Summary This source did not publish a separate summary. Review SKILL.md before using the skill.
Version Compatibility
Reference examples tested with: GATK 4.5+, bcftools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
CLI: <tool> --version then <tool> --help to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
DeepVariant Variant Calling
Installation
Goal: Install DeepVariant via Docker or Singularity container.
Approach: Pull the pre-built container image matching the target platform (CPU or GPU).
Docker (Recommended)
docker pull google/deepvariant:1.6.1
# Or with GPU support
docker pull google/deepvariant:1.6.1-gpu
Singularity
singularity pull docker://google/deepvariant:1.6.1
Basic Usage
Goal: Call germline variants from aligned reads using DeepVariant's deep learning model.
Approach: Run the all-in-one run_deepvariant wrapper specifying model type, reference, reads, and output paths.
"Call variants with DeepVariant" → Convert aligned read pileups into image tensors, classify with a CNN, and output genotyped VCF.
One-Step Run (run_deepvariant)
docker run -v "${PWD}:/input" -v "${PWD}/output:/output" \
google/deepvariant:1.6.1 \
/opt/deepvariant/bin/run_deepvariant \
--model_type=WGS \
--ref=/input/reference.fa \
--reads=/input/sample.bam \
--output_vcf=/output/sample.vcf.gz \
--output_gvcf=/output/sample.g.vcf.gz \
--num_shards=16
Model Types
WGSIllumina WGS Whole genome sequencing WESIllumina WES Whole exome/targeted PACBIOPacBio HiFi Long-read HiFi ONT_R104ONT R10.4 Oxford Nanopore HYBRID_PACBIO_ILLUMINAMixed Hybrid assemblies
Step-by-Step Workflow Goal: Run DeepVariant in three explicit stages for more control over intermediate outputs.
Approach: Generate pileup image tensors (make_examples), classify with the CNN (call_variants), then merge and genotype (postprocess_variants).
For more control, run each step separately:
Step 1: Make Examples docker run -v "${PWD}:/data" google/deepvariant:1.6.1 \
/opt/deepvariant/bin/make_examples \
--mode calling \
--ref /data/reference.fa \
--reads /data/sample.bam \
--examples /data/examples.tfrecord.gz \
--gvcf /data/gvcf.tfrecord.gz
Step 2: Call Variants docker run -v "${PWD}:/data" google/deepvariant:1.6.1 \
/opt/deepvariant/bin/call_variants \
--outfile /data/call_variants.tfrecord.gz \
--examples /data/examples.tfrecord.gz \
--checkpoint /opt/models/wgs/model.ckpt
Step 3: Postprocess Variants docker run -v "${PWD}:/data" google/deepvariant:1.6.1 \
/opt/deepvariant/bin/postprocess_variants \
--ref /data/reference.fa \
--infile /data/call_variants.tfrecord.gz \
--outfile /data/output.vcf.gz \
--gvcf_outfile /data/output.g.vcf.gz \
--nonvariant_site_tfrecord_path /data/gvcf.tfrecord.gz
GPU Acceleration Goal: Speed up DeepVariant inference using GPU hardware.
Approach: Use the GPU-enabled container image with Docker --gpus flag.
docker run --gpus all -v "${PWD}:/data" \
google/deepvariant:1.6.1-gpu \
/opt/deepvariant/bin/run_deepvariant \
--model_type=WGS \
--ref=/data/reference.fa \
--reads=/data/sample.bam \
--output_vcf=/data/output.vcf.gz \
--num_shards=16
PacBio HiFi Calling Goal: Call variants from PacBio HiFi long reads.
Approach: Use the PACBIO model type which is trained on HiFi read characteristics.
docker run -v "${PWD}:/data" google/deepvariant:1.6.1 \
/opt/deepvariant/bin/run_deepvariant \
--model_type=PACBIO \
--ref=/data/reference.fa \
--reads=/data/hifi_aligned.bam \
--output_vcf=/data/hifi_variants.vcf.gz \
--num_shards=16
ONT Calling Goal: Call variants from Oxford Nanopore long reads.
Approach: Use the ONT_R104 model type trained on Nanopore R10.4 chemistry.
docker run -v "${PWD}:/data" google/deepvariant:1.6.1 \
/opt/deepvariant/bin/run_deepvariant \
--model_type=ONT_R104 \
--ref=/data/reference.fa \
--reads=/data/ont_aligned.bam \
--output_vcf=/data/ont_variants.vcf.gz \
--num_shards=16
Exome/Targeted Sequencing Goal: Call variants from exome or targeted panel data.
