 0.009300 0.004650 0.016280 0.030230 0.948840 0.018610 0.630230 0.009300 0.341860 0.002330 0.009300 0.004650 0.983720 0.006980 0.988370 0.004650 0.000000 0.002330 0.051160 0.000000 0.946510 0.002330 0.006980 0.013950 0.976740 0.009300 0.004650 0.981400 0.004650 0.495350 0.025580 0.011630 0.467440 0.967440 0.004650 0.018610 0.009300 0.093020 0.009300 0.848840 0.048840 0.486045 0.002330 0.509295 0.002330 0.004650 0.995350 0.000000 0.000000 0.995350 0.000000 0.004650 0.000000 0.216280 0.000000 0.783720 0.000000 0.004650 0.993020 0.002330 0.000000 1.000000 0.000000 0.000000 0.000000 0.046510 0.079070 0.295350 0.579070 0.972080 0.002330 0.023260 0.002330 MOTIF AC0505:ZNF:C2H2_ZF AC0505:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.083655 0.097870 0.745843 0.072633 0.010870 0.949537 0.024708 0.014885 0.041600 0.040385 0.021258 0.896757 0.006353 0.006740 0.960832 0.026075 0.005668 0.880049 0.044198 0.070085 0.010433 0.961090 0.008395 0.020082 0.023965 0.829608 0.004705 0.141722 0.528725 0.055290 0.239359 0.176626 0.533589 0.057870 0.335624 0.072917 0.103725 0.174728 0.075082 0.646465 0.111465 0.157002 0.133960 0.597573 0.139805 0.610631 0.154477 0.095087 0.600726 0.222225 0.079545 0.097505 0.070157 0.070492 0.065320 0.794031 MOTIF AC0506:ZNF:['C2H2_zinc_finger_factors'] AC0506:ZNF:['C2H2_zinc_finger_factors'] letter-probability matrix: alength= 4 w= 18 nsites= 20 E= 0 0.023235 0.079525 0.105511 0.791729 0.039705 0.097520 0.079405 0.783370 0.673715 0.232430 0.018055 0.075800 0.069830 0.771550 0.085375 0.073245 0.038990 0.155451 0.060390 0.745169 0.012735 0.046800 0.005840 0.934625 0.016470 0.018695 0.876755 0.088080 0.014765 0.949200 0.026915 0.009120 0.792476 0.127444 0.016340 0.063740 0.026325 0.034055 0.907720 0.031900 0.023370 0.961985 0.009065 0.005580 0.014375 0.767225 0.005840 0.212560 0.342965 0.267625 0.230270 0.159140 0.803369 0.056710 0.073965 0.065955 0.706571 0.058155 0.196809 0.038465 0.382393 0.243854 0.107834 0.265919 0.188290 0.058085 0.679860 0.073765 0.139511 0.559428 0.152046 0.149016 MOTIF AC0507:PAX:Paired_box AC0507:PAX:Paired_box letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.118618 0.700440 0.029706 0.151236 0.665746 0.046446 0.242988 0.044820 0.172208 0.247393 0.382489 0.197911 0.009868 0.305564 0.042003 0.642565 0.057144 0.539763 0.380608 0.022485 0.726283 0.011590 0.235132 0.026996 0.441829 0.051075 0.115880 0.391215 0.006595 0.196540 0.788455 0.008410 0.013617 0.903674 0.017650 0.065059 0.177943 0.011009 0.794217 0.016831 0.042092 0.068127 0.115935 0.773846 0.248204 0.017626 0.694347 0.039823 0.727077 0.061173 0.099991 0.111759 0.232508 0.630023 0.070148 0.067321 MOTIF AC0508:FOSL:bZIP AC0508:FOSL:bZIP letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.056130 0.131730 0.788090 0.024050 0.049560 0.000000 0.096920 0.853520 0.067050 0.891330 0.041620 0.000000 0.943000 0.027260 0.024780 0.004960 0.024480 0.364750 0.031820 0.578950 0.073291 0.684747 0.081321 0.160642 0.150148 0.120709 0.636894 0.092249 0.600000 0.060120 0.293640 0.046240 0.000000 0.035630 0.040380 0.923990 0.016560 0.011460 0.971980 0.000000 0.822780 0.078060 0.028480 0.070680 0.045040 0.723640 0.131010 0.100310 MOTIF AC0509:MBNL:CCCH_ZF AC0509:MBNL:CCCH_ZF letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.090660 0.421307 0.029435 0.458597 0.072915 0.092690 0.766329 0.068065 0.032945 0.880346 0.019320 0.067390 0.055706 0.433364 0.009510 0.501420 0.113705 0.062915 0.032600 0.790780 0.142425 0.459010 0.021030 0.377535 0.086790 0.102165 0.763210 0.047835 0.025255 0.912775 0.012955 0.049015 0.072430 0.322420 0.030200 0.574950 0.102120 0.137690 0.050640 0.709550 MOTIF AC0510:ZNF:C2H2_ZF AC0510:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 20 nsites= 20 E= 0 0.119796 0.071710 0.764769 0.043725 0.198435 0.058555 0.718120 0.024890 0.049300 0.193114 0.031445 0.726141 0.031110 0.907855 0.027490 0.033545 0.085670 0.107546 0.120146 0.686638 0.053790 0.389017 0.183161 0.374032 0.280100 0.055850 0.600190 0.063860 0.069810 0.872020 0.038045 0.020125 0.168281 0.056610 0.039990 0.735119 0.359847 0.226131 0.360502 0.053520 0.093685 0.271545 0.077665 0.557105 0.108369 0.253742 0.516870 0.121019 0.054065 0.256179 0.076585 0.613172 0.041560 0.034640 0.226245 0.697555 0.004535 0.026300 0.951255 0.017910 0.008540 0.596618 0.220611 0.174231 0.012540 0.880254 0.069810 0.037395 0.018715 0.958030 0.009950 0.013305 0.533822 0.267429 0.046435 0.152314 0.047350 0.050060 0.866985 0.035605 MOTIF AC0511:ZNF:C2H2_ZF AC0511:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.047788 0.020540 0.884273 0.047400 0.006100 0.979922 0.002958 0.011020 0.006030 0.098400 0.004930 0.890640 0.030775 0.008090 0.958765 0.002370 0.011070 0.022015 0.016170 0.950745 0.574037 0.003723 0.409445 0.012795 0.004645 0.925732 0.050138 0.019485 0.006018 0.984619 0.004618 0.004745 0.007430 0.065352 0.005010 0.922208 0.255132 0.416254 0.168389 0.160225 MOTIF AC0512:ZNF:C2H2_ZF AC0512:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.074632 0.031552 0.855072 0.038745 0.024003 0.030445 0.922925 0.022628 0.963085 0.020327 0.011263 0.005325 0.956767 0.009793 0.013983 0.019457 0.061153 0.702674 0.215992 0.020182 0.710880 0.086190 0.192482 0.010448 0.008545 0.009375 0.972180 0.009900 0.326852 0.583928 0.045515 0.043705 MOTIF AC0513:NR3C/AR:Nuclear_receptor AC0513:NR3C/AR:Nuclear_receptor letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.442840 0.032243 0.409300 0.115617 0.054413 0.073083 0.811097 0.061407 0.443540 0.173440 0.199460 0.183560 0.919217 0.000750 0.012313 0.067720 0.038297 0.910976 0.012430 0.038297 0.889274 0.001070 0.053663 0.055993 MOTIF AC0514:NR3C/AR:Nuclear_receptor AC0514:NR3C/AR:Nuclear_receptor letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.049777 0.015501 0.899580 0.035142 0.386445 0.117820 0.202762 0.292973 0.939738 0.013526 0.021803 0.024933 0.008763 0.966851 0.012061 0.012325 0.794454 0.009756 0.115555 0.080235 0.116247 0.283471 0.205493 0.394789 0.267318 0.191341 0.211039 0.330302 0.349411 0.214634 0.293976 0.141979 0.073289 0.151969 0.016615 0.758127 0.013174 0.031524 0.941726 0.013576 0.038004 0.031511 0.019449 0.911036 0.297241 0.187375 0.121471 0.393913 0.039304 0.877822 0.011659 0.071215 MOTIF AC0515:NR3C:Nuclear_receptor AC0515:NR3C:Nuclear_receptor letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.000000 0.000000 0.712230 0.287770 0.000000 0.221342 0.098811 0.679847 0.886840 0.000000 0.000000 0.113160 0.000000 0.792810 0.000000 0.207190 0.708630 0.291370 0.000000 0.000000 0.584430 0.116170 0.196410 0.102990 0.301800 0.098200 0.201200 0.398800 0.312570 0.370060 0.317370 0.000000 0.193050 0.000000 0.000000 0.806950 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.322157 0.095809 0.582034 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 MOTIF AC0516:ZNF:C2H2_ZF AC0516:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 18 nsites= 20 E= 0 0.135170 0.604070 0.202150 0.058610 0.192582 0.489235 0.053831 0.264353 0.044260 0.058610 0.135170 0.761960 0.034690 0.106460 0.532300 0.326550 0.197370 0.154310 0.254780 0.393540 0.001200 0.044260 0.675833 0.278707 0.005980 0.005980 0.025120 0.962920 0.029900 0.025120 0.943780 0.001200 0.001200 0.001200 0.226078 0.771522 0.130380 0.001200 0.709330 0.159090 0.001200 0.996400 0.001200 0.001200 0.001200 0.881571 0.001200 0.116029 0.254783 0.254783 0.106461 0.383974 0.139951 0.087321 0.245212 0.527515 0.044260 0.053830 0.589710 0.312200 0.168660 0.120810 0.494020 0.216510 0.068180 0.819380 0.053830 0.058610 0.053830 0.135170 0.020330 0.790670 MOTIF AC0517:ZNF585A:C2H2_ZF AC0517:ZNF585A:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.141980 0.053530 0.021760 0.782730 0.004040 0.943720 0.007280 0.044960 0.140290 0.176990 0.035820 0.646900 0.000810 0.000810 0.997570 0.000810 0.000810 0.043830 0.041720 0.913639 0.442176 0.000810 0.120889 0.436126 0.000810 0.591920 0.000810 0.406460 0.000810 0.000810 0.000810 0.997570 0.010510 0.713130 0.000810 0.275550 MOTIF AC0518:NKX/MSANTD:['Homeo_domain_factors'] AC0518:NKX/MSANTD:['Homeo_domain_factors'] letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.086866 0.064764 0.254537 0.593833 0.059818 0.026518 0.148634 0.765030 0.067396 0.021662 0.880424 0.030518 0.961632 0.004948 0.009476 0.023944 0.028964 0.017500 0.932078 0.021458 0.077660 0.006030 0.026142 0.890168 0.112368 0.015110 0.836024 0.036498 MOTIF AC0519:NKX:Homeodomain AC0519:NKX:Homeodomain letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.004528 0.864631 0.003562 0.127279 0.951143 0.012379 0.001428 0.035050 0.007227 0.990869 0.000560 0.001344 0.003712 0.003677 0.004912 0.987699 0.004194 0.124412 0.004768 0.866626 0.504038 0.145880 0.295215 0.054867 MOTIF AC0520:NKX:Homeodomain AC0520:NKX:Homeodomain letter-probability matrix: alength= 4 w= 5 nsites= 20 E= 0 0.565601 0.046901 0.289067 0.098431 0.866882 0.017394 0.009284 0.106440 0.086101 0.048037 0.738514 0.127347 0.267625 0.042231 0.131217 0.558927 0.274624 0.034101 0.607052 0.084223 MOTIF AC0521:ZNF:C2H2_ZF AC0521:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 19 nsites= 20 E= 0 0.230750 0.032980 0.696630 0.039640 0.628534 0.107609 0.247538 0.016320 0.251403 0.552036 0.056971 0.139591 0.839890 0.031640 0.076960 0.051510 0.558710 0.086820 0.179570 0.174900 0.142120 0.278850 0.066960 0.512070 0.516720 0.126930 0.040980 0.315370 0.274180 0.338150 0.239540 0.148130 0.034300 0.685980 0.016320 0.263400 0.965700 0.016320 0.008990 0.008990 0.933709 0.024990 0.032310 0.008990 0.298170 0.001000 0.699830 0.001000 0.308827 0.005000 0.001000 0.685173 0.008330 0.001000 0.989670 0.001000 0.001000 0.291513 0.294043 0.413444 0.246200 0.094940 0.024320 0.634540 0.064300 0.067630 0.729150 0.138920 0.521390 0.016990 0.409450 0.052170 0.024320 0.777780 0.012990 0.184910 MOTIF AC0522:ZKSCAN:C2H2_ZF AC0522:ZKSCAN:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.122000 0.533000 0.050000 0.295000 0.707000 0.116000 0.027000 0.150000 0.584000 0.186000 0.064000 0.166000 0.900000 0.068000 0.026000 0.006000 0.008990 0.951040 0.024980 0.014990 0.123879 0.013990 0.018980 0.843152 0.123121 0.083081 0.549555 0.244242 0.084000 0.020000 0.113000 0.783000 0.465000 0.165000 0.188000 0.182000 0.449000 0.014000 0.517000 0.020000 0.725000 0.044000 0.096000 0.135000 0.024000 0.008000 0.949000 0.019000 0.086000 0.299000 0.122000 0.493000 0.119000 0.124000 0.631000 0.126000 MOTIF AC0523:NKX:Homeodomain AC0523:NKX:Homeodomain letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.026315 0.051315 0.000000 0.922370 0.948685 0.000000 0.000000 0.051315 0.948685 0.026315 0.000000 0.025000 0.025000 0.000000 0.948685 0.026315 0.000000 0.000000 0.000000 1.000000 0.922370 0.000000 0.077630 0.000000 0.153949 0.153949 0.025000 0.667102 MOTIF AC0524:FOXC:['Fork_head/winged_helix_factors'] AC0524:FOXC:['Fork_head/winged_helix_factors'] letter-probability matrix: alength= 4 w= 5 nsites= 20 E= 0 0.375000 0.250000 0.312500 0.062500 0.437500 0.187500 0.125000 0.250000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000 0.000000 0.000000 0.000000 MOTIF AC0525:NFYA/NFYB:CBF/NF-Y AC0525:NFYA/NFYB:CBF/NF-Y letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.382306 0.041500 0.495702 0.080492 0.004148 0.968188 0.007726 0.019938 0.008571 0.983494 0.003682 0.004253 0.976212 0.008194 0.002885 0.012709 0.955072 0.019711 0.019891 0.005326 0.008411 0.017068 0.005223 0.969298 0.067160 0.669209 0.233189 0.030441 0.737130 0.045413 0.204948 0.012508 0.168725 0.179390 0.584378 0.067507 MOTIF AC0526:GFI/GFI1B:C2H2_ZF AC0526:GFI/GFI1B:C2H2_ZF letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.677013 0.224370 0.057271 0.041346 0.998969 0.000150 0.000083 0.000798 0.996983 0.001864 0.001061 0.000092 0.000838 0.010534 0.005673 0.982955 0.000578 0.998013 0.000690 0.000719 0.684700 0.015706 0.007499 0.292095 0.062602 0.585193 0.331811 0.020394 0.347280 0.044059 0.215039 0.393621 0.115426 0.078833 0.723919 0.081821 0.083927 0.649996 0.071501 0.194576 0.394908 0.194748 0.202991 0.207354 0.244672 0.278782 0.201798 0.274749 0.203376 0.280018 0.232814 0.283792 0.160527 0.253553 0.233585 0.352336 0.148917 0.498363 0.196586 0.156135 0.525548 0.163530 0.143468 0.167453 MOTIF AC0527:PBX/PKNOX:Homeodomain AC0527:PBX/PKNOX:Homeodomain letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.130144 0.048390 0.094319 0.727147 0.037320 0.036753 0.903672 0.022255 0.940324 0.017176 0.021464 0.021036 0.040544 0.191516 0.429044 0.338897 0.053539 0.036004 0.087125 0.823332 0.018566 0.016514 0.951059 0.013861 0.786700 0.012086 0.191640 0.009574 0.019151 0.938144 0.012690 0.030015 0.797165 0.018095 0.124351 0.060389 0.061191 0.098374 0.738419 0.102016 MOTIF AC0528:ANHX:Homeodomain AC0528:ANHX:Homeodomain letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.071559 0.125089 0.160778 0.642574 0.037140 0.000120 0.012460 0.950280 0.071510 0.000120 0.642670 0.285700 0.999640 0.000120 0.000120 0.000120 0.000120 0.883421 0.000120 0.116339 0.836932 0.139589 0.011740 0.011740 0.526170 0.039580 0.079040 0.355210 0.220360 0.017110 0.762360 0.000170 MOTIF AC0529:ZNF:C2H2_ZF AC0529:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.007995 0.981385 0.004300 0.006320 0.311417 0.085214 0.598464 0.004905 0.684720 0.001925 0.313355 0.000000 0.000110 0.007360 0.001225 0.991305 0.001915 0.990680 0.000215 0.007190 0.986055 0.000180 0.004970 0.008795 0.094705 0.740985 0.000780 0.163530 0.008280 0.050195 0.070440 0.871085 0.011290 0.533832 0.048355 0.406523 0.001975 0.000000 0.984630 0.013395 0.162377 0.534375 0.222472 0.080776 0.052345 0.901150 0.008235 0.038270 MOTIF AC0530:ZNF:C2H2_ZF AC0530:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 18 nsites= 20 E= 0 0.862250 0.001020 0.107140 0.029590 0.021430 0.009180 0.968370 0.001020 0.001020 0.168368 0.013270 0.817342 0.001020 0.152040 0.001020 0.845920 0.001020 0.164288 0.821422 0.013270 0.792860 0.005100 0.005100 0.196940 0.045920 0.005100 0.943880 0.005100 0.001020 0.025510 0.001020 0.972450 0.160200 0.001020 0.821430 0.017350 0.796940 0.180610 0.009180 0.013270 0.005100 0.747960 0.001020 0.245920 0.050000 0.013270 0.066329 0.870401 0.005100 0.119390 0.870410 0.005100 0.005100 0.552046 0.009180 0.433674 0.307140 0.013270 0.482650 0.196940 0.009180 0.278573 0.682657 0.029590 0.013270 0.074489 0.013270 0.898971 0.029590 0.029590 0.013270 0.927550 MOTIF AC0531:ZNF:C2H2_ZF AC0531:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.045280 0.743325 0.030050 0.181345 0.659417 0.078250 0.071965 0.190369 0.121745 0.193910 0.136095 0.548250 0.058080 0.036670 0.063460 0.841790 0.041520 0.695197 0.027530 0.235754 0.868301 0.005890 0.050035 0.075775 0.008035 0.020220 0.265841 0.705904 0.005095 0.022555 0.009860 0.962490 0.023070 0.884195 0.003185 0.089550 0.907275 0.014580 0.013825 0.064320 0.146670 0.074000 0.166210 0.613120 0.022325 0.070655 0.023610 0.883410 0.045430 0.761930 0.019555 0.173085 MOTIF AC0532:ZNF:C2H2_ZF AC0532:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.061959 0.018480 0.879341 0.040220 0.840212 0.022830 0.096739 0.040220 0.831520 0.018480 0.131520 0.018480 0.027170 0.027170 0.044570 0.901090 0.005430 0.009780 0.001090 0.983700 0.022830 0.305437 0.009780 0.661953 0.566306 0.001090 0.427174 0.005430 0.005430 0.001090 0.001090 0.992390 0.001090 0.757610 0.005430 0.235870 0.892390 0.040220 0.035870 0.031520 0.048910 0.792398 0.005430 0.153262 0.079350 0.627170 0.031520 0.261960 0.801090 0.057610 0.066300 0.075000 0.031520 0.822822 0.044570 0.101089 0.009780 0.109781 0.005430 0.875009 MOTIF AC0533:POU3F/POU1F:Homeodomain,POU AC0533:POU3F/POU1F:Homeodomain,POU letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.050969 0.019866 0.911508 0.017657 0.156200 0.833215 0.000142 0.010443 0.947784 0.006511 0.026963 0.018742 0.016456 0.011959 0.003361 0.968224 0.801162 0.017617 0.042762 0.138459 0.758229 0.163832 0.024482 0.053457 0.837232 0.022372 0.037024 0.103372 0.000718 0.006457 0.004823 0.988002 0.050606 0.032011 0.020029 0.897354 0.942280 0.011980 0.025753 0.019987 MOTIF AC0534:POU3F:Homeodomain,POU AC0534:POU3F:Homeodomain,POU letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.823530 0.117650 0.000000 0.058820 0.000000 0.058820 0.000000 0.941180 0.117650 0.000000 0.882350 0.000000 0.470590 0.529410 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.357140 0.000000 0.000000 0.642860 0.500000 0.000000 0.000000 0.500000 0.882350 0.000000 0.000000 0.117650 0.058820 0.000000 0.000000 0.941180 0.000000 0.000000 0.000000 1.000000 0.117650 0.705880 0.000000 0.176470 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 MOTIF AC0535:POU4F/POU5F:Homeodomain,POU AC0535:POU4F/POU5F:Homeodomain,POU letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.779909 0.065412 0.100457 0.054222 0.007895 0.029772 0.007092 0.955241 0.132124 0.134222 0.079868 0.653786 0.854914 0.039510 0.011003 0.094573 0.871136 0.025424 0.024957 0.078484 0.155227 0.025546 0.027441 0.791786 0.223926 0.021789 0.017095 0.737190 0.983827 0.002096 0.002300 0.011777 0.021564 0.005429 0.009027 0.963980 0.011037 0.003564 0.609036 0.376363 0.076254 0.769130 0.034668 0.119948 0.788546 0.028304 0.144855 0.038294 MOTIF AC0536:POU3F/POU2F:Homeodomain,POU AC0536:POU3F/POU2F:Homeodomain,POU letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.118366 0.130076 0.045004 0.706554 0.151472 0.025438 0.775634 0.047456 0.276582 0.668748 0.015542 0.039128 0.838020 0.047071 0.063007 0.051902 0.046237 0.016983 0.010989 0.925791 0.785140 0.029657 0.056317 0.128886 0.663699 0.080244 0.039491 0.216566 0.278023 0.080400 0.073702 0.567875 0.119219 0.141640 0.204388 0.534752 MOTIF AC0537:POU4F:Homeodomain,POU AC0537:POU4F:Homeodomain,POU letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.007938 0.039211 0.005179 0.947672 0.170462 0.072153 0.668363 0.089022 0.251833 0.660971 0.013109 0.074087 0.935372 0.013479 0.035356 0.015793 0.015293 0.011209 0.009932 0.963566 0.427741 0.059642 0.071078 0.441540 0.907273 0.027024 0.027504 0.038199 0.065451 0.062956 0.054186 0.817407 0.114294 0.043579 0.373832 0.468296 0.179932 0.398558 0.171228 0.250282 0.859244 0.047081 0.059806 0.033869 MOTIF AC0538:POU3F:Homeodomain,POU AC0538:POU3F:Homeodomain,POU letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.083331 0.750008 0.083331 0.083331 0.916670 0.000000 0.000000 0.083330 0.083330 0.000000 0.000000 0.916670 0.100000 0.300000 0.300000 0.300000 0.100000 0.500000 0.300000 0.100000 0.400000 0.000000 0.400000 0.200000 0.500000 0.000000 0.000000 0.500000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.083330 0.000000 0.000000 0.916670 0.333330 0.166670 0.416670 0.083330 0.500005 0.333333 0.083331 0.083331 0.416666 0.000000 0.416666 0.166668 MOTIF AC0539:IRX:Homeodomain AC0539:IRX:Homeodomain letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.875659 0.022215 0.073700 0.028425 0.041055 0.892440 0.029080 0.037425 0.908815 0.021080 0.052275 0.017830 0.096290 0.343775 0.022570 0.537365 0.295470 0.032835 0.541370 0.130325 0.814461 0.054345 0.053185 0.078010 0.094245 0.767680 0.047750 0.090325 0.751913 0.041450 0.093916 0.112721 0.325665 0.193095 0.050830 0.430410 MOTIF AC0540:ASCL:bHLH AC0540:ASCL:bHLH letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.632080 0.159130 0.147680 0.061110 0.000000 0.958500 0.009650 0.031850 0.808630 0.067590 0.094460 0.029320 0.001750 0.868761 0.056869 0.072619 0.197410 0.088500 0.675970 0.038120 0.853821 0.106619 0.011180 0.028380 0.025000 0.919449 0.010180 0.045370 0.181670 0.005730 0.812600 0.000000 0.132370 0.681070 0.078190 0.108370 MOTIF AC0541:CREB3L/XBP:bZIP AC0541:CREB3L/XBP:bZIP letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.414030 0.157049 0.373443 0.055479 0.032473 0.069670 0.038238 0.859619 0.023066 0.038650 0.879173 0.059111 0.967561 0.008161 0.012449 0.011829 0.001380 0.912517 0.022541 0.063562 0.016305 0.001617 0.979052 0.003026 0.007571 0.037410 0.006387 0.948632 0.030661 0.022437 0.924286 0.022616 0.029747 0.014931 0.741359 0.213963 0.114777 0.759074 0.047772 0.078377 MOTIF AC0542:ZBTB:C2H2_ZF AC0542:ZBTB:C2H2_ZF letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.000000 0.052440 0.000000 0.947560 0.000000 0.000000 0.000000 1.000000 0.087540 0.256750 0.105840 0.549870 0.001150 0.724580 0.004020 0.270250 0.012190 0.005950 0.967580 0.014280 0.000000 0.999700 0.000000 0.000300 0.004030 0.492830 0.128140 0.375000 0.012910 0.004890 0.060380 0.921820 0.014890 0.000540 0.980240 0.004330 0.215530 0.039590 0.685010 0.059870 0.069340 0.837090 0.041270 0.052300 0.451640 0.322550 0.222970 0.002840 0.000000 0.423490 0.408230 0.168280 0.001790 0.000000 0.998210 0.000000 0.000000 0.039870 0.001760 0.958370 0.000000 1.000000 0.000000 0.000000 MOTIF AC0543:PROX:Prospero AC0543:PROX:Prospero letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.846255 0.014270 0.118035 0.021440 0.814775 0.082810 0.034738 0.067677 0.010775 0.031353 0.921374 0.036497 0.428498 0.009420 0.533230 0.028852 0.076927 0.833209 0.001585 0.088278 0.046587 0.006823 0.942075 0.004515 0.030430 0.032868 0.009888 0.926815 0.013000 0.952380 0.014465 0.020155 0.137132 0.047652 0.106405 0.708811 0.041780 0.085865 0.022653 0.849701 MOTIF AC0544:GMEB:SAND AC0544:GMEB:SAND letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.113852 0.076450 0.219635 0.590063 0.896660 0.009678 0.086707 0.006955 0.024310 0.974980 0.000000 0.000710 0.007310 0.021340 0.970773 0.000577 0.002417 0.187285 0.047942 0.762356 0.645072 0.163380 0.125573 0.065975 0.542288 0.285439 0.114042 0.058231 MOTIF AC0545:CREB:bZIP AC0545:CREB:bZIP letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.000000 0.117650 0.823530 0.058820 0.000000 0.117650 0.882350 0.000000 0.058820 0.000000 0.941180 0.000000 0.058821 0.117651 0.235292 0.588236 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.823530 0.176470 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.058821 0.411764 0.117651 0.411764 0.647060 0.294120 0.058820 0.000000 MOTIF AC0546:ZNF:C2H2_ZF AC0546:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.000000 0.271865 0.000000 0.728135 0.000000 0.000000 1.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.110265 0.000000 0.889735 0.000000 0.000000 1.000000 0.000000 0.913045 0.000000 0.000000 0.086955 0.000000 1.000000 0.000000 0.000000 MOTIF AC0547:ATF/CREB:bZIP AC0547:ATF/CREB:bZIP letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.023809 0.046502 0.023723 0.905966 0.024015 0.052180 0.787747 0.136058 0.915797 0.010114 0.044030 0.030059 0.014602 0.934574 0.022783 0.028041 0.162790 0.029428 0.796156 0.011626 0.020174 0.037518 0.012954 0.929354 0.120425 0.815307 0.045477 0.018791 0.916234 0.016991 0.043082 0.023693 MOTIF AC0548:NR2E:Nuclear_receptor AC0548:NR2E:Nuclear_receptor letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.593426 0.103261 0.103261 0.200052 0.322713 0.121761 0.433224 0.122301 0.900830 0.098750 0.000210 0.000210 0.104120 0.195540 0.216680 0.483660 0.804790 0.000210 0.194790 0.000210 0.122000 0.024210 0.024210 0.829580 0.024210 0.927370 0.024210 0.024210 0.927370 0.024210 0.024210 0.024210 MOTIF AC0549:ZNF:C2H2_ZF AC0549:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.074205 0.027935 0.141756 0.756104 0.000625 0.002620 0.994695 0.002060 0.030415 0.575253 0.047400 0.346932 0.062085 0.013510 0.091855 0.832551 0.016190 0.009325 0.679845 0.294640 0.003520 0.007965 0.984690 0.003825 0.085435 0.898750 0.005415 0.010400 0.171869 0.056790 0.140864 0.630477 0.000000 0.968410 0.000000 0.031590 0.001755 0.000875 0.995815 0.001555 0.000970 0.791120 0.000715 0.207195 0.006130 0.006815 0.973770 0.013285 MOTIF AC0550:HES/EGR:bHLH AC0550:HES/EGR:bHLH letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.311948 0.327033 0.223928 0.137090 0.116850 0.653601 0.084965 0.144585 0.631894 0.175147 0.157310 0.035650 0.000200 0.860608 0.003740 0.135452 0.292639 0.148163 0.432509 0.126690 0.028425 0.544864 0.021898 0.404813 MOTIF AC0551:CREB3L/XBP:bZIP AC0551:CREB3L/XBP:bZIP letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.025612 0.004873 0.948938 0.020577 0.105725 0.880257 0.010035 0.003983 0.013302 0.972138 0.007370 0.007190 0.990986 0.003172 0.002380 0.003462 0.002567 0.990210 0.000675 0.006548 0.105540 0.004533 0.889872 0.000055 0.034213 0.218379 0.004162 0.743246 0.193678 0.108347 0.665594 0.032382 0.046447 0.095997 0.254209 0.603347 0.546215 0.126067 0.234351 0.093367 0.067083 0.808057 0.050007 0.074852 MOTIF AC0552:HES/MAX:bHLH AC0552:HES/MAX:bHLH letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.035930 0.915990 0.023485 0.024595 0.880500 0.011166 0.084512 0.023822 0.008589 0.866434 0.009965 0.115012 0.047059 0.013151 0.936082 0.003708 0.013934 0.105764 0.007229 0.873073 0.026282 0.022751 0.923051 0.027916 MOTIF AC0553:ZBTB:C2H2_ZF AC0553:ZBTB:C2H2_ZF letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.041618 0.125443 0.072387 0.760552 0.020365 0.046725 0.838220 0.094690 0.038687 0.902620 0.026775 0.031918 0.067265 0.002940 0.899958 0.029837 0.037058 0.933102 0.017760 0.012080 0.034722 0.000682 0.957658 0.006938 0.003077 0.094810 0.048787 0.853326 0.004752 0.016588 0.896085 0.082575 0.005130 0.987967 0.002675 0.004228 0.944561 0.037452 0.010622 0.007365 0.000730 0.933332 0.003380 0.062558 0.046420 0.013250 0.926965 0.013365 0.126515 0.247938 0.025453 0.600094 MOTIF AC0554:HIF1A/TCFL:bHLH AC0554:HIF1A/TCFL:bHLH letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.690461 0.008475 0.273523 0.027541 0.014250 0.954081 0.004392 0.027277 0.016428 0.003344 0.975538 0.004690 0.010822 0.165649 0.001898 0.821631 0.023932 0.010823 0.960976 0.004269 0.251433 0.601755 0.067246 0.079566 MOTIF AC0555:SREBF:bHLH AC0555:SREBF:bHLH letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.621873 0.093573 0.270390 0.014164 0.067932 0.045683 0.033843 0.852542 0.007900 0.902670 0.075687 0.013743 0.871101 0.008148 0.096789 0.023962 0.010000 0.821943 0.133079 0.034978 0.054884 0.293053 0.648439 0.003624 0.039351 0.119797 0.011472 0.829380 0.013563 0.084391 0.888492 0.013554 0.924888 0.014789 0.029229 0.031094 0.009956 0.392919 0.072599 0.524526 MOTIF AC0556:USF/BHLHE:bHLH AC0556:USF/BHLHE:bHLH letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.261487 0.084024 0.607000 0.047489 0.069465 0.088897 0.129534 0.712104 0.006179 0.979434 0.012525 0.001862 0.970953 0.005984 0.013338 0.009725 0.001471 0.929132 0.017138 0.052259 0.143684 0.014908 0.839518 0.001890 0.014132 0.018702 0.009192 0.957974 0.002074 0.008443 0.982635 0.006848 MOTIF AC0557:PBX/HOXA:Homeodomain AC0557:PBX/HOXA:Homeodomain letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.058876 0.027189 0.119670 0.794266 0.046714 0.032035 0.900405 0.020846 0.846589 0.039145 0.057424 0.056842 0.049759 0.054145 0.081301 0.814795 0.031732 0.020120 0.268741 0.679407 0.030551 0.028007 0.937936 0.003506 0.858052 0.040850 0.074190 0.026908 0.016511 0.264167 0.048206 0.671116 MOTIF AC0558:HOXB/PBX:Homeodomain AC0558:HOXB/PBX:Homeodomain letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.881283 0.020833 0.022757 0.075127 0.062500 0.083333 0.031543 0.822624 0.152237 0.380530 0.041667 0.425567 0.937500 0.020833 0.000000 0.041667 0.958333 0.000000 0.000000 0.041667 0.041667 0.000000 0.000000 0.958333 0.083333 0.825650 0.000000 0.091017 0.945707 0.000000 0.033460 0.020833 0.689892 0.016563 0.005853 0.287692 MOTIF AC0559:MEIS/PBX:Homeodomain AC0559:MEIS/PBX:Homeodomain letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.136363 0.019715 0.033583 0.810339 0.070480 0.012137 0.858608 0.058775 0.932347 0.009528 0.035345 0.022780 0.025955 0.045358 0.024762 0.903925 0.068203 0.006162 0.038293 0.887342 0.079038 0.032405 0.311523 0.577034 0.916266 0.014327 0.037462 0.031945 0.065788 0.079817 0.021627 0.832768 0.151652 0.036215 0.570368 0.241765 MOTIF AC0560:PAX:Homeodomain,Paired_box AC0560:PAX:Homeodomain,Paired_box letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.820429 0.045053 0.067766 0.066752 0.018556 0.020286 0.017592 0.943566 0.018262 0.062808 0.025284 0.893646 0.477550 0.346176 0.113600 0.062674 0.006733 0.003291 0.967557 0.022419 0.058679 0.048097 0.013768 0.879456 0.022621 0.774944 0.013644 0.188791 0.875512 0.029076 0.079894 0.015518 0.013807 0.830026 0.004190 0.151977 0.094509 0.003423 0.895024 0.007044 0.001922 0.445825 0.544320 0.007933 MOTIF AC0561:PAX:Homeodomain,Paired_box AC0561:PAX:Homeodomain,Paired_box letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.008299 0.001570 0.936375 0.053756 0.076750 0.026264 0.005631 0.891355 0.015183 0.821873 0.016226 0.146717 0.925329 0.001103 0.072624 0.000944 0.006996 0.902802 0.002229 0.087973 0.050200 0.000643 0.944077 0.005080 0.005683 0.609404 0.378999 0.005914 0.159646 0.268724 0.078011 0.493619 0.332727 0.134340 0.069661 0.463271 0.685968 0.059536 0.230672 0.023824 0.027589 0.019264 0.018109 0.935038 0.047121 0.072803 0.022066 0.858010 0.826318 0.022787 0.050224 0.100671 MOTIF AC0562:PAX:Homeodomain,Paired_box AC0562:PAX:Homeodomain,Paired_box letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.007530 0.026213 0.919479 0.046778 0.093412 0.052937 0.022770 0.830881 0.014977 0.907019 0.018012 0.059992 0.908037 0.038487 0.025973 0.027503 0.020900 0.848650 0.025812 0.104638 0.161630 0.027168 0.794495 0.016707 0.013978 0.663885 0.297535 0.024602 0.157310 0.194524 0.084275 0.563891 MOTIF AC0563:SPDEF:Ets AC0563:SPDEF:Ets letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.078045 0.001375 0.019195 0.901385 0.571168 0.000410 0.417532 0.010890 0.926195 0.002055 0.030345 0.041405 0.006145 0.000000 0.011520 0.982335 0.007095 0.992905 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.005705 0.009675 0.984310 0.000310 0.001540 0.026055 0.967780 0.004625 0.893976 0.013770 0.013835 0.078420 0.120340 0.804590 0.054070 0.021000 0.219539 0.668932 0.029610 0.081920 0.613208 0.221021 0.029400 0.136371 MOTIF AC0564:PITX/OTX:Homeodomain AC0564:PITX/OTX:Homeodomain letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.028174 0.012834 0.003407 0.955585 0.941956 0.007361 0.030887 0.019796 0.991557 0.002272 0.002557 0.003614 0.007958 0.008446 0.083926 0.899670 0.037552 0.887980 0.036039 0.038429 0.035724 0.800016 0.051264 0.112996 MOTIF AC0565:ZNF:C2H2_ZF AC0565:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 1.000000 0.000000 0.000000 0.199580 0.350780 0.052020 0.397620 0.026740 0.002870 0.002260 0.968130 0.976560 0.001990 0.008170 0.013280 0.989200 0.002420 0.004030 0.004350 0.002610 0.002390 0.058790 0.936210 0.003960 0.983800 0.007770 0.004470 0.002850 0.926200 0.014630 0.056320 0.027440 0.197792 0.498075 0.276693 MOTIF AC0566:ZSCAN:C2H2_ZF,MADF AC0566:ZSCAN:C2H2_ZF,MADF letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.408570 0.032550 0.150310 0.408570 0.743830 0.000370 0.053080 0.202720 0.804720 0.129090 0.000130 0.066060 0.098541 0.204492 0.024790 0.672177 0.047200 0.563630 0.070420 0.318750 0.047920 0.804000 0.074410 0.073670 0.046430 0.073550 0.810750 0.069270 0.327190 0.126540 0.432910 0.113360 0.592460 0.044890 0.133520 0.229130 0.159228 0.096579 0.058579 0.685613 MOTIF AC0567:HMX/ISL:Homeodomain AC0567:HMX/ISL:Homeodomain letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.124223 0.068243 0.014660 0.792874 0.807907 0.000412 0.118836 0.072845 0.971766 0.017360 0.005075 0.005799 0.041038 0.015625 0.559842 0.383495 0.012749 0.073599 0.007630 0.906022 0.078581 0.008368 0.894781 0.018270 MOTIF AC0568:ZNF:C2H2_ZF AC0568:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 15 nsites= 20 E= 0 0.068069 0.096099 0.069069 0.766762 0.653000 0.030000 0.292000 0.025000 0.046000 0.859000 0.050000 0.045000 0.567000 0.180000 0.205000 0.048000 0.015020 0.840840 0.013010 0.131130 0.204000 0.239000 0.513000 0.044000 0.614000 0.217000 0.083000 0.086000 0.006010 0.048050 0.017020 0.928921 0.007000 0.112000 0.221000 0.660000 0.684687 0.133131 0.175172 0.007010 0.535540 0.085080 0.307310 0.072070 0.113000 0.181000 0.339000 0.367000 0.834000 0.040000 0.098000 0.028000 0.006000 0.074000 0.052000 0.868000 0.041960 0.901100 0.020980 0.035960 MOTIF AC0569:CUX:CUT,Homeodomain AC0569:CUX:CUT,Homeodomain letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.154942 0.084920 0.342990 0.417148 0.946124 0.002290 0.005633 0.045953 0.017445 0.020790 0.000233 0.961532 0.089835 0.490847 0.031707 0.387610 0.260053 0.030135 0.641714 0.068098 0.353241 0.113173 0.452543 0.081042 MOTIF AC0570:TLX:Homeodomain AC0570:TLX:Homeodomain letter-probability matrix: alength= 4 w= 21 nsites= 20 E= 0 0.165350 0.171541 0.178666 0.484443 0.487929 0.087990 0.093045 0.331036 0.679134 0.022955 0.254543 0.043368 0.292454 0.107865 0.075037 0.524644 0.116288 0.137857 0.124857 0.620999 0.118414 0.093354 0.564695 0.223537 0.215195 0.262911 0.305646 0.216248 0.218325 0.236525 0.264958 0.280193 0.268631 0.233218 0.214537 0.283614 0.264802 0.206545 0.216420 0.312233 0.272915 0.198628 0.217015 0.311443 0.309914 0.230644 0.168174 0.291267 0.295219 0.224426 0.231607 0.248747 0.272283 0.221411 0.272213 0.234094 0.304572 0.237315 0.246955 0.211157 0.236015 0.258411 0.278111 0.227463 0.118836 0.657877 0.069093 0.154194 0.537734 0.298188 0.045280 0.118798 0.793350 0.073125 0.040730 0.092795 0.273975 0.018297 0.013298 0.694430 0.060683 0.036848 0.029552 0.872917 MOTIF AC0571:BARHL/MSX:Homeodomain AC0571:BARHL/MSX:Homeodomain letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.124403 0.095350 0.087170 0.693076 0.693083 0.086057 0.095427 0.125433 0.740123 0.086327 0.087387 0.086163 0.346814 0.098447 0.147417 0.407321 0.145806 0.301236 0.135362 0.417595 0.176388 0.099887 0.588925 0.134800 0.215514 0.261618 0.272874 0.249994 0.132083 0.406736 0.106570 0.354612 0.207532 0.296801 0.320335 0.175332 0.155670 0.303037 0.261243 0.280050 0.064627 0.036170 0.029467 0.869736 0.799117 0.028513 0.034087 0.138283 0.838797 0.053313 0.001597 0.106293 0.390158 0.061850 0.113500 0.434491 0.051433 0.268464 0.072113 0.607989 0.131360 0.040097 0.747575 0.080967 MOTIF AC0572:BARHL:Homeodomain AC0572:BARHL:Homeodomain letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.036335 0.000788 0.000485 0.962392 0.854762 0.004259 0.012078 0.128901 0.993243 0.002877 0.000126 0.003754 0.721548 0.010584 0.060560 0.207308 0.006408 0.682045 0.003206 0.308341 0.079292 0.000090 0.866828 0.053790 MOTIF AC0573:ZNF354A/ZNF354B:C2H2_ZF AC0573:ZNF354A/ZNF354B:C2H2_ZF letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.096717 0.043907 0.087840 0.771536 0.135153 0.055137 0.013183 0.796527 0.004327 0.033950 0.018820 0.942903 0.910141 0.007143 0.049810 0.032907 0.177241 0.007413 0.579644 0.235702 0.299047 0.179577 0.098373 0.423003 0.039077 0.731397 0.045387 0.184139 0.060097 0.892871 0.002853 0.044180 0.867857 0.107523 0.011840 0.012780 0.025127 0.026197 0.008753 0.939923 0.002853 0.002180 0.006477 0.988490 0.096613 0.118213 0.087493 0.697681 0.739801 0.083870 0.089373 0.086957 MOTIF AC0574:HOXD/FOXC:Homeodomain AC0574:HOXD/FOXC:Homeodomain letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.407973 0.128567 0.155781 0.307678 0.183459 0.063895 0.134305 0.618342 0.137470 0.046797 0.096838 0.718895 0.344655 0.080884 0.105655 0.468805 0.831548 0.021778 0.086329 0.060345 0.076964 0.206959 0.062335 0.653742 MOTIF AC0575:ZNF:C2H2_ZF AC0575:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.099140 0.024420 0.064660 0.811780 0.012930 0.041670 0.030170 0.915230 0.622124 0.277297 0.099139 0.001440 0.035920 0.840520 0.035920 0.087640 0.001440 0.053160 0.012930 0.932470 0.639370 0.087640 0.041670 0.231320 0.783050 0.001440 0.168100 0.047410 0.007180 0.024420 0.007180 0.961220 0.242818 0.104889 0.455455 0.196838 0.604884 0.219828 0.110629 0.064659 0.760052 0.001440 0.116379 0.122129 0.007180 0.001440 0.966960 0.024420 0.127870 0.196840 0.219830 0.455460 MOTIF AC0576:PAX/CRX:Homeodomain AC0576:PAX/CRX:Homeodomain letter-probability matrix: alength= 4 w= 5 nsites= 20 E= 0 0.200046 0.219943 0.136776 0.443235 0.481415 0.144854 0.184112 0.189620 0.674695 0.084654 0.145420 0.095230 0.096775 0.147944 0.107254 0.648028 0.174565 0.262333 0.170922 0.392181 MOTIF AC0577:POU2F:Homeodomain,POU AC0577:POU2F:Homeodomain,POU letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.654510 0.130520 0.214970 0.000000 0.056180 0.056180 0.121350 0.766290 0.050630 0.202530 0.027430 0.719410 0.687500 0.020160 0.022180 0.270160 0.052940 0.273530 0.344120 0.329410 0.017590 0.486805 0.431084 0.064521 0.416424 0.258063 0.161292 0.164222 0.225000 0.064580 0.000000 0.710420 0.789358 0.064811 0.064811 0.081021 0.852500 0.135000 0.000000 0.012500 0.035050 0.220620 0.041240 0.703090 MOTIF AC0578:LCORL:Pipsqueak AC0578:LCORL:Pipsqueak letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.154340 0.566320 0.075130 0.204210 0.083410 0.580410 0.056470 0.279710 0.335920 0.256440 0.140740 0.266900 0.930400 0.000620 0.037930 0.031050 0.925440 0.052470 0.006750 0.015340 0.564864 0.062589 0.026490 0.346057 0.320740 0.153030 0.097580 0.428650 MOTIF AC0579:POU3F:Homeodomain,POU AC0579:POU3F:Homeodomain,POU letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.666663 0.166668 0.166668 0.000000 0.000000 0.333333 0.333333 0.333333 0.000000 0.166668 0.166668 0.666663 0.000000 0.333333 0.333333 0.333333 0.166668 0.000000 0.166668 0.666663 0.666670 0.000000 0.000000 0.333330 1.000000 0.000000 0.000000 0.000000 0.333330 0.000000 0.000000 0.666670 0.166670 0.000000 0.000000 0.833330 0.000000 0.666663 0.166668 0.166668 0.333330 0.500000 0.166670 0.000000 MOTIF AC0580:VENTX:Homeodomain AC0580:VENTX:Homeodomain letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.894220 0.000270 0.105240 0.000270 0.000270 0.195570 0.000270 0.803890 0.999190 0.000270 0.000270 0.000270 0.000270 0.309860 0.000270 0.689600 0.800598 0.000270 0.198862 0.000270 0.000270 0.000270 0.000270 0.999190 0.899710 0.000270 0.099750 0.000270 MOTIF AC0581:ZSCAN:C2H2_ZF AC0581:ZSCAN:C2H2_ZF letter-probability matrix: alength= 4 w= 22 nsites= 20 E= 0 0.043800 0.062040 0.062040 0.832120 0.510950 0.000000 0.452550 0.036500 0.047450 0.105839 0.025550 0.821162 0.587590 0.040150 0.346710 0.025550 0.029200 0.072990 0.010950 0.886860 0.598540 0.040150 0.361310 0.000000 0.000000 0.072990 0.010950 0.916060 0.751820 0.007300 0.237230 0.003650 0.036500 0.083940 0.000000 0.879560 0.558390 0.000000 0.441610 0.000000 0.000000 0.003650 0.029200 0.967150 0.401456 0.047450 0.551094 0.000000 0.003650 0.007300 0.014600 0.974450 0.536495 0.000000 0.423356 0.040150 0.025550 0.076640 0.007300 0.890510 0.726280 0.000000 0.273720 0.000000 0.000000 0.043800 0.000000 0.956200 0.751820 0.018250 0.175180 0.054750 0.000000 0.058390 0.000000 0.941610 0.832112 0.010950 0.127739 0.029200 0.000000 0.142339 0.029200 0.828462 0.631390 0.058390 0.277370 0.032850 MOTIF AC0582:TBP/HOXD:TBP AC0582:TBP/HOXD:TBP letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.043182 0.085232 0.086352 0.785234 0.875900 0.000014 0.017020 0.107066 0.012494 0.019694 0.015188 0.952624 0.895760 0.012094 0.013208 0.078938 0.760244 0.006726 0.005384 0.227646 0.893624 0.007126 0.087354 0.011896 0.544233 0.032296 0.147383 0.276088 0.369590 0.112082 0.414566 0.103762 0.135008 0.329045 0.417069 0.118878 MOTIF AC0583:WT/FOXC:C2H2_ZF AC0583:WT/FOXC:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.609659 0.087774 0.153731 0.148836 0.121075 0.089394 0.034673 0.754857 0.760736 0.021702 0.072703 0.144859 0.164096 0.049873 0.023867 0.762164 0.874794 0.000326 0.049081 0.075799 0.277892 0.054090 0.040025 0.627993 0.184325 0.013876 0.088490 0.713309 0.585327 0.096399 0.096901 0.221373 MOTIF AC0584:DMRT/HOXB:DM AC0584:DMRT/HOXB:DM letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.999355 0.000215 0.000215 0.000215 0.000215 0.000215 0.000215 0.999355 0.414472 0.000215 0.293941 0.291371 0.051996 0.050536 0.000215 0.897254 0.904995 0.000215 0.000215 0.094575 0.144000 0.000215 0.000215 0.855570 0.203940 0.110205 0.061395 0.624460 MOTIF AC0585:HESX/HOXB:Homeodomain AC0585:HESX/HOXB:Homeodomain letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.192605 0.100770 0.046105 0.660520 0.904480 0.000280 0.047470 0.047770 0.049670 0.000280 0.000280 0.949770 0.701415 0.000280 0.099470 0.198835 0.052505 0.000280 0.000280 0.946935 0.764465 0.000280 0.139670 0.095585 0.060025 0.207890 0.012210 0.719875 MOTIF AC0586:ZNF:C2H2_ZF AC0586:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.000520 0.000520 0.000520 0.998440 0.000520 0.000520 0.000520 0.998440 0.998440 0.000520 0.000520 0.000520 0.000520 0.000520 0.000520 0.998440 0.998440 0.000520 0.000520 0.000520 0.000520 0.000520 0.000520 0.998440 MOTIF AC0587:POU5F/NANOG:Homeodomain,POU AC0587:POU5F/NANOG:Homeodomain,POU letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.506012 0.039275 0.088572 0.366141 0.094778 0.052085 0.051150 0.801987 0.061150 0.014217 0.133738 0.790896 0.093367 0.250729 0.571817 0.084087 0.287504 0.036910 0.098565 0.577021 0.275924 0.194383 0.140505 0.389188 0.928603 0.015733 0.015272 0.040392 0.030803 0.012825 0.020617 0.935755 0.032790 0.012585 0.884025 0.070600 0.021148 0.666082 0.155643 0.157127 0.751488 0.015545 0.053282 0.179685 0.687431 0.044390 0.172079 0.096100 0.718327 0.052875 0.084795 0.144003 MOTIF AC0588:POU2F/POU3F:Homeodomain,POU AC0588:POU2F/POU3F:Homeodomain,POU letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.097111 0.107567 0.042118 0.753204 0.977141 0.005420 0.011494 0.005945 0.009601 0.028061 0.013488 0.948850 0.015032 0.004278 0.930157 0.050533 0.030319 0.722918 0.059071 0.187692 0.624250 0.005031 0.008432 0.362287 0.873341 0.017518 0.049826 0.059315 0.967110 0.006612 0.010522 0.015756 0.059841 0.034143 0.050323 0.855693 MOTIF AC0589:POU2F:Homeodomain,POU AC0589:POU2F:Homeodomain,POU letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.500000 0.000000 0.500000 0.000000 0.166670 0.500000 0.000000 0.333330 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.333330 0.500000 0.166670 0.333330 0.166670 0.000000 0.500000 0.500000 0.500000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.333330 0.000000 0.166670 0.500000 0.166668 0.000000 0.166668 0.666663 0.333330 0.166670 0.166670 0.333330 0.333330 0.166670 0.000000 0.500000 0.000000 0.166668 0.666663 0.166668 MOTIF AC0590:HOXC/CDX:Homeodomain AC0590:HOXC/CDX:Homeodomain letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.027961 0.011814 0.040871 0.919354 0.034637 0.004018 0.005913 0.955432 0.182714 0.016397 0.019655 0.781234 0.974069 0.002918 0.003644 0.019369 0.013923 0.066650 0.024718 0.894709 0.041790 0.036382 0.374137 0.547691 0.448259 0.005308 0.531234 0.015199 0.049415 0.711927 0.111914 0.126744 MOTIF AC0591:HOXA/HOXD:Homeodomain AC0591:HOXA/HOXD:Homeodomain letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.033578 0.746844 0.028436 0.191142 0.641532 0.218450 0.018197 0.121821 0.932067 0.005295 0.023772 0.038866 0.002096 0.002434 0.000677 0.994793 0.917424 0.005585 0.006077 0.070914 0.972476 0.001484 0.004610 0.021430 0.944976 0.031707 0.002133 0.021184 0.729214 0.163076 0.042050 0.065660 MOTIF AC0592:CDX/HOXA:Homeodomain AC0592:CDX/HOXA:Homeodomain letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.049450 0.798055 0.000000 0.152495 0.813620 0.016130 0.046595 0.123655 0.016130 0.080645 0.000000 0.903225 0.826895 0.064515 0.026880 0.081710 0.967740 0.016130 0.000000 0.016130 0.891530 0.048385 0.000000 0.060085 0.170419 0.178039 0.165819 0.485723 MOTIF AC0593:ELK/ETV:Ets AC0593:ELK/ETV:Ets letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.104940 0.126162 0.070625 0.698272 0.043570 0.939515 0.000000 0.016915 0.003960 0.000000 0.994630 0.001410 0.015097 0.004488 0.119473 0.860942 0.979811 0.004417 0.007692 0.008080 0.965438 0.013572 0.007955 0.013035 0.955364 0.002103 0.034510 0.008023 0.000567 0.066880 0.029287 0.903266 0.111035 0.161312 0.704042 0.023612 0.086270 0.782096 0.054818 0.076815 MOTIF AC0594:HOXC/HOXD:Homeodomain AC0594:HOXC/HOXD:Homeodomain letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.012511 0.039280 0.005487 0.942722 0.021040 0.938638 0.004074 0.036248 0.160171 0.000934 0.835646 0.003249 0.002286 0.003075 0.001471 0.993168 0.817160 0.004405 0.004403 0.174032 0.971957 0.005332 0.002137 0.020574 0.927973 0.031464 0.008371 0.032192 MOTIF AC0595:NKX:Homeodomain AC0595:NKX:Homeodomain letter-probability matrix: alength= 4 w= 7 nsites= 20 E= 0 0.187513 0.017815 0.095805 0.698867 0.028430 0.545323 0.153227 0.273020 0.921178 0.009782 0.045742 0.023298 0.899872 0.030173 0.011370 0.058585 0.000000 0.890643 0.032865 0.076492 0.037085 0.163598 0.777944 0.021372 0.580069 0.196673 0.062843 0.160415 MOTIF AC0596:HOXB/LHX:Homeodomain AC0596:HOXB/LHX:Homeodomain letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.045530 0.055425 0.021817 0.877228 0.795217 0.023709 0.063516 0.117558 0.897143 0.013528 0.062029 0.027300 0.001997 0.535543 0.007631 0.454829 0.065135 0.038816 0.823819 0.072230 0.658938 0.134212 0.074259 0.132591 MOTIF AC0597:NKX:Homeodomain AC0597:NKX:Homeodomain letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.026690 0.903720 0.026160 0.043430 0.810740 0.029620 0.119980 0.039660 0.516900 0.018540 0.452020 0.012540 0.008760 0.097980 0.009850 0.883410 0.994960 0.000000 0.005040 0.000000 0.998880 0.000000 0.001120 0.000000 0.000000 0.992740 0.004470 0.002790 0.000000 0.017750 0.979480 0.002770 0.996070 0.000000 0.000560 0.003370 0.000000 0.914821 0.007890 0.077289 0.891700 0.000000 0.047570 0.060730 0.035800 0.152300 0.009080 0.802820 MOTIF AC0598:PAX:Homeodomain,Paired_box AC0598:PAX:Homeodomain,Paired_box letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.126902 0.038495 0.059498 0.775105 0.963005 0.008467 0.023097 0.005431 0.995041 0.001520 0.001295 0.002144 0.002711 0.018740 0.002178 0.976371 0.001143 0.503631 0.021045 0.474181 0.138142 0.021290 0.839219 0.001349 0.965803 0.004913 0.025324 0.003960 0.001482 0.001449 0.002323 0.994746 0.007529 0.021490 0.010382 0.960599 MOTIF AC0599:CUX:CUT,Homeodomain AC0599:CUX:CUT,Homeodomain letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.055559 0.888881 0.000000 0.055559 0.000000 0.944440 0.000000 0.055560 0.944440 0.000000 0.000000 0.055560 0.944440 0.055560 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.944440 0.000000 0.000000 0.055560 0.000000 0.166670 0.000000 0.833330 0.000000 0.777780 0.111110 0.111110 0.277780 0.000000 0.722220 0.000000 0.777772 0.000000 0.055559 0.166668 0.111109 0.055559 0.055559 0.777772 MOTIF AC0600:HOMEZ:Homeodomain AC0600:HOMEZ:Homeodomain letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.514240 0.046770 0.084760 0.354230 0.257547 0.038230 0.027500 0.676723 0.698190 0.112680 0.070420 0.118710 0.780332 0.052819 0.011510 0.155338 0.005960 0.005960 0.000000 0.988080 0.039230 0.929020 0.017430 0.014320 0.000000 0.005200 0.969460 0.025340 0.108139 0.002280 0.040410 0.849172 0.101090 0.031590 0.010340 0.856980 0.124241 0.040130 0.015390 0.820238 MOTIF AC0601:ONECUT/CUX:CUT,Homeodomain AC0601:ONECUT/CUX:CUT,Homeodomain letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.121754 0.105250 0.135753 0.637243 0.977499 0.002693 0.010792 0.009016 0.011351 0.008171 0.003641 0.976837 0.006596 0.554639 0.010021 0.428744 0.019753 0.005777 0.967597 0.006873 0.974690 0.003797 0.016943 0.004570 0.009177 0.030626 0.003975 0.956222 0.056192 0.365367 0.049787 0.528654 MOTIF AC0602:ZZZ:Myb/SANT AC0602:ZZZ:Myb/SANT letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.936191 0.021270 0.021270 0.021270 0.999670 0.000110 0.000110 0.000110 0.000110 0.000110 0.000110 0.999670 0.000110 0.999670 0.000110 0.000110 0.000110 0.910321 0.089459 0.000110 0.910321 0.000110 0.000110 0.089459 MOTIF AC0603:ZNF:C2H2_ZF AC0603:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 20 nsites= 20 E= 0 0.013480 0.973290 0.009325 0.003905 0.025575 0.165669 0.001890 0.806866 0.079850 0.016635 0.698678 0.204836 0.010075 0.013985 0.973420 0.002520 0.863935 0.021420 0.072190 0.042455 0.022295 0.024190 0.182185 0.771330 0.042070 0.856274 0.075205 0.026450 0.405245 0.493220 0.059340 0.042195 0.709729 0.142491 0.091090 0.056690 0.182780 0.131905 0.652060 0.033255 0.005290 0.939650 0.036165 0.018895 0.053290 0.629670 0.025575 0.291465 0.388035 0.141600 0.325000 0.145365 0.096875 0.175346 0.141986 0.585793 0.356838 0.063375 0.412243 0.167544 0.018265 0.899715 0.068420 0.013600 0.009325 0.967995 0.018020 0.004660 0.020665 0.018020 0.026085 0.935230 0.010075 0.036915 0.946455 0.006555 0.765285 0.174995 0.048380 0.011340 MOTIF AC0604:ZNF:C2H2_ZF AC0604:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.025200 0.888520 0.054140 0.032140 0.262535 0.142400 0.146150 0.448915 0.014965 0.047075 0.289215 0.648745 0.032140 0.866115 0.009260 0.092485 0.659310 0.194560 0.089175 0.056955 0.347955 0.555300 0.047495 0.049250 0.040560 0.946620 0.007260 0.005560 0.008030 0.034845 0.020140 0.936985 0.048890 0.015560 0.924820 0.010730 0.589548 0.343657 0.049125 0.017670 0.007795 0.034905 0.005560 0.951740 0.097755 0.079005 0.425645 0.397595 0.702385 0.069240 0.185300 0.043075 0.188730 0.095880 0.589215 0.126175 MOTIF AC0605:SCRT:C2H2_ZF AC0605:SCRT:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.026279 0.038871 0.667764 0.267086 0.005890 0.972230 0.004868 0.017012 0.858616 0.044452 0.071067 0.025865 0.986506 0.001191 0.007836 0.004467 0.002493 0.995368 0.001094 0.001045 0.992756 0.001637 0.000779 0.004828 0.116263 0.003706 0.874421 0.005610 0.005889 0.004133 0.982665 0.007313 0.005102 0.010427 0.002481 0.981990 MOTIF AC0606:SNAI/ZEB:C2H2_ZF AC0606:SNAI/ZEB:C2H2_ZF letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.042027 0.861121 0.009855 0.086997 0.984940 0.002035 0.001132 0.011893 0.009326 0.978632 0.002127 0.009915 0.011941 0.935831 0.008971 0.043257 0.011779 0.002166 0.002456 0.983599 0.028779 0.010728 0.913694 0.046799 MOTIF AC0607:OLIG/NEUROG:bHLH AC0607:OLIG/NEUROG:bHLH letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.553751 0.058271 0.338600 0.049378 0.387064 0.430649 0.150406 0.031880 0.014091 0.971866 0.012475 0.001568 0.939465 0.003256 0.046341 0.010938 0.023380 0.058781 0.039032 0.878807 0.869709 0.054272 0.052023 0.023996 0.008674 0.052526 0.003847 0.934953 0.001453 0.008615 0.969743 0.020189 0.032300 0.182849 0.393126 0.391725 MOTIF AC0608:TFAP/MYOD:bHLH AC0608:TFAP/MYOD:bHLH letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.597066 0.144468 0.133156 0.125310 0.002092 0.985677 0.007724 0.004507 0.972118 0.006770 0.012186 0.008926 0.044058 0.101693 0.787698 0.066551 0.077224 0.810971 0.061995 0.049810 0.010539 0.011736 0.007174 0.970551 0.004542 0.005891 0.984351 0.005216 0.171310 0.136021 0.143695 0.548974 MOTIF AC0609:ZBTB/OLIG:bHLH AC0609:ZBTB/OLIG:bHLH letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.648144 0.130973 0.196575 0.024308 0.013396 0.969342 0.012532 0.004730 0.956808 0.025272 0.008620 0.009300 0.021400 0.023564 0.499785 0.455252 0.337334 0.624280 0.030522 0.007864 0.038003 0.018686 0.008828 0.934483 0.012238 0.004242 0.957561 0.025959 0.027498 0.128016 0.633107 0.211379 MOTIF AC0610:TCF/ASCL:bHLH AC0610:TCF/ASCL:bHLH letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.006565 0.983091 0.004883 0.005461 0.961313 0.013405 0.009806 0.015476 0.004422 0.627653 0.311488 0.056437 0.033280 0.876162 0.083338 0.007220 0.013393 0.015352 0.007543 0.963712 0.008831 0.013420 0.965986 0.011763 MOTIF AC0611:NHLH/TFAP:bHLH AC0611:NHLH/TFAP:bHLH letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.325934 0.587718 0.069081 0.017267 0.098905 0.137140 0.562429 0.201526 0.004133 0.993488 0.000791 0.001588 0.994688 0.001788 0.003369 0.000155 0.006831 0.056304 0.894196 0.042669 0.032589 0.892150 0.073290 0.001971 0.004357 0.003967 0.013960 0.977716 0.001432 0.013751 0.981992 0.002825 0.051181 0.755869 0.067295 0.125655 0.037214 0.032728 0.759134 0.170924 0.110283 0.289082 0.213351 0.387284 MOTIF AC0612:MEIS/TGIF:Homeodomain AC0612:MEIS/TGIF:Homeodomain letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.061702 0.029772 0.029521 0.879005 0.000364 0.003413 0.995859 0.000364 0.946503 0.007571 0.026081 0.019845 0.009595 0.973105 0.008378 0.008922 0.924806 0.002626 0.054585 0.017983 0.127130 0.133539 0.623676 0.115655 MOTIF AC0613:MEIS/PKNOX:Homeodomain AC0613:MEIS/PKNOX:Homeodomain letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.050595 0.093468 0.051524 0.804413 0.012873 0.033205 0.942142 0.011780 0.850295 0.012844 0.039158 0.097703 0.008787 0.965694 0.007190 0.018329 0.930677 0.004887 0.056640 0.007796 0.019461 0.195114 0.643491 0.141934 0.130396 0.565821 0.278209 0.025574 0.009423 0.067077 0.005683 0.917817 0.024388 0.014139 0.937344 0.024129 0.103094 0.054530 0.023741 0.818635 0.018573 0.907893 0.046299 0.027235 0.775072 0.048075 0.112081 0.064772 MOTIF AC0614:LHX:Homeodomain AC0614:LHX:Homeodomain letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.044062 0.040024 0.016170 0.899744 0.477233 0.010840 0.495199 0.016728 0.952882 0.015494 0.026702 0.004922 0.017658 0.025216 0.035706 0.921420 0.016396 0.025098 0.031660 0.926846 0.099036 0.011458 0.887810 0.001696 0.004192 0.876974 0.013002 0.105832 0.918032 0.029098 0.037122 0.015748 0.917804 0.034376 0.031006 0.016814 0.006852 0.026786 0.011368 0.954994 0.017452 0.493397 0.011150 0.478001 0.901858 0.013772 0.036692 0.047678 MOTIF AC0615:ZNF:C2H2_ZF AC0615:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.000630 0.163518 0.000630 0.835222 0.030690 0.000630 0.968050 0.000630 0.005640 0.790100 0.090850 0.113410 0.093359 0.000630 0.905381 0.000630 0.910400 0.000630 0.085840 0.003130 0.000630 0.000630 0.000630 0.998110 0.013160 0.932951 0.000630 0.053259 0.421670 0.010650 0.000630 0.567050 0.003130 0.652270 0.013160 0.331440 MOTIF AC0616:ZNF:C2H2_ZF AC0616:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.016450 0.876860 0.018815 0.087875 0.016450 0.955405 0.013855 0.014290 0.007475 0.946745 0.006180 0.039600 0.060305 0.799606 0.024060 0.116029 0.820378 0.037415 0.108536 0.033670 0.013505 0.940285 0.006280 0.039930 0.002620 0.008875 0.003155 0.985350 0.004020 0.802140 0.007045 0.186795 0.049600 0.891655 0.006180 0.052565 0.020625 0.149880 0.013605 0.815890 0.040585 0.021595 0.809506 0.128314 0.063554 0.019765 0.895191 0.021490 0.050710 0.097715 0.095120 0.756455 MOTIF AC0617:ERG/FLI:Ets AC0617:ERG/FLI:Ets letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.101301 0.760891 0.091264 0.046544 0.081862 0.910751 0.005844 0.001543 0.000434 0.002473 0.996712 0.000381 0.001244 0.001238 0.996733 0.000785 0.998218 0.001109 0.000533 0.000140 0.337434 0.046017 0.000031 0.616518 0.715310 0.000184 0.052442 0.232064 0.000130 0.000443 0.001163 0.998264 0.000901 0.996372 0.001501 0.001226 0.000791 0.996327 0.002521 0.000361 0.001683 0.005956 0.914558 0.077803 0.049481 0.085102 0.765780 0.099637 MOTIF AC0618:ZNF:C2H2_ZF AC0618:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.000000 1.000000 0.000000 0.000000 0.000000 0.840000 0.160000 0.000000 0.080000 0.040000 0.840000 0.040000 1.000000 0.000000 0.000000 0.000000 0.800000 0.160000 0.040000 0.000000 0.760000 0.000000 0.120000 0.120000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.960000 0.000000 0.040000 0.040000 0.000000 0.960000 0.000000 0.000000 0.000000 1.000000 0.000000 MOTIF AC0619:SPDEF:Ets AC0619:SPDEF:Ets letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.051990 0.843489 0.068965 0.035555 0.898145 0.053690 0.031505 0.016660 0.022550 0.007500 0.958050 0.011900 0.355525 0.072745 0.234230 0.337500 0.995010 0.001970 0.000345 0.002675 0.983265 0.004830 0.011905 0.000000 0.217994 0.009250 0.767551 0.005205 0.883036 0.002235 0.108014 0.006715 0.648640 0.325390 0.003190 0.022780 0.005695 0.029010 0.965295 0.000000 0.011125 0.006195 0.065815 0.916865 0.945240 0.000000 0.011580 0.043180 0.549600 0.000260 0.000000 0.450140 0.503210 0.300960 0.001420 0.194410 MOTIF AC0620:ZNF:C2H2_ZF AC0620:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 18 nsites= 20 E= 0 0.981460 0.008930 0.006180 0.003430 0.003430 0.011680 0.006180 0.978710 0.165518 0.039150 0.742443 0.052889 0.041900 0.429256 0.011680 0.517165 0.014420 0.044640 0.006180 0.934759 0.080359 0.011680 0.849582 0.058379 0.022670 0.014420 0.171018 0.791892 0.187500 0.058380 0.717720 0.036400 0.943000 0.003430 0.047390 0.006180 0.000690 0.000690 0.997930 0.000690 0.000690 0.000690 0.995190 0.003430 0.901781 0.019920 0.066619 0.011680 0.797390 0.099590 0.017170 0.085850 0.140799 0.017170 0.841342 0.000690 0.017170 0.956730 0.006180 0.019920 0.063871 0.624316 0.014420 0.297393 0.017170 0.921011 0.039150 0.022670 0.877060 0.028160 0.017170 0.077610 MOTIF AC0621:FOXO1::ELK/ERF::FOXO:['Tryptophan_cluster_factors',_'Fork_head/winged_helix_factors'] AC0621:FOXO1::ELK/ERF::FOXO:['Tryptophan_cluster_factors',_'Fork_head/winged_helix_factors'] letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.229916 0.111487 0.151729 0.506867 0.575891 0.306423 0.060505 0.057181 0.832485 0.059715 0.058588 0.049212 0.892100 0.013154 0.078273 0.016473 0.015892 0.913831 0.025077 0.045200 0.828825 0.142992 0.024170 0.004013 0.026458 0.026099 0.941308 0.006135 0.019953 0.009581 0.940273 0.030193 0.924029 0.031369 0.016702 0.027900 0.733388 0.044815 0.023923 0.197874 0.240115 0.066304 0.668092 0.025489 MOTIF AC0622:ELF/SPIB:Ets AC0622:ELF/SPIB:Ets letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.618972 0.086269 0.132633 0.162126 0.365223 0.238897 0.197012 0.198868 0.178815 0.372860 0.400739 0.047586 0.590215 0.271745 0.120126 0.017914 0.022797 0.005187 0.965035 0.006981 0.012748 0.002566 0.975776 0.008910 0.966950 0.017794 0.008556 0.006700 0.945812 0.017433 0.006527 0.030228 0.141521 0.050500 0.796697 0.011282 0.070717 0.097564 0.140368 0.691352 MOTIF AC0623:ZNF:C2H2_ZF AC0623:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 6 nsites= 20 E= 0 0.295074 0.094060 0.494438 0.116429 0.072945 0.158481 0.099815 0.668758 0.411885 0.395730 0.034455 0.157930 0.028525 0.074550 0.000105 0.896821 0.008755 0.814086 0.002165 0.174994 0.075835 0.713794 0.037650 0.172721 MOTIF AC0624:ZBTB:C2H2_ZF AC0624:ZBTB:C2H2_ZF letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.113770 0.092820 0.087700 0.705710 0.074330 0.083470 0.031185 0.811015 0.128394 0.115259 0.021455 0.734891 0.082280 0.908490 0.001650 0.007580 0.000910 0.996200 0.002590 0.000300 0.000000 0.044505 0.955495 0.000000 0.000550 0.000460 0.916420 0.082570 0.001210 0.000490 0.002115 0.996185 0.815940 0.005760 0.033275 0.145025 MOTIF AC0625:ELF:Ets AC0625:ELF:Ets letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.955990 0.002825 0.011205 0.029980 0.055440 0.016060 0.011425 0.917075 0.487490 0.024955 0.257730 0.229825 0.483658 0.056780 0.459563 0.000000 0.001715 0.998285 0.000000 0.000000 0.002425 0.003585 0.993700 0.000290 0.001460 0.001265 0.994940 0.002335 0.994460 0.003800 0.000000 0.001740 0.070365 0.006885 0.001215 0.921535 0.009785 0.005525 0.983260 0.001430 MOTIF AC0626:ELF/ETV:Ets AC0626:ELF/ETV:Ets letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.092767 0.710315 0.167699 0.029218 0.206338 0.767196 0.016460 0.010006 0.004553 0.002294 0.990037 0.003116 0.002830 0.002364 0.984905 0.009901 0.987184 0.005200 0.003323 0.004293 0.815680 0.018184 0.006794 0.159342 0.206150 0.043524 0.739546 0.010780 0.055449 0.216735 0.061484 0.666332 MOTIF AC0627:ELF:Ets AC0627:ELF:Ets letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.031750 0.044970 0.124340 0.798940 0.028040 0.006230 0.940810 0.024920 0.000000 0.006580 0.000000 0.993420 0.159780 0.008260 0.831960 0.000000 0.000000 0.996700 0.003300 0.000000 0.000000 0.003300 0.996700 0.000000 0.000000 0.003300 0.996700 0.000000 0.996700 0.000000 0.003300 0.000000 0.000000 0.000000 0.000000 1.000000 0.022580 0.000000 0.974190 0.003230 0.007250 0.729463 0.060389 0.202898 MOTIF AC0628:SMAD:SMAD AC0628:SMAD:SMAD letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.227360 0.064390 0.573440 0.134810 0.062371 0.189132 0.696187 0.052311 0.142861 0.092551 0.052311 0.712277 0.016100 0.016100 0.939631 0.028170 0.289740 0.370220 0.146880 0.193160 0.046280 0.597584 0.211268 0.144869 0.989940 0.002010 0.006040 0.002010 0.008050 0.008050 0.981890 0.002010 0.730380 0.177060 0.046280 0.046280 0.026160 0.945670 0.010060 0.018110 0.539240 0.052310 0.100600 0.307850 MOTIF AC0629:SMAD:SMAD AC0629:SMAD:SMAD letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.064000 0.696000 0.060000 0.180000 0.216000 0.004000 0.014000 0.766000 0.070000 0.294000 0.554000 0.082000 0.212000 0.312000 0.106000 0.370000 0.038000 0.870000 0.008000 0.084000 0.466000 0.118000 0.022000 0.394000 0.008000 0.566000 0.332000 0.094000 0.032000 0.912000 0.014000 0.042000 0.352000 0.056000 0.004000 0.588000 MOTIF AC0630:ZNF:C2H2_ZF AC0630:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 10 nsites= 20 E= 0 0.164890 0.214540 0.051420 0.569150 0.101060 0.306740 0.136530 0.455670 0.171990 0.604610 0.164890 0.058510 0.001770 0.136530 0.001770 0.859930 0.001770 0.001770 0.994690 0.001770 0.725187 0.228722 0.037230 0.008860 0.001770 0.994690 0.001770 0.001770 0.994690 0.001770 0.001770 0.001770 0.001770 0.994690 0.001770 0.001770 0.023050 0.959230 0.008860 0.008860 MOTIF AC0631:T/TBX:T-box AC0631:T/TBX:T-box letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.018772 0.040611 0.015854 0.924763 0.227829 0.553945 0.182464 0.035762 0.686657 0.026473 0.236723 0.050147 0.017381 0.951297 0.015743 0.015579 0.878946 0.008851 0.100892 0.011311 0.058527 0.751323 0.074454 0.115696 0.148892 0.548203 0.205464 0.097441 0.099414 0.072640 0.064411 0.763535 0.783946 0.047686 0.084814 0.083554 0.180362 0.151432 0.512330 0.155876 0.093582 0.031931 0.836183 0.038304 0.005407 0.047650 0.003492 0.943451 0.005083 0.002233 0.989076 0.003608 0.054949 0.185416 0.012830 0.746806 0.026924 0.106383 0.714105 0.152588 0.966835 0.007661 0.019813 0.005691 MOTIF AC0632:TBX/MGA:T-box AC0632:TBX/MGA:T-box letter-probability matrix: alength= 4 w= 16 nsites= 20 E= 0 0.140226 0.042897 0.773130 0.043747 0.028849 0.109959 0.166682 0.694510 0.002323 0.004280 0.989917 0.003480 0.056494 0.144732 0.019511 0.779263 0.023177 0.091189 0.752934 0.132700 0.699597 0.035847 0.067427 0.197129 0.352769 0.127589 0.267455 0.252188 0.706230 0.039261 0.066752 0.187758 0.282360 0.093648 0.070396 0.553596 0.230321 0.203339 0.187474 0.378865 0.129131 0.053168 0.012802 0.804899 0.189349 0.610531 0.162729 0.037390 0.772460 0.017362 0.145637 0.064541 0.001471 0.995179 0.001492 0.001858 0.792418 0.106704 0.097587 0.003291 0.042112 0.863042 0.047063 0.047783 MOTIF AC0633:TBX/MGA::EVX:T-box AC0633:TBX/MGA::EVX:T-box letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.102650 0.094113 0.725402 0.077834 0.019332 0.008122 0.956128 0.016418 0.005955 0.061975 0.007815 0.924255 0.001290 0.001360 0.996370 0.000980 0.078583 0.112518 0.028528 0.780371 0.020796 0.092086 0.649141 0.237977 0.983879 0.001219 0.010162 0.004740 0.708274 0.105926 0.040310 0.145489 0.350907 0.058859 0.035925 0.554309 0.193456 0.033628 0.180978 0.591937 0.053025 0.018770 0.027710 0.900495 0.178163 0.627629 0.144025 0.050184 0.739410 0.045418 0.146516 0.068655 0.032232 0.902531 0.031926 0.033311 0.829063 0.038380 0.100221 0.032336 0.046019 0.859041 0.041665 0.053275 0.105041 0.622667 0.145956 0.126336 MOTIF AC0634:ZNF:C2H2_ZF AC0634:ZNF:C2H2_ZF letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.231480 0.178543 0.576330 0.013647 0.041607 0.177513 0.033990 0.746890 0.029557 0.009137 0.026230 0.935076 0.000000 0.993333 0.004667 0.002000 0.823144 0.123260 0.002313 0.051283 0.001333 0.988000 0.002000 0.008667 0.983883 0.004000 0.000000 0.012117 0.000000 0.966290 0.023637 0.010073 MOTIF AC0635:TBX/EOMES:T-box AC0635:TBX/EOMES:T-box letter-probability matrix: alength= 4 w= 8 nsites= 20 E= 0 0.749746 0.031698 0.139142 0.079414 0.104370 0.093595 0.733419 0.068616 0.005121 0.017217 0.966233 0.011429 0.007324 0.093973 0.009852 0.888851 0.003568 0.001146 0.993374 0.001912 0.050090 0.115968 0.030176 0.803766 0.035428 0.112125 0.679993 0.172454 0.883652 0.019037 0.064617 0.032694 MOTIF AC0636:SREBF:bHLH AC0636:SREBF:bHLH letter-probability matrix: alength= 4 w= 9 nsites= 20 E= 0 0.021530 0.059721 0.003839 0.914910 0.006638 0.963366 0.026419 0.003577 0.923379 0.004324 0.031341 0.040956 0.002758 0.857251 0.099879 0.040112 0.197848 0.460651 0.308872 0.032630 0.007778 0.905704 0.003476 0.083042 0.012360 0.897395 0.073921 0.016324 0.947213 0.015064 0.018426 0.019297 0.006769 0.601750 0.072364 0.319117 MOTIF AC0637:AHR:bHLH AC0637:AHR:bHLH letter-probability matrix: alength= 4 w= 17 nsites= 20 E= 0 0.333330 0.000000 0.000000 0.666670 0.000000 0.555560 0.333330 0.111110 0.111110 0.111110 0.111110 0.666670 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.333333 0.111111 0.222222 0.333333 1.000000 0.000000 0.000000 0.000000 0.000000 0.444440 0.555560 0.000000 0.111111 0.444444 0.111111 0.333333 0.000000 0.555560 0.000000 0.444440 0.111111 0.333333 0.333333 0.222222 0.000000 0.222220 0.777780 0.000000 0.333330 0.111110 0.555560 0.000000 0.333330 0.000000 0.666670 0.000000 TPCAV-0.8.4/data/motif-clustering-v2.1beta_consensus_pwms.test.meme000066400000000000000000000120171516353663300251310ustar00rootroot00000000000000MEME version 4 ALPHABET= ACGT strands: + - Background letter frequencies (from uniform background): A 0.25000 C 0.25000 G 0.25000 T 0.25000 MOTIF AC0001:GATA/PROP:GATA AC0001:GATA/PROP:GATA letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.676859 0.103120 0.104093 0.115927 0.023893 0.035573 0.009097 0.931437 0.934550 0.021763 0.019600 0.024087 0.917327 0.041030 0.018193 0.023450 0.038483 0.317794 0.210558 0.433165 0.190786 0.100360 0.553218 0.155636 0.303647 0.276917 0.157037 0.262400 0.193229 0.356892 0.190209 0.259669 0.486882 0.219741 0.239644 0.053733 0.021013 0.020403 0.043023 0.915561 0.026120 0.019233 0.026153 0.928494 0.917833 0.008397 0.043430 0.030340 0.112230 0.107057 0.106463 0.674251 MOTIF AC0002:PROP/ALX:Homeodomain AC0002:PROP/ALX:Homeodomain letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.070020 0.039968 0.011852 0.878160 0.737015 0.006383 0.210664 0.045937 0.991269 0.002983 0.002646 0.003102 0.012670 0.044178 0.013890 0.929262 0.024947 0.111357 0.062462 0.801234 0.491825 0.106176 0.245316 0.156683 0.388738 0.163204 0.414210 0.033848 0.696075 0.035808 0.214818 0.053300 0.005811 0.009027 0.005716 0.979446 0.039457 0.104083 0.010127 0.846333 0.813842 0.023174 0.061061 0.101923 MOTIF AC0003:HNF1A/HNF1B:Homeodomain AC0003:HNF1A/HNF1B:Homeodomain letter-probability matrix: alength= 4 w= 12 nsites= 20 E= 0 0.043390 0.101613 0.085711 0.769286 0.121847 0.146660 0.046618 0.684875 0.949010 0.011809 0.030862 0.008319 0.842458 0.063088 0.020894 0.073560 0.098126 0.017778 0.012976 0.871120 0.272067 0.213304 0.348447 0.166182 0.828818 0.002082 0.032306 0.136794 0.050111 0.018913 0.059806 0.871170 0.008976 0.027477 0.001556 0.961991 0.799415 0.018407 0.092076 0.090102 0.858469 0.055420 0.065752 0.020359 0.051574 0.853694 0.024065 0.070667 MOTIF AC0004:ZSCAN:C2H2_ZF AC0004:ZSCAN:C2H2_ZF letter-probability matrix: alength= 4 w= 14 nsites= 20 E= 0 0.045050 0.830650 0.027755 0.096545 0.946450 0.017965 0.023565 0.012020 0.003235 0.000000 0.066100 0.930665 0.037160 0.015575 0.947150 0.000115 0.000395 0.026730 0.000500 0.972375 0.000640 0.000060 0.998795 0.000505 0.027480 0.972465 0.000000 0.000055 0.000000 0.000290 0.000000 0.999710 0.981575 0.017090 0.000055 0.001280 0.990570 0.000000 0.009370 0.000060 0.000115 0.060000 0.000000 0.939885 0.009440 0.048450 0.002060 0.940050 0.935975 0.013145 0.050880 0.000000 0.000445 0.994320 0.000000 0.005235 MOTIF AC0005:POU3F/POU1F:Homeodomain,POU AC0005:POU3F/POU1F:Homeodomain,POU letter-probability matrix: alength= 4 w= 11 nsites= 20 E= 0 0.028714 0.009164 0.001321 0.960801 0.989711 0.001851 0.005834 0.002604 0.997031 0.001043 0.001073 0.000853 0.000439 0.010054 0.002120 0.987387 0.014909 0.040320 0.779626 0.165145 0.932318 0.005882 0.032534 0.029266 0.010606 0.100105 0.504943 0.384346 0.966061 0.006411 0.017753 0.009775 0.001870 0.003164 0.003369 0.991597 0.022515 0.002735 0.919093 0.055657 0.004801 0.944495 0.012714 0.037990 MOTIF AC0078:CTCF/CTCFL:C2H2_ZF AC0078:CTCF/CTCFL:C2H2_ZF letter-probability matrix: alength= 4 w= 13 nsites= 20 E= 0 0.036609 0.576729 0.032043 0.354619 0.081579 0.023185 0.799122 0.096114 0.054816 0.818625 0.039634 0.086925 0.004737 0.982003 0.002393 0.010867 0.336382 0.592947 0.034155 0.036516 0.006422 0.593501 0.007549 0.392528 0.004117 0.987708 0.001873 0.006302 0.031729 0.055670 0.022278 0.890323 0.304343 0.077714 0.494273 0.123670 0.018072 0.325949 0.618554 0.037425 0.088867 0.172486 0.028103 0.710544 0.008761 0.009301 0.968005 0.013933 0.058971 0.060564 0.795785 0.084680 MOTIF AC0111:CTCF/KLF:C2H2_ZF AC0111:CTCF/KLF:C2H2_ZF letter-probability matrix: alength= 4 w= 18 nsites= 20 E= 0 0.187500 0.203125 0.390625 0.218750 0.203000 0.208750 0.260875 0.327375 0.368896 0.072490 0.379981 0.178633 0.151168 0.163332 0.423917 0.261582 0.204682 0.141320 0.518838 0.135160 0.121015 0.199470 0.610578 0.068937 0.005312 0.037218 0.803300 0.154170 0.111423 0.078630 0.675373 0.134575 0.089362 0.304339 0.442106 0.164193 0.088350 0.143830 0.617242 0.150578 0.101713 0.261749 0.331839 0.304699 0.268488 0.131160 0.171031 0.429321 0.368800 0.132021 0.195903 0.303277 0.150122 0.158900 0.357021 0.333956 0.183653 0.139593 0.410175 0.266580 0.175090 0.246932 0.395022 0.182955 0.232126 0.230838 0.368649 0.168388 0.184750 0.164750 0.455750 0.194750 TPCAV-0.8.4/data/report_tpcav_score.html.zip000066400000000000000000005361261516353663300207330ustar00rootroot00000000000000PKwtd\ĒIreport_tpcav_score.htmlUT iiux 9>YwoȒ{Sp2Hy<1r %=w%3ogY]?t_@u~ͿGrPhF 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&Qس<눮_44\"?{WZWU[X>,o:ڽ^[dM@ifoIGwszl)aREu#hOˤ8?^d K"Lg ԚhŤjZ7l&2$;c';,С֍LJ[~;6r3rMtip-$#Xm3FwID ئez96 y˶ _lq˵3G _"RJbJEl$ng ZrWFQ_1R="c6 P`n:f jcۢ3=>9Pu2 ݌jar 7%H ,ww" |VzS"[Gxg;  :98Yr D>ꏓo9v+\$jLN;hʯU>p^+nI[ *[Vy4b7GG̝GT]BBb &eW`A-^P!`ESeg񖅔.#ԅ%hsk>>Y^F ρ{P$PQh "tZU /q'qm&3 ڠNşjN2L"5|F&oŰe F4`o rVN }a;B"((T zqh[Qo* !T@)ޢAbc5E1bz3(p"D0wNDm BHȉGHD $lLe1wGc<;°/0tCgpG'qa1&z~ֲ/A/ b ?ʝNpyVP g hK_)z,LC{/U->>T7择'ȾBݨB.<tPr6\pD2=ߨנ؉eCAM~.L]p e6N(p |Z4'V5^Qq`f^{rK0?4PP tLv"٤GG?Dր=M&WH~B~(>]:xH[8;TO |f҃#TR!f6qH 8p\@H@6< ShxqܞBIwTOɩoiL,d,h:Pbq5 <#)  w~qCh<$A!]O&BBu2aEǚW-Own^r6&+PE ^~؏zj,\F+JzKz0.1T!7u ÄtEMԅg\~`oA=)@I Wd6)bbKM05Fey.?3Lt5AK GEB&[ QyGűskÏ׆8m)HYT{YR.r>1I wT%d5k\k$˜i|awVihc}b/BS%J&s Lh߾:Q856l,; KSI\P#(& h%L]f Wn82zq P/ 7F)+/0?)!D!JkP<Ӊ|3:G\WƷW BңC=߫p3;5 07Eq dCF5R^R?JgQȺ'(d(qA#",Q;KPK]TPCAV-0.8.4/examples/000077500000000000000000000000001516353663300142315ustar00rootroot00000000000000TPCAV-0.8.4/examples/toy_model_training.ipynb000066400000000000000000041124641516353663300211760ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "2225231d-36c1-4095-881f-31ad7149810f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/data/lab/jianyu/git/TPCAV/examples'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%pwd" ] }, { "cell_type": "code", "execution_count": 2, "id": "2cd77575-6c00-4aa1-ad64-d6b3cd754bad", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jmy5455/miniforge3/envs/test/lib/python3.14/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } ], "source": [ "import torch\n", "from tpcav import run_tpcav, helper\n", "\n", "device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n", "#==================== Prepare Model and Data transform function ================================\n", "class DummyModelSeq(torch.nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.conv_layer_1 = torch.nn.Sequential(\n", " torch.nn.Conv1d(4, 64, 25, padding=12, bias=True),\n", " torch.nn.BatchNorm1d(64),\n", " torch.nn.LeakyReLU()\n", " )\n", " self.conv_layer_2 = torch.nn.Sequential(\n", " torch.nn.Conv1d(64, 128, 3, padding=1, stride=2, bias=True),\n", " torch.nn.BatchNorm1d(128),\n", " torch.nn.LeakyReLU(),\n", " )\n", "\n", " self.linear_layer_1 = torch.nn.Sequential(\n", " torch.nn.Linear(512, 1),\n", " torch.nn.LeakyReLU()\n", " )\n", " self.linear_layer_2 = torch.nn.Linear(128, 1)\n", "\n", " def forward(self, seq):\n", " y_hat = self.conv_layer_1(seq)\n", " y_hat = self.conv_layer_2(y_hat)\n", " y_hat = self.linear_layer_1(y_hat)\n", " y_hat = y_hat.squeeze(-1)\n", " y_hat = self.linear_layer_2(y_hat)\n", " return y_hat" ] }, { "cell_type": "markdown", "id": "41b300ac-242e-49fe-abab-02b75feaef88", "metadata": { "jp-MarkdownHeadingCollapsed": true }, "source": [ "## model training" ] }, { "cell_type": "code", "execution_count": 32, "id": "6e704a4c-34e4-4987-b2fe-bcde68b98e11", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "

