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Proc Natl Acad Sci U S A. 2017 Feb 28;114(9):E1633-E1640. doi: 10.1073/pnas.1618353114. Epub 2017 Feb 13.

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

Author information

1
Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037.
2
Bioinformatics Program, University of California, San Diego, La Jolla, CA 92093.
3
Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, CA 92093.
4
Lawrence Berkeley National Laboratory, Berkeley, CA 94720.
5
Institute for Human Genetics, University of California, San Francisco, CA 94143.
6
Department of Neurology, University of California, San Francisco, CA 94143.
7
US Department of Energy Joint Genome Institute, Walnut Creek, CA 94598.
8
School of Natural Sciences, University of California, Merced, CA 95343.
9
Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093.
10
Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037; ecker@salk.edu.
11
Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA 92037.

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, regulatory 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. REPTILE is available at https://github.com/yupenghe/REPTILE/.

KEYWORDS:

DNA methylation; bioinformatics; enhancer prediction; epigenetics; gene regulation

PMID:
28193886
PMCID:
PMC5338528
DOI:
10.1073/pnas.1618353114
[Indexed for MEDLINE]
Free PMC Article

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