Promoter-enhancer interactions identified from Hi-C data using probabilistic models and hierarchical topological domains
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 a...
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Veröffentlicht in: | Nature communications 2017-12, Vol.8 (1), p.2237-2237, Article 2237 |
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Zusammenfassung: | 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.
Proximity-ligation methods like Hi-C map DNA-DNA interactions and reveal its organization into topologically associating domains (TADs). Here the authors describe PSYCHIC, a computational approach for analysing Hi-C data that allows the identification of promoter-enhancer interactions. |
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ISSN: | 2041-1723 2041-1723 |
DOI: | 10.1038/s41467-017-02386-3 |