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PLoS Comput Biol. 2014 Jun 26;10(6):e1003677. doi: 10.1371/journal.pcbi.1003677. eCollection 2014 Jun.

Integrating diverse datasets improves developmental enhancer prediction.

Author information

1
Gladstone Institute of Cardiovascular Disease, San Francisco, California, United States of America; Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America.
2
Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America; Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America.
3
Gladstone Institute of Cardiovascular Disease, San Francisco, California, United States of America.
4
Department of Developmental Biology and Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
5
Gladstone Institute of Cardiovascular Disease, San Francisco, California, United States of America; Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America; Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America.
6
Center for Human Genetics Research and Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, United States of America.

Abstract

Gene-regulatory enhancers have been identified using various approaches, including evolutionary conservation, regulatory protein binding, chromatin modifications, and DNA sequence motifs. To integrate these different approaches, we developed EnhancerFinder, a two-step method for distinguishing developmental enhancers from the genomic background and then predicting their tissue specificity. EnhancerFinder uses a multiple kernel learning approach to integrate DNA sequence motifs, evolutionary patterns, and diverse functional genomics datasets from a variety of cell types. In contrast with prediction approaches that define enhancers based on histone marks or p300 sites from a single cell line, we trained EnhancerFinder on hundreds of experimentally verified human developmental enhancers from the VISTA Enhancer Browser. We comprehensively evaluated EnhancerFinder using cross validation and found that our integrative method improves the identification of enhancers over approaches that consider a single type of data, such as sequence motifs, evolutionary conservation, or the binding of enhancer-associated proteins. We find that VISTA enhancers active in embryonic heart are easier to identify than enhancers active in several other embryonic tissues, likely due to their uniquely high GC content. We applied EnhancerFinder to the entire human genome and predicted 84,301 developmental enhancers and their tissue specificity. These predictions provide specific functional annotations for large amounts of human non-coding DNA, and are significantly enriched near genes with annotated roles in their predicted tissues and lead SNPs from genome-wide association studies. We demonstrate the utility of EnhancerFinder predictions through in vivo validation of novel embryonic gene regulatory enhancers from three developmental transcription factor loci. Our genome-wide developmental enhancer predictions are freely available as a UCSC Genome Browser track, which we hope will enable researchers to further investigate questions in developmental biology.

PMID:
24967590
PMCID:
PMC4072507
DOI:
10.1371/journal.pcbi.1003677
[Indexed for MEDLINE]
Free PMC Article

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