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chromstartend
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" ], "text/plain": [ " chrom start end\n", "0 chr17 75754688 75755712\n", "1 chr3 127747022 127748046\n", "2 chr22 46130993 46132017\n", "3 chr3 139677291 139678315\n", "4 chr8 123394394 123395418\n", "... ... ... ...\n", "56198 chr20 58209484 58210508\n", "56199 chr17 45134930 45135954\n", "56200 chr9 128117284 128118308\n", "56201 chr1 150979121 150980145\n", "56202 chr12 53675711 53676735\n", "\n", "[56184 rows x 3 columns]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ctcf_peaks = helper.load_bed_and_center(\"data/MCF-7_CTCF_ENCFF942TCG.bed\", window=1024)\n", "ctcf_peaks = ctcf_peaks.loc[ctcf_peaks.chrom != 'chrM']\n", "ctcf_peaks" ] }, { "cell_type": "code", "execution_count": 33, "id": "6d9604b7-4597-4f4f-85c4-98cb47afb881", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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chromstartend
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56184 rows × 3 columns

\n", "
" ], "text/plain": [ " chrom start end\n", "0 chr8 39267215 39268239\n", "1 chr17 43229869 43230893\n", "2 chr2 73528924 73529948\n", "3 chr8 86008837 86009861\n", "4 chr10 50239667 50240691\n", "... ... ... ...\n", "56179 chr4 76365762 76366786\n", "56180 chr3 27330197 27331221\n", "56181 chr14 1634799 1635823\n", "56182 chr3 49255000 49256024\n", "56183 chr1 220796727 220797751\n", "\n", "[56184 rows x 3 columns]" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "random_regs = helper.random_regions_dataframe(\"data/hg38.analysisSet.fa.fai\", window=1024, n=len(ctcf_peaks))\n", "random_regs = random_regs.loc[random_regs.chrom != 'chrM']\n", "random_regs" ] }, { "cell_type": "code", "execution_count": 34, "id": "167ad27b-bbdb-4c9f-93ef-6e954a968f82", "metadata": { "scrolled": true }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "train_df_pos, test_df_pos = train_test_split(ctcf_peaks, test_size=0.05)\n", "train_df_neg, test_df_neg = train_test_split(random_regs, test_size=0.05)" ] }, { "cell_type": "code", "execution_count": 35, "id": "fe1e77a9-e133-428e-93a9-fc9f85002753", "metadata": {}, "outputs": [], "source": [ "def df_to_one_hot_iter(df, infinite=False):\n", " it = iter(helper.dataframe_to_fasta_iter(df, \"data/hg38.analysisSet.fa\", batch_size=8))\n", " while True:\n", " try:\n", " yield helper.fasta_to_one_hot_sequences(next(it))\n", " except StopIteration:\n", " if infinite:\n", " it = iter(helper.dataframe_to_fasta_iter(df, \"data/hg38.analysisSet.fa\", batch_size=8))\n", " yield helper.fasta_to_one_hot_sequences(next(it))\n", " else:\n", " return\n", " " ] }, { "cell_type": "code", "execution_count": 36, "id": "eede5edc-b6e0-433f-9c99-b2aa81af7778", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Evaluation loss after batch 0: 0.6937556266784668\n", "Evaluation loss after batch 100: 0.6942372918128967\n", "Evaluation loss after batch 200: 0.6925637722015381\n", "Evaluation loss after batch 300: 0.6919495463371277\n", "Evaluation loss after batch 400: 0.6913040280342102\n", "Evaluation loss after batch 500: 0.6903418898582458\n", "Evaluation loss after batch 600: 0.6883369088172913\n", "Evaluation loss after batch 700: 0.6819443702697754\n", "Evaluation loss after batch 800: 0.6625214219093323\n", "Evaluation loss after batch 900: 0.6370221376419067\n", "Evaluation loss after batch 1000: 0.6176310777664185\n", "Evaluation loss after batch 1100: 0.6073943376541138\n", "Evaluation loss after batch 1200: 0.5926850438117981\n", "Evaluation loss after batch 1300: 0.5794664621353149\n", "Evaluation loss after batch 1400: 0.5670604705810547\n", "Evaluation loss after batch 1500: 0.5582072138786316\n", "Evaluation loss after batch 1600: 0.5516800880432129\n", "Evaluation loss after batch 1700: 0.5421598553657532\n", "Evaluation loss after batch 1800: 0.5393367409706116\n", "Evaluation loss after batch 1900: 0.5279940962791443\n", "Evaluation loss after batch 2000: 0.5209202766418457\n", "Evaluation loss after batch 2100: 0.5119447112083435\n", "Evaluation loss after batch 2200: 0.5058941841125488\n", "Evaluation loss after batch 2300: 0.49614405632019043\n", "Evaluation loss after batch 2400: 0.48771438002586365\n", "Evaluation loss after batch 2500: 0.4763661324977875\n", "Evaluation loss after batch 2600: 0.46442487835884094\n", "Evaluation loss after batch 2700: 0.4521169066429138\n", "Evaluation loss after batch 2800: 0.43591076135635376\n", "Evaluation loss after batch 2900: 0.4250583350658417\n", "Evaluation loss after batch 3000: 0.41229158639907837\n", "Evaluation loss after batch 3100: 0.4034208059310913\n", "Evaluation loss after batch 3200: 0.3960020840167999\n", "Evaluation loss after batch 3300: 0.3864622116088867\n", "Evaluation loss after batch 3400: 0.38422316312789917\n", "Evaluation loss after batch 3500: 0.37985554337501526\n", "Evaluation loss after batch 3600: 0.37480998039245605\n", "Evaluation loss after batch 3700: 0.3682119846343994\n", "Evaluation loss after batch 3800: 0.36069247126579285\n", "Evaluation loss after batch 3900: 0.3596401810646057\n", "Evaluation loss after batch 4000: 0.3561800718307495\n", "Evaluation loss after batch 4100: 0.3543008267879486\n", "Evaluation loss after batch 4200: 0.35068196058273315\n", "Evaluation loss after batch 4300: 0.35159268975257874\n", "Evaluation loss after batch 4400: 0.34504151344299316\n", "Evaluation loss after batch 4500: 0.3449777066707611\n", "Evaluation loss after batch 4600: 0.3428124487400055\n", "Evaluation loss after batch 4700: 0.33861663937568665\n", "Evaluation loss after batch 4800: 0.3395470082759857\n", "Evaluation loss after batch 4900: 0.337537944316864\n", "Evaluation loss after batch 5000: 0.33596113324165344\n", "Evaluation loss after batch 5100: 0.33065593242645264\n", "Evaluation loss after batch 5200: 0.33615007996559143\n", "Evaluation loss after batch 5300: 0.3293520510196686\n", "Evaluation loss after batch 5400: 0.3264411985874176\n", "Evaluation loss after batch 5500: 0.3246862292289734\n", "Evaluation loss after batch 5600: 0.32377514243125916\n", "Evaluation loss after batch 5700: 0.32407236099243164\n", "Evaluation loss after batch 5800: 0.32550048828125\n", "Evaluation loss after batch 5900: 0.32608461380004883\n", "Evaluation loss after batch 6000: 0.31514811515808105\n", "Evaluation loss after batch 6100: 0.3212358057498932\n", "Evaluation loss after batch 6200: 0.31468433141708374\n", "Evaluation loss after batch 6300: 0.3136895000934601\n", "Evaluation loss after batch 6400: 0.3111656904220581\n", "Evaluation loss after batch 6500: 0.3136947751045227\n", "Evaluation loss after batch 6600: 0.31020092964172363\n", "Evaluation loss after batch 6700: 0.30775704979896545\n", "Evaluation loss after batch 6800: 0.3137766718864441\n", "Evaluation loss after batch 6900: 0.3080938458442688\n", "Evaluation loss after batch 7000: 0.30662912130355835\n", "Evaluation loss after batch 7100: 0.30252090096473694\n", "Evaluation loss after batch 7200: 0.30684593319892883\n", "Evaluation loss after batch 7300: 0.30277639627456665\n", "Evaluation loss after batch 7400: 0.3032972812652588\n", "Evaluation loss after batch 7500: 0.30140456557273865\n", "Evaluation loss after batch 7600: 0.30448102951049805\n", "Evaluation loss after batch 7700: 0.3037923276424408\n", "Evaluation loss after batch 7800: 0.30271974205970764\n", "Evaluation loss after batch 7900: 0.3004668056964874\n", "Evaluation loss after batch 8000: 0.2994200587272644\n", "Evaluation loss after batch 8100: 0.2959950566291809\n", "Evaluation loss after batch 8200: 0.29507961869239807\n", "Evaluation loss after batch 8300: 0.2947378158569336\n", "Evaluation loss after batch 8400: 0.29273247718811035\n", "Evaluation loss after batch 8500: 0.2946201264858246\n", "Evaluation loss after batch 8600: 0.295011967420578\n", "Evaluation loss after batch 8700: 0.29328325390815735\n", "Evaluation loss after batch 8800: 0.2950860261917114\n", "Evaluation loss after batch 8900: 0.28916841745376587\n", "Evaluation loss after batch 9000: 0.2975997030735016\n", "Evaluation loss after batch 9100: 0.294389009475708\n", "Evaluation loss after batch 9200: 0.29023122787475586\n", "Evaluation loss after batch 9300: 0.28775253891944885\n", "Evaluation loss after batch 9400: 0.2850485146045685\n", "Evaluation loss after batch 9500: 0.2926185429096222\n", "Evaluation loss after batch 9600: 0.2908644676208496\n", "Evaluation loss after batch 9700: 0.2893913984298706\n", "Evaluation loss after batch 9800: 0.288402795791626\n", "Evaluation loss after batch 9900: 0.2841135561466217\n", "Evaluation loss after batch 10000: 0.2800595164299011\n", "Evaluation loss after batch 10100: 0.28185147047042847\n", "Evaluation loss after batch 10200: 0.28194180130958557\n", "Evaluation loss after batch 10300: 0.2872686982154846\n", "Evaluation loss after batch 10400: 0.28600311279296875\n", "Evaluation loss after batch 10500: 0.2899096608161926\n", "Evaluation loss after batch 10600: 0.281756192445755\n", "Evaluation loss after batch 10700: 0.27953630685806274\n", "Evaluation loss after batch 10800: 0.2810150980949402\n", "Evaluation loss after batch 10900: 0.2809644341468811\n", "Evaluation loss after batch 11000: 0.27607646584510803\n", "Evaluation loss after batch 11100: 0.2794179320335388\n", "Evaluation loss after batch 11200: 0.28031015396118164\n", "Evaluation loss after batch 11300: 0.2863805294036865\n", "Evaluation loss after batch 11400: 0.28191637992858887\n", "Evaluation loss after batch 11500: 0.27828916907310486\n", "Evaluation loss after batch 11600: 0.2728225290775299\n", "Evaluation loss after batch 11700: 0.27511900663375854\n", "Evaluation loss after batch 11800: 0.27224817872047424\n", "Evaluation loss after batch 11900: 0.27437493205070496\n", "Evaluation loss after batch 12000: 0.27383923530578613\n", "Evaluation loss after batch 12100: 0.27727770805358887\n", "Evaluation loss after batch 12200: 0.27413931488990784\n", "Evaluation loss after batch 12300: 0.2696428596973419\n", "Evaluation loss after batch 12400: 0.27333322167396545\n", "Evaluation loss after batch 12500: 0.2759076654911041\n", "Evaluation loss after batch 12600: 0.2721416652202606\n", "Evaluation loss after batch 12700: 0.2702060639858246\n", "Evaluation loss after batch 12800: 0.27123725414276123\n", "Evaluation loss after batch 12900: 0.26947394013404846\n", "Evaluation loss after batch 13000: 0.26807308197021484\n", "Evaluation loss after batch 13100: 0.26731419563293457\n", "Evaluation loss after batch 13200: 0.26474180817604065\n", "Evaluation loss after batch 13300: 0.2744033634662628\n", "Evaluation loss after batch 13400: 0.26792287826538086\n", "Evaluation loss after batch 13500: 0.27208659052848816\n", "Evaluation loss after batch 13600: 0.2652274966239929\n", "Evaluation loss after batch 13700: 0.26365673542022705\n", "Evaluation loss after batch 13800: 0.2618635892868042\n", "Evaluation loss after batch 13900: 0.26717641949653625\n", "Evaluation loss after batch 14000: 0.26400643587112427\n", "Evaluation loss after batch 14100: 0.26155367493629456\n", "Evaluation loss after batch 14200: 0.2657955288887024\n", "Evaluation loss after batch 14300: 0.26814544200897217\n", "Evaluation loss after batch 14400: 0.26133400201797485\n", "Evaluation loss after batch 14500: 0.2637538015842438\n", "Evaluation loss after batch 14600: 0.2600990831851959\n", "Evaluation loss after batch 14700: 0.2626963257789612\n", "Evaluation loss after batch 14800: 0.269621878862381\n", "Evaluation loss after batch 14900: 0.2658490240573883\n", "Evaluation loss after batch 15000: 0.25614482164382935\n", "Evaluation loss after batch 15100: 0.25463759899139404\n", "Evaluation loss after batch 15200: 0.257185161113739\n", "Evaluation loss after batch 15300: 0.25754693150520325\n", "Evaluation loss after batch 15400: 0.25792503356933594\n", "Evaluation loss after batch 15500: 0.2532142698764801\n", "Evaluation loss after batch 15600: 0.2608712315559387\n", "Evaluation loss after batch 15700: 0.2541520297527313\n", "Evaluation loss after batch 15800: 0.2567054033279419\n", "Evaluation loss after batch 15900: 0.25150883197784424\n", "Evaluation loss after batch 16000: 0.256671667098999\n", "Evaluation loss after batch 16100: 0.2559809386730194\n", "Evaluation loss after batch 16200: 0.2554147243499756\n", "Evaluation loss after batch 16300: 0.25299271941185\n", "Evaluation loss after batch 16400: 0.25477033853530884\n", "Evaluation loss after batch 16500: 0.2507142424583435\n", "Evaluation loss after batch 16600: 0.2550141215324402\n", "Evaluation loss after batch 16700: 0.24872644245624542\n", "Evaluation loss after batch 16800: 0.25556305050849915\n", "Evaluation loss after batch 16900: 0.2552239000797272\n", "Evaluation loss after batch 17000: 0.25605523586273193\n", "Evaluation loss after batch 17100: 0.25009942054748535\n", "Evaluation loss after batch 17200: 0.24783039093017578\n", "Evaluation loss after batch 17300: 0.2523411810398102\n", "Evaluation loss after batch 17400: 0.24978014826774597\n", "Evaluation loss after batch 17500: 0.24643300473690033\n", "Evaluation loss after batch 17600: 0.2493063360452652\n", "Evaluation loss after batch 17700: 0.24342229962348938\n", "Evaluation loss after batch 17800: 0.24402856826782227\n", "Evaluation loss after batch 17900: 0.24692270159721375\n", "Evaluation loss after batch 18000: 0.2487742304801941\n", "Evaluation loss after batch 18100: 0.24775318801403046\n", "Evaluation loss after batch 18200: 0.24500888586044312\n", "Evaluation loss after batch 18300: 0.24569523334503174\n", "Evaluation loss after batch 18400: 0.24211788177490234\n", "Evaluation loss after batch 18500: 0.24748393893241882\n", "Evaluation loss after batch 18600: 0.24285492300987244\n", "Evaluation loss after batch 18700: 0.241144597530365\n", "Evaluation loss after batch 18800: 0.24039356410503387\n", "Evaluation loss after batch 18900: 0.24457277357578278\n", "Evaluation loss after batch 19000: 0.24079284071922302\n", "Evaluation loss after batch 19100: 0.23801422119140625\n", "Evaluation loss after batch 19200: 0.24262282252311707\n", "Evaluation loss after batch 19300: 0.24535487592220306\n", "Evaluation loss after batch 19400: 0.23978261649608612\n", "Evaluation loss after batch 19500: 0.23935513198375702\n", "Evaluation loss after batch 19600: 0.24893540143966675\n", "Evaluation loss after batch 19700: 0.2352587878704071\n", "Evaluation loss after batch 19800: 0.23788417875766754\n", "Evaluation loss after batch 19900: 0.2366536408662796\n", "Evaluation loss after batch 20000: 0.23615340888500214\n" ] } ], "source": [ "def compute_loss(model, seq_pos, seq_neg):\n", " label_pos = torch.ones(seq_pos.shape[0])\n", " label_neg = torch.zeros(seq_neg.shape[0])\n", " label = torch.cat([label_pos, label_neg]).unsqueeze(-1).to(seq_pos.device)\n", " \n", " y_pred = model(torch.cat([seq_pos, seq_neg]))\n", " loss = torch.nn.functional.binary_cross_entropy_with_logits(y_pred, label)\n", "\n", " return loss\n", "\n", "def eval_model(model):\n", " model.eval()\n", " losses = []\n", " for seq_pos, seq_neg in zip(df_to_one_hot_iter(test_df_pos), df_to_one_hot_iter(test_df_neg)):\n", " loss = compute_loss(model, seq_pos.to(device), seq_neg.to(device))\n", " losses.append(loss.unsqueeze(-1).detach())\n", " losses = torch.cat(losses).mean()\n", " model.train()\n", " return losses\n", "\n", "model = DummyModelSeq()\n", "model.to(device)\n", "model.train()\n", "optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)\n", "\n", "training_losses = []\n", "best_eval_loss = 100\n", "early_stop_counter = 0\n", "max_epochs = 100000\n", "patience = 100\n", "for i, (seq_pos, seq_neg) in enumerate(zip(df_to_one_hot_iter(train_df_pos, infinite=True), df_to_one_hot_iter(train_df_neg, infinite=True))):\n", "\n", " loss = compute_loss(model, seq_pos.to(device), seq_neg.to(device))\n", " loss.backward()\n", "\n", " optimizer.step()\n", " optimizer.zero_grad()\n", "\n", " if i % 100 == 0:\n", " eval_loss = eval_model(model)\n", " if eval_loss < best_eval_loss:\n", " best_eval_loss = eval_loss.item()\n", " torch.save(model, 'data/mcf7_ctcf_best.pt')\n", " else:\n", " early_stop_counter += 1\n", " if early_stop_counter > patience:\n", " break\n", "\n", " if i > max_epochs: break\n", " #print(f\"Training loss after batch {i}: {loss}\")\n", " print(f\"Evaluation loss after batch {i}: {eval_loss}\")\n" ] }, { "cell_type": "markdown", "id": "b53ad165-2703-4ca6-a79a-3a6c4cba7bfc", "metadata": {}, "source": [ "## test TPCAV" ] }, { "cell_type": "code", "execution_count": 3, "id": "3e50a2d7-c167-4759-b6ca-382dbf87eaf1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/data/lab/jianyu/git/TPCAV\n" ] } ], "source": [ "%cd .." ] }, { "cell_type": "code", "execution_count": 4, "id": "bd8354cd-d8e1-4d35-912b-647a3d2f7759", "metadata": {}, "outputs": [], "source": [ "# By default, every concept extracts fasta sequences and bigwig signals from the given region\n", "# Use your own custom transformation function to get your desired inputs\n", "# Here we transform them into one-hot coded DNA sequences\n", "def transform_fasta_to_one_hot_seq(seq, chrom):\n", " # `seq` is a list of fasta sequences\n", " # `chrom` is a numpy array of bigwig signals of shape [-1, # bigwigs, len]\n", " return (helper.fasta_to_one_hot_sequences(seq),) # it has to return a tuple of inputs, even if there is only one input" ] }, { "cell_type": "code", "execution_count": 5, "id": "e081c395-170d-4ab6-92c1-2eb00d5c1f98", "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:tpcav.concepts:Concept Simple_repeat_(TAACCC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Satellite/telo_TAR1 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MC5a has 139 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER5B has 126 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCTCC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie15a has 42 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGG)n has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_L3 has 283 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_Plat_L3 has 18 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1K has 212 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/MIR_MIR1_Amn has 209 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER33 has 97 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACGCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MB3 has 208 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT_MER53 has 69 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1A has 63 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TC)n has 71 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MB5 has 114 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_Alu has 109 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA6 has 20 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1P1 has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1L has 54 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1J2 has 50 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie5 has 15 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1F2-int has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1F2 has 64 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSx3 has 488 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16C has 51 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_ERVL-E-int has 48 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR_MamRep1527 has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA8 has 36 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAT)n has 69 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME3G has 93 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA9 has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR12F has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MER45A has 39 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER58A has 125 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA14 has 18 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AACAA)n has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GT)n has 175 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCCT)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYj4 has 32 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCT)n has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Low_complexity_A-rich has 252 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L2_L2 has 356 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTC)n has 31 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_FLAM_A has 162 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER47A has 18 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME4c has 60 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSc has 390 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AC)n has 204 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA4 has 58 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AT)n has 159 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL?_LTR89 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MC has 47 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TA)n has 129 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAC)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA7 has 34 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA16 has 32 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA2 has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M2 has 22 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PREC2 has 14 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PB has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger5b has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSc5 has 41 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger5 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M5 has 298 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M4 has 101 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAAA)n has 11 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_HAL1M8 has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1?_LTR103b_Mam has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1H2 has 52 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGT)n has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYk2 has 66 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4D1 has 14 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4CL34 has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/RTE-X_L5 has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1H has 99 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAC)n has 33 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTGTG)n has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1I has 104 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTA)n has 30 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(T)n has 271 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR?_MamRep605 has 22 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR33 has 56 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MSTB1 has 47 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSq has 159 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CT)n has 60 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1C has 104 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TG)n has 222 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_FAM has 40 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME3E has 48 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR33B has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER65D has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1D has 181 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_THE1C has 80 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTATATA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAA)n has 35 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA5 has 14 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AACTGT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAA)n has 22 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_FLAM_C has 347 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Retroposon/SVA_SVA_E has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER1A has 41 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER21C has 66 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR41 has 37 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAA)n has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MB8 has 137 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger12 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR37A has 16 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTTT)n has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSg4 has 144 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_MER11B has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCATCC)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME4b has 142 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluJr4 has 126 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept snRNA_U6 has 21 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER8 has 11 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie29b has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MSTD has 62 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Low_complexity_GA-rich has 301 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSg7 has 83 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie4z has 76 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAAC)n has 33 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTAT)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCCTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTT)n has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2B3 has 25 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER31-int has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGGCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER92-int has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER34A has 24 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MC3 has 70 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MSTA1 has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4A1 has 17 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTAACC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4E has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTTT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYc has 56 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4A has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCT)n has 31 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie30a has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACA)n has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER31B has 18 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA4 has 39 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_FRAM has 244 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER112 has 20 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/MULE-MuDR_Ricksha_0 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger4b has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie20a has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MD3 has 76 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYf1 has 30 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MC4a has 65 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER39B has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME1 has 258 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTA)n has 16 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATTTAA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR9B has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER5A has 252 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4E1 has 23 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1E3 has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR37B has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1O has 30 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MSTA has 139 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER103C has 65 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR39 has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME3B has 55 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA5 has 26 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME3D has 31 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1P4 has 18 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA2 has 23 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR22C2 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA15-16 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME3A has 158 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA8A has 11 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA8 has 75 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA10 has 27 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MEd has 132 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_ERVL-int has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA7 has 27 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATA)n has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M7 has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_HAL1b has 15 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1H1 has 28 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_THE1B has 177 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_THE1B-int has 14 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1H1-int has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR64 has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_THE1D has 65 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AG)n has 30 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACACA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TACACACTCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Gln-CAA_ has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Ser-TCA(m) has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA?_MER124 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA11 has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATATA)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATC)n has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTTT)n has 11 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTATTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER3 has 118 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER5A1 has 119 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME3 has 63 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MEg has 76 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MC4 has 189 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_LTR104_Mam has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Blackjack_BLACKJACK has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M3 has 42 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR7 has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER67C has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAATG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR67B has 31 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR79 has 48 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie1b has 38 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie1a has 56 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger3a has 42 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER113A has 23 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACATACAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER44B has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Chap1a_Mam has 22 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1C2 has 210 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M8 has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER102c has 27 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L2_L2d2 has 325 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger7 has 19 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATT)n has 18 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER41D has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_HAL1 has 127 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1A1 has 57 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR78 has 19 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTTTT)n has 18 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACC)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Retroposon/SVA_SVA_D has 20 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M4b has 14 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(A)n has 191 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON76 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1B has 179 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MC1 has 73 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L2_L2d has 176 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA3 has 32 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie19a has 20 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTGCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME4a has 192 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCT)n has 22 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR2C has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATGTG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GATAGTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MB4 has 41 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Satellite_BSR/Beta has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSc8 has 243 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER101-int has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LOR1-int has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR57-int has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_HERVK9-int has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_MER9a2 has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER102a has 35 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGATG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER67A has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER2 has 48 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_HERV16-int has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCAG)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCGC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L2_X24_LINE has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MEc has 125 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCAG)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCCC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGC)n has 11 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCCT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCT)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCTGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCCC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGGTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCCTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCC)n has 11 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCG)n has 15 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGCC)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCC)n has 24 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTC)n has 25 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGCC)n has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGA)n has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCGGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGC)n has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGGG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCACCC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Blackjack_MER63B has 17 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCGTG)n has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAC)n has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTAT)n has 26 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4A1_ has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTCT)n has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCCG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1G1 has 27 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCCCC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYa5 has 17 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MD has 60 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR12C has 15 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCGC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCC)n has 16 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCCTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCAGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCCC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGCCC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(C)n has 19 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCACAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGCC)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGCC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTGCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCATC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCGGCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCC)n has 26 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGG)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR13A has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTGCA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGCAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCAGG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCG)n has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger19b has 23 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MEf has 52 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M4a2 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTCC)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER49 has 36 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME2 has 115 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER50 has 19 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSx4 has 143 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PB1 has 46 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCAGC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGCGG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR?_MamRep1151 has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTG)n has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER41B has 29 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTGG)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER30 has 43 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTCC)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_Arthur1B has 16 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCTCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCATCCCCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCGGA)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTCTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACCCC)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCC)n has 19 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR46 has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTG)n has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCGCCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCCC)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATC)n has 28 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCATGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_MER9a1 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCGCGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAG)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTGCC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGCC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGCCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CA)n has 154 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCG)n has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGCGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCAGCT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGCCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCCGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCCAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR13 has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR13_ has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCAGCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER54A has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER68 has 15 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGTCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_L3b has 66 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4B has 26 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4B-int has 11 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYe5 has 15 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCA)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCTG)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCACAT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGGAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCA)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGCGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(G)n has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCG)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGC)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCAGG)n has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCT)n has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGCA)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGGACA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACTGGTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCGC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCCC)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16B1 has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCAGG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSq4 has 20 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYc3 has 11 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGTGAATG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYh3 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER20 has 196 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCGGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tag1_hAT-1_Mam has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Blackjack_MER63D has 19 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTG)n has 19 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER66C has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYk3 has 67 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER66-int has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept srpRNA_7SLRNA has 17 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYh9 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER74B has 11 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR5_Hs has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGGTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTCCCAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR2B has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_Harlequin-int has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCCTGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR75 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAGCT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCTGC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGAG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCCCCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYa8 has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR10A has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERV9N-int has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERVIP10F-int has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA?_MER131 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HUERS-P1-int has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR7B has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCGCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGG)n has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGGC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M1 has 19 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie22a has 16 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGGCC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGGCC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGAGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACG)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER65A has 29 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER65-int has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER52A has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TACA)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTG)n has 19 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAT)n has 53 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER52C has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTCC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER83 has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAAA)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCCC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GC)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGCTCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGCC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER113 has 39 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR26B has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR42 has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4D has 11 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGTGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCCAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCCT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCAGGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCC)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCATC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M4c has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M4a1 has 28 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger1a_Art has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER65B has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCTCAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYi6 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1P5 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAC)n has 38 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Ac_OldhAT1 has 28 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MC5 has 91 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGT)n has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCACCCA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_ORSL-2b has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGTGCGGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYm1 has 39 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger1 has 62 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATG)n has 17 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAACA)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger16a has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_Arthur1A has 16 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MEb has 18 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTAA)n has 14 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTTT)n has 20 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger15a has 37 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTA)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_X26_DNA has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie15b has 22 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger2 has 11 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER44A has 22 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER119 has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_X7A_LINE has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER1B has 29 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie1 has 26 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept rRNA_LSU-rRNA_Hsa has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CG)n has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCTCC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACGCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTT)n has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACATGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16E1 has 19 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_ERV3-16A3_I-int has 16 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_ERV3-16A3_LTR has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2D has 36 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MB2 has 54 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGCTG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCAGCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie2b has 15 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Blackjack_MER81 has 53 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGAGT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER89 has 14 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCC)n has 26 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MER91B has 21 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGAA)n has 39 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER31A has 22 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1A0 has 113 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MSTB has 64 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCCCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR18B has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie8 has 42 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAAT)n has 23 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACAGCCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAAC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER46C has 18 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCTCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCA)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAGGCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_LTR81B has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR40a has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_LTR81 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTT)n has 17 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCCTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGAGGCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER58B has 78 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER74A has 18 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Blackjack_MER63A has 31 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCA)n has 26 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR52 has 11 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tc2_Kanga1 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGTGG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT-int has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie16 has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2C1 has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCA)n has 18 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger3c has 17 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MER91A has 28 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGTGA)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGTGATGAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2B1 has 19 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER47B has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MB7 has 225 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GACGCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCA)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGC)n has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGGCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/tRNA-Deu_AmnSINE2 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCAG)n has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCAG)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTC)n has 22 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAG)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/RTE-BovB_MamRTE1 has 50 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACACGGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTTTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_THE1A has 32 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACT)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MER45B has 11 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCTGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTGAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGG)n has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGGGC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGGCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCCCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTT)n has 31 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTGAG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATTT)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_LTR81C has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1J has 101 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger2a has 21 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAG)n has 25 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER66B has 23 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR41C has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER34C_ has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER58C has 17 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCCCT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCGAGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCGCGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR26 has 21 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGCAGAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCTGGA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAGGC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCACG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AACC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGCCG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGA)n has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGCCC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCGG)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACATA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGCTCCTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCAAGAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCC)n has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCTGGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCGCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCGGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTG)n has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGGCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCT)n has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAGGCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATA)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCTCT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTCCC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATT)n has 21 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCCTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTT)n has 26 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept scRNA_BC200 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR40c has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER34 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTCTGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTCTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGCCGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR19A has 11 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCGC)n has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGTGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGAGTCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTGGAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MCc has 17 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/5S-Deu-L2_AmnSINE1 has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MSTA-int has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_HERVL-int has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCACGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCA)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR27C has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCTGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger2b_Pri has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER117 has 47 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTCTGGGCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCAGGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCTTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR_LTR106_Mam has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1H-int has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/PiggyBac_MER75 has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTGCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2B4 has 36 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger17c has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger17d has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGGTGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie25 has 17 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER51A has 11 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MER96 has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR40b has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCT)n has 15 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGTGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGGA)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTT)n has 20 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGAGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACCCCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1E2 has 23 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACCC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACACATGCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_ERVL-B4-int has 23 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER107 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTTCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_Arthur1C has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MER45C has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_THE1C-int has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCC)n has 18 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCC)n has 11 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT?_MER121 has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA12 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYg6 has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16A1 has 22 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MSTB-int has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTGTC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MDa has 11 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATG)n has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCTTC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTTC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME3Cz has 122 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME2z has 53 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA15 has 26 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTGCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCTG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAT)n has 30 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTC)n has 18 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGCC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGAGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCATTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACACATG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGT)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGTGCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTA)n has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Gly-GGA has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTTCT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAAACA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCATG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON16 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTA)n has 37 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGGCCA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTTAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA?_Eulor10 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAT)n has 14 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGGCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGT)n has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTACACATG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGTGTGCAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATGCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCA)n has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCTC)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTCC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA10 has 19 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGG)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA_X30_DNA has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGACA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MC2 has 39 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCAT)n has 26 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGA)n has 15 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MamTip2b has 47 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCGGAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGGGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGAAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGTTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTC)n has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTTC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON80_AMi has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGA)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACATG)n has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTCT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTG)n has 14 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME3C has 25 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGGCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MEh has 25 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA5A has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGCA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCACCCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGTTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGCAAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACAGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTCCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCACCTCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR10B2 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR21C has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTC)n has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR62 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCAGC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGACA)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCACC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGCCC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GACCTCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_THE1D-int has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCGG)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER44C has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATATT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCTGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCC)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie4a has 33 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER34C2 has 16 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATTTTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER39 has 18 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYh3a3 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Mariner_MADE2 has 21 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCAGGTG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATGTGC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTTCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCTGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCCC)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Satellite_MSR1 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGTGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTCGTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGCCC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGGC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCGCGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept RC/Helitron_Helitron3Na_Mam has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR5B has 16 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR45C has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGTG)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGATGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger3d has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GACCAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR8A has 22 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR38 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR38-int has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_HERVK13-int has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR3B_ has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1E1 has 15 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GACA)n has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR78B has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACTGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MB1 has 11 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger4a has 23 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGCGCC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTGT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MD2 has 50 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MER115 has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MEi has 17 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER77 has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR3A has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAT)n has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER77B has 20 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR33C has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy?_LTR85c has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tc2_Kanga1a has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tc2_Kanga1d has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR105_Mam has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTGA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2B2 has 11 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR40A1 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_HERVL40-int has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTTGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/PiggyBac_MER85 has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1J1 has 19 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCCTG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER92B has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR10E has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Blackjack_MER63C has 18 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16E2 has 14 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTG)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCTAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1E1A has 18 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA13 has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tc2_Kanga1b has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTCTT)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1F1 has 15 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/RTE-X_L4_B_Mam has 15 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MamRep4096 has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME5 has 31 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCACACACA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCACTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCTCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER21B has 17 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1P2 has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy?_LTR85a has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger19a has 36 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGG)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_HAL1ME has 24 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR24C has 15 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA1 has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATGG)n has 22 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MSTC has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGGCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYb8 has 16 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER21A has 15 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTACA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_X6a_DNA has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MSTB2 has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCTG)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTGCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCA)n has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATT)n has 34 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTTTC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTC)n has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2A2 has 20 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGACCTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER48 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERVH-int has 25 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA17 has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PB4 has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR53B has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAT)n has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER61B has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME3F has 45 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Leu-TTA has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1A0-int has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TT)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATAT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTCA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LOR1a has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAT)n has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR33A_ has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTC)n has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER57B1 has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GATCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYh7 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCCA)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTTCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGACT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16A2 has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR53 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_X17_LINE has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGGCA)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER90 has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTATAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR50 has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GA)n has 27 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR1A2 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTGTT)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL?_LTR89B has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAAGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_MER11A has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_LTR81AB has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR1B1 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER61D has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER61A has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_Arthur1 has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_X7B_LINE has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR8 has 20 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCTA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100?_Eutr15 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA6 has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MamRep434 has 25 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCACACACATGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PB2 has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1P3 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR_EUTREP8 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_CR1_Mam has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MER45R has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTTT)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCT)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger17a has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MamTip1 has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGTT)n has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR101_Mam has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTGT)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTCTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATCAT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCAGCAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1G has 19 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger11a has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER110 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1N2 has 29 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR66 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_HERVL66-int has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATAA)n has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER54B has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATATA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER70A has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1E has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCACAG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACAGCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MD1 has 49 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_MamGypsy2-I has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATCCATT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATGAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR_LTR90B has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGGAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTTC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGTGAGGCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGATGA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER20B has 17 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGAGCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAACC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCATGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCCT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATCAT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAA)n has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/tRNA-RTE_MamSINE1 has 33 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTTG)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16D has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1F has 31 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR24 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR24B has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER57A1 has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATATACGTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACATAC)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR10C has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1J-int has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Mariner_MADE1 has 33 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2A1 has 14 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR48B has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER34-int has 17 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR49-int has 14 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Glu-GAA has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATATC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER70B has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT?_MER121B has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/tRNA_LFSINE_Vert has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger9b has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L2_UCON49 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTA)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tc2_Kanga2_a has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGATTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGG)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GATA)n has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_MamGypLTR1a has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTCCT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATGA)n has 16 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAT)n has 20 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GATG)n has 19 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger23a has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATTT)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MEj has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/RTE-X_L4_A_Mam has 38 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT_MamRep38 has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PB3 has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR23 has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_ERV24_Prim-int has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR23-int has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER34B has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR54 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTC)n has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER34C has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/PiggyBac_MER75A has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16A has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M6 has 15 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER44D has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER34A1 has 11 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAATCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGAT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/RTE-X_L4_C_Mam has 25 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACACAT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AACTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100?_MamRep1879 has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTGTTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Blackjack_MER94 has 24 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_X12_DNA has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAAA)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16B2 has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTCTCCCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGCTG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTAT)n has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATC)n has 14 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_Zaphod3 has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTT)n has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAGAC)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACTATT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER58D has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MamTip2 has 35 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR33A has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER41A has 24 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR8B has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER5C has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON63 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGA)n has 32 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GACCCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCATGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGAGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAAGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1C2-int has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger13a has 20 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16D2 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_ORSL has 20 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Mariner_MARNA has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAG)n has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAGGAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCTCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1G1-int has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR84b has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR1C has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_THE1A-int has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTT)n has 14 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACAC)n has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATACA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR_LTR91 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGTGG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCTGCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTTAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTGTGTGCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCA)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGGTTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR41B has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR_LTR91A has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGTA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAGA)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGA)n has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSq10 has 21 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGAG)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/Jockey_X12_LINE has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGCAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCAGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER105 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR19B has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGACCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERVIP10FH-int has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie16a has 18 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_FordPrefect_a has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_MER11C has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCCACCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGACCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy?_LTR85b has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR71B has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATG)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16D1 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA3 has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_X9_LINE has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GATGGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTGGGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCGCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCAGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie18a has 26 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER51E has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATACA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MER91C has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCACAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCTTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR86A1 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCTCAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept snRNA_U2 has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR83 has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCAC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR75_1 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER72 has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCTCCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAA)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGGGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCCGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAGGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGG)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L2_L2-3_Crp has 11 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON88 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTC)n has 19 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACCCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCACA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAAGCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAAAA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie32a has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCAAGAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCTCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCC)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAAT)n has 14 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1D-int has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCCAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCCCT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER52D has 21 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L2_X15_LINE has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR12E has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATA)n has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACACAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCCC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTCCCCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCAAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGGCAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGTGTGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCTGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCTCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_MamRep564 has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCCCC)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCTGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCGCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAAAAAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAATA)n has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR21B has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERVFH21-int has 14 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR14B has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYk4 has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTATT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGC)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Cheshire has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR?_MamRep605b has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGTTTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR12 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACCTC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER101 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_X7C_LINE has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTTTT)n has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR36 has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATACAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA_MER126 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA_MER130 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON22 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCATC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1M has 22 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCCAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR71A has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept snRNA_U13_ has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/Penelope_Penelope1_Vert has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCGGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TACATA)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTTT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTTTTG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACTATTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger14a has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie7a has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MamRep137 has 15 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAG)n has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie29a has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTCC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTG)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie17a has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Blackjack_MER94B has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTTA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAC)n has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTATT)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA?_Eulor2A has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACCAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON20 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTCC)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTGGT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_CR1-3_Croc has 15 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCTT)n has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCTTCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCTCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTTCT)n has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR86A2 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATGAT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER102b has 23 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_X5B_LINE has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTATTAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_X20_LINE has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept snRNA_U1 has 11 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTG)n has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATAGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATATACAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GACATTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGCAGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/Dong-R4_Mam_R4 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTATC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTAGCTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTTTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2C2 has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/MULE-MuDR_Ricksha_c has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1G3 has 15 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA_X9a_DNA has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAGA)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_CR1-16_AMi has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MER96B has 22 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACTCA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAGCAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGCTGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACC)n has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTACTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR1F has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON57 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCAGTGCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie24 has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGTGAG)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGCCTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGG)n has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCTCCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAATT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGCGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATTTT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/PiggyBac_MER75B has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAACA)n has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATTT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACACACG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATAT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTATGCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAGTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept rRNA_5S has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGGTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAA)n has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTCTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER90a has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA4A has 21 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGT)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATACC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATAT)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATA)n has 18 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger3b has 22 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAAA)n has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Glu-GAG_ has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATAACAA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_Zaphod has 14 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER84 has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER84-int has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept scRNA_HY1 has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATCAC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCGCGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MARE4 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTATAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAATAG)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie10 has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR9D has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Retroposon/SVA_SVA_F has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCTGAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie26a has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie21a has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_MamGypLTR2c has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTTCTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Ac_EuthAT-2 has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AACA)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATGTAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTTA)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGGG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR29 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Retroposon/SVA_SVA_B has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATTTTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_MamGypLTR1b has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATAGAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATTAA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAGG)n has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie10a has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger16b has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGAGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGGTCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR86C has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCACA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGTGGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept snRNA_U5 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCACTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L2_L2-2_Mam has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATAAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie2a has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGGAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAGAAAA)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTAA)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tc2_MER104 has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAGAA)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Retroposon/SVA_SVA_A has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATAATA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tc2_Kanga11a has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER5C1 has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATGAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATTAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATAAGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie23a has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTATC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger9a has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTTTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie17 has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATACTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCAA)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCGCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATTA)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTATA)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MCb has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAATGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Retroposon/SVA_SVA_C has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAG)n has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tc1_X32_DNA has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGCCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MCa has 27 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCT)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA_MARE8 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGCGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER51B has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTTAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERV9-int has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR47A has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_MER9a3 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR82A has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR21A has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR77 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR61 has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAACT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER106B has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCTAC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCCCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR56 has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTTTT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4C has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER41E has 10 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYe6 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERVFH19-int has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER110A has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGTGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYd8 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_MER11D has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCAGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger10 has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_hAT-N1a_Mam has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_MamGypLTR3a has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL?_Eutr10 has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR26E has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAGAA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAAAC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTATTT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MamRep488 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCGCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGGC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGCGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2B5 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAGAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCTCC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCACC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie7 has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTCTG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGCTGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTTTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Low_complexity_polypurine has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_Zaphod2 has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCCCCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACAAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGC)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCCAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAACAC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR9 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR60B has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR2 has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGGCGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATTC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER57C1 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGCT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTATT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER21-int has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYi6_4d has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAAT)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCCTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCTGGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCCTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger4 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER47C has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCTTT)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_ORSL-2a has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGCG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACA)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept RNA_7SK has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAAAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR1D1 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR51 has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCTC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTTTTATT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tc2_Kanga1c has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTAT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTCTCTGTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCATT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCAGCC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_Zaphod5a has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGTGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATATAT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACCGCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGTTTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTTTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_X7D_LINE has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAATT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER106A has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCTTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTCTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATTTTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATAAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR84a has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACAGAAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTGTT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGTTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGA)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGTAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA?_UCON14 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON70 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/Kolobok_UCON29 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie13a has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2F has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAATTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L2_L2-1_AMi has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATCTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTATACA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTTCC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTCCCTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCGTCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGCGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GACAGA)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCAGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTTCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGCCCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTCCT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCCTC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCCCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCCCGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGG)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGGCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGCTGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTGC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT?_UCON105 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TACATG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_UCON81 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTATTTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCTCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT_UCON73 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAAG)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCAGAGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MamTip3 has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie4 has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR26C has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTTC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER2B has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAACC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTAAT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tc1_EutTc1-N2 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAAGGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar_X5a_DNA has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAC)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTTATT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie13b has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCTGG)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TACAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATG)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCACTC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16 has 15 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR43B has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCCAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTCTCCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCC)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCCA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGACAG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1?_LTR103_Mam has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCACG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTATA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_MamGypLTR4 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR47B2 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATTTTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCTCCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCCCA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_X1_DNA has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCAGCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGCTGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCTCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCCCAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tc1_X33a_DNA has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger22N1 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTTTT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCACC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCTCTAGAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATGTG)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept scRNA_HY3 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_ERV24B_Prim-int has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAAATT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Mariner_HSMAR1 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCGCCGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTATTT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTTATTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER34B-int has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGATGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCCTC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR75B has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTTTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACACCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTCTC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT_MamRep1894 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR27B has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGGAGAGTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCAGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR1C1 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTATCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTA)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER67B has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_X6b_DNA has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_UCON34 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTATTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept snRNA_U3 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA_Eutr11 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar_X4b_DNA has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MER97c has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2E has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR65 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Mariner_HSMAR2 has 9 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTCT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTTAGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger8 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER52-int has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR22B1 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERV4_I-int has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_Zaphod4a has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER68-int has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER70C has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR102_Mam has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR10F has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGAGA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER6 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept snRNA_U13 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger6a has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGAAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_PRIMA4-int has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_HERVK-int has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTG)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AACAGAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER41G has 13 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTAATA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1HS has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACTT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR44 has 18 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR45B has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER57A-int has 12 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCCT)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR45 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M3f has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR57 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERV3-int has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger6 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATTAG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Satellite/centr_GSATII has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERVE-int has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTGAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTCTAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAGAACAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAGAAT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCATT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAAGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCCTTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATAA)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR1C3 has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR7Y has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATATTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAAGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTTCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AACATAT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGAGG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GATATA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTGT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERV9NC-int has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR12D has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCTTATA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LOR1b has 7 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy?_LTR88a has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGACA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTATT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTCTT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER6B has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR10B has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGTCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTGTCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR14A has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGCT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATTTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TACATT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TACATTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTGTTTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTCATA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA?_UCON58 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTTGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTCAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MamTip1b has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCTGGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER6C has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR22A has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGCCGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger18a has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAATA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MST-int has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYb9 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/MULE-MuDR_Ricksha has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/MULE-MuDR_Ricksha_a has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR80A has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTCTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATTATT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie31a has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGGAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON35 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR86B1 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON41 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAACAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_MamGypLTR1c has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR82B has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy?_LTR88b has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATGTACATA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATAAC)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept RC?/Helitron?_Helitron2Na_Mam has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAATTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1F-int has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL?_LTR55 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTTCCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA_X34_DNA has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGATAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACAA)n has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER6A has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1I-int has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACAGAG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR9A1 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATATG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger12c has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger12A has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_HERVL74-int has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGAATT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTCCC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTTTCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON71_Crp has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA_MER135 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Thr-ACA has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR32 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCTCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_CR1-1_Amn has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_X1_LINE has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAAAA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16B has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCTCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER127 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGTCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON65 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATCATT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTAATAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCACCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_X6B_LINE has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTCCT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCTCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR10D has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCATCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGGTCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACACT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_PRIMA4_LTR has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGCTCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER57E2 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_MamGypLTR1d has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGCCTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCCAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAGGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_CR1-L3A_Croc has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TACAA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGTGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA_UCON78 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCCTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATGTTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_EUTREP2 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAATTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER57F has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar_DNA1_Mam has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTGTGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie17b has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept snRNA_U7 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTCCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1J1-int has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCTCTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGATGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCATT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGG)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR?_MER129 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_X6A_LINE has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGTCGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATT)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTCAAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGTGCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTGCCCAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MERX has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCGCCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger17 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGCAGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTCCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTATT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTAA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGTGCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCCCA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR_EUTREP16 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTATAAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATTTCT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGCAG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTAGGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCGGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGAAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCCT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR4 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER87 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR49 has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER51D has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Asn-AAC has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_PRIMA41-int has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-His-CAY_ has 6 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Gly-GGG has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Val-GTG has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACTAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Gln-CAG has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTTTTT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAAG)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAACAAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCACA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACCTG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCAGCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTATA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTAATAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR39-int has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_X9b_DNA has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGGAG)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCTAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGCAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCTGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGCTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER57B2 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTTGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGTGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCAGTAACCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTCCTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR54B has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR76 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_MamGypLTR2b has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER50C has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Met_ has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_PABL_B has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCTCTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGGCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGTGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR15 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAGGAAAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCATTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR38B has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERV-Fc2_LTR has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTGACA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCGCGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER61-int has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAGTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGGCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTTTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAAAG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGGCTCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGAGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_LTR81A has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar_UCON44 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON47 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT?_UCON89 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCAGGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTTTTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Chap1_Mam has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTCTCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept RC/Helitron_Helitron1Na_Mam has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept RC/Helitron_Helitron1Nb_Mam has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATTAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGACC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATTA)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCAGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTATTTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATTGTTAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGATGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGA)n has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGCT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGAGGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTCCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGACCTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCACT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCACTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR?_Eutr18 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCAGTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_X11_DNA has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AACCCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA_X23_DNA has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_UCON11 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGTCAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTACTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GATGGA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER41-int has 15 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATTAGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAATAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATTAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTCTCTGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR1F1 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGACGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCTT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCACC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATGCAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCATG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATGC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_X8_LINE has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTGAAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCATCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATGT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AACACC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCCCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAACCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCACC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GACAGAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCGCCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCCCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTACT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCCTCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L2_L2-1_Crp has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAGTGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4D0 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATTCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACCAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTTTT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTATTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER30B has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAGATA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAACT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTAAATGACA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HUERS-P3-int has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA_Eutr2B has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCCCCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAACAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERV35I-int has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept snRNA_U4 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR37-int has 11 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie30b has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTCTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCAGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCCAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR28B has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCATC)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTAAAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGACCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie14a has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAACAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MEg2 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTGTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR14 has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_HERVK14C-int has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER66A has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_MamGyp-int has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1A1-int has 8 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR46-int has 5 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M2a has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4-int has 26 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1P4e has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GATCAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie9 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR26D has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGTGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L2_UCON86 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR_LTR107_Mam has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCTATT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTAGGT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGACAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCGAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GATT)n has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATGATG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA?