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

Gil Ron, Yuval Globerson, Dror Moran and Tommy Kaplan ()
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Gil Ron: The Hebrew University of Jerusalem
Yuval Globerson: The Hebrew University of Jerusalem
Dror Moran: The Hebrew University of Jerusalem
Tommy Kaplan: The Hebrew University of Jerusalem

Nature Communications, 2017, vol. 8, issue 1, 1-12

Abstract: 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.

Date: 2017
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DOI: 10.1038/s41467-017-02386-3

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