Shuhua-Group / PanGenome_VCF_PostProcess

A simple pipeline to process the VCF deconstructed from the VG pangenome graph

PanGenome_VCF_PostProcess

A simple pipeline to process the VCF deconstructed from the VG pangenome graph

1. Remove large variants with vcfbub -l 0 -r 10000000;
2. Trim ALT alleles not seen in the genotype fields with bcftools view -a;
3. Group the alternative alleles in multiallelic SV loci by their length (see allele-group.pdf);
4. Split multiallelics with bcftools norm -m -any;
5. Classify the variants into small variants and SVs (abs(length(ALT_allele)-length(REF_allele))>=50).

Usage

1. Installation

conda or mamba is required

# clone the repo
git clone https://github.com/Shuhua-Group/PanGenome_VCF_PostProcess.git
# create the enviroment
cd PanGenome_VCF_PostProcess
mamba env create -f environment.yaml
# activate the enviroment
mamba activate PanVCF

2. Modify the configfile

Add the working directory work_dir: and PanGenome VCF file pan_vcf to config.yaml

# where you want the pipeline work
work_dir: ${wd}
# the vcf file deconstructed from the vg pangenome 
pan_vcf: ${PanGenome_VCF}
# prefix of output files 
out_prefix: CPC.HPRC.Phase1.processed
# allele grouping threads
threads: 16

3. Run the pipeline

Make sure that PanGenome_vcf.post_process.smk, config.yaml, and module.py are under the same directory:

snakemake -c 128 -s PanGenome_vcf.post_process.smk

It cost about two hours in a 128 core sever to process the CPC+HPRC pangenome, and the processed VCF files are:

# biallelics small variants (abs(length(ALT_allele)-length(REF_allele))<50)
CPC.HPRC.Phase1.processed.small_variants.normed.vcf.gz
# biallelics SVs (abs(length(ALT_allele)-length(REF_allele))>=50)
CPC.HPRC.Phase1.processed.SVs.normed.vcf.gz

[Note] We have updated the process strategy about the complex loci with both small variants and SVs alleles, so that the number of variants is slightly different than in the paper.