Publication Date:
2015-10-15
Description:
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 subnetworks 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.
Keywords:
Computational Methods, Genomics, Transcriptome Mapping - Monitoring Gene Expression
Print ISSN:
0305-1048
Electronic ISSN:
1362-4962
Topics:
Biology
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