_UCON21 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger20a has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTCCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGCCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAATTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTTCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCACTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCCCCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCCCCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGTTAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA_Eulor1 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR7C has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCTCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGCCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTTCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCCGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAGTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCAATT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTAC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATATAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER73 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTA)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACTAAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PBa has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR06 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR1E has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR48 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGATAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAGCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTATG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCGCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAAAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAACC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCAGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100?_hAT-N1_Mam has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAACCCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCCTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAATC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTCCCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATGTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGACAGAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCAGGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAAGTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1E3-int has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_PABL_A has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTCTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR28C has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGAAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1K-int has 4 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGCGCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTTTTTT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER57-int has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACAGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCTCCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACACAG)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGGTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTCTGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Val-GTY has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCATG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTATTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCTCTCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAATG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGGGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACCA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCCGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR47A2 has 3 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_UCON38 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATTATA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_Arthur2 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCCCGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTCT)n has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGCT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger21a has 2 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGTCC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR?_Eutr5 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GATTAAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGACTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAAT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER50-int has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTTTTA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar_X4a_DNA has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCATT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGACA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCAAGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGCAGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGCCAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAAGGGA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCTCTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATGTG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TACACA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAAGAAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGGGAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_PABL_B-int has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy?_LTR88c has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGCGGC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie10b has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON4 has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAGCAG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCATTTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTTC)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACAGAGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTAACG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGGT)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tag1_hAT-5_Mam has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCGCG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAGG)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTAAA)n has 1 samples, fewer than min_samples=500; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCAA)n has 1 samples, fewer than min_samples=500; skipping\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0001:GATA-PROP:GATA\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0002:PROP-ALX:Homeodomain\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0003:HNF1A-HNF1B:Homeodomain\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0004:ZSCAN:C2H2_ZF\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0005:POU3F-POU1F:Homeodomain,POU\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0078:CTCF-CTCFL:C2H2_ZF\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0111:CTCF-KLF:C2H2_ZF\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept random_regions\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/MIR_MIR3\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept LINE/L2_L2a\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/MIR_MIR\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept LINE/L2_L2b\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept LINE/L2_L2c\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/Alu_AluSp\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/MIR_MIRb\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/MIR_MIRc\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/Alu_AluJo\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/Alu_AluJr\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/Alu_AluSx\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/Alu_AluSz\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/Alu_AluY\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/Alu_AluJb\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/Alu_AluSq2\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/Alu_AluSg\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/Alu_AluSx1\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/Alu_AluSz6\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept Low_complexity_G-rich\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept random_regions\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0001:GATA-PROP:GATA\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0002:PROP-ALX:Homeodomain\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0003:HNF1A-HNF1B:Homeodomain\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0004:ZSCAN:C2H2_ZF\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0005:POU3F-POU1F:Homeodomain,POU\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0078:CTCF-CTCFL:C2H2_ZF\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 13\n", "INFO:tpcav.cavs:[train] Accuracy: 0.6381\n", "INFO:tpcav.cavs:[val] Accuracy: 0.6600\n", "INFO:tpcav.cavs:[test] Accuracy: 0.5700\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0111:CTCF-KLF:C2H2_ZF\n", "INFO:tpcav.cavs:Best Params: {'alpha': 1e-08} | Iterations: 21\n", "INFO:tpcav.cavs:[train] Accuracy: 0.6953\n", "INFO:tpcav.cavs:[val] Accuracy: 0.6800\n", "INFO:tpcav.cavs:[test] Accuracy: 0.4400\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 16\n", "INFO:tpcav.cavs:[train] Accuracy: 0.6517\n", "INFO:tpcav.cavs:[val] Accuracy: 0.6400\n", "INFO:tpcav.cavs:[test] Accuracy: 0.5400\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 14\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7662\n", "INFO:tpcav.cavs:[val] Accuracy: 0.7300\n", "INFO:tpcav.cavs:[test] Accuracy: 0.7000\n", "INFO:tpcav.cavs:Best Params: {'alpha': 1e-06} | Iterations: 12\n", "INFO:tpcav.cavs:[train] Accuracy: 0.6866\n", "INFO:tpcav.cavs:[val] Accuracy: 0.6800\n", "INFO:tpcav.cavs:[test] Accuracy: 0.5600\n", "INFO:tpcav.cavs:Best Params: {'alpha': 1e-06} | Iterations: 18\n", "INFO:tpcav.cavs:[train] Accuracy: 0.6754\n", "INFO:tpcav.cavs:[val] Accuracy: 0.6600\n", "INFO:tpcav.cavs:[test] Accuracy: 0.5300\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 14\n", "INFO:tpcav.cavs:[train] Accuracy: 0.6841\n", "INFO:tpcav.cavs:[val] Accuracy: 0.6500\n", "INFO:tpcav.cavs:[test] Accuracy: 0.5300\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0001:GATA-PROP:GATA\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0002:PROP-ALX:Homeodomain\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0003:HNF1A-HNF1B:Homeodomain\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0004:ZSCAN:C2H2_ZF\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 11\n", "INFO:tpcav.cavs:[train] Accuracy: 0.6940\n", "INFO:tpcav.cavs:[val] Accuracy: 0.6800\n", "INFO:tpcav.cavs:[test] Accuracy: 0.6500\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0005:POU3F-POU1F:Homeodomain,POU\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 26\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7239\n", "INFO:tpcav.cavs:[val] Accuracy: 0.6700\n", "INFO:tpcav.cavs:[test] Accuracy: 0.6500\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0078:CTCF-CTCFL:C2H2_ZF\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.0001} | Iterations: 17\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7289\n", "INFO:tpcav.cavs:[val] Accuracy: 0.7200\n", "INFO:tpcav.cavs:[test] Accuracy: 0.6200\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 21\n", "INFO:tpcav.cavs:[train] Accuracy: 0.8881\n", "INFO:tpcav.cavs:[val] Accuracy: 0.9200\n", "INFO:tpcav.cavs:[test] Accuracy: 0.8800\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0111:CTCF-KLF:C2H2_ZF\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.0001} | Iterations: 27\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7475\n", "INFO:tpcav.cavs:[val] Accuracy: 0.7300\n", "INFO:tpcav.cavs:[test] Accuracy: 0.6700\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.0001} | Iterations: 13\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7861\n", "INFO:tpcav.cavs:[val] Accuracy: 0.7700\n", "INFO:tpcav.cavs:[test] Accuracy: 0.6400\n", "INFO:tpcav.cavs:Best Params: {'alpha': 1e-06} | Iterations: 18\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7910\n", "INFO:tpcav.cavs:[val] Accuracy: 0.8300\n", "INFO:tpcav.cavs:[test] Accuracy: 0.7900\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0001:GATA-PROP:GATA\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0002:PROP-ALX:Homeodomain\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0003:HNF1A-HNF1B:Homeodomain\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0004:ZSCAN:C2H2_ZF\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.0001} | Iterations: 12\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7662\n", "INFO:tpcav.cavs:[val] Accuracy: 0.8100\n", "INFO:tpcav.cavs:[test] Accuracy: 0.7700\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0005:POU3F-POU1F:Homeodomain,POU\n", "INFO:tpcav.cavs:Best Params: {'alpha': 1e-06} | Iterations: 21\n", "INFO:tpcav.cavs:[train] Accuracy: 0.8172\n", "INFO:tpcav.cavs:[val] Accuracy: 0.8000\n", "INFO:tpcav.cavs:[test] Accuracy: 0.6400\n", "INFO:tpcav.cavs:Best Params: {'alpha': 1e-06} | Iterations: 13\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7935\n", "INFO:tpcav.cavs:[val] Accuracy: 0.8000\n", "INFO:tpcav.cavs:[test] Accuracy: 0.6700\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0078:CTCF-CTCFL:C2H2_ZF\n", "INFO:tpcav.cavs:Best Params: {'alpha': 1e-06} | Iterations: 23\n", "INFO:tpcav.cavs:[train] Accuracy: 0.8284\n", "INFO:tpcav.cavs:[val] Accuracy: 0.8200\n", "INFO:tpcav.cavs:[test] Accuracy: 0.7500\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 11\n", "INFO:tpcav.cavs:[train] Accuracy: 0.9664\n", "INFO:tpcav.cavs:[val] Accuracy: 0.9600\n", "INFO:tpcav.cavs:[test] Accuracy: 0.9300\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0111:CTCF-KLF:C2H2_ZF\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 19\n", "INFO:tpcav.cavs:[train] Accuracy: 0.8520\n", "INFO:tpcav.cavs:[val] Accuracy: 0.8700\n", "INFO:tpcav.cavs:[test] Accuracy: 0.8200\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 15\n", "INFO:tpcav.cavs:[train] Accuracy: 0.8731\n", "INFO:tpcav.cavs:[val] Accuracy: 0.8600\n", "INFO:tpcav.cavs:[test] Accuracy: 0.8100\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/MIR_MIR3\n", "INFO:tpcav.cavs:Submitted CAV training for concept LINE/L2_L2a\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/MIR_MIR\n", "INFO:tpcav.cavs:Submitted CAV training for concept LINE/L2_L2b\n", "INFO:tpcav.cavs:Submitted CAV training for concept LINE/L2_L2c\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/Alu_AluSp\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/MIR_MIRb\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/MIR_MIRc\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/Alu_AluJo\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/Alu_AluJr\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/Alu_AluSx\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/Alu_AluSz\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 14\n", "INFO:tpcav.cavs:[train] Accuracy: 0.8287\n", "INFO:tpcav.cavs:[val] Accuracy: 0.8265\n", "INFO:tpcav.cavs:[test] Accuracy: 0.7347\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 22\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7766\n", "INFO:tpcav.cavs:[val] Accuracy: 0.8469\n", "INFO:tpcav.cavs:[test] Accuracy: 0.6735\n", "INFO:tpcav.cavs:Best Params: {'alpha': 1e-08} | Iterations: 29\n", "INFO:tpcav.cavs:[train] Accuracy: 0.8261\n", "INFO:tpcav.cavs:[val] Accuracy: 0.8469\n", "INFO:tpcav.cavs:[test] Accuracy: 0.7143\n", "INFO:tpcav.cavs:Best Params: {'alpha': 1e-06} | Iterations: 11\n", "INFO:tpcav.cavs:[train] Accuracy: 0.9569\n", "INFO:tpcav.cavs:[val] Accuracy: 0.9490\n", "INFO:tpcav.cavs:[test] Accuracy: 0.9490\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 12\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7982\n", "INFO:tpcav.cavs:[val] Accuracy: 0.6837\n", "INFO:tpcav.cavs:[test] Accuracy: 0.7347\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.0001} | Iterations: 22\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7525\n", "INFO:tpcav.cavs:[val] Accuracy: 0.7449\n", "INFO:tpcav.cavs:[test] Accuracy: 0.6429\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 23\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7741\n", "INFO:tpcav.cavs:[val] Accuracy: 0.7449\n", "INFO:tpcav.cavs:[test] Accuracy: 0.6429\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 21\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.0001} | Iterations: 11\n", "INFO:tpcav.cavs:[train] Accuracy: 0.9086\n", "INFO:tpcav.cavs:[val] Accuracy: 0.8878\n", "INFO:tpcav.cavs:[test] Accuracy: 0.8469\n", "INFO:tpcav.cavs:[train] Accuracy: 0.9036\n", "INFO:tpcav.cavs:[val] Accuracy: 0.9082\n", "INFO:tpcav.cavs:[test] Accuracy: 0.8776\n", "INFO:tpcav.cavs:Best Params: {'alpha': 1e-06} | Iterations: 19\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7728\n", "INFO:tpcav.cavs:[val] Accuracy: 0.7245\n", "INFO:tpcav.cavs:[test] Accuracy: 0.6735\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 17\n", "INFO:tpcav.cavs:[train] Accuracy: 0.9327\n", "INFO:tpcav.cavs:[val] Accuracy: 0.9388\n", "INFO:tpcav.cavs:[test] Accuracy: 0.9184\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 12\n", "INFO:tpcav.cavs:[train] Accuracy: 0.9391\n", "INFO:tpcav.cavs:[val] Accuracy: 0.9388\n", "INFO:tpcav.cavs:[test] Accuracy: 0.8367\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/Alu_AluY\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/Alu_AluJb\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/Alu_AluSq2\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 11\n", "INFO:tpcav.cavs:[train] Accuracy: 0.9772\n", "INFO:tpcav.cavs:[val] Accuracy: 0.9796\n", "INFO:tpcav.cavs:[test] Accuracy: 0.9388\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/Alu_AluSg\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 13\n", "INFO:tpcav.cavs:[train] Accuracy: 0.9023\n", "INFO:tpcav.cavs:[val] Accuracy: 0.9184\n", "INFO:tpcav.cavs:[test] Accuracy: 0.8980\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 12\n", "INFO:tpcav.cavs:[train] Accuracy: 0.9518\n", "INFO:tpcav.cavs:[val] Accuracy: 0.9184\n", "INFO:tpcav.cavs:[test] Accuracy: 0.9184\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/Alu_AluSx1\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/Alu_AluSz6\n", "INFO:tpcav.cavs:Submitted CAV training for concept Low_complexity_G-rich\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.0001} | Iterations: 11\n", "INFO:tpcav.cavs:[train] Accuracy: 0.9695\n", "INFO:tpcav.cavs:[val] Accuracy: 0.9490\n", "INFO:tpcav.cavs:[test] Accuracy: 0.9184\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 11\n", "INFO:tpcav.cavs:[train] Accuracy: 0.9505\n", "INFO:tpcav.cavs:[val] Accuracy: 0.9796\n", "INFO:tpcav.cavs:[test] Accuracy: 0.9490\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 11\n", "INFO:tpcav.cavs:[train] Accuracy: 0.9797\n", "INFO:tpcav.cavs:[val] Accuracy: 0.9796\n", "INFO:tpcav.cavs:[test] Accuracy: 0.9490\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 13\n", "INFO:tpcav.cavs:[train] Accuracy: 0.9264\n", "INFO:tpcav.cavs:[val] Accuracy: 0.9592\n", "INFO:tpcav.cavs:[test] Accuracy: 0.8061\n", "INFO:tpcav.cavs:Skipping CAV AC0001:GATA-PROP:GATA with F-score 0.763 below threshold 0.900\n", "INFO:tpcav.cavs:Skipping CAV AC0003:HNF1A-HNF1B:Homeodomain with F-score 0.747 below threshold 0.900\n", "INFO:tpcav.cavs:Skipping CAV AC0111:CTCF-KLF:C2H2_ZF with F-score 0.824 below threshold 0.900\n", "INFO:tpcav.cavs:Skipping CAV AC0002:PROP-ALX:Homeodomain with F-score 0.617 below threshold 0.900\n", "INFO:tpcav.cavs:Skipping CAV AC0004:ZSCAN:C2H2_ZF with F-score 0.812 below threshold 0.900\n", "INFO:tpcav.cavs:Skipping CAV AC0005:POU3F-POU1F:Homeodomain,POU with F-score 0.680 below threshold 0.900\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "motif_path = \"data/motif-clustering-v2.1beta_consensus_pwms.test.meme\" # motif file in meme format for constructing motif concepts\n", "bed_seq_concept = \"data/hg38_rmsk.sample.bed\" # a bed file to supply concepts described by a set of regions, format [chrom, start, end, label, concept_name]\n", "genome_fasta = \"data/hg38.analysisSet.fa\"\n", "model = torch.load(\"data/mcf7_ctcf_best.pt\", map_location=device, weights_only=False) # load the model\n", "layer_name = \"linear_layer_1\" # name of the layer to be interpreted, you should be able to retrieve the layer object by getattr(model, layer_name)\n", "\n", "# concept_fscores_dataframe: fscores of each concept\n", "# motif_cav_trainers: each trainer contains the cav weights of motifs inserted different number of times\n", "# bed_cav_trainer: trainer contains the cav weights of the sequence concepts provided in bed file\n", "concept_fscores_dataframe, motif_cav_trainers, bed_cav_trainer = run_tpcav(\n", " model=model,\n", " layer_name=layer_name,\n", " motif_file=motif_path,\n", " motif_file_fmt='meme', # specify your motif file format, either meme or consensus (tab delimited file in form [motif_name, consensus_sequence])\n", " genome_fasta=genome_fasta,\n", " num_motif_insertions=[12, 24, 36],\n", " bed_seq_file=bed_seq_concept,\n", " num_samples_for_cav=500,\n", " output_dir=\"test_run_tpcav_output/\",\n", " input_transform_func=transform_fasta_to_one_hot_seq,\n", " p=4) # number of concurrent SGDClassifier can be run at the same time, increase it if you have available CPU power, it speeds up training significantly" ] }, { "cell_type": "code", "execution_count": 6, "id": "fe76a7b3-e897-4cab-9504-0b7191ac52f3", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jmy5455/miniforge3/envs/test/lib/python3.14/site-packages/captum/attr/_core/deep_lift.py:294: UserWarning: Input Tensor 1 did not already require gradients, required_grads has been set automatically.\n", " gradient_mask = apply_gradient_requirements(inputs_tuple)\n", "/home/jmy5455/miniforge3/envs/test/lib/python3.14/site-packages/captum/log/dummy_log.py:39: UserWarning: Setting forward, backward hooks and attributes on non-linear\n", " activations. The hooks and attributes will be removed\n", " after the attribution is finished\n", " return func(*args, **kwargs)\n", "INFO:tpcav.cavs:Skipping CAV AC0001:GATA-PROP:GATA with F-score 0.763 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV AC0003:HNF1A-HNF1B:Homeodomain with F-score 0.747 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV AC0002:PROP-ALX:Homeodomain with F-score 0.617 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV AC0005:POU3F-POU1F:Homeodomain,POU with F-score 0.680 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV SINE/MIR_MIR3 with F-score 0.714 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV LINE/L2_L2a with F-score 0.653 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV SINE/MIR_MIR with F-score 0.692 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV LINE/L2_L2b with F-score 0.735 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV LINE/L2_L2c with F-score 0.711 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV SINE/MIR_MIRb with F-score 0.588 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV SINE/MIR_MIRc with F-score 0.673 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV AC0001:GATA-PROP:GATA with F-score 0.763 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV AC0003:HNF1A-HNF1B:Homeodomain with F-score 0.747 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV AC0002:PROP-ALX:Homeodomain with F-score 0.617 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV AC0005:POU3F-POU1F:Homeodomain,POU with F-score 0.680 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV SINE/MIR_MIR3 with F-score 0.714 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV LINE/L2_L2a with F-score 0.653 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV SINE/MIR_MIR with F-score 0.692 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV LINE/L2_L2b with F-score 0.735 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV LINE/L2_L2c with F-score 0.711 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV SINE/MIR_MIRb with F-score 0.588 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV SINE/MIR_MIRc with F-score 0.673 below threshold 0.800\n", "INFO:tpcav.cavs:PWM not found for Low_complexity_G-rich\n", "INFO:tpcav.cavs:PWM not found for SINE/Alu_AluSz\n", "INFO:tpcav.cavs:PWM not found for SINE/Alu_AluSx\n", "INFO:tpcav.cavs:PWM not found for SINE/Alu_AluSq2\n", "INFO:tpcav.cavs:PWM not found for SINE/Alu_AluSg\n", "INFO:tpcav.cavs:PWM not found for SINE/Alu_AluSz6\n", "INFO:tpcav.cavs:PWM not found for SINE/Alu_AluSx1\n", "INFO:tpcav.cavs:PWM not found for SINE/Alu_AluJb\n", "INFO:tpcav.cavs:PWM not found for SINE/Alu_AluY\n", "INFO:tpcav.cavs:PWM not found for SINE/Alu_AluJr\n", "INFO:tpcav.cavs:PWM not found for SINE/Alu_AluSp\n", "INFO:tpcav.cavs:PWM not found for SINE/Alu_AluJo\n" ] }, { "data": { "image/png": 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yDsGJ78BeQSp4scqGYnYbbJ10/t80Fgs0+73jsbglNVubiMhlRqGYiIiIiIiIiIhcUdq3d9zfvt2EM1XWdBi4lFpDzG6DDTfBT/eaDqL8dDhzAE5vLXdqaKhZY6u0NWtMgHSujOPwYRg/3vzaYoEBFcx0d/w4jB1bsv5ZWhr85S8wY0bJmLLPfeoU7N17nmctxTLL4rC5v2Bh0PcW/hVsYfvvLRy6y0LCAxZibrfw5TUWZtgt9PzMjAUzZaV7BbMLLl8OL79s1hgDM+Xi735nOtIuGX3fhTYPA+ZZKMyC/fNhw1D4tBV82hLWXge2SuaxvFANe5c/dvQT2DvH/N4DOLkZsuLLj2taJhQ7/oXpdBMRuUIpFBMRERERERERkSvKu+9CgwYl+4cOmbW2Sq9/VVZWFvzv7NJh3k2g818cB9gL4cBC+LIzfOAPn7WGuIqnVZwypfyxWbOgSxdYtAh27jQdVsuXw513mqkNS3d53XxzxTWuXg2RkWbKw2bN4PnnHV//61+hU6eS/cJC+MMfzNSRNlvF1ywshE2bzv16dfj4QFRUxa899ZTpNOvRw4R369Zd/P1qlIs79JoPN+2Crn81a3BZXMqP8wyDJjdDj1fguk9q5t5hv634ePQTJpD7sius6QcFmeXHhA4Av3aOx7Y8AOt+C0dXmhA3eQsc+6xmahURucTVwiS3IiIiIiIiIiIil65OneCbb8xaWz/+aI599pnZOnUy0/yFhJjjKSlw4ADs2QM9e5aMp8MTcGqLCRaq6cYb4fHHzRSEpe3ZY9Y7O5+bbjr3axkZpcK7Mnx84OuvzXpgH3xggq59+0xQ1bSpCeXCwsxaYunppkNt1y7IzIS8PLDPKN/KdrYL7KyKxpR2880mvKvIkSNmqzW2AsiMg/RfIeMAFJyBgiyz2QvMemAunuDmCx4Nwb8LhPR1uERscmc27+hMbOyfOXwgi9QTp6Egk4xsLzIKGuLu1YDQUBPw9etnpoC8aD4tzRSKmYfLv5YZV/m5Vjfo/Y7pYis9tWPSerOJiFxhFIqJiIiIiIiIiMgV55prYPNmE/qsXw+7d5tt/34TguXmmqn+goKgSRMYPRqGDi11Aasr9F8Gu5+DmH+agOVc3PxNZ1mzkoTk5ZfBy8us+VVd4eGmo2rHjuqf27QpLFsGc+eatcZ27zZh3C+/wLffQnY2uLmBr69ZH+yGG8x6YS4VNEVdiFtuMWFkncrPgF3PwMFFZj0wgOa3Q9hACG4Nbn5g9QRsJiDLOw3ZCZB1BDCh2KpV8Oc/m6k2z/L29iYqypvINmatN6vVTFt59Ch8+qkJIGskFLNYoNFgOPD2hZ0f0hcG/Be+v63q0zt6Nb6we4mIXOIUiomIiIiIiIiIyBWlbHcTAM2KtlLBVy6QULRtA5YcgLGU6oRy8YRuz0OHx2Hf3yF+BWQfM695hELDq6HxEIi4DVy9HWuwwLPPwpgx8PbbsHhxyXpgpVmtMHiwmV6wtAkT4JFHqvvkJZo2hfuu3QszroeEhJIXXF3hvaUwatSFX7wSLVrAb39rOvXqzIG3IebVkv1Gg6H/UscxP4yBwhwzBWFhqQXmfFry0carGTXKcQrJWbPgiSfA0/Pctz1TSU5abc3/78JDMYBmw+CWX02Ae+Ats+5dWS5e0Gw4tJ9qfu+KiNRDCsVERERERERERC5EcLD5Rjwnx9mVXDk8Pc37fpEudhrACkO1ctKBWGAZMP6c12zf3kyj+PzzsGYNxMebKRu9vaF1a+jTp2Qqx9LuvNOEaidOVKGUiuzeDddfby5gsZgFx159FZKTzQJr+fkmsasFjz9ex6FY24km6Ip9y0w3mLgG1g2CRoPAtzW4+kLrB8CeD7FvQnyp+R3z03j0UcdArH9/ePpp87ZVxtfX/DMry3QlxsZCUpLZ7sq007Ah+PmZ4POFF8xYu93cKz8fhg0zU3YCEBoFvm3hzK9VfOgKimvQHK6aA12egWNfQvZxsOWCexD4d4Kgq8qFtyIi9Y1CMRERERERERGRCxERATExJkSQuhEcbN53J6uJtbUqOxfAPqXy8/38TI51//1Vu4fVWmonOhoGDSppTXvjDbjvPrPY2cCBZg7AO+80ycxdd1XtBtVw441mKspVq6o23qH2InkVzAIYG2uCKldXMwWkl5dZR83NzQ06TTdbXgqc2Amr34Po78G6HqxFF2vWHDr1h8Y3mjXFvBqDfyeaNoVjx0ruc/y4WWfNx+f8tW/ZYjr90ks1Zv3pTyYI9fKq2vObN8EFusyATXdUbXyHP53zJcsL/g771fm9KiJyuVMoJiIiIiIiIiJyoSIiLomQRpzs+PHyx5KTa6SrrTL33AOvvWYyrspERMDddxftbNtmUpqUFLPv7Q2LFpkNTFKTlmbalcaPN8HYvffWeO1z58LatVBQUPm4O++E5s1h5kzYuNEEX6dPQ1gY/H2ynebNzXpenp6mbDDXzMmBjAwTHg4YUHSxlBTzhv3973DyJFx3HTzzjGnPe+45OLQBOm+DJ5+E0TeaZA344ANzjfh4c5lDh8xaa08+aZrtyoZjeXmwb59Zm+7WW+GHH0wAeLZTbNP3Nu6+PY9mjW34+9mxWMClgQe4uGKzlXSKDR9eqlMMIGI07HkB0vZU/qY1GgQdn6p8jIjIFUqhmIiIiIiIiIiIyIU4dcrMezd/PvZc4Pe/N0nMN9/AnBZmnsCpU0vm0QOI/xAOvQtZR6HgDAR2xz7uPdOVZHWH/FQ4sx/2vQqpu806V9gg6Gro8JjD7V1c4NNP4Te/MUFNRYKC4OOPi/K5n36CIUNK0qOZM2HEiPIn7d9vpk7MyTEdZPn58NBDF/9+ldKxIyxfDrfdBoWFFY+59lpYsMA854QJ0L17SSiWlphF8uZjuP+aS4ZXAS5WOxYvT6zt24KLCzabua6XFyVh2BtvmIW+unUzaZanJ+zYYW72wAPms/vgA5g0yaR2jz0Go0cTEeHGzp2mlmXLYOdOMx3i8OGmtoYNzftssZi3NinJ3LtLFxOKde4Mndvkwtdfw4oVsPtT84F0ucPMl/jee6bGYcNg9GgTWrq7l39DrC5w7Qewpr/peKtI6HUw4EOwupCdbabl/N//TE2ZmWZNtzdGmjDR29t04f3wgznVYjGBXEGBWXOuTZuL/JBFRC5BFrvdrv7YOjZlyhReffXV8w+sp/evl/bvd3YFtSMy0tkV1LyDB51dQc0LDXV2BbWjKvNQXG5KT0JfX8TFObuCmlfLf5vXaerjtE718d9/GRnOrqB2NGvm7ApqXlXne7qc3HSTsyuoHQsWOLuCGpWenY3/lCmkpaXh5+fn7HJELinp6en4+/tX7efjxPeQsh2yjkB+OtAQ8gLBpQFYXM36U/ZMk3TkHQGLFSwuJphqdotJF+bNg5dfLpkXb/BgE7TY7bByJfz4ozkeEmIWoLr/fvDwMMcKc+H0Adj2A+zYDAf2QePG0LaD+Y7hyDFo0xq6d4ROXcDbH9x8IaBLhY9z+DCMHQvff+94vFs3Ezy1a1d0IO4nOLYRMo9AYTo0CgP/zuAeCC6eZn2pnKJFyhJ+hexs8+weIdD/SRPKlHEx00YCfPKJyaCOHi11TYvpbHvtNZNbFcvIMCcsXQqrV5s2sDvuMO1Uy5eb0Ck42ARLY8aYBdmKFv7KzDRhWkYG5Oaarc8qx9pjbi+p/ezaXhER0KCBY81ZWaYLbP9+879E2dmmO8zVFfz9zR9TW7QwwZ+vazZ8tgJWfgypKRAaAD07QwM/E4JaLOZm2bmwdSckJUOAL9wyBIbdXfJ7prQT35tpFLPiSx20QKt7oOc/wdVxXsaMDEjacZzM77aT+dNusk9nYe/QCXc/Tyw7o7Fgh65doXt37M0jwWqlRQsTjImI1DfqFBMRERERERERkSuLa0dIc4fYYNNi5elpko9WrUw4deiQmSvvTBpYA6B1a7MFtjIh2Ouvm790dM89pg1o1Kji8AWAiRNNq9B//mP+cubBg6ZL6f77TUfQd9/B1q2mJadTf+g4zLQbAbQYatKb5GRYtwc2JJvQZ8AACKj4cZo3N5fcsgW++sqUcvXVZt0uh/W4InubrYwKg61WF/UOV9nw4XDzzfDRR6YDKzTULHfWuXPpguym3WnlSpNIdetmOvBKp1XPPGO2AwfMFJErVpj3+LbbOOUaxurV5q0/G4q1awfbbrHj6Wm6vaDkIzxfC4G3N3T9qMw6cFawP13Bifn50McHegyB/DOAHRr2AbeiUAxMOGvLhRsaQU6SCWCtuZBzEDw6lL9m6LVwy344/D6k/QLeTSF8hPlnaYWFsGsXPt9+i09cnOlYHNPJhL1nFzT73W/M77UD+2H7dxAfbH6vNbwG8Cx7ZxGRy546xZxg2rRp5OTkOO3+J06cYMmSJU67f70UE+PsCmpHffwb+GX/2lx90KiRsyuoHfXxbz7Xx2cq/dc564v6+DlB/exAqmjF9cudZz394sHb29kV1LyAAGdXUPOWLXN2BbXjwQedXUGNSgf8QZ1iIhWoVqeYXBpOb4PNd0PabsfjgVfB1f+EkH5OKUtEROovdYo5wYsvvujU+0+ZMsWp9xcRERERERERkStcwmr49lYzVWVZKdth7QC46lVo92jd1yYiIvVWPfzrtSIiIiIiIiIiInLJyjkBm8ZUHIidZbfBtj9C/Id1V5eIiNR7CsVERERERERERESk7mz7I+SeqtrYjIO1W4uIiFxRNH2iiIiIiIiIiMiFio+H5GRnV3HlCA6GiAhnVyEXI2UnHK6n61iKiMglT6GYiIiIiIiIiMiFiI+Hdu0gJ8fZlVw5PD0hJkbB2OVs/wJnVyAiIlcwTZ8oIiIiIiIiInIhkpMViNW1nBx15l3ObIVw9CNnVyEiIlcwhWIiIiIiIiIiIiJS+05+Bzknyh8P6AI37oCRp+H6byBsYN3XJiIiVwSFYiIiIiIiIiIiIlL7EteVP9awN1y/AQK7g3ugCcR+uw46/aWOixMRkSuB1hQTERERERERERGR2ncmxnHf4gL93wePoDLHLdDtObBpelIREalZCsVERERERERERESk9qWXCcWa3Qo+Lc49vtuLcOp/tVuTiIhcUTR9ooiIiIiIiIiIiNQuuw3O7Hc8Fn5r5edYXSGkb+3VJCIiVxyFYiIiIiIiIiIiIlK7so5CYbbjsbDfOqcWERG5YikUExERERERERERkdpVdupEVx/wDHNOLSIicsVSKCYiIiIiIiIiIiK1q2wo5tMSLBbn1CIiIlcshWIiIiIiIiIiIiJSu8quJ+bTwjl1iIjIFU2hmIiIiIiIiIiIiNSu/DTH/QYtnVOHiIhc0RSKiYiIiIiIiIiISO0qzHHc927inDpEROSK5ursAkRERERERERERKSes5UJxayejvspu2BVj4rPvS0bXNxrpy4REbmiKBQTERERERERERGR2lW2U8zFo/wYu61uahERkSuWpk8UERERERERERGR2mUrcNy3uDinDhERuaIpFBMREREREREREZHaVbYzzJbnnDpEROSKplBMREREREREREREapdLmTXECnOdU4fIZWLz5s24uLhw8803l3stLy+Pl19+mW7duuHt7U1wcDD9+/fnnXfeIT8/v3jc/PnziYyMxNPTk969e7NlyxaH67z55ptERUXh5+eHxWIhNTW13L2ef/55+vXrh7e3NwEBARXW+uijj9KzZ088PDzo3r17lZ8xMTGRSZMm0bJlSzw8PAgPD+eWW25h3bp1bNiwAYvFUum2YcMGAD788EOioqLw9/fHx8eHrl278uyzz3L69GkAFi9eXOH5b7/99jlrmzlzZrln+e677wgICGDy5MnY7fYKx5QWFRVV4X0LCgoqHG+323nzzTfp3bs3Pj4+BAQEcPXVVzNv3jyysrIAeOuttxgwYACBgYEEBgYyaNCgcp9rVFQUkydPLnf9xYsXO3yGH330EYMHDyYkJAQ/Pz/69u3L119/fc7nKe18n8/AgQMBiIuLq/D1sWPHVuk+tUGhmIiIiIiIiIiIiNQua5lQLD/dOXWIXCYWLlzIpEmT+Pbbbzl+/Hjx8by8PIYMGcLs2bO5//772bRpE1u2bGHixIn885//ZM+ePQAsX76cqVOnMmPGDLZv3063bt0YMmQIJ06cKL5WVlYWQ4cOZfr06eesIy8vj1GjRvHQQw9VWu8999zD6NGjq/x8cXFx9OzZk2+++YZXXnmFn3/+mVWrVjFw4EAmTpxIv379SEhIKN5uu+02hg4d6nCsX79+/PnPf2b06NH06tWLr776it27dzN37lx27tzJf/7zn+L7+fn5OZybkJDAHXfcUeV6v/jiC4YMGcLUqVOZN28eFoulSufdd9995e7r6upa4dg777yTyZMnM2zYMNavX090dDRPP/00K1euZPXq1YAJo26//XbWr1/P5s2bCQ8P54YbbuDYsWNVfpazvv32WwYPHsyXX37Jtm3bGDhwILfccgs7duw477llP5+z2xtvvIHFYuHhhx92GL927VqHcfPnz692vTWl4ndfRERERERERETkMpSe7hi2eHh44OHhcY7RUmdcvRz3M+Mc962u4Blqfp17GuwVd1KIXAkyMjJYvnw5W7duJTExkcWLFxcHV/PmzePbb79l69at9OjRo/icli1bMmrUKPLyzNSkf/vb37jvvvsYP348AAsWLOCLL75g0aJFPPXUUwDF3URnO64qMmvWLMB0GZ3LP/7xDwBOnjzJrl27qvSMDz/8MBaLhS1bttCgQYPi4506deKee+7B3d2dRo0aFR/38vIiNzfX4diWLVt44YUXmDdvHn/84x+Lj0dGRjJ48GCHzjeLxeJwbnUsXbqU8ePHM3fuXB555JFqnevt7V2l+65YsYIlS5bwySefMGzYsOLjkZGR/P73vy/+b9uSJUscznv77bf58MMPWbduHePGjatWbfPmzXPYf+GFF1i5ciWfffaZw++tipT9fAD27t3L448/zvTp0xk1apTDaw0bNrzg97+mKRS7Anl6ejJlyhRnl1GvvPrgg84uQURERERERESA8PBwh/0ZM2Ywc+ZM5xQjJbwdP5dyoZh/R/hDkvn12oFwYkPl17Plw/FVEP8BZB81nWeuDcC7OQT1gMZDwb9DTVUvUqdWrFhB+/btadeuHWPHjmXy5MlMmzYNi8XCkiVLGDRoUIWhhZubG25ubuTl5bFt2zamTZtW/JrVamXQoEFs3ry5Lh+lmMVi4Z133uHuu+/m9OnTrFq1iueff94hEDvrXNM0lrVkyRJ8fHzKdSVV9zqVmT9/PlOnTmXRokXV6iw7n7vvvpu4uLjiQHLJkiW0a9fOIRA7y2Kx4O/vX+F1srKyyM/PJygo6KJrstlsnDlz5oKulZqayrBhw4iKiuK555676Fpqk0KxK9CLL77o7BLqn5gYZ1cgIiIiIiIiIsCRI0fw8/Mr3leXWC3KOgbx/4XjX0BOEhRkgqsPNIiAgK7Q6HoIuQ6sLuDb1vHcjEMXds/CHPh5Jhx8B3JOVDwm7j/AVAjuCzdsurD7iDjRwoULi9dcGjp0KIeSp4kAACXzSURBVGlpaWzcuJGoqCj2799PVFRUpecnJydTWFhIWFiYw/GwsDD27dtXW2VXql27dsXBTmxsLHa7nfbt21/UNffv30/Lli1xc3M779i0tDR8fHyK9318fEhMTKz0nL179/LII4+wcOHCCw7EXnvtNYe1yx544AHmzp1L48aNsdlsxcf3799Pu3btqn39J598kiZNmjBo0KBK7wtQUFCAp2eZqWxLmTNnDhkZGdx2223VqsFmszFmzBhcXV1ZsmRJhVNL9uvXD6u1ZDWv77777rzdaLVFoZiIiIiIiIiIiNQbfn5+DqGY1IK0vfC/h8/dzZW6E459Bnueh4AucNMu8CvzZW/WEbAVmGkTqyo/Hb4ZDKe2VG18snM6YkQuRkxMDFu2bOHjjz8GwNXVldGjR7Nw4UKioqKw2+1OrvDClA7jauoZqnMdX19ftm/fXrx/NqCJj4+nY8eOxcenT59ePFVls2bNCAgI4JVXXuHGG2+kcePG1a7xjjvu4M9//nPx/tnutbKNKxfynsyePZtly5axYcOGcmFX2fsCfPTRR7zwwgsVXmvp0qXMmjWLlStXEhoaWq06pk+fzubNm9myZQu+vr4Vjlm+fDkdOpR075bt6q5LCsVERERERERERESkak58B+uHQmFW1cZnF3Vi+LZxPG4vgLRfILBr1e/94/jygZhXE2hxF/h3MtfMOAhJ38DJ76t+XZFLyMKFCykoKKBJkybFx+x2Ox4eHvzrX/+ibdu25+32Cg4OxsXFhaSkJIfjSUlJl8S6Tm3atMFisVx011rbtm35/vvvyc/PP2+3mNVqpXXr1uWON2nShOjo6OL90lMH+vr6snbtWgYPHszAgQNZv359tYMxf3//Cu9bVlU+19LmzJnD7NmzWbt2LV27lv/3aEX3PVfYtWzZMu69914++OCDch1n57Ns2TLmzJnDF198QZs2bc45Ljw8vErvQ12wnn+IiIiIiIiIiIiIXPHyUuDb4Y6BmGcYdHkWfhcD/5cHt2WazrCr50NI/5Jxbr7gVebL5ISvqn7v1D1w5CPHYx2nwbDD0P0FaHEHtLwLus6Cwd/B736FyDur/YgizlRQUMC7777L3LlziY6OLt527txJkyZNeP/99xkzZgxr165lx44d5c7Pz88nMzMTd3d3evbsybp164pfs9lsrFu3jr59+9blI1UoKCiIIUOGMH/+fDIzM8u9npqaWqXrjBkzhoyMDF577bUKX6/KdVxdXWndunXxVnY9rcDAQNauXYufnx9RUVEcP368SrVV15gxY/j1119ZuXJludfsdjtpaWnF+y+//DLPPfccq1at4uqrr76o+77//vuMHz+e999/n5tvvrla50ZHRzNhwgRmz57NkCFDLqqOuqRQTERERERERERERM7v0HuQd7pk36spDN0GXZ4Gv7ZgdQNXbzNlYtuHYfD3MHBVyfiy64odeg+qOmVY3HuO+40GQ7fnzz39ol8b6Pdu1a4tcon4/PPPSUlJYcKECXTu3NlhGzFiBAsXLmTy5Mn079+f66+/nvnz57Nz504OHjzIihUr6NOnD/v37wdg6tSpvPXWW/z73/9m7969PPTQQ2RmZjJ+/Pji+yUmJhIdHU1sbCwAP//8M9HR0Zw+XfJzHh8fT3R0NPHx8RQWFhYHdRkZGcVjYmNjiY6OJjExkezs7OIxeXl5xWPat29fPCUkwPz58yksLOSaa67hww8/ZP/+/ezdu5d//OMfVQ7uevfuzRNPPMFjjz3GE088webNmzl8+DDr1q1j1KhR/Pvf/76wD6KMgIAA1qxZQ2BgYLlgrPTznt0OHDhw3mtOmzaNcePGFe/fdtttjB49mttvv50XXniBrVu3cvjwYT7//HMGDRrE+vXrAXjppZd4+umnWbRoEZGRkSQmJpKYmOjweVTV0qVLGTduHHPnzqV3797F1yodwJ1LcnIyw4cPJyoqirFjxxafe3Y7efJkteupK5o+UURERERERERERCpnt8OBtxyPdX4avJtWfl7QVSW/9msHJzaW7KfthvgV0Hz0ee5tg7gljsda3g0Wy3nLFrmcLFy4kEGDBuHv71/utREjRvDyyy8TExPDmjVrePXVV3njjTd4/PHH8fb2pkOHDjz66KN07twZgNGjR3Py5EmeeeYZEhMT6d69O6tWrSIsLKz4mgsWLGDWrFnF+9dddx0A77zzDnfffTcAzzzzjEO41KNHDwDWr19PVFQUAPfeey8bN24sN+bQoUNERkYCZq200mFLy5Yt2b59O88//zyPPfYYCQkJhISE0LNnT15//fUqv2cvvfQSPXv2ZP78+SxYsACbzUarVq0YOXIkd911V5Wvcz7+/v6sXr2aoUOH8pvf/IYNGzYA8OuvvxY/71nXX389a9eurfR6CQkJxMfHF+9bLBaWLl3Km2++yaJFi3j++edxdXWlTZs2jBs3rrgT6/XXXycvL4+RI0c6XG/GjBnMnDmzWs/05ptvUlBQwMSJE5k4cWLx8bvuuovFixdXeu4XX3zB4cOHOXz4cIVTSjZv3py4uLhq1VNXLPbLdWU+kUtJTIyzK6gd1VxU8bLwfT2cU/wSmAu6VtTHhbHr4zMdPersCmpeffycAC7gb41d8qz1cNKDMosj1xve3s6uoOYVLZBdryxb5uwKaseDDzq7ghqVDvgDaWlp+NXX/2ZdTrZvh549nV3FlWfbNrjqqnKH09PT8ff3189HbTkTC5+VWi/G4gK3JoJncNWvsf91+N/DjsesbnDtB9BsmNm3FcDXvSAlumTM7+Pg00jH80amgHtAyX5BNtjzy9/T6g4utfBnrMzDcHIT5Cab6SSt7uAeBA2am045j4Y1f08REblo6hQTERERERERERGRymWXWUfHO7x8ILb1UUj+sfy5bR6EVvdA09+XD8Vs+fDdSIgYCZ6NIHENpO1xHJN72nHf1dcxEAPYcn/5KRYB2j4KV//9nI9VLekx8MtLkLgWso5UPta/Ewz+AdzLd/yIiIjzKBQTERERERERERGRytkLHfctFXTtp8fA6f+VP55d1AXm3RSC+0Ly5jLXLoDDlXQsW13KHLCdt9wat38BbJ1kaj3LzQ9CBkCDSMBuusbS90LqbhPs2XLrvk4RkRoQHx9Px44dz/n6L7/8QkRERB1WVHMUiomIiIiIiIiIiEjlPMossZB1DAoywbVByTHvJuDTyhzPSaz4Os3HlA/FznvvEMf9gkzIOgrezUqOeTUC37ZQmANZ8dSo09uLOtyKVqGxekCv16DFOLBW8PVq7mk49im4eNVsHSIidaRJkyZER0dX+vrlSqGYiIiIiIiIiIiIVM6/g+mIyowz+7ZcOP4lRIwqGdPnHfPPY1/Cxpsrvk7Lu2H3TMg9VfV7ezSEwO6O64wd+9xMy3hWj1fMdmqrWZOsJu34E8WBGECn6WY6yHPWG2SeU0TkMuXq6krr1q2dXUatUCgmIiIiIiIiIiIilbNYoeU98PMzJcf2vABNfgeu1eiIcvOBHnPhx7urNr7xUNONFXmHYyi2fwG0mgBWt6rf+0IUZELSN47HWt/vuJ+03oRxZfm1h2a31F5tIiJSbRVM/isiIiIiIiIiIiJSRqvxZurAs1KiYe0AOPIxFOaVHC/IqPw6LcZBoxvOf7+Q/jDgvyaQa347YCl5LXUnfD8aMo9U5wmqrzCn/DE3X8f9Y59D9BPlt7gltVubiIhUmzrFRERERERERERE6pLdDmm/QMoOSP3ZTElYkFG0ZQIWsLqDq4+ZOtCrMbR5GHxbObdu72bQZxFsuqPk2Olt8N0fzNpiHqFQmAU5SZVfx2KB6z6C70ZAwtcVj2kzEa6aAy6eRfduCp2mme60s45+DMdWQqMh5r0pzIVTP17cM5blHgQNmkPm4ZJjx7+CiJEl+81uhQYtIO80/DyjZu8vIiI1SqGYiIiIiIiIiIhIXUndDZvGmDAMTOjS7o8Q3Mes2eXmawKxwlzIT4XsBMg4hMOaVs4UOQbcA+GnCaa2swoyoeBQ+fFu/mYawbJcG8B1n8Lh9+HAQjjzK3g2goa9TAAY1KP8OV2ehexEOLio5JjdBglfQUL54TXCYoGuf4XNd5Yc2zkdgvuaoA4g9FqzZcYrFBMRucQpFBMREREREREREakr34+E9JiS/b7/hqa/K9k/+QP8Or/8eWm/QM+/1UwNhbmQtgdSdkLGAccuNbvdhHJuPiaw82oMTW4Gn8iS85vcCMOOwIn1EPc+JK6GnJNgzwdXP/BpCYHdoPEQ82yuDbDMspyznBJJwE5Y9bbDUfuMokDQ6gK934YmQyH2TUhce+5LNYiElnebjrOz0mPM2l9peyAjFvLPFIV5mWAvNOuTuXqDWwB4hkD4CPOskWPg8DI4/oW5zpn98HkHCL8VwgaCV1MTzqXuqsIzioiIMykUExERERERERERqSudZ5guq8Jss//zLNM1FdTLBFH+naHzM+a11X0gP8382rfNxYdi+RmwfbJZ6+rsWllNb4GQa8313XzNmmG2XMhLg5xEyDhopg4sCsXOnIG1a2HnThd27RpEbOwgzpyBzEw7GRlgsViwWMDbGxo2hCZNYNKkUsFWGWXDsnONKznBAhGjzJYRB0dXmjoLs01Xmm9rCLwK/DuasWDq33QnnPzO7Lt4QcdpEPabouf2A6sn2AtMUJabDFlHwD2g6J5WuO4TiJkHe1400yQWnIFD75rtXLwaVf4sIiJS5xSKiYiIiIiIiIiI1JXI2yHst2YtrNPbTbfWpjvBlmM6tKyeYHUFW54JY3xam3Cl4TUXf+8fbofjn5fsd38JOj5Rsp95GOL/63iOVxNI/Rl76G946y2YPBmyi/I8d3d4/HEYNAg6d7YQEABubqbZLCsLEhLgwAEICbn40itimdvCYf+cgdovL5UEYgBtHoIuT5fs556C9TeaX9vyTNfXWb/5DLzCzGfS4XFoN9msBXdiIyT/CHkpJkR08QKPENOhFtgdQvqXTK8oIiKXDIViIiIiIiIiIiIidckrDFrfb3793//CvfcWJU12cAEa+MK778GwG2v2vqEDHEOxk99D3v2lOqLczJSJALv+XLJmmJs/W9Mf4YEHHC/34oswdWr521gs0KABtG5tNqfr8qyZ3vHoRybwivk72ApMp5hPa9Mhd+1yM63k/x6EhK9LzrXlOV7L6goNe2H5l2NIed4OtxpU7e46uXAF2ZB9DHJOmO5KW25RcGoHiwu4eJhOTzd/aBBhug5F5JKmUExERERERERERKSu2WwwYwb89a9mv2tX+Ne/TED2669w880wezb86U8l0wAC2PIh9WfIOAQ5SWaz5Zkv4y2upruMovH2ArNWllcTaHmX6QoLuhoOvgOnt8Kxz+HDEAjoYtYBc/M13WoFWWYawbM8gunQAW68Eb76quTw22/Db34DV13lWGLZx8zONiGZ03gGw4APICcZUnaQd3I32SdiKUhaCQWZWAoysdkt2HDDbg3C7vsQVq9QXP3DCXBriLUma7EVmqkXbXnms7QXmKDOYjWfn9UDXDzN2maWGr2zVNXpbaa78MS35ufLOxxa3Al+HcAzFNwDTYBsLzDTdmYnQupuE5o1vNrZ1YvIeSgUExERERERERG5EMHB4OkJOTnOruTK4elp3vfLXXo6jB0Ln31m9kND4cknIS0Npk83cxSmpppj0dEmffL2hpRoWD/UfFEP4BYA16+DgK6mgwng8AqzJlZhjvnC3m6H9H3mi/6gntDot2bLyoKPlsI3H4AtG5oWwqlfTBDTsw8MXglBzc3Uja4N8AE+/xw++gg+/RR27oS9e+HqqyEgADp3Bn9/E34VFppHTEiAgwdN9vfEE1yUggLYutXkhUlJcOIE3JdmJzDQ3N/FBebMKQnnCgvNOT17QpcuJm9csyaY2NjBpKYOpnt3E/K1aGHWPvP0MtfIzzdvTWqCuccjbSDA3Vzz7JSQCQnl63v9dbBazZSS3t7g6wutWkG7kB3wy4smNMlJBPeG0PZhaNDcfH5WN0hcC9hNSFaYDQWZ4BkGHf4E7v4X98bVsLJdalCFTjW73QSBeammG89eYJ4VG2A1v3ctbiYMdPMt6Vx0lqSNcOwLKMwy+0FXQcRo8GtnOsOyjsOBt81rBxaa9efOGmMrfz0RuaQoFBMRERERERERuRARERATA8nJzq7kyhEcbN73mpCfYb7Mzk83W2GOCYSs7kXJigWwmy/0sZvXGvY2X9pfjF9/heHDTaIEMGqUSW0OHCgZM3WqSYA+/RTefx/27YNPPoGI7hD1BSSshsw4M73h7mfBt62Z9vBstxHAmRhI/qlkardG15tn+ekneOcdWLbMtHAFBcHIkdC2F/zyCyxdCu+8DtY3YMQIGD8eoqLAasVqNUNHjgTOnCFv3mvEbzhIRqEXmTYvMk55kOfSDLfbbsWneUOCg6FxYxOWXYyUFBNuHTpUcuyGG2D5chOInc/bb8M//wkZGSXHpkyBP/wBfHzOfV5+vnnLZs2CJUtg/35zPCIC5v7RTteu5vm8vU0YZrdDXh6cOWNqTksDWrWHyDshbbfpKCoo+v2Wc9J05GExXXoAp7aYDkBXLxMa5aeWhGJ2O2zZAkuWYP+uu/ncevaEDRvg637w+9/DmDHn//lISioqrJSwsMo/pPwzZt21vNMVv378KxPuWYs63PzamQDsp/uKnvu4+X3YYy74tjadVlYPM31nwRlz/ZwTRSGUBTo+CX5tK3+O2tRhqpneNGWH+TnLOgIHF5nPxMXTjLG6mw6/pr8rCvP8TOApIpc8i91u16SzIhcrJsbZFdSO0FBnV1Dzvv/e2RXUvEaNnF1B7fCrh/Nw18dnOnrU2RXUvPr4OYHjNwD1hbUeTifj6ensCmqHt7ezK6h5VfkG7nKzbJmzK6gdDz7o7ApqVDrgD6SlpeFXX/+bJXKB0tPT8ff3r9rPxy+vwOGl5svuvDRoNhwix4BXUxNI5CTBmQOmo+Xs+lK2fNOR1eOli6rzqgcsBBQ1F+a6wqZwimc7LM1ig2vjwbWo8STZG3a9XvHXeFVdY6rlHy10KMpw7Zh7p3mVH+eVBwNK3ftQAPwyv+iamZkwfz68/DKcOgXdusFLL5l0aNo0+PJL8PCAhx4ynW4V/D9rbq7pwjpxAq7+3LH2lT3suLqaSzRoYLKa9u1N0LRunfkK5sQJOHG8AFtKGuFheQT4FuLqYsfq1wACg7AXZZn5+SY3GjLEdK799BPExsLx43DsmPkjsp+fCcYsFtMpBmbKx4IC88+ZM00X2NKl5t52O4SHw6RJOIRiHh5mfF6eeYtOnzbH+/Sp8KOo+rpg+/ebRG7JEtN216+fCb5uuMHc9ORJWLvWPNgvv8B118Edd5jk0t8f9uyBH34w265dpoWuRQszLisLNm40b6inJ/TqBf37m61FC/h5BhxYZNbUAmh1L7SaYLrYXH1MKJuy3UzReSbWdLgVZEDgVdDuEciIM6FY1jHISzE/T16NzblYzJpcFovpvMo8VDRtZANoOgxyT5jpC7MTTGjm1w4a9jLnWt1MsJhxyJxfkFUyDaV/J2g8xARa2QkmnLPlgneE6UBz8QCs5pjdZrazPxEWFwjsAS7u5T+H/HwsLzge11puIpcnhWIiNUGh2OVDodjloz5+yVMfn0mh2OVDodjlQaHY5UOh2OVDoZjIFaNaoRgm9DhzBnKy7eTnZNNtSQNcAFcLuACbRh7DYrFiwxW71QMbHng3cKNxE0uFU8hVxbm+RK/qlHSZmbB6tckwUlPNzJ3h4dC2rfnPrZubyQgcliCzmSCnaVMzTeDZYxkZ5vmbvV3+3j8OtePigkM4FdEoz3SYvfSSKaBpUxO+uLmVKdxugrHoaFPUww/D5Mms2xbAP/4BP/5oTrdY4P774dprzaX8/c29rFYTZmVnm4amU6fgtttKAitSUkwoN2+eeXHwYDN344IF5g0ZNcqEc927n/fzqHIwVSQnBxJ/TSdh2UZObosn1+ZKrrsfeRl5FHg0wHp1D9zbtcTL24KvL7RsaZ5t0yY4csQ0lWZkmD9GRESY//Vwd3f8vM5+U1tYCL162vA78jYkrIIz+8GWCU2vhdBrwdNMa0nuKTMtY2EeHN1UEvSE3wxt73Gc3tXLC1xdK37u3FyT6J3l7U1yci6nD+yiMGUf5CbjUngKtwaBuHo3xOrmgcVigi0LNjwzNmO3uGGzeGH3jiSk732Ob15KCvzyC5a11zree/AP0KlTuU61vbtz2LPlMFmnEqAgA1cyiWjtT0BwA7y83czvdasLFosd16xfsdlsFNjcyceP8F6D2bp6KzkpCdhy07AX5mL3bkF4q0C8fDxwcbXiWnACqz0Xa0Eqrrnx2Cxu2HDHo9kA/Jp3Nz8c330H69ebbrydO03C2r+/CR83bIBmzWDgQNNJOXBg/f1+RqSeUSgmUhMUil0+FIpdPurjlzz18ZkUil0+FIpdHhSKXT4Uil0+6lkolgYEAEeOHFEoJlJGeno64eHhpKam4n+e+fq2bDFNM6nHs8hNOIVL6il6BR2kUcdAPBo3xHLsKJw8iaVxI2jeHCIisHl64+oKjUOyOPrzDnLSksjPOoMtLxc7ofj4eOPu4YbVClZbGi7uFvCwYyUXOxZsFk/8Wl1LRl4QiYnmj2e5uSYEuWFT+WDqyAQ7pb+1c3GBJk3M7HcnD54hZfshcmPiyD8Qj69XAd6dW+Lmaseyby+W/Dxo0wbatsXeug02Hz+aNjXf48+ebfKqggITek2bZpqGIiJKgimLxbyemWmyjNOnzXf+FalqsPTttybL+vFH06VVWAh33VUSivn5lYRiBQWmiSk93eRe48aBS3ISvPoqvPaaCSxatzZhWN++5gZJSSYxjI01QdBNN5mHu7YohLHZTKfV9u2wYwfs3m0SxR49IDHRHA8MhKuuMse6dSuZWzEvD1atgvfeM1NaWq2m6LFjTUfV55+bbq20NLM/dqwJDNu04fhxkxEeOQKnEvPJOJlN50bJtAjLwqexL+72HMjIxMXHCxo2xB4UhM3iis1m1mv76iuTy5w+mUdhXiZ+3pncdGM+zcML8fQoxGKxYbFYsFgt2HHFjiuFdnc8GjSgXVs7aTFfU5gWhz03GXthAXkNuuHm0xBXd08sFgsu+QlYsGHJT8FSkAq4YHPxxaPtaI6cbMThODspB06TcygB27EEWlkO4ts6DLfwRliPHsZy7Jj5OWnVCnuLltgbBuPuYaFxSAGxq2Kx7dqNx/7dWM+kQbNmeEeGYu/YCRerHff9e7CePolLwlHs/oHktelEXtvO+F/VCt9AMw1ofq6N3KMnyYs7TsOvbgK7tWizgN1K3MBPsDRpjCUkGFcPF9zcIDjIhv3YcbJ+iePMvmPkHztBXmY++TYXXCLDcfVwwRJ/2CSvPj4mLQ4Px9YsgqBOjTnycyq7393OmZQCsnMg3qs9V98USmSk+eO6q2tR+JydhVeM+WGy2yE7sgOdb2lR6b97RMT5FIqJiIiIiIiIXCYOHjxIq1atnF2GyCXtwIEDtGzZ0tlliIiIyCXI1dkFiIiIiIiIiEjVBAUFARAfH3/eTphL2dmOnsu9403PcWlJS0sjIiKi+OdEREREpCyFYiIiIiIiIiKXCWvR1LH+/v6XdXhxlp+fn57jElJfnsNaH6dYFhERkRqhPyWIiIiIiIiIiIiIiIhIvadQTEREREREREREREREROo9hWIiIiIiIiIilwkPDw9mzJiBh4eHs0u5KHqOS4ueQ0RERK4UFrvdbnd2ESIiIiIiIiIiIiIiIiK1SZ1iIiIiIiIiIiIiIiIiUu8pFBMREREREREREREREZF6T6GYiIiIiIiIiIiIiIiI1HsKxURERERERERERERERKTeUygmIiIiIiIiIiIiIiIi9Z5CMREREREREZFLWGRkJBaLxWGbPXt2pefk5OQwceJEGjZsiI+PDyNGjCApKamOKi4vLi6OCRMm0KJFC7y8vGjVqhUzZswgLy+v0vOioqLKPfuDDz5YR1Ub8+fPJzIyEk9PT3r37s2WLVsqHf/BBx/Qvn17PD096dKlC19++WUdVVqxF198kV69euHr60toaCjDhw8nJiam0nMWL15c7n339PSso4orNnPmzHI1tW/fvtJzLrXPQkRERJxPoZiIiIiIiIjIJe7ZZ58lISGheJs0aVKl46dMmcJnn33GBx98wMaNGzl+/Dh/+MMf6qja8vbt24fNZuONN95gz549vPrqqyxYsIDp06ef99z77rvP4dlffvnlOqjYWL58OVOnTmXGjBls376dbt26MWTIEE6cOFHh+E2bNnH77bczYcIEduzYwfDhwxk+fDi7d++us5rL2rhxIxMnTuTHH39kzZo15Ofnc8MNN5CZmVnpeX5+fg7v++HDh+uo4nPr1KmTQ03ff//9Ocdeip+FiIiIOJ/FbrfbnV2EiIiIiIiIiFQsMjKSyZMnM3ny5CqNT0tLIyQkhKVLlzJy5EjAhFIdOnRg8+bN9OnTpxarrbpXXnmF119/nYMHD55zTFRUFN27d2fevHl1V1gpvXv3plevXvzrX/8CwGazER4ezqRJk3jqqafKjR89ejSZmZl8/vnnxcf69OlD9+7dWbBgQZ3VXZmTJ08SGhrKxo0bue666yocs3jxYiZPnkxqamrdFleJmTNn8sknnxAdHV2l8ZfDZyEiIiJ1T51iIiIiIiIiIpe42bNn07BhQ3r06MErr7xCQUHBOcdu27aN/Px8Bg0aVHysffv2REREsHnz5root0rS0tIICgo677glS5YQHBxM586dmTZtGllZWXVQHeTl5bFt2zaH99FqtTJo0KBzvo+bN292GA8wZMiQS+59B8773mdkZNC8eXPCw8MZNmwYe/bsqYvyKrV//36aNGlCy5YtueOOO4iPjz/n2MvhsxAREZG65+rsAkRERERERETk3B599FGuuuoqgoKC2LRpE9OmTSMhIYG//e1vFY5PTEzE3d2dgIAAh+NhYWEkJibWQcXnFxsbyz//+U/mzJlT6bgxY8bQvHlzmjRpwq5du3jyySeJiYnho48+qvUak5OTKSwsJCwszOF4WFgY+/btq/CcxMTECsdfKu+7zWZj8uTJ9O/fn86dO59zXLt27Vi0aBFdu3YlLS2NOXPm0K9fP/bs2UOzZs3qsOISvXv3ZvHixbRr146EhARmzZrFgAED2L17N76+vuXGX+qfhYiIiDiHQjERERERERGROvbUU0/x0ksvVTpm7969tG/fnqlTpxYf69q1K+7u7jzwwAO8+OKLeHh41HaplarOc5x17Ngxhg4dyqhRo7jvvvsqPff+++8v/nWXLl1o3Lgx119/PQcOHKBVq1YXV/wVaOLEiezevbvStbgA+vbtS9++fYv3+/XrR4cOHXjjjTd47rnnarvMCt14443Fv+7atSu9e/emefPmrFixggkTJjilJhEREbn8KBQTERERERERqWOPPfYYd999d6VjWrZsWeHx3r17U1BQQFxcHO3atSv3eqNGjcjLyyM1NdWhWywpKYlGjRpdTNnlVPc5jh8/zsCBA+nXrx9vvvlmte/Xu3dvwHSa1XYoFhwcjIuLC0lJSQ7HK3sfGzVqVK3xdemRRx7h888/59tvv612t5ebmxs9evQgNja2lqqrvoCAANq2bXvOmi7lz0JEREScR6GYiIiIiIiISB0LCQkhJCTkgs6Njo7GarUSGhpa4es9e/bEzc2NdevWMWLECABiYmKIj4936P6pCdV5jmPHjjFw4EB69uzJO++8g9Va/WXOo6OjAWjcuHG1z60ud3d3evbsybp16xg+fDhgph9ct24djzzySIXn9O3bl3Xr1jF58uTiY2vWrKnx97067HY7kyZN4uOPP2bDhg20aNGi2tcoLCzk559/5qabbqqFCi9MRkYGBw4c4M4776zw9UvxsxARERHnq/6fQEVERERERESkTmzevJl58+axc+dODh48yJIlS5gyZQpjx44lMDAQMGFT+/bt2bJlCwD+/v5MmDCBqVOnsn79erZt28b48ePp27cvffr0ccpzHDt2jKioKCIiIpgzZw4nT54kMTHRYX2nss9x4MABnnvuObZt20ZcXByffvop48aN47rrrqNr1651UvfUqVN56623+Pe//83evXt56KGHyMzMZPz48QCMGzeOadOmFY//4x//yKpVq5g7dy779u1j5syZbN269ZwhWl2YOHEi7733HkuXLsXX17f4fc/Ozi4eU/Y5nn32WVavXs3BgwfZvn07Y8eO5fDhw9x7773OeAQAHn/8cTZu3EhcXBybNm3i1ltvxcXFhdtvvx24PD4LERERcT51iomIiIiIiIhcojw8PFi2bBkzZ84kNzeXFi1aMGXKFId1xvLz84mJiSErK6v42KuvvorVamXEiBHk5uYyZMgQXnvtNWc8AmA6dGJjY4mNjS03dZ/dbgfKP4e7uztr165l3rx5ZGZmEh4ezogRI/jLX/5SZ3WPHj2akydP8swzz5CYmEj37t1ZtWoVYWFhAMTHxzt0vPXr14+lS5fyl7/8henTp9OmTRs++eQTOnfuXGc1l/X6668DEBUV5XD8nXfeKZ76suxzpKSkcN9995GYmEhgYCA9e/Zk06ZNdOzYsa7KLufo0aPcfvvtnDp1ipCQEK699lp+/PHH4k7Fy+GzEBEREeez2M/+6VNERERERERERERERESkntL0iSIiIiIiIiIiIiIiIlLvKRQTERERERERERERERGRek+hmIiIiIiIiIiIiIiIiNR7CsVERERERERERERERESk3lMoJiIiIiIiIiIiIiIiIvWeQjERERERERERERERERGp9xSKiYiIiIiIiIiIiIiISL2nUExERERERERERERERETqPYViIiIiIiIiIiIiIiIiUu8pFBMREREREREREREREZF6T6GYiIiIiIiIiIiIiIiI1Hv/D3IIlmf75kXtAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from tpcav import report\n", "\n", "#==================== Compuate layer attributions of target testing regions ================================\n", "# retrieve the tpcav model\n", "tpcav_model = bed_cav_trainer.tpcav\n", "\n", "# create input regions and baseline regions for attribution\n", "ctcf_peaks = helper.load_bed_and_center(\"data/MCF-7_CTCF_ENCFF942TCG.bed\", window=1024).sample(n=100)\n", "random_regions = helper.random_regions_dataframe(genome_fasta + \".fai\", 1024, 100, seed=2)\n", "# create iterators to yield one-hot encoded sequences from the region dataframes\n", "def pack_data_iters(df):\n", " seq_fasta_iter = helper.dataframe_to_fasta_iter(df, genome_fasta, batch_size=8)\n", " seq_one_hot_iter = (helper.fasta_to_one_hot_sequences(seq_fasta) for seq_fasta in seq_fasta_iter)\n", " return zip(seq_one_hot_iter, )\n", "# compute layer attributions given the iterators of testing regions and control regions\n", "attributions = tpcav_model.layer_attributions(pack_data_iters(ctcf_peaks), pack_data_iters(random_regions))\n", "# generate a new html report with TPCAV score computed\n", "report.generate_tpcav_html_report(\"report_tpcav_score.html\", motif_cav_trainers,\n", " non_motif_cav_trainers = {'repeats': bed_cav_trainer},\n", " attributions = [attributions, ], # if you have multiple sets of attributions you can provide a list\n", " motif_file=motif_path, motif_file_fmt='meme', fscore_thresh=0.8)\n", "\n", "# save the trainers for future use\n", "torch.save(motif_cav_trainers, \"motif_cav_trainers.pt\")\n", "torch.save(bed_cav_trainer, \"bed_cav_trainer.pt\")" ] }, { "cell_type": "markdown", "id": "d74495fd-5534-435a-bf7e-e94c0b361e8d", "metadata": {}, "source": [ "## test" ] }, { "cell_type": "code", "execution_count": 3, "id": "4a5faa7d-2307-405f-8e4e-023ba134265a", "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:tpcav.concepts:Concept Simple_repeat_(TAACCC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Satellite/telo_TAR1 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MC5a has 139 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER5B has 126 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCTCC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie15a has 42 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGG)n has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_L3 has 283 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_Plat_L3 has 18 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1K has 212 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/MIR_MIR1_Amn has 209 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSp has 884 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER33 has 97 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACGCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MB3 has 208 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT_MER53 has 69 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1A has 63 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TC)n has 71 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluJo has 676 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MB5 has 114 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_Alu has 109 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA6 has 20 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1P1 has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1L has 54 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1J2 has 50 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie5 has 15 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1F2-int has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1F2 has 64 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSx3 has 488 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16C has 51 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_ERVL-E-int has 48 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR_MamRep1527 has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA8 has 36 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAT)n has 69 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME3G has 93 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA9 has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR12F has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MER45A has 39 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER58A has 125 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA14 has 18 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AACAA)n has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GT)n has 175 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCCT)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYj4 has 32 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCT)n has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Low_complexity_A-rich has 252 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L2_L2 has 356 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTC)n has 31 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_FLAM_A has 162 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER47A has 18 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME4c has 60 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSc has 390 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AC)n has 204 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA4 has 58 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AT)n has 159 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL?_LTR89 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MC has 47 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TA)n has 129 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAC)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA7 has 34 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA16 has 32 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA2 has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M2 has 22 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PREC2 has 14 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PB has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger5b has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSc5 has 41 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger5 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M5 has 298 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M4 has 101 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAAA)n has 11 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_HAL1M8 has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1?_LTR103b_Mam has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1H2 has 52 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGT)n has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYk2 has 66 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4D1 has 14 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4CL34 has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/RTE-X_L5 has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1H has 99 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAC)n has 33 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTGTG)n has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1I has 104 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTA)n has 30 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(T)n has 271 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR?_MamRep605 has 22 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR33 has 56 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MSTB1 has 47 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSq has 159 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CT)n has 60 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1C has 104 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TG)n has 222 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_FAM has 40 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSq2 has 657 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME3E has 48 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR33B has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER65D has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1D has 181 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_THE1C has 80 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTATATA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAA)n has 35 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA5 has 14 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AACTGT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAA)n has 22 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_FLAM_C has 347 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Retroposon/SVA_SVA_E has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSg has 559 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER1A has 41 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER21C has 66 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR41 has 37 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAA)n has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MB8 has 137 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger12 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR37A has 16 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTTT)n has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSg4 has 144 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_MER11B has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCATCC)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME4b has 142 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluJr4 has 126 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept snRNA_U6 has 21 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER8 has 11 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie29b has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MSTD has 62 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Low_complexity_GA-rich has 301 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSg7 has 83 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSz6 has 523 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie4z has 76 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAAC)n has 33 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTAT)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCCTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTT)n has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2B3 has 25 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER31-int has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGGCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER92-int has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER34A has 24 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MC3 has 70 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MSTA1 has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4A1 has 17 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTAACC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4E has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTTT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYc has 56 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4A has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCT)n has 31 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie30a has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACA)n has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER31B has 18 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA4 has 39 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_FRAM has 244 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER112 has 20 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/MULE-MuDR_Ricksha_0 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger4b has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie20a has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MD3 has 76 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYf1 has 30 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MC4a has 65 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER39B has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME1 has 258 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTA)n has 16 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATTTAA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR9B has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER5A has 252 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4E1 has 23 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1E3 has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR37B has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1O has 30 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MSTA has 139 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER103C has 65 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR39 has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME3B has 55 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA5 has 26 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME3D has 31 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1P4 has 18 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA2 has 23 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR22C2 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA15-16 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME3A has 158 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA8A has 11 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA8 has 75 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA10 has 27 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MEd has 132 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_ERVL-int has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA7 has 27 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATA)n has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M7 has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_HAL1b has 15 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1H1 has 28 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_THE1B has 177 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_THE1B-int has 14 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1H1-int has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR64 has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_THE1D has 65 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AG)n has 30 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACACA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TACACACTCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Gln-CAA_ has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Ser-TCA(m) has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA?_MER124 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA11 has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATATA)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATC)n has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTTT)n has 11 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTATTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER3 has 118 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER5A1 has 119 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME3 has 63 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MEg has 76 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MC4 has 189 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_LTR104_Mam has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Blackjack_BLACKJACK has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M3 has 42 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR7 has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER67C has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAATG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR67B has 31 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR79 has 48 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie1b has 38 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie1a has 56 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger3a has 42 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER113A has 23 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACATACAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER44B has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Chap1a_Mam has 22 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1C2 has 210 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M8 has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER102c has 27 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L2_L2d2 has 325 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger7 has 19 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATT)n has 18 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER41D has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_HAL1 has 127 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1A1 has 57 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR78 has 19 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTTTT)n has 18 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACC)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Retroposon/SVA_SVA_D has 20 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M4b has 14 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(A)n has 191 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON76 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1B has 179 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MC1 has 73 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L2_L2d has 176 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA3 has 32 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie19a has 20 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTGCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME4a has 192 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCT)n has 22 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR2C has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATGTG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GATAGTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MB4 has 41 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Satellite_BSR/Beta has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSc8 has 243 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER101-int has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LOR1-int has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR57-int has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_HERVK9-int has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_MER9a2 has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER102a has 35 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGATG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER67A has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER2 has 48 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_HERV16-int has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCAG)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCGC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L2_X24_LINE has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MEc has 125 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCAG)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCCC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGC)n has 11 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCCT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCT)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCTGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCCC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGGTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCCTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCC)n has 11 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCG)n has 15 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGCC)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCC)n has 24 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTC)n has 25 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGCC)n has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGA)n has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCGGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGC)n has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGGG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCACCC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Blackjack_MER63B has 17 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCGTG)n has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAC)n has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTAT)n has 26 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4A1_ has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTCT)n has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCCG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1G1 has 27 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCCCC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYa5 has 17 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MD has 60 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR12C has 15 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCGC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCC)n has 16 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCCTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCAGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCCC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGCCC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(C)n has 19 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCACAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGCC)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGCC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTGCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCATC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCGGCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCC)n has 26 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGG)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR13A has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTGCA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGCAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCAGG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCG)n has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger19b has 23 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MEf has 52 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M4a2 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTCC)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER49 has 36 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME2 has 115 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER50 has 19 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSx4 has 143 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PB1 has 46 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCAGC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGCGG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR?_MamRep1151 has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTG)n has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER41B has 29 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTGG)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER30 has 43 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTCC)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_Arthur1B has 16 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCTCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCATCCCCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCGGA)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTCTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACCCC)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCC)n has 19 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR46 has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTG)n has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCGCCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCCC)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATC)n has 28 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCATGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_MER9a1 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCGCGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAG)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTGCC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGCC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGCCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CA)n has 154 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCG)n has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGCGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCAGCT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGCCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCCGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCCAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR13 has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR13_ has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCAGCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER54A has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER68 has 15 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGTCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_L3b has 66 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4B has 26 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4B-int has 11 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYe5 has 15 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCA)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCTG)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCACAT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGGAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCA)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGCGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(G)n has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCG)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGC)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCAGG)n has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCT)n has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGCA)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGGACA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACTGGTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCGC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCCC)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16B1 has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCAGG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSq4 has 20 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYc3 has 11 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGTGAATG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYh3 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER20 has 196 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCGGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tag1_hAT-1_Mam has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Blackjack_MER63D has 19 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTG)n has 19 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER66C has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYk3 has 67 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER66-int has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept srpRNA_7SLRNA has 17 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYh9 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER74B has 11 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR5_Hs has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGGTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTCCCAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR2B has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_Harlequin-int has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCCTGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR75 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAGCT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCTGC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGAG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCCCCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYa8 has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR10A has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERV9N-int has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERVIP10F-int has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA?_MER131 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HUERS-P1-int has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR7B has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCGCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGG)n has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGGC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M1 has 19 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie22a has 16 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGGCC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGGCC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGAGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACG)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER65A has 29 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER65-int has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER52A has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TACA)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTG)n has 19 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAT)n has 53 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER52C has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTCC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER83 has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAAA)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCCC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GC)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGCTCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGCC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER113 has 39 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR26B has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR42 has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4D has 11 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGTGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCCAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCCT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCAGGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCC)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCATC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M4c has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M4a1 has 28 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger1a_Art has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER65B has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCTCAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYi6 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1P5 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAC)n has 38 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Ac_OldhAT1 has 28 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MC5 has 91 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGT)n has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCACCCA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_ORSL-2b has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGTGCGGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYm1 has 39 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger1 has 62 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATG)n has 17 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAACA)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger16a has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_Arthur1A has 16 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MEb has 18 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTAA)n has 14 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTTT)n has 20 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger15a has 37 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTA)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_X26_DNA has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie15b has 22 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger2 has 11 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER44A has 22 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER119 has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_X7A_LINE has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER1B has 29 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie1 has 26 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept rRNA_LSU-rRNA_Hsa has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CG)n has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCTCC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACGCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTT)n has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACATGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16E1 has 19 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_ERV3-16A3_I-int has 16 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_ERV3-16A3_LTR has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2D has 36 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MB2 has 54 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGCTG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCAGCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie2b has 15 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Blackjack_MER81 has 53 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGAGT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER89 has 14 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCC)n has 26 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MER91B has 21 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGAA)n has 39 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER31A has 22 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1A0 has 113 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MSTB has 64 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCCCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR18B has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie8 has 42 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAAT)n has 23 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACAGCCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAAC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER46C has 18 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCTCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCA)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAGGCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_LTR81B has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR40a has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_LTR81 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTT)n has 17 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCCTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGAGGCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER58B has 78 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER74A has 18 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Blackjack_MER63A has 31 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCA)n has 26 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR52 has 11 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tc2_Kanga1 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGTGG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT-int has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie16 has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2C1 has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCA)n has 18 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger3c has 17 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MER91A has 28 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGTGA)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGTGATGAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2B1 has 19 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER47B has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MB7 has 225 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GACGCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCA)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGC)n has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGGCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/tRNA-Deu_AmnSINE2 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCAG)n has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCAG)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTC)n has 22 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAG)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/RTE-BovB_MamRTE1 has 50 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACACGGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTTTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_THE1A has 32 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACT)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MER45B has 11 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCTGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTGAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGG)n has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGGGC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGGCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCCCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTT)n has 31 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTGAG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATTT)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_LTR81C has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1J has 101 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger2a has 21 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAG)n has 25 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER66B has 23 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR41C has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER34C_ has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER58C has 17 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCCCT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCGAGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCGCGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR26 has 21 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGCAGAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCTGGA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAGGC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCACG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AACC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGCCG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGA)n has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGCCC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCGG)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACATA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGCTCCTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCAAGAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCC)n has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCTGGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCGCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCGGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTG)n has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGGCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCT)n has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAGGCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATA)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCTCT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTCCC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATT)n has 21 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCCTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTT)n has 26 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept scRNA_BC200 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR40c has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER34 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTCTGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTCTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGCCGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR19A has 11 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCGC)n has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGTGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGAGTCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTGGAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MCc has 17 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/5S-Deu-L2_AmnSINE1 has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MSTA-int has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_HERVL-int has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCACGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCA)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR27C has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCTGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger2b_Pri has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER117 has 47 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTCTGGGCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCAGGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCTTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR_LTR106_Mam has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1H-int has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/PiggyBac_MER75 has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTGCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2B4 has 36 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger17c has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger17d has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGGTGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie25 has 17 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER51A has 11 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MER96 has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR40b has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCT)n has 15 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGTGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGGA)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTT)n has 20 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGAGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACCCCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1E2 has 23 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACCC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACACATGCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_ERVL-B4-int has 23 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER107 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTTCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_Arthur1C has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MER45C has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_THE1C-int has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCC)n has 18 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCC)n has 11 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT?_MER121 has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA12 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYg6 has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16A1 has 22 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MSTB-int has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTGTC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MDa has 11 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATG)n has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCTTC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTTC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME3Cz has 122 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME2z has 53 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA15 has 26 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTGCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCTG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAT)n has 30 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTC)n has 18 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGCC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGAGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCATTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACACATG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGT)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGTGCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTA)n has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Gly-GGA has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTTCT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAAACA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCATG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON16 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTA)n has 37 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGGCCA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTTAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA?_Eulor10 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAT)n has 14 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGGCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGT)n has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTACACATG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGTGTGCAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATGCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCA)n has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCTC)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTCC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA10 has 19 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGG)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA_X30_DNA has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGACA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MC2 has 39 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCAT)n has 26 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGA)n has 15 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MamTip2b has 47 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCGGAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGGGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGAAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGTTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTC)n has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTTC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON80_AMi has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGA)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACATG)n has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTCT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTG)n has 14 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME3C has 25 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGGCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MEh has 25 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA5A has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGCA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCACCCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGTTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGCAAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACAGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTCCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCACCTCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR10B2 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR21C has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTC)n has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR62 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCAGC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGACA)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCACC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGCCC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GACCTCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_THE1D-int has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCGG)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER44C has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATATT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCTGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCC)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie4a has 33 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER34C2 has 16 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATTTTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER39 has 18 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYh3a3 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Mariner_MADE2 has 21 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCAGGTG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATGTGC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTTCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCTGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCCC)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Satellite_MSR1 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGTGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTCGTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGCCC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGGC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCGCGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept RC/Helitron_Helitron3Na_Mam has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR5B has 16 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR45C has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGTG)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGATGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger3d has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GACCAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR8A has 22 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR38 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR38-int has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_HERVK13-int has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR3B_ has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1E1 has 15 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GACA)n has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR78B has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACTGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MB1 has 11 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger4a has 23 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGCGCC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTGT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MD2 has 50 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MER115 has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MEi has 17 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER77 has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR3A has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAT)n has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER77B has 20 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR33C has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy?_LTR85c has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tc2_Kanga1a has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tc2_Kanga1d has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR105_Mam has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTGA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2B2 has 11 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR40A1 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_HERVL40-int has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTTGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/PiggyBac_MER85 has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1J1 has 19 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCCTG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER92B has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR10E has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Blackjack_MER63C has 18 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16E2 has 14 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTG)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCTAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1E1A has 18 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA13 has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tc2_Kanga1b has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTCTT)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1F1 has 15 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/RTE-X_L4_B_Mam has 15 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MamRep4096 has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME5 has 31 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCACACACA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCACTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCTCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER21B has 17 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1P2 has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy?_LTR85a has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger19a has 36 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGG)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_HAL1ME has 24 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR24C has 15 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA1 has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATGG)n has 22 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MSTC has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGGCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYb8 has 16 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER21A has 15 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTACA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_X6a_DNA has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MSTB2 has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCTG)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTGCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCA)n has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATT)n has 34 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTTTC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTC)n has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2A2 has 20 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGACCTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER48 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERVH-int has 25 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PA17 has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PB4 has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR53B has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAT)n has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER61B has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1ME3F has 45 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Leu-TTA has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1A0-int has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TT)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATAT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTCA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LOR1a has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAT)n has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR33A_ has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTC)n has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER57B1 has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GATCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYh7 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCCA)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTTCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGACT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16A2 has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR53 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_X17_LINE has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGGCA)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER90 has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTATAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR50 has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GA)n has 27 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR1A2 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTGTT)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL?_LTR89B has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAAGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_MER11A has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_LTR81AB has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR1B1 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER61D has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER61A has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_Arthur1 has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_X7B_LINE has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR8 has 20 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCTA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100?_Eutr15 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA6 has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MamRep434 has 25 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCACACACATGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PB2 has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1P3 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR_EUTREP8 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_CR1_Mam has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MER45R has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTTT)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCT)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger17a has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MamTip1 has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGTT)n has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR101_Mam has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTGT)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTCTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATCAT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCAGCAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1G has 19 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger11a has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER110 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1N2 has 29 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR66 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_HERVL66-int has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATAA)n has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER54B has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATATA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER70A has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1E has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCACAG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACAGCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MD1 has 49 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_MamGypsy2-I has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATCCATT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATGAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR_LTR90B has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGGAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTTC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGTGAGGCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGATGA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER20B has 17 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGAGCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAACC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCATGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCCT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATCAT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAA)n has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/tRNA-RTE_MamSINE1 has 33 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTTG)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16D has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1F has 31 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR24 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR24B has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER57A1 has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATATACGTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACATAC)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR10C has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1J-int has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Mariner_MADE1 has 33 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2A1 has 14 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR48B has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER34-int has 17 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR49-int has 14 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Glu-GAA has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATATC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER70B has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT?_MER121B has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/tRNA_LFSINE_Vert has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger9b has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L2_UCON49 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTA)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tc2_Kanga2_a has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGATTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGG)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GATA)n has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_MamGypLTR1a has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTCCT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATGA)n has 16 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAT)n has 20 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GATG)n has 19 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger23a has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATTT)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MEj has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/RTE-X_L4_A_Mam has 38 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT_MamRep38 has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PB3 has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR23 has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_ERV24_Prim-int has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR23-int has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER34B has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR54 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTC)n has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER34C has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/PiggyBac_MER75A has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16A has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M6 has 15 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER44D has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER34A1 has 11 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAATCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGAT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/RTE-X_L4_C_Mam has 25 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACACAT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AACTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100?_MamRep1879 has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTGTTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Blackjack_MER94 has 24 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_X12_DNA has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAAA)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16B2 has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTCTCCCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGCTG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTAT)n has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATC)n has 14 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_Zaphod3 has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTT)n has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAGAC)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACTATT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER58D has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MamTip2 has 35 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR33A has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER41A has 24 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR8B has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER5C has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON63 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGA)n has 32 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GACCCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCATGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGAGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAAGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1C2-int has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger13a has 20 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16D2 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_ORSL has 20 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Mariner_MARNA has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAG)n has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAGGAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCTCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1G1-int has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR84b has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR1C has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_THE1A-int has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTT)n has 14 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACAC)n has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATACA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR_LTR91 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGTGG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCTGCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTTAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTGTGTGCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCA)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGGTTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR41B has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR_LTR91A has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGTA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAGA)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGA)n has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluSq10 has 21 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGAG)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/Jockey_X12_LINE has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGCAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCAGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER105 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR19B has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGACCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERVIP10FH-int has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie16a has 18 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_FordPrefect_a has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_MER11C has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCCACCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGACCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy?_LTR85b has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR71B has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATG)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16D1 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA3 has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_X9_LINE has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GATGGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTGGGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCGCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCAGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie18a has 26 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER51E has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATACA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MER91C has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCACAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCTTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR86A1 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCTCAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept snRNA_U2 has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR83 has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCAC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR75_1 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER72 has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCTCCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAA)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGGGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCCGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAGGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGG)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L2_L2-3_Crp has 11 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON88 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTC)n has 19 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACCCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCACA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAAGCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAAAA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie32a has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCAAGAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCTCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCC)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAAT)n has 14 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1D-int has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCCAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCCCT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER52D has 21 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L2_X15_LINE has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR12E has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATA)n has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACACAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCCC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTCCCCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCAAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGGCAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGTGTGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCTGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCTCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_MamRep564 has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCCCC)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCTGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCGCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAAAAAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAATA)n has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR21B has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERVFH21-int has 14 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR14B has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYk4 has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTATT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGC)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Cheshire has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR?_MamRep605b has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGTTTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR12 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACCTC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER101 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_X7C_LINE has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTTTT)n has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR36 has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATACAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA_MER126 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA_MER130 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON22 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCATC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1M has 22 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCCAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR71A has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept snRNA_U13_ has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/Penelope_Penelope1_Vert has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCGGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TACATA)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTTT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTTTTG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACTATTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger14a has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie7a has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MamRep137 has 15 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAG)n has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie29a has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTCC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTG)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie17a has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Blackjack_MER94B has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTTA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAC)n has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTATT)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA?_Eulor2A has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACCAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON20 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTCC)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTGGT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_CR1-3_Croc has 15 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCTT)n has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCTTCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCTCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTTCT)n has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR86A2 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATGAT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER102b has 23 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_X5B_LINE has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTATTAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_X20_LINE has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept snRNA_U1 has 11 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTG)n has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATAGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATATACAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GACATTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGCAGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/Dong-R4_Mam_R4 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTATC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTAGCTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTTTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2C2 has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/MULE-MuDR_Ricksha_c has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1G3 has 15 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA_X9a_DNA has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAGA)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_CR1-16_AMi has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MER96B has 22 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACTCA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAGCAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGCTGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACC)n has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTACTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR1F has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON57 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCAGTGCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie24 has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGTGAG)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGCCTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGG)n has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCTCCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAATT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGCGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATTTT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/PiggyBac_MER75B has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAACA)n has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATTT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACACACG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATAT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTATGCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAGTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept rRNA_5S has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGGTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAA)n has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTCTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER90a has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MA4A has 21 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGT)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATACC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATAT)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATA)n has 18 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger3b has 22 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAAA)n has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Glu-GAG_ has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATAACAA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_Zaphod has 14 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER84 has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER84-int has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept scRNA_HY1 has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATCAC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCGCGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MARE4 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTATAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAATAG)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie10 has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR9D has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Retroposon/SVA_SVA_F has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCTGAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie26a has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie21a has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_MamGypLTR2c has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTTCTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Ac_EuthAT-2 has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AACA)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATGTAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTTA)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGGG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR29 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Retroposon/SVA_SVA_B has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATTTTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_MamGypLTR1b has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATAGAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATTAA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAGG)n has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie10a has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger16b has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGAGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGGTCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR86C has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCACA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGTGGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept snRNA_U5 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCACTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L2_L2-2_Mam has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATAAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie2a has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGGAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAGAAAA)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTAA)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tc2_MER104 has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAGAA)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Retroposon/SVA_SVA_A has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATAATA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tc2_Kanga11a has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER5C1 has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATGAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATTAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATAAGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie23a has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTATC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger9a has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTTTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie17 has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATACTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCAA)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCGCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATTA)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTATA)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MCb has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAATGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Retroposon/SVA_SVA_C has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAG)n has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tc1_X32_DNA has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGCCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MCa has 27 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCT)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA_MARE8 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGCGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER51B has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTTAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERV9-int has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR47A has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_MER9a3 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR82A has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR21A has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR77 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR61 has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAACT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER106B has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCTAC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCCCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR56 has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTTTT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4C has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER41E has 10 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYe6 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERVFH19-int has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER110A has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGTGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYd8 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_MER11D has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCAGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger10 has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_hAT-N1a_Mam has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_MamGypLTR3a has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL?