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Nat Commun. 2017 Dec 21;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.

Author information

1
School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel.
2
School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel. tommy@cs.huji.ac.il.

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.

PMID:
29269730
PMCID:
PMC5740158
DOI:
10.1038/s41467-017-02386-3
[Indexed for MEDLINE]
Free PMC Article

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