deepvariant

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

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DeepVariant is a deep learning-based variant caller that takes aligned reads (in
BAM or CRAM format), produces pileup image tensors from them, classifies each
tensor using a convolutional neural network, and finally reports the results in
a standard VCF or gVCF file.

DeepVariant supports germline variant-calling in diploid organisms.

Please also note:

  • For somatic data or any other samples where the genotypes go beyond two
    copies of DNA, DeepVariant will not work out of the box because the only
    genotypes supported are hom-alt, het, and hom-ref.
  • The models included with DeepVariant are only trained on human data. For
    other organisms, see the
    blog post on non-human variant-calling
    for some possible pitfalls and how to handle them.

DeepTrio

DeepTrio is a deep learning-based trio variant caller built on top of
DeepVariant. DeepTrio extends DeepVariant’s functionality, allowing it to
utilize the power of neural networks to predict genomic variants in trios or
duos. See this page for more details and
instructions on how to run DeepTrio.

DeepTrio supports germline variant-calling in diploid organisms for the
following types of input data:

Please also note:

  • All DeepTrio models were trained on human data.
  • It is possible to use DeepTrio with only 2 samples (child, and one parent).
  • External tool GLnexus is used to
    merge output VCFs.

How to run DeepVariant

We recommend using our Docker solution. The command will look like this:

BIN_VERSION="1.6.1"
docker run \
  -v "YOUR_INPUT_DIR":"/input" \
  -v "YOUR_OUTPUT_DIR:/output" \
  google/deepvariant:"${BIN_VERSION}" \
  /opt/deepvariant/bin/run_deepvariant \
  --model_type=WGS \ **Replace this string with exactly one of the following [WGS,WES,PACBIO,ONT_R104,HYBRID_PACBIO_ILLUMINA]**
  --ref=/input/YOUR_REF \
  --reads=/input/YOUR_BAM \
  --output_vcf=/output/YOUR_OUTPUT_VCF \
  --output_gvcf=/output/YOUR_OUTPUT_GVCF \
  --num_shards=$(nproc) \ **This will use all your cores to run make_examples. Feel free to change.**
  --logging_dir=/output/logs \ **Optional. This saves the log output for each stage separately.
  --haploid_contigs="chrX,chrY" \ **Optional. Heterozygous variants in these contigs will be re-genotyped as the most likely of reference or homozygous alternates. For a sample with karyotype XY, it should be set to "chrX,chrY" for GRCh38 and "X,Y" for GRCh37. For a sample with karyotype XX, this should not be used.
  --par_regions_bed="/input/GRCh3X_par.bed" \ **Optional. If --haploid_contigs is set, then this can be used to provide PAR regions to be excluded from genotype adjustment. Download links to this files are available in this page.
  --dry_run=false **Default is false. If set to true, commands will be printed out but not executed.

For details on X,Y support, please see
DeepVariant haploid support and the case
study in
DeepVariant X, Y case study. You
can download the PAR bed files from here:
GRCh38_par.bed,
GRCh37_par.bed.

To see all flags you can use, run: docker run google/deepvariant:"${BIN_VERSION}"

If you’re using GPUs, or want to use Singularity instead, see
Quick Start for more details or see all the
setup options available.

For more information, also see:

How to cite

If you’re using DeepVariant in your work, please cite:

A universal SNP and small-indel variant caller using deep neural networks. Nature Biotechnology 36, 983–987 (2018).

Ryan Poplin, Pi-Chuan Chang, David Alexander, Scott Schwartz, Thomas Colthurst, Alexander Ku, Dan Newburger, Jojo Dijamco, Nam Nguyen, Pegah T. Afshar, Sam S. Gross, Lizzie Dorfman, Cory Y. McLean, and Mark A. DePristo.

doi: https://doi.org/10.1038/nbt.4235

Additionally, if you are generating multi-sample calls using our
DeepVariant and GLnexus Best Practices, please
cite:

Accurate, scalable cohort variant calls using DeepVariant and GLnexus.
Bioinformatics (2021).

Taedong Yun, Helen Li, Pi-Chuan Chang, Michael F. Lin, Andrew Carroll, and Cory
Y. McLean.

doi: https://doi.org/10.1093/bioinformatics/btaa1081

Why Use DeepVariant?

  • High accuracy - DeepVariant won 2020
    PrecisionFDA Truth Challenge V2
    for All Benchmark Regions for ONT, PacBio, and Multiple Technologies
    categories, and 2016
    PrecisionFDA Truth Challenge
    for best SNP Performance. DeepVariant maintains high accuracy across data
    from different sequencing technologies, prep methods, and species. For
    lower coverage,
    using DeepVariant makes an especially great difference. See
    metrics for the latest accuracy numbers on each of the
    sequencing types.
  • Flexibility - Out-of-the-box use for
    PCR-positive
    samples and
    low quality sequencing runs,
    and easy adjustments for
    different sequencing technologies
    and
    non-human species.
  • Ease of use - No filtering is needed beyond setting your preferred
    minimum quality threshold.
  • Cost effectiveness - With a single non-preemptible n1-standard-16
    machine on Google Cloud, it costs ~$11.8 to call a 30x whole genome and
    ~$0.89 to call an exome. With preemptible pricing, the cost is $2.84 for a
    30x whole genome and $0.21 for whole exome (not considering preemption).
  • Speed - See metrics for the runtime of all supported
    datatypes on a 64-core CPU-only machine. Multiple options for
    acceleration exist.
  • Usage options - DeepVariant can be run via Docker or binaries, using
    both on-premise hardware or in the cloud, with support for hardware
    accelerators like GPUs and TPUs.

(1): Time estimates do not include mapping.

How DeepVariant works

Stages in DeepVariant

For more information on the pileup images and how to read them, please see the
“Looking through DeepVariant’s Eyes” blog post.

DeepVariant relies on Nucleus, a library of
Python and C++ code for reading and writing data in common genomics file formats
(like SAM and VCF) designed for painless integration with the
TensorFlow machine learning framework. Nucleus
was built with DeepVariant in mind and open-sourced separately so it can be used
by anyone in the genomics research community for other projects. See this blog
post on
Using Nucleus and TensorFlow for DNA Sequencing Error Correction.

DeepVariant Setup

Prerequisites

  • Unix-like operating system (cannot run on Windows)
  • Python 3.8

Official Solutions

Below are the official solutions provided by the
Genomics team in Google Health.

Name Description
Docker This is the recommended method.
Build from source DeepVariant comes with scripts to build it on Ubuntu 20.04. To build and run on other Unix-based systems, you will need to modify these scripts.
Prebuilt Binaries Available at gs://deepvariant/. These are compiled to use SSE4 and AVX instructions, so you will need a CPU (such as Intel Sandy Bridge) that supports them. You can check the /proc/cpuinfo file on your computer, which lists these features under “flags”.

Contribution Guidelines

Please open a pull request if
you wish to contribute to DeepVariant. Note, we have not set up the
infrastructure to merge pull requests externally. If you agree, we will test and
submit the changes internally and mention your contributions in our
release notes. We apologize
for any inconvenience.

If you have any difficulty using DeepVariant, feel free to
open an issue. If you have
general questions not specific to DeepVariant, we recommend that you post on a
community discussion forum such as BioStars.

License

BSD-3-Clause license

Acknowledgements

DeepVariant happily makes use of many open source packages. We would like to
specifically call out a few key ones:

We thank all of the developers and contributors to these packages for their
work.

Disclaimer

This is not an official Google product.

NOTE: the content of this research code repository (i) is not intended to be a
medical device; and (ii) is not intended for clinical use of any kind, including
but not limited to diagnosis or prognosis.