Agent skill

bio-variant-calling-deepvariant

Deep learning-based variant calling with Google DeepVariant. Provides high accuracy for germline SNPs and indels from Illumina, PacBio, and ONT data. Use when calling variants with DeepVariant deep learning caller.

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Install this agent skill to your Project

npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-variant-calling-deepvariant

SKILL.md

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)

bash
docker pull google/deepvariant:1.6.1

# Or with GPU support
docker pull google/deepvariant:1.6.1-gpu

Singularity

bash
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)

bash
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

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

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

bash
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

bash
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

bash
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.

bash
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.

bash
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.

bash
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.

bash
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:

bash
# 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
DeepVariantWGS Illumina WGS
DeepVariantWES Illumina exome
DeepVariant_unfiltered Keep 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.

bash
# 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.

bash
# 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.

bash
#!/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

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