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COMPUTATIONAL BIOLOGY

Automated sequence-based annotation and interpretation of the human genome

Nature Genetics volume 54, pages 916–917 (2022)Cite this article

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A machine-learning model produces summarized sequence representations of genomic regulatory activity, and provides a functional view of regulatory DNA variation in the human genome, with the aim of better understanding the role of sequence variation in health and disease.

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Fig. 1: Conceptual overview of the Sei framework.

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Author information

Authors and Affiliations

  1. Department of Genetics, Stanford University, Palo Alto, CA, USA

    Anshul Kundaje

  2. Department of Computer Science, Stanford University, Palo Alto, CA, USA

    Anshul Kundaje

  3. Altius Institute for Biomedical Sciences, Seattle, WA, USA

    Wouter Meuleman

  4. Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA

    Wouter Meuleman

Authors
  1. Anshul Kundaje
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  2. Wouter Meuleman
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Corresponding authors

Correspondence to Anshul Kundaje or Wouter Meuleman.

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Competing interests

A.K. is scientific co-founder of Ravel Biotechnology, is on the scientific advisory board of PatchBio, SerImmune, AINovo, TensorBio and OpenTargets, is a consultant with Illumina and owns shares in DeepGenomics, Immuni and Freenome. W.M. has no competing interests.

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Kundaje, A., Meuleman, W. Automated sequence-based annotation and interpretation of the human genome. Nat Genet 54, 916–917 (2022). https://doi.org/10.1038/s41588-022-01123-x

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