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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Gil Ron & Yuval Globerson & Dror Moran & Tommy Kaplan, 2017. "Promoter-enhancer interactions identified from Hi-C data using probabilistic models and hierarchical topological domains," Nature Communications, Nature, vol. 8(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-02386-3
    DOI: 10.1038/s41467-017-02386-3
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    Cited by:

    1. Jingxuan Xu & Xiang Xu & Dandan Huang & Yawen Luo & Lin Lin & Xuemei Bai & Yang Zheng & Qian Yang & Yu Cheng & An Huang & Jingyi Shi & Xiaochen Bo & Jin Gu & Hebing Chen, 2024. "A comprehensive benchmarking with interpretation and operational guidance for the hierarchy of topologically associating domains," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    2. Long Jin & Danyang Wang & Jiaman Zhang & Pengliang Liu & Yujie Wang & Yu Lin & Can Liu & Ziyin Han & Keren Long & Diyan Li & Yu Jiang & Guisen Li & Yu Zhang & Jingyi Bai & Xiaokai Li & Jing Li & Lu Lu, 2023. "Dynamic chromatin architecture of the porcine adipose tissues with weight gain and loss," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    3. Markus Götz & Olivier Messina & Sergio Espinola & Jean-Bernard Fiche & Marcelo Nollmann, 2022. "Multiple parameters shape the 3D chromatin structure of single nuclei at the doc locus in Drosophila," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    4. M S Vijayabaskar & Debbie K Goode & Nadine Obier & Monika Lichtinger & Amber M L Emmett & Fatin N Zainul Abidin & Nisar Shar & Rebecca Hannah & Salam A Assi & Michael Lie-A-Ling & Berthold Gottgens & , 2019. "Identification of gene specific cis-regulatory elements during differentiation of mouse embryonic stem cells: An integrative approach using high-throughput datasets," PLOS Computational Biology, Public Library of Science, vol. 15(11), pages 1-29, November.
    5. Michael Lattke & Robert Goldstone & James K. Ellis & Stefan Boeing & Jerónimo Jurado-Arjona & Nicolás Marichal & James I. MacRae & Benedikt Berninger & Francois Guillemot, 2021. "Extensive transcriptional and chromatin changes underlie astrocyte maturation in vivo and in culture," Nature Communications, Nature, vol. 12(1), pages 1-18, December.

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