_Eutr10 has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR26E has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAGAA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAAAC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTATTT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MamRep488 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCGCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGGC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGCGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2B5 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAGAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCTCC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCACC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie7 has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTCTG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGCTGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTTTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Low_complexity_polypurine has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_Zaphod2 has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCCCCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACAAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGC)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCCAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAACAC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR9 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR60B has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR2 has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGGCGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATTC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER57C1 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGCT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTATT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER21-int has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYi6_4d has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAAT)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCCTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCTGGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCCTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger4 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER47C has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCTTT)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_ORSL-2a has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGCG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACA)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept RNA_7SK has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAAAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR1D1 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR51 has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCTC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTTTTATT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tc2_Kanga1c has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTAT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTCTCTGTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCATT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCAGCC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_Zaphod5a has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGTGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATATAT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACCGCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGTTTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTTTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_X7D_LINE has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAATT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER106A has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCTTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTCTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATTTTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATAAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR84a has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACAGAAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTGTT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGTTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGA)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGTAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA?_UCON14 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON70 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/Kolobok_UCON29 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie13a has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2F has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAATTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L2_L2-1_AMi has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATCTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTATACA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTTCC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTCCCTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCGTCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGCGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GACAGA)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCAGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTTCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGCCCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTCCT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCCTC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCCCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCCCGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGG)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGGCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGCTGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTGC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT?_UCON105 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TACATG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_UCON81 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTATTTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCTCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT_UCON73 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAAG)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCAGAGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MamTip3 has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie4 has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR26C has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTTC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER2B has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAACC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTAAT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tc1_EutTc1-N2 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAAGGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar_X5a_DNA has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAC)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTTATT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie13b has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCTGG)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TACAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATG)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCACTC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16 has 15 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR43B has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCCAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTCTCCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCC)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCCA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGACAG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1?_LTR103_Mam has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCACG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTATA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_MamGypLTR4 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR47B2 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATTTTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCTCCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCCCA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_X1_DNA has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCAGCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGCTGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCTCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCCCAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tc1_X33a_DNA has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger22N1 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTTTT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCACC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCTCTAGAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATGTG)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept scRNA_HY3 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_ERV24B_Prim-int has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAAATT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Mariner_HSMAR1 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCCGCCGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTATTT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTTATTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER34B-int has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGATGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCCTC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR75B has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTTTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACACCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTCTC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT_MamRep1894 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR27B has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGGAGAGTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCAGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR1C1 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTATCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTA)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER67B has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_X6b_DNA has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_UCON34 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTATTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept snRNA_U3 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA_Eutr11 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar_X4b_DNA has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MER97c has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MLT2E has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR65 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Mariner_HSMAR2 has 9 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTCT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTTAGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger8 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER52-int has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR22B1 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERV4_I-int has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_Zaphod4a has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER68-int has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER70C has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR102_Mam has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR10F has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGAGA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER6 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept snRNA_U13 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger6a has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGAAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_PRIMA4-int has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_HERVK-int has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTG)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AACAGAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER41G has 13 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTAATA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1HS has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACTT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR44 has 18 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR45B has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER57A-int has 12 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCCT)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR45 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M3f has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR57 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERV3-int has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger6 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATTAG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Satellite/centr_GSATII has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERVE-int has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTGAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTCTAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAGAACAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAGAAT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCATT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAAGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCCTTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATAA)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR1C3 has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR7Y has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATATTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAAGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTTCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AACATAT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGAGG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GATATA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTGT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERV9NC-int has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR12D has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCTTATA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LOR1b has 7 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy?_LTR88a has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGACA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTATT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTCTT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER6B has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR10B has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGTCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTGTCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR14A has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGCT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATTTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TACATT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TACATTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTGTTTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTCATA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA?_UCON58 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTTGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTCAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_MamTip1b has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCTGGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER6C has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR22A has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGCCGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger18a has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAATA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MST-int has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept SINE/Alu_AluYb9 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/MULE-MuDR_Ricksha has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/MULE-MuDR_Ricksha_a has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR80A has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTCTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATTATT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie31a has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGGAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON35 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR86B1 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON41 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAACAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_MamGypLTR1c has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR82B has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy?_LTR88b has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATGTACATA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATAAC)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept RC?/Helitron?_Helitron2Na_Mam has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAATTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1F-int has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL?_LTR55 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTTCCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA_X34_DNA has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGATAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACAA)n has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER6A has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1I-int has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACAGAG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR9A1 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATATG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger12c has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger12A has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_HERVL74-int has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGAATT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTCCC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTTTCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON71_Crp has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA_MER135 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Thr-ACA has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR32 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCTCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_CR1-1_Amn has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_X1_LINE has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAAAA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR16B has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCTCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MER127 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGTCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON65 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATCATT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTAATAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCACCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_X6B_LINE has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTCCT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCTCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR10D has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCATCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGGTCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACACT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_PRIMA4_LTR has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGCTCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER57E2 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_MamGypLTR1d has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGCCTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCCAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAGGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_CR1-L3A_Croc has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TACAA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGTGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA_UCON78 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCCTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATGTTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_EUTREP2 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAATTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER57F has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar_DNA1_Mam has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTGTGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie17b has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept snRNA_U7 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTCCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1J1-int has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCTCTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGATGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCATT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGG)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR?_MER129 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_X6A_LINE has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGTCGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATT)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTCAAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGTGCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTGCCCAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_MERX has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCGCCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger17 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGCAGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTCCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTATT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTAA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGTGCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCCCA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR_EUTREP16 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTATAAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATTTCT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGGCAG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTAGGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCGGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGAAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCCT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR4 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER87 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR49 has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER51D has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Asn-AAC has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_PRIMA41-int has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-His-CAY_ has 6 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Gly-GGG has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Val-GTG has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACTAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Gln-CAG has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTTTTT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAAG)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAACAAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCACA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACCTG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCAGCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTATA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTAATAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR39-int has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_X9b_DNA has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGGAG)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCTAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGCAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGCTGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGCTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER57B2 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTTGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGTGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCAGTAACCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTCCTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR54B has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR76 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_MamGypLTR2b has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER50C has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Met_ has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_PABL_B has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCTCTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGGCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGTGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR15 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAGGAAAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCATTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR38B has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERV-Fc2_LTR has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTGACA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCGCGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER61-int has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAGTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGGCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTTTTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAAAG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGGCTCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGAGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_LTR81A has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar_UCON44 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON47 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT?_UCON89 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCAGGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTTTTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Chap1_Mam has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTCTCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept RC/Helitron_Helitron1Na_Mam has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept RC/Helitron_Helitron1Nb_Mam has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATTAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGACC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATTA)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCAGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTATTTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATTGTTAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGATGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGA)n has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGCT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGAGGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTCCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGACCTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCACT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCACTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR?_Eutr18 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCAGTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_X11_DNA has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AACCCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA_X23_DNA has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_UCON11 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGTCAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTACTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GATGGA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER41-int has 15 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATTAGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TAATAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATTAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTCTCTGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR1F1 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGACGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCCTT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCACC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATGCAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTTAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCATG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATGC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/CR1_X8_LINE has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTGAAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCATCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATGT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AACACC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCCCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAACCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCACC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GACAGAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCGCCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCCCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTACT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCCTCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L2_L2-1_Crp has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAGTGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4D0 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CATTCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACCAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTTTT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTATTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_MER30B has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAGATA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAACT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTTAAATGACA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HUERS-P3-int has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA_Eutr2B has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGCCCCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAACAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_HERV35I-int has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept snRNA_U4 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR37-int has 11 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie30b has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTCTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCAGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCCAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR28B has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCATC)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTAAAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGACCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie14a has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAACAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1MEg2 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTGTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_LTR14 has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVK_HERVK14C-int has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER66A has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy_MamGyp-int has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1A1-int has 8 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR46-int has 5 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1M2a has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER4-int has 26 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1P4e has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GATCAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie9 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR26D has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGTGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L2_UCON86 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR_LTR107_Mam has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCTATT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTAGGT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGACAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCGAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GATT)n has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATGATG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA?_UCON21 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger20a has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCTCCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCAGCCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAATTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTTCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCACTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCCCGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCCCCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCCCCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTGTTAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA_Eulor1 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR7C has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCTCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGCCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTTCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CGCCGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAGTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCAATT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTAC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATCCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AATATAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_MER73 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTA)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACTAAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LINE/L1_L1PBa has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR06 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR1E has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR48 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGATAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAGCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTATG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCGCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAAAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTGGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAACC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCAGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100?_hAT-N1_Mam has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAACCCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCCCTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAATC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTCCCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATGTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGACAGAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCAGGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAAGTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1E3-int has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_PABL_A has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCTCTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTCTCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_LTR28C has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGAAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL-MaLR_MLT1K-int has 4 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCGCGCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTTTTTT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER57-int has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACAGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCTCCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACACAG)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGGTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTCTGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept tRNA_tRNA-Val-GTY has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCATG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTATTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCTCTCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAATG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGGGGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CACCA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCCGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERVL_LTR47A2 has 3 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_UCON38 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TATTATA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tip100_Arthur2 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAGCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGCCCGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCTCT)n has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGGCT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar-Tigger_Tigger21a has 2 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTGTCC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR?_Eutr5 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GATTAAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGACTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGAAT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_MER50-int has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGTTTTA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/TcMar_X4a_DNA has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CCTCATT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGACA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCAAGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGCAGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GTGCCAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CAAGGGA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTCTCTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATATGTG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATAAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TACACA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAAGAAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GAGGGAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/ERV1_PABL_B-int has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept LTR/Gypsy?_LTR88c has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCGCGGC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Charlie_Charlie10b has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Unknown_UCON4 has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AGAGCAG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TCATTTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ATTTC)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(ACAGAGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TTAACG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(TGGGT)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept DNA/hAT-Tag1_hAT-5_Mam has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GCCCGCG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(AAAGG)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(CTAAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "WARNING:tpcav.concepts:Concept Simple_repeat_(GGCCAA)n has 1 samples, fewer than min_samples=1000; skipping\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0001:GATA-PROP:GATA\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0002:PROP-ALX:Homeodomain\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0003:HNF1A-HNF1B:Homeodomain\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0004:ZSCAN:C2H2_ZF\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0005:POU3F-POU1F:Homeodomain,POU\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0078:CTCF-CTCFL:C2H2_ZF\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0111:CTCF-KLF:C2H2_ZF\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept random_regions\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/MIR_MIR3\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept LINE/L2_L2a\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/MIR_MIR\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept LINE/L2_L2b\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept LINE/L2_L2c\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/MIR_MIRb\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/MIR_MIRc\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/Alu_AluJr\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/Alu_AluSx\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/Alu_AluSz\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/Alu_AluY\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/Alu_AluJb\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept SINE/Alu_AluSx1\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept Low_complexity_G-rich\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept random_regions\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0001:GATA-PROP:GATA\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0002:PROP-ALX:Homeodomain\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0003:HNF1A-HNF1B:Homeodomain\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0004:ZSCAN:C2H2_ZF\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 17\n", "INFO:tpcav.cavs:[train] Accuracy: 0.6225\n", "INFO:tpcav.cavs:[val] Accuracy: 0.6050\n", "INFO:tpcav.cavs:[test] Accuracy: 0.5300\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0005:POU3F-POU1F:Homeodomain,POU\n", "INFO:tpcav.cavs:Best Params: {'alpha': 1e-06} | Iterations: 13\n", "INFO:tpcav.cavs:[train] Accuracy: 0.6312\n", "INFO:tpcav.cavs:[val] Accuracy: 0.6550\n", "INFO:tpcav.cavs:[test] Accuracy: 0.6200\n", "INFO:tpcav.cavs:Best Params: {'alpha': 1e-08} | Iterations: 13\n", "INFO:tpcav.cavs:[train] Accuracy: 0.6306\n", "INFO:tpcav.cavs:[val] Accuracy: 0.6550\n", "INFO:tpcav.cavs:[test] Accuracy: 0.6200\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0078:CTCF-CTCFL:C2H2_ZF\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.0001} | Iterations: 18\n", "INFO:tpcav.cavs:[train] Accuracy: 0.6162\n", "INFO:tpcav.cavs:[val] Accuracy: 0.5900\n", "INFO:tpcav.cavs:[test] Accuracy: 0.5600\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 17\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7619\n", "INFO:tpcav.cavs:[val] Accuracy: 0.8150\n", "INFO:tpcav.cavs:[test] Accuracy: 0.6850\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0111:CTCF-KLF:C2H2_ZF\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.0001} | Iterations: 15\n", "INFO:tpcav.cavs:[train] Accuracy: 0.6331\n", "INFO:tpcav.cavs:[val] Accuracy: 0.5850\n", "INFO:tpcav.cavs:[test] Accuracy: 0.5600\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 19\n", "INFO:tpcav.cavs:[train] Accuracy: 0.6619\n", "INFO:tpcav.cavs:[val] Accuracy: 0.6750\n", "INFO:tpcav.cavs:[test] Accuracy: 0.5950\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0001:GATA-PROP:GATA\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0002:PROP-ALX:Homeodomain\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 22\n", "INFO:tpcav.cavs:[train] Accuracy: 0.6787\n", "INFO:tpcav.cavs:[val] Accuracy: 0.6950\n", "INFO:tpcav.cavs:[test] Accuracy: 0.6600\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0003:HNF1A-HNF1B:Homeodomain\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.0001} | Iterations: 25\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7306\n", "INFO:tpcav.cavs:[val] Accuracy: 0.7150\n", "INFO:tpcav.cavs:[test] Accuracy: 0.7100\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0004:ZSCAN:C2H2_ZF\n", "INFO:tpcav.cavs:Best Params: {'alpha': 1e-08} | Iterations: 11\n", "INFO:tpcav.cavs:[train] Accuracy: 0.6975\n", "INFO:tpcav.cavs:[val] Accuracy: 0.7450\n", "INFO:tpcav.cavs:[test] Accuracy: 0.6350\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0005:POU3F-POU1F:Homeodomain,POU\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.0001} | Iterations: 13\n", "INFO:tpcav.cavs:[train] Accuracy: 0.6737\n", "INFO:tpcav.cavs:[val] Accuracy: 0.7250\n", "INFO:tpcav.cavs:[test] Accuracy: 0.6900\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0078:CTCF-CTCFL:C2H2_ZF\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 16\n", "INFO:tpcav.cavs:[train] Accuracy: 0.8956\n", "INFO:tpcav.cavs:[val] Accuracy: 0.8550\n", "INFO:tpcav.cavs:[test] Accuracy: 0.8500\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 19\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7725\n", "INFO:tpcav.cavs:[val] Accuracy: 0.7400\n", "INFO:tpcav.cavs:[test] Accuracy: 0.7650\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0111:CTCF-KLF:C2H2_ZF\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 16\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7462\n", "INFO:tpcav.cavs:[val] Accuracy: 0.7400\n", "INFO:tpcav.cavs:[test] Accuracy: 0.6850\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0001:GATA-PROP:GATA\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0002:PROP-ALX:Homeodomain\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0003:HNF1A-HNF1B:Homeodomain\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.0001} | Iterations: 11\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7612\n", "INFO:tpcav.cavs:[val] Accuracy: 0.7700\n", "INFO:tpcav.cavs:[test] Accuracy: 0.7250\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0004:ZSCAN:C2H2_ZF\n", "INFO:tpcav.cavs:Best Params: {'alpha': 1e-06} | Iterations: 19\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7963\n", "INFO:tpcav.cavs:[val] Accuracy: 0.7750\n", "INFO:tpcav.cavs:[test] Accuracy: 0.7600\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.0001} | Iterations: 11\n", "INFO:tpcav.cavs:[train] Accuracy: 0.8594\n", "INFO:tpcav.cavs:[val] Accuracy: 0.8550\n", "INFO:tpcav.cavs:[test] Accuracy: 0.8100\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.0001} | Iterations: 12\n", "INFO:tpcav.cavs:[train] Accuracy: 0.8131\n", "INFO:tpcav.cavs:[val] Accuracy: 0.8000\n", "INFO:tpcav.cavs:[test] Accuracy: 0.7800\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0005:POU3F-POU1F:Homeodomain,POU\n", "INFO:tpcav.cavs:Best Params: {'alpha': 1e-06} | Iterations: 17\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7725\n", "INFO:tpcav.cavs:[val] Accuracy: 0.7750\n", "INFO:tpcav.cavs:[test] Accuracy: 0.7300\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0078:CTCF-CTCFL:C2H2_ZF\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 17\n", "INFO:tpcav.cavs:[train] Accuracy: 0.9712\n", "INFO:tpcav.cavs:[val] Accuracy: 0.9700\n", "INFO:tpcav.cavs:[test] Accuracy: 0.9450\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0111:CTCF-KLF:C2H2_ZF\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.0001} | Iterations: 11\n", "INFO:tpcav.cavs:[train] Accuracy: 0.8488\n", "INFO:tpcav.cavs:[val] Accuracy: 0.8750\n", "INFO:tpcav.cavs:[test] Accuracy: 0.7650\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/MIR_MIR3\n", "INFO:tpcav.cavs:Submitted CAV training for concept LINE/L2_L2a\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/MIR_MIR\n", "INFO:tpcav.cavs:Submitted CAV training for concept LINE/L2_L2b\n", "INFO:tpcav.cavs:Submitted CAV training for concept LINE/L2_L2c\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/MIR_MIRb\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/MIR_MIRc\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/Alu_AluJr\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/Alu_AluSx\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/Alu_AluSz\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 24\n", "INFO:tpcav.cavs:[train] Accuracy: 0.8035\n", "INFO:tpcav.cavs:[val] Accuracy: 0.8081\n", "INFO:tpcav.cavs:[test] Accuracy: 0.7424\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/Alu_AluY\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/Alu_AluJb\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 22\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7563\n", "INFO:tpcav.cavs:[val] Accuracy: 0.7374\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 21\n", "INFO:tpcav.cavs:[test] Accuracy: 0.7121\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7670\n", "INFO:tpcav.cavs:[val] Accuracy: 0.7424\n", "INFO:tpcav.cavs:[test] Accuracy: 0.7172\n", "INFO:tpcav.cavs:Submitted CAV training for concept SINE/Alu_AluSx1\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.0001} | Iterations: 13\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7758\n", "INFO:tpcav.cavs:[val] Accuracy: 0.8182\n", "INFO:tpcav.cavs:[test] Accuracy: 0.7929\n", "INFO:tpcav.cavs:Submitted CAV training for concept Low_complexity_G-rich\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.0001} | Iterations: 23\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 24\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7506\n", "INFO:tpcav.cavs:[val] Accuracy: 0.7475\n", "INFO:tpcav.cavs:[test] Accuracy: 0.7020\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7513\n", "INFO:tpcav.cavs:[val] Accuracy: 0.7929\n", "INFO:tpcav.cavs:[test] Accuracy: 0.6919\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 16\n", "INFO:tpcav.cavs:[train] Accuracy: 0.7462\n", "INFO:tpcav.cavs:[val] Accuracy: 0.7879\n", "INFO:tpcav.cavs:[test] Accuracy: 0.6768\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 11\n", "INFO:tpcav.cavs:[train] Accuracy: 0.8923\n", "INFO:tpcav.cavs:[val] Accuracy: 0.8485\n", "INFO:tpcav.cavs:[test] Accuracy: 0.8939\n", "INFO:tpcav.cavs:Best Params: {'alpha': 1e-08} | Iterations: 21\n", "INFO:tpcav.cavs:[train] Accuracy: 0.9219\n", "INFO:tpcav.cavs:[val] Accuracy: 0.9444\n", "INFO:tpcav.cavs:[test] Accuracy: 0.9192\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.0001} | Iterations: 13\n", "INFO:tpcav.cavs:[train] Accuracy: 0.9125\n", "INFO:tpcav.cavs:[val] Accuracy: 0.9242\n", "INFO:tpcav.cavs:[test] Accuracy: 0.8737\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 11\n", "INFO:tpcav.cavs:[train] Accuracy: 0.9628\n", "INFO:tpcav.cavs:[val] Accuracy: 0.9747\n", "INFO:tpcav.cavs:[test] Accuracy: 0.9343\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 11\n", "INFO:tpcav.cavs:[train] Accuracy: 0.8892\n", "INFO:tpcav.cavs:[val] Accuracy: 0.9091\n", "INFO:tpcav.cavs:[test] Accuracy: 0.9091\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.0001} | Iterations: 11\n", "INFO:tpcav.cavs:[train] Accuracy: 0.9421\n", "INFO:tpcav.cavs:[val] Accuracy: 0.9545\n", "INFO:tpcav.cavs:[test] Accuracy: 0.9141\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.0001} | Iterations: 11\n", "INFO:tpcav.cavs:[train] Accuracy: 0.9717\n", "INFO:tpcav.cavs:[val] Accuracy: 0.9697\n", "INFO:tpcav.cavs:[test] Accuracy: 0.9495\n", "INFO:tpcav.cavs:Skipping CAV AC0003:HNF1A-HNF1B:Homeodomain with F-score 0.782 below threshold 0.900\n", "INFO:tpcav.cavs:Skipping CAV AC0002:PROP-ALX:Homeodomain with F-score 0.753 below threshold 0.900\n", "INFO:tpcav.cavs:Skipping CAV AC0001:GATA-PROP:GATA with F-score 0.696 below threshold 0.900\n", "INFO:tpcav.cavs:Skipping CAV AC0111:CTCF-KLF:C2H2_ZF with F-score 0.764 below threshold 0.900\n", "INFO:tpcav.cavs:Skipping CAV AC0005:POU3F-POU1F:Homeodomain,POU with F-score 0.724 below threshold 0.900\n", "INFO:tpcav.cavs:Skipping CAV AC0004:ZSCAN:C2H2_ZF with F-score 0.819 below threshold 0.900\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import torch\n", "from tpcav import run_tpcav, helper\n", "device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n", "\n", "#==================== Prepare Model and Data transform function ================================\n", "class DummyModelSeq(torch.nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.conv_layer_1 = torch.nn.Sequential(\n", " torch.nn.Conv1d(4, 64, 25, padding=12, bias=True),\n", " torch.nn.BatchNorm1d(64),\n", " torch.nn.LeakyReLU()\n", " )\n", " self.conv_layer_2 = torch.nn.Sequential(\n", " torch.nn.Conv1d(64, 128, 3, padding=1, stride=2, bias=True),\n", " torch.nn.BatchNorm1d(128),\n", " torch.nn.LeakyReLU(),\n", " )\n", "\n", " self.linear_layer_1 = torch.nn.Sequential(\n", " torch.nn.Linear(512, 1),\n", " torch.nn.LeakyReLU()\n", " )\n", " self.linear_layer_2 = torch.nn.Linear(128, 1)\n", "\n", " def forward(self, seq):\n", " y_hat = self.conv_layer_1(seq)\n", " y_hat = self.conv_layer_2(y_hat)\n", " y_hat = self.linear_layer_1(y_hat)\n", " y_hat = y_hat.squeeze(-1)\n", " y_hat = self.linear_layer_2(y_hat)\n", " return y_hat\n", "\n", "# By default, every concept extracts fasta sequences and bigwig signals from the given region\n", "# Use your own custom transformation function to get your desired inputs\n", "# Here we transform them into one-hot coded DNA sequences\n", "def transform_fasta_to_one_hot_seq(seq, chrom):\n", " # `seq` is a list of fasta sequences\n", " # `chrom` is a numpy array of bigwig signals of shape [-1, # bigwigs, len]\n", " return (helper.fasta_to_one_hot_sequences(seq),) # it has to return a tuple of inputs, even if there is only one input\n", "\n", "#==================== Construct concepts ================================\n", "motif_path = \"data/motif-clustering-v2.1beta_consensus_pwms.test.meme\" # motif file in meme format for constructing motif concepts\n", "bed_seq_concept = \"data/hg38_rmsk.sample.bed\" # a bed file to supply concepts described by a set of regions, format [chrom, start, end, label, concept_name]\n", "genome_fasta = \"data/hg38.analysisSet.fa\"\n", "model = torch.load(\"data/mcf7_ctcf_best.pt\", map_location=device, weights_only=False) # load the model\n", "layer_name = \"linear_layer_1\" # name of the layer to be interpreted, you should be able to retrieve the layer object by getattr(model, layer_name)\n", "\n", "# concept_fscores_dataframe: fscores of each concept\n", "# motif_cav_trainers: each trainer contains the cav weights of motifs inserted different number of times\n", "# bed_cav_trainer: trainer contains the cav weights of the sequence concepts provided in bed file\n", "concept_fscores_dataframe, motif_cav_trainers, bed_cav_trainer = run_tpcav(\n", " model=model,\n", " layer_name=layer_name,\n", " motif_file=motif_path,\n", " motif_file_fmt='meme', # specify your motif file format, either meme or consensus (tab delimited file in form [motif_name, consensus_sequence])\n", " genome_fasta=genome_fasta,\n", " num_motif_insertions=[12, 24, 36],\n", " bed_seq_file=bed_seq_concept, \n", " output_dir=\"test_run_tpcav_output/\",\n", " input_transform_func=transform_fasta_to_one_hot_seq,\n", " p=4) # number of concurrent SGDClassifier can be run at the same time, increase it if you have available CPU power, it speeds up training significantly" ] }, { "cell_type": "code", "execution_count": 4, "id": "dd86a288-3bcd-4e54-b441-c5b7db5ab982", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jmy5455/miniforge3/envs/test/lib/python3.14/site-packages/captum/attr/_core/deep_lift.py:294: UserWarning: Input Tensor 1 did not already require gradients, required_grads has been set automatically.\n", " gradient_mask = apply_gradient_requirements(inputs_tuple)\n", "/home/jmy5455/miniforge3/envs/test/lib/python3.14/site-packages/captum/log/dummy_log.py:39: UserWarning: Setting forward, backward hooks and attributes on non-linear\n", " activations. The hooks and attributes will be removed\n", " after the attribution is finished\n", " return func(*args, **kwargs)\n", "INFO:tpcav.cavs:Skipping CAV AC0003:HNF1A-HNF1B:Homeodomain with F-score 0.782 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV AC0002:PROP-ALX:Homeodomain with F-score 0.753 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV AC0001:GATA-PROP:GATA with F-score 0.696 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV AC0111:CTCF-KLF:C2H2_ZF with F-score 0.764 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV AC0005:POU3F-POU1F:Homeodomain,POU with F-score 0.724 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV SINE/MIR_MIR3 with F-score 0.721 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV LINE/L2_L2a with F-score 0.716 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV SINE/MIR_MIR with F-score 0.694 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV LINE/L2_L2b with F-score 0.798 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV LINE/L2_L2c with F-score 0.723 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV SINE/MIR_MIRb with F-score 0.686 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV SINE/MIR_MIRc with F-score 0.677 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV AC0003:HNF1A-HNF1B:Homeodomain with F-score 0.782 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV AC0002:PROP-ALX:Homeodomain with F-score 0.753 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV AC0001:GATA-PROP:GATA with F-score 0.696 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV AC0111:CTCF-KLF:C2H2_ZF with F-score 0.764 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV AC0005:POU3F-POU1F:Homeodomain,POU with F-score 0.724 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV SINE/MIR_MIR3 with F-score 0.721 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV LINE/L2_L2a with F-score 0.716 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV SINE/MIR_MIR with F-score 0.694 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV LINE/L2_L2b with F-score 0.798 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV LINE/L2_L2c with F-score 0.723 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV SINE/MIR_MIRb with F-score 0.686 below threshold 0.800\n", "INFO:tpcav.cavs:Skipping CAV SINE/MIR_MIRc with F-score 0.677 below threshold 0.800\n", "INFO:tpcav.cavs:PWM not found for Low_complexity_G-rich\n", "INFO:tpcav.cavs:PWM not found for SINE/Alu_AluSx1\n", "INFO:tpcav.cavs:PWM not found for SINE/Alu_AluSx\n", "INFO:tpcav.cavs:PWM not found for SINE/Alu_AluY\n", "INFO:tpcav.cavs:PWM not found for SINE/Alu_AluJb\n", "INFO:tpcav.cavs:PWM not found for SINE/Alu_AluJr\n", "INFO:tpcav.cavs:PWM not found for SINE/Alu_AluSz\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from tpcav import report\n", "\n", "#==================== Compuate layer attributions of target testing regions ================================\n", "# retrieve the tpcav model\n", "tpcav_model = bed_cav_trainer.tpcav\n", "\n", "# create input regions and baseline regions for attribution\n", "ctcf_peaks = helper.load_bed_and_center(\"data/MCF-7_CTCF_ENCFF942TCG.bed\", window=1024).sample(n=100)\n", "random_regions = helper.random_regions_dataframe(genome_fasta + \".fai\", 1024, 100, seed=2)\n", "\n", "# create iterators to yield one-hot encoded sequences from the region dataframes\n", "# adjust this funtion to fit your model input format requirements\n", "def pack_data_iters(df):\n", " seq_fasta_iter = helper.dataframe_to_fasta_iter(df, genome_fasta, batch_size=8)\n", " seq_one_hot_iter = (helper.fasta_to_one_hot_sequences(seq_fasta) for seq_fasta in seq_fasta_iter)\n", " return zip(seq_one_hot_iter, )\n", "\n", "# compute layer attributions given the iterators of testing regions and control regions\n", "attributions = tpcav_model.layer_attributions(pack_data_iters(ctcf_peaks), pack_data_iters(random_regions))\n", "\n", "# generate a new html report with TPCAV score computed\n", "report.generate_tpcav_html_report(\"report_tpcav_score.html\", motif_cav_trainers,\n", " non_motif_cav_trainers = {'repeats': bed_cav_trainer},\n", " attributions = [attributions, ], # if you have multiple sets of attributions you can provide a list\n", " motif_file=motif_path, motif_file_fmt='meme', fscore_thresh=0.8)\n", "\n", "# save the trainers for future use\n", "torch.save(motif_cav_trainers, \"motif_cav_trainers.pt\")\n", "torch.save(bed_cav_trainer, \"bed_cav_trainer.pt\")" ] } ], "metadata": { "kernelspec": { "display_name": "test", "language": "python", "name": "test" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.2" } }, "nbformat": 4, "nbformat_minor": 5 } TPCAV-0.8.4/examples/tpcav_detailed_usage.ipynb000066400000000000000000002336251516353663300214430ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "dbbe425c-4a8b-439c-97a1-cee37a070f41", "metadata": {}, "source": [ "# TPCAV detailed usage tutorial\n", "\n", "This tutorial goes through the detailed steps of TPCAV, make sure you installed tpcav package first:\n", "\n", "`pip install tpcav`" ] }, { "cell_type": "code", "execution_count": 1, "id": "3a7dc41c-124f-448a-a9fb-5625a54d4729", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2026-02-25 15:38:56-- https://hgdownload.gi.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz\n", "Resolving hgdownload.gi.ucsc.edu (hgdownload.gi.ucsc.edu)... 128.114.119.163\n", "Connecting to hgdownload.gi.ucsc.edu (hgdownload.gi.ucsc.edu)|128.114.119.163|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 983659424 (938M) [application/x-gzip]\n", "Saving to: ‘data/hg38.fa.gz’\n", "\n", "hg38.fa.gz 100%[===================>] 938.09M 39.0MB/s in 26s \n", "\n", "2026-02-25 15:39:23 (36.4 MB/s) - ‘data/hg38.fa.gz’ saved [983659424/983659424]\n", "\n", "--2026-02-25 15:40:00-- https://raw.githubusercontent.com/seqcode/TPCAV/main/data/motif-clustering-v2.1beta_consensus_pwms.test.meme\n", "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.109.133, 185.199.108.133, ...\n", "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 3504 (3.4K) [text/plain]\n", "Saving to: ‘data/motif-clustering-v2.1beta_consensus_pwms.test.meme’\n", "\n", "motif-clustering-v2 100%[===================>] 3.42K --.-KB/s in 0s \n", "\n", "2026-02-25 15:40:00 (25.5 MB/s) - ‘data/motif-clustering-v2.1beta_consensus_pwms.test.meme’ saved [3504/3504]\n", "\n" ] }, { "data": { "text/plain": [ "Fasta(\"data/hg38.fa\")" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# data download\n", "!mkdir data/\n", "!wget https://hgdownload.gi.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz -P data/\n", "!gunzip data/hg38.fa.gz\n", "!wget https://raw.githubusercontent.com/seqcode/TPCAV/main/data/motif-clustering-v2.1beta_consensus_pwms.test.meme -P data/\n", "\n", "import pyfaidx\n", "pyfaidx.Fasta('data/hg38.fa')" ] }, { "cell_type": "markdown", "id": "7fbed531-7834-457b-a9fe-c878537a026c", "metadata": {}, "source": [ "## Prepare the model\n", "\n", "Since TPCAV is a concept based attribution method, the first step is to construct candidate concepts for testing, each concept is a set of input examples that share a similar pattern (e.g. motif).\n", "\n", "Assume we start with a dummy model that has two linear layers taking 1024bp long one-hot coded DNA input for predicting a scalar value." ] }, { "cell_type": "code", "execution_count": 2, "id": "bf92bb3c-89de-472e-8872-3a54ce835476", "metadata": {}, "outputs": [], "source": [ "import torch\n", "\n", "class DummyModelSeq(torch.nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.layer1 = torch.nn.Linear(1024, 1)\n", " self.layer2 = torch.nn.Linear(4, 1)\n", "\n", " def forward(self, seq):\n", " y_hat = self.layer1(seq)\n", " y_hat = y_hat.squeeze(-1)\n", " y_hat = self.layer2(y_hat)\n", " return y_hat" ] }, { "cell_type": "markdown", "id": "80d1891d-ab65-4299-aa6a-723b7150f233", "metadata": {}, "source": [ "## Construct concepts\n", "\n", "Now we start constructing concepts, a concept is basically an iterator of the inputs that represent a prominent pattern. TPCAV provides a class `ConceptBuilder` to construct common concepts in genomics field, here we use it to construct motif concepts, defined as sets of random genomic sequences inserted by motif instances.\n", "\n", "There are 10 motif concepts built in this case given the test meme file" ] }, { "cell_type": "code", "execution_count": 3, "id": "16ba9d74-a736-4ffb-8c9c-724ab68c7699", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jmy5455/miniforge3/envs/test/lib/python3.14/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] }, { "data": { "text/plain": [ "[(Concept(1, 'AC0001:GATA-PROP:GATA'),\n", " Concept(2, 'AC0001:GATA-PROP:GATA_perm')),\n", " (Concept(3, 'AC0002:PROP-ALX:Homeodomain'),\n", " Concept(4, 'AC0002:PROP-ALX:Homeodomain_perm')),\n", " (Concept(5, 'AC0003:HNF1A-HNF1B:Homeodomain'),\n", " Concept(6, 'AC0003:HNF1A-HNF1B:Homeodomain_perm')),\n", " (Concept(7, 'AC0004:ZSCAN:C2H2_ZF'), Concept(8, 'AC0004:ZSCAN:C2H2_ZF_perm')),\n", " (Concept(9, 'AC0005:POU3F-POU1F:Homeodomain,POU'),\n", " Concept(10, 'AC0005:POU3F-POU1F:Homeodomain,POU_perm'))]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pathlib import Path\n", "from tpcav.concepts import ConceptBuilder\n", "\n", "motif_path = Path(\"data\") / \"motif-clustering-v2.1beta_consensus_pwms.test.meme\"\n", "\n", "# create concept builder to generate concepts\n", "builder = ConceptBuilder(\n", " genome_fasta=\"data/hg38.fa\",\n", " input_window_length=1024,\n", " bws=None,\n", " num_motifs=12,\n", " include_reverse_complement=True,\n", " min_samples=1000,\n", " batch_size=8,\n", ")\n", "# use random regions as control \n", "builder.build_control()\n", "# use meme motif PWMs to build motif concepts, one concept per motif\n", "builder.add_meme_motif_concepts(str(motif_path))" ] }, { "cell_type": "markdown", "id": "71e96db7-d9a8-47b5-9d7e-d4eae2b631dc", "metadata": {}, "source": [ "Concept constructed by `ConceptBuilder` contains an iterator of two things: fasta sequences strings and array of bigwig signal tracks. If your model takes different formats of inputs, you can provide a transformation function to fit your model. Make sure your transformation function returns a tuple and your model forward function can be called by `model(*inputs)`\n", "\n", "Below is the example of obtaining one hot coded DNA sequence inputs." ] }, { "cell_type": "code", "execution_count": 4, "id": "2ef8bb2a-c6bc-49b6-a369-3e672e6ef5af", "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jmy5455/miniforge3/envs/test/lib/python3.14/site-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n", " warnings.warn(warn_msg)\n" ] }, { "data": { "text/plain": [ "(['ATTTTACTAACTGCCACACTGCATAGCTTTTATAAGCATATTAATAGATTTACGGGAGAGAGGCACCAGAGTGGATTACAACACAGGACTGACCTTTTACCACCCGGCTGAAGCTCGAAAAACAAAACCAAATCCCATCTTACTAACCATACCATAACTAGGTTATGGAAATACCCCAGAATACAAGTTTGTTTGTTTTTTAAGATGGTTTTCCTTCCCTGAGAGAACAATCCTGAAACTTAAGTCAATAAAGATACTGCCATTATTAAGCATTTTGCCTCCTCGGGTCAGACTTGAGGCCTTTTCCCCCCTTTCTTCTCGGTAATAAGCTTACTGCCTTAGATCACGGTGCATCCTAGGCAGGTTATGCCATTTTAATCTCATAAATATGAAAATAAAAGACATAATCAGAGCAATGTCGTTATTTTTTATTTTTAAACCAAAGGAAGCCAAAATAGCAACAGAGAAGAAAGGAAATATAATATCATTAACTCTGGGGCCAAGGTGCATTTGCCCTAGTTCTTTTTTTAATAATGCGCTTATTTTTCTTCCTCCCCATTCCACAGCACTCAGCATCTTCTATCTTGTCTCCCTTGGTGTAGCTCTAGTGTTCTAATTCTATCCCATCTTCTTCTACTGAAATAATGAACTTTTTAAAGAGAAAATTAAGTGAATGTCTTTGTAGCAATGAACCTCCCCCTCTCGAGAATAAAAGGGAGCTTTGGAGATAAGGCAATGCTTAACCCAGTGAACGAATTGCTTACTGATGGGGTCAGGTAAAAGGATTACAGGGTGGCAGAGCTGACTCTGCCTACTGATATCGGCCATGTTAAATAATTCCCGTATTATATACTCAACATCTATATAACAATGTTTTTGTTAGCTTGTTTCTATTTTATAACAGCCCTGAAATGTAAAATGGTTTTATTTTATTTATTTATTATAATGCTGTAAGTTTTTTGAGATAATGACATAACCTGTCGCCCAGGCTGGAGTGCAGTGGCGCGACCTCGGCTCACTGCAA',\n", 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'CTCTTATTTCCTGCTATTATGCCTAATAGCGTTATACCTGTAGTAACCTATCTGTAGTTACCTGTAAGTAACTCTAATAAACTGATCACTAAAATAGAGTGTCTCAAAAGGATATTTTAAGCTGATAAGATCCAAATATAAAGAAAGACGGAAATACATGGAATTGTCTGGGGCCAAGAAATACTTGATAATTTTTGCCAGTAAAGTAAGAGTTGCCATAGTTCCCTTGGCTTTAGGGCATTGGCTATAAGGTCATAATCTTGCCCCTAGAGATAAATAAGGTTATTATAACTATGAAACATTATTTTATTAAAAGTGAATATACAAAATAACTCAGAAATACATGATCATATTTTGTCTGTGGAGTACAAGCATTTATTTGTCTGCTGATTACTTCTTTGGGGGAACTACCCATCCTTCATTCTTGGTCAGTATAAGGGAGTTATCTAAAGTAACCATGGCTCCAGGGGTGGGCATGTGATCCAAATTTTGCTAATTATTATTAGACATCCTTGGAGGGATATAACGACGTTATGGTGAGCATATTACTCAAGCTTGCCAATTAATGTCGTTATTTGAGAATTTAGCAAAAATTACTGGGAAAGAGGCATTTCCTTTCCATTTGAGTTACTGAACTGATAGAAAGTAACTGATAAACTCTATAATGCCATTATGGCCGTTCCTACCCTCTGCATCGGGAAGGCATATTTGACAATAAAGCTGATACAGAAGAAAACAGATTTAAGACGTGGAGAAACAGTGTTCTGATAAGGCAATTATAGCAAATTGATTCAACACCCAATTTAGTCATGTGAGGAAATGAGTTCTTTTTTGTTTTATGATAATGCTATTATGGTTTTTGTGTAATGGTATAATGGAAATGAGTTCTTTTTTGTTTTATGCATAACGATATTTTGTTTTTGTCCCCTGTAACTGAAAGGAGCTTGGCTAATTAAGTCAGGAATTTGCATATAGCTTTTAGATATAAATTAAATAGTTTTGTAATTGATATGCTTACTTTTCT',\n", 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" 'CTCTTATTTCCTGCTATTATGCCCAATAGCGTTATACCTGTAGTAACCTATCTGTAGTTACCTGTAAGTAACTCTAATAAACTTATCACTAAAATAGAGTGTCTCAAAAGGATATTTTAAGCTGATAAGATCCAAATATAAAGAAAGACGGAAATACATGGAATTGTCTGGGGCCAAGAAATACTTGATAATTTTTGCCAGTAAAGTAAGAGTTGCCATAGTTCCCTTGGCTTTAGGGCATTGGCTATAAGGTCATTATCTTGCCCCTAGAGATAAATAAGCTTATTATAACTATGAAACATTATTTTATTAAAAGTGAATATACAAAATAACTCAGAAATACATGATCATATTTTGTCTGTGGAGTACAAGCATTTATTTGTCTGCTGATTACTTCTTTGGGGGAACTACCCATCCTTCATTCTTGGTCAGTATCATGGCGTTATCTAAAGTAACCATGGCTCCAGGGGTGGGCATGTGATCCAAATTTTGCTAATTATTATTAGACATCCTTGGAGGGATATAACGACGTTATGGTGAGCATATTACTCAAGCTTGCCAATTAATGTCGTTATTTGAGAATTTAGCAAAAATTACTGGGAAAGAGGCATTTCCTTTCCATTTGAGTTACTGAACTGATAGAAAGTAACTGATAAACTCTATAATGGCATTATGGCCGTTCCTACCCTCTGCATCGGGAAGGCATATTTGACAATAAAGCTGATACAGAAGAAAACAGATTTAAGACGTGGAGAAACAGTGTTCTGATAAGGCACTTATAGCAAATTGATTCAACACCCAATTTAGTCATGTGAGGAAATGAGTTCTTTTTTGTTTTATGATTATGCTATTATGGTTTTTGTGTAATGGTATTATGGAAATGAGTTCTTTTTTGTTTTATGCATAACGAGATTGTGTTTTTGTCCCCTGTAACTGAAAGGAGCTTGGCTAATTAAGTCAGGAATTTGCATATAGCTTTTAGATATAAATTAAATAGTTTTGTAATTGATATGCTTACTTTTCT',\n", " 'TAAGTAGTATTCTATCGAATATATATATACCACAGTTTCTTTATGCACTCATTGATTGTTGGGCATTTGGGTTGGCTCTGTTTTTGCAATTGTGAATTGTGCTGCTATATACATACTATCATTAATATCTTTCTAAGATCTTTATTTCTTTTACTTGCCTTACATACCCAGTAGTGGGATTGCTGGATCAAATGGTAGTTTTACTTTTAGTCCTTTAAGCAATCTCTCCACGCTGTTTTCCACAGTGGTTGTTTGTTTACATTCCCATTAGCAGTGCAGAAGTGTTCCCTATTATAAACCGCTTGTCCAACATCTACTATTTTTAAAATAATGGTACTATGGCCATTCTAACAGAAGTAAGGTGATATCACACTGTGCTTTTGATTTGTGTTTCCCTGATCATTAGTGAATAACTCCATTATTTCATATGTTTGTTGGCATAAGATCGTTAATCTTTTGACTAATGTCCTTATATGTCCTTAGCCCACTTTTTGATGGGATTATAACACCATTATTTGTTGATTTGTTTGAGTTCATTGTAGAATCTGGATATTAGTCCTCTGTCAGGTGTATAGATTGTAAAGATTTTTTCCCATTCTGTGGGTTGTCTGTTTACTTTGCTGACTGTCCCTTTTCCCACTCAAAAGTTTCTAGCAACCGCAGTTAAAAAAGACAAAGTGGAACATTATGTAATGATAAAAGGACTAGTCCAACAAGAAAATATCACAATCCTAGCCATACATGCACCTAACACTGGAGCTCCCAGATTTGTAAAACGATTACTAATATAATTGCATTATGAGATAGACAGCAACACAGTAATAGCTTAATACCGTTATACTCCACTGACAGCACTAGGCAGGTCATTAAGACAGAAACTCAAGAAAGAACAATGGATTTAAACTACACTCCGGAACAAATGGACTTAACACCTATATACAGAGCATTTTATCCAATAACCGCAGAATATACATTCTTTTCAACAGCATATGTTAATGGTGTTATGATAGGTCATATGACAGGC',\n", " 'TTATCAAGTATCTGCAGCTAATGGACTTATCTCAAACCTAAAAATAAGAAATGAAAAAAATGCCTAAGAAAATTGTAATATAACGAAGTTTTTCATCACACATGCTAAAACTTTTCTGGATGAAAGATACAAACTGGCCATTAATGCGGTTATTGAATGGAACTTAACCGAGGAGTTTGGCAGAGGAATGGCTGGGTTATGTAATGCCCGTATTTTCTCTGCATTTAGCTGAAAGCATGGTTGTCCAGAATTAAGGCTATTTCTCAGTCTATAATTATATTATGCAATGGTCATGTACCTCTGCATTTAGCTGAAAGCATGGTTGTCCAGAATTAAATAACGAAATTATTCTCTCTTAACAGCTGGCAATGGTCATGTAACTAAGTTCTGGGTAATGATATGTTGAGTGGGAGTGGTGTGTGCAGTATTCTGATTGAGCCTGTGGAGGGAAGCATGTGCTCTTCACTTCCTGTTTCCTCCCTTCTAGCTGCCTGGAACATAAACTTAGTAATGAAATTACTTAGGAAAAATCGTAACAACGTTATCTTCCTTGCTCTTAATCTTATTAAATGAATAATAGAACTTAAACATAGGTATTTTGACACAAATTATCCACTAAGCATATAATTCAAAATATTCGATTGAATTCTTTACTCCCACAATAATTTAGCATTCTAAAACATTAATTTGCTGAAGAGAAATTGGCTTTTGGATGAGTTAGGGCATTTACTAAAAGGTTCCTTTATTACTGAAGAGTGGTTAGTAATAATGAGATTAAAGATAATTTGAAATTATTAATTTTATGAGATGATCTTTAAAAATACTTTTTGAAAATCAAATTATGCTTTCAGATTTTCCAGGTCAAAATTTGTATTGTAATTTTATTACAATTGTGTTAGAATTAAACACACATACAATCAAATTATGAAAGACAGACACAGTTGGAAGAGTCATACTCCCTCATTATAAAAGTTACTACAAAATTATAGAATTCAAATAATCTACTTATTGGCAAAAGGATAAA',\n", " 'TTATCAAGTATCTGCAGCCTATGTACTTATCTCAAACCTAAAAATAAGAAATGAAAAAAATGCCTAAGAAAATTGTAATATAACGACGTTGTTCATCACACATGCTAAAACTTTTCTGGATGAAAGATACAAACTGGCCATTAATGCCGTTATTGAATGGAACTTAACCGAGGAGTTTGGCAGAGGAATGGCTGGGTTATGTAATTCCCGTATTTTCTCTGCATTTAGCTGAAAGCATGGTTGTCCAGAATTAAGGCTATTTCTCAGTCTATAATTATCTTATGCAATGGTCATGTACCTCTGCATTTAGCTGAAAGCATGGTTGTCCAGAATTAAATAACCAAATTATTCTCTCTTAACAGCTGGCAATGGTCATGTAACTAAGTTCTGGGTAATGATATGTTGAGTGGGAGTGGTGTGTGCAGTATTCTGATTGAGCCTGTGGAGGGAAGCATGTGCTCTTCACTTCCTGTTTCCTCCCTTCTAGCTGCCTGGAACATAAACTTAGTAATGAAATTACTTAGGAAAAATCGTAACACCGTTATCTTCCTTGCTCTTAATCTTATTAAATGAATAATAGAACTTAAACATAGGTATTTTGACACAAATTATCCACTAAGCATATAATTCAAAATATTCGATTGAATTCTTTACTCCCACAATAATTTAGCATTCTAAAACATTAATTTGCTGAAGAGAAATTGGCTTTTGGATGAGTTAGGGCATTTACTAAAAGGTTCCTTTATTACTGAAGAGTGGTTAGTAATAATGACATTAAAGATAATTTGAAATTATTAATTTTATGAGATGATCTTTAAAAATACTTTTTGAAAATCAAATTATGCTTTCAGATTTTCCAGGTCAAAATTTGTATTGTAATTTTATTACAATTGTGTTAGAATTAAACACACATAAAATCAAATTATGAAAGACAGACACAGTTGGAAGAGTCATACTCCCTCATTATAAAAGTTACTACAAAATTATAGAATTCAAATAATCTACTTATTGGCAAAAGGATAAA'],\n", " tensor([[nan],\n", " [nan],\n", " [nan],\n", " [nan],\n", " [nan],\n", " [nan],\n", " [nan],\n", " [nan]]))" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# each batch is a tuple of a fasta sequence list and an array of bigwig signals\n", "next(iter(builder.concepts[0].data_iter))" ] }, { "cell_type": "code", "execution_count": 5, "id": "0504fa0f-f05b-4878-80d7-58c697cb5344", "metadata": {}, "outputs": [], "source": [ "from tpcav import helper\n", "\n", "# apply transformation function to obtain one-hot encoded sequences\n", "def transform_fasta_to_one_hot_seq(seq, chrom):\n", " # `seq` is a list of fasta sequences\n", " # `chrom` is a numpy array of bigwig signals of shape [-1, # bigwigs, len]\n", " return (helper.fasta_to_one_hot_sequences(seq),) # it has to return a tuple of inputs, even if there is only one input\n", "builder.apply_transform(transform_fasta_to_one_hot_seq)" ] }, { "cell_type": "code", "execution_count": 6, "id": "689f99f3-ebda-4fb2-8661-44577017c198", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "(tensor([[[1., 0., 0., ..., 0., 1., 1.],\n", " [0., 0., 0., ..., 1., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 1., 1., ..., 0., 0., 0.]],\n", " \n", " [[0., 0., 0., ..., 0., 0., 0.],\n", " [1., 0., 1., ..., 0., 1., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 1., 0., ..., 1., 0., 1.]],\n", " \n", " [[1., 0., 0., ..., 0., 1., 1.],\n", " [0., 0., 0., ..., 1., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 1., 1., ..., 0., 0., 0.]],\n", " \n", " ...,\n", " \n", " [[0., 0., 1., ..., 1., 1., 1.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [1., 1., 0., ..., 0., 0., 0.]],\n", " \n", " [[0., 0., 0., ..., 0., 1., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [1., 1., 1., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 1., 0., 1.]],\n", " \n", " [[1., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 1., 1., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 1., 1., ..., 0., 0., 1.]]]),)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# after transformation each batch is one-hot coded DNA arrays wrapped in tuple\n", "next(iter(builder.concepts[0].data_iter))" ] }, { "cell_type": "markdown", "id": "f476a6dd-c2b8-42a4-8580-6cafba3dd43f", "metadata": {}, "source": [ "## Train concept activaton vector (CAV) classifier\n", "\n", "After concept construction, now it's time to train the linear classifier for every concept in your model. You need to wrap your model instance by `TPCAV` class and the layer for interpretation provided, then we apply PCA transformation given the concepts we just built to decorrelate the embedding space to solve the correlated redundant feature issue.\n", "\n", "Note: You can either provide a layer name (should be able to retrieve the corresponding layer by `getattr(model, layer_name)`) or the exact layer variable (make sure it's from the instantiated model)" ] }, { "cell_type": "code", "execution_count": 7, "id": "5d492620-87d0-45e4-9764-eb7943cc099e", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0001:GATA-PROP:GATA\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0002:PROP-ALX:Homeodomain\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0003:HNF1A-HNF1B:Homeodomain\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0004:ZSCAN:C2H2_ZF\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0005:POU3F-POU1F:Homeodomain,POU\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept random_regions\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0001:GATA-PROP:GATA_perm\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0002:PROP-ALX:Homeodomain_perm\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0003:HNF1A-HNF1B:Homeodomain_perm\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0004:ZSCAN:C2H2_ZF_perm\n", "INFO:tpcav.tpcav_model:Sampled 10 activations from concept AC0005:POU3F-POU1F:Homeodomain,POU_perm\n" ] } ], "source": [ "from tpcav.tpcav_model import TPCAV\n", "# create TPCAV model on top of your model\n", "tpcav_model = TPCAV(DummyModelSeq(), layer_name=\"layer1\")\n", "# fit PCA on sampled all concept activations\n", "tpcav_model.fit_pca(\n", " concepts=builder.all_concepts(),\n", " num_samples_per_concept=10,\n", " num_pc=\"full\",\n", ")\n", "# you can save the tpcav model in case of future use\n", "torch.save(tpcav_model, \"data/tmp_tpcav_model.pt\")" ] }, { "cell_type": "code", "execution_count": 8, "id": "7177e0eb-6e56-4141-b5ab-94fed823f282", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "tensor([[-0.1796, 0.0593, -0.6600, 0.7271],\n", " [ 0.7280, -0.6324, -0.2646, -0.0088],\n", " [-0.4566, -0.7081, 0.4251, 0.3308],\n", " [-0.4789, -0.3085, -0.5601, -0.6015]])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# now tpcav_model contains the necessary parameters for PCA transformation\n", "tpcav_model.Vh" ] }, { "cell_type": "markdown", "id": "a3d8dbd7-1acb-4811-9cbd-581781f68126", "metadata": {}, "source": [ "Using the fitted TPCAV model, we can train the concept activation vectors (CAVs) on the concepts we built, here we use the random genomic background concept as control" ] }, { "cell_type": "code", "execution_count": 9, "id": "6c07c332-ee51-4925-80f2-ab06e397715f", "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:tpcav.cavs:Submitted CAV training for concept AC0001:GATA-PROP:GATA\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0002:PROP-ALX:Homeodomain\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0003:HNF1A-HNF1B:Homeodomain\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0004:ZSCAN:C2H2_ZF\n", "INFO:tpcav.cavs:Submitted CAV training for concept AC0005:POU3F-POU1F:Homeodomain,POU\n", "INFO:tpcav.cavs:Best Params: {'alpha': 1e-06} | Iterations: 20\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.01} | Iterations: 18\n", "INFO:tpcav.cavs:[train] Accuracy: 0.5536\n", "INFO:tpcav.cavs:[val] Accuracy: 0.5500\n", "INFO:tpcav.cavs:[train] Accuracy: 0.5417\n", "INFO:tpcav.cavs:[val] Accuracy: 0.6000\n", "INFO:tpcav.cavs:[test] Accuracy: 0.5000\n", "INFO:tpcav.cavs:[test] Accuracy: 0.4500\n", "INFO:tpcav.cavs:Best Params: {'alpha': 1e-08} | Iterations: 14\n", "INFO:tpcav.cavs:Best Params: {'alpha': 0.0001} | Iterations: 18\n", "INFO:tpcav.cavs:[train] Accuracy: 0.5893\n", "INFO:tpcav.cavs:[train] Accuracy: 0.5833\n", "INFO:tpcav.cavs:[val] Accuracy: 0.4000\n", "INFO:tpcav.cavs:[val] Accuracy: 0.3000\n", "INFO:tpcav.cavs:[test] Accuracy: 0.3500\n", "INFO:tpcav.cavs:[test] Accuracy: 0.4500\n", "INFO:tpcav.cavs:Best Params: {'alpha': 1e-06} | Iterations: 13\n", "INFO:tpcav.cavs:[train] Accuracy: 0.5893\n", "INFO:tpcav.cavs:[val] Accuracy: 0.5500\n", "INFO:tpcav.cavs:[test] Accuracy: 0.6000\n" ] } ], "source": [ "from tpcav.cavs import CavTrainer\n", "\n", "# create trainer for computing CAVs\n", "cav_trainer = CavTrainer(tpcav_model, penalty=\"l2\")\n", "# set control concept for CAV training\n", "cav_trainer.set_control(builder.control_concepts[0], num_samples=100)\n", "# train CAVs for all concepts\n", "cav_trainer.train_concepts(\n", " builder.concepts, 100, output_dir=\"data/cavs/\", num_processes=2\n", ")" ] }, { "cell_type": "code", "execution_count": 10, "id": "684ebb1f-a0b6-4c87-9142-a4fce408206d", "metadata": { "scrolled": true }, "outputs": [], "source": [ "# after training, the cavs are stored\n", "cav_trainer.cav_weights\n", "# you can save the cav_trainer for future use\n", "torch.save(cav_trainer, \"cav_trainer.pt\")" ] }, { "cell_type": "markdown", "id": "a9f92e1d-7af4-4b75-bc3f-13a498764a01", "metadata": {}, "source": [ "## Layer attribution & TPCAV score calculation\n", "\n", "Finally, we can compute the layer attribution scores on the test regions against control regions, and obtain the final TPCAV score for each concept\n", "\n", "Note the default layer attribution in TPCAV uses DeepLIFT multiplier x abs(input - baselines), which is different from default Captum implementation" ] }, { "cell_type": "code", "execution_count": 11, "id": "81403bb9-5649-4307-94d8-d44a6f523241", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jmy5455/miniforge3/envs/test/lib/python3.14/site-packages/captum/attr/_core/deep_lift.py:294: UserWarning: Input Tensor 1 did not already require gradients, required_grads has been set automatically.\n", " gradient_mask = apply_gradient_requirements(inputs_tuple)\n", "/home/jmy5455/miniforge3/envs/test/lib/python3.14/site-packages/captum/log/dummy_log.py:39: UserWarning: Setting forward, backward hooks and attributes on non-linear\n", " activations. The hooks and attributes will be removed\n", " after the attribution is finished\n", " return func(*args, **kwargs)\n" ] }, { "data": { "text/plain": [ "{'AC0001:GATA-PROP:GATA': 3.912023005428146,\n", " 'AC0002:PROP-ALX:Homeodomain': 4.61512051684126,\n", " 'AC0003:HNF1A-HNF1B:Homeodomain': 1.0216512475319812,\n", " 'AC0004:ZSCAN:C2H2_ZF': 2.9652730660692823,\n", " 'AC0005:POU3F-POU1F:Homeodomain,POU': 3.4965075614664802}" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# create input regions and baseline regions for attribution\n", "random_regions_1 = helper.random_regions_dataframe(\n", " \"data/hg38.fa.fai\", 1024, 100, seed=1\n", ")\n", "random_regions_2 = helper.random_regions_dataframe(\n", " \"data/hg38.fa.fai\", 1024, 100, seed=2\n", ")\n", "# create iterators to yield one-hot encoded sequences from the region dataframes\n", "def pack_data_iters(df):\n", " seq_fasta_iter = helper.dataframe_to_fasta_iter(\n", " df, \"data/hg38.fa\", batch_size=8\n", " )\n", " seq_one_hot_iter = (\n", " helper.fasta_to_one_hot_sequences(seq_fasta)\n", " for seq_fasta in seq_fasta_iter\n", " )\n", " return zip(seq_one_hot_iter, )\n", "# compute layer attributions given the iterators of testing regions and control regions\n", "attributions = tpcav_model.layer_attributions(\n", " pack_data_iters(random_regions_1), pack_data_iters(random_regions_2)\n", ")\n", "# compute TPCAV scores for the concept\n", "cav_trainer.tpcav_score_all_concepts_log_ratio(attributions)" ] }, { "cell_type": "markdown", "id": "11f64f51-dd1d-48c3-b75f-70d5fa8fee95", "metadata": {}, "source": [ "You can visualize the cosine similarity matrix between concepts vectors combined with the TPCAV scores" ] }, { "cell_type": "code", "execution_count": 12, "id": "37a3ff15-bfc3-436a-9866-37530e4aac25", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cav_trainer.plot_cavs_similaritiy_heatmap(attributions, fscore_thresh=0.0)" ] } ], "metadata": { "kernelspec": { "display_name": "test", "language": "python", "name": "test" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.2" } }, "nbformat": 4, "nbformat_minor": 5 } TPCAV-0.8.4/pyproject.toml000066400000000000000000000015031516353663300153260ustar00rootroot00000000000000[build-system] requires = ["setuptools>=61.0"] build-backend = "setuptools.build_meta" [project] name = "tpcav" version = "0.8.4" description = "Testing with PCA projected Concept Activation Vectors" authors = [{name = "Jianyu Yang", email = "yztxwd@gmail.com"},] readme = "README.md" requires-python = ">=3.8" dependencies = [ "torch", "pandas", "numpy", "seqchromloader", "deeplift", "pyfaidx", "pybedtools", "captum", "scikit-learn", "biopython", "seaborn", "matplotlib", "logomaker" ] license = "MIT AND (Apache-2.0 OR BSD-2-Clause)" keywords = ["interpretation", "attribution", "concept", "genomics", "deep learning"] [project.urls] Homepage = "https://github.com/seqcode/TPCAV" [tool.setuptools.packages.find] exclude = ["data", "model", "node_modules", "test", "scripts"] TPCAV-0.8.4/scripts/000077500000000000000000000000001516353663300141025ustar00rootroot00000000000000TPCAV-0.8.4/scripts/compute_input_attrs_pca_x_cav.py000066400000000000000000000270311516353663300225720ustar00rootroot00000000000000#!/usr/bin/env python3 DESCRIPTION = """ Provide test regions and cavs, compute attribution scores on the input that in parallel to the cav directions and those orthogonal to cav directions. """ import logging import os import warnings from argparse import ArgumentParser from glob import glob import numpy as np import pandas as pd import seqchromloader as scl import torch import utils from captum.attr import DeepLift from pybedtools import BedTool from sklearn.metrics import precision_recall_fscore_support as score from tqdm import tqdm warnings.simplefilter(action="ignore", category=FutureWarning) device = "cuda:0" if torch.cuda.is_available() else "cpu" SEED = 1001 random_state = np.random.RandomState(SEED) logging.basicConfig( format="%(asctime)s %(levelname)-8s %(message)s", level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S", ) logger = logging.getLogger(__name__) logger.setLevel("INFO") logger.info("Compute Layer Attrs~") def abs_attribution_func(multipliers, inputs, baselines): "Multiplier x abs(inputs - baselines), this is to avoid duplicate sign coming from both inputs-baselines and concept cavs" attributions = tuple( (input - baseline).abs() * multiplier for input, baseline, multiplier in zip(inputs, baselines, multipliers) ) return attributions def main(): parser = ArgumentParser(description=DESCRIPTION) parser.add_argument("tpcav_model", help="TPCAV model") parser.add_argument( "test_bed", type=str, default=None, help="Test bed files will compute layer attributions on", ) parser.add_argument( "input_window_length", default=1024, type=int, help="input window length" ) parser.add_argument("genome_fasta_file", help="Genome fasta file") parser.add_argument("genome_size_file", help="Genome size file") parser.add_argument( "output_prefix", help="Prefix of output file name for attributions saved in npz format", ) parser.add_argument( "--cavs-dir", type=str, default=None, help="Directory containing CAV subdirs, this would be used to disentangle the attributions into those explained by CAVs and those not", ) parser.add_argument( "--cavs", type=str, nargs="+", default=None, help="CAVs to use, each CAV should be a folder containing classifier_weights.pt and classifier_perform_on_test.txt", ) parser.add_argument( "--num-baselines-per-sample", type=int, default=10, help="How many regions sampled as background when attributing each region", ) args = parser.parse_args() # ============================== load cavs ========================================= # iterate through all cavs, check performance of classifier cavs_list = [] total_cavs = 0 if args.cavs_dir is not None: cav_weight_fn_list = glob( os.path.join(args.cavs_dir, "**/*classifier_weights.pt") ) logger.info(f"Found {len(cav_weight_fn_list)} CAVs in {args.cavs_dir}") for fn in cav_weight_fn_list: perform = pd.read_table( fn.replace("classifier_weights.pt", "classifier_perform_on_test.txt"), comment="#", ) # two headers [Pred, Truth] _, _, fscores, _ = score(perform.Truth, perform.Pred) cavs_list.append(torch.load(fn, map_location="cpu")[0]) logger.info(f"Loading CAV from {fn}, fscore: {np.mean(fscores):.3f}") total_cavs += 1 if args.cavs is not None: for cav_dir in args.cavs: cav_weight_fn = glob(os.path.join(cav_dir, "*classifier_weights.pt"))[0] cav_perform_fn = cav_weight_fn.replace( "classifier_weights.pt", "classifier_perform_on_test.txt" ) perform = pd.read_table(cav_perform_fn, comment="#") _, _, fscores, _ = score(perform.Truth, perform.Pred) cavs_list.append(torch.load(cav_weight_fn, map_location="cpu")[0]) logger.info( f"Loading CAV from {cav_weight_fn}, fscore: {np.mean(fscores):.3f}" ) total_cavs += 1 logger.info(f"{len(cavs_list)} CAVs loaded") if len(cavs_list) == 0: cavs_list = None # ============================== load data ========================================= ## target test bed samples target_df = BedTool(args.test_bed).to_dataframe() target_df = utils.center_windows(target_df, window_len=args.input_window_length) target_df["label"] = -1 target_df["strand"] = "+" target_dl = scl.SeqChromDatasetByDataFrame( target_df, genome_fasta=args.genome_fasta_file, bigwig_filelist=[], return_region=True, dataloader_kws={"batch_size": 8, "drop_last": False}, ) ## baseline samples random_regs = scl.random_coords( gs=args.genome_size_file, l=args.input_window_length, n=len(target_df) * 20, ) random_regs["label"] = -1 random_regs["strand"] = "+" baseline_dl = scl.SeqChromDatasetByDataFrame( random_regs, genome_fasta=args.genome_fasta_file, bigwig_filelist=[], dataloader_kws={ "batch_size": 8 * args.num_baselines_per_sample, "drop_last": True, }, ) # ============================== Attribution in PCA space ================================ # load the TPCAV model tpcav_model = torch.load(args.tpcav_model) tpcav_model.eval() tpcav_model.to(device) tpcav_model.forward = tpcav_model.forward_from_start # set forward function deeplift = DeepLift(tpcav_model, multiply_by_inputs=True) # attribution each test sample # NOTE: there should be only one attribution tensor coming out of layer attribution attributions_seq = [] attributions_chrom = [] attributions_x_avs_seq = [] attributions_remainder_seq = [] attributions_x_avs_chrom = [] attributions_remainder_chrom = [] regions_save = [] for (region, seq, chrom, _, _), (bseq, bchrom, _, _) in tqdm( zip(target_dl, baseline_dl) ): assert bseq.shape[0] == args.num_baselines_per_sample * 8 regions_save.extend(region) # match repeated input shape seq = torch.repeat_interleave(seq, repeats=args.num_baselines_per_sample, dim=0) chrom = torch.repeat_interleave(chrom, repeats=args.num_baselines_per_sample, dim=0) bseq = bseq[: seq.shape[0]] bchrom = bchrom[: chrom.shape[0]] seq = utils.seq_transform_fn(seq.to(device)) bseq = utils.seq_transform_fn(bseq.to(device)) chrom = utils.chrom_transform_fn(chrom.to(device)) bchrom = utils.chrom_transform_fn(bchrom.to(device)) inputs = seq if chrom is None else (seq, chrom) binputs = bseq if chrom is None else (bseq, bchrom) # attribution on full sequence attribution = deeplift.attribute( inputs, baselines=binputs, additional_forward_args=( cavs_list, False, False, ), # custom_attribution_func=( # None if args.no_multiply_by_inputs else abs_attribution_func # ), ) # [# batch, dim_projected+dim_residual] def reduce_attrs(attrs): return attrs.reshape(-1, args.num_baselines_per_sample, *attrs.shape[1:]).mean(axis=1).detach().cpu() if chrom is None: attributions_seq.append(reduce_attrs(attribution)) else: attributions_seq.append(reduce_attrs(attribution[0])) attributions_chrom.append(reduce_attrs(attribution[1])) if cavs_list is not None: # attribution on x avs directions attribution_x_avs = deeplift.attribute( inputs, baselines=binputs, additional_forward_args=( cavs_list, False, True, ), ) # [# batch, dim_projected+dim_residual] # attribution on remainder attribution_remainder = deeplift.attribute( inputs, baselines=binputs, additional_forward_args=( cavs_list, True, False, ), ) # [# batch, dim_projected+dim_residual] if chrom is None: attributions_x_avs_seq.append(reduce_attrs(attribution_x_avs)) attributions_remainder_seq.append(reduce_attrs(attribution_remainder)) else: attributions_x_avs_seq.append(reduce_attrs(attribution_x_avs[0])) attributions_x_avs_chrom.append(reduce_attrs(attribution_x_avs[1])) attributions_remainder_seq.append(reduce_attrs(attribution_remainder[0])) attributions_remainder_chrom.append(reduce_attrs(attribution_remainder[1])) with torch.no_grad(): del ( seq, bseq, attribution, ) torch.cuda.empty_cache() # save regions np.savetxt(f"{args.output_prefix}.regions.txt", regions_save, fmt="%s") # save attributions def save_attrs(attrs, name): attrs = torch.cat(attrs) assert len(attrs.shape) == 3 and attrs.shape[2] == 4 torch.save(attrs, f"{args.output_prefix}.{name}.pt") return attrs # sum over the last dimension to get per base pair attributions attrs_all_seq = save_attrs(attributions_seq, "attributions_seq").sum(dim=2) if chrom is not None: attrs_all_chrom = save_attrs(attributions_chrom, "attributions_chrom").sum(dim=2) if cavs_list is not None: attrs_x_avs_seq = save_attrs(attributions_x_avs_seq, "attributions_x_avs_seq").sum(dim=2) attrs_remainder_seq = save_attrs( attributions_remainder_seq, "attributions_remainder_seq" ).sum(dim=2) if chrom is not None: attrs_x_avs_chrom = save_attrs(attributions_x_avs_chrom, "attributions_x_avs_chrom").sum(dim=2) attrs_remainder_chrom = save_attrs( attributions_remainder_chrom, "attributions_remainder_chrom" ).sum(dim=2) # save attr x avs and total attrs per region if chrom is not None: pd.DataFrame( { "region": regions_save, "attrs_total_seq": attrs_all_seq.sum(dim=1).numpy(), "attrs_x_avs_seq": attrs_x_avs_seq.sum(dim=1).numpy(), "attrs_total_chrom": attrs_all_chrom.sum(dim=1).numpy(), "attrs_x_avs_chrom": attrs_x_avs_chrom.sum(dim=1).numpy(), } ).assign(attrs_x_avs_total=lambda x: x['attrs_x_avs_seq'] + x['attrs_x_avs_chrom']).sort_values("attrs_x_avs_total", ascending=False).to_csv( f"{args.output_prefix}.regions_with_attrs_x_avs.txt", index=False, header=True, sep="\t", ) else: pd.DataFrame( { "region": regions_save, "attrs_total_seq": attrs_all_seq.sum(dim=1).numpy(), "attrs_x_avs_seq": attrs_x_avs_seq.sum(dim=1).numpy(), } ).sort_values("attrs_x_avs_seq", ascending=False).to_csv( f"{args.output_prefix}.regions_with_attrs_x_avs.txt", index=False, header=True, sep="\t", ) if __name__ == "__main__": main() TPCAV-0.8.4/scripts/compute_layer_attrs_only.py000066400000000000000000000220101516353663300215750ustar00rootroot00000000000000#!/usr/bin/env python3 DESCRIPTION = """ Given concepts, train linear classifier and evaluate """ import argparse import logging import re import numpy as np import pandas as pd import seqchromloader as scl import torch import utils from captum.attr import DeepLift from pybedtools import BedTool device = "cuda:0" if torch.cuda.is_available() else "cpu" SEED = 1001 random_state = np.random.RandomState(SEED) logging.basicConfig( format="%(asctime)s %(levelname)-8s %(message)s", level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S", ) logger = logging.getLogger(__name__) logger.setLevel("INFO") logger.info("Compute Layer Attrs~") def abs_attribution_func(multipliers, inputs, baselines): "Multiplier x abs(inputs - baselines), this is to avoid duplicate sign coming from both inputs-baselines and concept cavs" attributions = tuple( (input - baseline).abs() * multiplier for input, baseline, multiplier in zip(inputs, baselines, multipliers) ) return attributions def main(): parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter, description=DESCRIPTION ) parser.add_argument("tpcav_model", help="saved TPCAV model") parser.add_argument( "test_bed", type=str, default=None, help="Test bed files will compute layer attributions on", ) parser.add_argument( "input_window_length", default=1024, type=int, help="input window length" ) parser.add_argument("genome_fasta_file") parser.add_argument("genome_size_file") parser.add_argument( "output_prefix", help="Prefix of output file name for attributions saved in npz format", ) parser.add_argument( "--no-multiply-by-inputs", action="store_true", default=False, help="Specify if you don't want to factor in the activation difference between input and baseline", ) parser.add_argument( "--num-baselines-per-sample", type=int, default=10, help="How many regions sampled as background when attributing each region", ) parser.add_argument( "--chroms", nargs="+", default=None, help="Restrict attribution to certain chromosomes", ) args = parser.parse_args() ## target test bed samples target_df = BedTool(args.test_bed).to_dataframe() target_df = utils.center_windows(target_df, window_len=args.input_window_length) target_df["label"] = -1 target_df["strand"] = "+" if args.chroms is not None: target_df = scl.filter_chromosomes(target_df, to_keep=args.chroms) target_dl = scl.SeqChromDatasetByDataFrame( target_df, genome_fasta=args.genome_fasta_file, bigwig_filelist=[], return_region=True, dataloader_kws={"batch_size": 8, "drop_last": False}, ) ## baseline samples random_regs = scl.random_coords( gs=args.genome_size_file, l=args.input_window_length, n=len(target_df) * 20, ) random_regs["label"] = -1 random_regs["strand"] = "+" baseline_dl = scl.SeqChromDatasetByDataFrame( random_regs, genome_fasta=args.genome_fasta_file, bigwig_filelist=[], dataloader_kws={ "batch_size": 8 * args.num_baselines_per_sample, "drop_last": True, }, ) # load the saved model with tpcav parameters defined tpcav_model = torch.load(args.tpcav_model, map_location=device) tpcav_model.forward = ( tpcav_model.forward_from_projected_and_residual ) # set forward pass starting from projected and residual tpcav_model.eval() deeplift = DeepLift( tpcav_model, multiply_by_inputs=False if args.no_multiply_by_inputs else True ) # Atrtribute on the PCA embedding space attributions = [] avs_projected_save = [] avs_residual_save = [] regions = [] target_preds = [] baseline_preds = [] # for inpt, binpt in zip(target_dl, baseline_dl): for (region, seq, chrom, _, _), (bseq, bchrom, _, _) in zip(target_dl, baseline_dl): assert bseq.shape[0] == 8 * args.num_baselines_per_sample # save regions for r in region: regions.append(re.split(r"[:-]", r)) # project input seq = utils.seq_transform_fn(seq) chrom = utils.chrom_transform_fn(chrom) if chrom is not None: avs = tpcav_model.forward_until_select_layer( seq.to(device), chrom.to(device) ) else: avs = tpcav_model.forward_until_select_layer(seq.to(device)) avs_residual, avs_projected = tpcav_model.project_avs_to_pca( avs.flatten(start_dim=1).to(device) ) ## save if avs_projected is not None: avs_projected_save.append(avs_projected.detach().cpu()) avs_residual_save.append(avs_residual.detach().cpu()) # match repeated input shape avs_projected = ( torch.repeat_interleave( avs_projected, repeats=args.num_baselines_per_sample, dim=0 ) if avs_projected is not None else None ) avs_residual = torch.repeat_interleave( avs_residual, repeats=args.num_baselines_per_sample, dim=0 ) bseq = bseq[: (avs.shape[0] * args.num_baselines_per_sample)] bchrom = bchrom[: (avs.shape[0] * args.num_baselines_per_sample)] # do normal model forward on baselines to get activations bseq = utils.seq_transform_fn(bseq) bchrom = utils.chrom_transform_fn(bchrom) if bchrom is not None: bavs = tpcav_model.forward_until_select_layer( bseq.to(device), bchrom.to(device) ) else: bavs = tpcav_model.forward_until_select_layer(bseq.to(device)) bavs_residual, bavs_projected = tpcav_model.project_avs_to_pca( bavs.flatten(start_dim=1).to(device) ) # attribution if avs_projected is not None: attribution = deeplift.attribute( (avs_residual.to(device), avs_projected.to(device)), baselines=(bavs_residual.to(device), bavs_projected.to(device)), custom_attribution_func=( None if args.no_multiply_by_inputs else abs_attribution_func ), ) attr_residual, attr_projected = attribution attribution = torch.cat((attr_projected, attr_residual), dim=1) with torch.no_grad(): del attr_residual, attr_projected else: attribution = deeplift.attribute( (avs_residual.to(device),), baselines=(bavs_residual.to(device),), additional_forward_args=(None,), custom_attribution_func=( None if args.no_multiply_by_inputs else abs_attribution_func ), ) assert len(attribution) == 1 attribution = attribution[0] attributions.append(attribution.detach().cpu()) # make predictions, save both projected activations and residuals if avs_projected is not None: target_preds.append( tpcav_model(avs_residual.to(device), avs_projected.to(device)) .detach() .cpu() ) baseline_preds.append( tpcav_model(bavs_residual.to(device), bavs_projected.to(device)) .detach() .cpu() ) else: target_preds.append(tpcav_model(avs_residual.to(device), None).detach().cpu()) baseline_preds.append(tpcav_model(bavs_residual.to(device), None).detach().cpu()) with torch.no_grad(): del avs, avs_projected, avs_residual, bavs, bavs_projected, bavs_residual, seq, chrom, bseq, bchrom, attribution torch.cuda.empty_cache() print(f"Average target predictions: {torch.concat(target_preds).mean()}") print(f"Average baseline predictions: {torch.concat(baseline_preds).mean()}") # save regions regions = pd.DataFrame(regions, columns=["chrom", "start", "end"]) regions.to_csv( f"{args.output_prefix}.regions.bed", header=False, index=False, sep="\t", ) # save activations if len(avs_projected_save) > 0: avs_projected_save = torch.cat(avs_projected_save) torch.save( avs_projected_save, f"{args.output_prefix}.activations_projected.pt", ) else: torch.save(None, f"{args.output_prefix}.activations_projected.pt") avs_residual_save = torch.cat(avs_residual_save) torch.save( avs_residual_save, f"{args.output_prefix}.activations_residual.pt", ) # save attributions attrs = torch.cat(attributions) attrs = attrs.reshape(-1, args.num_baselines_per_sample, *attrs.shape[1:]).mean( axis=1 ) torch.save(attrs, f"{args.output_prefix}.attributions.pt") with torch.no_grad(): del attrs if __name__ == "__main__": main() TPCAV-0.8.4/scripts/compute_tcav_v2_pwm.example.yaml000066400000000000000000000004771516353663300224130ustar00rootroot00000000000000# parameters cav_root_dir: "./cavs_test/cavs/" attrs: "./cavs_test/test.attributions.pt" activations_projected: "./cavs_test/test.activations_projected.pt" activations_residual: "./cavs_test/test.activations_residual.pt" regions: "./cavs_test/test.regions.bed" output_dir: "output/tcavs/" fscore_threshold: 0.95 TPCAV-0.8.4/scripts/compute_tcav_v2_pwm.py.ipynb000066400000000000000000004114341516353663300215660ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "23d23ed8-0561-4f77-92cb-49e183bee21a", "metadata": {}, "outputs": [], "source": [ "# start coding here" ] }, { "cell_type": "code", "execution_count": 1, "id": "106abf26-c714-4afb-a4a3-b2339dc23078", "metadata": { "editable": true, "papermill": { "duration": 5.432492, "end_time": "2025-07-08T14:12:28.182980", "exception": false, "start_time": "2025-07-08T14:12:22.750488", "status": "completed" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "import logomaker\n", "import torch\n", "import re, os\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import textwrap\n", "\n", "from Bio import motifs\n", "from functools import partial\n", "from glob import glob\n", "from pyjaspar import jaspardb\n", "from sklearn.metrics import precision_recall_fscore_support as score\n", "from tqdm import tqdm\n", "from matplotlib import gridspec\n", "from pybedtools import BedTool\n", "from gprofiler import GProfiler" ] }, { "cell_type": "code", "execution_count": 2, "id": "1f795f12-92bb-4511-8d9b-a43de85b2a58", "metadata": { "editable": true, "papermill": { "duration": 0.259521, "end_time": "2025-07-08T14:12:28.458244", "exception": false, "start_time": "2025-07-08T14:12:28.198723", "status": "completed" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "import matplotlib.style as mplstyle\n", "mplstyle.use('fast')\n", "import matplotlib\n", "matplotlib.rc('pdf', fonttype=42)\n", "from fastcluster import linkage\n", "from scipy.spatial import distance\n", "from scipy.spatial.distance import pdist\n", "from scipy.cluster import hierarchy\n", "from scipy.cluster.hierarchy import fcluster, leaves_list\n", "from scipy.stats import zscore\n", "from collections import defaultdict\n", "from sklearn.cluster import AgglomerativeClustering\n", "#from cuml.cluster import AgglomerativeClustering " ] }, { "cell_type": "code", "execution_count": 3, "id": "cfa2f3ab-87cb-4aa1-a2b4-ef3465a2c6a3", "metadata": { "papermill": { "duration": 0.023997, "end_time": "2025-07-08T14:12:28.498762", "exception": false, "start_time": "2025-07-08T14:12:28.474765", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "rng = np.random.default_rng(1)" ] }, { "cell_type": "code", "execution_count": 4, "id": "ecfef380-f9dd-43cb-bb96-0fd260e9d552", "metadata": { "editable": true, "papermill": { "duration": 0.025713, "end_time": "2025-04-01T16:59:29.537950", "exception": false, "start_time": "2025-04-01T16:59:29.512237", "status": "completed" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "def compute_tcav(cav, attr):\n", "\n", " tcav = torch.matmul(torch.flatten(attr, start_dim=1), cav.T)\n", " print(cav.shape, attr.shape)\n", " tcav_score = ((tcav>0)*1.).mean(axis=0).cpu().numpy()\n", "\n", " return tcav, tcav_score" ] }, { "cell_type": "code", "execution_count": 48, "id": "457d1a36-692b-4ac1-ba2b-3dccbedf9ae3", "metadata": { "editable": true, "papermill": { "duration": 0.024606, "end_time": "2025-04-01T16:59:29.576178", "exception": false, "start_time": "2025-04-01T16:59:29.551572", "status": "completed" }, "slideshow": { "slide_type": "" }, "tags": [ "parameters" ] }, "outputs": [], "source": [ "# parameters\n", "cav_root_dir = \"./cavs_test/cavs/\"\n", "\n", "attrs = \"./cavs_test/test.attributions.pt\"\n", "\n", "activations_projected = \"./cavs_test/test.activations_projected.pt\"\n", "activations_residual = \"./cavs_test/test.activations_residual.pt\"\n", "\n", "regions = \"./cavs_test/test.regions.bed\"\n", "\n", "output_dir = \"./cavs_test/tcav_report/\"\n", "\n", "fscore_threshold:float = 0.95" ] }, { "cell_type": "code", "execution_count": 59, "id": "7d246c15-3672-4281-bfa1-64cff98fc5cf", "metadata": {}, "outputs": [], "source": [ "cav_dirs = {i: os.path.join(cav_root_dir, i) for i in os.listdir(cav_root_dir)}" ] }, { "cell_type": "code", "execution_count": 60, "id": "c076f0b6-fcd6-48aa-a4ae-21c9e595acd2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'AC0001:GATA-PROP:GATA_control_random_regions': './cavs_test/cavs/AC0001:GATA-PROP:GATA_control_random_regions',\n", " 'AC0002:PROP-ALX:Homeodomain_control_random_regions': './cavs_test/cavs/AC0002:PROP-ALX:Homeodomain_control_random_regions',\n", " 'AC0003:HNF1A-HNF1B:Homeodomain_control_random_regions': './cavs_test/cavs/AC0003:HNF1A-HNF1B:Homeodomain_control_random_regions',\n", " 'AC0004:ZSCAN:C2H2_ZF_control_random_regions': './cavs_test/cavs/AC0004:ZSCAN:C2H2_ZF_control_random_regions',\n", " 'AC0005:POU3F-POU1F:Homeodomain,POU_control_random_regions': './cavs_test/cavs/AC0005:POU3F-POU1F:Homeodomain,POU_control_random_regions'}" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cav_dirs" ] }, { "cell_type": "code", "execution_count": 51, "id": "da79b725-64bb-445e-8139-5ff0c545d05f", "metadata": { "papermill": { "duration": 0.09174, "end_time": "2025-07-08T14:12:28.947321", "exception": false, "start_time": "2025-07-08T14:12:28.855581", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "if regions is not None:\n", " regions = np.loadtxt(regions, dtype=str)" ] }, { "cell_type": "code", "execution_count": 52, "id": "cfdaccc8-3b6f-40a0-ba1f-8fb4f232cbf1", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2367435/806406243.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " avs_projected = torch.load(activations_projected, map_location='cpu')\n", "/tmp/ipykernel_2367435/806406243.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " avs_residual = torch.load(activations_residual, map_location='cpu')\n" ] } ], "source": [ "avs_projected = torch.load(activations_projected, map_location='cpu')\n", "avs_residual = torch.load(activations_residual, map_location='cpu')" ] }, { "cell_type": "code", "execution_count": 53, "id": "87d12372-5d53-4354-b609-e838f12366d3", "metadata": {}, "outputs": [], "source": [ "if avs_projected is not None:\n", " activations = torch.concat([avs_projected, avs_residual], dim=1)\n", "else:\n", " activations = avs_residual" ] }, { "cell_type": "code", "execution_count": 54, "id": "0676be06-2ffb-4c6d-9ac1-428e8bc81100", "metadata": {}, "outputs": [], "source": [ "os.makedirs(output_dir)" ] }, { "cell_type": "code", "execution_count": 55, "id": "9a74b33b-51cf-44d0-aaee-ca9be09fa325", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2367435/777408744.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " attrs = torch.load(attrs)\n" ] } ], "source": [ "attrs = torch.load(attrs)" ] }, { "cell_type": "code", "execution_count": 61, "id": "b51d5840-321d-42b0-8529-1f4080e79d3d", "metadata": { "papermill": { "duration": 0.027998, "end_time": "2025-04-01T16:59:30.388033", "exception": false, "start_time": "2025-04-01T16:59:30.360035", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "tcav_expts = []\n", "for cn, cav_dir in cav_dirs.items():\n", " concept, control = cn.split(\"_control_\")\n", " tcav_expts.append((re.sub(r'control_rep[0-9]_insert_', '', concept), control, cav_dir))\n", "tcav_expts = pd.DataFrame(tcav_expts, columns=['concept', 'rep', 'cav_dir'])" ] }, { "cell_type": "code", "execution_count": 62, "id": "fdb86eb8-03af-4ca4-a266-a94e8f7e129a", "metadata": { "papermill": { "duration": 0.04468, "end_time": "2025-04-01T16:59:30.446445", "exception": false, "start_time": "2025-04-01T16:59:30.401765", "status": "completed" }, "scrolled": true, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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conceptrepcav_dir
0AC0001:GATA-PROP:GATArandom_regions./cavs_test/cavs/AC0001:GATA-PROP:GATA_control...
1AC0002:PROP-ALX:Homeodomainrandom_regions./cavs_test/cavs/AC0002:PROP-ALX:Homeodomain_c...
2AC0003:HNF1A-HNF1B:Homeodomainrandom_regions./cavs_test/cavs/AC0003:HNF1A-HNF1B:Homeodomai...
3AC0004:ZSCAN:C2H2_ZFrandom_regions./cavs_test/cavs/AC0004:ZSCAN:C2H2_ZF_control_...
4AC0005:POU3F-POU1F:Homeodomain,POUrandom_regions./cavs_test/cavs/AC0005:POU3F-POU1F:Homeodomai...
\n", "
" ], "text/plain": [ " concept rep \\\n", "0 AC0001:GATA-PROP:GATA random_regions \n", "1 AC0002:PROP-ALX:Homeodomain random_regions \n", "2 AC0003:HNF1A-HNF1B:Homeodomain random_regions \n", "3 AC0004:ZSCAN:C2H2_ZF random_regions \n", "4 AC0005:POU3F-POU1F:Homeodomain,POU random_regions \n", "\n", " cav_dir \n", "0 ./cavs_test/cavs/AC0001:GATA-PROP:GATA_control... \n", "1 ./cavs_test/cavs/AC0002:PROP-ALX:Homeodomain_c... \n", "2 ./cavs_test/cavs/AC0003:HNF1A-HNF1B:Homeodomai... \n", "3 ./cavs_test/cavs/AC0004:ZSCAN:C2H2_ZF_control_... \n", "4 ./cavs_test/cavs/AC0005:POU3F-POU1F:Homeodomai... " ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tcav_expts" ] }, { "cell_type": "code", "execution_count": 66, "id": "dc4c32e3-9050-4641-9f5c-439d3fa4f6d1", "metadata": { "editable": true, "papermill": { "duration": 1.318247, "end_time": "2025-04-01T16:59:31.780317", "exception": false, "start_time": "2025-04-01T16:59:30.462070", "status": "completed" }, "scrolled": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "0it [00:00, ?it/s]/tmp/ipykernel_2367435/1255733344.py:26: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n", "/tmp/ipykernel_2367435/1255733344.py:26: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n", "/tmp/ipykernel_2367435/1255733344.py:26: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n", "/tmp/ipykernel_2367435/1255733344.py:26: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n", "/tmp/ipykernel_2367435/1255733344.py:26: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n", "5it [00:00, 129.84it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 188]) torch.Size([1000, 188])\n", "torch.Size([2, 188]) torch.Size([1000, 188])\n", "torch.Size([2, 188]) torch.Size([1000, 188])\n", "torch.Size([2, 188]) torch.Size([1000, 188])\n", "torch.Size([2, 188]) torch.Size([1000, 188])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\r", "760it [00:02, 260.22it/s]" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\r", "787it [00:02, 261.06it/s]" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\r", "815it [00:03, 265.68it/s]" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\r", "846it [00:03, 275.63it/s]" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\r", "860it [00:03, 272.85it/s]" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "dfs = []\n", "cavs = defaultdict(list)\n", "tcavs = defaultdict(list)\n", "\n", "fscores = []\n", "for item in tqdm(tcav_expts.itertuples()):\n", "\n", " prefix_dir = item.cav_dir\n", " \n", " perform = pd.read_table(f\"{prefix_dir}/classifier_perform_on_test.txt\", \n", " comment=\"#\")\n", "\n", " precision, recall, fscore, support = score(perform.Truth, perform.Pred)\n", "\n", " fscores.append(np.mean(fscore))\n", "\n", " # here predictions are model outputs, so it's usually expected that concepts 0 has a higher probability of being bound\n", " #model_predictions_0 = np.loadtxt(f\"{prefix_dir}/predictions_concept_0.txt\")\n", " #model_predictions_1 = np.loadtxt(f\"{prefix_dir}/predictions_concept_1.txt\")\n", " \n", " classifier_scores = pd.DataFrame({'precision': precision,\n", " 'recall': recall,\n", " 'fscore': fscore,\n", " 'support': support})\n", "\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n", " tcav, tcav_score = compute_tcav(cav, attrs)\n", " cavs[item.concept].append(cav)\n", " tcavs[item.concept].append(tcav)\n", "\n", " dfs.append((np.mean(fscore), support, np.median(tcav.numpy(), axis=0)[0], tcav_score[0], tcav_score[1], item.concept, item.rep))\n", "\n", " # dfs.append((fscore[0], support, tcav_score[0], np.median(tcav.numpy(), axis=0)[0], \n", " # cav, tcav,\n", " # #model_predictions_0.mean(), \n", " # item.concept, item.rep, item.layer, 0))\n", " # dfs.append((fscore[1], support, tcav_score[1], np.median(tcav.numpy(), axis=0)[1],\n", " # cav, tcav,\n", " # #model_predictions_1.mean(), \n", " # item.concept, item.rep, item.layer, 1))\n", " \n", "# dfs_merge = pd.DataFrame(dfs, columns=['fscore', 'support', 'tcav_score', 'tcav_median', \n", "# \"cav\", \"tcav\",\n", "# #'model_preds',\n", "# 'concept', 'rep', 'layer', 'concept_id'])\n", "dfs_merge = pd.DataFrame(dfs, columns=['fscore', 'support', 'tcav_score_median_test', 'tcav_pos_ratio_test', 'tcav_pos_ratio_control', \n", " 'concept', 'rep'])\n", "\n", "#dfs_merge['concept_name'] = dfs_merge.apply(lambda x: f'Random genome with\\n{x[\"concept\"]} inserted' if x['concept_id']==0 else 'Random genome', axis=1)" ] }, { "cell_type": "code", "execution_count": 67, "id": "4c46072b-f17c-4186-bb86-ea385aac1dbc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.histplot(x=fscores)" ] }, { "cell_type": "code", "execution_count": 68, "id": "c965f052-8510-4751-ab16-b296678e3f61", "metadata": {}, "outputs": [], "source": [ "dfs_merge\n", "dfs_merge.to_csv(f\"{output_dir}/tcav_all_concepts.tsv\")" ] }, { "cell_type": "code", "execution_count": 69, "id": "d34fabef-b521-4a95-8a76-cc3176610089", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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fscoresupporttcav_score_median_testtcav_pos_ratio_testtcav_pos_ratio_controlconceptrep
20.514597[461, 439]1.1888911.00.0AC0003:HNF1A-HNF1B:Homeodomainrandom_regions
30.556389[461, 434]0.2944931.00.0AC0004:ZSCAN:C2H2_ZFrandom_regions
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" ], "text/plain": [ " fscore support tcav_score_median_test tcav_pos_ratio_test \\\n", "2 0.514597 [461, 439] 1.188891 1.0 \n", "3 0.556389 [461, 434] 0.294493 1.0 \n", "\n", " tcav_pos_ratio_control concept rep \n", "2 0.0 AC0003:HNF1A-HNF1B:Homeodomain random_regions \n", "3 0.0 AC0004:ZSCAN:C2H2_ZF random_regions " ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfs_merge = dfs_merge.loc[dfs_merge.fscore>fscore_threshold]\n", "dfs_merge" ] }, { "cell_type": "code", "execution_count": 72, "id": "d8cfc13b-fdcf-4496-881b-c97a7acb45b8", "metadata": {}, "outputs": [], "source": [ "motif_clustering_pwms = motifs.parse(open('data/motif-clustering-v2.1beta_consensus_pwms.meme'), fmt='MINIMAL')\n", "motif_clustering_pwms = {m.name.replace('/', '-'): m.pwm for m in motif_clustering_pwms}\n", "\n", "if len(dfs_merge) > 0:\n", " dfs_merge['motif_cluster'] = dfs_merge.concept.apply(lambda x: x if x.startswith('AC') else None).str.replace(r'_rep[0-9]', '', regex=True)" ] }, { "cell_type": "markdown", "id": "bed7a289-3d63-4a8f-a316-89f439012e45", "metadata": { "papermill": { "duration": 0.027845, "end_time": "2025-04-01T16:59:47.005057", "exception": false, "start_time": "2025-04-01T16:59:46.977212", "status": "completed" }, "tags": [] }, "source": [ "### Log ratio" ] }, { "cell_type": "code", "execution_count": 73, "id": "e76aa30e-43b9-414f-8146-90923efb6a3f", "metadata": { "editable": true, "papermill": { "duration": 0.526734, "end_time": "2025-04-01T16:59:47.559457", "exception": true, "start_time": "2025-04-01T16:59:47.032723", "status": "failed" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "def log_ratio(x):\n", " # the upper and lower bound should be ~5\n", " return np.log((x['tcav_pos_ratio_test']+0.01)/(x['tcav_pos_ratio_control']+0.01))\n", " \n", "if len(dfs_merge) > 0:\n", " dfs_merge['log_ratio'] = dfs_merge.apply(log_ratio, axis=1)" ] }, { "cell_type": "markdown", "id": "0ae4dd89-6cf5-4ba4-b1c6-dd295446f475", "metadata": { "papermill": { "duration": null, "end_time": null, "exception": null, "start_time": null, "status": "pending" }, "tags": [] }, "source": [ "### CAV correlation" ] }, { "cell_type": "code", "execution_count": 76, "id": "b8c502ef-5d98-4935-8ef2-625759841d46", "metadata": { "papermill": { "duration": 0.051424, "end_time": "2025-07-08T14:18:29.597858", "exception": false, "start_time": "2025-07-08T14:18:29.546434", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "motif_clustering_pwms = motifs.parse(open('data/motif-clustering-v2.1beta_consensus_pwms.meme'), fmt='MINIMAL')\n", "motif_clustering_pwms = {re.findall(r'AC[0-9]*', m.name)[0]: m.pwm for m in motif_clustering_pwms} # index pwm by AC id\n", "def seq_logo(cav_key, ax=None, max_len=20):\n", " ac_id = re.findall('AC[0-9]*', cav_key)\n", " if len(ac_id) == 1:\n", " pwm = motif_clustering_pwms.get(ac_id[0])\n", " \n", " pwm_df = pd.DataFrame(pwm)\n", " motif_len = len(pwm_df)\n", " \n", " # Logomaker expects columns as A,C,G,T, so ensure correct order\n", " pwm_df = pwm_df[['A', 'C', 'G', 'T']]\n", " \n", " # Compute information content at each position (2 - entropy)\n", " def compute_ic(pwm_row):\n", " entropy = -sum([p * np.log2(p) if p > 0 else 0 for p in pwm_row])\n", " return 2 - entropy\n", " \n", " # Compute IC matrix: IC_letter = p * IC_total\n", " ic_df = pwm_df.copy()\n", " for i in range(len(pwm_df)):\n", " ic_total = compute_ic(pwm_df.iloc[i])\n", " ic_df.iloc[i] = pwm_df.iloc[i] * ic_total\n", " \n", " \n", " # Plot with logomaker\n", " x0, y0, width, height = ax.get_position().bounds\n", " ax.set_position([x0, y0, width * min(1., motif_len/max_len), height])\n", " logo = logomaker.Logo(ic_df, color_scheme={'A': 'red', 'C': 'blue',\n", " 'G': 'orange', 'T': 'green'},\n", " ax=ax)\n", " logo.ax.axis('off')\n", " else:\n", " ax.axis('off')\n" ] }, { "cell_type": "code", "execution_count": 81, "id": "9b08abf5-4d72-47a5-86b8-28659305d7da", "metadata": { "editable": true, "papermill": { "duration": 21.95088, "end_time": "2025-07-08T14:19:15.506164", "exception": false, "start_time": "2025-07-08T14:18:53.555284", "status": "completed" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "clustermap_width = 0.5\n", "barplot_width = 0.2\n", "logo_width = 0.3\n", "\n", "if len(dfs_merge) > 0:\n", " cavs_stack = {}\n", " #pwms_toplot = []\n", " colors_toplot = []\n", " log_ratios = []\n", " for concept, cav_list in cavs.items():\n", " if concept in dfs_merge.concept.tolist():\n", " motif_cluster = dfs_merge.loc[dfs_merge.concept==concept, 'motif_cluster'].item()\n", "\n", " cavs_key = concept\n", " cavs_stack[cavs_key] = torch.stack(cav_list).mean(dim=0)[0]\n", " #pwms_toplot.append(motif_clustering_pwms.get(dfs_merge.loc[dfs_merge.concept==concept, 'motif_cluster'].item()))\n", " colors_toplot.append('red' if dfs_merge.loc[dfs_merge.concept==concept, 'tcav_pos_ratio_test'].item()>0.5 else 'blue')\n", " log_ratios.append(dfs_merge.loc[dfs_merge.concept==concept, 'log_ratio'].item())\n", " cavs_keys = list(cavs_stack.keys())\n", " cavs_stack = torch.stack(list(cavs_stack.values()))\n", " \n", " from sklearn.metrics.pairwise import cosine_similarity\n", " if cavs_stack.shape[0] > 1:\n", "\n", " fig = plt.figure(figsize=(13, 8))\n", " sfigs = fig.subfigures(2, 1)\n", " \n", " plt.rc('xtick', labelsize=50/np.log(len(dfs_merge)))\n", " plt.rc('ytick', labelsize=50/np.log(len(dfs_merge)))\n", " \n", " matrix_similarity = cosine_similarity(cavs_stack.detach().numpy())\n", " cm = sns.clustermap(matrix_similarity, xticklabels=False, yticklabels=False, cmap='bwr',\n", " vmin=-1, vmax=1)\n", " labels = fcluster(cm.dendrogram_row.linkage, 10, criterion='maxclust')\n", " labels_toplot = np.array([labels[i] for i in cm.dendrogram_row.reordered_ind])\n", " \n", " colors_toplot = [colors_toplot[i] for i in cm.dendrogram_col.reordered_ind]\n", " for c, text in zip(colors_toplot, cm.ax_heatmap.get_yticklabels()):\n", " text.set_color(c)\n", "\n", " cm.gs.update(left=0.05, right=clustermap_width)\n", " cm.ax_cbar.set_position([.01, 0.9, .05, .05])\n", "\n", " cavs_keys_reordered = [cavs_keys[i] for i in cm.dendrogram_col.reordered_ind]\n", "\n", " #create new gridspec for the log ratio barplot\n", " gs2 = gridspec.GridSpec(1, 1)\n", " # create axes within this new gridspec\n", " ax2 = cm.fig.add_subplot(gs2[0])\n", "\n", " heatmap_bbox = cm.ax_heatmap.get_position()\n", " ax2.set_position([0.5, heatmap_bbox.y0, barplot_width, heatmap_bbox.height])\n", "\n", " # plot log ratio\n", " ax2.tick_params(axis='y', which='major', pad=fig.get_size_inches()[0] * logo_width * 72)\n", " log_ratios_reordered = [log_ratios[i] for i in cm.dendrogram_col.reordered_ind]\n", " sns.barplot(y=cavs_keys_reordered, x=log_ratios_reordered, orient=\"y\", ax=ax2)\n", " # set color of bar by value\n", " for idx in range(len((ax2.containers[0]))):\n", " if ax2.containers[0].datavalues[idx] > 0:\n", " ax2.containers[0][idx].set_color('red')\n", " else:\n", " ax2.containers[0][idx].set_color('blue')\n", " \n", " ax2.set_xlim(left=-5, right=5)\n", " ax2.yaxis.tick_right()\n", " ax2.set_title('TCAV log ratio')\n", "\n", " gs3 = gridspec.GridSpec(len(cavs_keys_reordered), 1)\n", "\n", " logo_height = heatmap_bbox.height/len(cavs_keys_reordered)\n", " for i, (cav_key, g) in enumerate(zip(reversed(cavs_keys_reordered), gs3)):\n", " ax3 = plt.subplot(g)\n", " ax3.set_position([0.71, heatmap_bbox.y0+i*logo_height, logo_width+0.01, logo_height])\n", " if cav_key is not None:\n", " seq_logo(cav_key, ax=ax3)\n", " else:\n", " ax3.axis('off')\n", "\n", " gs4 = gridspec.GridSpec(1, 1)\n", " # create axes within this new gridspec\n", " ax4 = cm.fig.add_subplot(gs4[0])\n", " ax4.imshow(labels_toplot.reshape(-1, 1), cmap=\"tab10\", aspect='auto')\n", " ax4.axis('off')\n", "\n", " ax4.set_position([-0.05, heatmap_bbox.y0, 0.05, heatmap_bbox.height])\n", " \n", " plt.savefig(f\"{output_dir}/tcav_cav_correlation_full.pdf\", bbox_inches=\"tight\")\n" ] }, { "cell_type": "code", "execution_count": 78, "id": "34707485-04fd-4602-b7f4-1ca97dacbbe9", "metadata": { "papermill": { "duration": 32.652877, "end_time": "2025-07-08T14:19:48.276459", "exception": false, "start_time": "2025-07-08T14:19:15.623582", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1000,)\n", "990 regions are positively associated with cluster 1\n" ] } ], "source": [ "if len(dfs_merge) > 0 and regions is not None:\n", " # reuse the stacked cavs\n", " if cavs_stack.shape[0] > 1:\n", " # compute cosine similarity between the activations and cavs\n", " ## reorder according to previous clustermap dendrogram order\n", " cavs_stack_reordered = torch.stack([cavs_stack[i] for i in cm.dendrogram_col.reordered_ind])\n", " ## compute cosine similarity\n", " similarity_avs_cavs = cosine_similarity(activations, cavs_stack_reordered)\n", "\n", " posrs = {}\n", " for l in np.unique(labels_toplot):\n", " # subset concepts in the cluster\n", " posr = []\n", " #print(f\"subsample columns: \", np.where(labels_toplot==l)[0])\n", " ss = similarity_avs_cavs[:, np.where(labels_toplot==l)[0]].mean(axis=1) # take avg of cosine similarity among selected concepts\n", " print(ss.shape)\n", " c = 0\n", " ks = [cavs_keys_reordered[i] for i in np.where(labels_toplot==l)[0]]\n", " for idx in np.argsort(ss):\n", " if ss[idx] > 0: # take regions that are positive\n", " posr.append(regions[idx])\n", " c+=1\n", " if len(posr) > 0:\n", " bed_df = []\n", " with open(f\"{output_dir}/cluster_{l}_positive_cavs_x_avs.bed\", 'w') as f:\n", " f.write(f\"#{ks}\")\n", " for r in posr:\n", " chrom, start, end = r\n", " f.write(f\"{chrom}\\t{start}\\t{end}\\n\")\n", " bed_df.append((chrom, start, end, l))\n", "\n", " posrs[l.item()] = pd.DataFrame(bed_df, columns=['chrom', 'start', 'end', 'label'])\n", " print(f\"{len(bed_df)} regions are positively associated with cluster {l}\")\n", " else:\n", " print(f\"No regions are positively associated with cluster {l}\")" ] } ], "metadata": { "kernelspec": { "display_name": "torch_latest", "language": "python", "name": "torch_latest" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.6" } }, "nbformat": 4, "nbformat_minor": 5 } TPCAV-0.8.4/scripts/compute_tcav_v2_pwm_pattern_multi.ipynb000066400000000000000000025264341516353663300241170ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "1efe9e34-6aaf-47ea-8524-d105541105cd", "metadata": { "editable": true, "papermill": { "duration": 0.032786, "end_time": "2025-07-08T14:12:22.734785", "exception": false, "start_time": "2025-07-08T14:12:22.701999", "status": "completed" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/data/lab/jianyu/projects/TCAV/1_code/0_Kipoi_models\n" ] } ], "source": [ "%cd ~/group/lab/jianyu/projects/TCAV/1_code/0_Kipoi_models/" ] }, { "cell_type": "code", "execution_count": 2, "id": "939a1ece-7599-46a9-865b-4bfe98acf08b", "metadata": { "editable": true, "papermill": { "duration": 5.432492, "end_time": "2025-07-08T14:12:28.182980", "exception": false, "start_time": "2025-07-08T14:12:22.750488", "status": "completed" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "import logomaker\n", "import torch\n", "import re, os\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import textwrap\n", "\n", "from Bio import motifs\n", "from functools import partial\n", "from glob import glob\n", "from pyjaspar import jaspardb\n", "from sklearn.metrics import precision_recall_fscore_support as score\n", "from tqdm import tqdm\n", "from matplotlib import gridspec\n", "from pybedtools import BedTool\n", "from gprofiler import GProfiler" ] }, { "cell_type": "code", "execution_count": 3, "id": "1dea4a82-8d11-4182-ac0d-f14c6af30b1e", "metadata": { "editable": true, "papermill": { "duration": 0.259521, "end_time": "2025-07-08T14:12:28.458244", "exception": false, "start_time": "2025-07-08T14:12:28.198723", "status": "completed" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "import matplotlib.style as mplstyle\n", "mplstyle.use('fast')\n", "import matplotlib\n", "matplotlib.rc('pdf', fonttype=42)\n", "from fastcluster import linkage\n", "from scipy.spatial import distance\n", "from scipy.spatial.distance import pdist\n", "from scipy.cluster import hierarchy\n", "from scipy.cluster.hierarchy import fcluster, leaves_list\n", "from scipy.stats import zscore\n", "from collections import defaultdict\n", "from sklearn.cluster import AgglomerativeClustering\n", "#from cuml.cluster import AgglomerativeClustering " ] }, { "cell_type": "code", "execution_count": 18, "id": "f104a219", "metadata": { "editable": true, "papermill": { "duration": 0.025584, "end_time": "2025-07-08T14:12:28.843593", "exception": false, "start_time": "2025-07-08T14:12:28.818009", "status": "completed" }, "slideshow": { "slide_type": "" }, "tags": [ "injected-parameters", "parameters" ] }, "outputs": [], "source": [ "# Parameters\n", "cav_root_dir = \"output/pipeline_test_5000_36_motifs/cavs/\"\n", "cav_dir_patterns = [\"AC0134:HSF:HSF*\",\"AC0077:ZIC-GLIS:C2H2_ZF*\",\"AC0108:KLF-SP:C2H2_ZF*\",\"AC0290:FOXP:Forkhead*\",\"AC0587:POU5F-NANOG:Homeodomain,POU*\",\n", " \"AC0109:SP-KLF:C2H2_ZF*\",\"AC0588:POU2F-POU3F:Homeodomain,POU*\",\"AC0581:ZSCAN:C2H2_ZF*\",\"AC0004:ZSCAN:C2H2_ZF*\",\n", " \"AC0008:VENTX:Homeodomain*\",\"AC0262:SOX-SRY:Sox*\",\"AC0424:NR4A:Nuclear_receptor*\",\"AC0373:ZBTB:C2H2_ZF*\",\"AC0115:ZNF:C2H2_ZF*\",\n", " \"AC0114:ZNF-VEZF:C2H2_ZF*\",\"AC0456:ZNF-PRDM:C2H2_ZF*\",\"AC0259:SOX:Sox*\",\"AC0110:ZNF-MAZ:C2H2_ZF*\",\"AC0442:ZNF:C2H2_ZF*\",\n", " \"AC0395:SOX:Sox*\",\"AC0104:ZNF:C2H2_ZF*\",\"AC0309:ZNF:C2H2_ZF*\",\"AC0614:LHX:Homeodomain*\",\"AC0525:NFYA-NFYB:CBF-NF-Y*\",\n", " \"AC0529:ZNF:C2H2_ZF*\",\"AC0120:RREB:C2H2_ZF*\",\"AC0267:SOX-SRY:Sox*\",\"AC0051:PLAG:C2H2_ZF*\",\"AC0098:ZNF:C2H2_ZF*\",\"AC0126:ZNF:C2H2_ZF*\",\n", " \"DNA*\",\n", " \"LINE*\",\n", " \"Low*\",\n", " \"LTR*\",\n", " \"Other*\",\n", " \"Satellite*\",\n", " \"scRNA*\",\n", " \"Simple*\",\n", " \"SINE*\"]\n", "\n", "layer_attrs_prefixes = {\"Nanog\": \"output/pipeline_test_5000_36_motifs/Nanog_peaks_task_Nanog_counts\",\n", " \"Klf4\": \"output/pipeline_test_5000_36_motifs/Klf4_peaks_task_Klf4_counts\",\n", " \"Sox2\": \"output/pipeline_test_5000_36_motifs/Sox2_peaks_task_Sox2_counts\",\n", " \"Oct4\": \"output/pipeline_test_5000_36_motifs/Oct4_peaks_task_Oct4_counts\",}\n", "\n", "output_dir = \"output/tcavs/TEST/\"\n", "fscore_threshold = 0.90\n", "\n", "motifs_pwm_meme = \"TPCAV/data/motif-clustering-v2.1beta_consensus_pwms.meme\"" ] }, { "cell_type": "code", "execution_count": 19, "id": "4c210bc3-0620-403a-8928-c5f7690cca2e", "metadata": {}, "outputs": [], "source": [ "cav_dirs = {}\n", "for p in cav_dir_patterns:\n", " for cd in glob(f\"{cav_root_dir}/{p}\"):\n", " cn = os.path.basename(cd)\n", " cav_dirs[cn] = cd" ] }, { "cell_type": "code", "execution_count": 20, "id": "0676be06-2ffb-4c6d-9ac1-428e8bc81100", "metadata": { "papermill": { "duration": 0.02177, "end_time": "2025-07-08T14:14:44.707699", "exception": false, "start_time": "2025-07-08T14:14:44.685929", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "os.makedirs(output_dir, exist_ok=True)" ] }, { "cell_type": "code", "execution_count": 21, "id": "9a74b33b-51cf-44d0-aaee-ca9be09fa325", "metadata": { "papermill": { "duration": 83.118634, "end_time": "2025-07-08T14:16:07.837780", "exception": false, "start_time": "2025-07-08T14:14:44.719146", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3491937093.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " k: torch.load(f'{prefix}.attributions.pt') for k, prefix in layer_attrs_prefixes.items()\n" ] } ], "source": [ "layer_attrs = {\n", " k: torch.load(f'{prefix}.attributions.pt') for k, prefix in layer_attrs_prefixes.items()\n", "}" ] }, { "cell_type": "code", "execution_count": 22, "id": "b51d5840-321d-42b0-8529-1f4080e79d3d", "metadata": { "papermill": { "duration": 2.019248, "end_time": "2025-07-08T14:16:09.903103", "exception": false, "start_time": "2025-07-08T14:16:07.883855", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "tcav_expts = []\n", "for cn, cav_dir in cav_dirs.items():\n", " concept, control = cn.split(\"_control_\")\n", " tcav_expts.append((concept, control, cav_dir))\n", "tcav_expts = pd.DataFrame(tcav_expts, columns=['concept', 'control', 'cav_dir'])" ] }, { "cell_type": "code", "execution_count": 23, "id": "fdb86eb8-03af-4ca4-a266-a94e8f7e129a", "metadata": { "papermill": { "duration": 3.80461, "end_time": "2025-07-08T14:16:13.730822", "exception": false, "start_time": "2025-07-08T14:16:09.926212", "status": "completed" }, "scrolled": true, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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conceptcontrolcav_dir
0AC0134:HSF:HSFrandom_regionsoutput/pipeline_test_5000_36_motifs/cavs/AC013...
1AC0077:ZIC-GLIS:C2H2_ZFrandom_regionsoutput/pipeline_test_5000_36_motifs/cavs/AC007...
2AC0108:KLF-SP:C2H2_ZFrandom_regionsoutput/pipeline_test_5000_36_motifs/cavs/AC010...
3AC0290:FOXP:Forkheadrandom_regionsoutput/pipeline_test_5000_36_motifs/cavs/AC029...
4AC0587:POU5F-NANOG:Homeodomain,POUrandom_regionsoutput/pipeline_test_5000_36_motifs/cavs/AC058...
............
208SINE_PB1D10random_regionsoutput/pipeline_test_5000_36_motifs/cavs/SINE_...
209SINE_PB1D11random_regionsoutput/pipeline_test_5000_36_motifs/cavs/SINE_...
210SINE_PB1D7random_regionsoutput/pipeline_test_5000_36_motifs/cavs/SINE_...
211SINE_PB1D9random_regionsoutput/pipeline_test_5000_36_motifs/cavs/SINE_...
212SINE_RSINE1random_regionsoutput/pipeline_test_5000_36_motifs/cavs/SINE_...
\n", "

