Improved regulatory element prediction based on tissue-specific local epigenomic signatures
Abstract
Accurate enhancer identification is critical for understanding the spatiotemporal transcriptional regulation during development as well as the functional impact of disease-related noncoding genetic variants. Computational methods have been developed to predict the genomic locations of active enhancers based on histone modifications, but the accuracy and resolution of these methods remain limited. Here, we present an algorithm, regulator y element prediction based on tissue-specific local epigenetic marks (REPTILE), which integrates histone modification and whole-genome cytosine DNA methylation profiles to identify the precise location of enhancers. We tested the ability of REPTILE to identify enhancers previously validated in reporter assays. Compared with existing methods, REPTILE shows consistently superior performance across diverse cell and tissue types, and the enhancer locations are significantly more refined. We show that, by incorporating base-resolution methylation data, REPTILE greatly improves upon current methods for annotation of enhancers across a variety of cell and tissue types.
- Authors:
-
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037,, Bioinformatics Program, University of California, San Diego, La Jolla, CA 92093,
- Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, CA 92093,
- Lawrence Berkeley National Laboratory, Berkeley, CA 94720,
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037,
- Institute for Human Genetics, University of California, San Francisco, CA 94143,, Department of Neurology, University of California, San Francisco, CA 94143,
- Lawrence Berkeley National Laboratory, Berkeley, CA 94720,, US Department of Energy Joint Genome Institute, Walnut Creek, CA 94598,, School of Natural Sciences, University of California, Merced, CA 95343,
- Lawrence Berkeley National Laboratory, Berkeley, CA 94720,, US Department of Energy Joint Genome Institute, Walnut Creek, CA 94598,
- Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, CA 92093,, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093,
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037,, Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA 92037
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); National Institutes of Health (NIH); Gordon and Betty Moore Foundation
- OSTI Identifier:
- 1343636
- Alternate Identifier(s):
- OSTI ID: 1393127
- Grant/Contract Number:
- AC02-05CH11231; U54 HG006997; K12 GM068524; GBMF3034; R01 MH094670; U01 MH105985; GC1R-06673-B
- Resource Type:
- Published Article
- Journal Name:
- Proceedings of the National Academy of Sciences of the United States of America
- Additional Journal Information:
- Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Volume: 114 Journal Issue: 9; Journal ID: ISSN 0027-8424
- Publisher:
- Proceedings of the National Academy of Sciences
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 60 APPLIED LIFE SCIENCES; 59 BASIC BIOLOGICAL SCIENCES; enhancer prediction; DNA methylation; bioinformatics; gene regulation; epigenetics
Citation Formats
He, Yupeng, Gorkin, David U., Dickel, Diane E., Nery, Joseph R., Castanon, Rosa G., Lee, Ah Young, Shen, Yin, Visel, Axel, Pennacchio, Len A., Ren, Bing, and Ecker, Joseph R. Improved regulatory element prediction based on tissue-specific local epigenomic signatures. United States: N. p., 2017.
Web. doi:10.1073/pnas.1618353114.
He, Yupeng, Gorkin, David U., Dickel, Diane E., Nery, Joseph R., Castanon, Rosa G., Lee, Ah Young, Shen, Yin, Visel, Axel, Pennacchio, Len A., Ren, Bing, & Ecker, Joseph R. Improved regulatory element prediction based on tissue-specific local epigenomic signatures. United States. https://doi.org/10.1073/pnas.1618353114
He, Yupeng, Gorkin, David U., Dickel, Diane E., Nery, Joseph R., Castanon, Rosa G., Lee, Ah Young, Shen, Yin, Visel, Axel, Pennacchio, Len A., Ren, Bing, and Ecker, Joseph R. Mon .
"Improved regulatory element prediction based on tissue-specific local epigenomic signatures". United States. https://doi.org/10.1073/pnas.1618353114.
@article{osti_1343636,
title = {Improved regulatory element prediction based on tissue-specific local epigenomic signatures},
author = {He, Yupeng and Gorkin, David U. and Dickel, Diane E. and Nery, Joseph R. and Castanon, Rosa G. and Lee, Ah Young and Shen, Yin and Visel, Axel and Pennacchio, Len A. and Ren, Bing and Ecker, Joseph R.},
abstractNote = {Accurate enhancer identification is critical for understanding the spatiotemporal transcriptional regulation during development as well as the functional impact of disease-related noncoding genetic variants. Computational methods have been developed to predict the genomic locations of active enhancers based on histone modifications, but the accuracy and resolution of these methods remain limited. Here, we present an algorithm, regulator y element prediction based on tissue-specific local epigenetic marks (REPTILE), which integrates histone modification and whole-genome cytosine DNA methylation profiles to identify the precise location of enhancers. We tested the ability of REPTILE to identify enhancers previously validated in reporter assays. Compared with existing methods, REPTILE shows consistently superior performance across diverse cell and tissue types, and the enhancer locations are significantly more refined. We show that, by incorporating base-resolution methylation data, REPTILE greatly improves upon current methods for annotation of enhancers across a variety of cell and tissue types.},
doi = {10.1073/pnas.1618353114},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
number = 9,
volume = 114,
place = {United States},
year = {2017},
month = {2}
}
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https://doi.org/10.1073/pnas.1618353114
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