A predictive modeling approach for cell line-specific long-range regulatory interactions.
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.
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.
Sushmita
S
Roy
Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA.
Alireza Fotuhi
AF
Siahpirani
Wisconsin Institute for Discovery, 330 N. Orchard Street, Madison, WI, USA.
Deborah
D
Chasman
Wisconsin Institute for Discovery, 330 N. Orchard Street, Madison, WI, USA.
Sara
S
Knaack
Department of Genome Sciences, University of Washington, Seattle, WA, USA.
Ferhat
F
Ay
Morgridge Institute for Research, Madison, WI 53715, USA.
Ron
R
Stewart
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.
Michael
M
Wilson
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.
Rupa
R
Sridharan
D000465:Algorithms; D000076246:CCCTC-Binding Factor; D018797:Cell Cycle Proteins / Q000032:analysis; D002460:Cell Line; D002843:Chromatin / Q000737:chemistry / Q000378:metabolism; D002868:Chromosomal Proteins, Non-Histone / Q000032:analysis; D004742:Enhancer Elements, Genetic; D023281:Genomics / Q000379:methods*; D042421:Histone Code; D006801:Humans; D008957:Models, Genetic; D011401:Promoter Regions, Genetic; D012097:Repressor Proteins / Q000032:analysis; D035181:TATA-Box Binding Protein / Q000032:analysis; D000097722:Cohesins
D000076246:CCCTC-Binding Factor; D018797:Cell Cycle Proteins; D002843:Chromatin; D002868:Chromosomal Proteins, Non-Histone; D012097:Repressor Proteins; D035181:TATA-Box Binding Protein; D000097722:Cohesins; C000619697:CTCF protein, human