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.

Parabricks has accelerated the Google Deepvariant to extensively use GPUs and finish 30x WGS analysis in 4 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.

Getting Started

Quick Start Guide

For further information visit the deepvariant help page.

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