213 rows × 3 columns

\n", "
" ], "text/plain": [ " concept control \\\n", "0 AC0134:HSF:HSF random_regions \n", "1 AC0077:ZIC-GLIS:C2H2_ZF random_regions \n", "2 AC0108:KLF-SP:C2H2_ZF random_regions \n", "3 AC0290:FOXP:Forkhead random_regions \n", "4 AC0587:POU5F-NANOG:Homeodomain,POU random_regions \n", ".. ... ... \n", "208 SINE_PB1D10 random_regions \n", "209 SINE_PB1D11 random_regions \n", "210 SINE_PB1D7 random_regions \n", "211 SINE_PB1D9 random_regions \n", "212 SINE_RSINE1 random_regions \n", "\n", " cav_dir \n", "0 output/pipeline_test_5000_36_motifs/cavs/AC013... \n", "1 output/pipeline_test_5000_36_motifs/cavs/AC007... \n", "2 output/pipeline_test_5000_36_motifs/cavs/AC010... \n", "3 output/pipeline_test_5000_36_motifs/cavs/AC029... \n", "4 output/pipeline_test_5000_36_motifs/cavs/AC058... \n", ".. ... \n", "208 output/pipeline_test_5000_36_motifs/cavs/SINE_... \n", "209 output/pipeline_test_5000_36_motifs/cavs/SINE_... \n", "210 output/pipeline_test_5000_36_motifs/cavs/SINE_... \n", "211 output/pipeline_test_5000_36_motifs/cavs/SINE_... \n", "212 output/pipeline_test_5000_36_motifs/cavs/SINE_... \n", "\n", "[213 rows x 3 columns]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tcav_expts" ] }, { "cell_type": "code", "execution_count": 4, "id": "02db5eae-d2ff-48b6-ad59-08fea529bc54", "metadata": { "papermill": { "duration": 0.023997, "end_time": "2025-07-08T14:12:28.498762", "exception": false, "start_time": "2025-07-08T14:12:28.474765", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "rng = np.random.default_rng(1)" ] }, { "cell_type": "code", "execution_count": 5, "id": "ecfef380-f9dd-43cb-bb96-0fd260e9d552", "metadata": { "editable": true, "papermill": { "duration": 0.246636, "end_time": "2025-07-08T14:12:28.760214", "exception": false, "start_time": "2025-07-08T14:12:28.513578", "status": "completed" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "def compute_tcav(cav, attr):\n", "\n", " tcav = torch.matmul(torch.flatten(attr, start_dim=1), cav.T)\n", " print(cav.shape, attr.shape)\n", " tcav_score = ((tcav>0)*1.).mean(axis=0).cpu().numpy()\n", "\n", " return tcav, tcav_score" ] }, { "cell_type": "code", "execution_count": 36, "id": "dc4c32e3-9050-4641-9f5c-439d3fa4f6d1", "metadata": { "editable": true, "papermill": { "duration": 104.426731, "end_time": "2025-07-08T14:17:58.418790", "exception": false, "start_time": "2025-07-08T14:16:13.992059", "status": "completed" }, "scrolled": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "0it [00:00, ?it/s]/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "1it [00:02, 2.65s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2it [00:04, 2.05s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "3it [00:05, 1.84s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "4it [00:07, 1.76s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "5it [00:09, 1.66s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "6it [00:10, 1.62s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "7it [00:12, 1.67s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "8it [00:14, 1.71s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "9it [00:15, 1.71s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "10it [00:17, 1.70s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "11it [00:19, 1.70s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "12it [00:20, 1.71s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "13it [00:22, 1.73s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "14it [00:24, 1.73s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "15it [00:26, 1.70s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "16it [00:27, 1.69s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "17it [00:29, 1.71s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "18it [00:31, 1.75s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "19it [00:33, 1.77s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "20it [00:34, 1.78s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "21it [00:36, 1.73s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "22it [00:38, 1.70s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "23it [00:39, 1.67s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "24it [00:41, 1.64s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "25it [00:42, 1.61s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "26it [00:44, 1.59s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "27it [00:46, 1.64s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "28it [00:48, 1.69s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "29it [00:49, 1.73s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "30it [00:51, 1.71s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "31it [00:53, 1.67s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "32it [00:54, 1.66s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "33it [00:56, 1.68s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "34it [00:58, 1.71s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "35it [00:59, 1.73s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "36it [01:01, 1.79s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "37it [01:03, 1.80s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "38it [01:05, 1.76s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "39it [01:07, 1.74s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "40it [01:08, 1.72s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "41it [01:10, 1.69s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "42it [01:12, 1.68s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "43it [01:13, 1.63s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "44it [01:15, 1.61s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "45it [01:16, 1.57s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "46it [01:18, 1.59s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "47it [01:19, 1.57s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "48it [01:21, 1.58s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "49it [01:23, 1.63s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "50it [01:24, 1.66s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "51it [01:26, 1.70s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "52it [01:28, 1.67s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "53it [01:29, 1.64s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "54it [01:31, 1.64s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "55it [01:33, 1.67s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "56it [01:34, 1.68s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "57it [01:36, 1.68s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "58it [01:38, 1.65s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "59it [01:39, 1.66s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "60it [01:41, 1.70s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "61it [01:43, 1.70s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "62it [01:45, 1.72s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "63it [01:46, 1.71s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "64it [01:48, 1.72s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "65it [01:50, 1.73s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "66it [01:52, 1.75s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "67it [01:53, 1.70s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "68it [01:55, 1.68s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "69it [01:56, 1.65s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "70it [01:58, 1.62s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "71it [01:59, 1.60s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "72it [02:01, 1.60s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "73it [02:03, 1.59s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "74it [02:04, 1.64s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "75it [02:06, 1.67s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "76it [02:08, 1.70s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "77it [02:10, 1.74s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "78it [02:12, 1.74s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "79it [02:13, 1.72s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "80it [02:15, 1.72s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "81it [02:16, 1.68s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "82it [02:18, 1.65s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "83it [02:20, 1.69s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "84it [02:22, 1.71s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "85it [02:23, 1.73s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "86it [02:25, 1.75s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "87it [02:27, 1.78s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "88it [02:29, 1.77s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "89it [02:31, 1.76s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "90it [02:32, 1.71s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "91it [02:34, 1.67s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "92it [02:35, 1.65s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "93it [02:37, 1.65s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "94it [02:39, 1.63s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "95it [02:40, 1.59s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "96it [02:42, 1.58s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "97it [02:43, 1.56s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "98it [02:45, 1.55s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "99it [02:46, 1.58s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100it [02:48, 1.56s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "101it [02:49, 1.60s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "102it [02:51, 1.65s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "103it [02:53, 1.68s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "104it [02:55, 1.73s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "105it [02:57, 1.75s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "106it [02:58, 1.74s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "107it [03:00, 1.73s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "108it [03:02, 1.76s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "109it [03:04, 1.79s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "110it [03:05, 1.74s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "111it [03:07, 1.73s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "112it [03:09, 1.74s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "113it [03:11, 1.78s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "114it [03:13, 1.80s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "115it [03:14, 1.73s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "116it [03:16, 1.70s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "117it [03:17, 1.66s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "118it [03:19, 1.66s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "119it [03:20, 1.60s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "120it [03:22, 1.61s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "121it [03:24, 1.62s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "122it [03:25, 1.66s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "123it [03:27, 1.71s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "124it [03:29, 1.72s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "125it [03:31, 1.69s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "126it [03:32, 1.68s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "127it [03:34, 1.68s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "128it [03:36, 1.69s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "129it [03:37, 1.70s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "130it [03:39, 1.74s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "131it [03:41, 1.75s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "132it [03:43, 1.75s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "133it [03:44, 1.73s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "134it [03:46, 1.73s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "135it [03:48, 1.69s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "136it [03:49, 1.67s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "137it [03:51, 1.64s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "138it [03:53, 1.65s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "139it [03:54, 1.61s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "140it [03:56, 1.57s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "141it [03:57, 1.63s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "142it [03:59, 1.69s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "143it [04:01, 1.70s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "144it [04:03, 1.72s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "145it [04:05, 1.74s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "146it [04:06, 1.74s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "147it [04:08, 1.72s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "148it [04:10, 1.72s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "149it [04:11, 1.66s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "150it [04:13, 1.67s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "151it [04:15, 1.69s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "152it [04:16, 1.73s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "153it [04:18, 1.77s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "154it [04:20, 1.77s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "155it [04:22, 1.75s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "156it [04:23, 1.70s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "157it [04:25, 1.70s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "158it [04:27, 1.68s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "159it [04:28, 1.64s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "160it [04:30, 1.63s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "161it [04:31, 1.60s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "162it [04:33, 1.60s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "163it [04:35, 1.59s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "164it [04:36, 1.58s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "165it [04:38, 1.61s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "166it [04:40, 1.64s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "167it [04:41, 1.61s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "168it [04:43, 1.61s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "169it [04:44, 1.65s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "170it [04:46, 1.67s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "171it [04:48, 1.67s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "172it [04:49, 1.66s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "173it [04:51, 1.63s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "174it [04:53, 1.64s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "175it [04:54, 1.68s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "176it [04:56, 1.71s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "177it [04:58, 1.73s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "178it [05:00, 1.77s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "179it [05:02, 1.76s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "180it [05:03, 1.76s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "181it [05:05, 1.76s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "182it [05:07, 1.85s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "183it [05:09, 1.79s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "184it [05:10, 1.72s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "185it [05:12, 1.72s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "186it [05:14, 1.78s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "187it [05:16, 1.80s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "188it [05:18, 1.81s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "189it [05:19, 1.80s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "190it [05:21, 1.78s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "191it [05:23, 1.80s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "192it [05:25, 1.80s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "193it [05:27, 1.81s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "194it [05:28, 1.73s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "195it [05:30, 1.73s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "196it [05:32, 1.74s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "197it [05:33, 1.74s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "198it [05:35, 1.74s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "199it [05:37, 1.75s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "200it [05:39, 1.74s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "201it [05:40, 1.75s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "202it [05:42, 1.72s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "203it [05:45, 2.01s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "204it [05:47, 2.05s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "205it [05:49, 2.03s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "206it [05:51, 2.01s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "207it [05:53, 2.01s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "208it [05:55, 1.99s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "209it [05:57, 1.99s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "210it [05:59, 1.96s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "211it [06:00, 1.90s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "212it [06:02, 1.86s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2300022/3675804983.py:29: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([56459, 72210])\n", "torch.Size([2, 72210]) torch.Size([57601, 72210])\n", "torch.Size([2, 72210]) torch.Size([10999, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "213it [06:04, 1.71s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([2, 72210]) torch.Size([25849, 72210])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "def log_ratio(x):\n", " return np.log((x['tcav_pos_ratio_test']+0.01)/(x['tcav_pos_ratio_control']+0.01))\n", "\n", "dfs = []\n", "cavs = defaultdict(list)\n", "tcavs = defaultdict(list)\n", "\n", "fscores = []\n", "for item in tqdm(tcav_expts.itertuples()):\n", "\n", " prefix_dir = item.cav_dir\n", " \n", " perform = pd.read_table(f\"{prefix_dir}/classifier_perform_on_test.txt\", \n", " comment=\"#\")\n", "\n", " precision, recall, fscore, support = score(perform.Truth, perform.Pred)\n", "\n", " fscores.append(np.mean(fscore))\n", "\n", " # here predictions are model outputs, so it's usually expected that concepts 0 has a higher probability of being bound\n", " #model_predictions_0 = np.loadtxt(f\"{prefix_dir}/predictions_concept_0.txt\")\n", " #model_predictions_1 = np.loadtxt(f\"{prefix_dir}/predictions_concept_1.txt\")\n", " \n", " classifier_scores = pd.DataFrame({'precision': precision,\n", " 'recall': recall,\n", " 'fscore': fscore,\n", " 'support': support})\n", "\n", " cav = torch.load(f\"{prefix_dir}/classifier_weights.pt\").detach()\n", " cavs[item.concept].append(cav)\n", "\n", " for key, attr in layer_attrs.items():\n", " \n", " tcav, tcav_score = compute_tcav(cav, attr)\n", " tcavs[item.concept].append(tcav)\n", "\n", " tcav_log_ratio = np.log((tcav_score[0]+0.01)/(tcav_score[1]+0.01))\n", " \n", " dfs.append((np.mean(fscore), support, tcav_log_ratio, item.concept, item.control, key))\n", "\n", "dfs_merge = pd.DataFrame(dfs, columns=['fscore', 'support', 'tcav_log_ratio', \n", " 'concept', 'control', 'layer_attr_key'])\n", "\n", "#dfs_merge['concept_name'] = dfs_merge.apply(lambda x: f'Random genome with\\n{x[\"concept\"]} inserted' if x['concept_id']==0 else 'Random genome', axis=1)" ] }, { "cell_type": "code", "execution_count": 37, "id": "4c46072b-f17c-4186-bb86-ea385aac1dbc", "metadata": { "editable": true, "papermill": { "duration": 3.940561, "end_time": "2025-07-08T14:18:02.422171", "exception": false, "start_time": "2025-07-08T14:17:58.481610", "status": "completed" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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fscoresupporttcav_log_ratioconceptcontrollayer_attr_key
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8470.788863[498, 478]3.899257SINE_PB1D9random_regionsOct4
8480.790428[498, 467]0.337436SINE_RSINE1random_regionsNanog
8490.790428[498, 467]3.218748SINE_RSINE1random_regionsKlf4
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fscoresupporttcav_log_ratioconceptcontrollayer_attr_key
00.998932[484, 453]-1.459535AC0134:HSF:HSFrandom_regionsNanog
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30.998932[484, 453]4.615120AC0134:HSF:HSFrandom_regionsOct4
40.997812[484, 433]4.600910AC0077:ZIC-GLIS:C2H2_ZFrandom_regionsNanog
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7670.930051[498, 447]4.473102SINE_B2_Mm1arandom_regionsOct4
7680.905391[498, 485]-4.023066SINE_B2_Mm1trandom_regionsNanog
7690.905391[498, 485]4.507082SINE_B2_Mm1trandom_regionsKlf4
7700.905391[498, 485]2.800212SINE_B2_Mm1trandom_regionsSox2
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336 rows × 6 columns

\n", "
" ], "text/plain": [ " fscore support tcav_log_ratio concept \\\n", "0 0.998932 [484, 453] -1.459535 AC0134:HSF:HSF \n", "1 0.998932 [484, 453] 4.606392 AC0134:HSF:HSF \n", "2 0.998932 [484, 453] 4.615120 AC0134:HSF:HSF \n", "3 0.998932 [484, 453] 4.615120 AC0134:HSF:HSF \n", "4 0.997812 [484, 433] 4.600910 AC0077:ZIC-GLIS:C2H2_ZF \n", ".. ... ... ... ... \n", "767 0.930051 [498, 447] 4.473102 SINE_B2_Mm1a \n", "768 0.905391 [498, 485] -4.023066 SINE_B2_Mm1t \n", "769 0.905391 [498, 485] 4.507082 SINE_B2_Mm1t \n", "770 0.905391 [498, 485] 2.800212 SINE_B2_Mm1t \n", "771 0.905391 [498, 485] 4.473102 SINE_B2_Mm1t \n", "\n", " control layer_attr_key \n", "0 random_regions Nanog \n", "1 random_regions Klf4 \n", "2 random_regions Sox2 \n", "3 random_regions Oct4 \n", "4 random_regions Nanog \n", ".. ... ... \n", "767 random_regions Oct4 \n", "768 random_regions Nanog \n", "769 random_regions Klf4 \n", "770 random_regions Sox2 \n", "771 random_regions Oct4 \n", "\n", "[336 rows x 6 columns]" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfs_merge" ] }, { "cell_type": "markdown", "id": "0ae4dd89-6cf5-4ba4-b1c6-dd295446f475", "metadata": { "papermill": { "duration": 0.023525, "end_time": "2025-07-08T14:18:29.521959", "exception": false, "start_time": "2025-07-08T14:18:29.498434", "status": "completed" }, "tags": [] }, "source": [ "### CAV correlation" ] }, { "cell_type": "markdown", "id": "d83015ce-0a0c-4489-bc23-456a96ccf50a", "metadata": { "papermill": { "duration": 0.034163, "end_time": "2025-07-08T14:19:15.587940", "exception": false, "start_time": "2025-07-08T14:19:15.553777", "status": "completed" }, "tags": [] }, "source": [ "Save the positively associated regions for each cluster" ] }, { "cell_type": "code", "execution_count": 53, "id": "6e2cba42-c252-4761-a9cd-689d78ce37bb", "metadata": { "papermill": { "duration": 0.051424, "end_time": "2025-07-08T14:18:29.597858", "exception": false, "start_time": "2025-07-08T14:18:29.546434", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "motif_clustering_pwms = motifs.parse(open(motifs_pwm_meme), fmt='MINIMAL')\n", "motif_clustering_pwms = {m.name: m.pwm for m in motif_clustering_pwms} # index pwm by AC id\n", "def seq_logo(cav_key, ax=None, max_len=20):\n", " pwm = motif_clustering_pwms.get(cav_key)\n", "\n", " if pwm is not None:\n", " pwm_df = pd.DataFrame(pwm)\n", " motif_len = len(pwm_df)\n", " \n", " # Logomaker expects columns as A,C,G,T, so ensure correct order\n", " pwm_df = pwm_df[['A', 'C', 'G', 'T']]\n", " \n", " # Compute information content at each position (2 - entropy)\n", " def compute_ic(pwm_row):\n", " entropy = -sum([p * np.log2(p) if p > 0 else 0 for p in pwm_row])\n", " return 2 - entropy\n", " \n", " # Compute IC matrix: IC_letter = p * IC_total\n", " ic_df = pwm_df.copy()\n", " for i in range(len(pwm_df)):\n", " ic_total = compute_ic(pwm_df.iloc[i])\n", " ic_df.iloc[i] = pwm_df.iloc[i] * ic_total\n", " \n", " \n", " # Plot with logomaker\n", " x0, y0, width, height = ax.get_position().bounds\n", " ax.set_position([x0, y0, width * min(1., motif_len/max_len), height])\n", " logo = logomaker.Logo(ic_df, color_scheme={'A': 'red', 'C': 'blue',\n", " 'G': 'orange', 'T': 'green'},\n", " ax=ax)\n", " logo.ax.axis('off')\n", " else:\n", " print(f'PWM not found for {cav_key}')\n", " ax3.axis('off')\n" ] }, { "cell_type": "code", "execution_count": 68, "id": "e74400df-15a9-4e41-aa56-470ea14ed603", "metadata": { "editable": true, "papermill": { "duration": 21.95088, "end_time": "2025-07-08T14:19:15.506164", "exception": false, "start_time": "2025-07-08T14:18:53.555284", "status": "completed" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "
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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "clustermap_width = 0.5\n", "barplot_width = 0.4 / len(layer_attrs_prefixes) # compute barplot width for each of the layer attrs\n", "logo_width = 0.1\n", "text_width = 0.1\n", "\n", "text_size = 40/np.log(len(dfs_merge))\n", "\n", "if len(dfs_merge) > 0:\n", " cavs_stack = {}\n", " for concept, cav_list in cavs.items():\n", " if concept in dfs_merge.concept.tolist():\n", " cavs_key = concept\n", " cavs_stack[cavs_key] = torch.stack(cav_list).mean(dim=0)[0]\n", " cavs_keys = list(cavs_stack.keys())\n", " cavs_stack = torch.stack(list(cavs_stack.values()))\n", " \n", " from sklearn.metrics.pairwise import cosine_similarity\n", " if cavs_stack.shape[0] > 1:\n", "\n", " fig = plt.figure(figsize=(13, 8))\n", " sfigs = fig.subfigures(2, 1)\n", " \n", " plt.rc('xtick', labelsize=text_size)\n", " plt.rc('ytick', labelsize=text_size)\n", " \n", " matrix_similarity = cosine_similarity(cavs_stack.detach().numpy())\n", " cm = sns.clustermap(matrix_similarity, xticklabels=False, yticklabels=False, cmap='bwr',\n", " vmin=-1, vmax=1)\n", " labels = fcluster(cm.dendrogram_row.linkage, 10, criterion='maxclust')\n", "\n", " # set color bar loc\n", " cm.gs.update(left=0.05, right=clustermap_width)\n", " cm.ax_cbar.set_position([.01, 0.9, .05, .05])\n", "\n", " cavs_keys_reordered = [cavs_keys[i] for i in cm.dendrogram_col.reordered_ind]\n", "\n", " # plot Log ratio barplots\n", " for i, layer_attr_key in enumerate(layer_attrs_prefixes.keys()):\n", " log_ratios = dfs_merge.loc[dfs_merge.layer_attr_key==layer_attr_key].set_index('concept').loc[cavs_keys_reordered, 'tcav_log_ratio'].tolist()\n", " \n", " ## create new gridspec for the log ratio barplot\n", " gs2 = gridspec.GridSpec(1, 1)\n", " ## create axes within this new gridspec\n", " ax2 = cm.fig.add_subplot(gs2[0])\n", " \n", " heatmap_bbox = cm.ax_heatmap.get_position()\n", " ax2.set_position([0.5+i*barplot_width, heatmap_bbox.y0, barplot_width, heatmap_bbox.height])\n", " \n", " ## plot log ratio\n", " ax2.tick_params(axis='y', which='major', pad=fig.get_size_inches()[0] * logo_width * 72)\n", " sns.barplot(y=cavs_keys_reordered, x=log_ratios, orient=\"y\", ax=ax2)\n", " ## set color of bar by value\n", " for idx in range(len((ax2.containers[0]))):\n", " if ax2.containers[0].datavalues[idx] > 0:\n", " ax2.containers[0][idx].set_color('red')\n", " else:\n", " ax2.containers[0][idx].set_color('blue')\n", " \n", " ax2.set_xlim(left=-5, right=5)\n", " ax2.yaxis.tick_right()\n", " if i != 0:\n", " ax2.axes.get_xaxis().set_visible(False)\n", " ax2.axes.get_yaxis().set_visible(False)\n", " ax2.set_title(layer_attr_key)\n", "\n", " # plot PWM logos\n", " gs3 = gridspec.GridSpec(len(cavs_keys_reordered), 1)\n", "\n", " logo_height = heatmap_bbox.height/len(cavs_keys_reordered)\n", " for i, (cav_key, g) in enumerate(zip(cavs_keys_reordered[::-1], gs3)):\n", " ax3 = plt.subplot(g)\n", " ax3.set_position([0.5+len(layer_attrs_prefixes)*barplot_width+0.01, heatmap_bbox.y0+i*logo_height, logo_width+0.01, logo_height])\n", " if cav_key is not None:\n", " seq_logo(cav_key, ax=ax3)\n", " else:\n", " ax3.axis('off')\n", "\n", " # plot cav keys\n", " gs4 = gridspec.GridSpec(len(cavs_keys_reordered), 1)\n", " text_height = heatmap_bbox.height/len(cavs_keys_reordered)\n", " for i, (cav_key, g) in enumerate(zip(cavs_keys_reordered[::-1], gs4)):\n", " ax4 = plt.subplot(g)\n", " ax4.set_position([0.5+len(layer_attrs_prefixes)*barplot_width+logo_width+0.03, heatmap_bbox.y0+i*text_height, text_width+0.01, text_height])\n", " ax4.text(0, 0, cav_key, transform=ax4.transAxes, fontsize=text_size)\n", " ax4.axis('off')\n", " \n", " plt.savefig(f\"{output_dir}/tcav_cav_correlation_full.pdf\", bbox_inches=\"tight\")\n" ] } ], "metadata": { "kernelspec": { "display_name": "torch_latest", "language": "python", "name": "torch_latest" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.6" }, "papermill": { "default_parameters": {}, "duration": 524.532709, "end_time": "2025-07-08T14:21:05.238952", "environment_variables": {}, "exception": null, "input_path": "scripts/compute_tcav_v2_pwm.ipynb", "output_path": "output/tcavs/compute_tcav_v2_pwm_sample10_motif-clustering-v2.1beta_and_repeats_full_PC_pca_add_9_Nanog_counts.processed.ipynb", "parameters": { "activations_projected": "output/attrs/Nanog_counts_sample10_motif-clustering-v2.1beta_and_repeats_full_PC_pca_add_9.add.avs_projected.pt", "activations_residual": "output/attrs/Nanog_counts_sample10_motif-clustering-v2.1beta_and_repeats_full_PC_pca_add_9.add.avs_residual.pt", "attr_regions": null, "attrs_dict": { "add_9-add": "output/attrs/Nanog_counts_sample10_motif-clustering-v2.1beta_and_repeats_full_PC_pca_add_9.add.attributions.pt" }, "cav_root_dir": "output/cavs/sample10_motif-clustering-v2.1beta_and_repeats_full_PC/", "cluster_selection_epsilon": 5, "fscore_threshold": 0.95, "layer": "add_9-add", "max_clust": 4, "num_motif_per_cluster": "all", "output_dir": "output/tcavs/compute_tcav_sample10_motif-clustering-v2.1beta_and_repeats_full_PC_pca_add_9_Nanog_counts", "regions": "output/attrs/Nanog_counts_sample10_motif-clustering-v2.1beta_and_repeats_full_PC_pca_add_9.add.regions.txt" }, "start_time": "2025-07-08T14:12:20.706243", "version": "2.6.0" } }, "nbformat": 4, "nbformat_minor": 5 } TPCAV-0.8.4/scripts/models.example.py000066400000000000000000000256361516353663300174050ustar00rootroot00000000000000Description = "NN model collection" import math from copy import deepcopy import pytorch_lightning as pl import torch from einops.layers.torch import Rearrange from torch import nn class squeeze(nn.Module): def forward(self, x): return torch.squeeze(x) def default(val, d): return val if val is not None else d def exponential_linspace_int(start, end, num, divisible_by=1): def _round(x): return int(round(x / divisible_by) * divisible_by) base = math.exp(math.log(end / start) / (num - 1)) return [_round(start * base**i) for i in range(num)] def DenseBlock(num_f, activation=nn.LeakyReLU, dropout=0.0): return nn.Sequential( nn.Linear(num_f, num_f), activation(), nn.BatchNorm1d(num_f), nn.Dropout(dropout), ) def ConvBlock(dim, dim_out=None, kernel_size=1, stride=1, dilation=1): "Standard convolutional block" return nn.Sequential( nn.Conv1d( dim, default(dim_out, dim), kernel_size, padding=int(dilation * (kernel_size - 1) / 2), stride=stride, dilation=dilation, ), nn.GELU(), nn.BatchNorm1d(default(dim_out, dim)), ) def RConvBlock(dim, kernel_size=1, num_conv=1, skip=True): "A convolutional block stack with skip connnection" layer_list = [] for i in range(num_conv): layer_list.extend( [ nn.Conv1d(dim, dim, kernel_size=kernel_size, padding=kernel_size // 2), nn.GELU(), nn.BatchNorm1d(dim), ] ) if skip: return Residual(nn.Sequential(*layer_list)) else: return nn.Sequential(*layer_list) class Squeeze(nn.Module): def __init__(self, dim=None): super().__init__() self.dim = dim def forward(self, x): if self.dim is None: return torch.squeeze(x) else: return torch.squeeze(x, dim=self.dim) class Residual(nn.Module): def __init__(self, fn): super().__init__() self.fn = fn def forward(self, x, **kwargs): return self.fn(x, **kwargs) + x class PositionalEncoding(nn.Module): def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000): super().__init__() self.dropout = nn.Dropout(p=dropout) position = torch.arange(max_len).unsqueeze(1) div_term = torch.exp( torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model) ) pe = torch.zeros(max_len, 1, d_model) pe[:, 0, 0::2] = torch.sin(position * div_term) pe[:, 0, 1::2] = torch.cos(position * div_term) self.register_buffer("pe", pe) def forward(self, x: torch.Tensor) -> torch.Tensor: """ Args: x: Tensor, shape [seq_len, batch_size, embedding_dim] """ x = x + self.pe[: x.size(0)] return self.dropout(x) class ConvTowerDomain_v6(pl.LightningModule): """ Convolutional tower model v6 Separate sequence and chromatin convolutional tower for only applying domain adaptation to one of them Final Attention layers merge seq and chrom """ def __init__( self, chroms_channel=1, input_len=512, conv1d_filter=64, conv_tower_depth=4, conv_tower_kernel=5, conv_tower_dilation=1, attn_num_layers=2, attn_num_heads=8, attn_dim_feedforward=2048, activation="LeakyReLU", dropout=0.0, gamma=0.99995, lr=1e-4, lambd=0, classification=False, seqonly=True, ): super().__init__() self.chroms_channel = chroms_channel self.input_len = input_len self.conv1d_filter = conv1d_filter self.conv_tower_depth = conv_tower_depth self.conv_tower_kernel = conv_tower_kernel self.conv_tower_dilation = conv_tower_dilation self.attn_num_layers = attn_num_layers self.attn_num_heads = attn_num_heads self.attn_dim_feedforward = attn_dim_feedforward self.activation = getattr(nn, activation) self.dropout = dropout self.gamma = gamma self.lr = lr self.lambd = lambd self.classification = classification self.seqonly = seqonly self.save_hyperparameters() self.example_input_array = ( torch.zeros(512, 4, self.input_len).index_fill_(1, torch.tensor(2), 1), torch.ones(512, self.chroms_channel, self.input_len), ) # stem self.seq_stem = nn.Sequential( nn.Conv1d(4, self.conv1d_filter, 25, padding=12, bias=False), self.activation(), nn.BatchNorm1d(self.conv1d_filter), ) if not self.seqonly: self.chrom_stem = nn.Sequential( nn.Conv1d( self.chroms_channel, self.conv1d_filter, 25, padding=12, bias=False ), self.activation(), nn.BatchNorm1d(self.conv1d_filter), ) # convolutional tower ## compute the depth because the exponential base is fixed to 2 here self.conv_tower_outdim = int( self.conv1d_filter * (2 ** int(self.conv_tower_depth - 1)) ) self.conv_tower_outlen = int( self.input_len / (2 ** int(self.conv_tower_depth - 1)) ) dim_lists = exponential_linspace_int( self.conv1d_filter, self.conv_tower_outdim, num=self.conv_tower_depth, divisible_by=2, ) conv_tower = [] for dim_in, dim_out in zip(dim_lists[:-1], dim_lists[1:]): conv_tower.append( nn.Sequential( RConvBlock(dim_in, kernel_size=self.conv_tower_kernel, skip=True), ConvBlock(dim_in, dim_out=dim_out, kernel_size=1), nn.MaxPool1d(kernel_size=2), ) ) self.seq_conv_tower = nn.Sequential(*conv_tower) if not self.seqonly: self.chrom_conv_tower = deepcopy(self.seq_conv_tower) merge_dim = ( self.conv_tower_outdim if self.seqonly else self.conv_tower_outdim * 2 ) # self attention self.pos_encoder = PositionalEncoding( merge_dim, self.dropout, self.conv_tower_outlen ) # assume we merged sequence and chromatin inputs here transformer_encoder = [] for i in range(self.attn_num_layers): transformer_encoder.append( nn.TransformerEncoderLayer( d_model=merge_dim, nhead=self.attn_num_heads, dim_feedforward=self.attn_dim_feedforward, activation="relu", ) ) self.transformer_encoder = nn.Sequential(*transformer_encoder) self.pre_attn = nn.Sequential(Rearrange("b c l -> l b c"), self.pos_encoder) self.post_attn = nn.Sequential( Rearrange("l b c -> b c l"), nn.BatchNorm1d(merge_dim), self.activation(), nn.Dropout(self.dropout), nn.Conv1d(merge_dim, 1, 1), self.activation(), nn.BatchNorm1d(1), Squeeze(dim=1), ) # final output self.main_pred = nn.Sequential(nn.Linear(self.conv_tower_outlen, 1)) # tpcav parameters # self.register_buffer("zscore_mean", None) # self.register_buffer("zscore_std", None) # self.register_buffer("pca_inv", None) # self.orig_shape = None def forward(self, seq): seq = self.seq_stem(seq) seq = self.seq_conv_tower(seq) y_hat = seq y_hat = self.pre_attn(y_hat) y_hat = self.transformer_encoder(y_hat) y_hat = self.post_attn(y_hat) y_pred = self.main_pred(y_hat) return y_pred def merge_projected_and_residual(self, y_projected, y_residual): "Project PCA activations back to original space and add with residual, then multiply with zscore_std" "NOTE: zscore_mean is not added here , this is to avoid adding it twice if you disentange y_projected and y_residual" y_hat = torch.matmul(y_projected, self.pca_inv) + y_residual # PCA inverse y_hat = y_hat * self.zscore_std # reverse normalize return y_hat def forward_until_select_layer(self, seq): seq = self.seq_stem(seq) seq = self.seq_conv_tower(seq) y_hat = seq y_hat = self.pre_attn(y_hat) y_hat = self.transformer_encoder(y_hat) y_hat = self.post_attn(y_hat) return y_hat def resume_forward_from_select_layer(self, y_hat): return self.main_pred(y_hat) def forward_from_start( self, seq, cavs_list=None, mute_x_avs=False, mute_remainder=False, project_to_pca=True, ): y_hat = self.forward_until_select_layer(seq) if project_to_pca: avs_residual, avs_projected = self.project_post_attn_to_pca( y_hat.flatten(start_dim=1) ) return self.forward_from_projected_and_residual( avs_residual, avs_projected, cavs_list, mute_x_avs, mute_remainder, ) else: return self.resume_forward_from_select_layer(y_hat) def forward_from_projected_and_residual( self, y_residual, y_projected, cavs_list=None, mute_x_avs=False, mute_remainder=False, ): if cavs_list is not None: y_hat_x_avs, y_hat_remainder = self.disentangle_avs_x_cavs( y_projected, y_residual, cavs_list ) if mute_x_avs: # which part of the activations to use y_hat_x_avs.register_hook(lambda grad: torch.zeros_like(grad)) if mute_remainder: y_hat_remainder.register_hook(lambda grad: torch.zeros_like(grad)) y_hat = y_hat_x_avs + y_hat_remainder + self.zscore_mean else: y_hat = ( self.merge_projected_and_residual(y_projected, y_residual) + self.zscore_mean ) if self.orig_shape is not None: y_hat = y_hat.reshape((-1, *self.orig_shape[1:])) # resume back to normal forward process y_hat = self.resume_forward_from_select_layer(y_hat) return y_hat def project_avs_to_pca(self, y): return self.project_post_attn_to_pca(y) def project_post_attn_to_pca(self, y): "Project activations of post attn layer to PCA space" if self.pca_inv is not None: V = self.pca_inv.T y_standardized = (y - self.zscore_mean) / self.zscore_std y_projected = torch.matmul(y_standardized, V) y_residual = y_standardized - torch.matmul(y_projected, self.pca_inv) return y_residual, y_projected else: return y, None TPCAV-0.8.4/scripts/models.py000066400000000000000000000111421516353663300157360ustar00rootroot00000000000000Description = "NN model collection" import torch class Model_Class(torch.nn.Module): """ Model definition """ def __init__(self, *args, **kwargs): super().__init__() def merge_projected_and_residual(self, y_projected, y_residual): "Project PCA activations back to original space and add with residual, then multiply with zscore_std" "NOTE: zscore_mean is not added here , this is to avoid adding it twice if you disentange y_projected and y_residual" y_hat = torch.matmul(y_projected, self.pca_inv) + y_residual # PCA inverse y_hat = y_hat * self.zscore_std # reverse normalize return y_hat def forward_until_select_layer(self, seq, chrom): return y_hat def resume_forward_from_select_layer(self, y_hat): return y_pred def forward_from_start( self, seq, chrom, cavs_list=None, mute_x_avs=False, mute_remainder=False, project_to_pca=True, ): y_hat = self.forward_until_select_layer(seq, chrom) if project_to_pca: avs_residual, avs_projected = self.project_post_attn_to_pca( y_hat.flatten(start_dim=1) ) return self.forward_from_projected_and_residual( avs_residual, avs_projected, cavs_list, mute_x_avs, mute_remainder, ) else: return self.resume_forward_from_select_layer(y_hat) def forward_from_projected_and_residual( self, y_residual, y_projected, cavs_list=None, mute_x_avs=False, mute_remainder=False, ): if cavs_list is not None: y_projected, y_residual = self.disentangle_avs_x_cavs( y_projected, y_residual, cavs_list, mute_x_avs, mute_remainder ) y_hat = ( self.merge_projected_and_residual(y_projected, y_residual) + self.zscore_mean ) if self.orig_shape is not None: y_hat = y_hat.reshape((-1, *self.orig_shape[1:])) # resume back to normal forward process y_hat = self.resume_forward_from_select_layer(y_hat) return y_hat def project_avs_to_pca(self, y): return self.project_post_attn_to_pca(y) def project_post_attn_to_pca(self, y): "Project activations of post attn layer to PCA space" if self.pca_inv is not None: V = self.pca_inv.T y_standardized = (y - self.zscore_mean) / self.zscore_std y_projected = torch.matmul(y_standardized, V) y_residual = y_standardized - torch.matmul(y_projected, self.pca_inv) return y_residual, y_projected else: return y, None def disentangle_avs_x_cavs( self, y_projected, y_residual, cavs_list, mute_x_avs=False, mute_remainder=False ): "Given a list of CAVs, disentangle the activations" y_all = torch.cat( [y_projected, y_residual], dim=1 ) # merge projected and residual activations because cavs is computed on projected + residual cavs_matrix = torch.stack(cavs_list, dim=1).to(y_all.device) # [#dims, #cavs] if cavs_matrix.shape[1] > cavs_matrix.shape[0]: print( f"Warning: CAVs matrix has more CAVs than dimensions! Remainder should be super close to 0" ) # check the rank of cavs_matrix first mrank = torch.linalg.matrix_rank(cavs_matrix) cavs_ortho_matrix = ( torch.linalg.qr(cavs_matrix, mode="reduced").Q[:, :mrank].detach() ) # [#dims, #cavs], then keep the first mrank orthogonal basis as the remaining ones should be close to 0 and meaningless assert torch.allclose( cavs_ortho_matrix.T @ cavs_ortho_matrix, torch.eye(mrank).to(cavs_ortho_matrix.device), atol=1e-3, rtol=1e-3, ), f"Q^TQ is not identity matrix! Please check the CAVs matrix. {cavs_ortho_matrix.T @ cavs_ortho_matrix}" y_x_avs = y_all @ cavs_ortho_matrix @ cavs_ortho_matrix.T y_remainder = y_all - y_x_avs # [# batches, # dims] dim_projected = y_projected.shape[1] if y_projected is not None else 0 if mute_x_avs: y_x_avs.register_hook(lambda grad: torch.zeros_like(grad)) if mute_remainder: y_remainder.register_hook(lambda grad: torch.zeros_like(grad)) return ( y_x_avs[:, :dim_projected] + y_remainder[:, :dim_projected], y_x_avs[:, dim_projected:] + y_remainder[:, dim_projected:], ) TPCAV-0.8.4/scripts/run_tcav_sgd_pca.py000066400000000000000000000560161516353663300177650ustar00rootroot00000000000000#!/usr/bin/env python3 DESCRIPTION = """ Given concepts, train linear classifier and evaluate """ import warnings warnings.simplefilter(action="ignore", category=FutureWarning) import logging import os import time from argparse import ArgumentParser from multiprocessing import Pool from os import makedirs, path import numpy as np import pandas as pd import seqchromloader as scl import torch import webdataset as wds from Bio import motifs as Bio_motifs from captum.concept import Classifier, Concept from scipy.linalg import svd from sklearn.linear_model import SGDClassifier from sklearn.model_selection import GridSearchCV from torch.utils.data import DataLoader, TensorDataset, random_split device = "cuda:0" if torch.cuda.is_available() else "cpu" SEED = 1001 random_state = np.random.RandomState(SEED) logging.basicConfig( format="%(asctime)s %(levelname)-8s %(message)s", level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S", ) logger = logging.getLogger(__name__) logger.setLevel("INFO") logger.info("TCAV~") import numpy as np import seqchromloader as scl import utils def load_all_tensors_to_numpy(dataloaders): if not isinstance(dataloaders, list): dataloaders = [ dataloaders, ] avs = [] ls = [] for dataloader in dataloaders: for av, l in dataloader: avs.append(av.cpu().numpy()) ls.append(l.cpu().numpy()) avs = np.concatenate(avs) ls = np.concatenate(ls) return avs, ls class SGD(Classifier): def __init__(self, n_jobs=-1, penalty="l2"): self.lm = SGDClassifier( max_iter=1000, early_stopping=True, validation_fraction=0.1, learning_rate="optimal", n_iter_no_change=10, n_jobs=n_jobs, penalty=penalty, ) if penalty == "l2": self.parameters = {"alpha": [1e-2, 1e-4, 1e-6]} elif penalty == "l1": self.parameters = {"alpha": [1e-1, 1]} else: raise Exception(f"Unexpected penalty type {penalty} for SGD classifier") self.search = GridSearchCV(self.lm, self.parameters) def train_and_eval(self, train_dataloader, val_dataloader): # get all train/val tensors and convert into numpy train_avs, train_ls = load_all_tensors_to_numpy( [train_dataloader, val_dataloader] ) # fit self.search.fit(train_avs, train_ls) self.lm = self.search.best_estimator_ # print status logger.info(f"Best Params of hyperparam search: {self.search.best_params_}") logger.info(f"SGD iterations: {self.lm.n_iter_}") return def weights(self): if len(self.lm.coef_) == 1: return torch.tensor(np.array([-1 * self.lm.coef_[0], self.lm.coef_[0]])) else: return torch.tensor(self.lm.coef_) def classes(self): return self.lm.classes_ # train linear classifier def train_classifier(avs, args, output_dir, sgd_penalty="l2"): train_avds = [] val_avds = [] test_avds = [] for idx, av in avs.items(): avd = TensorDataset(av, torch.full((av.shape[0],), idx)) train_avd, val_avd, test_avd = random_split(avd, [0.8, 0.1, 0.1]) print(f"training dataset size: {len(train_avd)}") print(f"validation dataset size: {len(val_avd)}") print(f"test dataset size: {len(test_avd)}") train_avds.append(train_avd) val_avds.append(val_avd) test_avds.append(test_avd) train_avds = wds.RandomMix(train_avds) val_avds = wds.RandomMix(val_avds) test_avds = wds.RandomMix(test_avds) train_avdl = DataLoader(train_avds, batch_size=32) val_avdl = DataLoader(val_avds, batch_size=32) test_avdl = DataLoader(test_avds, batch_size=32) logger.info(f"Collected all activations for") input_dim = next(iter(train_avdl))[0].shape[1] logger.info(f"Layer activation input dimension is {input_dim}") classifier = SGD(penalty=sgd_penalty) classifier.train_and_eval(train_avdl, val_avdl) logger.info(f"Trained classifier") # summarize performance and save predictions def make_predictions_and_save(avdl, name): y_preds = [] y_trues = [] for x, y in avdl: y_pred = classifier.lm.predict(x.cpu().numpy()) y_preds.append(y_pred) y_trues.append(y.cpu().numpy()) y_preds = np.concatenate(y_preds) y_trues = np.concatenate(y_trues) df_plot = pd.DataFrame( # {"Pred": (y_preds.flatten() > 0.5) * 1, "Truth": y_trues.flatten()} {"Pred": y_preds.flatten(), "Truth": y_trues.flatten()} ) with open( f"{output_dir}/classifier_perform_on_{name}.txt", "w" ) as fout_perform: for i in range(len(avs)): df_sub = df_plot.loc[df_plot.Truth == i] logger.info( f"[{name}] Accuracy of concept id {i}: {(df_sub.Pred == df_sub.Truth).sum()/len(df_sub)}" ) fout_perform.write( f"#Accuracy of concept id {i}: {(df_sub.Pred == df_sub.Truth).sum()/len(df_sub)}\n" ) logger.info( f"[{name}] Overall accuracy: {(df_plot.Pred == df_plot.Truth).sum()/len(df_plot)}" ) fout_perform.write( f"#Overall accuracy: {(df_plot.Pred == df_plot.Truth).sum()/len(df_plot)}\n" ) df_plot.to_csv(fout_perform, header=True, index=False, sep="\t") make_predictions_and_save(train_avdl, "train") make_predictions_and_save(val_avdl, "val") make_predictions_and_save(test_avdl, "test") # save classifier weights torch.save(classifier.weights(), f"{output_dir}/classifier_weights.pt") # ================================================ Concept construction =========================================# def construct_motif_concept_dataloader_from_control( control_seq_bed_df, genome_fasta, motifs: list, num_motifs=128, motif_mode="pwm", start_buffer=0, end_buffer=0, batch_size=8, num_workers=0, infinite=False, ): "Construct a concept from control sequence bed file and insert motif sequence" dss = [] for motif in motifs: ds = utils.IterateSeqDataFrame( control_seq_bed_df, genome_fasta, motif=motif, num_motifs=num_motifs, motif_mode=motif_mode, start_buffer=start_buffer, end_buffer=end_buffer, print_warning=False, infinite=infinite, ) dss.append(ds) dl = torch.utils.data.DataLoader( wds.RandomMix(dss), batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, ) return dl def construct_bed_concept_dataloader_from_control( control_seq_bed_df, genome_fasta, regions_df, batch_size=8, num_workers=0, infinite=False, ): "Construct a concept from control sequence bed file and insert motif sequence" # take shard of the dataframe if specified dl = torch.utils.data.DataLoader( utils.IterateSeqDataFrame( control_seq_bed_df, genome_fasta, regions_insert=regions_df, print_warning=False, infinite=infinite, ), batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, ) return dl def construct_concept( cn, c_seq, chrom_bed, idx, genome_fasta, signal_bws, transforms=None, window_len=1024, batch_size=8, ): if c_seq.endswith(".fa"): seq_fa = c_seq sc_concept = utils.SeqChromConcept( seq_bed=None, seq_fa=seq_fa, chrom_bed=chrom_bed, genome_fasta=genome_fasta, bws=signal_bws, window_len=window_len, transforms=transforms, batch_size=batch_size, ) elif c_seq.endswith(".bed"): seq_bed = c_seq sc_concept = utils.SeqChromConcept( seq_bed=seq_bed, seq_fa=None, chrom_bed=chrom_bed, genome_fasta=genome_fasta, bws=signal_bws, window_len=window_len, transforms=transforms, batch_size=batch_size, ) else: raise Exception( f"Sequence concept file has to end with either .fa or .bed for concept {cn}!" ) return Concept(id=idx, name=cn, data_iter=sc_concept.seq_dataloader()) def main(): def pair(arg): # assume arg is a pair of strings separated by comma return [x for x in arg.split(",")] parser = ArgumentParser(description=DESCRIPTION) parser.add_argument("output_dir", help="Output folder of the run") parser.add_argument( "input_window_length", default=1024, type=int, help="input window length" ) parser.add_argument("genome_fasta_file") parser.add_argument("genome_size_file") parser.add_argument( "--concepts", default=[], nargs="+", type=pair, help="Prefixes of concepts in format: name1,concept1_seq.fa,concept1_chrom.bed name2,concept2_seq.fa,concept2_chrom.bed...", ) parser.add_argument( "--custom-motifs", type=str, default=None, help="Provide your own motifs via a two column text file in format: Motif_name\tConsensus_seq", ) parser.add_argument( "--meme-motifs", type=str, default=None, help="Motif file in MEME minimal format", ) parser.add_argument( "--bed-seq-concepts", type=str, nargs="+", default=None, help="Bed file of regions provided as sequence concepts, format: [chrom, start, end, strand, concept_name]", ) parser.add_argument( "--bed-chrom-concepts", type=str, nargs="+", default=None, help="Bed file of regions provided as chromatin concepts, format: [chrom, start, end, strand, concept_name]", ) parser.add_argument( "--bws", nargs="+", default=None, help="List of bigwig files to extract chromatin signal from, use this option if your model takes chromatin data as input", ) parser.add_argument( "--num-motifs", type=int, default=12, help="Number of motifs to insert" ) parser.add_argument( "--include-reverse-complement", action="store_true", help="Use both forward and reverse complement version of motifs", ) parser.add_argument( "--num-samples-per-concept", type=int, default=10, help="Number of samples per concept to draw to compute PCA matrix", ) parser.add_argument( "--num-samples", type=int, default=5000, help="Number of samples required for each concept to train classifiers, this would apply to filter out bed concepts that don't have enough samples", ) parser.add_argument( "--num-pc", default="full", help="Number of PCs to keep, or 'full' to keep all" ) parser.add_argument("--SGD-penalty", default="l2", help="SGD penalty type") parser.add_argument( "--save", action="store_true", default=False, help="Save activations per layer per concept", ) args = parser.parse_args() if path.exists(args.output_dir): logger.warning( f"Output directory {args.output_dir} exists! Contents could be overwritten" ) makedirs(args.output_dir, exist_ok=True) # make output directory logger.info(f"Created output directory {args.output_dir}") BATCH_SIZE = 8 # load model, this part is model specific model = utils.load_model() model.eval() model.to(device) logger.info(f"Loaded model") # load PCA model # model_pca = torch.load(args.tpcav_model).to(device) # model_pca.eval() # load concepts, be cautious about dataset specific transforms idx = 0 ## load control concepts first random_regions_fn = f"{args.output_dir}/random_regions.bed" random_regions_df = scl.random_coords( gs=args.genome_size_file, l=args.input_window_length, n=args.num_samples ) random_regions_df.to_csv(random_regions_fn, sep="\t", header=False, index=False) control_concepts = [] control_seq_dl = utils.seq_dataloader_from_bed( random_regions_fn, args.genome_fasta_file, args.input_window_length, BATCH_SIZE, ) control_chrom_dl = utils.chrom_dataloader_from_bed( random_regions_fn, args.genome_fasta_file, args.input_window_length, args.bws, BATCH_SIZE, ) control_concept = Concept( id=-idx, name="random_regions", data_iter=zip(control_seq_dl, control_chrom_dl), ) control_concepts.append(control_concept) idx += 1 ## load test concepts concepts = [] ## custom motifs, use the first control concept as a template if args.custom_motifs is not None: df = pd.read_table(args.custom_motifs, names=["motif_name", "consensus_seq"]) for m in np.unique(df.motif_name): motif_name = m consensus_seqs = df.loc[ df.motif_name == m, "consensus_seq" ].tolist() # take all consensus seqs that correspond to the same motif name motifs = [] for i, c in enumerate(consensus_seqs): motif = utils.CustomMotif(f"{m}_{i}", c) motifs.append(motif) if ( args.include_reverse_complement ): # add reverse complement if specified motif_rc = motif.reverse_complement() motifs.append(motif_rc) cn = f"{motif_name}" seq_dl = construct_motif_concept_dataloader_from_control( random_regions_df, args.genome_fasta_file, motifs=motifs, num_motifs=args.num_motifs, motif_mode="consensus", batch_size=BATCH_SIZE, infinite=False, ) concepts.append( Concept( id=idx, name=cn, data_iter=zip(seq_dl, control_chrom_dl), ) ) idx += 1 if args.meme_motifs is not None: with open(args.meme_motifs) as f: for motif in Bio_motifs.parse(f, fmt="MINIMAL"): motifs = [] motifs.append(motif) if args.include_reverse_complement: motif_rc = motif.reverse_complement() motifs.append(motif_rc) cn = f"{motif.name.replace('/', '-')}" seq_dl = construct_motif_concept_dataloader_from_control( random_regions_df, args.genome_fasta_file, motifs=motifs, num_motifs=args.num_motifs, motif_mode="pwm", batch_size=BATCH_SIZE, infinite=False, ) concepts.append( Concept( id=idx, name=cn, data_iter=zip(seq_dl, control_chrom_dl), ) ) idx += 1 if args.bed_seq_concepts is not None: for bed in args.bed_seq_concepts: bed_seq_df = pd.read_table( bed, header=None, usecols=[0, 1, 2, 3, 4], names=["chrom", "start", "end", "strand", "concept_name"], ) for cn in bed_seq_df.concept_name.unique(): cn_bed_seq_df = bed_seq_df.loc[bed_seq_df.concept_name == cn] if len(cn_bed_seq_df) < args.num_samples: logger.warning( f"Concept {cn} has only {len(cn_bed_seq_df)} samples, less than minimum required {args.num_samples}, skip" ) continue seq_dl = utils.seq_dataloader_from_dataframe( cn_bed_seq_df.sample(n=args.num_samples), args.genome_fasta_file, args.input_window_length, BATCH_SIZE, num_workers=0, ) concepts.append( Concept(id=idx, name=cn, data_iter=zip(seq_dl, control_chrom_dl)) ) idx += 1 if args.bed_chrom_concepts is not None: for bed in args.bed_chrom_concepts: bed_chrom_df = pd.read_table( bed, header=None, usecols=[0, 1, 2, 3, 4], names=["chrom", "start", "end", "strand", "concept_name"], ) for cn in bed_chrom_df.concept_name.unique(): cn_bed_chrom_df = bed_chrom_df.loc[bed_chrom_df.concept_name == cn] if len(cn_bed_chrom_df) < args.num_samples: logger.warning( f"Concept {cn} has only {len(cn_bed_chrom_df)} samples, less than minimum required {args.num_samples}, skip" ) continue chrom_dl = utils.chrom_dataloader_from_dataframe( cn_bed_chrom_df.sample(n=args.num_samples), args.genome_fasta_file, args.input_window_length, args.bws, BATCH_SIZE, num_workers=0, ) concepts.append( Concept(id=idx, name=cn, data_iter=zip(control_seq_dl, chrom_dl)) ) idx += 1 logger.info("Constructed concepts") logger.info(concepts) # register hook def get_activation(concept, num_samples=args.num_samples_per_concept): avs = [] num = 0 for seq, chrom in concept.data_iter: # print(seq) # print(concept) seq = utils.seq_transform_fn(seq) chrom = utils.chrom_transform_fn(chrom) if chrom is not None: av = model.forward_until_select_layer(seq.to(device), chrom.to(device)) else: av = model.forward_until_select_layer(seq.to(device)) avs.append(av.detach().cpu()) num += av.shape[0] if num >= num_samples: break return torch.cat(avs)[:num_samples] # load sampled concept examples and build tpcav model sampled_avs = [] for c in concepts + control_concepts: sampled_avs.append(get_activation(c, num_samples=args.num_samples_per_concept)) logging.info(f"Sampled {sampled_avs[-1].shape[0]} from concept {c.name}") sampled_avs = torch.cat(sampled_avs) orig_shape = sampled_avs.shape sampled_avs = sampled_avs.flatten(start_dim=1) # Zscore standardication avs_mean = sampled_avs.mean(dim=0) avs_std = sampled_avs.std(dim=0) avs_std[avs_std == 0] = ( -1 ) # to avoid division by zero when all values are the same along a dimension avs_standardized = (sampled_avs - avs_mean) / avs_std logger.warning(f"Activation matrix is of shape {avs_standardized.shape}") torch.save(avs_standardized, f"{args.output_dir}/avs_standardized.pt") # PCA if args.num_pc == "full": max_pc = min(avs_standardized.shape) U, S, Vh = svd(avs_standardized, lapack_driver="gesvd", full_matrices=False) V_inverse = torch.tensor(Vh) elif int(args.num_pc) == 0: V = None V_inverse = None else: U, S, Vh = svd(avs_standardized, lapack_driver="gesvd", full_matrices=False) V_inverse = torch.tensor(Vh) # estimate minimal explained variance ratio max_pc = min(avs_standardized.shape) S_imputed = torch.concat([S, S[-1].repeat(max_pc - len(S))]) min_pca_explained_ratio = (S_imputed / S_imputed.sum())[: len(S)].sum() logger.info( f"Estimated minimal explained PCA Ratio is {min_pca_explained_ratio}" ) # create tpcav model model.register_buffer("zscore_mean", avs_mean.to(device)) model.register_buffer("zscore_std", avs_std.to(device)) model.register_buffer( "pca_inv", V_inverse.to(device) if V_inverse is not None else None ) model.register_buffer("orig_shape", torch.tensor(orig_shape).to(device)) # save the new model torch.save(model, f"{args.output_dir}/tpcav_model.pt") # set to eval mode for sanity model.eval() def get_tpcav_activations(concept, num_samples=args.num_samples): avs_pca = [] num = 0 for seq, chrom in concept.data_iter: seq = utils.seq_transform_fn(seq) chrom = utils.chrom_transform_fn(chrom) if chrom is not None: av = model.forward_until_select_layer(seq.to(device), chrom.to(device)) else: av = model.forward_until_select_layer(seq.to(device)) av_residual, av_projected = model.project_avs_to_pca( av.flatten(start_dim=1) ) if av_projected is not None: av_pca = torch.cat((av_projected, av_residual), dim=1) else: av_pca = av_residual avs_pca.append(av_pca.detach().cpu()) num += av_pca.shape[0] if num >= num_samples: break with torch.no_grad(): del seq, av, av_projected, av_residual return torch.cat(avs_pca).detach().cpu()[:num_samples] # get activations of each concept and train classifier for each pair pool = Pool() pending = [] max_pending_jobs = 4 control_concept_avs = { cc.name: get_tpcav_activations(cc) for cc in control_concepts } logging.info(f"Collected all activations for control concepts") for c in concepts: idx = c.id try: test_avs = get_tpcav_activations(c) except RuntimeError as e: print(c) raise e for cc in control_concepts: cp = {0: test_avs, 1: control_concept_avs[cc.name]} dir_name = f"{args.output_dir}/cavs/{c.name}_control_{cc.name}" os.makedirs(dir_name, exist_ok=True) concept_pair = (cp, args, dir_name, args.SGD_penalty) while len(pending) >= max_pending_jobs: for job in pending: if job.ready(): job.get() # raise exception if any # keep all pending jobs pending = [job for job in pending if not job.ready()] if len(pending) >= max_pending_jobs: time.sleep(1) async_result = pool.apply_async(train_classifier, args=concept_pair) logging.info( f"Started training classifier for concept {c.name} vs control {cc.name}" ) pending.append(async_result) del c # wait for jobs finish if len(pending) > 0: for job in pending: job.wait() pool.close() pool.join() if __name__ == "__main__": main() TPCAV-0.8.4/scripts/run_tcav_sgd_pca_motif_bin.py000066400000000000000000000252711516353663300220120ustar00rootroot00000000000000#!/usr/bin/env python3 DESCRIPTION = """ Given concepts, train linear classifier and evaluate """ import warnings warnings.simplefilter(action="ignore", category=FutureWarning) import logging import os import time from argparse import ArgumentParser from multiprocessing import Pool from os import makedirs, path import numpy as np import pandas as pd import seqchromloader as scl import torch import utils from Bio import motifs as Bio_motifs from captum.concept import Concept from run_tcav_sgd_pca import ( construct_motif_concept_dataloader_from_control, train_classifier, ) logging.basicConfig( format="%(asctime)s %(levelname)-8s %(message)s", level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S", ) logger = logging.getLogger(__name__) logger.setLevel("INFO") logger.info("TCAV~") device = "cuda:0" if torch.cuda.is_available() else "cpu" SEED = 1001 random_state = np.random.RandomState(SEED) # ================================================ Concept construction =========================================# def get_buffer_list(window_len, num_bins): pos_list = np.linspace(0, window_len, num_bins + 1, dtype=int) buffer_list = [] for i in range(len(pos_list) - 1): start = pos_list[i] end = pos_list[i + 1] buffer_list.append((start, window_len - end)) return buffer_list def main(): def pair(arg): # assume arg is a pair of strings separated by comma return [x for x in arg.split(",")] parser = ArgumentParser(description=DESCRIPTION) parser.add_argument("output_dir", help="Output folder of the run") parser.add_argument("tpcav_model", help="Output folder of the run") parser.add_argument( "input_window_length", default=1024, type=int, help="input window length" ) parser.add_argument("genome_fasta_file") parser.add_argument("genome_size_file") parser.add_argument( "--custom-motifs", type=str, default=None, help="Provide your own motifs via a two column text file in format: Motif_name\tConsensus_seq", ) parser.add_argument( "--meme-motifs", type=str, default=None, help="Motif file in MEME minimal format", ) parser.add_argument( "--num-motifs", default=3, type=int, help="Number of motifs to insert per bin", ) parser.add_argument( "--include-reverse-complement", action="store_true", help="Use both forward and reverse complement version of motifs", ) parser.add_argument( "--bin-edges", required=True, nargs="+", type=int, help="Edges of the bins for restricting motif insert positions, for example: --bin-edges 64 128 with input window 256 will give bins [0-64, 64-128, 128-256]", ) parser.add_argument( "--max-samples", type=int, default=5000, help="Maximum number of samples to draw for training classifier on each concept", ) parser.add_argument( "--bws", nargs="+", default=None, help="List of bigwig files to extract chromatin signal from, use this option if your model takes chromatin data as input", ) parser.add_argument( "--classifier", default="sgd", help="choose from sgd or cuml_sgd" ) parser.add_argument("--SGD-penalty", default="l2", help="SGD penalty type") parser.add_argument( "--save", action="store_true", default=False, help="Save activations per layer per concept", ) args = parser.parse_args() if path.exists(args.output_dir): logger.warning( f"Output directory {args.output_dir} exists! Contents could be overwritten" ) makedirs(args.output_dir, exist_ok=True) # make output directory logger.info(f"Created output directory {args.output_dir}") BATCH_SIZE = 8 # load concepts, be cautious about dataset specific transforms idx = 0 ## load control concepts first random_regions_fn = f"{args.output_dir}/random_regions.bed" random_regions_df = scl.random_coords( gs=args.genome_size_file, l=args.input_window_length, n=args.max_samples ) random_regions_df.to_csv(random_regions_fn, sep="\t", header=False, index=False) control_concepts = [] control_seq_dl = utils.seq_dataloader_from_bed( random_regions_fn, args.genome_fasta_file, args.input_window_length, BATCH_SIZE, ) control_chrom_dl = utils.chrom_dataloader_from_bed( random_regions_fn, args.genome_fasta_file, args.input_window_length, args.bws, BATCH_SIZE, ) control_concept = Concept( id=-idx, name="random_regions", data_iter=zip(control_seq_dl, control_chrom_dl), ) control_concepts.append(control_concept) idx += 1 # load tpcav model model = torch.load(args.tpcav_model, map_location=device) model.eval() ## load test concepts concepts = [] ## custom motifs, use the first control concept as a template ### according to # bins, get the list of buffer breakpoints = [0, *args.bin_edges, args.input_window_length] bin_list = [ (breakpoints[i], breakpoints[i + 1]) for i in range(len(breakpoints) - 1) ] if args.custom_motifs is not None: df = pd.read_table(args.custom_motifs, names=["motif_name", "consensus_seq"]) for m in np.unique(df.motif_name): motif_name = m consensus_seqs = df.loc[ df.motif_name == m, "consensus_seq" ].tolist() # take all consensus seqs that correspond to the same motif name motifs = [] for i, c in enumerate(consensus_seqs): motif = utils.CustomMotif(f"{m}_{i}", c) motifs.append(motif) if ( args.include_reverse_complement ): # add reverse complement if specified motif_rc = motif.reverse_complement() motifs.append(motif_rc) for buffer_idx, (bin_start, bin_end) in enumerate(bin_list): start_buffer = bin_start end_buffer = args.input_window_length - bin_end cn = f"{motif_name}_bin_start_{bin_start}_end_{bin_end}" seq_dl = construct_motif_concept_dataloader_from_control( random_regions_df, args.genome_fasta_file, motifs=motifs, num_motifs=args.num_motifs, start_buffer=start_buffer, end_buffer=end_buffer, batch_size=BATCH_SIZE, ) concepts.append( Concept( id=idx, name=cn, data_iter=zip(seq_dl, control_chrom_dl), ) ) idx += 1 if args.meme_motifs is not None: with open(args.meme_motifs) as f: for motif in Bio_motifs.parse(f, fmt="MINIMAL"): motifs = [] motifs.append(motif) if args.include_reverse_complement: motif_rc = motif.reverse_complement() motifs.append(motif_rc) for buffer_idx, (bin_start, bin_end) in enumerate(bin_list): start_buffer = bin_start end_buffer = args.input_window_length - bin_end cn = f"{motif.name.replace('/', '-')}_bin_start_{bin_start}_end_{bin_end}" seq_dl = construct_motif_concept_dataloader_from_control( random_regions_df, args.genome_fasta_file, motifs=motifs, num_motifs=args.num_motifs, start_buffer=start_buffer, end_buffer=end_buffer, batch_size=BATCH_SIZE, ) concepts.append( Concept( id=idx, name=cn, data_iter=zip(seq_dl, control_chrom_dl), ) ) idx += 1 logger.info("Constructed concepts") logger.info(concepts) def get_tpcav_activations(concept): avs_pca = [] for seq, chrom in concept.data_iter: seq = utils.seq_transform_fn(seq) chrom = utils.chrom_transform_fn(chrom) if chrom is not None: av = model.forward_until_select_layer(seq.to(device), chrom.to(device)) else: av = model.forward_until_select_layer(seq.to(device)) av_residual, av_projected = model.project_avs_to_pca( av.flatten(start_dim=1) ) if av_projected is not None: av_pca = torch.cat((av_projected, av_residual), dim=1) else: av_pca = av_residual avs_pca.append(av_pca.detach().cpu()) with torch.no_grad(): del seq, av, av_projected, av_residual return torch.cat(avs_pca).detach().cpu() # get activations of each concept and train classifier for each pair pool = Pool() pending = [] max_pending_jobs = 4 control_concept_avs = { cc.name: get_tpcav_activations(cc) for cc in control_concepts } logging.info(f"Collected all activations for control concepts") for c in concepts: idx = c.id try: test_avs = get_tpcav_activations(c) except RuntimeError as e: print(c) raise e for cc in control_concepts: cp = {0: test_avs, 1: control_concept_avs[cc.name]} dir_name = f"{args.output_dir}/{c.name}_control_{cc.name}" os.makedirs(dir_name, exist_ok=True) concept_pair = (cp, args, dir_name, args.SGD_penalty) while len(pending) >= max_pending_jobs: for job in pending: if job.ready(): job.get() # raise exception if any # keep all pending jobs pending = [job for job in pending if not job.ready()] if len(pending) >= max_pending_jobs: time.sleep(1) async_result = pool.apply_async(train_classifier, args=concept_pair) logging.info( f"Started training classifier for concept {c.name} vs control {cc.name}" ) pending.append(async_result) del c # wait for jobs finish if len(pending) > 0: for job in pending: job.wait() pool.close() pool.join() if __name__ == "__main__": main() TPCAV-0.8.4/scripts/run_tcav_sgd_pca_motif_restrict_position.py000066400000000000000000000225741516353663300250300ustar00rootroot00000000000000#!/usr/bin/env python3 DESCRIPTION = """ Given concepts, train linear classifier and evaluate """ import warnings warnings.simplefilter(action="ignore", category=FutureWarning) import logging import os import time from argparse import ArgumentParser from multiprocessing import Pool from os import makedirs, path import numpy as np import seqchromloader as scl import torch import utils from Bio import motifs from captum.concept import Concept from run_tcav_sgd_pca import ( construct_motif_concept_dataloader_from_control, train_classifier, ) logging.basicConfig( format="%(asctime)s %(levelname)-8s %(message)s", level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S", ) logger = logging.getLogger(__name__) logger.setLevel("INFO") logger.info("TCAV~") device = "cuda:0" if torch.cuda.is_available() else "cpu" SEED = 1001 random_state = np.random.RandomState(SEED) # ================================================ Concept construction =========================================# def get_buffer_list(window_len, num_bins): pos_list = np.linspace(0, window_len, num_bins + 1, dtype=int) buffer_list = [] for i in range(len(pos_list) - 1): start = pos_list[i] end = pos_list[i + 1] buffer_list.append((start, window_len - end)) return buffer_list def main(): def pair(arg): # assume arg is a pair of strings separated by comma return [x for x in arg.split(",")] parser = ArgumentParser(description=DESCRIPTION) parser.add_argument("output_dir", help="Output folder of the run") parser.add_argument("tpcav_model", help="Output folder of the run") parser.add_argument( "input_window_length", default=1024, type=int, help="input window length" ) parser.add_argument("genome_fasta_file") parser.add_argument("genome_size_file") parser.add_argument( "--custom-motifs", type=str, default=None, help="Provide your own motifs via a two column text file in format: Motif_name\tConsensus_seq", ) parser.add_argument( "--meme-motifs", type=str, default=None, help="Motif file in MEME minimal format", ) parser.add_argument( "--num-motifs", default=3, type=int, help="Number of motifs to insert per bin", ) parser.add_argument( "--num-bins", default=3, type=int, help="Number of bins to split the region for inserting motifs", ) parser.add_argument( "--max-samples", type=int, default=5000, help="Maximum number of samples to draw for training classifier on each concept", ) parser.add_argument( "--bws", nargs="+", default=None, help="List of bigwig files to extract chromatin signal from, use this option if your model takes chromatin data as input", ) parser.add_argument( "--classifier", default="sgd", help="choose from sgd or cuml_sgd" ) parser.add_argument("--SGD-penalty", default="l2", help="SGD penalty type") parser.add_argument( "--save", action="store_true", default=False, help="Save activations per layer per concept", ) args = parser.parse_args() if path.exists(args.output_dir): logger.warning( f"Output directory {args.output_dir} exists! Contents could be overwritten" ) makedirs(args.output_dir, exist_ok=True) # make output directory logger.info(f"Created output directory {args.output_dir}") BATCH_SIZE = 8 # load concepts, be cautious about dataset specific transforms idx = 0 ## load control concepts first random_regions_fn = f"{args.output_dir}/random_regions.bed" random_regions_df = scl.random_coords( gs=args.genome_size_file, l=args.input_window_length, n=args.max_samples ) random_regions_df.to_csv(random_regions_fn, sep="\t", header=False, index=False) control_concepts = [] control_seq_dl = utils.seq_dataloader_from_bed( random_regions_fn, args.genome_fasta_file, args.input_window_length, BATCH_SIZE, ) control_chrom_dl = utils.chrom_dataloader_from_bed( random_regions_fn, args.genome_fasta_file, args.input_window_length, args.bws, BATCH_SIZE, ) control_concept = Concept( id=-idx, name="random_regions", data_iter=zip(control_seq_dl, control_chrom_dl), ) control_concepts.append(control_concept) idx += 1 # load tpcav model model = torch.load(args.tpcav_model, map_location=device) model.eval() ## load test concepts concepts = [] ## custom motifs, use the first control concept as a template ### according to # bins, get the list of buffer buffer_list = get_buffer_list(args.input_window_length, args.num_bins) if args.custom_motifs is not None: with open(args.custom_motifs) as f: for m in f: for buffer_idx, (start_buffer, end_buffer) in enumerate(buffer_list): motif_name, consensus_seq = m.strip().split("\t") motif = utils.CustomMotif("motif", consensus_seq) cn = f"{motif_name}_bin_{buffer_idx}" seq_dl = construct_motif_concept_dataloader_from_control( random_regions_df, args.genome_fasta_file, motif=motif, num_motifs=args.num_motifs, start_buffer=start_buffer, end_buffer=end_buffer, batch_size=BATCH_SIZE, ) concepts.append( Concept( id=idx, name=cn, data_iter=zip(seq_dl, control_chrom_dl), ) ) idx += 1 if args.meme_motifs is not None: with open(args.meme_motifs) as f: for motif in motifs.parse(f, fmt="MINIMAL"): for buffer_idx, (start_buffer, end_buffer) in enumerate(buffer_list): cn = f"{motif.name.replace('/', '-')}_bin_{buffer_idx}" seq_dl = construct_motif_concept_dataloader_from_control( random_regions_df, args.genome_fasta_file, motif=motif, num_motifs=args.num_motifs, start_buffer=start_buffer, end_buffer=end_buffer, batch_size=BATCH_SIZE, ) concepts.append( Concept( id=idx, name=cn, data_iter=zip(seq_dl, control_chrom_dl), ) ) idx += 1 logger.info("Constructed concepts") logger.info(concepts) def get_tpcav_activations(concept): avs_pca = [] for seq, chrom in concept.data_iter: seq = utils.seq_transform_fn(seq) chrom = utils.chrom_transform_fn(chrom) if chrom is not None: av = model.forward_until_select_layer(seq.to(device), chrom.to(device)) else: av = model.forward_until_select_layer(seq.to(device)) av_residual, av_projected = model.project_avs_to_pca( av.flatten(start_dim=1) ) if av_projected is not None: av_pca = torch.cat((av_projected, av_residual), dim=1) else: av_pca = av_residual avs_pca.append(av_pca.detach().cpu()) with torch.no_grad(): del seq, av, av_projected, av_residual return torch.cat(avs_pca).detach().cpu() # get activations of each concept and train classifier for each pair pool = Pool() pending = [] max_pending_jobs = 4 control_concept_avs = { cc.name: get_tpcav_activations(cc) for cc in control_concepts } logging.info(f"Collected all activations for control concepts") for c in concepts: idx = c.id try: test_avs = get_tpcav_activations(c) except RuntimeError as e: print(c) raise e for cc in control_concepts: cp = {0: test_avs, 1: control_concept_avs[cc.name]} dir_name = f"{args.output_dir}/{c.name}_control_{cc.name}" os.makedirs(dir_name, exist_ok=True) concept_pair = (cp, args, dir_name, args.SGD_penalty) while len(pending) >= max_pending_jobs: for job in pending: if job.ready(): job.get() # raise exception if any # keep all pending jobs pending = [job for job in pending if not job.ready()] if len(pending) >= max_pending_jobs: time.sleep(1) async_result = pool.apply_async(train_classifier, args=concept_pair) logging.info( f"Started training classifier for concept {c.name} vs control {cc.name}" ) pending.append(async_result) del c # wait for jobs finish if len(pending) > 0: for job in pending: job.wait() pool.close() pool.join() if __name__ == "__main__": main() TPCAV-0.8.4/scripts/seq_utils.py000066400000000000000000000152321516353663300164670ustar00rootroot00000000000000#!/usr/bin/env python3 """ Generate motif concepts for TCAV """ from copy import deepcopy import Bio import numpy as np import seqchromloader as scl from Bio.Seq import Seq from Bio.SeqRecord import SeqRecord from deeplift.dinuc_shuffle import dinuc_shuffle scl.mute_warning() def sample_from_pwm(motif, n_seqs=1, rng=None): """ Draw `n_seqs` independent sequences from a Bio.motifs.Motif PWM. Parameters ---------- motif : Bio.motifs.Motif Motif whose `.pwm` is used for sampling. n_seqs : int, default 1 How many sequences to generate. rng : numpy.random.Generator or None Leave None for np.random.default_rng(). Returns ------- str | list[str] A single string if n_seqs==1, otherwise a list of strings. """ rng = rng or np.random.default_rng() # ---- 1. Build a (L, A) probability matrix -------------------------------- alphabet = list(motif.alphabet) # e.g. ['A', 'C', 'G', 'T'] L = motif.length pwm_dict = motif.pwm # dict base → list(float) # shape (L, A) with rows = positions, cols = alphabet order prob_mat = np.column_stack([pwm_dict[b] for b in alphabet]) # ---- 2. Vectorised multinomial sampling ----------------------------------- # Draw U(0,1) numbers of shape (n_seqs, L) u = rng.random((n_seqs, L)) # cumulative probabilities along alphabet axis cum = np.cumsum(prob_mat, axis=1) # still (L, A) # Broadcast cum to (n_seqs, L, A) and pick first index where cum > u idx = (u[..., None] < cum).argmax(axis=2) # (n_seqs, L) int indices # ---- 3. Convert indices back to letters ----------------------------------- letters = np.array(alphabet, dtype="U1") seq_arr = letters[idx] # (n_seqs, L) array of chars # Join per sequence seqs = ["".join(row) for row in seq_arr] return seqs[0] if n_seqs == 1 else seqs class PairedMotif: def __init__(self, motif1, motif2, spacing=0): self.motif1 = motif1 self.motif2 = motif2 self.rc = False self.spacing = spacing self.pname = f"{self.motif1.name}_and_{self.motif2.name}" self.pmatrix_id = f"{self.motif1.matrix_id}_and_{self.motif2.matrix_id}" def reverse_complement(self): self.rc = True self.motif1 = self.motif1.reverse_complement() self.motif2 = self.motif2.reverse_complement() return self @property def name(self): return self.pname @property def matrix_id(self): return self.pmatrix_id def __len__(self): return len(self.motif1) + len(self.motif2) + self.spacing class CustomMotif: def __init__(self, name, consensus): self.name = name self.matrix_id = "custom" self.consensus = consensus.upper() self.rc = False def __len__(self): return len(self.consensus) def reverse_complement(self): self.consensus = Bio.Seq.reverse_complement(self.consensus) return self def saturate_motif_in_seq(seq, motif, start=0, gap=10, mode="consensus"): "Insert motif into sequence in a tiling way" seq_ins = list(deepcopy(seq)) for i in range(start, len(seq), len(motif) + gap): if i + len(motif) <= len(seq): seq_ins[i : (i + len(motif))] = ( list(motif.consensus) if mode == "consensus" else sample_from_pwm(motif) ) assert len(seq_ins) == len(seq), "Length of sequence is changed!" return "".join(seq_ins) def insert_region_into_seq( seq, regions_df, genome, rng=np.random.default_rng(1), ): "Insert a sample sequence from the given regions dataframe into the sequence" seq_ins = list(deepcopy(seq)) region_sampled = regions_df.sample(1, random_state=rng) region_sampled_seq = genome[region_sampled.chrom][ region_sampled.start : region_sampled.end ] if getattr(region_sampled, "strand", "+") == "-": region_sampled_seq = region_sampled_seq.reverse_complement() region_sampled_seq = str(region_sampled_seq).upper() if len(region_sampled_seq) >= len(seq_ins): return region_sampled_seq[: len(seq_ins)] else: start = rng.integers(low=0, high=len(seq_ins) - len(region_sampled_seq)) seq[start : (start + len(region_sampled_seq))] = list(region_sampled_seq) return "".join(seq_ins) def insert_motif_into_seq( seq, motif, num_motifs=3, start_buffer=50, end_buffer=50, rng=np.random.default_rng(1), mode="consensus", ): assert mode in ["consensus", "pwm"] seq_ins = list(deepcopy(seq)) pos_motif_overlap = np.ones(len(seq)) pos_motif_overlap[:start_buffer] = 0 pos_motif_overlap[(-end_buffer - len(motif)) :] = 0 num_insert_motifs = 0 for i in range(num_motifs): try: motif_start = rng.choice( np.where(pos_motif_overlap > 0)[0] ).item() # randomly pick insert location except ValueError: # print( # f"No samples can be taken for motif {motif.name}, skip inserting the rest of motifs" # ) break if isinstance(motif, PairedMotif): seq_ins[motif_start : (motif_start + len(motif.motif1))] = ( list(motif.motif1.consensus) if mode == "consensus" else sample_from_pwm(motif.motif1) ) seq_ins[ (motif_start + len(motif.motif1) + motif.spacing) : ( motif_start + len(motif) ) ] = ( list(motif.motif2.consensus) if mode == "consensus" else sample_from_pwm(motif.motif2) ) else: seq_ins[motif_start : (motif_start + len(motif))] = ( list(motif.consensus) if mode == "consensus" else sample_from_pwm(motif) ) pos_motif_overlap[(motif_start - len(motif)) : (motif_start + len(motif))] = 0 num_insert_motifs += 1 if num_insert_motifs < num_motifs * 0.5: print( "Less than 50% of specified # motifs inserted, make sure this is the desired behavior, consider increasing the length of the sequence or decreasing the number of motifs" ) return "".join(seq_ins) def dinucleotide_shuffle_seq(dnaSeqs): "Dinucleotide shuffle the given DNA sequences" records = [] for idx, record in enumerate(dnaSeqs): seq = str(record.seq) seq_shuf = dinuc_shuffle(seq) records.append( SeqRecord( Seq(seq_shuf), id=f"dinucleotide shuffle idx", description=f"dinucleotide shuffled dna seqs inserted by motifs", ) ) return records TPCAV-0.8.4/scripts/summarize_fscores_across_num_motifs_insertions.py.ipynb000066400000000000000000037765161516353663300274540ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "f4e79665-63a3-4ca2-8f76-7c1323d20e49", "metadata": {}, "outputs": [], "source": [ "# parameters\n", "# set a fscore threshold for filtering low quality concepts\n", "fscore_threshold = 0.95\n", "\n", "# folders of your cavs classifiers, key should be the number of motif insertions, value should be the folder containing classifier folders\n", "cavs_folders = {\n", " 12: \"output/pipeline_test_5000_12_motifs_motif_only/cavs/\",\n", " 24: \"output/pipeline_test_5000_24_motifs_motif_only/cavs/\",\n", " 36: \"output/pipeline_test_5000_36_motifs_motif_only/cavs/\"\n", "}" ] }, { "cell_type": "code", "execution_count": null, "id": "c90809b7-99bd-4d95-bf66-98864e09f3ca", "metadata": {}, "outputs": [], "source": [ "num_insertions = list(cavs_folders.keys())" ] }, { "cell_type": "code", "execution_count": 3, "id": "84f2ebae-0c00-4055-9250-1c1fdf01bcf1", "metadata": {}, "outputs": [], "source": [ "import os, re\n", "import torch\n", "import pandas as pd\n", "import seaborn as sns\n", "from glob import glob\n", "from functools import reduce\n", "from scipy import stats\n", "from sklearn.metrics import precision_recall_fscore_support as score" ] }, { "cell_type": "code", "execution_count": 4, "id": "fc60f620-ce62-467f-b478-ca6ebf91d932", "metadata": {}, "outputs": [], "source": [ "def load_cavs_from_folder(cavs_dir):\n", " all_perform_on_test_fns = glob(os.path.join(cavs_dir, \"*/*perform_on_test.txt\"))\n", "\n", " df = []\n", " for fn in all_perform_on_test_fns:\n", " concept, _ = os.path.relpath(fn, cavs_dir).split(\"/\")\n", " concept = re.split(r'_control_', concept)[0]\n", "\n", " # compute fscore on test dataset\n", " performance = pd.read_table(fn, comment=\"#\")\n", " precision, recall, fscore, support = score(performance.Truth, performance.Pred)\n", " \n", " df.append((concept, fscore.mean().item()))\n", " return pd.DataFrame(df, columns=['concept', 'fscore'])" ] }, { "cell_type": "code", "execution_count": 7, "id": "52e18d0a-b1ca-4fd4-8ee7-bb8b888e76b4", "metadata": {}, "outputs": [], "source": [ "concept_fscore_tables = []\n", "for key, folder in cavs_folders.items():\n", " concept_fscore_table = load_cavs_from_folder(folder).rename(columns={'fscore': int(key)})\n", " concept_fscore_tables.append(concept_fscore_table)" ] }, { "cell_type": "code", "execution_count": 8, "id": "a6419ffe-e646-4061-be2d-a407392b4b82", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[ concept 12\n", " 0 AC0001:GATA-PROP:GATA 0.462226\n", " 1 AC0002:PROP-ALX:Homeodomain 0.631410\n", " 2 AC0003:HNF1A-HNF1B:Homeodomain 0.725731\n", " 3 AC0004:ZSCAN:C2H2_ZF 0.904799\n", " 4 AC0005:POU3F-POU1F:Homeodomain,POU 0.648016\n", " .. ... ...\n", " 632 AC0633:TBX-MGA::EVX:T-box 0.593544\n", " 633 AC0634:ZNF:C2H2_ZF 0.467501\n", " 634 AC0635:TBX-EOMES:T-box 0.583873\n", " 635 AC0636:SREBF:bHLH 0.558728\n", " 636 AC0637:AHR:bHLH 0.608945\n", " \n", " [637 rows x 2 columns],\n", " concept 24\n", " 0 AC0001:GATA-PROP:GATA 0.747827\n", " 1 AC0002:PROP-ALX:Homeodomain 0.882589\n", " 2 AC0003:HNF1A-HNF1B:Homeodomain 0.947691\n", " 3 AC0004:ZSCAN:C2H2_ZF 0.984007\n", " 4 AC0005:POU3F-POU1F:Homeodomain,POU 0.908629\n", " .. ... ...\n", " 632 AC0633:TBX-MGA::EVX:T-box 0.854638\n", " 633 AC0634:ZNF:C2H2_ZF 0.732609\n", " 634 AC0635:TBX-EOMES:T-box 0.840731\n", " 635 AC0636:SREBF:bHLH 0.826703\n", " 636 AC0637:AHR:bHLH 0.867890\n", " \n", " [637 rows x 2 columns],\n", " concept 36\n", " 0 AC0001:GATA-PROP:GATA 0.870802\n", " 1 AC0002:PROP-ALX:Homeodomain 0.960273\n", " 2 AC0003:HNF1A-HNF1B:Homeodomain 0.992772\n", " 3 AC0004:ZSCAN:C2H2_ZF 0.997898\n", " 4 AC0005:POU3F-POU1F:Homeodomain,POU 0.980448\n", " .. ... ...\n", " 632 AC0633:TBX-MGA::EVX:T-box 0.946887\n", " 633 AC0634:ZNF:C2H2_ZF 0.899784\n", " 634 AC0635:TBX-EOMES:T-box 0.932412\n", " 635 AC0636:SREBF:bHLH 0.946743\n", " 636 AC0637:AHR:bHLH 0.982428\n", " \n", " [637 rows x 2 columns]]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "concept_fscore_tables" ] }, { "cell_type": "code", "execution_count": 9, "id": "15eda0f3-a668-4a28-9a02-c4a800b4d6a3", "metadata": {}, "outputs": [], "source": [ "def reduce_tables(left, right):\n", " return pd.merge(left, right, on='concept', how='left')\n", "\n", "merged_concepts_tables = reduce(reduce_tables, concept_fscore_tables)" ] }, { "cell_type": "code", "execution_count": 10, "id": "10e31a3b-2246-464a-9130-ef1709cb1062", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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concept122436
0AC0001:GATA-PROP:GATA0.4622260.7478270.870802
1AC0002:PROP-ALX:Homeodomain0.6314100.8825890.960273
2AC0003:HNF1A-HNF1B:Homeodomain0.7257310.9476910.992772
3AC0004:ZSCAN:C2H2_ZF0.9047990.9840070.997898
4AC0005:POU3F-POU1F:Homeodomain,POU0.6480160.9086290.980448
...............
632AC0633:TBX-MGA::EVX:T-box0.5935440.8546380.946887
633AC0634:ZNF:C2H2_ZF0.4675010.7326090.899784
634AC0635:TBX-EOMES:T-box0.5838730.8407310.932412
635AC0636:SREBF:bHLH0.5587280.8267030.946743
636AC0637:AHR:bHLH0.6089450.8678900.982428
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637 rows × 4 columns

