GATK Best Practices for germline SNP/indel calling from WGS/WES BAMs. Per-sample HaplotypeCaller GVCFs, GenomicsDBImport, GenotypeGVCFs joint calling, VQSR or hard filters. Requires BWA-MEM2-aligned, markdup, BQSR BAMs. Use DeepVariant for a faster DL alternative; GATK is the NIH/ENCODE standard.
SkillScore100Maintained
Automatic
◆Research pack
Last verified 8/24/2026
What it changes
Passed 24 of 24 test cases with the skill, and 22 of 24 without it.
Without skill92%
With skill100%
Checks it passed that the model alone missed
Invokes vep with -i specifying the input VCF
Uses gatk SelectVariants with --select-type-to-include SNP
2 fixed22 passed either way0 still failing0 broke
Pass-rate lift
+8 pp
Tokens
+314%
Agent turns
0%
Response time
-13%
Model gemini-3.6-flashMeasured 8/24/2026v1 · latest is v224 of 24 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
See it in action
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 2
Prompt
Before applying distinct hard filter thresholds to SNPs and Indels in a joint VCF file (cohort_all.vcf.gz), which GATK tool and option should be used to isolate SNPs into a standalone VCF?
What was checked, without and with the skill
Checked
Without skill
With skill
Uses gatk SelectVariants with --select-type-to-include SNP
Missed
Passed
case-14 · gemini-3.6-flash · 8/24/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md382 linessingle file
GATK — Germline Variant Calling Pipeline
Overview
GATK (Genome Analysis Toolkit) implements the GATK Best Practices workflow for calling SNPs and indels from Illumina WGS and WES data. The pipeline runs HaplotypeCaller per sample (producing GVCF files), consolidates GVCFs with GenomicsDBImport, performs joint genotyping with GenotypeGVCFs, and filters variants with VQSR (Variant Quality Score Recalibration) or hard filters. GATK requires BWA-MEM2-aligned, duplicate-marked, and base quality score recalibrated (BQSR) BAM files as input. It integrates with Picard tools, samtools, and bcftools for pre- and post-processing. The GATK4 workflow is the NIH/ENCODE standard for germline variant calling in research and clinical genomics.
When to Use
Calling germline SNPs and indels from WGS or WES samples for population genetics or clinical variant analysis
Running joint genotyping across multiple samples for cohort-scale studies (families, case-control)
Applying base quality score recalibration (BQSR) to improve variant calling accuracy before HaplotypeCaller
Generating GVCF files for scalable cohort expansion: add new samples without reprocessing existing ones
Producing variant call sets for downstream annotation with Ensembl VEP, ANNOVAR, or SnpEff
Use DeepVariant (Google) instead for a faster deep-learning approach with comparable accuracy
Use bcftools call instead for rapid variant calling without assembly-based local realignment
Prerequisites
Software: GATK4, Java 17+, samtools, BWA-MEM2
Reference files: genome FASTA + known variants VCF (dbSNP, 1000G, Mills indels)
Input: duplicate-marked, sorted BAM with @RG read group headers (from BWA-MEM2)
Check before installing: The tool may already be available in the current environment (e.g., inside a pixi / conda env). Run command -v gatk first and skip the install commands below if it returns a path. When running inside a pixi project, invoke the tool via pixi run gatk rather than bare gatk.
# Install GATK4
wget https://github.com/broadinstitute/gatk/releases/download/4.6.0.0/gatk-4.6.0.0.zip
unzip gatk-4.6.0.0.zip
export GATK="$PWD/gatk-4.6.0.0/gatk"
# Or with conda
conda install -c bioconda gatk4
# Verify
gatk --version
# GATK v4.6.0.0
# Download GATK resource bundle files (GRCh38)
# From gs://gcp-public-data--broad-references/hg38/v0/ (requires gsutil or Broad FTP)