snakemake-workflows/dna-seq-benchmark

A snakemake workflow for benchmarking variant calling approaches with Genome in a Bottle (GIAB), CHM (syndip) or other custom datasets

Overview

Latest release: v1.17.0, Last update: 2026-07-15

Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=snakemake-workflows/dna-seq-benchmark

Quality control: linting: passed formatting: passed

Wrappers: bio/bcftools/index bio/bcftools/norm bio/bcftools/sort bio/bcftools/view bio/bwa/index bio/bwa/mem bio/mosdepth bio/picard/markduplicates bio/reference/ensembl-sequence bio/samtools/faidx bio/samtools/index bio/tabix/index bio/vembrane/table bio/vep/annotate bio/vep/cache bio/vep/plugins utils/datavzrd

Workflow Rule Graph

This visualization of the workflow’s rule graph was automatically generated using Snakevision

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Conda package manager. It is recommended to install conda via Miniforge. Run

conda create -c conda-forge -c bioconda -c nodefaults --name snakemake snakemake snakedeploy

to install both Snakemake and Snakedeploy in an isolated environment. For all following commands ensure that this environment is activated via

conda activate snakemake

For other installation methods, refer to the Snakemake and Snakedeploy documentation.

Step 2: Deploy workflow

With Snakemake and Snakedeploy installed, the workflow can be deployed as follows. First, create an appropriate project working directory on your system and enter it:

mkdir -p path/to/project-workdir
cd path/to/project-workdir

In all following steps, we will assume that you are inside of that directory. Then run

snakedeploy deploy-workflow https://github.com/snakemake-workflows/dna-seq-benchmark . --tag v1.17.0

Snakedeploy will create two folders, workflow and config. The former contains the deployment of the chosen workflow as a Snakemake module, the latter contains configuration files which will be modified in the next step in order to configure the workflow to your needs.

Step 4: Run workflow

The deployment method is controlled using the --software-deployment-method (short --sdm) argument.

To run the workflow with automatic deployment of all required software via conda/mamba, use

snakemake --cores all --sdm conda

Snakemake will automatically detect the main Snakefile in the workflow subfolder and execute the workflow module that has been defined by the deployment in step 2.

For further options such as cluster and cloud execution, see the docs.

Step 5: Generate report

After finalizing your data analysis, you can automatically generate an interactive visual HTML report for inspection of results together with parameters and code inside of the browser using

snakemake --report report.zip

Configuration

The following section is imported from the workflow’s config/README.md.

Configuration Guide

Please follow the instructions in the template config.yaml.

VAF (Variant Allele Frequency) Calculation

If your variant-calling pipeline does not write a VAF FORMAT field to the VCF, you can have the workflow calculate it and inject it into the VCF automatically. This enables VAF-stratified precision/recall reporting.

Option A: Calculate VAF from the AD (Allelic Depth) field

If your VCF (for example called with strelka) contains a standard AD FORMAT field (i.e., [ref_count, alt_count] for biallelic sites), simply set:

variant-calls:
  callset:
    my_callset:
      path: path/to/variant-calls.vcf.gz
      vaf-field: tbc # ← "to-be-calculated"
      benchmark: giab-na12878
      genome-build: grch38

vaf-field: tbc tells the Snakemake workflow to run calc-vaf.py --from-ad on the VCF, computing VAF as:

VAF = alt_count / (ref_count + alt_count)

The calculated VAF FORMAT field (Type=Float, Number=A) is written to results/calculate-vaf/{callset}.added-vaf.bcf, which then feeds downstream precision/recall and FP/FN analyses.

Option B: Calculate VAF from custom numerator / denominator fields

If your VCF lacks an AD field but has separate numerator and denominator FORMAT fields (e.g., AO and RO as in VCFs generated by freebayes), specify both:

variant-calls:
  callset:
    my_callset:
      path: path/to/variant-calls.vcf.gz
      vaf-field: tbc
      vaf-numerator:
        field: FORMAT
        name: AO
      vaf-denominator:
        field: FORMAT
        name: RO
      benchmark: giab-na12878
      genome-build: grch38

The workflow will run:

calc-vaf.py --num-field FORMAT --num-name AO \
            --den-field FORMAT --den-name RO

which computes VAF = AO / RO per sample.

The same vaf-numerator/vaf-denominator pattern works with field: INFO for INFO-level fields.

Option C: Use a pre-existing VAF field

If your VCF already contains a VAF-like field, point to it directly instead of using tbc:

variant-calls:
  callset:
    my_callset:
      path: path/to/variant-calls.vcf.gz
      vaf-field:
        field: FORMAT
        name: AF
      benchmark: giab-na12878
      genome-build: grch38

The workflow will not recalculate VAF; it reads the field directly during precision/recall analysis. You can verify the field name with:

bcftools view -h path/to/variant-calls.vcf.gz