Promoter-Enhancer Interactions Identified from Hi-C Data using Probabilistic Models and Hierarchical Topological Domains

Abstract

Proximity-ligation methods as Hi-C allow us to map physical DNA-DNA interactions along the genome, and reveal its organization in topologically associating domains (TADs). As Hi-C data accumulate, computational methods were developed for identifying domain borders in multiple cell types and organisms.\n\nHere, we present PSYCHIC, a computational approach for analyzing Hi-C data and identifying Promoter-Enhancer interactions. We use a unified probabilistic model to segment the genome into domains, which we merge hierarchically and fit the Hi-C interaction map with a local background model. This allows us to estimate the expected number of interactions for every DNA-DNA pair, thus identifying over-represented interactions across the genome.\n\nBy analyzing published Hi-C data in human and mouse, we identified hundreds of thousands of putative enhancers and their target genes in multiple cell types, and compiled an extensive genome-wide catalog of gene regulation in human and mouse.