\n", "
" ], "text/plain": [ " concept 12 24 36\n", "0 AC0001:GATA-PROP:GATA 0.462226 0.747827 0.870802\n", "1 AC0002:PROP-ALX:Homeodomain 0.631410 0.882589 0.960273\n", "2 AC0003:HNF1A-HNF1B:Homeodomain 0.725731 0.947691 0.992772\n", "3 AC0004:ZSCAN:C2H2_ZF 0.904799 0.984007 0.997898\n", "4 AC0005:POU3F-POU1F:Homeodomain,POU 0.648016 0.908629 0.980448\n", ".. ... ... ... ...\n", "632 AC0633:TBX-MGA::EVX:T-box 0.593544 0.854638 0.946887\n", "633 AC0634:ZNF:C2H2_ZF 0.467501 0.732609 0.899784\n", "634 AC0635:TBX-EOMES:T-box 0.583873 0.840731 0.932412\n", "635 AC0636:SREBF:bHLH 0.558728 0.826703 0.946743\n", "636 AC0637:AHR:bHLH 0.608945 0.867890 0.982428\n", "\n", "[637 rows x 4 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merged_concepts_tables" ] }, { "cell_type": "code", "execution_count": 11, "id": "eeea7c2b-fac8-4f99-8f83-8394a6c83331", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_plot = merged_concepts_tables.loc[(merged_concepts_tables[num_insertions]>fscore_threshold).any(axis=1)]\n", "df_plot = df_plot.melt(value_vars=num_insertions,\n", " id_vars='concept')\n", "ax = sns.lineplot(data=df_plot, x='variable', y='value', hue='concept')\n", "sns.move_legend(\n", " ax, \"lower center\",\n", " bbox_to_anchor=(.5, 1), ncol=5, title=None, frameon=False,\n", ")" ] }, { "cell_type": "code", "execution_count": 12, "id": "82aa3503-6d8a-4e90-9191-d56bedee3eca", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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concept122436
0AC0001:GATA-PROP:GATA0.4622260.7478270.870802
1AC0002:PROP-ALX:Homeodomain0.6314100.8825890.960273
2AC0003:HNF1A-HNF1B:Homeodomain0.7257310.9476910.992772
3AC0004:ZSCAN:C2H2_ZF0.9047990.9840070.997898
4AC0005:POU3F-POU1F:Homeodomain,POU0.6480160.9086290.980448
...............
632AC0633:TBX-MGA::EVX:T-box0.5935440.8546380.946887
633AC0634:ZNF:C2H2_ZF0.4675010.7326090.899784
634AC0635:TBX-EOMES:T-box0.5838730.8407310.932412
635AC0636:SREBF:bHLH0.5587280.8267030.946743
636AC0637:AHR:bHLH0.6089450.8678900.982428
\n", "

637 rows × 4 columns

\n", "
" ], "text/plain": [ " concept 12 24 36\n", "0 AC0001:GATA-PROP:GATA 0.462226 0.747827 0.870802\n", "1 AC0002:PROP-ALX:Homeodomain 0.631410 0.882589 0.960273\n", "2 AC0003:HNF1A-HNF1B:Homeodomain 0.725731 0.947691 0.992772\n", "3 AC0004:ZSCAN:C2H2_ZF 0.904799 0.984007 0.997898\n", "4 AC0005:POU3F-POU1F:Homeodomain,POU 0.648016 0.908629 0.980448\n", ".. ... ... ... ...\n", "632 AC0633:TBX-MGA::EVX:T-box 0.593544 0.854638 0.946887\n", "633 AC0634:ZNF:C2H2_ZF 0.467501 0.732609 0.899784\n", "634 AC0635:TBX-EOMES:T-box 0.583873 0.840731 0.932412\n", "635 AC0636:SREBF:bHLH 0.558728 0.826703 0.946743\n", "636 AC0637:AHR:bHLH 0.608945 0.867890 0.982428\n", "\n", "[637 rows x 4 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merged_concepts_tables" ] }, { "cell_type": "code", "execution_count": 13, "id": "d48e3989-86db-48ff-8254-228351dd9ca5", "metadata": {}, "outputs": [], "source": [ "def compute_linregress_slope(item):\n", " x = []; y = []\n", " for k, v in item.items():\n", " if k == 'concept':\n", " continue\n", " x.append(k)\n", " y.append(item[k])\n", " res = stats.linregress(x, y)\n", " return res.slope, res.rvalue**2\n", "merged_concepts_tables[['slope', 'R^2']] = merged_concepts_tables.apply(compute_linregress_slope, axis=1, result_type='expand')" ] }, { "cell_type": "code", "execution_count": 14, "id": "9d971b99-cd89-42f0-8787-c8c95e5f24fb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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concept122436slopeR^2
0AC0001:GATA-PROP:GATA0.4622260.7478270.8708020.0170240.949839
1AC0002:PROP-ALX:Homeodomain0.6314100.8825890.9602730.0137030.915103
2AC0003:HNF1A-HNF1B:Homeodomain0.7257310.9476910.9927720.0111270.872414
3AC0004:ZSCAN:C2H2_ZF0.9047990.9840070.9978980.0038790.859054
4AC0005:POU3F-POU1F:Homeodomain,POU0.6480160.9086290.9804480.0138510.902926
.....................
632AC0633:TBX-MGA::EVX:T-box0.5935440.8546380.9468870.0147230.929271
633AC0634:ZNF:C2H2_ZF0.4675010.7326090.8997840.0180120.983179
634AC0635:TBX-EOMES:T-box0.5838730.8407310.9324120.0145220.930351
635AC0636:SREBF:bHLH0.5587280.8267030.9467430.0161670.953786
636AC0637:AHR:bHLH0.6089450.8678900.9824280.0155620.952532
\n", "

637 rows × 6 columns

\n", "
" ], "text/plain": [ " concept 12 24 36 \\\n", "0 AC0001:GATA-PROP:GATA 0.462226 0.747827 0.870802 \n", "1 AC0002:PROP-ALX:Homeodomain 0.631410 0.882589 0.960273 \n", "2 AC0003:HNF1A-HNF1B:Homeodomain 0.725731 0.947691 0.992772 \n", "3 AC0004:ZSCAN:C2H2_ZF 0.904799 0.984007 0.997898 \n", "4 AC0005:POU3F-POU1F:Homeodomain,POU 0.648016 0.908629 0.980448 \n", ".. ... ... ... ... \n", "632 AC0633:TBX-MGA::EVX:T-box 0.593544 0.854638 0.946887 \n", "633 AC0634:ZNF:C2H2_ZF 0.467501 0.732609 0.899784 \n", "634 AC0635:TBX-EOMES:T-box 0.583873 0.840731 0.932412 \n", "635 AC0636:SREBF:bHLH 0.558728 0.826703 0.946743 \n", "636 AC0637:AHR:bHLH 0.608945 0.867890 0.982428 \n", "\n", " slope R^2 \n", "0 0.017024 0.949839 \n", "1 0.013703 0.915103 \n", "2 0.011127 0.872414 \n", "3 0.003879 0.859054 \n", "4 0.013851 0.902926 \n", ".. ... ... \n", "632 0.014723 0.929271 \n", "633 0.018012 0.983179 \n", "634 0.014522 0.930351 \n", "635 0.016167 0.953786 \n", "636 0.015562 0.952532 \n", "\n", "[637 rows x 6 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merged_concepts_tables" ] }, { "cell_type": "code", "execution_count": 15, "id": "75c10066-1f47-4a4c-8733-859259b44b70", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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T9cwzzzhWHAAAgJMuKPg0NDRIOvngwB07dqh79+6uFAUAAOCGZt3OXlxc7HQdAAAArmv2c3zeffddvfvuuyovLw+eCTrlpZdeuujCAAAAnNas4LNw4UItWrRIgwYNUmJi4nl/CR0AACBSNCv4vPDCC1q3bp0efPBBp+sBAABwTbOe41NbW8uvnwMAgFanWcHnkUce0YYNG5yuBQAAwFXN+lPX999/r9WrV+udd97Rtddeq6ioqJDPly9f7khxAAAATmpW8NmzZ4+uv/56SdK+fftCPuNCZwAAEKmaFXzef/99p+sAAABwXbOu8QEAAGiNmnXGZ+TIkef9k9Z7773X7IIAAADc0qzgc+r6nlPq6uq0e/du7du3r9GPlwIAAESKZgWfZ5999qzj2dnZOn78+EUVBAAA4BZHr/EZP348v9MFAAAilqPBZ/v27erYsaOTSwIAADimWX/qGjt2bMh7Y4xKS0v16aef6je/+Y0jhQEAADitWcEnLi4u5P0ll1yi3r17a9GiRcrMzHSkMAAAAKc1K/isXbvW6ToAAABc16zgc8rOnTtVVFQkj8ejvn376oYbbnCqLgAAAMc1K/iUl5frvvvuU35+vi677DIZY1RZWamRI0dq48aNuvzyy52uEwAA4KI1666u6dOny+/3a//+/frmm29UUVGhffv2ye/3a8aMGU7XCAAA4IhmnfF566239M4776hPnz7Bsb59+2rVqlVc3AwAACJWs874NDQ0KCoqqtF4VFSUGhoaLrooAAAANzQr+PzkJz/RzJkz9fXXXwfHvvrqKz3++OO67bbbHCsOAADASc0KPitXrlRVVZVSU1N19dVX65prrlFaWpqqqqq0YsUKp2sEAABwRLOu8UlOTtZnn32mvLw8HTx4UMYY9e3bV6NGjXK6PgAAAMdc0Bmf9957T3379pXf75ckjR49WtOnT9eMGTM0ePBg9evXTx9++KErhQIAAFysCwo+ubm5evTRRxUbG9vos7i4OE2ePFnLly93rDgAAAAnXVDw+ctf/qLbb7/9nJ9nZmZq586dF10UAACAGy4o+PzP//zPWW9jP6V9+/b6xz/+cdFFAQAAuOGCgs8VV1yhvXv3nvPzPXv2KDEx8aKLAgAAcMMFBZ+f/vSn+u1vf6vvv/++0Wc1NTVasGCB7rrrLseKAwAAcNIF3c7+61//Wq+//rp69eqladOmqXfv3vJ4PCoqKtKqVat04sQJzZ8/361aAQAALsoFBZ/4+HgVFhbqscce07x582SMkSR5PB6NGTNGzz33nOLj410pFAAA4GJd8AMMe/bsqTfffFMVFRX661//KmOM0tPT1aVLFzfqAwAAcEyzntwsSV26dNHgwYOdrAUA8H9KSkrk8/kcX7eoqMjxNYHWpNnBBwDgjpKSEmVk9FFNTbVr26gL1Lq2NhDJCD4AEGF8Pp9qaqo1ZNICxSamOrp26d7t2rdlterr6x1dF2gtCD4AEKFiE1PVNaW3o2v6Sw87uh7Q2lzQc3wAAABaM4IPAACwBsEHAABYg+ADAACsQfABAADWIPgAAABrEHwAAIA1CD4AAMAaBB8AAGANgg8AALAGwQcAAFiD4AMAAKxB8AEAANYg+AAAAGu0quCzePFieTweZWVlBceMMcrOzlZSUpKio6M1YsQI7d+/P3xFAgCAiNVqgs+OHTu0evVqXXvttSHjy5Yt0/Lly7Vy5Urt2LFDCQkJGj16tKqqqsJUKQAAiFStIvgcP35cDzzwgNasWaMuXboEx40xys3N1fz58zV27Fj1799f69evV3V1tTZs2BDGigEAQCRqFcFn6tSpuvPOOzVq1KiQ8eLiYpWVlSkzMzM45vV6NXz4cBUWFp5zvUAgIL/fH/ICAABtX/twF/BDNm7cqM8++0w7duxo9FlZWZkkKT4+PmQ8Pj5eR44cOeeaixcv1sKFC50tFAAARLyIPuNz9OhRzZw5Uy+//LI6dux4znkejyfkvTGm0djp5s2bp8rKyuDr6NGjjtUMAAAiV0Sf8dm5c6fKy8s1cODA4NiJEyf0wQcfaOXKlTp06JCkk2d+EhMTg3PKy8sbnQU6ndfrldfrda9wAAAQkSL6jM9tt92mvXv3avfu3cHXoEGD9MADD2j37t266qqrlJCQoLy8vOB3amtrVVBQoGHDhoWxcgAAEIki+oxPTEyM+vfvHzLWuXNndevWLTielZWlnJwcpaenKz09XTk5OerUqZPGjRsXjpIBAEAEi+jg0xSzZ89WTU2NpkyZooqKCg0ZMkRbt25VTExMuEsDAAARptUFn/z8/JD3Ho9H2dnZys7ODks9AOxVUlIin8/n+LpFRUWOrwngpFYXfAAgEpSUlCgjo49qaqpd20ZdoNa1tQFbEXwAoBl8Pp9qaqo1ZNICxSamOrp26d7t2rdlterr6x1dFwDBBwAuSmxiqrqm9HZ0TX/pYUfXA/D/Ivp2dgAAACcRfAAAgDUIPgAAwBoEHwAAYA2CDwAAsAbBBwAAWIPgAwAArEHwAQAA1iD4AAAAaxB8AACANQg+AADAGgQfAABgDYIPAACwBsEHAABYg+ADAACsQfABAADWIPgAAABrEHwAAIA1CD4AAMAaBB8AAGANgg8AALAGwQcAAFiD4AMAAKxB8AEAANYg+AAAAGsQfAAAgDUIPgAAwBoEHwAAYA2CDwAAsAbBBwAAWIPgAwAArEHwAQAA1iD4AAAAaxB8AACANQg+AADAGgQfAABgDYIPAACwBsEHAABYg+ADAACsQfABAADWIPgAAABrEHwAAIA1CD4AAMAaBB8AAGANgg8AALBG+3AXAAAALkxRUZEr63bv3l0pKSmurB0pCD4AALQSNZXHJHk0fvx4V9aPju6kgweL2nT4IfgAANBK1FVXSTK6ftwcXZ6W4eja/tLD+vilhfL5fAQfAAAQOS7tkaKuKb3DXUarxMXNAADAGgQfAABgDYIPAACwBsEHAABYg+ADAACsQfABAADWIPgAAABr8BwfABekpKREPp/P8XXdfFS+GzW79ZMBANwV0cFn8eLFev3113Xw4EFFR0dr2LBhWrp0qXr3/v+HNhljtHDhQq1evVoVFRUaMmSIVq1apX79+oWxcqBtKikpUUZGH9XUVDu+tluPynezZkmqC9S6si4Ad0R08CkoKNDUqVM1ePBg1dfXa/78+crMzNSBAwfUuXNnSdKyZcu0fPlyrVu3Tr169dLvfvc7jR49WocOHVJMTEyYOwDaFp/Pp5qaag2ZtECxiamOrevmo/Ldqrl073bt27Ja9fX1jq0JwH0RHXzeeuutkPdr165Vjx49tHPnTt16660yxig3N1fz58/X2LFjJUnr169XfHy8NmzYoMmTJ5913UAgoEAgEHzv9/vdawJog2ITU1vd4/KdrtlfetixtQC0nFZ1cXNlZaUkqWvXrpKk4uJilZWVKTMzMzjH6/Vq+PDhKiwsPOc6ixcvVlxcXPCVnJzsbuEAACAitJrgY4zRrFmzdPPNN6t///6SpLKyMklSfHx8yNz4+PjgZ2czb948VVZWBl9Hjx51r3AAABAxIvpPXaebNm2a9uzZo23btjX6zOPxhLw3xjQaO53X65XX63W8RgAAENlaRfCZPn26tmzZog8++EBXXnllcDwhIUHSyTM/iYmJwfHy8vJGZ4EAm7h1yzm3cANo7SI6+BhjNH36dG3evFn5+flKS0sL+TwtLU0JCQnKy8vTDTfcIEmqra1VQUGBli5dGo6SgbBz+/ZtiVu4AbReER18pk6dqg0bNui///u/FRMTE7xuJy4uTtHR0fJ4PMrKylJOTo7S09OVnp6unJwcderUSePGjQtz9UB4uHX7tsQt3ABav4gOPs8//7wkacSIESHja9eu1cSJEyVJs2fPVk1NjaZMmRJ8gOHWrVt5hg+s58Yt59zCDaC1i+jgY4z5wTkej0fZ2dnKzs52vyAAANCqtZrb2QEAAC4WwQcAAFiD4AMAAKxB8AEAANYg+AAAAGtE9F1dQFvnxhOWeboyAJwbwQcIE7efsMzTlQGgMYIPECZuPWGZpysDwLkRfIAwc/oJyzxdGQDOjeADIGK4cX0S1zwBOB3BB0DY1VQek+TR+PHjXdsG1zwBkAg+ACJAXXWVJKPrx83R5WkZjq7NNU8ATkfwARAxLu2Rwi/KA3AVDzAEAADWIPgAAABrEHwAAIA1CD4AAMAaBB8AAGANgg8AALAGwQcAAFiD4AMAAKxB8AEAANYg+AAAAGsQfAAAgDUIPgAAwBoEHwAAYA2CDwAAsEb7cBcARLqSkhL5fD7H1y0qKnJ8TQDA+RF8gPMoKSlRRkYf1dRUu7aNukCta2sDAEIRfIDz8Pl8qqmp1pBJCxSbmOro2qV7t2vfltWqr693dF0AwLkRfIAmiE1MVdeU3o6u6S897Oh6AIAfxsXNAADAGgQfAABgDYIPAACwBtf4oM1w47ZzbjkHgLaF4IM2we3bzrnlHADaBoIP2gS3bjvnlnMAaFsIPmhTnL7tnFvOAaBt4eJmAABgDYIPAACwBsEHAABYg+ADAACsQfABAADWIPgAAABrcDu7y9x4mvAp3bt3V0pKiitrAwDQFhF8XOT204Sjozvp4MEiwg8AAE1E8HGRW08Tlk4+WO/jlxbK5/MRfAAAaCKCTwtw+mnCAACgeQg+aFFuXfPEr6gDAJqC4IMW4/Y1TxK/og4AOD+CD1qMm9c88SvqAICmIPigxblxzRO/og4AznDj0oFIevwKwQcAAKim8pgkj8aPH+/42pH0+BWCDwAAUF11lSSj68fN0eVpGY6tG2mPXyH4AACAoEt7pLTpR7AQfFo5t27jDgQC8nq9jq7JLecAgHAj+LRSbv4tVpLk8UjGuLI0t5wDAMKF4NNKufW3WOn/bw13em1uOQcAhBvBp5Vz42+xp24Nd3ptbjkHAITbJeEuwCnPPfec0tLS1LFjRw0cOFAffvhhuEsCAAARpk0En1dffVVZWVmaP3++du3apVtuuUV33HGHSkpKwl0aAACIIG0i+CxfvlwPP/ywHnnkEfXp00e5ublKTk7W888/H+7SAABABGn11/jU1tZq586dmjt3bsh4ZmamCgsLz/qdQCCgQCAQfF9ZWSlJ8vv9jtZ2/PhxSdI3Rw6pPlDj6Nr+0iOSpMqvvlBUe0+rWJuaW2Ztam6ZtVtjzW6uTc0ts3arrLns5F9fjh8/7vh/Z0+tZy7kLmTTyn311VdGkvnzn/8cMv7000+bXr16nfU7CxYsMJJ48eLFixcvXm3gdfTo0SbnhlZ/xucUjyc0nRpjGo2dMm/ePM2aNSv4vqGhQd988426det2zu9EGr/fr+TkZB09elSxsbHhLqdF2NizZGff9GxHz5KdfdOzcz0bY1RVVaWkpKQmf6fVB5/u3burXbt2KisrCxkvLy9XfHz8Wb/j9XobPZX4sssuc6tEV8XGxlpz4JxiY8+SnX3Tsz1s7JuenREXF3dB81v9xc0dOnTQwIEDlZeXFzKel5enYcOGhakqAAAQiVr9GR9JmjVrlh588EENGjRIQ4cO1erVq1VSUqJf/epX4S4NAABEkDYRfO69914dO3ZMixYtUmlpqfr3768333xTPXv2DHdprvF6vVqwYIHjPyQayWzsWbKzb3q2h41903N4eYxx6ZcoAQAAIkyrv8YHAACgqQg+AADAGgQfAABgDYIPAACwBsGnBTz33HNKS0tTx44dNXDgQH344YfnnV9QUKCBAweqY8eOuuqqq/TCCy80mrNp0yb17dtXXq9Xffv21ebNm0M+X7x4sQYPHqyYmBj16NFDd999tw4dOhQyZ+LEifJ4PCGvG2+88eIb/j/h6Ds7O7tRTwkJCSFzjDHKzs5WUlKSoqOjNWLECO3fv//iG1Z4ek5NTW3Us8fj0dSpU4Nz3NzXTve8f/9+/dM//VOwr9zc3GZt18393JTtn8mJvsN9XIej53Af01J4+m5rx/WaNWt0yy23qEuXLurSpYtGjRqlTz755IK368i+btYPZKHJNm7caKKiosyaNWvMgQMHzMyZM03nzp3NkSNHzjr/yy+/NJ06dTIzZ840Bw4cMGvWrDFRUVHmtddeC84pLCw07dq1Mzk5OaaoqMjk5OSY9u3bm48++ig4Z8yYMWbt2rVm3759Zvfu3ebOO+80KSkp5vjx48E5EyZMMLfffrspLS0Nvo4dO9aq+16wYIHp169fSE/l5eUh21qyZImJiYkxmzZtMnv37jX33nuvSUxMNH6/v1X2XF5eHtJvXl6ekWTef//94By39rUbPX/yySfmySefNK+88opJSEgwzz77bLO269Z+Dmff4Tyuw9VzOI/pcPbd1o7rcePGmVWrVpldu3aZoqIi88///M8mLi7O/P3vf7+g7Tqxrwk+Lvvxj39sfvWrX4WMZWRkmLlz5551/uzZs01GRkbI2OTJk82NN94YfH/PPfeY22+/PWTOmDFjzH333XfOOsrLy40kU1BQEBybMGGC+cUvftHUVi5IuPpesGCBue66685ZV0NDg0lISDBLliwJjn3//fcmLi7OvPDCCz/Y1/lEyr6eOXOmufrqq01DQ0NwzK197UbPp+vZs+dZ/6PwQ9t1cz83ZftncqrvM7XkcR2unsN5TBsTOfu6LR3XxhhTX19vYmJizPr165u8Xaf2NX/qclFtba127typzMzMkPHMzEwVFhae9Tvbt29vNH/MmDH69NNPVVdXd94551pTkiorKyVJXbt2DRnPz89Xjx491KtXLz366KMqLy9vWnPnEe6+v/jiCyUlJSktLU333Xefvvzyy+BnxcXFKisrC1nH6/Vq+PDh5/3f74eEu+fT63j55Zc1adKkRj+46/S+dqtnJ7br1n5u6vbP5ETfZ9NSx3W4ew7HMS2Fv+/T62hrx3V1dbXq6uqC/99tyeOa4OMin8+nEydONPqx1Pj4+EY/qnpKWVnZWefX19fL5/Odd8651jTGaNasWbr55pvVv3//4Pgdd9yh//zP/9R7772nZ555Rjt27NBPfvITBQKBC+71dOHse8iQIfr3f/93vf3221qzZo3Kyso0bNgwHTt2LLjGqe81tbamiJR9/cYbb+jbb7/VxIkTQ8bd2Ndu9ezEdt3az03d/pmc6PtMLXlch7PncB3TUuTs67Z4XM+dO1dXXHGFRo0a1eTtOrWv28RPVkS6MxO6MabR2A/NP3P8QtacNm2a9uzZo23btoWM33vvvcF/9+/fX4MGDVLPnj31xz/+UWPHjj1PR00Tjr7vuOOO4L8HDBigoUOH6uqrr9b69es1a9asZtfWVOHe1y+++KLuuOMOJSUlhYy7ua/d6Nmp7bq1n5uztlN9nxKO4zocPYf7mG7O2k7v67Z2XC9btkyvvPKK8vPz1bFjxwve7sXua874uKh79+5q165doyRaXl7eKLGekpCQcNb57du3V7du3c4752xrTp8+XVu2bNH777+vK6+88rz1JiYmqmfPnvriiy9+sLfziYS+T+ncubMGDBgQ7OnU3SAXus4PiYSejxw5onfeeUePPPLID9brxL52q2cntuvWfm7q9s/kRN+na+njOhJ6PqWljmkpMvpua8f1H/7wB+Xk5Gjr1q269tprL2i7Tu1rgo+LOnTooIEDByovLy9kPC8vT8OGDTvrd4YOHdpo/tatWzVo0CBFRUWdd87paxpjNG3aNL3++ut67733lJaW9oP1Hjt2TEePHlViYmKT+juXcPZ9pkAgoKKiomBPaWlpSkhICFmntrZWBQUF513nh0RCz2vXrlWPHj105513/mC9Tuxrt3p2Yrtu7eembv9MTvQthe+4DmfPZ2qpY1qKjL7b0nH9+9//Xv/6r/+qt956S4MGDbrg7Tq2r5t8GTSa5dTteS+++KI5cOCAycrKMp07dzaHDx82xhgzd+5c8+CDDwbnn7ot8PHHHzcHDhwwL774YqPbAv/85z+bdu3amSVLlpiioiKzZMmSRrc4P/bYYyYuLs7k5+eH3OpYXV1tjDGmqqrKPPHEE6awsNAUFxeb999/3wwdOtRcccUVjt4C2tJ9P/HEEyY/P998+eWX5qOPPjJ33XWXiYmJCW7XmJO3Q8bFxZnXX3/d7N2719x///2O3s7e0j0bY8yJEydMSkqKmTNnTqO63NzXbvQcCATMrl27zK5du0xiYqJ58sknza5du8wXX3zR5O0a495+Dmff4Tyuw9VzOI/pcPZtTNs6rpcuXWo6dOhgXnvttZD/71ZVVTV5u8Y4s68JPi1g1apVpmfPnqZDhw7mRz/6UaNbT4cPHx4yPz8/39xwww2mQ4cOJjU11Tz//PON1vyv//ov07t3bxMVFWUyMjLMpk2bQj6XdNbX2rVrjTHGVFdXm8zMTHP55ZebqKgok5KSYiZMmGBKSkpadd+nnukQFRVlkpKSzNixY83+/ftD5jQ0NJgFCxaYhIQE4/V6za233mr27t3bans2xpi3337bSDKHDh1q9Jnb+9rpnouLi8/6/90z1znfdo1xdz+Hq+9wH9fh6Dncx3S4+jambR3XPXv2PGvPCxYsaPJ2jXFmX3uM+b8rkAAAANo4rvEBAADWIPgAAABrEHwAAIA1CD4AAMAaBB8AAGANgg8AALAGwQcAAFiD4AMAAKxB8AHQKk2cOFF33313uMsA0MoQfAAAgDUIPgAAwBoEHwAR7bXXXtOAAQMUHR2tbt26adSoUfruu+8azQsEApoxY4Z69Oihjh076uabb9aOHTuCn+fn58vj8eiPf/yjrrvuOnXs2FFDhgzR3r17Q9YpLCzUrbfequjoaCUnJ2vGjBln3R6A1ongAyBilZaW6v7779ekSZNUVFSk/Px8jR07Vmf7beXZs2dr06ZNWr9+vT777DNdc801GjNmjL755puQeU899ZT+8Ic/aMeOHerRo4d+/vOfq66uTpK0d+9ejRkzRmPHjtWePXv06quvatu2bZo2bVqL9AvAffw6O4CI9dlnn2ngwIE6fPiwevbsGfLZxIkT9e233+qNN97Qd999py5dumjdunUaN26cJKmurk6pqanKysrSU089pfz8fI0cOVIbN27UvffeK0n65ptvdOWVV2rdunW655579NBDDyk6Olr/9m//FtzOtm3bNHz4cH333Xfq2LFjyzUPwBWc8QEQsa677jrddtttGjBggH75y19qzZo1qqioaDTvb3/7m+rq6nTTTTcFx6KiovTjH/9YRUVFIXOHDh0a/HfXrl3Vu3fv4JydO3dq3bp1uvTSS4OvMWPGqKGhQcXFxS51CaAltQ93AQBwLu3atVNeXp4KCwu1detWrVixQvPnz9fHH38cMu/UiWuPx9No/Myxszk1p6GhQZMnT9aMGTMazUlJSWluGwAiCGd8AEQ0j8ejm266SQsXLtSuXbvUoUMHbd68OWTONddcow4dOmjbtm3Bsbq6On366afq06dPyNyPPvoo+O+Kigp9/vnnysjIkCT96Ec/0v79+3XNNdc0enXo0MHFLgG0FM74AIhYH3/8sd59911lZmaqR48e+vjjj/WPf/xDffr00Z49e4LzOnfurMcee0xPPfWUunbtqpSUFC1btkzV1dV6+OGHQ9ZctGiRunXrpvj4eM2fP1/du3cPPghxzpw5uvHGGzV16lQ9+uij6ty5s4qKipSXl6cVK1a0ZOsAXELwARCxYmNj9cEHHyg3N1d+v189e/bUM888ozvuuEOvvvpqyNwlS5aooaFBDz74oKqqqjRo0CC9/fbb6tKlS6N5M2fO1BdffKHrrrtOW7ZsCZ7Nufbaa1VQUKD58+frlltukTFGV199dfBiaACtH3d1AbDCqbu6KioqdNlll4W7HABhwjU+AADAGgQfAABgDf7UBQAArMEZHwAAYA2CDwAAsAbBBwAAWIPgAwAArEHwAQAA1iD4AAAAaxB8AACANQg+AADAGv8LAD0Slykbm+0AAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.histplot(data=merged_concepts_tables, x='slope')" ] }, { "cell_type": "code", "execution_count": 16, "id": "605f1575-e77b-44b0-a7f9-5cbe6ea5723a", "metadata": { "scrolled": true }, "outputs": [], "source": [ "merged_concepts_tables_sorted_by_slope = merged_concepts_tables.loc[((merged_concepts_tables[num_insertions]>fscore_threshold).any(axis=1))].sort_values('slope')" ] }, { "cell_type": "code", "execution_count": 18, "id": "bc53e738-4aa2-4490-8696-b1df21a0c740", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " concept 12 24 \\\n", "0 AC0134:HSF:HSF 0.957552 0.998949 \n", "1 AC0077:ZIC-GLIS:C2H2_ZF 0.944478 0.983656 \n", "2 AC0108:KLF-SP:C2H2_ZF 0.946736 0.989602 \n", "3 AC0290:FOXP:Forkhead 0.929897 0.981421 \n", "4 AC0587:POU5F-NANOG:Homeodomain,POU 0.933423 0.982020 \n", "5 AC0109:SP-KLF:C2H2_ZF 0.932978 0.994893 \n", "6 AC0588:POU2F-POU3F:Homeodomain,POU 0.918495 0.979441 \n", "7 AC0581:ZSCAN:C2H2_ZF 0.907063 0.983622 \n", "8 AC0004:ZSCAN:C2H2_ZF 0.904799 0.984007 \n", "9 AC0008:VENTX:Homeodomain 0.887748 0.991718 \n", "10 AC0262:SOX-SRY:Sox 0.870168 0.975153 \n", "11 AC0424:NR4A:Nuclear_receptor 0.876931 0.968783 \n", "12 AC0373:ZBTB:C2H2_ZF 0.847913 0.975558 \n", "13 AC0115:ZNF:C2H2_ZF 0.862602 0.985691 \n", "14 AC0114:ZNF-VEZF:C2H2_ZF 0.859138 0.982008 \n", "15 AC0456:ZNF-PRDM:C2H2_ZF 0.857196 0.982061 \n", "16 AC0259:SOX:Sox 0.855221 0.958724 \n", "17 AC0110:ZNF-MAZ:C2H2_ZF 0.849792 0.969611 \n", "18 AC0442:ZNF:C2H2_ZF 0.845285 0.975716 \n", "19 AC0395:SOX:Sox 0.852110 0.968447 \n", "20 AC0104:ZNF:C2H2_ZF 0.824863 0.973635 \n", "21 AC0309:ZNF:C2H2_ZF 0.840434 0.953012 \n", "22 AC0614:LHX:Homeodomain 0.844613 0.983400 \n", "23 AC0525:NFYA-NFYB:CBF-NF-Y 0.828494 0.978550 \n", "24 AC0529:ZNF:C2H2_ZF 0.810311 0.972926 \n", "25 AC0120:RREB:C2H2_ZF 0.821083 0.976214 \n", "26 AC0267:SOX-SRY:Sox 0.823460 0.975384 \n", "27 AC0051:PLAG:C2H2_ZF 0.817095 0.966933 \n", "28 AC0098:ZNF:C2H2_ZF 0.826131 0.975201 \n", "29 AC0126:ZNF:C2H2_ZF 0.820799 0.965393 \n", "30 AC0427:NR6A:Nuclear_receptor 0.805930 0.948729 \n", "31 AC0100:ZNF:C2H2_ZF 0.815298 0.984676 \n", "32 AC0106:EGR-WT:C2H2_ZF 0.823898 0.977242 \n", "33 AC0092:ZNF:C2H2_ZF 0.821983 0.968943 \n", "34 AC0372:ZNF:C2H2_ZF 0.805758 0.984149 \n", "35 AC0113:ZNF:C2H2_ZF 0.811802 0.965906 \n", "36 AC0090:ZNF:C2H2_ZF 0.819568 0.986133 \n", "37 AC0172:ZNF:C2H2_ZF 0.816189 0.987924 \n", "38 AC0434:NR2F-RXRG:Nuclear_receptor 0.792795 0.953912 \n", "39 AC0358:ZNF:C2H2_ZF 0.787971 0.965376 \n", "40 AC0534:POU3F:Homeodomain,POU 0.796838 0.948908 \n", "41 AC0334:ZNF:C2H2_ZF 0.810894 0.957178 \n", "42 AC0396:POU2F:Homeodomain,POU 0.793572 0.968576 \n", "43 AC0235:HINFP:C2H2_ZF 0.803211 0.962653 \n", "44 AC0530:ZNF:C2H2_ZF 0.804015 0.950362 \n", "45 AC0444:ZNF:C2H2_ZF 0.805063 0.971803 \n", "46 AC0535:POU4F-POU5F:Homeodomain,POU 0.795523 0.964722 \n", "47 AC0430:TCF7L-LEF:Sox 0.787437 0.944566 \n", "48 AC0263:SOX-SRY:Sox 0.798333 0.962490 \n", "49 AC0405:ZNF:C2H2_ZF 0.798568 0.970968 \n", "50 AC0085:ZNF:C2H2_ZF 0.788466 0.972964 \n", "51 AC0406:ZNF:C2H2_ZF 0.775749 0.936861 \n", "52 AC0443:CTCF-ZNF:C2H2_ZF 0.787447 0.964982 \n", "53 AC0448:ZNF:C2H2_ZF 0.784999 0.973735 \n", "54 AC0599:CUX:CUT,Homeodomain 0.782883 0.968197 \n", "55 AC0185:ZNF:C2H2_ZF 0.784552 0.968508 \n", "56 AC0410:RARA-RARG:Nuclear_receptor 0.753471 0.919529 \n", "57 AC0101:ZBTB:C2H2_ZF 0.784874 0.973843 \n", "58 AC0500:ZNF:C2H2_ZF 0.767584 0.963992 \n", "59 AC0270:BBX:Sox 0.777558 0.960548 \n", "60 AC0006:MEOX:Homeodomain 0.774054 0.976229 \n", "61 AC0261:SOX-SRY:Sox 0.775248 0.939882 \n", "62 AC0558:HOXB-PBX:Homeodomain 0.773058 0.954552 \n", "63 AC0170:FLI-ZNF:Ets 0.785328 0.961933 \n", "64 AC0546:ZNF:C2H2_ZF 0.769160 0.956577 \n", "65 AC0494:ZNF:C2H2_ZF 0.763408 0.951078 \n", "66 AC0044:ZNF:C2H2_ZF 0.776755 0.953214 \n", "67 AC0323:ZNF:C2H2_ZF 0.766964 0.931819 \n", "68 AC0093:ZNF:C2H2_ZF 0.774423 0.967746 \n", "69 AC0116:GLIS:C2H2_ZF 0.772494 0.965646 \n", "70 AC0423:NR2E:Nuclear_receptor 0.765777 0.947851 \n", "71 AC0598:PAX:Homeodomain,Paired_box 0.777245 0.959949 \n", "72 AC0501:ZNF:C2H2_ZF 0.768516 0.958772 \n", "73 AC0168:ZNF:C2H2_ZF 0.762549 0.945158 \n", "74 AC0050:ZNF:C2H2_ZF 0.774522 0.967862 \n", "75 AC0397:POU2F-POU3F:Homeodomain,POU 0.749815 0.918894 \n", "76 AC0095:E2F:E2F 0.758123 0.960575 \n", "77 AC0260:SOX:Sox 0.769825 0.940635 \n", "78 AC0041:ZNF:C2H2_ZF 0.765810 0.936662 \n", "79 AC0460:ZNF:C2H2_ZF 0.761085 0.958042 \n", "80 AC0415:RARA-ESRRA:Nuclear_receptor 0.766928 0.959413 \n", "81 AC0165:ZNF:C2H2_ZF 0.770282 0.962194 \n", "82 AC0088:ZNF:C2H2_ZF 0.729480 0.925201 \n", "83 AC0257:FOXG-FOXK:Forkhead 0.725518 0.895068 \n", "84 AC0537:POU4F:Homeodomain,POU 0.726483 0.888545 \n", "85 AC0039:TFAP2A-TFAP2C:AP-2 0.744479 0.922890 \n", "86 AC0344:ZNF:C2H2_ZF 0.748187 0.956743 \n", "87 AC0316:ZNF:C2H2_ZF 0.750630 0.924015 \n", "88 AC0089:ZNF:C2H2_ZF 0.747222 0.955168 \n", "89 AC0091:ZNF:C2H2_ZF 0.741185 0.961474 \n", "90 AC0229:ZFP:C2H2_ZF 0.751026 0.922066 \n", "91 AC0132:ZSCAN-KLF:C2H2_ZF 0.741448 0.933643 \n", "92 AC0169:ZNF-PRDM:C2H2_ZF 0.753097 0.966106 \n", "93 AC0326:ZNF:C2H2_ZF 0.736190 0.944400 \n", "94 AC0314:ZNF:C2H2_ZF 0.745980 0.941955 \n", "95 AC0429:GLI:C2H2_ZF 0.744780 0.935464 \n", "96 AC0557:PBX-HOXA:Homeodomain 0.742047 0.927992 \n", "97 AC0107:KLF:C2H2_ZF 0.737888 0.951815 \n", "98 AC0346:ZNF:C2H2_ZF 0.731989 0.950656 \n", "99 AC0203:ZNF:C2H2_ZF 0.714198 0.947139 \n", "100 AC0271:SOX-SRY:Sox 0.720320 0.940941 \n", "101 AC0601:ONECUT-CUX:CUT,Homeodomain 0.712820 0.929157 \n", "102 AC0342:ZNF:C2H2_ZF 0.708143 0.962961 \n", "103 AC0313:ZNF:C2H2_ZF 0.726995 0.933393 \n", "104 AC0096:ZNF:C2H2_ZF 0.714128 0.949003 \n", "105 AC0300:ZNF:C2H2_ZF 0.723741 0.930756 \n", "106 AC0003:HNF1A-HNF1B:Homeodomain 0.725731 0.947691 \n", "107 AC0604:ZNF:C2H2_ZF 0.704422 0.913780 \n", "108 AC0422:ESRRA-NR5A:Nuclear_receptor 0.685381 0.893154 \n", "109 AC0565:ZNF:C2H2_ZF 0.701470 0.921922 \n", "110 AC0345:ZNF:C2H2_ZF 0.700920 0.943914 \n", "111 AC0137:ZNF:C2H2_ZF 0.685142 0.918853 \n", "112 AC0238:ZNF:C2H2_ZF 0.696961 0.931204 \n", "113 AC0138:GTF3A:C2H2_ZF 0.688309 0.913179 \n", "114 AC0211:ZNF:C2H2_ZF 0.711033 0.914769 \n", "115 AC0312:ZNF:C2H2_ZF 0.686910 0.910753 \n", "116 AC0174:ZSCAN:C2H2_ZF 0.699988 0.922792 \n", "117 AC0294:ZNF:C2H2_ZF 0.675214 0.873531 \n", "118 AC0195:ZNF:C2H2_ZF 0.695515 0.919548 \n", "119 AC0218:ZSCAN:C2H2_ZF 0.686390 0.917402 \n", "120 AC0488:ZNF:C2H2_ZF 0.689895 0.910787 \n", "121 AC0016:KLF:C2H2_ZF 0.683394 0.911863 \n", "122 AC0171:RBAK-ZNF:C2H2_ZF 0.703987 0.932573 \n", "123 AC0264:FOXD:Forkhead 0.685080 0.919038 \n", "124 AC0310:ZNF:C2H2_ZF 0.700672 0.898330 \n", "125 AC0531:ZNF:C2H2_ZF 0.677382 0.908050 \n", "126 AC0180:ZSCAN-ZNF:C2H2_ZF 0.692986 0.931106 \n", "127 AC0301:ZNF:C2H2_ZF 0.692675 0.917043 \n", "128 AC0481:ZNF:C2H2_ZF 0.697025 0.922052 \n", "129 AC0277:ZNF:C2H2_ZF 0.685754 0.903970 \n", "130 AC0518:NKX-MSANTD:['Homeo_domain_factors'] 0.692751 0.901618 \n", "131 AC0158:E2F-TFDP:E2F 0.667035 0.880265 \n", "132 AC0117:GLI-ZIC:C2H2_ZF 0.670669 0.896587 \n", "133 AC0616:ZNF:C2H2_ZF 0.699121 0.919593 \n", "134 AC0386:FOXR:Forkhead 0.672154 0.900480 \n", "135 AC0493:ZNF:C2H2_ZF 0.693654 0.926624 \n", "136 AC0332:ZNF:C2H2_ZF 0.683990 0.945132 \n", "137 AC0111:CTCF-KLF:C2H2_ZF 0.693238 0.925180 \n", "138 AC0164:ZNF:C2H2_ZF 0.682188 0.925983 \n", "139 AC0319:ZBTB:C2H2_ZF 0.661190 0.892632 \n", "140 AC0618:ZNF:C2H2_ZF 0.685104 0.914774 \n", "141 AC0105:EGR-ZNF:C2H2_ZF 0.665526 0.926854 \n", "142 AC0049:PLAGL:C2H2_ZF 0.673946 0.890368 \n", "143 AC0286:ZNF33A:C2H2_ZF 0.676681 0.908058 \n", "144 AC0047:ZNF:C2H2_ZF 0.665483 0.874134 \n", "145 AC0149:ZNF:C2H2_ZF 0.684284 0.935859 \n", "146 AC0167:ZNF:C2H2_ZF 0.658632 0.898749 \n", "147 AC0553:ZBTB:C2H2_ZF 0.668134 0.896254 \n", "148 AC0040:ZNF:C2H2_ZF 0.680665 0.915964 \n", "149 AC0131:ZNF:C2H2_ZF 0.677907 0.913264 \n", "150 AC0031:POU3F-POU2F:Homeodomain,POU 0.670829 0.907772 \n", "151 AC0296:ZNF:C2H2_ZF 0.661203 0.893332 \n", "152 AC0477:ZNF:C2H2_ZF 0.669595 0.895205 \n", "153 AC0204:ZNF33B:C2H2_ZF 0.675479 0.938076 \n", "154 AC0240:ZNF:C2H2_ZF 0.674583 0.926228 \n", "155 AC0603:ZNF:C2H2_ZF 0.665921 0.893043 \n", "156 AC0478:ZNF-PAX:C2H2_ZF 0.665355 0.904271 \n", "157 AC0617:ERG-FLI:Ets 0.665198 0.916129 \n", "158 AC0498:ZNF:C2H2_ZF 0.651739 0.909091 \n", "159 AC0347:ZNF:C2H2_ZF 0.651889 0.904282 \n", "160 AC0447:ZFP:C2H2_ZF 0.673771 0.931879 \n", "161 AC0615:ZNF:C2H2_ZF 0.668301 0.911942 \n", "162 AC0223:ZNF:C2H2_ZF 0.658601 0.904615 \n", "163 AC0499:ZBTB:C2H2_ZF 0.645400 0.900274 \n", "164 AC0508:FOSL:bZIP 0.664465 0.925377 \n", "165 AC0178:ZNF:C2H2_ZF 0.654347 0.882591 \n", "166 AC0123:FOXO:Forkhead 0.641947 0.903387 \n", "167 AC0465:ZIK:C2H2_ZF 0.648219 0.885196 \n", "168 AC0024:ZNF:C2H2_ZF 0.646445 0.902875 \n", "169 AC0162:ZNF:C2H2_ZF 0.665723 0.917501 \n", "170 AC0571:BARHL-MSX:Homeodomain 0.650249 0.875631 \n", "171 AC0166:ZFP:C2H2_ZF 0.646997 0.893961 \n", "172 AC0022:ZNF:C2H2_ZF 0.657493 0.905216 \n", "173 AC0037:ZNF:C2H2_ZF 0.645565 0.882100 \n", "174 AC0078:CTCF-CTCFL:C2H2_ZF 0.643865 0.908790 \n", "175 AC0625:ELF:Ets 0.647598 0.884604 \n", "176 AC0161:E2F-ZBTB:E2F 0.632679 0.885031 \n", "177 AC0458:ZFP:C2H2_ZF 0.656318 0.917007 \n", "178 AC0469:ZNF:C2H2_ZF 0.648725 0.906829 \n", "179 AC0139:ZNF:C2H2_ZF 0.645695 0.930250 \n", "180 AC0353:PRDM:C2H2_ZF 0.651451 0.893689 \n", "181 AC0279:ZNF:C2H2_ZF 0.649845 0.925508 \n", "182 AC0464:ZNF:C2H2_ZF 0.642236 0.865578 \n", "183 AC0017:RUNX:Runt 0.657417 0.903230 \n", "184 AC0413:THRB-RARA:Nuclear_receptor 0.634492 0.877832 \n", "185 AC0627:ELF:Ets 0.648063 0.901716 \n", "186 AC0136:INSM:C2H2_ZF 0.639871 0.895452 \n", "187 AC0285:ZFP:C2H2_ZF 0.638322 0.896578 \n", "188 AC0468:ZNF780A:C2H2_ZF 0.649230 0.896936 \n", "189 AC0094:ZNF:C2H2_ZF 0.657555 0.915883 \n", "190 AC0418:ZNF:C2H2_ZF 0.657494 0.908046 \n", "191 AC0306:TOPORS:Unknown 0.632853 0.891201 \n", "192 AC0231:PAX:Homeodomain,Paired_box 0.650918 0.880755 \n", "193 AC0559:MEIS-PBX:Homeodomain 0.623601 0.853841 \n", "194 AC0002:PROP-ALX:Homeodomain 0.631410 0.882589 \n", "195 AC0335:NRF:Unknown 0.639880 0.886836 \n", "196 AC0018:ZBTB:C2H2_ZF 0.635013 0.873622 \n", "197 AC0527:PBX-PKNOX:Homeodomain 0.631624 0.860852 \n", "198 AC0291:ZFP-ZNF:C2H2_ZF 0.644980 0.888585 \n", "199 AC0181:ZNF:C2H2_ZF 0.649817 0.910307 \n", "200 AC0445:ZNF:C2H2_ZF 0.635564 0.897486 \n", "201 AC0005:POU3F-POU1F:Homeodomain,POU 0.648016 0.908629 \n", "202 AC0182:ZNF:C2H2_ZF 0.654121 0.909328 \n", "203 AC0459:ZNF:C2H2_ZF 0.618992 0.858461 \n", "204 AC0042:ZSCAN:C2H2_ZF 0.628568 0.852782 \n", "205 AC0055:ZNF:C2H2_ZF 0.647884 0.886137 \n", "206 AC0062:ZNF:C2H2_ZF 0.624023 0.863999 \n", "207 AC0145:ZNF:C2H2_ZF 0.640319 0.906404 \n", "208 AC0034:NR2F-NR1H:Nuclear_receptor 0.612144 0.853421 \n", "209 AC0029:ZNF:C2H2_ZF 0.616351 0.868455 \n", "210 AC0010:DLX-LHX:Homeodomain 0.623741 0.887295 \n", "211 AC0549:ZNF:C2H2_ZF 0.629207 0.884304 \n", "212 AC0069:ZNF:C2H2_ZF 0.620651 0.878876 \n", "213 AC0027:ZNF:C2H2_ZF 0.627656 0.916581 \n", "214 AC0526:GFI-GFI1B:C2H2_ZF 0.624174 0.908861 \n", "215 AC0449:ZNF:C2H2_ZF 0.615602 0.875925 \n", "216 AC0613:MEIS-PKNOX:Homeodomain 0.607875 0.866024 \n", "217 AC0542:ZBTB:C2H2_ZF 0.627102 0.884565 \n", "218 AC0473:ZNF:C2H2_ZF 0.613565 0.907441 \n", "219 AC0191:IRF:IRF 0.605428 0.874745 \n", "220 AC0234:HINFP:C2H2_ZF 0.632008 0.880690 \n", "221 AC0495:ZNF:C2H2_ZF 0.613167 0.890121 \n", "222 AC0177:ZNF-MZF:C2H2_ZF 0.607694 0.886978 \n", "223 AC0155:ETS-SIX:Ets 0.607515 0.856703 \n", "224 AC0463:ZNF:C2H2_ZF 0.625297 0.898592 \n", "225 AC0253:GATA:GATA 0.605306 0.860107 \n", "226 AC0295:ZNF:C2H2_ZF 0.606749 0.856819 \n", "227 AC0227:SPI-BCL11A:Ets 0.605956 0.874104 \n", "228 AC0510:ZNF:C2H2_ZF 0.619510 0.891190 \n", "229 AC0563:SPDEF:Ets 0.612601 0.880214 \n", "230 AC0324:MBD:MBD 0.604310 0.860201 \n", "231 AC0209:ZNF:C2H2_ZF 0.599791 0.860194 \n", "232 AC0159:E2F:E2F 0.597494 0.845987 \n", "233 AC0266:ZNF:C2H2_ZF 0.595918 0.873963 \n", "234 AC0079:ZNF:C2H2_ZF 0.600629 0.877796 \n", "235 AC0189:IRF:IRF 0.627115 0.896879 \n", "236 AC0573:ZNF354A-ZNF354B:C2H2_ZF 0.594093 0.832705 \n", "237 AC0160:BCL11B:C2H2_ZF 0.610607 0.883187 \n", "238 AC0273:MTF:C2H2_ZF 0.602265 0.877823 \n", "239 AC0366:ZNF:C2H2_ZF 0.597758 0.880081 \n", "240 AC0147:ZNF:C2H2_ZF 0.587340 0.867747 \n", "241 AC0504:ZNF:C2H2_ZF 0.609066 0.895883 \n", "242 AC0409:NR2E:Nuclear_receptor 0.585314 0.851039 \n", "243 AC0173:ZNF:C2H2_ZF 0.604769 0.885809 \n", "244 AC0146:ZNF-ZNF324B:['C2H2_zinc_finger_factors'] 0.597672 0.887265 \n", "245 AC0348:TP:p53 0.588453 0.863204 \n", "246 AC0220:ZNF:C2H2_ZF 0.579542 0.857188 \n", "247 AC0228:CTCF:C2H2_ZF 0.591172 0.883757 \n", "248 AC0637:AHR:bHLH 0.608945 0.867890 \n", "249 AC0007:BARX-NKX:Homeodomain 0.581194 0.855097 \n", "250 AC0072:MYBL:Myb-SANT 0.579822 0.878704 \n", "251 AC0222:MYNN:C2H2_ZF 0.598300 0.876948 \n", "252 AC0023:ZNF:C2H2_ZF 0.574132 0.862535 \n", "253 AC0251:MECOM:C2H2_ZF 0.579569 0.868002 \n", "254 AC0619:SPDEF:Ets 0.579556 0.872876 \n", "255 AC0224:ZNF:C2H2_ZF 0.589362 0.867561 \n", "256 AC0371:REST:C2H2_ZF 0.580509 0.854200 \n", "257 AC0099:ZNF:C2H2_ZF 0.565014 0.858303 \n", "258 AC0486:ZFP-ZFP69B:C2H2_ZF 0.565648 0.839959 \n", "259 AC0401:GMEB:SAND 0.564955 0.849142 \n", "260 AC0330:LIN28B:CSD 0.587405 0.865152 \n", "261 AC0127:ZFP:C2H2_ZF 0.585743 0.885697 \n", "262 AC0245:ZBTB:C2H2_ZF 0.576110 0.843376 \n", "263 AC0521:ZNF:C2H2_ZF 0.557120 0.814841 \n", "264 AC0019:AIRE:SAND 0.555236 0.843938 \n", "265 AC0193:ZNF:C2H2_ZF 0.562691 0.844223 \n", "266 AC0021:ZNF:C2H2_ZF 0.564882 0.835024 \n", "267 AC0140:ZNF75A-ZNF75D:C2H2_ZF 0.548088 0.862700 \n", "268 AC0216:ZNF:C2H2_ZF 0.558092 0.839817 \n", "269 AC0376:ZBED:BED_ZF 0.548618 0.845173 \n", "270 AC0327:ZBTB:C2H2_ZF 0.551066 0.856423 \n", "271 AC0496:ZNF:C2H2_ZF 0.550519 0.835031 \n", "272 AC0466:ZNF:C2H2_ZF 0.543797 0.849366 \n", "273 AC0083:ZBTB:C2H2_ZF 0.535465 0.847795 \n", "274 AC0311:CTCF:C2H2_ZF 0.525237 0.810870 \n", "\n", " 36 slope R^2 \n", "0 0.995638 0.001587 0.685249 \n", "1 0.992871 0.002016 0.886690 \n", "2 1.000000 0.002219 0.889792 \n", "3 0.984349 0.002269 0.790207 \n", "4 0.990697 0.002386 0.860638 \n", "5 0.997912 0.002706 0.784796 \n", "6 1.000000 0.003396 0.924344 \n", "7 0.998933 0.003828 0.870958 \n", "8 0.997898 0.003879 0.859054 \n", "9 0.998977 0.004635 0.798728 \n", "10 0.989602 0.004976 0.839251 \n", "11 0.998960 0.005085 0.921534 \n", "12 0.972420 0.005188 0.731109 \n", "13 0.997863 0.005636 0.816898 \n", "14 0.996929 0.005741 0.830164 \n", "15 0.995749 0.005773 0.823304 \n", "16 0.994908 0.005820 0.928146 \n", "17 0.992707 0.005955 0.867545 \n", "18 0.988718 0.005976 0.817378 \n", "19 0.996841 0.006030 0.890413 \n", "20 0.972530 0.006153 0.744386 \n", "21 0.992610 0.006341 0.928793 \n", "22 0.998958 0.006431 0.824757 \n", "23 0.986467 0.006582 0.787488 \n", "24 0.974295 0.006833 0.756261 \n", "25 0.987652 0.006940 0.801243 \n", "26 0.990469 0.006959 0.817140 \n", "27 0.984647 0.006981 0.828311 \n", "28 0.994543 0.007017 0.834871 \n", "29 0.990654 0.007077 0.858717 \n", "30 0.976676 0.007114 0.868947 \n", "31 0.987704 0.007184 0.763167 \n", "32 0.996941 0.007210 0.834148 \n", "33 0.995444 0.007228 0.861513 \n", "34 0.983164 0.007392 0.745838 \n", "35 0.989311 0.007396 0.846947 \n", "36 0.997930 0.007432 0.799374 \n", "37 0.995913 0.007489 0.783269 \n", "38 0.973380 0.007524 0.829813 \n", "39 0.970666 0.007612 0.771699 \n", "40 0.981420 0.007691 0.877308 \n", "41 0.995801 0.007704 0.898472 \n", "42 0.981306 0.007822 0.800605 \n", "43 0.991828 0.007859 0.862814 \n", "44 0.995910 0.007996 0.915773 \n", "45 0.997957 0.008037 0.849573 \n", "46 0.988514 0.008041 0.840883 \n", "47 0.980472 0.008043 0.883817 \n", "48 0.991673 0.008056 0.860250 \n", "49 0.993713 0.008131 0.836092 \n", "50 0.983887 0.008143 0.791785 \n", "51 0.971458 0.008155 0.877733 \n", "52 0.985685 0.008260 0.827385 \n", "53 0.985217 0.008342 0.792864 \n", "54 0.984647 0.008407 0.810710 \n", "55 0.990815 0.008594 0.830061 \n", "56 0.962754 0.008720 0.897002 \n", "57 0.995792 0.008788 0.827116 \n", "58 0.978690 0.008796 0.801947 \n", "59 0.989272 0.008821 0.849631 \n", "60 0.986756 0.008863 0.787023 \n", "61 0.988528 0.008887 0.910265 \n", "62 0.986540 0.008895 0.859488 \n", "63 0.998912 0.008899 0.875307 \n", "64 0.983603 0.008935 0.842834 \n", "65 0.978212 0.008950 0.843039 \n", "66 0.992668 0.008996 0.881669 \n", "67 0.983469 0.009021 0.916478 \n", "68 0.992761 0.009097 0.834672 \n", "69 0.991370 0.009120 0.836788 \n", "70 0.985158 0.009141 0.873246 \n", "71 0.996777 0.009147 0.871703 \n", "72 0.988541 0.009168 0.849368 \n", "73 0.982617 0.009170 0.873356 \n", "74 0.994802 0.009178 0.840188 \n", "75 0.971337 0.009230 0.915410 \n", "76 0.979655 0.009231 0.814077 \n", "77 0.993287 0.009311 0.914751 \n", "78 0.989683 0.009328 0.915466 \n", "79 0.985348 0.009344 0.839803 \n", "80 0.994763 0.009493 0.863143 \n", "81 0.998949 0.009528 0.866950 \n", "82 0.963838 0.009765 0.869750 \n", "83 0.960040 0.009772 0.937838 \n", "84 0.962926 0.009852 0.956169 \n", "85 0.981748 0.009886 0.921977 \n", "86 0.986371 0.009924 0.841673 \n", "87 0.989265 0.009943 0.935939 \n", "88 0.986212 0.009958 0.845570 \n", "89 0.980491 0.009971 0.809193 \n", "90 0.990634 0.009984 0.942536 \n", "91 0.983282 0.010076 0.896196 \n", "92 0.996905 0.010159 0.843046 \n", "93 0.980448 0.010177 0.857929 \n", "94 0.993669 0.010320 0.898412 \n", "95 0.992781 0.010333 0.912078 \n", "96 0.990342 0.010346 0.923708 \n", "97 0.989414 0.010480 0.859245 \n", "98 0.988033 0.010668 0.856817 \n", "99 0.972391 0.010758 0.822583 \n", "100 0.979288 0.010790 0.858271 \n", "101 0.972076 0.010802 0.870210 \n", "102 0.967804 0.010819 0.763983 \n", "103 0.989701 0.010946 0.901872 \n", "104 0.979633 0.011063 0.835239 \n", "105 0.990541 0.011117 0.907846 \n", "106 0.992772 0.011127 0.872414 \n", "107 0.974434 0.011251 0.908182 \n", "108 0.959010 0.011401 0.917714 \n", "109 0.975485 0.011417 0.889957 \n", "110 0.975384 0.011436 0.834736 \n", "111 0.959835 0.011446 0.859042 \n", "112 0.971870 0.011455 0.858166 \n", "113 0.964100 0.011491 0.882920 \n", "114 0.987360 0.011514 0.930162 \n", "115 0.963315 0.011517 0.886526 \n", "116 0.976530 0.011523 0.889217 \n", "117 0.952095 0.011537 0.941306 \n", "118 0.974117 0.011608 0.890211 \n", "119 0.966900 0.011688 0.877522 \n", "120 0.971483 0.011733 0.902622 \n", "121 0.965395 0.011750 0.886310 \n", "122 0.986667 0.011778 0.887302 \n", "123 0.968335 0.011802 0.875910 \n", "124 0.984237 0.011815 0.950778 \n", "125 0.961073 0.011820 0.884403 \n", "126 0.977492 0.011854 0.868515 \n", "127 0.977505 0.011868 0.900592 \n", "128 0.982037 0.011876 0.899462 \n", "129 0.971392 0.011902 0.914996 \n", "130 0.978744 0.011916 0.933941 \n", "131 0.953214 0.011924 0.925845 \n", "132 0.957589 0.011955 0.900800 \n", "133 0.986514 0.011975 0.913114 \n", "134 0.960069 0.011996 0.897271 \n", "135 0.983089 0.012060 0.889709 \n", "136 0.973496 0.012063 0.822707 \n", "137 0.983347 0.012088 0.893177 \n", "138 0.973083 0.012121 0.867752 \n", "139 0.952131 0.012123 0.895719 \n", "140 0.980553 0.012310 0.906972 \n", "141 0.962313 0.012366 0.838177 \n", "142 0.971064 0.012380 0.934965 \n", "143 0.974058 0.012391 0.906546 \n", "144 0.966520 0.012543 0.952634 \n", "145 0.985372 0.012545 0.869467 \n", "146 0.959870 0.012552 0.894700 \n", "147 0.969559 0.012559 0.919174 \n", "148 0.982468 0.012575 0.905577 \n", "149 0.980102 0.012591 0.906079 \n", "150 0.973111 0.012595 0.902996 \n", "151 0.963761 0.012607 0.913066 \n", "152 0.972419 0.012618 0.925885 \n", "153 0.980062 0.012691 0.851157 \n", "154 0.980493 0.012746 0.878139 \n", "155 0.972250 0.012764 0.927886 \n", "156 0.972386 0.012793 0.906491 \n", "157 0.972821 0.012818 0.882692 \n", "158 0.959792 0.012836 0.869563 \n", "159 0.960194 0.012846 0.880760 \n", "160 0.983880 0.012921 0.871656 \n", "161 0.978483 0.012924 0.901987 \n", "162 0.969290 0.012945 0.898023 \n", "163 0.956178 0.012949 0.879792 \n", "164 0.976402 0.012997 0.868880 \n", "165 0.967872 0.013064 0.935185 \n", "166 0.956590 0.013110 0.872600 \n", "167 0.963263 0.013127 0.921822 \n", "168 0.961602 0.013132 0.884036 \n", "169 0.982670 0.013206 0.896419 \n", "170 0.967492 0.013218 0.944247 \n", "171 0.964285 0.013220 0.906362 \n", "172 0.975733 0.013260 0.906327 \n", "173 0.963842 0.013262 0.926919 \n", "174 0.962600 0.013281 0.872421 \n", "175 0.966757 0.013298 0.927240 \n", "176 0.952136 0.013311 0.899210 \n", "177 0.976264 0.013331 0.883295 \n", "178 0.968860 0.013339 0.888859 \n", "179 0.966021 0.013347 0.832592 \n", "180 0.972386 0.013372 0.920340 \n", "181 0.970847 0.013375 0.853527 \n", "182 0.963264 0.013376 0.951412 \n", "183 0.979402 0.013416 0.915310 \n", "184 0.957747 0.013469 0.921491 \n", "185 0.971471 0.013475 0.902707 \n", "186 0.963319 0.013477 0.899063 \n", "187 0.961847 0.013480 0.893968 \n", "188 0.972936 0.013488 0.914254 \n", "189 0.981437 0.013495 0.894385 \n", "190 0.982207 0.013530 0.910446 \n", "191 0.958973 0.013588 0.897803 \n", "192 0.978493 0.013649 0.948580 \n", "193 0.951179 0.013649 0.947988 \n", "194 0.960273 0.013703 0.915103 \n", "195 0.969220 0.013723 0.923161 \n", "196 0.964358 0.013723 0.937032 \n", "197 0.961185 0.013732 0.951484 \n", "198 0.974559 0.013732 0.929152 \n", "199 0.979505 0.013737 0.899104 \n", "200 0.965398 0.013743 0.896598 \n", "201 0.980448 0.013851 0.902926 \n", "202 0.986569 0.013852 0.912807 \n", "203 0.951808 0.013867 0.939624 \n", "204 0.962240 0.013903 0.962069 \n", "205 0.984445 0.014023 0.945508 \n", "206 0.961088 0.014044 0.943484 \n", "207 0.978493 0.014091 0.901148 \n", "208 0.950759 0.014109 0.943190 \n", "209 0.954991 0.014110 0.926199 \n", "210 0.962980 0.014135 0.907252 \n", "211 0.969845 0.014193 0.923712 \n", "212 0.963254 0.014275 0.920957 \n", "213 0.971574 0.014330 0.866384 \n", "214 0.969103 0.014372 0.876319 \n", "215 0.960584 0.014374 0.920447 \n", "216 0.953105 0.014385 0.924346 \n", "217 0.973831 0.014447 0.927267 \n", "218 0.961206 0.014485 0.862800 \n", "219 0.953183 0.014490 0.908739 \n", "220 0.980304 0.014512 0.942455 \n", "221 0.961929 0.014532 0.896594 \n", "222 0.956966 0.014553 0.893100 \n", "223 0.957811 0.014596 0.943783 \n", "224 0.976062 0.014615 0.905885 \n", "225 0.956501 0.014633 0.936491 \n", "226 0.958035 0.014637 0.943528 \n", "227 0.957440 0.014645 0.915619 \n", "228 0.971280 0.014657 0.910017 \n", "229 0.964687 0.014670 0.917274 \n", "230 0.957446 0.014714 0.936966 \n", "231 0.953760 0.014749 0.931055 \n", "232 0.951693 0.014758 0.948612 \n", "233 0.951026 0.014796 0.903527 \n", "234 0.955863 0.014801 0.905214 \n", "235 0.983621 0.014854 0.919242 \n", "236 0.950852 0.014865 0.963386 \n", "237 0.970349 0.014989 0.918651 \n", "238 0.962097 0.014993 0.913913 \n", "239 0.962615 0.015202 0.909134 \n", "240 0.952816 0.015228 0.913058 \n", "241 0.976386 0.015305 0.904847 \n", "242 0.952772 0.015311 0.937742 \n", "243 0.972864 0.015337 0.915268 \n", "244 0.966354 0.015362 0.901985 \n", "245 0.958054 0.015400 0.926807 \n", "246 0.951286 0.015489 0.924844 \n", "247 0.964023 0.015535 0.902454 \n", "248 0.982428 0.015562 0.952532 \n", "249 0.955624 0.015601 0.933299 \n", "250 0.955134 0.015638 0.895173 \n", "251 0.975502 0.015717 0.929380 \n", "252 0.951548 0.015726 0.914884 \n", "253 0.957642 0.015753 0.915619 \n", "254 0.960455 0.015871 0.911367 \n", "255 0.972669 0.015971 0.936353 \n", "256 0.965046 0.016022 0.943593 \n", "257 0.950043 0.016043 0.916306 \n", "258 0.951546 0.016079 0.944046 \n", "259 0.951117 0.016090 0.930913 \n", "260 0.973620 0.016092 0.939819 \n", "261 0.977436 0.016321 0.913916 \n", "262 0.968537 0.016351 0.958121 \n", "263 0.951242 0.016422 0.969382 \n", "264 0.950964 0.016489 0.934357 \n", "265 0.959423 0.016531 0.944651 \n", "266 0.962281 0.016558 0.958687 \n", "267 0.950656 0.016774 0.904432 \n", "268 0.960865 0.016782 0.949625 \n", "269 0.951918 0.016804 0.931241 \n", "270 0.956963 0.016912 0.921765 \n", "271 0.967346 0.017368 0.957451 \n", "272 0.965299 0.017563 0.936793 \n", "273 0.957253 0.017574 0.928407 \n", "274 0.950241 0.017709 0.962021 \n" ] } ], "source": [ "with pd.option_context('display.max_rows', None, 'display.max_columns', None): # more options can be specified also\n", " print(merged_concepts_tables_sorted_by_slope.reset_index(drop=True))" ] }, { "cell_type": "code", "execution_count": 19, "id": "988d509c-7eba-4d62-ba15-2940873d05d0", "metadata": {}, "outputs": [], "source": [ "# save top 30 concepts\n", "#merged_concepts_tables_sorted_by_slope.iloc[:30].to_csv(f\"output/top30_motifs_fscore{fscore_threshold}_slope.tsv\", header=True, index=False, sep=\"\\t\")" ] } ], "metadata": { "kernelspec": { "display_name": "torch_latest", "language": "python", "name": "torch_latest" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.6" } }, "nbformat": 4, "nbformat_minor": 5 } TPCAV-0.8.4/scripts/summary_classifiers.ipynb000066400000000000000000007466241516353663300212540ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "a5fd5ea5-5545-4717-a109-d8ba2d14f3d5", "metadata": {}, "source": [ "# Summarize CAV classifiers" ] }, { "cell_type": "code", "execution_count": 53, "id": "6ba258e4-b037-4c8c-8278-b19986b49cd8", "metadata": {}, "outputs": [], "source": [ "import torch\n", "import os\n", "import logomaker\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "\n", "from Bio import motifs\n", "from collections import defaultdict\n", "from glob import glob\n", "from matplotlib import gridspec\n", "from tqdm import tqdm\n", "from sklearn.metrics import precision_recall_fscore_support as score" ] }, { "cell_type": "code", "execution_count": 30, "id": "e406da46-e862-4105-a5ad-b98e8478c317", "metadata": { "editable": true, "papermill": { "duration": 0.024606, "end_time": "2025-04-01T16:59:29.576178", "exception": false, "start_time": "2025-04-01T16:59:29.551572", "status": "completed" }, "slideshow": { "slide_type": "" }, "tags": [ "parameters" ] }, "outputs": [], "source": [ "# parameters\n", "cav_root_dir = \"output/512bp_regions_insert_3_motifs/cavs/\"\n", "\n", "motif_meme = \"0_data/motif-clustering-v2.1beta_consensus_pwms.meme\"\n", "\n", "topN = 30 # how many motifs to plot, ranked by fscore" ] }, { "cell_type": "code", "execution_count": 31, "id": "670e20d7-7a19-4e63-9fc4-2382375f5310", "metadata": { "papermill": { "duration": 0.027998, "end_time": "2025-04-01T16:59:30.388033", "exception": false, "start_time": "2025-04-01T16:59:30.360035", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "tcav_expts = []\n", "for cav_dir in os.listdir(cav_root_dir):\n", " concept, control = cav_dir.split(\"_control_\")\n", " tcav_expts.append((concept, control, os.path.join(cav_root_dir, cav_dir )))\n", "tcav_expts = pd.DataFrame(tcav_expts, columns=['concept', 'control', 'cav_dir'])" ] }, { "cell_type": "code", "execution_count": 32, "id": "d228f8db-eb69-47eb-8d6f-857668042f3d", "metadata": { "papermill": { "duration": 0.04468, "end_time": "2025-04-01T16:59:30.446445", "exception": false, "start_time": "2025-04-01T16:59:30.401765", "status": "completed" }, "scrolled": true, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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conceptcontrolcav_dir
0AC0001:GATA-PROP:GATArandom_regionsoutput/512bp_regions_insert_3_motifs/cavs/AC00...
1AC0002:PROP-ALX:Homeodomainrandom_regionsoutput/512bp_regions_insert_3_motifs/cavs/AC00...
2AC0003:HNF1A-HNF1B:Homeodomainrandom_regionsoutput/512bp_regions_insert_3_motifs/cavs/AC00...
3AC0004:ZSCAN:C2H2_ZFrandom_regionsoutput/512bp_regions_insert_3_motifs/cavs/AC00...
4AC0005:POU3F-POU1F:Homeodomain,POUrandom_regionsoutput/512bp_regions_insert_3_motifs/cavs/AC00...
............
632AC0633:TBX-MGA::EVX:T-boxrandom_regionsoutput/512bp_regions_insert_3_motifs/cavs/AC06...
633AC0634:ZNF:C2H2_ZFrandom_regionsoutput/512bp_regions_insert_3_motifs/cavs/AC06...
634AC0635:TBX-EOMES:T-boxrandom_regionsoutput/512bp_regions_insert_3_motifs/cavs/AC06...
635AC0636:SREBF:bHLHrandom_regionsoutput/512bp_regions_insert_3_motifs/cavs/AC06...
636AC0637:AHR:bHLHrandom_regionsoutput/512bp_regions_insert_3_motifs/cavs/AC06...
\n", "

