Snakemake pipeline to call variants using illumina fastq data

docker build vcf_sm:latest

docker run -v /path/to/repo/variant_call_snakemake:/usr/src/vcf vcf:latest snakemake --cores all

You will need to replace '/path/to/repo/' with your actual path This command will create a directory called 'results' with all of the output files.

docker run -v /path/to/repo/variant_call_snakemake:/usr/src/vcf vcf:latest pytest test.py

results_final/ Contains the results of this pipeline when run with the data and config file provided.

Summary of the results found in /path/to/repo/vcf_nextflow_test/results_final/reports/sample1_snpEff_report.genes.txt E: 0 MODERATE impact M: 1 MODERATE impact, 1 missense variant N: 4 MODERATE impact, 4 missense variants ORF10: 0 MODERATE impact ORF3a: 2 MODERATE impact, 2 missense variants ORF6: 0 MODERATE impact ORF7a: 2 MODERATE impact, 2 missense variants ORF8: 1 MODERATE impact S: 7 MODERATE impact, 6 missense variants, 1 disruptive inframe deletion orf1ab: 10 MODERATE impact, 10 missense variants

There are several reports available. The MultiQC report is the most informative for sequencing QC and some basic information about the VCF results. There is also a snpEff Report that has more detailed information about the VCF. Generally speaking, the fastq files look good enough for reliable results.