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PMID: 26213851 Published · ppublish English Journal Article Research Support, Non-U.S. Gov't

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

Nature biotechnology ·Vol. 33 ·No. 8 ·2015-08-00 ·Pages 831-8

Alipanahi B, Delong A, Weirauch MT, Frey BJ

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.

MeSH Terms
Computational Biology/methods DNA-Binding Proteins/chemistry Position-Specific Scoring Matrices RNA-Binding Proteins/chemistry Sequence Analysis, Protein/methods Software
Chemicals
DNA-Binding Proteins RNA-Binding Proteins
Authors & Affiliations
4 authors, click to expand affiliations / ORCID
Alipanahi Babak
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.
Delong Andrew
Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada.
Weirauch Matthew T
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.
Frey Brendan J
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.
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Article Info
Journal
Nature biotechnology
Abbr.
Nat Biotechnol
ISSN
1546-1696
DOI
10.1038/nbt.3300
Published
2015-08-00
Epub
2015-00-27
Pages
831-8
Language
English
Region
United States
NLM ID
9604648
Subset
IM
Grants
Canadian Institutes of Health Research · OGP-106690 · Canada
Corrections
CommentIn
PMID: 26252139
External Links
PubMed source DOI: 10.1038/nbt.3300 View journal details
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