Approach: Use WES model type with a BED file restricting calling to target regions.
docker run -v "${PWD}:/data" google/deepvariant:1.6.1 \
/opt/deepvariant/bin/run_deepvariant \
--model_type=WES \
--ref=/data/reference.fa \
--reads=/data/exome.bam \
--regions=/data/targets.bed \
--output_vcf=/data/exome_variants.vcf.gz \
--num_shards=8
Joint Calling with GLnexus Goal: Perform joint genotyping across a cohort from DeepVariant gVCFs.
Approach: Generate per-sample gVCFs, then merge and jointly genotype with GLnexus using a DeepVariant-specific config.
For multi-sample cohorts, use gVCFs with GLnexus:
# Generate gVCFs for each sample
for bam in *.bam; do
sample=$(basename $bam .bam)
docker run -v "${PWD}:/data" google/deepvariant:1.6.1 \
/opt/deepvariant/bin/run_deepvariant \
--model_type=WGS \
--ref=/data/reference.fa \
--reads=/data/$bam \
--output_vcf=/data/${sample}.vcf.gz \
--output_gvcf=/data/${sample}.g.vcf.gz \
--num_shards=16
done
# Joint genotyping with GLnexus
docker run -v "${PWD}:/data" quay.io/mlin/glnexus:v1.4.1 \
/usr/local/bin/glnexus_cli \
--config DeepVariantWGS \
/data/*.g.vcf.gz \
| bcftools view - -Oz -o cohort.vcf.gz
GLnexus Configurations Config Use Case DeepVariantWGSIllumina WGS DeepVariantWESIllumina exome DeepVariant_unfilteredKeep all variants
Output Quality Metrics Goal: Assess the quality of DeepVariant calls.
Approach: Generate summary statistics with bcftools stats and check Ti/Tv ratio as a quality indicator.
# Variant statistics
bcftools stats output.vcf.gz > stats.txt
# Filter by quality
bcftools view -i 'QUAL>20 && FMT/GQ>20' output.vcf.gz -Oz -o filtered.vcf.gz
# Ti/Tv ratio (expect ~2.0-2.1 for WGS)
bcftools stats output.vcf.gz | grep TSTV
Benchmarking Against Truth Set Goal: Evaluate DeepVariant accuracy against a GIAB truth set.
Approach: Run hap.py to compute precision, recall, and F1 for SNPs and indels.
# Using hap.py for GIAB benchmarking
docker run -v "${PWD}:/data" jmcdani20/hap.py:latest \
/opt/hap.py/bin/hap.py \
/data/HG002_GRCh38_truth.vcf.gz \
/data/deepvariant_output.vcf.gz \
-r /data/reference.fa \
-o /data/benchmark \
--threads 16
Complete Workflow Script Goal: Run DeepVariant end-to-end with indexing and statistics in a single script.
Approach: Wrap run_deepvariant, bcftools index, and bcftools stats in a parameterized shell script.
#!/bin/bash
set -euo pipefail
BAM=$1
REFERENCE=$2
OUTPUT_PREFIX=$3
MODEL_TYPE=${4:-WGS}
THREADS=${5:-16}
echo "=== DeepVariant: ${MODEL_TYPE} mode ==="
docker run -v "${PWD}:/data" google/deepvariant:1.6.1 \
/opt/deepvariant/bin/run_deepvariant \
--model_type=${MODEL_TYPE} \
--ref=/data/${REFERENCE} \
--reads=/data/${BAM} \
--output_vcf=/data/${OUTPUT_PREFIX}.vcf.gz \
--output_gvcf=/data/${OUTPUT_PREFIX}.g.vcf.gz \
--intermediate_results_dir=/data/${OUTPUT_PREFIX}_tmp \
--num_shards=${THREADS}
echo "=== Indexing ==="
bcftools index -t ${OUTPUT_PREFIX}.vcf.gz
bcftools index -t ${OUTPUT_PREFIX}.g.vcf.gz
echo "=== Statistics ==="
bcftools stats ${OUTPUT_PREFIX}.vcf.gz > ${OUTPUT_PREFIX}_stats.txt
echo "=== Complete ==="
echo "VCF: ${OUTPUT_PREFIX}.vcf.gz"
echo "gVCF: ${OUTPUT_PREFIX}.g.vcf.gz"
Comparison with Other Callers Caller Speed Accuracy Best For DeepVariant Moderate Highest Production, benchmarking GATK HaplotypeCaller Moderate High GATK ecosystem bcftools Fast Good Quick analysis Clair3 Fast High Long reads
Resource Requirements Data Type Memory CPU Time (30x WGS) WGS 64 GB ~4-6 hours WES 32 GB ~30 min With GPU 32 GB ~1-2 hours (WGS)
Related Skills
variant-calling/gatk-variant-calling - GATK alternative
variant-calling/variant-calling - bcftools calling
long-read-sequencing/clair3-variants - Long-read alternative
variant-calling/filtering-best-practices - Post-calling filtering
