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

Gil Ron, Yuval Globerson, Dror Moran, Tommy Kaplan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

90 Scopus citations


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.

Original languageAmerican English
Article number2237
JournalNature Communications
Issue number1
StatePublished - 1 Dec 2017

Bibliographical note

Funding Information:
We would like to thank Nir Friedman, Eran Rosenthal, Shira Strauss, and members of the Kaplan lab for helpful discussions and comments. T.K. is a member of the Israeli Center of Excellence (I-CORE) for Gene Regulation in Complex Human Disease (no. 41/11) and the Israeli Center of Excellence (I-CORE) for Chromatin and RNA in Gene Regulation (no. 1796/12). This research was also supported by a Marie Curie Career Integration Grant (PCIG13-GA-2013-618327), and an Israel Science Foundation grant (no. 913/15) to T.K. Y.G. is supported by a Leibniz Fellowship.

Publisher Copyright:
© 2017 The Author(s).


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