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

DeepVariant

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

How to run

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

BIN_VERSION="0.9.0"
sudo docker run \
  -v "YOUR_INPUT_DIR":"/input" \
  -v "YOUR_OUTPUT_DIR:/output" \
  google/deepvariant:"${BIN_VERSION}" \
  /opt/deepvariant/bin/run_deepvariant \
  --model_type=WGS \ **Replace this string with exactly one of the following [WGS,WES,PACBIO]**
  --ref=/input/YOUR_REF \
  --reads=/input/YOUR_BAM \
  --output_vcf=/output/YOUR_OUTPUT_VCF \
  --output_gvcf=/output/YOUR_OUTPUT_GVCF \
  --num_shards=$(nproc) **This will use all your cores to run make_examples. Feel free to change.**

For more information, see:

• Quick Start
• Full documentation list
• Best practices for multi-sample variant calling with DeepVariant

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. bioRxiv 10.1101/2020.02.10.942086v1 (2020). Taedong Yun, Helen Li, Pi-Chuan Chang, Michael F. Lin, Andrew Carroll, and Cory Y. McLean. doi: https://doi.org/10.1101/2020.02.10.942086

Why Use DeepVariant?

• High accuracy - In 2016 DeepVariant won PrecisionFDA Truth Challenge for best SNP Performance. DeepVariant maintains high accuracy across data from different sequencing technologies, prep methods, and species.
• 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 ~$9.11 to call a 30x whole genome and ~$0.39 to call an exome. With preemptible pricing, the cost is $2.19 for a 30x whole genome and $0.09 for whole exome (not considering preemption).
• Speed - On a 64-core CPU-only machine, DeepVariant completes a 50x WGS in 5 hours and an exome in 16 minutes (1). Multiple options for acceleration exist, taking the WGS pipeline to as fast as 40 minutes (see external solutions).
• 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.

DeepVariant Setup

Prerequisites

• Unix-like operating system (cannot run on Windows)
• Python 2.7

Official Solutions

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

External Solutions

The following pipelines are not created or maintained by the Genomics team in Google Brain. Please contact the relevant teams if you have any questions or concerns.

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:

• Boost Graph Library
• abseil-cpp and abseil-py
• CLIF
• GNU Parallel
• htslib & samtools
• Nucleus
• numpy
• SSW Library
• TensorFlow and Slim

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

Disclaimer

This is not an official Google product.