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Modeling and analysis of Hi-C data by HiSIF identifies characteristic promoter-distal loops.

ABSTRACT: Current computational methods on Hi-C analysis focused on identifying Mb-size domains often failed to unveil the underlying functional and mechanistic relationship of chromatin structure and gene regulation. We developed a novel computational method HiSIF to identify genome-wide interacting loci. We illustrated HiSIF outperformed other tools for identifying chromatin loops. We applied it to Hi-C data in breast cancer cells and identified 21 genes with gained loops showing worse relapse-free survival in endocrine-treated patients, suggesting the genes with enhanced loops can be used for prognostic signatures for measuring the outcome of the endocrine treatment. HiSIF is available at https://github.com/yufanzhouonline/HiSIF .

SUBMITTER: Zhou Y

PROVIDER: S-EPMC7425017 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Modeling and analysis of Hi-C data by HiSIF identifies characteristic promoter-distal loops.

Zhou Yufan Y Cheng Xiaolong X Yang Yini Y Li Tian T Li Jingwei J Huang Tim H-M TH Wang Junbai J Lin Shili S Jin Victor X VX

Genome medicine 20200812 1

Current computational methods on Hi-C analysis focused on identifying Mb-size domains often failed to unveil the underlying functional and mechanistic relationship of chromatin structure and gene regulation. We developed a novel computational method HiSIF to identify genome-wide interacting loci. We illustrated HiSIF outperformed other tools for identifying chromatin loops. We applied it to Hi-C data in breast cancer cells and identified 21 genes with gained loops showing worse relapse-free surv ...[more]

PMID: 32787954

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