The annotation statistics process for SNP uses the in-house script "annotationAnaysisSNP" and for SV the script "annotationAnaysisSV", these scripts can be found in "AnalysisScripts/annotationAnaysis/". The analysis is divided into three processes, which are to count a piece of data and generate the result json file, then to merge all the results and generate the merged json file, and finally, to use the json file to generate a spreadsheet. The specific execution commands are as follows:

(1)Execute annotation statistics, where "SNP_anno_file.txt" is the annotation result file, and "output_fn.json" is the statistics result.

./annotationAnaysisSNP anno_analysis SNP_anno_file.txt -1 > output_fn.json

(2)Results Merge.

find ../annotationAnaysisSNP_rst/* | grep json > all_json_file_list_SNP.txt
./annotationAnaysisSNP combine all_json_file_list_SNP.txt > all_combine.json

(3)Generate preadsheet.

./annotationAnaysisSNP SV_excel all_combine.json title_str > final_excel_SV.tsv

(1) Using 500/1000/10000/100000 as window lengths, count the number of variants in each window on each chromosome, and the number of variants distributed in each frequency class

regulation_region/submit_find_out.py

(2) Combine all the analysis results in all windows

regulation_region/cmb_region_variants.py

(3) Split the results according to the frequency level

regulation_region/split_region_variants_col.py

(4) Sort the number of variants distributed in frequency classes and fetch the top 1000 windows with the most variants

regulation_region/submit_fetch_1000.py

(1) Split the promoter and enhancer raw dependency dataset by chromosome for subsequent parallel analysis

regulation_region/split_regulation.py

(2) Count the variants located in the regulation region

regulation_region/submit_find_regu.py

(3) Combine the results of each chromosome

regulation_region/combine_regulation.py

(1) Count the number of variants on each gene

submit_fetch_gene_nums.py

(2) Combine the statistical results

cmb_gene_nums.py

(1)Execute annotation statistics, where "SV_var.vcf" is the joint SV file, "annoteSV_sesult.tsv" is the annotation result generate by annoteSV, and "output_fn.json" is the statistics result.

./annotationAnaysisSV anno_analysis SV_var.vcf annoteSV_sesult.tsv -1 0 NULL output.json 

(2)Results Merge.

./annotationAnaysisSV combine all_json_file_list.txt > all_combine.json

(3)Generate preadsheet.

./annotationAnaysisSV SV_excel all_combine.json title_str > final_excel_SNP.tsv