This repository contains the supplementary R Markdown notebooks for [Sturiale et al., BMC Biology, in revision]. We used a high-density SNP chip (131,048 SNPs) to analyze 60 Aedes albopictus mosquitoes across three populations: AUTO (autogenous selected line, n=28), NON-AUTO (Manassas VA lab colony, n=10), and NON-AUTO-FIELD (Montclair NJ field-collected, n=22). Analyses include quality control, genome-wide selection scans (OutFLANK, pcadapt), linkage disequilibrium network analysis (LDna), functional SNP annotation, and population genetic statistics.
| File | Analysis | View |
|---|---|---|
| File S1 | Quality control | HTML |
| File S2 | Selection scan with OutFLANK | HTML |
| File S3 | Selection scan with pcadapt | HTML |
| File S4 | Linkage network analysis (LDna) | HTML |
| File S5 | Gene expression x selection scan intersection | HTML |
| File S6 | Functional annotation of SNPs | HTML |
| File S7 | Allele and genotype frequencies | HTML |
| File S8 | FST estimations | HTML |
Choose the method that fits your setup. All three produce identical results.
pixi manages the full R + Python + genomics tool environment from pixi.toml. No Docker or Singularity needed.
# 1. Clone
git clone https://github.com/cosmelab/aealbo_autogeny && cd aealbo_autogeny
# 2. Install pixi (if not already installed)
curl -fsSL https://pixi.sh/install.sh | bash
# 3. Install all dependencies (R, Python, plink2, etc.)
pixi install
pixi run install-r-extras # installs OutFLANK, LDna, and other CRAN/GitHub packages
# 4. Download data
bash scripts/00_download_data.sh
# 5. Render a notebook
pixi run Rscript -e "rmarkdown::render('notebooks/File_S1.Quality_control.Rmd', output_dir='docs/html/')"
# Render all notebooks in dependency order
bash scripts/render_notebooks.sh# 1. Clone and download data
git clone https://github.com/cosmelab/aealbo_autogeny && cd aealbo_autogeny
bash scripts/00_download_data.sh
# 2. Pull container
docker pull ghcr.io/cosmelab/aealbo_autogeny:latest
# 3. Render a notebook
docker run --rm -v $PWD:/workspace --workdir /workspace \
ghcr.io/cosmelab/aealbo_autogeny:latest \
bash -c 'eval "$(pixi shell-hook)" && Rscript -e \
"rmarkdown::render(\"notebooks/File_S1.Quality_control.Rmd\", output_dir=\"docs/html/\")"'
# Render all
bash scripts/render_notebooks.sh # auto-detects Docker# 1. Clone and download data
git clone https://github.com/cosmelab/aealbo_autogeny && cd aealbo_autogeny
bash scripts/00_download_data.sh
# 2. Pull container
singularity pull docker://ghcr.io/cosmelab/aealbo_autogeny:latest
# 3. Render a notebook
singularity exec --bind $PWD:/workspace --pwd /workspace \
aealbo_autogeny_latest.sif \
bash -c 'eval "$(pixi shell-hook)" && Rscript -e \
"rmarkdown::render(\"notebooks/File_S1.Quality_control.Rmd\", output_dir=\"docs/html/\")"'
# Render all
bash scripts/render_notebooks.sh # auto-detects SingularitySee docs/spark_instructions.md for the full HPC workflow and docs/spark_agent_task.json for agent-driven rendering.
For programmatic or HPC use, run the full pipeline from the command line without opening notebooks. Each step maps directly to its supplementary notebook.
# Check what's pending (dry run — nothing executes)
bash scripts/run_pipeline.sh --dry-run
# Run the full pipeline (skips already-completed steps)
bash scripts/run_pipeline.shRequires the container and data (see Reproducing the Analysis above). The pipeline auto-detects Docker or Podman via CONTAINER_RUNTIME.
| Step | Script | Output | Notebook |
|---|---|---|---|
| 01 | scripts/01_qc/run_qc.sh |
output/quality_control/file7.* |
File S1 |
| 02 | scripts/02_selection_scans/run_selection_scans.sh |
output/selection_scans/ |
Files S2, S3 |
| 03 | scripts/05_ldna/run_ldna.sh |
output/ldna/ |
Files S4a–S4e |
| 04 | scripts/03_annotation/run_snpeff.sh |
output/snpeff/ |
File S6 |
| 05 | scripts/07_gene_expression/run_gene_expression.sh |
output/gene_expression/ |
File S5 |
| 06 | scripts/04_diversity/run_diversity.sh |
output/diversity/, output/fst/ |
Files S7, S8 |
Note: Step 03 (LDna) requires ~45 min and 32 GB RAM. See
scripts/README.mdfor per-step details.
Input data (VCF files, SNP chip annotations, gene annotations) are archived at Zenodo (DOI: 10.5281/zenodo.19451616). Reference genome AalbF3 is available at NCBI (GCA_006496715.1). See data/README.md for complete provenance and docs/zenodo_manifest.md for the full archive contents.
Sturiale SL, Heilig MC, Aardema ML, Cosme LV, Corley M, Marzec S, Hamilton M, Vizcarra D, Anderson L, Holzapfel CM, Bradshaw WE, Meuti ME, Caccone A, Armbruster PA. [Title]. BMC Biology [in revision]. GitHub: https://github.com/cosmelab/aealbo_autogeny
MIT