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Nucleic Acids Res. 2015 Oct 15;43(18):8694-712. doi: 10.1093/nar/gkv865. Epub 2015 Sep 3.

A predictive modeling approach for cell line-specific long-range regulatory interactions.

Author information

1
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA Wisconsin Institute for Discovery, 330 N. Orchard Street, Madison, WI, USA sroy@biostat.wisc.edu.
2
Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA.
3
Wisconsin Institute for Discovery, 330 N. Orchard Street, Madison, WI, USA.
4
Department of Genome Sciences, University of Washington, Seattle, WA, USA.
5
Morgridge Institute for Research, Madison, WI 53715, USA.
6
Genetics & Genome Biology Program, Hospital for Sick Children (SickKids) and Department of Molecular Genetics, University of Toronto,Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, ON, Canada.
7
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA Department of Cell and Regenerative biology, University of Wisconsin, Madison, WI 53715, USA.

Abstract

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.

PMID:
26338778
PMCID:
PMC4605315
DOI:
10.1093/nar/gkv865
[Indexed for MEDLINE]
Free PMC Article

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