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

Nucleic Acids Research
Sushmita RoyRupa Sridharan

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 s...Continue Reading

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Citations

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Oct 31, 2017·Current Opinion in Systems Biology·Deborah Chasman, Sushmita Roy

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Datasets Mentioned

BETA
GSE63525
Gm12878

Methods Mentioned

BETA
RNA-seq
Hi-C
ChIP-seq
interaction prediction
interaction predictions
Hi-C.

Software Mentioned

CONCAT
Spectral Meta Learner
Group Lasso
PRODUCT
MATLAB
RIPPLE ( Regulatory Interaction Prediction for Promoters Enhan...
Segway
SimpleMerge
IMPET
SLEP

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