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Deep learning for regulatory genomics

Nature Biotechnology volume 33, pages 825–826 (2015)Cite this article

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Computational modeling of DNA and RNA targets of regulatory proteins is improved by a deep-learning approach.

A fundamental unit of gene-regulatory control is the contact between a regulatory protein and its target DNA or RNA molecule. Biophysical models that directly predict these interactions are incomplete and confined to specific types of structures, but computational analysis of large-scale experimental datasets allows regulatory motifs to be identified by their over-representation in target sequences. In this issue, Alipanahi et al.1 describe the use of a deep learning strategy to calculate protein–nucleic acid interactions from diverse experimental data sets. They show that their algorithm, called DeepBind, is broadly applicable and results in increased predictive power compared to traditional single-domain methods, and they use its predictions to discover regulatory motifs, to predict RNA editing and alternative splicing, and to interpret genetic variants.

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Figure 1: Illustration of the deep convolutional neural network designed by Alipanahi et al.1.

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Authors and Affiliations

  1. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Yongjin Park & Manolis Kellis

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  1. Yongjin Park
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  2. Manolis Kellis
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Correspondence to Manolis Kellis.

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The authors declare no competing financial interests.

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Park, Y., Kellis, M. Deep learning for regulatory genomics. Nat Biotechnol 33, 825–826 (2015). https://doi.org/10.1038/nbt.3313

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