RFECS: a random-forest based algorithm for enhancer identification from chromatin state

Transcriptional enhancers play critical roles in regulation of gene expression, but their identification in the eukaryotic genome has been challenging. Recently, it was shown that enhancers in the mammalian genome are associated with characteristic histone modification patterns, which have been incr...

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Bibliographic Details
Published in:PLoS computational biology 2013-03, Vol.9 (3), p.e1002968
Main Authors: Rajagopal, Nisha, Xie, Wei, Li, Yan, Wagner, Uli, Wang, Wei, Stamatoyannopoulos, John, Ernst, Jason, Kellis, Manolis, Ren, Bing
Format: Article
Language:English
Subjects:
Algorithms
Area Under Curve
Binding Sites
Biology
Cell Line
Chromatin
Chromatin - chemistry
Chromatin - genetics
Chromatin - metabolism
Cluster Analysis
Computational Biology - methods
Computer Science
Databases, Genetic
Decision Trees
Enhancer Elements, Genetic - genetics
Gene expression
Genetic algorithms
Genetic aspects
Genomes
Histones
Histones - genetics
Histones - metabolism
Humans
Neural networks
Physiological aspects
Proteins
Ratios
Regulation
Reproducibility of Results
Stem cells
Transcription Factors - genetics
Transcription Factors - metabolism
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Summary:Transcriptional enhancers play critical roles in regulation of gene expression, but their identification in the eukaryotic genome has been challenging. Recently, it was shown that enhancers in the mammalian genome are associated with characteristic histone modification patterns, which have been increasingly exploited for enhancer identification. However, only a limited number of cell types or chromatin marks have previously been investigated for this purpose, leaving the question unanswered whether there exists an optimal set of histone modifications for enhancer prediction in different cell types. Here, we address this issue by exploring genome-wide profiles of 24 histone modifications in two distinct human cell types, embryonic stem cells and lung fibroblasts. We developed a Random-Forest based algorithm, RFECS (Random Forest based Enhancer identification from Chromatin States) to integrate histone modification profiles for identification of enhancers, and used it to identify enhancers in a number of cell-types. We show that RFECS not only leads to more accurate and precise prediction of enhancers than previous methods, but also helps identify the most informative and robust set of three chromatin marks for enhancer prediction.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1002968