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Nat Biotechnol. 2015 Aug;33(8):831-8. doi: 10.1038/nbt.3300. Epub 2015 Jul 27.

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning.

Author information

1
1] Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada. [2] Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada.
2
Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada.
3
1] Canadian Institute for Advanced Research, Programs on Genetic Networks and Neural Computation, Toronto, Ontario, Canada. [2] Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA. [3] Divisions of Biomedical Informatics and Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
4
1] Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada. [2] Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada. [3] Canadian Institute for Advanced Research, Programs on Genetic Networks and Neural Computation, Toronto, Ontario, Canada.

Abstract

Knowing the sequence specificities of DNA- and RNA-binding proteins is essential for developing models of the regulatory processes in biological systems and for identifying causal disease variants. Here we show that sequence specificities can be ascertained from experimental data with 'deep learning' techniques, which offer a scalable, flexible and unified computational approach for pattern discovery. Using a diverse array of experimental data and evaluation metrics, we find that deep learning outperforms other state-of-the-art methods, even when training on in vitro data and testing on in vivo data. We call this approach DeepBind and have built a stand-alone software tool that is fully automatic and handles millions of sequences per experiment. Specificities determined by DeepBind are readily visualized as a weighted ensemble of position weight matrices or as a 'mutation map' that indicates how variations affect binding within a specific sequence.

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PMID:
26213851
DOI:
10.1038/nbt.3300
[Indexed for MEDLINE]

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