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

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.

Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases

“…The fourth step is to link enhancers to their target gene isoforms, which is more challenging because enhancers are often distal and their targets may be cell type-specific 1,3 . We opted for a parsimonious approach weighting potential enhancer–isoform links based on just two factors: their genomic distance and activity level in the given tissue.…”

“…However, translating these findings into a functional understanding of disease processes remains a major challenge, notably because the effect of individual trait-associated variants is typically minute, underlying mechanisms are generally cell type-specific 1–3 , and most variants lie in poorly understood noncoding regions of the genome 4 .…”

Inference of cell type specific regulatory networks on mammalian lineages

“…A complementary set of methods use supervised learning, e.g., IM-PET [88], RIPPLE [89], TargetFinder [90], CITD [91]. These methods differ based on the 3C technology used for training, which can be ChIA-PET (IM-PET), 5C (RIPPLE), or Hi-C (TargetFinder, RIPPLE, CITD), input regulatory signals, and whether the methods use data from multiple cell types.…”

Applications of ENCODE data to systematic analyses via data integration

“…Cao et al developed a computational method called joint effect of multiple enhancers (JEME) and applied it to predict EP interactions in 935 human primary cell types, cell lines and tissues by the integration of histone modification, DNase-seq, RNA-seq and other data types [*32]. These analyses have revealed critical insights into EP interactions, including that i) they are not simply determined by genomic proximity [33–35]; ii) multiple enhancers might control the same promoter [36]; and iii) EPs are cell-type specific [37,*38]. …”