deepvariant - NVIDIA Docs

Run a GPU-accelerated DeepVariant algorithm.

What is DeepVariant?

DeepVariant is a deep learning based variant caller developed by Google for germline variant calling of high-throughput sequencing data. It works by taking aligned sequencing reads in BAM/CRAM format and utilizes a convolutional neural network (CNN) to classify the locus into true underlying genomic variation or sequencing error. DeepVariant can therefore call single nucleotide variants (SNVs) and insertions/deletions (InDels) from sequencing data at high accuracy in germline samples.

Why DeepVariant?

DeepVariant’s approach is able to detect variants that are often missed by traditional (for example Bayesian) variant callers, and is known to reduce false positives. It offers several advantages over similar tools, including its ability to detect a wide range of variants with high accuracy, its scalability for analyzing large datasets, and its open source availability. Additionally, its deep learning-based approach allows it to provide better support for different sequencing platforms, as it can be retrained to provide higher accuracy for specific protocols or research areas.

How should I use DeepVariant?

DeepVariant is designed for use as a germline variant caller that can apply different models trained for specific sample types (such as whole genome and whole exome samples) to yield higher accuracy results. DeepVariant can be deployed within NVIDIA’s Parabricks software suite, which is designed for accelerated secondary analysis in genomics, bringing industry standard tools and workflows from CPU to GPU, and delivering the same results at up to 60x faster runtimes. A 30x whole genome can be run through DeepVariant in as little as 8 minutes on an NVIDIA DGX station, compared to 5 hours on a CPU instance (m5.24xlarge, 96 x vCPU). DeepVariant in Parabricks is deployed in the same way as other command line tools that users are familiar with: It takes a BAM/CRAM and the reference genome as inputs, and produces the variants (a VCF file) as outputs. Currently, DeepVariant is supported for V100 and newer GPUs out of the box.

Note

In version 3.8 the --run-partition option was added, which can lead to a significant speed increase. However, using the --run-partition, --proposed-variants, and --gvcf options at the same time will lead to a substantial slowdown. A warning will be issued and the --run-partition option will be ignored.

Quick Start

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            # This command assumes all the inputs are in INPUT_DIR and all the outputs go to OUTPUT_DIR.
$ docker run --rm --gpus all --volume INPUT_DIR:/workdir --volume OUTPUT_DIR:/outputdir \
    --workdir /workdir \
    nvcr.io/nvidia/clara/clara-parabricks:4.1.0-1 \
    pbrun deepvariant \
    --ref /workdir/${REFERENCE_FILE} \
    --in-bam /workdir/${INPUT_BAM} \
    --out-variants /outputdir/${OUTPUT_VCF}

Compatible Google DeepVariant Commands

The commands below are the Google counterpart of the Parabricks command above. The output from these commands will be identical to the output from the above command. See the Output Comparison page for comparing the results.

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            sudo docker run \
--volume <INPUT_DIR>:/input \
--volume <OUTPUT_DIR>:/output \
google/deepvariant:1.5.0 \
/opt/deepvariant/bin/run_deepvariant \
--model_type WGS \
--ref /input/${REFERENCE_FILE} \
--reads /input/${INPUT_BAM} \
--output_vcf /output/${OUTPUT_VCF} \
--num_shards $(nproc) \
--make_examples_extra_args "ws_use_window_selector_model=true"