637 rows × 3 columns

\n", "
" ], "text/plain": [ " concept control \\\n", "0 AC0001:GATA-PROP:GATA random_regions \n", "1 AC0002:PROP-ALX:Homeodomain random_regions \n", "2 AC0003:HNF1A-HNF1B:Homeodomain random_regions \n", "3 AC0004:ZSCAN:C2H2_ZF random_regions \n", "4 AC0005:POU3F-POU1F:Homeodomain,POU random_regions \n", ".. ... ... \n", "632 AC0633:TBX-MGA::EVX:T-box random_regions \n", "633 AC0634:ZNF:C2H2_ZF random_regions \n", "634 AC0635:TBX-EOMES:T-box random_regions \n", "635 AC0636:SREBF:bHLH random_regions \n", "636 AC0637:AHR:bHLH random_regions \n", "\n", " cav_dir \n", "0 output/512bp_regions_insert_3_motifs/cavs/AC00... \n", "1 output/512bp_regions_insert_3_motifs/cavs/AC00... \n", "2 output/512bp_regions_insert_3_motifs/cavs/AC00... \n", "3 output/512bp_regions_insert_3_motifs/cavs/AC00... \n", "4 output/512bp_regions_insert_3_motifs/cavs/AC00... \n", ".. ... \n", "632 output/512bp_regions_insert_3_motifs/cavs/AC06... \n", "633 output/512bp_regions_insert_3_motifs/cavs/AC06... \n", "634 output/512bp_regions_insert_3_motifs/cavs/AC06... \n", "635 output/512bp_regions_insert_3_motifs/cavs/AC06... \n", "636 output/512bp_regions_insert_3_motifs/cavs/AC06... \n", "\n", "[637 rows x 3 columns]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tcav_expts" ] }, { "cell_type": "code", "execution_count": 33, "id": "2c796cee-f4d2-4a46-8078-df5282c672b1", "metadata": { "editable": true, "papermill": { "duration": 1.318247, "end_time": "2025-04-01T16:59:31.780317", "exception": false, "start_time": "2025-04-01T16:59:30.462070", "status": "completed" }, "scrolled": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "637it [00:11, 53.18it/s]\n" ] } ], "source": [ "dfs = []\n", "cavs = defaultdict(list)\n", "tcavs = defaultdict(list)\n", "\n", "fscores = []\n", "for item in tqdm(tcav_expts.itertuples()):\n", "\n", " prefix_dir = item.cav_dir\n", " \n", " perform = pd.read_table(f\"{prefix_dir}/classifier_perform_on_test.txt\", \n", " comment=\"#\")\n", "\n", " precision, recall, fscore, support = score(perform.Truth, perform.Pred)\n", "\n", " fscores.append(np.mean(fscore))\n", "\n", " # here predictions are model outputs, so it's usually expected that concepts 0 has a higher probability of being bound\n", " #model_predictions_0 = np.loadtxt(f\"{prefix_dir}/predictions_concept_0.txt\")\n", " #model_predictions_1 = np.loadtxt(f\"{prefix_dir}/predictions_concept_1.txt\")\n", " \n", " classifier_scores = pd.DataFrame({'precision': precision,\n", " 'recall': recall,\n", " 'fscore': fscore,\n", " 'support': support})\n", "\n", " dfs.append((np.mean(fscore), support, item.concept, item.control))\n", "\n", "dfs_merge = pd.DataFrame(dfs, columns=['fscore', 'support', 'concept', 'rep'])\n", "\n", "#dfs_merge['concept_name'] = dfs_merge.apply(lambda x: f'Random genome with\\n{x[\"concept\"]} inserted' if x['concept_id']==0 else 'Random genome', axis=1)" ] }, { "cell_type": "code", "execution_count": 34, "id": "5a9e0a62-86dd-4bad-8a26-c8748742bd0a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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fscoresupportconceptrep
00.570113[469, 448]AC0001:GATA-PROP:GATArandom_regions
10.627836[469, 456]AC0002:PROP-ALX:Homeodomainrandom_regions
20.624060[469, 462]AC0003:HNF1A-HNF1B:Homeodomainrandom_regions
30.882251[469, 483]AC0004:ZSCAN:C2H2_ZFrandom_regions
40.686810[469, 432]AC0005:POU3F-POU1F:Homeodomain,POUrandom_regions
...............
6320.683243[469, 415]AC0633:TBX-MGA::EVX:T-boxrandom_regions
6330.637607[469, 450]AC0634:ZNF:C2H2_ZFrandom_regions
6340.502615[469, 440]AC0635:TBX-EOMES:T-boxrandom_regions
6350.645897[469, 463]AC0636:SREBF:bHLHrandom_regions
6360.842541[469, 415]AC0637:AHR:bHLHrandom_regions
\n", "

637 rows × 4 columns

\n", "
" ], "text/plain": [ " fscore support concept rep\n", "0 0.570113 [469, 448] AC0001:GATA-PROP:GATA random_regions\n", "1 0.627836 [469, 456] AC0002:PROP-ALX:Homeodomain random_regions\n", "2 0.624060 [469, 462] AC0003:HNF1A-HNF1B:Homeodomain random_regions\n", "3 0.882251 [469, 483] AC0004:ZSCAN:C2H2_ZF random_regions\n", "4 0.686810 [469, 432] AC0005:POU3F-POU1F:Homeodomain,POU random_regions\n", ".. ... ... ... ...\n", "632 0.683243 [469, 415] AC0633:TBX-MGA::EVX:T-box random_regions\n", "633 0.637607 [469, 450] AC0634:ZNF:C2H2_ZF random_regions\n", "634 0.502615 [469, 440] AC0635:TBX-EOMES:T-box random_regions\n", "635 0.645897 [469, 463] AC0636:SREBF:bHLH random_regions\n", "636 0.842541 [469, 415] AC0637:AHR:bHLH random_regions\n", "\n", "[637 rows x 4 columns]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfs_merge" ] }, { "cell_type": "code", "execution_count": 42, "id": "26c409db-78ea-4336-829f-08fefa01ff03", "metadata": {}, "outputs": [], "source": [ "if topN is not None:\n", " df_plot = dfs_merge.sort_values('fscore', ascending=False).iloc[:topN]\n", "else:\n", " df_plot = dfs_merge.sort_values('fscore', ascending=False)" ] }, { "cell_type": "code", "execution_count": 73, "id": "3b862b20-5cf1-4c7c-aa1e-d192c3ecb742", "metadata": {}, "outputs": [], "source": [ "motif_pwms = motifs.parse(open(motif_meme), fmt='MINIMAL')\n", "motif_pwms = {m.name: m.pwm for m in motif_pwms} # index pwm by name\n", "def seq_logo(concept, ax=None, max_len=20):\n", " pwm = motif_pwms.get(concept)\n", "\n", " if pwm is not None: \n", " pwm_df = pd.DataFrame(pwm)\n", " motif_len = len(pwm_df)\n", " \n", " # Logomaker expects columns as A,C,G,T, so ensure correct order\n", " pwm_df = pwm_df[['A', 'C', 'G', 'T']]\n", " \n", " # Compute information content at each position (2 - entropy)\n", " def compute_ic(pwm_row):\n", " entropy = -sum([p * np.log2(p) if p > 0 else 0 for p in pwm_row])\n", " return 2 - entropy\n", " \n", " # Compute IC matrix: IC_letter = p * IC_total\n", " ic_df = pwm_df.copy()\n", " for i in range(len(pwm_df)):\n", " ic_total = compute_ic(pwm_df.iloc[i])\n", " ic_df.iloc[i] = pwm_df.iloc[i] * ic_total\n", " \n", " \n", " # Plot with logomaker\n", " x0, y0, width, height = ax.get_position().bounds\n", " ax.set_position([x0, y0, width * min(1., motif_len/max_len), height])\n", " logo = logomaker.Logo(ic_df, color_scheme={'A': 'red', 'C': 'blue',\n", " 'G': 'orange', 'T': 'green'},\n", " ax=ax)\n", " logo.ax.axis('off')\n", " else:\n", " print(f\"Can't find PWM for concept {concept}!\")\n", " ax.axis('off')" ] }, { "cell_type": "code", "execution_count": 74, "id": "fdc7a9f8-8b7c-4bb2-93c5-fd61cd77a43a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Can't find PWM for concept AC0161:E2F-ZBTB:E2F!\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "\n", "ax_bar = sns.barplot(data=df_plot, y='concept', x='fscore', orient=\"y\")\n", "ax_bar.set_position([0, 0, 0.7, 1])\n", "ax_bar_bbox = ax_bar.get_position()\n", "\n", "gs3 = gridspec.GridSpec(len(df_plot), 1)\n", "\n", "logo_height = ax_bar_bbox.height/len(df_plot)\n", "logo_width = 0.25\n", "for i, (concept, g) in enumerate(zip(reversed(df_plot.concept.tolist()), gs3)):\n", " ax3 = plt.subplot(g)\n", " ax3.set_position([0.71, ax_bar_bbox.y0+i*logo_height, logo_width+0.01, logo_height])\n", " if concept is not None:\n", " seq_logo(concept, ax=ax3)\n", " else:\n", " ax3.axis('off')" ] } ], "metadata": { "kernelspec": { "display_name": "torch_latest", "language": "python", "name": "torch_latest" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.6" } }, "nbformat": 4, "nbformat_minor": 5 } TPCAV-0.8.4/scripts/utils.example.py000066400000000000000000000350601516353663300172520ustar00rootroot00000000000000#!/usr/bin/env python3 """ Utility functions of loading model, dataset, etc. """ from itertools import cycle import numpy as np import pandas as pd import pyfaidx import seqchromloader as scl import torch from Bio import SeqIO from deeplift.dinuc_shuffle import dinuc_shuffle from generate_motif_concepts_v3 import insert_motif_into_seq from models import ConvTowerDomain_v6 from pybedtools import BedTool from pyfaidx import Fasta from seqchromloader.utils import dna2OneHot from torch.utils.data import TensorDataset, default_collate, get_worker_info def expand_dataframe(df, target_len=196_608): "expand interval dataframe to target length" halfR = int(target_len / 2) df = df.assign(mid=lambda x: ((x["start"] + x["end"]) / 2).astype(int)).assign( start=lambda x: x["mid"] - halfR, end=lambda x: x["mid"] + halfR ) # center 2*halfR window return df[["chrom", "start", "end"]] def split_dataframe(df, chunk_size=10000): chunks = list() num_chunks = len(df) // chunk_size + 1 for i in range(num_chunks): chunks.append(df[i * chunk_size : (i + 1) * chunk_size]) return chunks def seq_transform_fn(seq_one_hot): "modify this function if your model input is not one hot coded, seq_one_hot is one hot coded DNA by the order of [A, C, G, T], its shape is [batch_size, 4, len]" return seq_one_hot def iterate_seq_df_chunk( chunk, genome, motif=None, motif_mode="pwm", num_motifs=128, seq_transform_fn=seq_transform_fn, start_buffer=0, end_buffer=0, return_region=False, batch_size=None, print_warning=True, ): "Core function to generate motif concepts given a motif instance and a dataframe of regions" seq_one_hots = [] regions = [] for item in chunk.itertuples(): try: seq = str(genome[item.chrom][item.start : item.end]).upper() except KeyError as e: if print_warning: print(f"catch KeyError in region {item.chrom}:{item.start}-{item.end}") continue except pyfaidx.FetchError as e: if print_warning: print( f"catch FetchError in region {item.chrom}:{item.start}-{item.end}, probably it's due to the start coordinate is negative" ) continue unique_chars = np.unique(list(seq)) if "N" in seq: if print_warning: print(f"Skip {item.chrom}:{item.start}-{item.end} due to containing N") continue elif len(unique_chars) == 0: if print_warning: print( f"Skip region {item.chrom}:{item.start}-{item.end} due to no sequences avaiable" ) continue # if motif is not None, insert motif sequence if motif is not None: seq = insert_motif_into_seq( seq, motif, num_motifs=num_motifs, start_buffer=start_buffer, end_buffer=end_buffer, mode=motif_mode, ) if batch_size is None: seq_one_hot = dna2OneHot(seq) if seq_transform_fn is not None: seq_one_hot = seq_transform_fn(seq_one_hot) if return_region: yield f"{item.chrom}:{item.start}-{item.end}", seq_one_hot else: yield seq_one_hot else: seq_one_hots.append(dna2OneHot(seq)) regions.append(f"{item.chrom}:{item.start}-{item.end}") if len(seq_one_hots) >= batch_size: seq_one_hots = torch.stack(seq_one_hots) if seq_transform_fn is not None: seq_one_hots = seq_transform_fn(seq_one_hots) if return_region: yield regions, seq_one_hots else: yield seq_one_hots seq_one_hots = [] regions = [] if (len(seq_one_hots) > 0) and batch_size is not None: if return_region: yield regions, torch.stack(seq_one_hots) else: yield torch.stack(seq_one_hots) def iterate_seq_df( seq_df, genome_fasta, motif=None, num_motifs=128, motif_mode="pwm", start_buffer=0, end_buffer=0, batch_size=32, return_region=False, print_warning=True, ): genome = Fasta(genome_fasta) yield from iterate_seq_df_chunk( chunk=seq_df, genome=genome, motif=motif, num_motifs=num_motifs, motif_mode=motif_mode, start_buffer=start_buffer, end_buffer=end_buffer, return_region=return_region, batch_size=batch_size, print_warning=print_warning, ) def iterate_seq_bed( seq_bed, genome_fasta, motif=None, num_motifs=128, motif_mode="pwm", start_buffer=0, end_buffer=0, batch_size=32, print_warning=True, ): seq_df = pd.read_table(seq_bed, usecols=range(3), names=["chrom", "start", "end"]) yield from iterate_seq_df( seq_df, genome_fasta, motif=motif, motif_mode=motif_mode, num_motifs=num_motifs, start_buffer=start_buffer, end_buffer=end_buffer, batch_size=batch_size, print_warning=print_warning, ) class IterateSeqDataFrame(torch.utils.data.IterableDataset): def __init__( self, seq_df, genome_fasta, motif=None, motif_mode="pwm", num_motifs=128, start_buffer=0, end_buffer=0, return_region=False, print_warning=True, infinite=False, ): self.seq_df = seq_df self.genome_fasta = genome_fasta self.motif = motif self.motif_mode = motif_mode self.num_motifs = num_motifs self.start_buffer = start_buffer self.end_buffer = end_buffer self.return_region = return_region self.print_warning = print_warning self.infinite = infinite def __iter__(self): worker_info = get_worker_info() self.genome = pyfaidx.Fasta(self.genome_fasta) if self.infinite: while True: chunk = self.seq_df.sample(frac=1.0).reset_index( drop=True ) # shuffle the dataframe every time try: yield from iterate_seq_df_chunk( chunk, genome=self.genome, motif=self.motif, motif_mode=self.motif_mode, num_motifs=self.num_motifs, start_buffer=self.start_buffer, end_buffer=self.end_buffer, batch_size=None, return_region=self.return_region, print_warning=self.print_warning, ) except StopIteration: continue else: if worker_info is None: chunk = self.seq_df else: chunk = np.array_split(self.seq_df, worker_info.num_workers)[ worker_info.id ] yield from iterate_seq_df_chunk( chunk, genome=self.genome, motif=self.motif, motif_mode=self.motif_mode, num_motifs=self.num_motifs, start_buffer=self.start_buffer, end_buffer=self.end_buffer, batch_size=None, return_region=self.return_region, print_warning=self.print_warning, ) def center_windows(df, window_len=1024): "Get center window_len bp region of the given coordinate dataframe, input dataframe must have start and end colums" halfR = int(window_len / 2) return df.assign(mid=lambda x: ((x["start"] + x["end"]) / 2).astype(int)).assign( start=lambda x: x["mid"] - halfR, end=lambda x: x["mid"] + halfR ) def collate_seq(batch): seq, chrom, target, label = default_collate(batch) return seq def collate_chrom(batch): seq, chrom, target, label = default_collate(batch) return chrom def collate_seqchrom(batch): seq, chrom, target, label = default_collate(batch) return seq, chrom def seqs_to_tensords(seqs): "Turn a list of DNA sequences into one-hot coded tensordataset" onehots = [] for s in seqs: onehots.append(scl.dna2OneHot(s)) return TensorDataset(torch.tensor(np.stack(onehots))) def preprocess(x, y, device="cpu"): if isinstance(x, list): x = x[0] if isinstance(y, list): y = y[0] return x.to(device), y.to(device) class WrappedDataLoader: def __init__(self, dl1, dl2, func): self.dl1 = dl1 self.dl2 = dl2 self.func = func def __len__(self): return len(self.dl1.dataset.dataframe) def __iter__(self): if len(self.dl1) > len(self.dl2): dl1 = self.dl1 dl2 = cycle(self.dl2) else: dl1 = cycle(self.dl1) dl2 = self.dl2 for b1, b2 in zip(dl1, dl2): yield (self.func(b1, b2)) class SeqChromConcept: "Sequence + Chromatin concept given the bed files of sequences and chromatin regions" def __init__( self, seq_bed, seq_fa, chrom_bed, genome_fasta, bws: list, transforms=None, window_len=1024, batch_size=8, ): self.seq_bed = seq_bed self.seq_fa = seq_fa self.chrom_bed = chrom_bed self.seq_dl = None self.chrom_dl = None self.genome_fasta = genome_fasta self.bws = bws self.transforms = transforms self.window_len = window_len self.batch_size = batch_size def seq_dataloader(self): if self.seq_bed is not None: seq_df = pd.read_table( self.seq_bed, header=None, usecols=[0, 1, 2], names=["chrom", "start", "end"], ) seq_df = center_windows(seq_df, window_len=self.window_len) seq_df["label"] = -1 seq_df["strand"] = "+" print(f"Filtering out concept samples that don't exist in the genome...") seq_df = scl.filter_chromosomes( seq_df, to_keep=Fasta(self.genome_fasta).keys() ) dl = scl.SeqChromDatasetByDataFrame( seq_df, genome_fasta=self.genome_fasta, dataloader_kws={ "batch_size": self.batch_size, "num_workers": 0, "collate_fn": collate_seq, "drop_last": True, }, ) for seq in dl: if isinstance(seq, list): assert len(seq) == 1 seq = seq[0] yield seq else: with open(self.seq_fa) as handle: dnaSeqs = [] for record in SeqIO.parse(handle, "fasta"): dnaSeqs.append(torch.tensor(scl.dna2OneHot(record.seq))) if len(record.seq) != 1024: print(len(record.seq)) if len(dnaSeqs) >= self.batch_size: yield torch.stack(dnaSeqs) dnaSeqs = [] def chrom_dataloader(self): chrom_df = pd.read_table( self.chrom_bed, header=None, usecols=[0, 1, 2], names=["chrom", "start", "end"], ) chrom_df = center_windows(chrom_df, window_len=self.window_len) chrom_df["label"] = -1 chrom_df["strand"] = "+" # print(f"Filtering out concept samples that don't exist in the genome...") chrom_df = scl.filter_chromosomes( chrom_df, to_keep=Fasta(self.genome_fasta).keys() ) dl = scl.SeqChromDatasetByDataFrame( chrom_df, genome_fasta=self.genome_fasta, bigwig_filelist=self.bws, transforms=self.transforms, dataloader_kws={ "batch_size": self.batch_size, "num_workers": 0, "collate_fn": collate_chrom, "drop_last": True, }, ) yield from dl def dataloader(self): if ( self.seq_bed is not None or self.seq_fa is not None ) and self.seq_dl is None: self.seq_dl = self.seq_dataloader() if (self.chrom_bed is not None) and self.chrom_dl is None: self.chrom_dl = self.chrom_dataloader() yield from zip(self.seq_dl, self.chrom_dl) def dinuc_shuffle_several_times_seqchrom(seqchrom, times: int = 10, seed: int = 1): seq, chrom = seqchrom seq_to_return = dinuc_shuffle_several_times_seq(seq, times, seed) chrom_to_return = torch.stack( [torch.zeros_like(i) for j in range(times) for i in chrom] ) return (seq_to_return, chrom_to_return) def dinuc_shuffle_several_times_seq(seq, times: int = 10, seed: int = 1): assert len(seq.shape) == 3 # dim: N x D x L onehot_seq = torch.permute(seq, (0, 2, 1)) # dim: N x L x D assert onehot_seq.shape[-1] == 4 device = onehot_seq.device to_returns = [] for s in onehot_seq: # reset RandomState every loop to make it the same as v2 behavior on each example rng = np.random.RandomState(seed) to_return = torch.tensor( np.array( [ dinuc_shuffle(s.detach().cpu().numpy(), rng=rng).T for i in range(times) ] ) ).to(device) to_returns.append(to_return) return torch.cat(to_returns) def create_dataloader_from_bed( bed, data_config, window=1024, batch_size=8, transforms=None ): halfR = int(window / 2) target_df = BedTool(bed).to_dataframe() target_df = target_df.assign( mid=lambda x: ((x["start"] + x["end"]) / 2).astype(int) ).assign(start=lambda x: x["mid"] - halfR, end=lambda x: x["mid"] + halfR) target_df["label"] = -1 target_df["strand"] = "+" target_dl = scl.SeqChromDatasetByDataFrame( target_df, genome_fasta=data_config["genome_fasta_file"], bigwig_filelist=data_config["pre_bws"], transforms=transforms, return_region=True, dataloader_kws={"batch_size": batch_size, "drop_last": False}, ) return target_dl def load_model(): "Please put your model class intialization and loading here" model = ConvTowerDomain_v6.load_from_checkpoint( "data/checkpoint_epoch=19-val_loss=0.507421.ckpt" ) return model TPCAV-0.8.4/scripts/utils.py000066400000000000000000000454571516353663300156330ustar00rootroot00000000000000#!/usr/bin/env python3 """ Utility functions of loading model, dataset, etc. """ from itertools import cycle import Bio import numpy as np import pandas as pd import pyfaidx import seqchromloader as scl import torch from Bio import SeqIO from deeplift.dinuc_shuffle import dinuc_shuffle from pybedtools import BedTool from pyfaidx import Fasta from seq_utils import insert_motif_into_seq, insert_region_into_seq from seqchromloader.utils import dna2OneHot from torch.utils.data import TensorDataset, default_collate, get_worker_info def seq_transform_fn(seq_one_hot): "TODO: modify this function if your model input is not one hot coded, seq_one_hot is one hot coded DNA by the order of [A, C, G, T], its shape is [batch_size, 4, len]" return seq_one_hot def chrom_transform_fn(chrom_signal): "TODO: chrom_signal is of shape [batch_size, num_bigwigs, len], modify this function if you want to do some transformation on chromatin signal, if your model does not use chromatin signal, just return None" return None def load_model(): "TODO: Please load your model here" return model def get_mean_and_std(bws): ms = [] stds = [] for bwf in bws: mean, std = scl.utils.compute_mean_std_bigwig(bwf) ms.append(mean) stds.append(std) print(f"{bwf} Mean: {mean}, Standard Deviation {std} from {bwf}") return ms, stds def default_chroms_transform(c, mean: list, std: list): return (c - np.array(mean, dtype=np.float32)[np.newaxis, :, np.newaxis]) / np.array( std, dtype=np.float32 )[np.newaxis, :, np.newaxis] def split_dataframe(df, chunk_size=10000): chunks = list() num_chunks = len(df) // chunk_size + 1 for i in range(num_chunks): chunks.append(df[i * chunk_size : (i + 1) * chunk_size]) return chunks def iterate_seq_df_chunk( chunk, genome, motif=None, motif_mode="pwm", regions_insert=None, num_motifs=128, start_buffer=0, end_buffer=0, return_region=False, batch_size=None, print_warning=True, rng=np.random.default_rng(1), ): "Core function to generate motif concepts given a motif instance and a dataframe of regions" seq_one_hots = [] regions = [] for item in chunk.itertuples(): try: seq = str(genome[item.chrom][item.start : item.end]).upper() except KeyError as e: if print_warning: print(f"catch KeyError in region {item.chrom}:{item.start}-{item.end}") continue except pyfaidx.FetchError as e: if print_warning: print( f"catch FetchError in region {item.chrom}:{item.start}-{item.end}, probably it's due to the start coordinate is negative" ) continue unique_chars = np.unique(list(seq)) if "N" in seq: if print_warning: print(f"Skip {item.chrom}:{item.start}-{item.end} due to containing N") continue elif len(unique_chars) == 0: if print_warning: print( f"Skip region {item.chrom}:{item.start}-{item.end} due to no sequences avaiable" ) continue # if motif is not None, insert motif sequence if motif is not None: seq = insert_motif_into_seq( seq, motif, num_motifs=num_motifs, start_buffer=start_buffer, end_buffer=end_buffer, mode=motif_mode, rng=rng, ) elif regions_insert is not None: seq = insert_region_into_seq(seq, regions_insert, genome, rng=rng) if batch_size is None: seq_one_hot = dna2OneHot(seq) if return_region: yield f"{item.chrom}:{item.start}-{item.end}", seq_one_hot else: yield seq_one_hot else: seq_one_hots.append(dna2OneHot(seq)) regions.append(f"{item.chrom}:{item.start}-{item.end}") if len(seq_one_hots) >= batch_size: seq_one_hots = torch.stack(seq_one_hots) if return_region: yield regions, seq_one_hots else: yield seq_one_hots seq_one_hots = [] regions = [] if (len(seq_one_hots) > 0) and batch_size is not None: seq_one_hots = torch.stack(seq_one_hots) if return_region: yield regions, seq_one_hots else: yield seq_one_hots def iterate_seq_df( seq_df, genome_fasta, motif=None, num_motifs=128, motif_mode="pwm", start_buffer=0, end_buffer=0, regions_insert=None, batch_size=32, return_region=False, print_warning=True, rng=np.random.default_rng(1), ): genome = Fasta(genome_fasta) yield from iterate_seq_df_chunk( chunk=seq_df, genome=genome, motif=motif, num_motifs=num_motifs, motif_mode=motif_mode, start_buffer=start_buffer, end_buffer=end_buffer, regions_insert=regions_insert, return_region=return_region, batch_size=batch_size, print_warning=print_warning, rng=rng, ) def iterate_seq_bed( seq_bed, genome_fasta, motif=None, num_motifs=128, motif_mode="pwm", start_buffer=0, end_buffer=0, regions_insert=None, batch_size=32, print_warning=True, rng=np.random.default_rng(1), ): seq_df = pd.read_table(seq_bed, usecols=range(3), names=["chrom", "start", "end"]) yield from iterate_seq_df( seq_df, genome_fasta, motif=motif, motif_mode=motif_mode, num_motifs=num_motifs, start_buffer=start_buffer, end_buffer=end_buffer, regions_insert=regions_insert, batch_size=batch_size, print_warning=print_warning, rng=rng, ) class IterateSeqDataFrame(torch.utils.data.IterableDataset): def __init__( self, seq_df, genome_fasta, motif=None, motif_mode="pwm", num_motifs=128, start_buffer=0, end_buffer=0, regions_insert=None, return_region=False, print_warning=True, infinite=False, ): self.seq_df = seq_df self.genome_fasta = genome_fasta self.motif = motif self.motif_mode = motif_mode self.num_motifs = num_motifs self.start_buffer = start_buffer self.end_buffer = end_buffer self.regions_insert = regions_insert self.return_region = return_region self.print_warning = print_warning self.infinite = infinite def __iter__(self): worker_info = get_worker_info() self.genome = pyfaidx.Fasta(self.genome_fasta) rng = np.random.default_rng(worker_info.id if worker_info is not None else 1) if self.infinite: while True: chunk = self.seq_df.sample(frac=1.0).reset_index( drop=True ) # shuffle the dataframe every time try: yield from iterate_seq_df_chunk( chunk, genome=self.genome, motif=self.motif, motif_mode=self.motif_mode, num_motifs=self.num_motifs, start_buffer=self.start_buffer, end_buffer=self.end_buffer, regions_insert=self.regions_insert, batch_size=None, return_region=self.return_region, print_warning=self.print_warning, rng=rng, ) except StopIteration: continue else: if worker_info is None: chunk = self.seq_df else: chunk = np.array_split(self.seq_df, worker_info.num_workers)[ worker_info.id ] yield from iterate_seq_df_chunk( chunk, genome=self.genome, motif=self.motif, motif_mode=self.motif_mode, num_motifs=self.num_motifs, start_buffer=self.start_buffer, end_buffer=self.end_buffer, regions_insert=self.regions_insert, batch_size=None, return_region=self.return_region, print_warning=self.print_warning, rng=rng, ) def center_windows(df, window_len=1024): "Get center window_len bp region of the given coordinate dataframe, input dataframe must have start and end colums" halfR = int(window_len / 2) df = df.assign(mid=lambda x: ((x["start"] + x["end"]) / 2).astype(int)).assign( start=lambda x: x["mid"] - halfR, end=lambda x: x["mid"] + halfR ) if "strand" in df.columns: return df[["chrom", "start", "end", "strand"]] else: return df[["chrom", "start", "end"]] def collate_seq(batch): seq, chrom, target, label = default_collate(batch) return seq def collate_chrom(batch): seq, chrom, target, label = default_collate(batch) return chrom def collate_seqchrom(batch): seq, chrom, target, label = default_collate(batch) return seq, chrom def seqs_to_tensords(seqs): "Turn a list of DNA sequences into one-hot coded tensordataset" onehots = [] for s in seqs: onehots.append(scl.dna2OneHot(s)) return TensorDataset(torch.tensor(np.stack(onehots))) def preprocess(x, y, device="cpu"): if isinstance(x, list): x = x[0] if isinstance(y, list): y = y[0] return x.to(device), y.to(device) class WrappedDataLoader: def __init__(self, dl1, dl2, func): self.dl1 = dl1 self.dl2 = dl2 self.func = func def __len__(self): return len(self.dl1.dataset.dataframe) def __iter__(self): if len(self.dl1) > len(self.dl2): dl1 = self.dl1 dl2 = cycle(self.dl2) else: dl1 = cycle(self.dl1) dl2 = self.dl2 for b1, b2 in zip(dl1, dl2): yield (self.func(b1, b2)) def seq_dataloader_from_bed( seq_bed, genome_fasta, window_len=1024, batch_size=8, num_workers=0 ): seq_df = pd.read_table( seq_bed, header=None, usecols=[0, 1, 2], names=["chrom", "start", "end"], ) return seq_dataloader_from_dataframe( seq_df, genome_fasta, window_len, batch_size, num_workers ) def seq_dataloader_from_dataframe( seq_df, genome_fasta, window_len=1024, batch_size=8, num_workers=0 ): seq_df = center_windows(seq_df, window_len=window_len) seq_df["label"] = -1 if not "strand" in seq_df.columns: seq_df["strand"] = "+" # print(f"Filtering out concept samples that don't exist in the genome...") seq_df = scl.filter_chromosomes(seq_df, to_keep=Fasta(genome_fasta).keys()) dl = scl.SeqChromDatasetByDataFrame( seq_df, genome_fasta=genome_fasta, dataloader_kws={ "batch_size": batch_size, "num_workers": num_workers, "collate_fn": collate_seq, "drop_last": True, }, ) return dl def seq_dataloader_from_fa(seq_fa, input_window_length=1024, batch_size=8): with open(seq_fa) as handle: dnaSeqs = [] for record in SeqIO.parse(handle, "fasta"): if len(record.seq) != input_window_length: raise Exception( "Sequence length not equal to input_window_length, got {len(record.seq)}" ) dnaSeqs.append(torch.tensor(scl.dna2OneHot(record.seq))) if len(dnaSeqs) >= batch_size: yield torch.stack(dnaSeqs) dnaSeqs = [] if len(dnaSeqs) > 0: yield torch.stack(dnaSeqs) def chrom_dataloader_from_bed( chrom_bed, genome_fasta, input_window_length=1024, bigwigs=[], batch_size=8 ): chrom_df = pd.read_table( chrom_bed, header=None, usecols=[0, 1, 2], names=["chrom", "start", "end"], ) return chrom_dataloader_from_dataframe( chrom_df, genome_fasta, input_window_length, bigwigs, batch_size ) def chrom_dataloader_from_dataframe( chrom_df, genome_fasta, input_window_length=1024, bigwigs=[], batch_size=8, num_workers=0, ): chrom_df = center_windows(chrom_df, window_len=input_window_length) chrom_df["label"] = -1 if not "strand" in chrom_df.columns: chrom_df["strand"] = "+" # print(f"Filtering out concept samples that don't exist in the genome...") chrom_df = scl.filter_chromosomes(chrom_df, to_keep=Fasta(genome_fasta).keys()) dl = scl.SeqChromDatasetByDataFrame( chrom_df, genome_fasta=genome_fasta, bigwig_filelist=bigwigs, dataloader_kws={ "batch_size": batch_size, "num_workers": num_workers, "collate_fn": collate_chrom, "drop_last": True, }, ) return dl class SeqChromConcept: "Sequence + Chromatin concept given the bed files of sequences and chromatin regions" def __init__( self, seq_bed, seq_fa, chrom_bed, genome_fasta, bws: list, transforms=None, window_len=1024, batch_size=8, ): self.seq_bed = seq_bed self.seq_fa = seq_fa self.chrom_bed = chrom_bed self.seq_dl = None self.chrom_dl = None self.genome_fasta = genome_fasta self.bws = bws self.transforms = transforms self.window_len = window_len self.batch_size = batch_size def seq_dataloader(self): if self.seq_bed is not None: seq_df = pd.read_table( self.seq_bed, header=None, usecols=[0, 1, 2], names=["chrom", "start", "end"], ) seq_df = center_windows(seq_df, window_len=self.window_len) seq_df["label"] = -1 if not "strand" in seq_df.columns: seq_df["strand"] = "+" # print(f"Filtering out concept samples that don't exist in the genome...") seq_df = scl.filter_chromosomes( seq_df, to_keep=Fasta(self.genome_fasta).keys() ) dl = scl.SeqChromDatasetByDataFrame( seq_df, genome_fasta=self.genome_fasta, dataloader_kws={ "batch_size": self.batch_size, "num_workers": 0, "collate_fn": collate_seq, "drop_last": True, }, ) for seq in dl: if isinstance(seq, list): assert len(seq) == 1 seq = seq[0] yield seq else: with open(self.seq_fa) as handle: dnaSeqs = [] for record in SeqIO.parse(handle, "fasta"): dnaSeqs.append(torch.tensor(scl.dna2OneHot(record.seq))) if len(record.seq) != 1024: print(len(record.seq)) if len(dnaSeqs) >= self.batch_size: yield torch.stack(dnaSeqs) dnaSeqs = [] def chrom_dataloader(self): chrom_df = pd.read_table( self.chrom_bed, header=None, usecols=[0, 1, 2], names=["chrom", "start", "end"], ) chrom_df = center_windows(chrom_df, window_len=self.window_len) chrom_df["label"] = -1 if not "strand" in chrom_df.columns: chrom_df["strand"] = "+" # print(f"Filtering out concept samples that don't exist in the genome...") chrom_df = scl.filter_chromosomes( chrom_df, to_keep=Fasta(self.genome_fasta).keys() ) dl = scl.SeqChromDatasetByDataFrame( chrom_df, genome_fasta=self.genome_fasta, bigwig_filelist=self.bws, transforms=self.transforms, dataloader_kws={ "batch_size": self.batch_size, "num_workers": 0, "collate_fn": collate_chrom, "drop_last": True, }, ) yield from dl def dataloader(self): if ( self.seq_bed is not None or self.seq_fa is not None ) and self.seq_dl is None: self.seq_dl = self.seq_dataloader() if (self.chrom_bed is not None) and self.chrom_dl is None: self.chrom_dl = self.chrom_dataloader() yield from zip(self.seq_dl, self.chrom_dl) def dinuc_shuffle_several_times_seqchrom(seqchrom, times: int = 10, seed: int = 1): seq, chrom = seqchrom seq_to_return = dinuc_shuffle_several_times_seq(seq, times, seed) chrom_to_return = torch.stack( [torch.zeros_like(i) for j in range(times) for i in chrom] ) return (seq_to_return, chrom_to_return) def dinuc_shuffle_several_times_seq(seq, times: int = 10, seed: int = 1): assert len(seq.shape) == 3 # dim: N x D x L onehot_seq = torch.permute(seq, (0, 2, 1)) # dim: N x L x D assert onehot_seq.shape[-1] == 4 device = onehot_seq.device to_returns = [] for s in onehot_seq: # reset RandomState every loop to make it the same as v2 behavior on each example rng = np.random.RandomState(seed) to_return = torch.tensor( np.array( [ dinuc_shuffle(s.detach().cpu().numpy(), rng=rng).T for i in range(times) ] ) ).to(device) to_returns.append(to_return) return torch.cat(to_returns) def create_dataloader_from_bed( bed, data_config, window=1024, batch_size=8, transforms=None ): halfR = int(window / 2) target_df = BedTool(bed).to_dataframe() target_df = target_df.assign( mid=lambda x: ((x["start"] + x["end"]) / 2).astype(int) ).assign(start=lambda x: x["mid"] - halfR, end=lambda x: x["mid"] + halfR) target_df["label"] = -1 target_df["strand"] = "+" target_dl = scl.SeqChromDatasetByDataFrame( target_df, genome_fasta=data_config["genome_fasta_file"], bigwig_filelist=data_config["pre_bws"], transforms=transforms, return_region=True, dataloader_kws={"batch_size": batch_size, "drop_last": False}, ) return target_dl class CustomMotif: def __init__(self, name, consensus): self.name = name self.matrix_id = "custom" self.consensus = consensus.upper() self.rc = False def __len__(self): return len(self.consensus) def reverse_complement(self): self.consensus = Bio.Seq.reverse_complement(self.consensus) self.name = self.name + "_rc" return self TPCAV-0.8.4/test/000077500000000000000000000000001516353663300133725ustar00rootroot00000000000000TPCAV-0.8.4/test/test_cav_trainer.py000066400000000000000000000337661516353663300173170ustar00rootroot00000000000000import unittest from functools import partial from pathlib import Path import torch from Bio import motifs as Bio_motifs from captum.attr import DeepLift from tpcav import helper, run_tpcav, utils, report from tpcav.cavs import CavTrainer from tpcav.concepts import ConceptBuilder from tpcav.tpcav_model import TPCAV, _abs_attribution_func from line_profiler import LineProfiler class DummyModelSeq(torch.nn.Module): def __init__(self): super().__init__() self.layer1 = torch.nn.Linear(1024, 1) self.layer2 = torch.nn.Linear(4, 1) def forward(self, seq): y_hat = self.layer1(seq) y_hat = y_hat.squeeze(-1) y_hat = self.layer2(y_hat) return y_hat def foward_from_layer1(self, y_hat): y_hat = y_hat.squeeze(-1) y_hat = self.layer2(y_hat) return y_hat class DummyModelSeqChrom(torch.nn.Module): def __init__(self): super().__init__() self.layer1 = torch.nn.Linear(1024, 1) self.layer2 = torch.nn.Linear(4, 1) def forward(self, seq, chrom): y_hat = self.layer1(seq) y_hat = y_hat.squeeze(-1) y_hat = self.layer2(y_hat) return y_hat def transform_fasta_to_one_hot_seq(seq, chrom): return (helper.fasta_to_one_hot_sequences(seq),) class TPCAVTest(unittest.TestCase): def test_motif_concepts_insertion(self): motif_path = Path("data") / "motif-clustering-v2.1beta_consensus_pwms.test.meme" self.assertTrue(motif_path.exists(), "Motif file is missing") builder = ConceptBuilder( genome_fasta="data/hg38.analysisSet.fa", input_window_length=1024, bws=None, num_motifs=16, include_reverse_complement=True, min_samples=1000, batch_size=8, ) builder.build_control() builder.add_meme_motif_concepts(str(motif_path)) # load motifs motifs = Bio_motifs.parse(open(motif_path), fmt="minimal") for motif in motifs: motif_name = motif.name.replace("/", "-") concept = None for c in builder.concepts: if c.name == motif_name: concept = c break self.assertIsNotNone(concept) seq, chrom = next(iter(concept.data_iter)) matches = list(motif.pssm.search(seq[0], threshold=2.0)) self.assertGreaterEqual( len(matches), 16, f"Motif concept {motif_name} has insufficient matches {matches}", ) control_seq, _ = next(iter(builder.control_concepts[0].data_iter)) control_matches = list(motif.pssm.search(control_seq[0], threshold=2.0)) self.assertGreater( len(matches), len(control_matches), f"Control concept has more motif matches than Motif concept, motif concept: {len(matches)}, control concept: {len(control_matches)}", ) def test_run_tpcav_random_control(self): motif_path = Path("data") / "motif-clustering-v2.1beta_consensus_pwms.test.meme" genome_fasta = "data/hg38.analysisSet.fa" model = DummyModelSeq() layer_name = "layer1" cavs_fscores_df, motif_cav_trainers, bed_cav_trainer = run_tpcav( model=model, layer_name=layer_name, motif_file=str(motif_path), genome_fasta=genome_fasta, num_motif_insertions=[4, 8], bed_seq_file="data/hg38_rmsk.sample.bed", output_dir="data/test_run_tpcav_output/", ) random_regions_1 = helper.random_regions_dataframe( "data/hg38.analysisSet.fa.fai", 1024, 100, seed=1 ) random_regions_2 = helper.random_regions_dataframe( "data/hg38.analysisSet.fa.fai", 1024, 100, seed=2 ) def pack_data_iters(df): seq_fasta_iter = helper.dataframe_to_fasta_iter( df, "data/hg38.analysisSet.fa", batch_size=8 ) seq_one_hot_iter = ( helper.fasta_to_one_hot_sequences(seq_fasta) for seq_fasta in seq_fasta_iter ) chrom_iter = helper.dataframe_to_chrom_tracks_iter(df, None, batch_size=8) return zip( seq_one_hot_iter, ) attributions = bed_cav_trainer.tpcav.layer_attributions( pack_data_iters(random_regions_1), pack_data_iters(random_regions_2) ).cpu() report.generate_tpcav_html_report("data/test_html.html", motif_cav_trainers, non_motif_cav_trainers = {'repeats': bed_cav_trainer}, attributions = [attributions, ] * 3, motif_file=motif_path, fscore_thresh=0.1) def test_run_tpcav_consensus_random_control(self): motif_path = Path("data") / "custom_motifs_alan.tsv" genome_fasta = "data/hg38.analysisSet.fa" model = DummyModelSeq() layer_name = "layer1" cavs_fscores_df, motif_cav_trainers, bed_cav_trainer = run_tpcav( model=model, layer_name=layer_name, motif_file=str(motif_path), motif_file_fmt='consensus', genome_fasta=genome_fasta, num_motif_insertions=[4, 8], output_dir="data/test_run_tpcav_output/", ) def test_run_tpcav_permute_control(self): motif_path = Path("data") / "motif-clustering-v2.1beta_consensus_pwms.test.meme" genome_fasta = "data/hg38.analysisSet.fa" model = DummyModelSeq() layer_name = "layer1" cavs_fscores_df, motif_cav_trainers, bed_cav_trainer = run_tpcav( model=model, layer_name=layer_name, motif_file=str(motif_path), motif_control_type='permute', genome_fasta=genome_fasta, num_motif_insertions=[4, 8], bed_seq_file="data/hg38_rmsk.sample.bed", output_dir="data/test_run_tpcav_output/", ) def test_run_tpcav_no_pca(self): motif_path = Path("data") / "motif-clustering-v2.1beta_consensus_pwms.test.meme" genome_fasta = "data/hg38.analysisSet.fa" model = DummyModelSeq() layer_name = "layer1" cavs_fscores_df, motif_cav_trainers, bed_cav_trainer = run_tpcav( model=model, layer_name=layer_name, motif_file=str(motif_path), genome_fasta=genome_fasta, num_motif_insertions=[4, 8], bed_seq_file="data/hg38_rmsk.sample.bed", output_dir="data/test_run_tpcav_output/", num_pc=0 ) def test_write_bw(self): random_regions_1 = helper.random_regions_dataframe( "data/hg38.analysisSet.fa.fai", 1024, 100, seed=1 ) helper.write_attrs_to_bw(torch.rand((100, 1024)).numpy(), random_regions_1.apply(lambda x: f"{x.chrom}:{x.start}-{x.end}", axis=1).tolist(), "data/hg38.analysisSet.fa.fai", "data/test_run_tpcav_output/input_attrs.bw") def test_motif_concepts_against_permute_control(self): motif_path = Path("data") / "motif-clustering-v2.1beta_consensus_pwms.test.meme" self.assertTrue(motif_path.exists(), "Motif file is missing") builder = ConceptBuilder( genome_fasta="data/hg38.analysisSet.fa", input_window_length=1024, bws=None, num_motifs=16, include_reverse_complement=True, min_samples=1000, batch_size=8, ) builder.build_control() concepts_pairs = builder.add_meme_motif_concepts(str(motif_path), build_permute_control=True) builder.apply_transform(transform_fasta_to_one_hot_seq) tpcav_model = TPCAV(DummyModelSeq(), layer_name="layer1") tpcav_model.fit_pca( concepts=builder.all_concepts(), num_samples_per_concept=10, num_pc="full", ) cav_trainer = CavTrainer(tpcav_model) cav_trainer.train_concepts_pairs(concepts_pairs, 200, output_dir="data/cavs_permute/", num_processes=2) def test_all(self): lp = LineProfiler() # Add installed-package functions you care about lp.add_function(utils.iterate_seq_df_chunk) lp.add_function(CavTrainer.train_concepts) lp.enable_by_count() motif_path = Path("data") / "motif-clustering-v2.1beta_consensus_pwms.test.meme" self.assertTrue(motif_path.exists(), "Motif file is missing") builder = ConceptBuilder( genome_fasta="data/hg38.analysisSet.fa", input_window_length=1024, bws=None, num_motifs=12, include_reverse_complement=True, min_samples=1000, batch_size=8, ) builder.build_control() builder.add_meme_motif_concepts(str(motif_path)) builder.apply_transform(transform_fasta_to_one_hot_seq) batch = next(iter(builder.all_concepts()[0].data_iter)) self.assertTupleEqual(batch[0].shape, (builder.batch_size, 4, 1024)) device = 'cuda:0' if torch.cuda.is_available() else 'cpu' tpcav_model = TPCAV(DummyModelSeq(), layer_name="layer1").to(device) tpcav_model.fit_pca( concepts=builder.concepts_for_pca(), num_samples_per_concept=10, num_pc="full", ) torch.save(tpcav_model, "data/tmp_tpcav_model.pt") cav_trainer = CavTrainer(tpcav_model, penalty="l2") cav_trainer.set_control(builder.control_concepts[0], num_samples=100) cav_trainer.train_concepts( builder.concepts, 100, output_dir="data/cavs/", num_processes=2, backend='torch', device='cuda:1' if torch.cuda.device_count() > 1 else 'cuda:0', ) cav_trainer.train_concepts( builder.concepts, 100, output_dir="data/cavs/", num_processes=2 ) torch.save(cav_trainer, "data/tmp_cav_trainer.pt") random_regions_1 = helper.random_regions_dataframe( "data/hg38.analysisSet.fa.fai", 1024, 100, seed=1 ) random_regions_2 = helper.random_regions_dataframe( "data/hg38.analysisSet.fa.fai", 1024, 100, seed=2 ) def pack_data_iters(df): seq_fasta_iter = helper.dataframe_to_fasta_iter( df, "data/hg38.analysisSet.fa", batch_size=8 ) seq_one_hot_iter = ( helper.fasta_to_one_hot_sequences(seq_fasta) for seq_fasta in seq_fasta_iter ) chrom_iter = helper.dataframe_to_chrom_tracks_iter(df, None, batch_size=8) return zip( seq_one_hot_iter, ) attributions = tpcav_model.layer_attributions( pack_data_iters(random_regions_1), pack_data_iters(random_regions_2) ).cpu() cav_trainer.tpcav_score("AC0001:GATA-PROP:GATA", attributions) cav_trainer.plot_cavs_similaritiy_heatmap([attributions,]*2, fscore_thresh=0.0, motif_meme_file="data/motif-clustering-v2.1beta_consensus_pwms.test.meme") cav_trainer.plot_cavs_similaritiy_heatmap(attributions, fscore_thresh=0.0, motif_meme_file="data/motif-clustering-v2.1beta_consensus_pwms.test.meme") input_attrs = tpcav_model.input_attributions( pack_data_iters(random_regions_1), pack_data_iters(random_regions_2), multiply_by_inputs=True, cavs_list=[ cav_trainer.cav_weights["AC0001:GATA-PROP:GATA"], ], ) # compute layer attributions using the old way random1_avs = [] random2_avs = [] for inputs in pack_data_iters(random_regions_1): av = tpcav_model._layer_output(*[i.to(tpcav_model.device) for i in inputs]) random1_avs.append(av.detach().cpu()) for inputs in pack_data_iters(random_regions_2): av = tpcav_model._layer_output(*[i.to(tpcav_model.device) for i in inputs]) random2_avs.append(av.detach().cpu()) random1_avs = torch.cat(random1_avs, dim=0) random2_avs = torch.cat(random2_avs, dim=0) random1_avs_residual, random1_avs_projected = tpcav_model.project_activations( random1_avs ) random2_avs_residual, random2_avs_projected = tpcav_model.project_activations( random2_avs ) def forward_from_layer_1_embeddings(tm, avs_residual, avs_projected): y_hat = tm.embedding_to_layer_activation(avs_residual, avs_projected) y_hat = tm.model.foward_from_layer1(y_hat) return y_hat tpcav_model.forward = partial(forward_from_layer_1_embeddings, tpcav_model) dl = DeepLift(tpcav_model) attributions_old = dl.attribute( ( random1_avs_residual.to(tpcav_model.device), random1_avs_projected.to(tpcav_model.device), ), baselines=( random2_avs_residual.to(tpcav_model.device), random2_avs_projected.to(tpcav_model.device), ), custom_attribution_func=_abs_attribution_func, ) attr_residual, attr_projected = attributions_old attributions_old = torch.cat((attr_projected, attr_residual), dim=1).cpu() self.assertTrue( torch.allclose(attributions.cpu(), attributions_old.cpu(), atol=1e-6), f"Attributions do not match, max difference is {torch.abs(attributions - attributions_old).max()}", ) lp.disable_by_count() #lp.print_stats() # test save and restore states tpcav_model.save_state("data/tmp_tpcav_model.state.pt") tpcav_model = TPCAV.load_state(DummyModelSeq(), layer_name="layer1", state_path="data/tmp_tpcav_model.state.pt") cav_trainer.save_state("data/tmp_cav_trainer.state.pt") cav_trainer = CavTrainer.load_state(tpcav_model, state_path="data/tmp_cav_trainer.state.pt") if __name__ == "__main__": unittest.main() TPCAV-0.8.4/tpcav/000077500000000000000000000000001516353663300135305ustar00rootroot00000000000000TPCAV-0.8.4/tpcav/__init__.py000066400000000000000000000017441516353663300156470ustar00rootroot00000000000000""" Lightweight, reusable TCAV utilities built from the repository scripts. The package keeps existing scripts untouched while offering programmatic access to concept construction and PCA/attribution workflows. """ import logging # Set the logging level to INFO logging.basicConfig(level=logging.INFO) from .cavs import CavTrainer, run_tpcav from .concepts import ConceptBuilder from .helper import ( bed_to_chrom_tracks_iter, bed_to_fasta_iter, dataframe_to_chrom_tracks_iter, dataframe_to_fasta_iter, dinuc_shuffle_sequences, fasta_to_one_hot_sequences, random_regions_dataframe, ) from .logging_utils import set_verbose from .tpcav_model import TPCAV __all__ = [ "ConceptBuilder", "CavTrainer", "TPCAV", "bed_to_fasta_iter", "dataframe_to_fasta_iter", "bed_to_chrom_tracks_iter", "dataframe_to_chrom_tracks_iter", "fasta_to_one_hot_sequences", "random_regions_dataframe", "dinuc_shuffle_sequences", "set_verbose", ] TPCAV-0.8.4/tpcav/cavs.py000066400000000000000000001167011516353663300150440ustar00rootroot00000000000000#!/usr/bin/env python3 """ CAV training and attribution utilities built on TPCAV. """ import logging import os import gc from pathlib import Path from typing import Iterable, List, Optional, Tuple, Union import time from concurrent.futures import ProcessPoolExecutor from Bio import motifs import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns import torch from copy import deepcopy from scipy import stats import uuid from sklearn.linear_model import SGDClassifier from sklearn.metrics import precision_recall_fscore_support from sklearn.metrics.pairwise import cosine_similarity from sklearn.model_selection import GridSearchCV from torch.utils.data import DataLoader, TensorDataset, random_split from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split import logomaker import multiprocessing as mp from . import helper, utils, report from .concepts import ConceptBuilder from .tpcav_model import TPCAV from matplotlib import gridspec logger = logging.getLogger(__name__) class _TorchLinear(torch.nn.Module): """Torch linear layer classifier""" def __init__(self, input_dim, num_class=1, device='cuda:0'): super().__init__() self.linear = torch.nn.Linear(input_dim, num_class) self.device = device def forward(self, avs): return self.linear(avs).squeeze(-1) def shuffle_tensor(self, *tensors): p = torch.randperm(len(tensors[0])) new_tensors = [t[p] for t in tensors] return new_tensors def fit(self, train_avs: np.ndarray, train_ls: np.ndarray, val_avs: np.ndarray, val_ls: np.ndarray, patience=10, lr=1e-2, weight_decay=1e-2, max_epochs=1000): optimizer = torch.optim.AdamW(self.parameters(), lr=lr, weight_decay=weight_decay) best_loss = None best_state_dict = None epoch = 0 t = 0 while True: epoch += 1 if epoch > max_epochs: break #shuffle the tensors before every epoch train_avs, train_ls = self.shuffle_tensor(train_avs, train_ls) self.train() for i in range(0, len(train_avs), 32): optimizer.zero_grad() avs = torch.from_numpy(train_avs[i: (i+32)]) l = torch.from_numpy(train_ls[i: (i+32)]) y_hat = self(avs.to(self.device)) loss = torch.mean(torch.clamp(1 - l.to(self.device) * y_hat, min=0)) loss.backward() optimizer.step() logger.debug(f"Training loss at epoch {epoch}: {loss}") self.eval() val_loss_all = [] for i in range(0, len(val_avs), 32): avs = torch.from_numpy(val_avs[i: (i+32)]) l = torch.from_numpy(val_ls[i: (i+32)]) y_hat = self(avs.to(self.device)) val_loss = torch.mean(torch.clamp(1 - l.to(self.device) * y_hat, min=0)) val_loss_all.append(val_loss.item()) val_loss_all = np.mean(val_loss_all) #logger.debug(f"Validation loss at epoch {epoch}: {val_loss_all}, patience {t} out of {patience}") if (best_loss is None) or (val_loss_all < best_loss): best_loss = val_loss_all best_state_dict = deepcopy(self.state_dict()) else: t += 1 if t >= patience: break return best_state_dict, best_loss class _TorchLinearWrapper: def __init__(self, input_dim, num_class=1, lr=1e-2, weight_decay_search = [1e-2, 1e-4, 1e-6], device="cuda:0"): super().__init__() self.input_dim = input_dim self.num_class = num_class self.lr = lr self.weight_decay_search = weight_decay_search self.device = device def fit(self, train_val_avs: np.ndarray, train_val_ls: np.ndarray): train_avs, val_avs, train_ls, val_ls = train_test_split(train_val_avs, train_val_ls, test_size=0.1) best_state_dict = None; best_loss = None for w in self.weight_decay_search: model = _TorchLinear(self.input_dim, self.num_class, device=self.device).to(self.device) state_dict, loss = model.fit(train_avs, train_ls, val_avs, val_ls, lr=self.lr, weight_decay=w) if (best_loss is None) or (loss < best_loss): best_loss = loss best_state_dict = state_dict del model gc.collect() torch.cuda.empty_cache() self.best_model = _TorchLinear(self.input_dim, self.num_class) self.best_model.load_state_dict(best_state_dict) self.best_model.to(self.device) def predict(self, avs: np.ndarray): y_hat = self.best_model(torch.from_numpy(avs).to(self.device)) y_hat[y_hat>=0] = 1 y_hat[y_hat<0] = -1 return y_hat.detach().cpu().numpy() @property def weights(self): linear_weight = self.best_model.linear.weight.detach().cpu()[0] return torch.stack([-1 * linear_weight, linear_weight]) @property def classes_(self): return self.num_class class _SGDWrapper: """Lightweight SGD concept classifier.""" def __init__(self, penalty: str = "l2", n_jobs: int = -1): self.lm = SGDClassifier( max_iter=1000, early_stopping=True, validation_fraction=0.1, learning_rate="optimal", n_iter_no_change=10, n_jobs=n_jobs, penalty=penalty, average=True, ) if penalty == "l2": params = {"alpha": [1e-2, 1e-4, 1e-6, 1e-8]} elif penalty == "l1": params = {"alpha": [1e-1, 1]} else: raise ValueError(f"Unexpected penalty type {penalty}") self.search = GridSearchCV(self.lm, params) def fit(self, train_avs: np.ndarray, train_ls: np.ndarray): self.search.fit(train_avs, train_ls) self.lm = self.search.best_estimator_ logger.info( "Best Params: %s | Iterations: %s", self.search.best_params_, self.lm.n_iter_, ) @property def weights(self) -> torch.Tensor: if len(self.lm.coef_) == 1: return torch.tensor(np.array([-1 * self.lm.coef_[0], self.lm.coef_[0]])) return torch.tensor(self.lm.coef_) @property def classes_(self): return self.lm.classes_ def predict(self, x: np.ndarray) -> np.ndarray: return self.lm.predict(x) def prepare_xy(concept_embeddings, control_embeddings, seed=42): def _to_numpy(emb): return np.load(str(emb), mmap_mode="r") # move to CPU + numpy, just double confirm concept = _to_numpy(concept_embeddings) control = _to_numpy(control_embeddings) # labels y_concept = np.zeros(len(concept), dtype=np.int64) y_control = np.ones(len(control), dtype=np.int64) # combine X = np.concatenate([concept, control], axis=0) y = np.concatenate([y_concept, y_control], axis=0) # flatten if needed (SGDClassifier expects 2D) X = X.reshape(X.shape[0], -1) # split: train vs temp (val+test) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.1, random_state=seed, stratify=y, # keeps class balance ) return X_train, y_train, X_test, y_test def _train( concept_embeddings: str, control_embeddings: str, output_dir: str, penalty: str = "l2", backend: str = "sklearn", device=None, ) -> Tuple[float, torch.Tensor]: """ Train a binary CAV classifier for a concept vs cached control embeddings. Requires set_control to have been called beforehand. """ assert backend in ["sklearn", "torch"], "Backend has to be either sklearn or torch!" output_dir = Path(output_dir) train_avs, train_l, test_avs, test_l = prepare_xy(concept_embeddings, control_embeddings) if backend == "sklearn": clf = _SGDWrapper(penalty=penalty) else: # replace label 0 as -1 to accomodate hinge loss train_l[train_l==0] = -1 test_l[test_l==0] = -1 device = device or ('cuda:0' if torch.cuda.is_available else 'cpu') clf = _TorchLinearWrapper(input_dim= train_avs.shape[1], device=device) clf.fit(train_avs, train_l) #breakpoint() def _eval(avs, l, name: str): y_preds = clf.predict(avs) acc = (y_preds == l).sum() / len(l) precision, recall, fscore, support = precision_recall_fscore_support( l, y_preds, average="binary", pos_label=1 ) logger.info("[%s] Accuracy: %.4f", name, acc) (output_dir / f"classifier_perform_on_{name}.txt").write_text( f"Accuracy: {acc}\n" ) return fscore output_dir.mkdir(parents=True, exist_ok=True) _eval(train_avs, train_l, "train") test_fscore = _eval(test_avs, test_l, "test") weights = clf.weights assert len(weights.shape) == 2 and weights.shape[0] == 2 torch.save(weights, output_dir / "classifier_weights.pt") if backend == 'torch': # release gpu memroy del clf.best_model gc.collect() torch.cuda.empty_cache() return test_fscore, weights[0] class CavTrainer: """Train CAVs and compute attribution-driven TCAV scores.""" def __init__(self, tpcav: TPCAV, penalty: str = "l2") -> None: self.tpcav = tpcav self.penalty = penalty self.cav_fscores = {} self.cav_weights = {} self.control_embeddings: Optional[torch.Tensor] = None self.cavs_list: List[torch.Tensor] = [] def save_state(self, output_path: str = "cav_trainer_state.pt"): """ Save CavTrainer state to a file. """ state = { "penalty": self.penalty, "cav_fscores": self.cav_fscores, "cav_weights": self.cav_weights, "control_embeddings": self.control_embeddings, "cavs_list": self.cavs_list, } torch.save(state, output_path) @staticmethod def load_state(tpcav_model: TPCAV, state_path: str = "cav_trainer_state.pt"): """ Restore CavTrainer state from a file. """ state = torch.load(state_path, map_location="cpu") cav_trainer = CavTrainer(tpcav_model, penalty=state["penalty"]) cav_trainer.cav_fscores = state["cav_fscores"] cav_trainer.cav_weights = state["cav_weights"] cav_trainer.control_embeddings = state["control_embeddings"] cav_trainer.cavs_list = state["cavs_list"] logger.info("Successfully restored cav trainer states!") return cav_trainer def set_control(self, control_concept, num_samples: int) -> torch.Tensor: """ Set and cache control embeddings to avoid recomputation across CAV trainings. """ self.control_embeddings = self.tpcav.concept_embeddings( control_concept, num_samples=num_samples ) return self.control_embeddings @staticmethod def _save_tensor_npy(path: Path, tensor: torch.Tensor) -> str: path.parent.mkdir(parents=True, exist_ok=True) np.save(path, tensor.detach().cpu().numpy()) return str(path) @staticmethod def _cleanup_paths(paths: list[str]) -> None: for p in paths: try: Path(p).unlink(missing_ok=True) except Exception: pass @classmethod def _reap_done_futures(cls, futures: list): pending = [] for name, fut, paths in futures: if fut.done(): fut.result() # raises if worker failed cls._cleanup_paths(paths) else: pending.append((name, fut, paths)) return pending @classmethod def _wait_for_capacity( cls, futures: list, capacity: int, sleep_s: int = 5, ): while True: futures = cls._reap_done_futures(futures) if len(futures) < capacity: return futures time.sleep(sleep_s) def train_concepts( self, concept_list, num_samples: int, output_dir: str, num_processes: int = 1, max_pending: int = 8, backend='sklearn', device=None ): "Train concepts with a fixed control set by self.set_control()" if self.control_embeddings is None: raise ValueError( "Call set_control(control_concept, num_samples=...) before training CAVs." ) else: self.control_embeddings = self.control_embeddings.cpu() output_dir_path = Path(output_dir) output_dir_path.mkdir(parents=True, exist_ok=True) control_memmap_path = output_dir_path / f"_control_embeddings_{uuid.uuid4().hex}.npy" self._save_tensor_npy(control_memmap_path, self.control_embeddings) if num_processes == 1: for c in concept_list: concept_embeddings = self.tpcav.concept_embeddings( c, num_samples=num_samples ) concept_dir = output_dir_path / c.name concept_dir.mkdir(parents=True, exist_ok=True) concept_memmap_path = concept_dir / "concept_embeddings.npy" self._save_tensor_npy(concept_memmap_path, concept_embeddings) fscore, weight = _train( str(concept_memmap_path), str(control_memmap_path), concept_dir, self.penalty, backend=backend, device=device ) self.cav_fscores[c.name] = fscore self.cav_weights[c.name] = weight self.cavs_list.append(weight) self._cleanup_paths([str(concept_memmap_path)]) else: futures = [] ctx = mp.get_context("spawn") with ProcessPoolExecutor(mp_context=ctx, max_workers=num_processes) as executor: for c in concept_list: concept_embeddings = self.tpcav.concept_embeddings( c, num_samples=num_samples ) concept_dir = output_dir_path / c.name concept_dir.mkdir(parents=True, exist_ok=True) concept_memmap_path = concept_dir / "concept_embeddings.npy" self._save_tensor_npy(concept_memmap_path, concept_embeddings) # block the process to avoid too long queue futures = self._wait_for_capacity( futures, capacity=(max_pending + num_processes), sleep_s=5 ) future = executor.submit( _train, str(concept_memmap_path), str(control_memmap_path), concept_dir, self.penalty, backend=backend, device=device ) logger.info("Submitted CAV training for concept %s", c.name) futures.append((c.name, future, [str(concept_memmap_path)])) results = [] for name, fut, paths in futures: results.append((name, fut.result())) self._cleanup_paths(paths) for name, (fscore, weight) in results: self.cav_fscores[name] = fscore self.cav_weights[name] = weight self.cavs_list.append(weight) self._cleanup_paths([str(control_memmap_path)]) def train_concepts_pairs(self, concept_pair_list, num_samples: int, output_dir: str, num_processes: int = 1, max_pending: int = 8, backend='sklearn', device=None): """Train concept pairs (test concept, control concept) Note: It would compute embeddings on every control concept, use self.train_concepts if control concept is fixed """ output_dir_path = Path(output_dir) output_dir_path.mkdir(parents=True, exist_ok=True) if num_processes == 1: for c_test, c_control in concept_pair_list: concept_embeddings = self.tpcav.concept_embeddings( c_test, num_samples=num_samples ) control_embeddings = self.tpcav.concept_embeddings( c_control, num_samples=num_samples ) concept_dir = output_dir_path / c_test.name concept_dir.mkdir(parents=True, exist_ok=True) concept_memmap_path = concept_dir / "concept_embeddings.npy" control_memmap_path = concept_dir / "control_embeddings.npy" self._save_tensor_npy(concept_memmap_path, concept_embeddings) self._save_tensor_npy(control_memmap_path, control_embeddings) fscore, weight = _train( str(concept_memmap_path), str(control_memmap_path), concept_dir, self.penalty, backend=backend, device=device ) self.cav_fscores[c_test.name] = fscore self.cav_weights[c_test.name] = weight self.cavs_list.append(weight) self._cleanup_paths([str(concept_memmap_path), str(control_memmap_path)]) else: futures = [] with ProcessPoolExecutor(max_workers=num_processes) as executor: for c_test, c_control in concept_pair_list: concept_embeddings = self.tpcav.concept_embeddings( c_test, num_samples=num_samples ) control_embeddings = self.tpcav.concept_embeddings( c_control, num_samples=num_samples ) concept_dir = output_dir_path / c_test.name concept_dir.mkdir(parents=True, exist_ok=True) concept_memmap_path = concept_dir / "concept_embeddings.npy" control_memmap_path = concept_dir / "control_embeddings.npy" self._save_tensor_npy(concept_memmap_path, concept_embeddings) self._save_tensor_npy(control_memmap_path, control_embeddings) # block the process to avoid too long queue futures = self._wait_for_capacity( futures, capacity=(max_pending + num_processes), sleep_s=5 ) future = executor.submit( _train, str(concept_memmap_path), str(control_memmap_path), concept_dir, self.penalty, backend=backend, device=device ) logger.info("Submitted CAV training for concept %s", c_test.name) futures.append( ( c_test.name, future, [str(concept_memmap_path), str(control_memmap_path)], ) ) results = [] for name, fut, paths in futures: results.append((name, fut.result())) self._cleanup_paths(paths) for name, (fscore, weight) in results: self.cav_fscores[name] = fscore self.cav_weights[name] = weight self.cavs_list.append(weight) def tpcav_score( self, concept_name: str, attributions: torch.Tensor ) -> torch.Tensor: """ Compute a simple TCAV score: mean directional attribution along the concept CAV. """ if concept_name not in self.cav_weights: raise ValueError(f"No CAV weights stored for concept {concept_name}") weights = self.cav_weights[concept_name] flat_attr = attributions.flatten(start_dim=1) scores = torch.matmul(flat_attr, weights.to(flat_attr.device).unsqueeze(-1)) return scores def tpcav_score_all_concepts(self, attributions: torch.Tensor) -> dict: """ Compute TCAV scores for all trained concepts. """ scores_dict = {} for concept_name in self.cav_weights.keys(): scores = self.tpcav_score(concept_name, attributions) scores_dict[concept_name] = scores return scores_dict def tpcav_score_binary_log_ratio( self, concept_name: str, attributions: torch.Tensor, pseudocount: float = 1.0 ) -> float: """ Compute TCAV log ratio score: log2 of ratio of positive to negative directional attributions. """ scores = self.tpcav_score(concept_name, attributions) pos_count = (scores > 0).sum().item() neg_count = (scores < 0).sum().item() return np.log((pos_count + pseudocount) / (neg_count + pseudocount)) def tpcav_score_all_concepts_log_ratio( self, attributions: torch.Tensor, pseudocount: float = 1.0 ) -> dict: """ Compute TCAV log ratio scores for all trained concepts. """ log_ratio_dict = {} for concept_name in self.cav_weights.keys(): log_ratio = self.tpcav_score_binary_log_ratio( concept_name, attributions, pseudocount ) log_ratio_dict[concept_name] = log_ratio return log_ratio_dict def plot_cavs_similaritiy_heatmap( self, attributions: Optional[Union[List[torch.Tensor], torch.Tensor]] = None, concept_list: Optional[List[str]] = None, fscore_thresh=0.8, motif_meme_file: Optional[str] = None, output_path: str = "cavs_similarity_heatmap.png", ): if concept_list is None: cavs_names = list(self.cav_weights.keys()) else: cavs_names = concept_list cavs_pass = [] cavs_names_pass = [] for cname in cavs_names: if self.cav_fscores[cname] >= fscore_thresh: cavs_pass.append(self.cav_weights[cname].cpu().numpy()) cavs_names_pass.append(cname) else: logger.info( "Skipping CAV %s with F-score %.3f below threshold %.3f", cname, self.cav_fscores[cname], fscore_thresh, ) if len(cavs_pass) == 0: logger.warning(f"No CAVs passed the F-score threshold {fscore_thresh:.3f}.") return # compute similarity matrix matrix_similarity = cosine_similarity(cavs_pass) # plot cm = sns.clustermap( matrix_similarity, xticklabels=False, yticklabels=False if attributions is not None else cavs_names_pass, cmap="bwr", vmin=-1, vmax=1, ) cm.gs.update(left=0, right=1) cm.ax_cbar.set_position([0.01, 0.9, 0.05, 0.05]) cavs_names_sorted = [ cavs_names_pass[i] for i in cm.dendrogram_col.reordered_ind ] heatmap_bbox = cm.ax_heatmap.get_position() ax_logs = [] log_ratios_by_attr = None if attributions is not None: log_ratios_by_attr = {} attributions = ( attributions if isinstance(attributions, (list, tuple)) else [ attributions, ] ) for i, attrs in enumerate(attributions): offset = 1 + i*0.2 ## plot log ratio plot ax_log = cm.figure.add_subplot() ax_log.set_position([offset, heatmap_bbox.y0, 0.2, heatmap_bbox.height]) log_ratios_reordered = [ self.tpcav_score_binary_log_ratio(cname, attrs) for cname in cavs_names_sorted ] log_ratios_by_attr[i] = log_ratios_reordered sns.barplot(y=cavs_names_sorted, x=log_ratios_reordered, orient="y", ax=ax_log) # set color of bar by value for idx in range(len((ax_log.containers[0]))): if ax_log.containers[0].datavalues[idx] > 0: ax_log.containers[0][idx].set_color("red") else: ax_log.containers[0][idx].set_color("blue") ax_log.set_xlim(left=-5, right=5) ax_log.yaxis.tick_right() ax_log.set_title(f"TCAV log ratio {i}") ax_log.get_yaxis().set_visible(False) ax_logs.append(ax_log) ax_logs[-1].get_yaxis().set_visible(True) # only show yaxis labels in the last log ratio plot # plot motif logo if provided meme file, try to look for pwm for every concept in the file if motif_meme_file is not None: if attributions is not None: ax_logs[-1].tick_params( axis="y", which="major", pad=cm.figure.get_size_inches()[0] * 0.3 * 72 # leave space for motif logos ) else: cm.ax_heatmap.tick_params( axis="y", which="major", pad=cm.figure.get_size_inches()[0] * 0.3 * 72 # leave space for motif logos ) gs_logo = gridspec.GridSpec(len(cavs_names_pass), 1) logo_height = heatmap_bbox.height/len(cavs_names_pass) for i, (cav_key, g) in enumerate(zip(cavs_names_sorted[::-1], gs_logo)): ax_logo = plt.subplot(g) ax_logo.set_position([1+len(ax_logs)*0.2+0.01, heatmap_bbox.y0+i*logo_height, 0.3+0.01, logo_height]) if cav_key is not None: seq_logo(cav_key, motif_meme_file=motif_meme_file, ax=ax_logo) else: ax_logo.axis('off') plt.savefig(output_path, dpi=300, bbox_inches="tight") row_reordered_ind = list(cm.dendrogram_row.reordered_ind) col_reordered_ind = list(cm.dendrogram_col.reordered_ind) matrix_similarity_sorted = matrix_similarity[np.ix_(row_reordered_ind, col_reordered_ind)] return { "concept_names": cavs_names_pass, "matrix_similarity": matrix_similarity, "row_reordered_ind": row_reordered_ind, "col_reordered_ind": col_reordered_ind, "concept_names_sorted_rows": cavs_names_sorted, "concept_names_sorted_cols": cavs_names_sorted, "matrix_similarity_sorted": matrix_similarity_sorted, "log_ratios_by_attr": log_ratios_by_attr, "output_path": str(output_path), } def seq_logo(key, motif_meme_file, ax, max_len=20): "plot a pwm logo" with open(motif_meme_file) as handle: motif_pwms = motifs.parse(handle, fmt='MINIMAL') motif_pwms = {utils.clean_motif_name(m.name): m.pwm for m in motif_pwms} pwm = motif_pwms.get(key) if pwm is not None: pwm_df = pd.DataFrame(pwm) motif_len = len(pwm_df) # Logomaker expects columns as A,C,G,T, so ensure correct order pwm_df = pwm_df[['A', 'C', 'G', 'T']] # Compute information content at each position (2 - entropy) def compute_ic(pwm_row): entropy = -sum([p * np.log2(p) if p > 0 else 0 for p in pwm_row]) return 2 - entropy # Compute IC matrix: IC_letter = p * IC_total ic_df = pwm_df.copy() for i in range(len(pwm_df)): ic_total = compute_ic(pwm_df.iloc[i]) ic_df.iloc[i] = pwm_df.iloc[i] * ic_total # Plot with logomaker x0, y0, width, height = ax.get_position().bounds ax.set_position([x0, y0, width * min(1., motif_len/max_len), height]) logo = logomaker.Logo(ic_df, color_scheme={'A': 'red', 'C': 'blue', 'G': 'orange', 'T': 'green'}, ax=ax) logo.ax.axis('off') else: logger.info(f'PWM not found for {key}') ax.axis('off') def load_motifs_from_meme(motif_meme_file): return {utils.clean_motif_name(m.name): m for m in motifs.parse(open(motif_meme_file), fmt="MINIMAL")} def load_motifs_from_custom_motif(motif_file): df = pd.read_table(motif_file, names=["motif_name", "consensus_seq"]) motifs_dict = {} for motif_name in np.unique(df.motif_name): motif_name = utils.clean_motif_name(motif_name) consensus = [s.upper() for s in df.loc[df.motif_name == motif_name, "consensus_seq"].tolist()] motifs_dict[motif_name] = consensus return motifs_dict def plot_reg(data, x, y, ax=None): ax = sns.regplot(data=data, x=x, y=y, ax=ax) res = stats.linregress(data[x], data[y]) ax.text(0.05, 0.9, f"R^2: {res.rvalue**2:.4f}\nP value: {res.pvalue}", transform=ax.transAxes) return res def compute_motif_auc_fscore(num_motif_insertions: List[int], cav_trainers: List[CavTrainer], motif_file: Optional[str] = None, motif_file_fmt: str = 'meme', output_path: Optional[str]=None): assert motif_file_fmt in ['meme', 'consensus'] cavs_fscores_df = pd.DataFrame({f"fscore_{nm}_insertions": cav_trainer.cav_fscores for nm, cav_trainer in zip(num_motif_insertions, cav_trainers)}) cavs_fscores_df['concept'] = list(cav_trainers[0].cav_fscores.keys()) def compute_auc_fscore(row): y = [row[f"fscore_{nm}_insertions"] for nm in num_motif_insertions] return np.trapz(y, num_motif_insertions) / ( num_motif_insertions[-1] - num_motif_insertions[0] ) cavs_fscores_df["AUC_fscores"] = cavs_fscores_df.apply(compute_auc_fscore, axis=1) # if motif instances are provided, fit linear regression curve to remove the dependency of f-scores on either information_content_GC or motif length and motif gc fig, axes = plt.subplots(nrows=1, ncols=3, sharey=True, figsize=(15, 4)) if motif_file is not None: if motif_file_fmt == 'meme': motifs_dict = load_motifs_from_meme(motif_file) def load_meme_motif_info(key): m = motifs_dict[key] return (len(m.consensus), m.relative_entropy.sum(), np.dot(np.array(m.pwm['G']) + np.array(m.pwm['C']), m.relative_entropy)) cavs_fscores_df[['motif_len', 'information_content', 'information_content_GC']] = cavs_fscores_df.apply(lambda x: load_meme_motif_info(x['concept']), axis=1, result_type='expand') model = LinearRegression() model.fit(cavs_fscores_df[['information_content_GC',]].to_numpy(), cavs_fscores_df['AUC_fscores'].to_numpy()[:, np.newaxis]) y_pred = model.predict(cavs_fscores_df[['information_content_GC',]].to_numpy()) residuals = cavs_fscores_df['AUC_fscores'].to_numpy() - y_pred.flatten() cavs_fscores_df['Motif_concept_sensitivity_score (AUC_fscores_residual)'] = residuals plot_reg(data=cavs_fscores_df, x='information_content_GC', y='AUC_fscores', ax=axes[0]) plot_reg(data=cavs_fscores_df, x='information_content', y='AUC_fscores', ax=axes[1]) plot_reg(data=cavs_fscores_df, x='motif_len', y='AUC_fscores', ax=axes[2]) else: motifs_dict = load_motifs_from_custom_motif(motif_file) def load_custom_motif_info(key): consensus_seqs = motifs_dict[key] avg_len = np.mean([len(s) for s in consensus_seqs]) avg_gc = np.mean([s.count('G') + s.count('C') for s in consensus_seqs]) return (avg_len, avg_gc) cavs_fscores_df[['avg_len', 'avg_gc']] = cavs_fscores_df.apply(lambda x: load_custom_motif_info(x['concept']), axis=1, result_type='expand') model = LinearRegression() model.fit(cavs_fscores_df[['avg_gc',]].to_numpy(), cavs_fscores_df['AUC_fscores'].to_numpy()[:, np.newaxis]) y_pred = model.predict(cavs_fscores_df[['avg_gc',]].to_numpy()) residuals = cavs_fscores_df['AUC_fscores'].to_numpy() - y_pred.flatten() cavs_fscores_df['Motif_concept_sensitivity_score (AUC_fscores_residual)'] = residuals plot_reg(data=cavs_fscores_df, x='avg_len', y='AUC_fscores', ax=axes[0]) plot_reg(data=cavs_fscores_df, x='avg_gc', y='AUC_fscores', ax=axes[1]) axes[2].axis('off') cavs_fscores_df.sort_values("Motif_concept_sensitivity_score (AUC_fscores_residual)", ascending=False, inplace=True) else: cavs_fscores_df.sort_values("AUC_fscores", ascending=False, inplace=True) if output_path is not None: plt.savefig(output_path, dpi=300, bbox_inches='tight') return cavs_fscores_df def run_tpcav( model, motif_file: str, genome_fasta: str, motif_file_fmt: str = 'meme', num_motif_insertions: List[int] = [4, 8, 16], motif_control_type="random", bed_seq_file: Optional[str] = None, bed_chrom_file: Optional[str] = None, layer_name: Optional[str]=None, layer=None, output_dir: str = "tpcav/", num_samples_for_pca=10, num_samples_for_cav=1000, input_window_length=1024, batch_size=8, num_workers=0, bws=None, input_transform_func=helper.fasta_chrom_to_one_hot_seq, num_pc: Union[str,int]='full', p=1, max_pending_jobs=4, save_cav_trainer=True, generate_html_report=True, html_report_fscore_thresh=0.9, seed=1001, backend='sklearn', device=None, ): """ One-stop function to compute CAVs on motif concepts and bed concepts, compute AUC of motif concept f-scores after correction """ assert motif_control_type in ["random", "permute"], "motif_control_type has to be one of [random, permute]!" assert motif_file_fmt in ['meme', 'consensus'], "motif_file_fmt has to be one of [meme, consensus]!" if not os.path.exists(output_dir): os.makedirs(output_dir) output_path = Path(output_dir) # create concept builder to generate concepts ## motif concepts motif_concepts_pairs = {} motif_concept_builders = {} num_motif_insertions.sort() for nm in num_motif_insertions: builder = ConceptBuilder( genome_fasta=genome_fasta, input_window_length=input_window_length, bws=bws, num_motifs=nm, include_reverse_complement=True, min_samples=num_samples_for_cav, batch_size=batch_size, num_workers=num_workers, rng_seed = seed, ) # use random regions as control builder.build_control() if motif_file_fmt == 'meme': # use meme motif PWMs to build motif concepts, one concept per motif # each pair of concepts include the motif concept and the permuted motif concept concepts_pairs = builder.add_meme_motif_concepts(str(motif_file)) else: concepts_pairs = builder.add_custom_motif_concepts(str(motif_file)) # apply transform to convert fasta sequences to one-hot encoded sequences builder.apply_transform(input_transform_func) motif_concepts_pairs[nm] = concepts_pairs motif_concept_builders[nm] = builder ## bed concepts (optional) if bed_seq_file is not None or bed_chrom_file is not None: bed_builder = ConceptBuilder( genome_fasta=genome_fasta, input_window_length=input_window_length, bws=bws, num_motifs=0, include_reverse_complement=True, min_samples=num_samples_for_cav, batch_size=batch_size, rng_seed = seed, ) # use random regions as control bed_builder.build_control() if bed_seq_file is not None: # build concepts from fasta sequences in bed file bed_builder.add_bed_sequence_concepts(bed_seq_file) if bed_chrom_file is not None: # build concepts from chromatin tracks in bed file bed_builder.add_bed_chrom_concepts(bed_chrom_file) # apply transform to convert fasta sequences to one-hot encoded sequences bed_builder.apply_transform(input_transform_func) else: bed_builder = None # create TPCAV model on top of the given model tpcav_model = TPCAV(model, layer_name=layer_name, layer=layer) # fit PCA on sampled all concept activations of the last builder (should have the most motifs) tpcav_model.fit_pca( concepts=motif_concept_builders[num_motif_insertions[-1]].concepts_for_pca() + bed_builder.concepts_for_pca() if bed_builder is not None else motif_concept_builders[num_motif_insertions[-1]].concepts_for_pca(), num_samples_per_concept=num_samples_for_pca, num_pc=num_pc, ) #torch.save(tpcav_model, output_path / "tpcav_model.pt") # create trainer for computing CAVs motif_cav_trainers = {} for nm in num_motif_insertions: cav_trainer = CavTrainer(tpcav_model, penalty="l2") if motif_control_type == 'permute': cav_trainer.train_concepts_pairs(motif_concepts_pairs[nm], num_samples_for_cav, output_dir=str(output_path / f"cavs_{nm}_motifs/"), num_processes=p, max_pending=max_pending_jobs, backend=backend, device=device) else: cav_trainer.set_control(motif_concept_builders[nm].control_concepts[0], num_samples=num_samples_for_cav) cav_trainer.train_concepts([c for c, _ in motif_concepts_pairs[nm]], num_samples_for_cav, output_dir=str(output_path / f"cavs_{nm}_motifs/"), num_processes=p, max_pending=max_pending_jobs, backend=backend, device=device) if save_cav_trainer: torch.save(cav_trainer, str(output_path / f"cavs_{nm}_motifs/cav_trainer.pt")) motif_cav_trainers[nm] = cav_trainer if bed_builder is not None: bed_cav_trainer = CavTrainer(tpcav_model, penalty="l2") bed_cav_trainer.set_control( bed_builder.control_concepts[0], num_samples=num_samples_for_cav ) bed_cav_trainer.train_concepts( bed_builder.concepts, num_samples_for_cav, output_dir=str(output_path / f"cavs_bed_concepts/"), num_processes=p, backend=backend, device=device ) if save_cav_trainer: torch.save(bed_cav_trainer, str(output_path / f"cavs_bed_concepts/cav_trainer.pt")) else: bed_cav_trainer = None if len(num_motif_insertions) > 1: cavs_fscores_df = compute_motif_auc_fscore(num_motif_insertions, list(motif_cav_trainers.values()), motif_file=motif_file, motif_file_fmt=motif_file_fmt) else: cavs_fscores_df = None if generate_html_report: report.generate_tcav_html_report(str(output_path / f"report.html"), motif_cav_trainers, extra_cav_trainers = {'bed concepts': bed_cav_trainer}, motif_file=motif_file, motif_file_fmt=motif_file_fmt, fscore_thresh=html_report_fscore_thresh) return cavs_fscores_df, motif_cav_trainers, bed_cav_trainer TPCAV-0.8.4/tpcav/concepts.py000066400000000000000000000315461516353663300157310ustar00rootroot00000000000000import logging from typing import Dict, Iterable, List, Optional, Sequence, Tuple, Union import numpy as np import pandas as pd import pyfaidx import seqchromloader as scl import webdataset as wds from Bio import motifs as Bio_motifs from captum.concept import Concept from torch.utils.data import DataLoader from . import helper, utils logger = logging.getLogger(__name__) class _PairedLoader: """Allow repeated iteration over paired dataloaders.""" def __init__(self, seq_dl: Iterable, chrom_dl: Iterable) -> None: self.seq_dl = seq_dl self.chrom_dl = chrom_dl self.apply_func_list = [] def apply(self, apply_func): self.apply_func_list.append(apply_func) def __iter__(self): for inputs in zip(self.seq_dl, self.chrom_dl): for apply_func in self.apply_func_list: inputs = apply_func(*inputs) yield inputs def _construct_motif_concept_dataloader_from_control( control_seq_df: pd.DataFrame, genome_fasta: str, motifs: Sequence, num_motifs: int, batch_size: int, num_workers: int, start_buffer=0, end_buffer=0 ) -> DataLoader: """Mirror the motif-based dataloader logic used in the TCAV script.""" datasets = [] for motif in motifs: ds = utils.IterateSeqDataFrame( control_seq_df, genome_fasta, motif=motif, num_motifs=num_motifs, start_buffer=start_buffer, end_buffer=end_buffer, print_warning=False, infinite=False, ) datasets.append(ds) mixed_dl = DataLoader( wds.RandomMix(datasets), batch_size=batch_size, num_workers=num_workers, drop_last=False, ) return mixed_dl class ConceptBuilder: """Build and store concepts/control concepts in a reusable, programmatic way.""" def __init__( self, genome_fasta: str, input_window_length: int = 1024, bws: Optional[List[str]] = None, batch_size: int = 8, num_workers: int = 0, num_motifs: int = 12, include_reverse_complement: bool = False, min_samples: int = 5000, rng_seed: int = 1001, concept_name_suffix: str = "", ) -> None: self.genome_fasta = genome_fasta pyfaidx.Fasta( genome_fasta, build_index=True ) # validate genome fasta file and build index if needed self.genome_size_file = self.genome_fasta + ".fai" self.input_window_length = input_window_length self.bws = bws or [] self.batch_size = batch_size self.num_workers = num_workers self.num_motifs = num_motifs self.include_reverse_complement = include_reverse_complement self.min_samples = min_samples self.rng_seed = rng_seed self.concept_name_suffix = concept_name_suffix self.control_regions = None self.control_concepts: List[Concept] = [] self.motif_permute_concepts: List[Concept] = [] self.concepts: List[Concept] = [] self.metadata: Dict[str, object] = {} self._next_concept_id = 0 self._control_seq_loader: Optional[Iterable] = None self._control_chrom_loader: Optional[Iterable] = None def build_control(self, name: str = "random_regions") -> Concept: """Create the background/control concept.""" control_regions = scl.random_coords( gs=self.genome_size_file, l=self.input_window_length, n=self.min_samples, ) control_regions["label"] = -1 control_regions["strand"] = "+" self.control_regions = control_regions concept = Concept( id=self._reserve_id(is_control=True), name=name + self.concept_name_suffix, data_iter=_PairedLoader(self._control_seq_dl(), self._control_chrom_dl()), ) self.control_concepts = [concept] self.metadata["control_regions"] = control_regions return concept def _control_seq_dl(self): if self.control_regions is None: raise ValueError("Call build_control before creating control regions.") seq_fasta_iter = helper.DataFrame2FastaIterator( self.control_regions, self.genome_fasta, batch_size=self.batch_size ) return seq_fasta_iter def _control_chrom_dl(self): if self.control_regions is None: raise ValueError("Call build_control before creating control regions.") chrom_iter = helper.DataFrame2ChromTracksIterator( self.control_regions, self.bws, batch_size=self.batch_size, ) return chrom_iter def add_custom_motif_concepts( self, motif_table: str, control_regions: Optional[pd.DataFrame] = None, build_permute_control=True ) -> Union[List[Concept], List[Tuple[Concept]]]: """Add concepts from a tab-delimited motif table: motif_nameconsensus.""" df = pd.read_table(motif_table, names=["motif_name", "consensus_seq"]) added = [] for motif_name in np.unique(df.motif_name): motif_name = utils.clean_motif_name(motif_name) consensus = df.loc[df.motif_name == motif_name, "consensus_seq"].tolist() motifs = [] for idx, cons in enumerate(consensus): motif = utils.ConsensusMotif(f"{motif_name}_{idx}", cons) motifs.append(motif) concept = self.build_motif_concept(motifs, motif_name, control_regions=control_regions) self.concepts.append(concept) # build permute control if specified if build_permute_control: motifs_permuted = [utils.PermutedConsensusMotif(m.name + "_perm", m.consensus) for m in motifs] concept_permuted = self.build_motif_concept(motifs_permuted, motif_name + "_perm", control_regions=control_regions) self.motif_permute_concepts.append(concept_permuted) added.append((concept, concept_permuted)) else: added.append(concept) return added def add_meme_motif_concepts( self, meme_file: str, control_regions: Optional[pd.DataFrame] = None, build_permute_control=True) -> Union[List[Concept], List[Tuple[Concept]]]: """Add concepts from a MEME minimal-format motif file.""" added = [] with open(meme_file) as handle: for motif in Bio_motifs.parse(handle, fmt="MINIMAL"): motif_name = utils.clean_motif_name(motif.name) concept = self.build_motif_concept([utils.BioMotifWrapped(motif),], motif_name, control_regions=control_regions) self.concepts.append(concept) # build permute control if specified if build_permute_control: motif_permuted = utils.PermutedPWMMotif(motif) concept_permuted = self.build_motif_concept([motif_permuted,], motif_name + "_perm", control_regions=control_regions) self.motif_permute_concepts.append(concept_permuted) added.append((concept, concept_permuted)) else: added.append(concept) return added def build_motif_concept(self, motifs, concept_name, control_regions=None, start_buffer=0, end_buffer=0): if control_regions is None: if not self.control_concepts: raise ValueError("Call build_control or pass control_regions first.") control_regions = self.metadata.get("control_regions") assert control_regions is not None if self.include_reverse_complement: motifs.extend([m.reverse_complement() for m in motifs]) seq_dl = _construct_motif_concept_dataloader_from_control( control_regions, self.genome_fasta, motifs=motifs, num_motifs=self.num_motifs, batch_size=self.batch_size, num_workers=self.num_workers, start_buffer=start_buffer, end_buffer=end_buffer ) concept = Concept( id=self._reserve_id(), name=concept_name + self.concept_name_suffix, data_iter=_PairedLoader(seq_dl, self._control_chrom_dl()), ) return concept def add_bed_sequence_concepts(self, bed_path: str) -> List[Concept]: """Add concepts backed by BED sequences with concept_name in column 5.""" added: List[Concept] = [] bed_df = pd.read_table( bed_path, header=None, usecols=[0, 1, 2, 3, 4], names=["chrom", "start", "end", "strand", "concept_name"], ) added.extend(self.add_dataframe_sequence_concepts(bed_df)) return added def add_dataframe_sequence_concepts(self, dataframe: pd.DataFrame) -> List[Concept]: """Add concepts backed by BED sequences with concept_name in column 5.""" dataframe = helper.center_dataframe_regions(dataframe, self.input_window_length) added: List[Concept] = [] for concept_name in dataframe.concept_name.unique(): concept_df = dataframe.loc[dataframe.concept_name == concept_name] if len(concept_df) < self.min_samples: logger.warning( "Concept %s has %s samples, fewer than min_samples=%s; skipping", concept_name, len(concept_df), self.min_samples, ) continue seq_fasta_iter = helper.dataframe_to_fasta_iter( concept_df.sample(n=self.min_samples, random_state=self.rng_seed), self.genome_fasta, batch_size=self.batch_size, ) concept = Concept( id=self._reserve_id(), name=concept_name + self.concept_name_suffix, data_iter=_PairedLoader(seq_fasta_iter, self._control_chrom_dl()), ) self.concepts.append(concept) added.append(concept) return added def add_bed_chrom_concepts(self, bed_path: str) -> List[Concept]: """Add concepts backed by chromatin signal bigwigs and BED coordinates.""" added: List[Concept] = [] bed_df = pd.read_table( bed_path, header=None, usecols=[0, 1, 2, 3, 4], names=["chrom", "start", "end", "strand", "concept_name"], ) added.extend(self.add_dataframe_chrom_concepts(bed_df)) return added def add_dataframe_chrom_concepts(self, dataframe) -> List[Concept]: """Add concepts backed by chromatin signal bigwigs and BED coordinates.""" dataframe = helper.center_dataframe_regions(dataframe, self.input_window_length) added: List[Concept] = [] for concept_name in dataframe.concept_name.unique(): concept_df = dataframe.loc[dataframe.concept_name == concept_name] if len(concept_df) < self.min_samples: logger.warning( "Concept %s has %s samples, fewer than min_samples=%s; skipping", concept_name, len(concept_df), self.min_samples, ) continue chrom_dl = helper.dataframe_to_chrom_tracks_iter( concept_df.sample(n=self.min_samples, random_state=self.rng_seed), self.bws, batch_size=self.batch_size, ) concept = Concept( id=self._reserve_id(), name=concept_name + self.concept_name_suffix, data_iter=_PairedLoader(self._control_seq_dl(), chrom_dl), ) self.concepts.append(concept) added.append(concept) return added def all_concepts(self) -> List[Concept]: """Return test + control + permute concepts.""" return [*self.concepts, *self.control_concepts, *self.motif_permute_concepts] def concepts_for_pca(self) -> List[Concept]: """Return test + control concepts.""" return [*self.concepts, *self.control_concepts] def summary(self) -> Dict[str, object]: """Lightweight run metadata.""" return { "num_concepts": len(self.concepts), "num_control": len(self.control_concepts), "concept_names": [c.name for c in self.concepts], "control_names": [c.name for c in self.control_concepts], "input_window_length": self.input_window_length, "num_motifs": self.num_motifs, "bigwigs": self.bws, } def _reserve_id(self, is_control: bool = False) -> int: cid = -self._next_concept_id if is_control else self._next_concept_id self._next_concept_id += 1 return cid def apply_transform(self, transform): """Apply a transform function to all concepts""" for c in self.all_concepts(): c.data_iter.apply(transform) TPCAV-0.8.4/tpcav/helper.py000066400000000000000000000232741516353663300153710ustar00rootroot00000000000000#!/usr/bin/env python3 """ Lightweight data loading helpers for sequences and chromatin tracks. """ from typing import Iterable, List, Optional import itertools import logging import pyBigWig import re import sys import numpy as np import pandas as pd import seqchromloader as scl import torch from deeplift.dinuc_shuffle import dinuc_shuffle from pyfaidx import Fasta from seqchromloader.utils import dna2OneHot, extract_bw logger = logging.getLogger(__name__) def load_bed_and_center(bed_file: str, window: int) -> pd.DataFrame: """ Load a BED file and center the regions to a fixed window size. """ bed_df = pd.read_table(bed_file, usecols=[0, 1, 2], names=["chrom", "start", "end"]) return center_dataframe_regions(bed_df, window) def center_dataframe_regions(df: pd.DataFrame, window: int) -> pd.DataFrame: """ Center the regions in a DataFrame to a fixed window size, keep other columns. Put chrom, start, end as the first 3 columns. """ df = df.copy() df["center"] = ((df["start"] + df["end"]) // 2).astype(int) df["start"] = df["center"] - (window // 2) df["end"] = df["start"] + window df = df.drop(columns=["center"]) cols = ["chrom", "start", "end"] + [ col for col in df.columns if col not in ["chrom", "start", "end"] ] df = df[cols] return df def bed_to_fasta_iter( bed_file: str, genome_fasta: str, batch_size: int ) -> Iterable[List[str]]: """ Yield sequences from a BED file as fasta strings. """ bed_df = pd.read_table(bed_file, usecols=[0, 1, 2], names=["chrom", "start", "end"]) yield from dataframe_to_fasta_iter(bed_df, genome_fasta, batch_size) def dataframe_to_fasta_iter( df: pd.DataFrame, genome_fasta: str, batch_size: int ) -> Iterable[List[str]]: """ Yield sequences from a DataFrame with columns [chrom, start, end]. """ genome = Fasta(genome_fasta) fasta_seqs = [] for row in df.itertuples(index=False): seq = str(genome[row.chrom][row.start : row.end]).upper() if len(seq) != (row.end - row.start): raise ValueError( f"Extract Fasta sequence length mismatch with region coordinate length {row.chrom}:{row.start}-{row.end}" ) fasta_seqs.append(seq) if len(fasta_seqs) == batch_size: yield fasta_seqs fasta_seqs = [] if fasta_seqs: yield fasta_seqs class DataFrame2FastaIterator: """ Iterator class to yield sequences from a DataFrame with columns [chrom, start, end]. """ def __init__(self, df: pd.DataFrame, genome_fasta: str, batch_size: int): self.genome_fasta = genome_fasta self.df = df self.batch_size = batch_size def __iter__(self): return dataframe_to_fasta_iter( self.df, genome_fasta=self.genome_fasta, batch_size=self.batch_size ) def bed_to_chrom_tracks_iter( bed_file: str, genome_fasta: str, bigwigs: List[str] ) -> Iterable[torch.Tensor]: """ Yield chromatin tracks for BED regions using bigwig files. Each batch is shaped [batch, num_tracks, window]. """ bed_df = pd.read_table(bed_file, usecols=[0, 1, 2], names=["chrom", "start", "end"]) yield from dataframe_to_chrom_tracks_iter(bed_df, genome_fasta, bigwigs) def dataframe_to_chrom_tracks_iter( df: pd.DataFrame, bigwigs: Optional[List[str]], batch_size: int = 1, ) -> Iterable[torch.Tensor]: """ Yield chromatin tracks for regions from a DataFrame using bigwig files. """ if bigwigs is not None and len(bigwigs) > 0: chrom_arrs = [] for row in df.itertuples(index=False): chrom = extract_bw( row.chrom, row.start, row.end, getattr(row, "strand", "+"), bigwigs ) chrom_arrs.append(chrom) if batch_size is not None and len(chrom_arrs) == batch_size: yield torch.tensor(np.stack(chrom_arrs)) chrom_arrs = [] if chrom_arrs: yield torch.tensor(np.stack(chrom_arrs)) else: while True: yield torch.full((batch_size, 1), torch.nan) class DataFrame2ChromTracksIterator: """ Iterator class to yield chromatin tracks from a DataFrame with columns [chrom, start, end]. """ def __init__( self, df: pd.DataFrame, bigwigs: Optional[List[str]], batch_size: int = 1, ): self.bigwigs = bigwigs self.df = df self.batch_size = batch_size def __iter__(self): return dataframe_to_chrom_tracks_iter( self.df, bigwigs=self.bigwigs, batch_size=self.batch_size, ) def fasta_to_one_hot_sequences(seqs: List[str]) -> torch.Tensor: """ Return one-hot encoded numpy arrays [4, L] for list of fasta sequences. """ return torch.tensor(np.stack([dna2OneHot(seq) for seq in seqs])) def random_regions_dataframe( genome_size_file: str, window: int, n: int, seed: int = 1 ) -> pd.DataFrame: """ Generate random regions as a DataFrame with columns [chrom, start, end]. """ return scl.random_coords(gs=genome_size_file, l=window, n=n, seed=seed)[ ["chrom", "start", "end"] ] def dinuc_shuffle_sequences( seqs: Iterable[str], num_shuffles: int = 10, seed: int = 1 ) -> List[List[str]]: """ For each fasta sequence, yield a list of dinucleotide-shuffled sequences. """ rng = np.random.RandomState(seed) results = [] for seq in seqs: shuffles = dinuc_shuffle( seq, num_shufs=num_shuffles, rng=rng, ) results.append(shuffles) return results def fasta_chrom_to_one_hot_seq(seq, chrom): return (fasta_to_one_hot_sequences(seq),) def write_attrs_to_bw(arrs, regions, genome_info, bigwig_fn, smooth=False): """ write the attributions into bigwig files shape of arrs should be (# samples, length) Note: If regions overlap with each other, only base pairs not covered by previous regions would be assigned current region's attribution score """ # write header into bigwig bw = pyBigWig.open(bigwig_fn, "w") heads = [] with open(genome_info, "r") as f: for line in f: chrom, length = line.strip().split("\t")[:2] heads.append((chrom, int(length))) heads = sorted(heads, key=lambda x: x[0]) bw.addHeader(heads) # sort regions and arrs assert len(regions) == len(arrs) def get_key(x): chrom, start, end = re.split("[:-]", regions[x]) start = int(start) return chrom, start idx_sort = sorted(range(len(regions)), key=get_key) regions = [regions[i] for i in idx_sort] arrs = arrs[idx_sort] # construct iterables it = zip(arrs, regions) it = itertools.chain( it, zip([np.array([-1000])], ["chrNone:10-100"]) ) # add pseudo region to make sure the last entry will be added to bw file arr, lastRegion = next(it) lastChrom, start, end = re.split(r"[:-]", lastRegion) start = int(start) end = int(end) # extend coordinates if attribution arr is larger than interval length if end - start < len(arr): logger.warning( "Interval length is smaller than attribution array length, expand it!" ) diff = len(arr) - (end - start) if diff % 2 != 0: raise Exception( "The difference between attribution array length and interval length is not even! Can't do symmetric extension in this case, exiting..." ) start -= int(diff / 2) end += int(diff / 2) elif end - start == len(arr): diff = 0 else: raise Exception( "Interval length is larger than attribution array length, this is not expected situation, exiting..." ) arr_store_tmp = arr for arr, region in it: rchrom, rstart, rend = re.split(r"[:-]", region) rstart = int(rstart) rend = int(rend) # extend coordinates if attribution arr is larger than interval length rstart -= int(diff / 2) rend += int(diff / 2) if rstart < 0: break if end <= rstart or rchrom != lastChrom: arr_store_tmp = ( np.convolve(arr_store_tmp, np.ones(10) / 10, mode="same") if smooth else arr_store_tmp ) try: bw.addEntries( lastChrom, np.arange(start, end, dtype=np.int64), values=arr_store_tmp.astype(np.float64), span=1, ) except: print(lastChrom) print(start) print(end) print(arr_store_tmp.shape, arr_store_tmp.dtype) print(rchrom) print(rstart) print(rend) raise Exception( "Runtime error when adding entries to bigwig file, see above messages for region info" ) lastChrom = rchrom start = rstart end = rend arr_store_tmp = arr # get uncovered interval (defined by start coordinate `start` and relative start coordinate `start_idx`) else: assert ( end > rstart and rchrom == lastChrom ) # just double check make sure two intervals are overlapped start_idx = end - rstart end = rend try: arr_store_tmp = np.concatenate([arr_store_tmp, arr[start_idx:]]) except TypeError: print(start_idx) print(rstart, rend, rchrom, start, end, lastChrom) print(arr_store_tmp.shape, print(arr.shape)) sys.exit(1) bw.close() TPCAV-0.8.4/tpcav/logging_utils.py000066400000000000000000000004311516353663300167460ustar00rootroot00000000000000import logging def set_verbose(level: str = "INFO") -> None: """ Set logging level for the tpcav_package (and root logger). """ lvl = getattr(logging, level.upper(), logging.INFO) logging.getLogger().setLevel(lvl) logging.getLogger("tpcav").setLevel(lvl) TPCAV-0.8.4/tpcav/report.py000066400000000000000000001343511516353663300154240ustar00rootroot00000000000000from __future__ import annotations import base64 import datetime as _dt import html as _html import io import json import mimetypes import re from pathlib import Path from typing import Any, Mapping, Optional, Union def _read_as_data_uri(path: Path) -> str: mime, _ = mimetypes.guess_type(str(path)) if mime is None: mime = "application/octet-stream" data = base64.b64encode(path.read_bytes()).decode("ascii") return f"data:{mime};base64,{data}" def _render_kv_table(rows: list[tuple[str, str]]) -> str: cells = "\n".join( f"{_html.escape(k)}{_html.escape(v)}" for k, v in rows ) return f"{cells}
" def _render_df_table(df: Any, max_rows: int = 200) -> str: try: df2 = df.head(max_rows) return df2.to_html(index=False, escape=False, classes="df") except Exception: return f"
{_html.escape(str(df)[:20000])}
" def _json_for_html(payload: Any) -> str: return json.dumps(payload, ensure_ascii=False).replace("<", "\\u003c") def _maybe_build_motif_logo_data_uris( motif_meme_file: Optional[Union[str, Path]], concept_names: list[str] ) -> dict[str, str]: if motif_meme_file is None or not concept_names: return {} try: from Bio import motifs import matplotlib.pyplot as plt import numpy as np import pandas as pd import logomaker from . import utils as _utils except Exception: return {} motif_meme_file = Path(motif_meme_file) with motif_meme_file.open() as handle: motif_pwms = motifs.parse(handle, fmt="MINIMAL") pwm_by_name = {_utils.clean_motif_name(m.name): m.pwm for m in motif_pwms} def compute_ic(row: Any) -> float: entropy = -sum([p * np.log2(p) if p > 0 else 0 for p in row]) return 2 - entropy out: dict[str, str] = {} for name in concept_names: pwm = pwm_by_name.get(_utils.clean_motif_name(str(name))) if pwm is None: continue pwm_df = pd.DataFrame(pwm)[["A", "C", "G", "T"]] ic_df = pwm_df.copy() for i in range(len(pwm_df)): ic_total = compute_ic(pwm_df.iloc[i]) ic_df.iloc[i] = pwm_df.iloc[i] * ic_total fig, ax = plt.subplots(figsize=(2.2, 0.55)) logomaker.Logo( ic_df, color_scheme={"A": "red", "C": "blue", "G": "orange", "T": "green"}, ax=ax, ) ax.axis("off") buf = io.BytesIO() fig.savefig(buf, format="png", dpi=200, bbox_inches="tight", transparent=True) plt.close(fig) out[str(name)] = "data:image/png;base64," + base64.b64encode( buf.getvalue() ).decode("ascii") return out def generate_tpcav_html_report( output_html_path: Union[str, Path], motif_cav_trainers: Optional[Mapping[Union[int, str], Any]] = None, non_motif_cav_trainers: Optional[Mapping[str, Any]] = None, extra_cav_trainers: Optional[Mapping[str, Any]] = None, attributions: Optional[Union[Any, list[Any]]] = None, motif_file: Optional[Union[str, Path]] = None, motif_file_fmt: str = "meme", fscore_thresh: float = 0.8, top_motif_concepts: int = 10, title: str = "TPCAV report", embed_images: bool = False, ) -> Path: """ Generate a standalone HTML report from CavTrainer objects. Args: output_html_path: Path to write the HTML report. motif_cav_trainers: Dict mapping "# motif insertions" -> CavTrainer. non_motif_cav_trainers: Optional dict mapping a display name -> CavTrainer. extra_cav_trainers: Deprecated alias for non_motif_cav_trainers. attributions: Optional attribution tensor (or list of tensors) shared across all heatmaps (enables log-ratio barplots). motif_file: Motif file path passed through to compute/plot helpers when relevant. motif_file_fmt: 'meme' or 'consensus' (used by compute_motif_auc_fscore). fscore_thresh: Threshold passed to CavTrainer.plot_cavs_similaritiy_heatmap. title: Report title. embed_images: If True, embed PNGs as base64 data URIs; otherwise link to files. """ output_html_path = Path(output_html_path) assets_dir = output_html_path.parent / f"{output_html_path.stem}_assets" assets_dir.mkdir(parents=True, exist_ok=True) motif_cav_trainers = dict(motif_cav_trainers or {}) non_motif_cav_trainers = dict(non_motif_cav_trainers or {}) extra_cav_trainers = dict(extra_cav_trainers or {}) if non_motif_cav_trainers and extra_cav_trainers: raise ValueError("Provide only one of `non_motif_cav_trainers` or `extra_cav_trainers`.") if extra_cav_trainers and not non_motif_cav_trainers: non_motif_cav_trainers = extra_cav_trainers now = _dt.datetime.now().astimezone().strftime("%Y-%m-%d %H:%M:%S %Z") motif_auc_plot_path = assets_dir / "motif_auc_fscore_regression.png" motif_auc_df = None motif_auc_plot_exists = False ranked_motif_concepts: list[str] = [] motif_insertions: list[int] = [] if motif_cav_trainers: try: from .cavs import compute_motif_auc_fscore # local import (torch dependency) except Exception as e: # pragma: no cover raise ImportError( "Failed to import `compute_motif_auc_fscore` from `tpcav.cavs`. " "Ensure your environment can import torch and tpcav." ) from e try: import matplotlib.pyplot as plt except Exception: plt = None try: sorted_keys = sorted(motif_cav_trainers.keys(), key=lambda x: int(x)) motif_insertions = [int(k) for k in sorted_keys] trainers = [motif_cav_trainers[k] for k in sorted_keys] motif_auc_df = compute_motif_auc_fscore( num_motif_insertions=motif_insertions, cav_trainers=trainers, motif_file=str(motif_file) if motif_file is not None else None, motif_file_fmt=motif_file_fmt, output_path=str(motif_auc_plot_path), ) motif_auc_plot_exists = motif_auc_plot_path.exists() if motif_auc_df is not None and "concept" in motif_auc_df.columns: ranked_motif_concepts = motif_auc_df["concept"].astype(str).tolist() finally: if plt is not None: plt.close("all") # ----------------------------------------------------------------------------- # 1) Gather CavTrainers # ----------------------------------------------------------------------------- all_trainers: dict[str, Any] = {f"motifs_{k}": v for k, v in motif_cav_trainers.items()} all_trainers.update(non_motif_cav_trainers) motif_rank: dict[str, int] = {c: i + 1 for i, c in enumerate(ranked_motif_concepts)} motif_concept_set = set(ranked_motif_concepts) motif_weight_source = None if motif_cav_trainers and motif_insertions: motif_weight_source = motif_cav_trainers[max(motif_insertions)] # ----------------------------------------------------------------------------- # 2) Concept info table (motif first, then extras) # ----------------------------------------------------------------------------- concept_rows: list[dict[str, Any]] = [] if motif_auc_df is not None: for row in motif_auc_df.to_dict(orient="records"): concept = str(row.get("concept")) out: dict[str, Any] = { "concept": concept, "type": "motif", "rank": motif_rank.get(concept), "source": "motif", } for nm in motif_insertions: out[f"fscore_{nm}_insertions"] = row.get(f"fscore_{nm}_insertions") out["AUC_fscores"] = row.get("AUC_fscores") out["Motif_concept_sensitivity_score (AUC_fscores_residual)"] = row.get("Motif_concept_sensitivity_score (AUC_fscores_residual)") concept_rows.append(out) # Track all non-motif concepts (concept, source). extra_concepts: list[tuple[str, str, float]] = [] for trainer_name, trainer in non_motif_cav_trainers.items(): cav_fscores = getattr(trainer, "cav_fscores", None) if not isinstance(cav_fscores, dict): continue for concept, fscore in cav_fscores.items(): concept = str(concept) if concept in motif_concept_set: continue fs = float(fscore) extra_concepts.append((concept, str(trainer_name), fs)) concept_rows.append( { "concept": concept, "type": "non-motif", "rank": None, "source": str(trainer_name), "fscore": fs, } ) def _concept_sort_key(r: dict[str, Any]) -> tuple: if r.get("type") == "motif": return (0, r.get("rank") or 10**9) return (1, -float(r.get("fscore") or -1), r.get("concept", "")) concept_rows.sort(key=_concept_sort_key) # ----------------------------------------------------------------------------- # 3) Select concepts for heatmaps # ----------------------------------------------------------------------------- # No thresholding/selection logic beyond taking the top N motifs by ranking. top_motif_n = max(0, int(top_motif_concepts)) selected_motif_concepts: list[str] = ranked_motif_concepts[:top_motif_n] # Include ALL non-motif concepts (disambiguate duplicates by appending source). selected_extra_concepts: list[tuple[str, str, float]] = extra_concepts[:] # ----------------------------------------------------------------------------- # 4) Build synthetic CavTrainers + heatmap data (motif / non-motif / merged) # ----------------------------------------------------------------------------- heatmap_png_paths = { "motif": assets_dir / "cavs_similarity_heatmap__motif.png", "non-motif": assets_dir / "cavs_similarity_heatmap__non_motif.png", "merged": assets_dir / "cavs_similarity_heatmap__merged.png", } heatmap_data: dict[str, Any] = {"motif": None, "non-motif": None, "merged": None} if all_trainers: base_trainer = ( motif_weight_source if motif_weight_source is not None else next(iter(all_trainers.values())) ) try: from .cavs import CavTrainer def _new_trainer() -> Any: t = CavTrainer( getattr(base_trainer, "tpcav"), penalty=getattr(base_trainer, "penalty", "l2"), ) t.cav_weights = {} t.cav_fscores = {} t.cavs_list = [] return t motif_trainer = _new_trainer() extra_trainer = _new_trainer() merged_trainer = _new_trainer() # fill motif trainer if motif_weight_source is not None: for c in selected_motif_concepts: if c not in getattr(motif_weight_source, "cav_weights", {}): continue w = motif_weight_source.cav_weights[c] motif_trainer.cav_weights[c] = w motif_trainer.cav_fscores[c] = float( getattr(motif_weight_source, "cav_fscores", {}).get(c, 0.0) ) motif_trainer.cavs_list.append(w) # fill non-motif trainer (disambiguate duplicates) label_counts: dict[str, int] = {} for concept, source_name, fs in selected_extra_concepts: src_trainer = non_motif_cav_trainers.get(source_name) if src_trainer is None: continue if concept not in getattr(src_trainer, "cav_weights", {}): continue label = concept label_counts[label] = label_counts.get(label, 0) + 1 if label_counts[label] > 1: label = f"{concept} ({source_name})" w = src_trainer.cav_weights[concept] extra_trainer.cav_weights[label] = w extra_trainer.cav_fscores[label] = float(fs) extra_trainer.cavs_list.append(w) # merged = motif + non-motif for c, w in motif_trainer.cav_weights.items(): merged_trainer.cav_weights[c] = w merged_trainer.cav_fscores[c] = float(motif_trainer.cav_fscores.get(c, 0.0)) merged_trainer.cavs_list.append(w) for c, w in extra_trainer.cav_weights.items(): merged_trainer.cav_weights[c] = w merged_trainer.cav_fscores[c] = float(extra_trainer.cav_fscores.get(c, 0.0)) merged_trainer.cavs_list.append(w) motif_meme_file = None if motif_file is not None and motif_file_fmt == "meme": motif_meme_file = str(motif_file) heatmap_data["motif"] = motif_trainer.plot_cavs_similaritiy_heatmap( attributions=attributions, concept_list=list(motif_trainer.cav_weights.keys()), fscore_thresh=fscore_thresh, motif_meme_file=motif_meme_file, output_path=str(heatmap_png_paths["motif"]), ) heatmap_data["non-motif"] = extra_trainer.plot_cavs_similaritiy_heatmap( attributions=attributions, concept_list=list(extra_trainer.cav_weights.keys()), fscore_thresh=fscore_thresh, motif_meme_file=None, output_path=str(heatmap_png_paths["non-motif"]), ) heatmap_data["merged"] = merged_trainer.plot_cavs_similaritiy_heatmap( attributions=attributions, concept_list=list(merged_trainer.cav_weights.keys()), fscore_thresh=fscore_thresh, motif_meme_file=motif_meme_file, output_path=str(heatmap_png_paths["merged"]), ) except Exception: heatmap_data = {"motif": None, "non-motif": None, "merged": None} # ----------------------------------------------------------------------------- # 5) Build JS payload (used by Plotly) # ----------------------------------------------------------------------------- motif_logo_concepts = selected_motif_concepts[:] motif_logo_dict = _maybe_build_motif_logo_data_uris( motif_file if motif_file_fmt == "meme" else None, motif_logo_concepts, ) js_payload: dict[str, Any] = { "motif_file_fmt": motif_file_fmt, "motif_auc_rows": motif_auc_df.to_dict(orient="records") if motif_auc_df is not None else None, "motif_insertions": motif_insertions, "concept_rows": concept_rows, "motif_logos": motif_logo_dict, "heatmaps": { "motif": {"heatmap": None, "heatmap_div_id": "heatmap__motif", "hover_div_id": "hover__motif"}, "non-motif": {"heatmap": None, "heatmap_div_id": "heatmap__non_motif", "hover_div_id": "hover__non_motif"}, "merged": {"heatmap": None, "heatmap_div_id": "heatmap__merged", "hover_div_id": "hover__merged"}, }, } def _to_list(x: Any) -> Any: try: return x.tolist() except Exception: return x for key in ("motif", "non-motif", "merged"): if isinstance(heatmap_data.get(key), dict): js_payload["heatmaps"][key]["heatmap"] = { "concept_names_sorted_rows": heatmap_data[key].get("concept_names_sorted_rows"), "concept_names_sorted_cols": heatmap_data[key].get("concept_names_sorted_cols"), "matrix_similarity_sorted": _to_list(heatmap_data[key].get("matrix_similarity_sorted")), "log_ratios_by_attr": heatmap_data[key].get("log_ratios_by_attr"), } # expose png paths for optional embedding/debug js_payload["heatmap_pngs"] = {k: str(p) for k, p in heatmap_png_paths.items()} # ----------------------------------------------------------------------------- # 6) Per-trainer details (collapsed section) # ----------------------------------------------------------------------------- trainer_sections: list[str] = [] for name, trainer in all_trainers.items(): meta_rows = [ ("name", str(name)), ("penalty", str(getattr(trainer, "penalty", ""))), ("num_cavs", str(len(getattr(trainer, "cav_weights", {}) or {}))), ] table_html = "" cav_fscores = getattr(trainer, "cav_fscores", None) if isinstance(cav_fscores, dict) and cav_fscores: try: import pandas as pd df = pd.DataFrame( [{"concept": k, "fscore": float(v)} for k, v in cav_fscores.items()] ).sort_values("fscore", ascending=False) table_html = _render_df_table(df, max_rows=200) except Exception: pairs = sorted(cav_fscores.items(), key=lambda kv: kv[1], reverse=True) preview = "\n".join(f"{k}\t{v}" for k, v in pairs[:200]) table_html = f"
{_html.escape(preview)}
" trainer_sections.append( "\n".join( [ f"

