Journal Article
. 2017 Dec; 8(1):2237.
doi: 10.1038/s41467-017-02386-3.

Promoter-enhancer interactions identified from Hi-C data using probabilistic models and hierarchical topological domains

Gil Ron 1 Yuval Globerson 1 Dror Moran 1 Tommy Kaplan 2 
Affiliations
  • PMID: 29269730
  •     69 References
  •     35 citations

Abstract

Proximity-ligation methods such as Hi-C allow us to map physical DNA-DNA interactions along the genome, and reveal its organization into topologically associating domains (TADs). As the Hi-C data accumulate, computational methods were developed for identifying domain borders in multiple cell types and organisms. Here, 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 then merge hierarchically and fit using a local background model, allowing us to identify over-represented DNA-DNA interactions across the genome. By analyzing the published Hi-C data sets in human and mouse, we identify hundreds of thousands of putative enhancers and their target genes, and compile an extensive genome-wide catalog of gene regulation in human and mouse. As we show, our predictions are highly enriched for ChIP-seq and DNA accessibility data, evolutionary conservation, eQTLs and other DNA-DNA interaction data.

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