skip to main content

DOE PAGESDOE PAGES

Title: Improved regulatory element prediction based on tissue-specific local epigenomic signatures

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:
 [1] ;  [2] ;  [3] ;  [4] ;  [4] ;  [2] ;  [5] ;  [6] ;  [7] ;  [8] ;  [9]
  1. Salk Inst. for Biological Studies, La Jolla, CA (United States). Genomic Analysis Lab.; Univ. of California, San Diego, CA (United States). Bioinformatics Program
  2. Univ. of California, San Diego, CA (United States). Ludwig Inst. for Cancer Research
  3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  4. Salk Inst. for Biological Studies, La Jolla, CA (United States). Genomic Analysis Lab.
  5. Univ. of California, San Diego, CA (United States), Inst. for Human Genetics; Univ. of California, San Francisco, CA (United States). Dept. of Neurology
  6. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); Univ. of California, Merced, CA (United States). School of Natural Sciences
  7. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States)
  8. Univ. of California, San Diego, CA (United States). Ludwig Inst. for Cancer Research; Univ. of California, San Diego, CA (United States). Dept. of Cellular and Molecular Medicine
  9. Salk Inst. for Biological Studies, La Jolla, CA (United States). Genomic Analysis Lab.; Salk Inst. for Biological Studies, La Jolla, CA (United States). Howard Hughes Medical Inst.
Publication Date:
Grant/Contract Number:
AC02-05CH11231; U54 HG006997; K12 GM068524; GBMF3034; R01 MH094670; U01 MH105985; GC1R-06673-B
Type:
Published Article
Journal Name:
Proceedings of the National Academy of Sciences of the United States of America
Additional Journal Information:
Journal Volume: 114; Journal Issue: 9; Journal ID: ISSN 0027-8424
Publisher:
National Academy of Sciences, Washington, DC (United States)
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
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 59 BASIC BIOLOGICAL SCIENCES; enhancer prediction; DNA methylation; bioinformatics; gene regulation; epigenetics
OSTI Identifier:
1343636
Alternate Identifier(s):
OSTI ID: 1393127

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., 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. 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, and Ecker, Joseph R.. 2017. "Improved regulatory element prediction based on tissue-specific local epigenomic signatures". United States. doi: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}
}

Works referenced in this record:

Random Forests
journal, January 2001