期刊
NUCLEIC ACIDS RESEARCH
卷 43, 期 18, 页码 8694-8712出版社
OXFORD UNIV PRESS
DOI: 10.1093/nar/gkv865
关键词
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资金
- NIAID
- NIEHS
- NINDS
- NIDCD
- NIAAA of the NIH [U54AI117924]
- Sloan foundation
- NLM training, Computation and Informatics in Biology and Medicine Training Program [NLM 5T15LM007359]
- UW Madison startup funds
- tier II Canada Research Chair
- NSERC [436194-2013]
Long range regulatory interactions among distal enhancers and target genes are important for tissue-specific gene expression. Genome-scale identification of these interactions in a cell line-specific manner, especially using the fewest possible datasets, is a significant challenge. We develop a novel computational approach, Regulatory Interaction Prediction for Promoters and Long-range Enhancers (RIPPLE), that integrates published Chromosome Conformation Capture (3C) data sets with a minimal set of regulatory genomic data sets to predict enhancer-promoter interactions in a cell line-specific manner. Our results suggest that CTCF, RAD21, a general transcription factor (TBP) and activating chromatin marks are important determinants of enhancer-promoter interactions. To predict interactions in a new cell line and to generate genome-wide interaction maps, we develop an ensemble version of RIPPLE and apply it to generate interactions in five human cell lines. Computational validation of these predictions using existing ChIA-PET and Hi-C data sets showed that RIPPLE accurately predicts interactions among enhancers and promoters. Enhancer-promoter interactions tend to be organized into sub-networks representing coordinately regulated sets of genes that are enriched for specific biological processes and cis-regulatory elements. Overall, our work provides a systematic approach to predict and interpret enhancer-promoter interactions in a genome-wide cell-type specific manner using a few experimentally tractable measurements.
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