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Deciphering cis-regulatory grammar with deep learning

Nature Genetics volume 53, pages 266–268 (2021)Cite this article

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A new study builds a novel deep-learning approach to unravel the syntax of transcription-factor binding from high-resolution ChIP–nexus data. In silico simulations lead to experimental validation of complex sequence-based predictions: helical periodicity and directional cooperativity between transcription factors.

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Fig. 1: BPNet simulations predict a soft TF-motif syntax in mouse embryonic stem cells.

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Authors and Affiliations

  1. Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Emily R. Miraldi & Matthew T. Weirauch

  2. Division of Immunobiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Emily R. Miraldi

  3. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA

    Emily R. Miraldi & Matthew T. Weirauch

  4. Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Xiaoting Chen & Matthew T. Weirauch

  5. Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Matthew T. Weirauch

Authors
  1. Emily R. Miraldi
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  2. Xiaoting Chen
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  3. Matthew T. Weirauch
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Corresponding author

Correspondence to Matthew T. Weirauch.

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The authors declare no competing interests.

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Miraldi, E.R., Chen, X. & Weirauch, M.T. Deciphering cis-regulatory grammar with deep learning. Nat Genet 53, 266–268 (2021). https://doi.org/10.1038/s41588-021-00814-1

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