This tool is a deep-learning-based germline variant caller that can apply different models trained for specific sample types (such as whole genome vs. whole exome sequencing) to gain a higher accuracy result.

Parabricks has accelerated the Google Deepvariant to extensively use GPUs and finish 30x WGS analysis in 25 minutes instead of hours. The Parabricks flavor of Deepvariant is more like other command line tools that users are familiar with: It takes a BAM and reference as inputs and produces variants as outputs.

Currently, Deepvariant is supported for T4, V100, and A100 GPUs out of the box. Please visit the Models for additional GPUs section for more details.

For further information visit the deepvariant help page.

Quick Start

# 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
    -w /workdir \
    nvcr.io/nvidia/clara/clara-parabricks:<VERSION-TAG> \
    pbrun deepvariant \
    --ref /workdir/${REFERENCE_FILE} \
    --in-bam /workdir/${INPUT_BAM} \
    --out-variants /outputdir/${OUTPUT_VCF}