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Identifying topologically associating domains and subdomains by Gaussian Mixture model And Proportion test

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Listed:
  • Wenbao Yu

    (Children’s Hospital of Philadelphia
    Children’s Hospital of Philadelphia)

  • Bing He

    (Children’s Hospital of Philadelphia
    Children’s Hospital of Philadelphia)

  • Kai Tan

    (Children’s Hospital of Philadelphia
    Children’s Hospital of Philadelphia
    University of Pennsylvania)

Abstract

The spatial organization of the genome plays a critical role in regulating gene expression. Recent chromatin interaction mapping studies have revealed that topologically associating domains and subdomains are fundamental building blocks of the three-dimensional genome. Identifying such hierarchical structures is a critical step toward understanding the three-dimensional structure–function relationship of the genome. Existing computational algorithms lack statistical assessment of domain predictions and are computationally inefficient for high-resolution Hi-C data. We introduce the Gaussian Mixture model And Proportion test (GMAP) algorithm to address the above-mentioned challenges. Using simulated and experimental Hi-C data, we show that domains identified by GMAP are more consistent with multiple lines of supporting evidence than three state-of-the-art methods. Application of GMAP to normal and cancer cells reveals several unique features of subdomain boundary as compared to domain boundary, including its higher dynamics across cell types and enrichment for somatic mutations in cancer.

Suggested Citation

  • Wenbao Yu & Bing He & Kai Tan, 2017. "Identifying topologically associating domains and subdomains by Gaussian Mixture model And Proportion test," Nature Communications, Nature, vol. 8(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-00478-8
    DOI: 10.1038/s41467-017-00478-8
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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. 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. Yanlin Zhang & Mathieu Blanchette, 2022. "Reference panel guided topological structure annotation of Hi-C data," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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