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A primer on deep learning in genomics

Nature Genetics volume 51, pages 12–18 (2019)Cite this article

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Abstract

Deep learning methods are a class of machine learning techniques capable of identifying highly complex patterns in large datasets. Here, we provide a perspective and primer on deep learning applications for genome analysis. We discuss successful applications in the fields of regulatory genomics, variant calling and pathogenicity scores. We include general guidance for how to effectively use deep learning methods as well as a practical guide to tools and resources. This primer is accompanied by an interactive online tutorial.

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Fig. 1: Deep learning workflow in genomics.
Fig. 2: Applications of deep learning in genomics.

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Acknowledgements

We thank N. Wineinger, R. Dias, J. di Iulio and D. Evans for comments on the paper. The work of A. Telenti, A. Torkamani and P.M. is supported by the Qualcomm Foundation and the NIH Center for Translational Science Award (CTSA, grant UL1TR002550). Further support to A. Torkamani is from U54GM114833 and U24TR002306. J.Z. is supported by a Chan–Zuckerberg Biohub Investigator grant and National Science Foundation (NSF) grant CRII 1657155.

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Authors and Affiliations

  1. Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA

    James Zou

  2. Chan–Zuckerberg Biohub, San Francisco, CA, USA

    James Zou

  3. Department of Electrical Engineering, Stanford University, Palo Alto, CA, USA

    James Zou & Abubakar Abid

  4. Peltarion, Stockholm, Sweden

    Mikael Huss

  5. Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden

    Mikael Huss

  6. Scripps Research Translational Institute, La Jolla, CA, USA

    Pejman Mohammadi, Ali Torkamani & Amalio Telenti

  7. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA

    Pejman Mohammadi, Ali Torkamani & Amalio Telenti

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  1. James Zou
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  2. Mikael Huss
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  3. Abubakar Abid
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  4. Pejman Mohammadi
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Contributions

All authors conceived and designed the project. J.Z., M.H., P.M., A. Torkamani and A. Telenti wrote the manuscript. J.Z. and A.A. wrote the online tutorial.

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Correspondence to James Zou or Amalio Telenti.

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M.H. is an employee of Peltarion.

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Zou, J., Huss, M., Abid, A. et al. A primer on deep learning in genomics. Nat Genet 51, 12–18 (2019). https://doi.org/10.1038/s41588-018-0295-5

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