bio-variant-calling-deepvariant

name	bio-variant-calling-deepvariant
description	Deep learning-based variant calling with Google DeepVariant. Provides high accuracy for germline SNPs and indels from Illumina, PacBio, and ONT data.
tool_type	cli
primary_tool	DeepVariant

DeepVariant Variant Calling

Installation

Docker (Recommended)

docker pull google/deepvariant:1.6.0

# Or with GPU support
docker pull google/deepvariant:1.6.0-gpu

Singularity

singularity pull docker://google/deepvariant:1.6.0

Basic Usage

One-Step Run (run_deepvariant)

docker run -v "${PWD}:/input" -v "${PWD}/output:/output" \
    google/deepvariant:1.6.0 \
    /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

Model	Data Type	Use Case
WGS	Illumina WGS	Whole genome sequencing
WES	Illumina WES	Whole exome/targeted
PACBIO	PacBio HiFi	Long-read HiFi
ONT_R104	ONT R10.4	Oxford Nanopore
HYBRID_PACBIO_ILLUMINA	Mixed	Hybrid assemblies

Step-by-Step Workflow

For more control, run each step separately:

Step 1: Make Examples

docker run -v "${PWD}:/data" google/deepvariant:1.6.0 \
    /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:1.6.0 \
    /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:1.6.0 \
    /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

docker run --gpus all -v "${PWD}:/data" \
    google/deepvariant:1.6.0-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

docker run -v "${PWD}:/data" google/deepvariant:1.6.0 \
    /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

docker run -v "${PWD}:/data" google/deepvariant:1.6.0 \
    /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

docker run -v "${PWD}:/data" google/deepvariant:1.6.0 \
    /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

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:1.6.0 \
        /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
DeepVariantWGS	Illumina WGS
DeepVariantWES	Illumina exome
DeepVariant_unfiltered	Keep all variants

Output Quality Metrics

# 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

# 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

#!/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 "${PWD}:/data" google/deepvariant:1.6.0 \
    /opt/deepvariant/bin/run_deepvariant \
    --model_type=${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