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In silico prediction of high-resolution Hi-C interaction matrices

Author

Listed:
  • Shilu Zhang

    (Wisconsin Institute for Discovery)

  • Deborah Chasman

    (Wisconsin Institute for Discovery)

  • Sara Knaack

    (Wisconsin Institute for Discovery)

  • Sushmita Roy

    (Wisconsin Institute for Discovery
    University of Wisconsin-Madison)

Abstract

The three-dimensional (3D) organization of the genome plays an important role in gene regulation bringing distal sequence elements in 3D proximity to genes hundreds of kilobases away. Hi-C is a powerful genome-wide technique to study 3D genome organization. Owing to experimental costs, high resolution Hi-C datasets are limited to a few cell lines. Computational prediction of Hi-C counts can offer a scalable and inexpensive approach to examine 3D genome organization across multiple cellular contexts. Here we present HiC-Reg, an approach to predict contact counts from one-dimensional regulatory signals. HiC-Reg predictions identify topologically associating domains and significant interactions that are enriched for CCCTC-binding factor (CTCF) bidirectional motifs and interactions identified from complementary sources. CTCF and chromatin marks, especially repressive and elongation marks, are most important for HiC-Reg’s predictive performance. Taken together, HiC-Reg provides a powerful framework to generate high-resolution profiles of contact counts that can be used to study individual locus level interactions and higher-order organizational units of the genome.

Suggested Citation

  • Shilu Zhang & Deborah Chasman & Sara Knaack & Sushmita Roy, 2019. "In silico prediction of high-resolution Hi-C interaction matrices," Nature Communications, Nature, vol. 10(1), pages 1-18, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-13423-8
    DOI: 10.1038/s41467-019-13423-8
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    Cited by:

    1. Fan Feng & Yuan Yao & Xue Qing David Wang & Xiaotian Zhang & Jie Liu, 2022. "Connecting high-resolution 3D chromatin organization with epigenomics," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    2. Kevin B. Dsouza & Alexandra Maslova & Ediem Al-Jibury & Matthias Merkenschlager & Vijay K. Bhargava & Maxwell W. Libbrecht, 2022. "Learning representations of chromatin contacts using a recurrent neural network identifies genomic drivers of conformation," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    3. Hao Wang & Jiaxin Yang & Yu Zhang & Jianliang Qian & Jianrong Wang, 2022. "Reconstruct high-resolution 3D genome structures for diverse cell-types using FLAMINGO," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    4. Mattia Conte & Ehsan Irani & Andrea M. Chiariello & Alex Abraham & Simona Bianco & Andrea Esposito & Mario Nicodemi, 2022. "Loop-extrusion and polymer phase-separation can co-exist at the single-molecule level to shape chromatin folding," Nature Communications, Nature, vol. 13(1), pages 1-13, December.

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