{_html.escape(str(name))}

", _render_kv_table(meta_rows), "
", "CAV F-scores", table_html or "
(no cav_fscores found)
", "
", "
", ] ) ) if motif_auc_plot_exists: if embed_images: motif_src = _read_as_data_uri(motif_auc_plot_path) motif_auc_fig_html = f"Motif AUC regression" else: motif_src = _html.escape(motif_auc_plot_path.name) motif_auc_fig_html = "" elif motif_cav_trainers: motif_auc_fig_html = "
Motif AUC regression plot not generated.
" else: motif_auc_fig_html = "" motif_auc_table_html = "" if motif_auc_df is not None: # append motif logo column if exists if motif_file_fmt=='meme' and (len(motif_logo_dict)>0): motif_auc_df['motif_logo'] = motif_auc_df.apply(lambda x: "", axis=1) motif_auc_table_html = _render_df_table(motif_auc_df, max_rows=5000) if embed_images: assets_note = ( "
Images are embedded; the assets folder is optional.
" ) else: assets_note = ( f"
Images are stored in: {_html.escape(str(assets_dir))}
" ) param_rows = [ ("fscore_thresh", str(fscore_thresh)), ("top_motif_concepts", str(top_motif_concepts)), ("motif_file", str(motif_file) if motif_file is not None else ""), ("motif_file_fmt", str(motif_file_fmt)), ] params_html = _render_kv_table(param_rows) all_concepts: list[str] = [] seen = set() for c in ranked_motif_concepts: if c not in seen: all_concepts.append(c) seen.add(c) for trainer in all_trainers.values(): cav_fscores = getattr(trainer, "cav_fscores", None) if isinstance(cav_fscores, dict): for c in cav_fscores.keys(): c = str(c) if c not in seen: all_concepts.append(c) seen.add(c) concepts_html = "".join( f"
  • {_html.escape(c)}
    • " for c in all_concepts[:300] ) motif_concept_rows = [r for r in concept_rows if r.get("type") == "motif"] non_motif_concept_rows = [r for r in concept_rows if r.get("type") == "non-motif"] motif_table_html = "" extra_table_html = "" try: import pandas as pd motif_cols = ( ["rank", "concept"] + [f"fscore_{nm}_insertions" for nm in motif_insertions] + ["AUC_fscores", "Motif_concept_sensitivity_score (AUC_fscores_residual)", "source"] ) extra_cols = ["concept", "source", "fscore"] if motif_concept_rows: motif_df = pd.DataFrame(motif_concept_rows) motif_df = motif_df[[c for c in motif_cols if c in motif_df.columns]] # append motif logo column if exists if motif_file_fmt=='meme' and (len(motif_logo_dict)>0): motif_df['motif_logo'] = motif_df.apply(lambda x: "", axis=1) motif_table_html = _render_df_table(motif_df, max_rows=5000) if non_motif_concept_rows: extra_df = pd.DataFrame(non_motif_concept_rows) extra_df = extra_df[[c for c in extra_cols if c in extra_df.columns]] extra_table_html = _render_df_table(extra_df, max_rows=5000) except Exception: pass payload_json = _json_for_html(js_payload) def _barplot_divs(prefix: str, data: Any) -> str: if not isinstance(data, dict) or not data.get("log_ratios_by_attr"): return "" out = "" for attr_key in sorted(data["log_ratios_by_attr"].keys()): out += f"
    " return out def _png_html(path: Path, alt: str) -> str: if not embed_images or not path.exists(): return "" return ( "{_html.escape(alt)}" ) motif_barplots_html = _barplot_divs("motif", heatmap_data.get("motif")) extra_barplots_html = _barplot_divs("non-motif", heatmap_data.get("non-motif")) merged_barplots_html = _barplot_divs("merged", heatmap_data.get("merged")) motif_png_html = _png_html(heatmap_png_paths["motif"], "Motif heatmap PNG") extra_png_html = _png_html(heatmap_png_paths["non-motif"], "Non-motif heatmap PNG") merged_png_html = _png_html(heatmap_png_paths["merged"], "Merged heatmap PNG") html = f""" {_html.escape(title)}

    {_html.escape(title)}

    Generated: {now}
    {params_html} {assets_note}

    Concepts

    Motif concepts are listed first (ranked), then any additional concepts.

    Motif Concepts

    {motif_table_html or "
    (no motif concepts)
    "}

    Non-motif Concepts

    {extra_table_html or "
    (no non-motif concepts)
    "}
      {concepts_html}

    Motif Ranking

    {motif_auc_fig_html}
    {motif_auc_table_html or "
    (no motif ranking table)
    "}

    Trainer Heatmaps

    Motif heatmap (top motifs), non-motif heatmap (all non-motif concepts), and merged heatmap.

    Motif

    {motif_barplots_html} {motif_png_html}

    Non-motif

    {extra_barplots_html} {extra_png_html}

    Merged

    {merged_barplots_html} {merged_png_html}
    Per-trainer details {"".join(trainer_sections)}
    """ output_html_path.write_text(html, encoding="utf-8") return output_html_path # Backward-compatible alias (older name used `tcav` instead of `tpcav`). def generate_tcav_html_report(*args, **kwargs) -> Path: return generate_tpcav_html_report(*args, **kwargs) TPCAV-0.8.4/tpcav/tpcav_model.py000066400000000000000000000431171516353663300164050ustar00rootroot00000000000000import logging from functools import partial from typing import Dict, Iterable, List, Optional, Tuple, Union import numpy as np import torch from captum.attr import DeepLift from scipy.linalg import svd logger = logging.getLogger(__name__) def _abs_attribution_func(multipliers, inputs, baselines): "Multiplier x abs(inputs - baselines) to avoid double-sign effects." return tuple( (input_ - baseline).abs() * multiplier for input_, baseline, multiplier in zip(inputs, baselines, multipliers) ) class TPCAV(torch.nn.Module): """End-to-end PCA fitting, projection, and attribution utilities.""" def __init__( self, model, device: Optional[str] = None, layer_name: Optional[str] = None, layer: Optional[torch.nn.Module] = None, ) -> None: """ layer_name: optional module name to intercept activations via forward hook (useful if forward_until_select_layer is not implemented). """ super().__init__() self.model = model self.device = device or ("cuda:0" if torch.cuda.is_available() else "cpu") self.model.to(self.device) self.model.eval() self.fitted = False if layer is not None: self.layer = layer self.layer_name = "" elif layer_name is not None: self.layer = self._resolve_layer(layer_name) self.layer_name = layer_name else: raise Exception( "You have to specify either layer or layer_name to construct TPCAV model" ) def save_state(self, output_path="tpcav_state.pt"): """ Save TPCAV model state to a file. This is useful when model could not be restored by torch.save/load """ state = { "fitted": self.fitted, "zscore_mean": self.zscore_mean, "zscore_std": self.zscore_std, "Vh": self.Vh, "orig_shape": self.orig_shape } torch.save(state, output_path) @staticmethod def load_state(model, layer_name=None, layer=None, state_path: str = "tpcav_state.pt"): """ Restore CavTrainer state from a file. """ tpcav_model = TPCAV(model, layer_name=layer_name, layer=layer) state = torch.load(state_path, map_location="cpu") tpcav_model.fitted = state["fitted"] tpcav_model._set_buffer("zscore_mean", state["zscore_mean"].to(tpcav_model.device)) tpcav_model._set_buffer("zscore_std", state["zscore_std"].to(tpcav_model.device)) tpcav_model._set_buffer("Vh", state["Vh"].to(tpcav_model.device) if state["Vh"] is not None else None) tpcav_model._set_buffer("orig_shape", state["orig_shape"].to(tpcav_model.device)) logger.info("Successfully restored tpcav model states!") return tpcav_model def list_module_names(self) -> List[str]: """List all module names in the model for layer selection.""" return [name for name, _ in self.model.named_modules()] def fit_pca( self, concepts: Iterable, num_samples_per_concept: int = 10, num_pc: Optional[Union[int, str]] = None, ) -> Dict[str, torch.Tensor]: """Sample activations, compute PCA, and attach buffers to the model.""" logger.info("Start building PCA transformation.") sampled_avs = [] for concept in concepts: avs = self._sample_concept(concept, num_samples=num_samples_per_concept) logger.info( "Sampled %s activations from concept %s", avs.shape[0], concept.name ) sampled_avs.append(avs) all_avs = torch.cat(sampled_avs) orig_shape = all_avs.shape flat = all_avs.flatten(start_dim=1) mean = flat.mean(dim=0) std = flat.std(dim=0) std[std == 0] = -1 standardized = (flat - mean) / std if num_pc is None or num_pc == "full": _, S, Vh = svd(standardized, lapack_driver="gesvd", full_matrices=False) Vh = torch.tensor(Vh) elif int(num_pc) == 0: S = None Vh = None else: _, S, Vh = svd(standardized, lapack_driver="gesvd", full_matrices=False) Vh = torch.tensor(Vh[: int(num_pc)]) self.eigen_values = np.square(S) if S is not None else None self._set_buffer("zscore_mean", mean.to(self.device)) self._set_buffer("zscore_std", std.to(self.device)) self._set_buffer("Vh", Vh.to(self.device) if Vh is not None else None) self._set_buffer("orig_shape", torch.tensor(orig_shape).to(self.device)) self.fitted = True logger.info("PCA transformation built.") return { "zscore_mean": mean, "zscore_std": std, "Vh": Vh, "orig_shape": torch.tensor(orig_shape), } def project_activations( self, activations: torch.Tensor ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: """Project flattened activations into PCA space and residual.""" if not self.fitted: logger.warning( "PCA not fit before projecting activations, make sure this is intended" ) y = activations.flatten(start_dim=1).to(self.device) if self.Vh is not None: V = self.Vh.T zscore_mean = getattr(self, "zscore_mean", 0.0) zscore_std = getattr(self, "zscore_std", 1.0) y_standardized = (y - zscore_mean) / zscore_std y_projected = torch.matmul(y_standardized, V) y_residual = y_standardized - torch.matmul(y_projected, self.Vh) return y_residual, y_projected else: return y, None def concept_embeddings(self, concept, num_samples: int) -> torch.Tensor: """Return concatenated projected + residual activations for a concept.""" residual, projected = self._sample_concept_embeddings(concept, num_samples=num_samples) if projected is not None: return torch.cat((projected, residual), dim=1) return residual.detach() def forward_from_embeddings_at_layer( self, avs_residual: torch.Tensor, avs_projected: Optional[torch.Tensor] = None, model_inputs: Optional[Tuple[torch.Tensor, Optional[torch.Tensor]]] = None, ) -> torch.Tensor: """ Resume model forward by injecting activations at a named layer. If layer_name is not set, falls back to forward_with_embeddings which expects model.forward_from_projected_and_residual to exist. """ if model_inputs is None: raise ValueError( "model_inputs (seq, chrom) must be provided to run forward." ) y_hat = self.embedding_to_layer_activation(avs_residual, avs_projected) return self.forward_patched( model_inputs, layer_activation=y_hat, ) def layer_attributions( self, target_batches: Iterable, baseline_batches: Iterable, multiply_by_inputs: bool = True, abs_inputs_diff: bool = True, ) -> torch.Tensor: """ Compute DeepLift attributions on PCA embedding space. By default, it computes (input - baseline).abs() * multiplier to avoid double-sign effects (abs_inputs_diff=True). target_batches and baseline_batches should yield (seq, chrom) pairs of matching length. """ if not self.fitted: raise RuntimeError("Call fit_pca before attributing.") self.forward = self.forward_from_embeddings_at_layer deeplift = DeepLift(self, multiply_by_inputs=multiply_by_inputs) custom_attr_func = _abs_attribution_func if abs_inputs_diff else None attributions = [] for inputs, binputs in zip(target_batches, baseline_batches): avs = self._layer_output(*[i.to(self.device) for i in inputs]) avs_residual, avs_projected = self.project_activations(avs) bavs = self._layer_output(*[bi.to(self.device) for bi in binputs]) bavs_residual, bavs_projected = self.project_activations(bavs) # detach the projected tensor as it's connnected to the original input graph, # detaching it would keep the gradients on it if avs_projected is not None: avs_projected = avs_projected.detach() bavs_projected = bavs_projected.detach() attribution = deeplift.attribute( (avs_residual.to(self.device), avs_projected.to(self.device)), baselines=( bavs_residual.to(self.device), bavs_projected.to(self.device), ), additional_forward_args=( [torch.cat([i, bi]) for i, bi in zip(inputs, binputs)], ), custom_attribution_func=( None if not multiply_by_inputs else custom_attr_func ), ) attr_residual, attr_projected = attribution attribution = torch.cat((attr_projected, attr_residual), dim=1) else: attribution = deeplift.attribute( (avs_residual.to(self.device),), baselines=(bavs_residual.to(self.device),), additional_forward_args=( None, [torch.cat([i, bi]) for i, bi in zip(inputs, binputs)], ), custom_attribution_func=( None if not multiply_by_inputs else custom_attr_func ), )[0] attributions.append(attribution.detach().cpu()) with torch.no_grad(): del ( avs, avs_projected, avs_residual, bavs, bavs_projected, bavs_residual, ) torch.cuda.empty_cache() return torch.cat(attributions) def input_attributions( self, target_batches: Iterable, baseline_batches: Iterable, multiply_by_inputs: bool = True, cavs_list: Optional[List[torch.Tensor]] = None, ) -> List[torch.Tensor]: """Compute DeepLift attributions on PCA embedding space. target_batches and baseline_batches should yield (seq, chrom) pairs of matching length. """ if not self.fitted: raise RuntimeError("Call fit_pca before attributing.") self.forward = partial( self.forward_patched_tensor, layer_activation=None, cavs_list=cavs_list, mute_x_avs=False, mute_remainder=True, ) deeplift = DeepLift(self, multiply_by_inputs=multiply_by_inputs) attributions = [] for inputs, binputs in zip(target_batches, baseline_batches): attribution = deeplift.attribute( tuple([i.to(self.device) for i in inputs]), baselines=tuple([bi.to(self.device) for bi in binputs]), ) attributions.append( [a.detach().cpu() for a in attribution] if isinstance(attribution, tuple) else attribution.detach().cpu() ) return [torch.cat(z) for z in zip(*attributions)] def _sample_concept(self, concept, num_samples: int) -> torch.Tensor: avs: List[torch.Tensor] = [] num = 0 for inputs in concept.data_iter: av = self._layer_output(*[i.to(self.device) for i in inputs]) avs.append(av.detach().cpu()) num += av.shape[0] if num >= num_samples: break if not avs: raise ValueError(f"No activations gathered for concept {concept.name}") return torch.cat(avs)[:num_samples] def _sample_concept_embeddings(self, concept, num_samples: int) -> List[Union[torch.Tensor, None]]: num = 0 residuals = []; projected = [] for inputs in concept.data_iter: av = self._layer_output(*[i.to(self.device) for i in inputs]).detach() r, p = self.project_activations(av) residuals.append(r.cpu()) if p is not None: projected.append(p.cpu()) else: projected.append(p) num += av.shape[0] if num >= num_samples: break if not residuals: raise ValueError(f"No activations gathered for concept {concept.name}") residuals = torch.cat(residuals) projected = torch.cat(projected) if p is not None else None return residuals, projected def _layer_output(self, *inputs: Tuple[torch.Tensor]) -> torch.Tensor: """Return activations from the configured layer or model hook.""" cache: List[torch.Tensor] = [] def hook_fn(_module, _inputs, output): cache.append(output) handle = self.layer.register_forward_hook(hook_fn) try: inputs = [inp.to(self.device) if inp is not None else inp for inp in inputs] _ = self.model(*inputs) finally: handle.remove() if not cache: raise RuntimeError(f"No activation captured for layer {self.layer_name}; Are you sure it's used in forward function?") return cache[0] def _resolve_layer(self, name: str): for module_name, module in self.model.named_modules(): if module_name == name: return module raise ValueError(f"Layer {name} not found in model.") def embedding_to_layer_activation( self, avs_residual: torch.Tensor, avs_projected: Optional[torch.Tensor] ) -> torch.Tensor: """ Combine projected/residual embeddings into the layer activation space, mirroring scripts/models.py merge logic. """ if self.Vh is not None: y_hat = torch.matmul(avs_projected, self.Vh) + avs_residual else: y_hat = avs_residual y_hat = y_hat * self.zscore_std + self.zscore_mean return y_hat.reshape((-1, *self.orig_shape[1:])) def forward_patched_tensor( self, *model_inputs: torch.Tensor, layer_activation: Optional[torch.Tensor] = None, cavs_list: Optional[List[torch.Tensor]] = None, mute_x_avs: bool = False, mute_remainder: bool = True, ) -> torch.Tensor: return self.forward_patched( model_inputs, layer_activation, cavs_list, mute_x_avs, mute_remainder ) def forward_patched( self, model_inputs: Tuple[torch.Tensor, Optional[torch.Tensor]], layer_activation: Optional[torch.Tensor] = None, cavs_list: Optional[List[torch.Tensor]] = None, mute_x_avs: bool = False, mute_remainder: bool = True, ) -> torch.Tensor: """ Full forward pass with optional activation replacement and/or CAV-based gradient muting. """ def hook_fn(_module, _inputs, output): y = layer_activation if layer_activation is not None else output if cavs_list is None or len(cavs_list) == 0: return y return self._disentangle_with_cavs( y, cavs_list, mute_x_avs=mute_x_avs, mute_remainder=mute_remainder ) handle = self.layer.register_forward_hook(hook_fn) try: output = self.model(*[i.to(self.device) for i in model_inputs]) finally: handle.remove() return output def _disentangle_with_cavs( self, layer_output: torch.Tensor, cavs_list: List[torch.Tensor], mute_x_avs: bool = False, mute_remainder: bool = True, ) -> torch.Tensor: """ Project activations into CAV subspace and optionally zero gradients for subspaces (default mutes orthogonal/remainder gradients). """ y = layer_output.flatten(start_dim=1) y_residual, y_projected = self.project_activations(y) y_pca_all = torch.cat([y_projected, y_residual], dim=1) cavs_matrix = torch.stack(cavs_list, dim=1).to(y.device) # [dims, #cavs] if cavs_matrix.shape[1] > cavs_matrix.shape[0]: logger.warning( "CAV matrix has more columns than rows; remainder should be near zero." ) mrank = torch.linalg.matrix_rank(cavs_matrix) cavs_ortho = torch.linalg.qr(cavs_matrix, mode="reduced").Q[:, :mrank].detach() if not torch.allclose( cavs_ortho.T @ cavs_ortho, torch.eye(mrank, device=cavs_ortho.device), atol=1e-3, rtol=1e-3, ): logger.warning("Q^TQ not identity; check CAV matrix conditioning.") y_pca_x_avs = y_pca_all @ cavs_ortho @ cavs_ortho.T y_pca_remainder = y_pca_all - y_pca_x_avs if mute_x_avs: y_pca_x_avs.register_hook(lambda grad: torch.zeros_like(grad)) if mute_remainder: y_pca_remainder.register_hook(lambda grad: torch.zeros_like(grad)) y_pca_hat = y_pca_x_avs + y_pca_remainder dim_projected = y_projected.shape[1] if y_projected is not None else 0 return self.embedding_to_layer_activation( y_pca_hat[:, dim_projected:], y_pca_hat[:, :dim_projected] ) def _set_buffer(self, name: str, value: Optional[torch.Tensor]) -> None: if hasattr(self.model, "_buffers") and name in self.model._buffers: self._buffers[name] = value # type: ignore[index] else: self.register_buffer(name, value) TPCAV-0.8.4/tpcav/utils.py000066400000000000000000000463621516353663300152550ustar00rootroot00000000000000#!/usr/bin/env python3 """ Copied utility helpers from scripts/utils.py so the package can run standalone. """ import logging from copy import deepcopy import Bio import numpy as np import pandas as pd import pyfaidx import seqchromloader as scl import torch from Bio import SeqIO from pyfaidx import Fasta from torch.utils.data import default_collate, get_worker_info logger = logging.getLogger(__name__) def clean_motif_name(motif_name): return motif_name.replace("/", "-") def center_windows(df, window_len=1024): "Get center window_len bp region of the given coordinate dataframe." halfR = int(window_len / 2) df = df.assign(mid=lambda x: ((x["start"] + x["end"]) / 2).astype(int)).assign( start=lambda x: x["mid"] - halfR, end=lambda x: x["mid"] + halfR ) if "strand" in df.columns: return df[["chrom", "start", "end", "strand"]] else: return df[["chrom", "start", "end"]] def collate_seq(batch): seq, chrom, target, label = default_collate(batch) return seq def collate_chrom(batch): seq, chrom, target, label = default_collate(batch) return chrom def collate_seqchrom(batch): seq, chrom, target, label = default_collate(batch) return seq, chrom def seq_dataloader_from_bed( seq_bed, genome_fasta, window_len=1024, batch_size=8, num_workers=0 ): seq_df = pd.read_table( seq_bed, header=None, usecols=[0, 1, 2], names=["chrom", "start", "end"], ) return seq_dataloader_from_dataframe( seq_df, genome_fasta, window_len, batch_size, num_workers ) def seq_dataloader_from_dataframe( seq_df, genome_fasta, window_len=1024, batch_size=8, num_workers=0 ): seq_df = center_windows(seq_df, window_len=window_len) seq_df["label"] = -1 if "strand" not in seq_df.columns: seq_df["strand"] = "+" seq_df = scl.filter_chromosomes(seq_df, to_keep=Fasta(genome_fasta).keys()) dl = scl.SeqChromDatasetByDataFrame( seq_df, genome_fasta=genome_fasta, dataloader_kws={ "batch_size": batch_size, "num_workers": num_workers, "collate_fn": collate_seq, "drop_last": True, }, ) return dl def seq_dataloader_from_fa(seq_fa, input_window_length=1024, batch_size=8): with open(seq_fa) as handle: dnaSeqs = [] for record in SeqIO.parse(handle, "fasta"): if len(record.seq) != input_window_length: raise Exception( f"Sequence length {len(record.seq)} != input_window_length {input_window_length}" ) dnaSeqs.append(torch.tensor(scl.dna2OneHot(record.seq))) if len(dnaSeqs) >= batch_size: yield torch.stack(dnaSeqs) dnaSeqs = [] if len(dnaSeqs) > 0: yield torch.stack(dnaSeqs) def chrom_dataloader_from_bed( chrom_bed, genome_fasta, input_window_length=1024, bigwigs=None, batch_size=8 ): chrom_df = pd.read_table( chrom_bed, header=None, usecols=[0, 1, 2], names=["chrom", "start", "end"], ) return chrom_dataloader_from_dataframe( chrom_df, genome_fasta, input_window_length, bigwigs or [], batch_size ) def chrom_dataloader_from_dataframe( chrom_df, genome_fasta, input_window_length=1024, bigwigs=None, batch_size=8, num_workers=0, ): bigwigs = bigwigs or [] chrom_df = center_windows(chrom_df, window_len=input_window_length) chrom_df["label"] = -1 if "strand" not in chrom_df.columns: chrom_df["strand"] = "+" chrom_df = scl.filter_chromosomes(chrom_df, to_keep=Fasta(genome_fasta).keys()) dl = scl.SeqChromDatasetByDataFrame( chrom_df, genome_fasta=genome_fasta, bigwig_filelist=bigwigs, dataloader_kws={ "batch_size": batch_size, "num_workers": num_workers, "collate_fn": collate_chrom, "drop_last": True, }, ) return dl class IterateSeqDataFrame(torch.utils.data.IterableDataset): def __init__( self, seq_df, genome_fasta, motif=None, num_motifs=128, start_buffer=0, end_buffer=0, return_region=False, print_warning=True, infinite=False, ): self.seq_df = seq_df self.genome_fasta = genome_fasta self.motif = motif self.num_motifs = num_motifs self.start_buffer = start_buffer self.end_buffer = end_buffer self.return_region = return_region self.print_warning = print_warning self.infinite = infinite def __iter__(self): worker_info = get_worker_info() self.genome = pyfaidx.Fasta(self.genome_fasta) rng = np.random.default_rng(worker_info.id if worker_info is not None else 1) if self.infinite: while True: chunk = self.seq_df.sample(frac=1.0).reset_index(drop=True) try: yield from iterate_seq_df_chunk( chunk, genome=self.genome, motif=self.motif, num_motifs=self.num_motifs, start_buffer=self.start_buffer, end_buffer=self.end_buffer, batch_size=None, return_region=self.return_region, print_warning=self.print_warning, rng=rng, ) except StopIteration: continue else: if worker_info is None: chunk = self.seq_df else: chunk = [self.seq_df.iloc[idx] for idx in np.array_split(np.arange(len(self.seq_df)), worker_info.num_workers)][worker_info.id] yield from iterate_seq_df_chunk( chunk, genome=self.genome, motif=self.motif, num_motifs=self.num_motifs, start_buffer=self.start_buffer, end_buffer=self.end_buffer, batch_size=None, return_region=self.return_region, print_warning=self.print_warning, rng=rng, ) def iterate_seq_df_chunk( chunk, genome, motif=None, num_motifs=128, start_buffer=0, end_buffer=0, return_region=False, batch_size=None, print_warning=True, rng=np.random.default_rng(1), ): seqs = [] regions = [] for item in chunk.itertuples(): try: seq = str(genome[item.chrom][item.start : item.end]).upper() except KeyError: if print_warning: print(f"catch KeyError in region {item.chrom}:{item.start}-{item.end}") continue except pyfaidx.FetchError: if print_warning: print( f"catch FetchError in region {item.chrom}:{item.start}-{item.end}, probably start coordinate negative" ) continue unique_chars = np.unique(list(seq)) if "N" in seq: if print_warning: print(f"Skip {item.chrom}:{item.start}-{item.end} due to containing N") continue elif len(unique_chars) == 0: if print_warning: print( f"Skip region {item.chrom}:{item.start}-{item.end} due to no sequences available" ) continue if motif is not None: seq = insert_motif_into_seq( seq, motif, num_motifs=num_motifs, start_buffer=start_buffer, end_buffer=end_buffer, rng=rng, ) if batch_size is None: if return_region: yield f"{item.chrom}:{item.start}-{item.end}", seq else: yield seq else: seqs.append(seq) regions.append(f"{item.chrom}:{item.start}-{item.end}") if len(seqs) >= batch_size: if return_region: yield regions, seqs else: yield seqs seqs = [] regions = [] if (len(seqs) > 0) and batch_size is not None: if return_region: yield regions, seqs else: yield seqs def iterate_seq_df( seq_df, genome_fasta, motif=None, num_motifs=128, start_buffer=0, end_buffer=0, batch_size=32, return_region=False, print_warning=True, rng=np.random.default_rng(1), ): genome = Fasta(genome_fasta) yield from iterate_seq_df_chunk( chunk=seq_df, genome=genome, motif=motif, num_motifs=num_motifs, start_buffer=start_buffer, end_buffer=end_buffer, return_region=return_region, batch_size=batch_size, print_warning=print_warning, rng=rng, ) class SeqChromConcept: "Sequence + Chromatin concept given the bed files of sequences and chromatin regions." def __init__( self, seq_bed, seq_fa, chrom_bed, genome_fasta, bws: list, transforms=None, window_len=1024, batch_size=8, ): self.seq_bed = seq_bed self.seq_fa = seq_fa self.chrom_bed = chrom_bed self.seq_dl = None self.chrom_dl = None self.genome_fasta = genome_fasta self.bws = bws self.transforms = transforms self.window_len = window_len self.batch_size = batch_size def seq_dataloader(self): if self.seq_bed is not None: seq_df = pd.read_table( self.seq_bed, header=None, usecols=[0, 1, 2], names=["chrom", "start", "end"], ) seq_df = center_windows(seq_df, window_len=self.window_len) seq_df["label"] = -1 if "strand" not in seq_df.columns: seq_df["strand"] = "+" seq_df = scl.filter_chromosomes( seq_df, to_keep=Fasta(self.genome_fasta).keys() ) dl = scl.SeqChromDatasetByDataFrame( seq_df, genome_fasta=self.genome_fasta, dataloader_kws={ "batch_size": self.batch_size, "num_workers": 0, "collate_fn": collate_seq, "drop_last": True, }, ) for seq in dl: if isinstance(seq, list): assert len(seq) == 1 seq = seq[0] yield seq else: with open(self.seq_fa) as handle: dnaSeqs = [] for record in SeqIO.parse(handle, "fasta"): dnaSeqs.append(torch.tensor(scl.dna2OneHot(record.seq))) if len(dnaSeqs) >= self.batch_size: yield torch.stack(dnaSeqs) dnaSeqs = [] if len(dnaSeqs) > 0: yield torch.stack(dnaSeqs) def chrom_dataloader(self): chrom_df = pd.read_table( self.chrom_bed, header=None, usecols=[0, 1, 2], names=["chrom", "start", "end"], ) chrom_df = center_windows(chrom_df, window_len=self.window_len) chrom_df["label"] = -1 if "strand" not in chrom_df.columns: chrom_df["strand"] = "+" chrom_df = scl.filter_chromosomes( chrom_df, to_keep=Fasta(self.genome_fasta).keys() ) dl = scl.SeqChromDatasetByDataFrame( chrom_df, genome_fasta=self.genome_fasta, bigwig_filelist=self.bws, transforms=self.transforms, dataloader_kws={ "batch_size": self.batch_size, "num_workers": 0, "collate_fn": collate_chrom, "drop_last": True, }, ) yield from dl def dataloader(self): if ( self.seq_bed is not None or self.seq_fa is not None ) and self.seq_dl is None: self.seq_dl = self.seq_dataloader() if (self.chrom_bed is not None) and self.chrom_dl is None: self.chrom_dl = self.chrom_dataloader() yield from zip(self.seq_dl, self.chrom_dl) def sample_from_pwm(pwm_prob_mat, n_seqs=1, rng=None, alphabet=['A', 'C', 'G', 'T']): """ Draw `n_seqs` independent sequences from a Bio.motifs.Motif PWM. Parameters ---------- pwm_prob_mat : (L, A) probability matrix n_seqs : int, default 1 How many sequences to generate. rng : numpy.random.Generator or None Leave None for np.random.default_rng(). alphabet : letters to be used Returns ------- str | list[str] A single string if n_seqs==1, otherwise a list of strings. """ rng = rng or np.random.default_rng() # ---- 1. Build a (L, A) probability matrix -------------------------------- L = pwm_prob_mat.shape[0] # ---- 2. Vectorised multinomial sampling ----------------------------------- # Draw U(0,1) numbers of shape (n_seqs, L) u = rng.random((n_seqs, L)) # cumulative probabilities along alphabet axis cum = np.cumsum(pwm_prob_mat, axis=1) # still (L, A) # Broadcast cum to (n_seqs, L, A) and pick first index where cum > u idx = (u[..., None] < cum).argmax(axis=2) # (n_seqs, L) int indices # ---- 3. Convert indices back to letters ----------------------------------- letters = np.array(alphabet, dtype="U1") seq_arr = letters[idx] # (n_seqs, L) array of chars # Join per sequence seqs = ["".join(row) for row in seq_arr] return seqs[0] if n_seqs == 1 else seqs def get_prob_mat_from_motif_pwm(pwm_dict, alphabet=['A', 'C', 'G', 'T']): pwm_prob_mat = np.column_stack([pwm_dict[b] for b in alphabet]) return pwm_prob_mat def insert_motif_into_seq( seq, motif, num_motifs=3, start_buffer=50, end_buffer=50, rng=np.random.default_rng(1), ): seq_ins = list(deepcopy(seq)) pos_motif_overlap = np.ones(len(seq)) pos_motif_overlap[:start_buffer] = 0 pos_motif_overlap[(-end_buffer - len(motif)) :] = 0 num_insert_motifs = 0 for i in range(num_motifs): try: motif_start = rng.choice( np.where(pos_motif_overlap > 0)[0] ).item() # randomly pick insert location except ValueError: # print( # f"No samples can be taken for motif {motif.name}, skip inserting the rest of motifs" # ) break seq_ins[motif_start : (motif_start + len(motif))] = motif.sample_instance() pos_motif_overlap[(motif_start - len(motif)) : (motif_start + len(motif))] = 0 num_insert_motifs += 1 if num_insert_motifs < num_motifs: logger.warning( f"Only inserted {num_insert_motifs} out of {num_motifs} motifs for motif {motif.name}" ) return "".join(seq_ins) class ConsensusMotif: def __init__(self, name, consensus): self.name = name self.matrix_id = "custom" self.consensus = consensus.upper() def __len__(self): return len(self.consensus) def reverse_complement(self): rc = deepcopy(self) rc.consensus = Bio.Seq.reverse_complement(self.consensus) rc.name = self.name + "_rc" return rc def sample_instance(self): return self.consensus class PermutedConsensusMotif: def __init__(self, name, consensus, seed=None, min_shift=0.3): self.name = name self.consensus = consensus.upper() self.rng = np.random.RandomState(seed) self.min_shift = min_shift def __len__(self): return len(self.consensus) def reverse_complement(self): rc = deepcopy(self) rc.consensus = Bio.Seq.reverse_complement(self.consensus) rc.name = self.name + "_rc" return rc def sample_instance(self): return self._permute(self.consensus) def _permute(self, max_attempts=100): """ Permute the consensus sequence, return the object """ permuted = deepcopy(self) L = len(self.consensus) count = 0 while True: perm = self.rng.permutation(L) frac_moved = np.mean(perm != np.arange(L)) if frac_moved >= self.min_shift: break else: count += 1 if count > max_attempts: raise ValueError( f"Could not generate a permutation with min_shift={self.min_shift} for motif {self.name}" ) permuted_consensus = ''.join([self.consensus[i] for i in perm]) return permuted_consensus class PermutedPWMMotif: BASES = ("A", "C", "G", "T") # alphabet must be symmetric! RC_MAP = {"A": "T", "C": "G", "G": "C", "T": "A"} def __init__(self, motif, seed=None, min_shift=0.3, name_suffix="_perm"): """ motif: Bio.motifs.Motif seed: RNG seed min_shift: fraction of positions that must move """ self.original_motif = motif self.name = motif.name + name_suffix if motif.name else "permuted_motif" self.length = motif.length self.alphabet = motif.alphabet self.min_shift = min_shift self.rng = np.random.RandomState(seed) # extract PWM as dict of lists pwm = {b: list(motif.pwm[b]) for b in self.BASES} self.pwm_prob_mat = get_prob_mat_from_motif_pwm(pwm) def __len__(self): return self.length def _permute_pwm_positions(self, max_attempts=100): L = self.length count = 0 while True: perm = self.rng.permutation(L) frac_moved = np.mean(perm != np.arange(L)) if frac_moved >= self.min_shift: break else: count += 1 if count > max_attempts: raise ValueError( f"Could not generate a permutation with min_shift={self.min_shift} for motif {self.original_motif.name}" ) permuted_pwm_prob_mat = self.pwm_prob_mat[perm, :] return permuted_pwm_prob_mat def reverse_complement(self): """ Return a NEW PermutedMotif with reverse-complemented PWM """ rc = deepcopy(self) rc.pwm_prob_mat = self.pwm_prob_mat[::-1, ::-1] rc.name = self.name + "_rc" return rc def sample_instance(self): return sample_from_pwm(self._permute_pwm_positions(), rng=self.rng) class BioMotifWrapped: def __init__(self, motif, seed=None): """ Wrapper class for Bio Motif """ self.motif = motif self.name = motif.name self.length = len(motif.consensus) self.pwm_prob_mat = get_prob_mat_from_motif_pwm(motif.pwm) self.rng = np.random.RandomState(seed) def __len__(self): return self.length def reverse_complement(self): rc = deepcopy(self) rc.motif = self.motif.reverse_complement() rc.name = self.motif.name + "_rc" rc.pwm_prob_mat = get_prob_mat_from_motif_pwm(rc.motif.pwm) return rc def sample_instance(self): return sample_from_pwm(self.pwm_prob_mat, rng=self.rng)