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A universal SNP and small-indel variant caller using deep neural networks

Nature Biotechnology volume 36, pages 983–987 (2018)Cite this article

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Abstract

Despite rapid advances in sequencing technologies, accurately calling genetic variants present in an individual genome from billions of short, errorful sequence reads remains challenging. Here we show that a deep convolutional neural network can call genetic variation in aligned next-generation sequencing read data by learning statistical relationships between images of read pileups around putative variant and true genotype calls. The approach, called DeepVariant, outperforms existing state-of-the-art tools. The learned model generalizes across genome builds and mammalian species, allowing nonhuman sequencing projects to benefit from the wealth of human ground-truth data. We further show that DeepVariant can learn to call variants in a variety of sequencing technologies and experimental designs, including deep whole genomes from 10X Genomics and Ion Ampliseq exomes, highlighting the benefits of using more automated and generalizable techniques for variant calling.

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Figure 1: DeepVariant workflow overview.

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Acknowledgements

We thank J. Zook and his collaborators at NIST for their work developing the Genome in a Bottle resources, the Verily sequencing facility for running the NA12878 replicates, and our colleagues at Verily and Google for their feedback on this manuscript and the project in general. This work was supported by internal funding.

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Authors and Affiliations

  1. Verily Life Sciences, Mountain View, California, USA

    Ryan Poplin, Dan Newburger, Jojo Dijamco, Nam Nguyen, Pegah T Afshar, Sam S Gross, Lizzie Dorfman, Cory Y McLean & Mark A DePristo

  2. Google Inc., Mountain View, California, USA

    Ryan Poplin, Pi-Chuan Chang, David Alexander, Scott Schwartz, Thomas Colthurst, Alexander Ku, Lizzie Dorfman, Cory Y McLean & Mark A DePristo

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Contributions

R.P. and M.A.D. designed the study, analyzed and interpreted results and wrote the paper. R.P., P.-C.C., D.A., S.S., T.C., A.K., D.N., J.D., N.N., P.T.A., S.S.G., L.D., C.Y.M. and M.A.D. performed experiments and contributed to the software.

Corresponding author

Correspondence to Mark A DePristo.

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Competing interests

D.N., J.D., N.N., P.T.A. and S.S.G. are employees of Verily Life Sciences. P.-C.C., D.A., S.S, T.C. and A.K. are employees of Google Inc. R.P., L.D., C.Y.M. and M.A.D. are employees of Verily Life Sciences and Google Inc. This work was internally funded by Verily Life Sciences and Google Inc.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1 and 2, Supplementary Tables 1–8 and Supplementary Notes 1–11 (PDF 1348 kb)

Life Sciences Reporting Summary (PDF 142 kb)

Supplementary Data

Evaluation metrics (TXT 28 kb)

Supplementary Software

Benchmarking script (TXT 19 kb)

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Poplin, R., Chang, PC., Alexander, D. et al. A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol 36, 983–987 (2018). https://doi.org/10.1038/nbt.4235

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