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

Yufan Zhou, Xiaolong Cheng, Yini Yang, Tian Li, Jingwei Li, Tim H.M. Huang, Junbai Wang, Shili Lin, Victor X. Jin

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Article number69
JournalGenome Medicine
Volume12
Issue number1
DOIs
StatePublished - Aug 12 2020

ASJC Scopus subject areas

  • Molecular Medicine
  • Molecular Biology
  • Genetics
  • Genetics(clinical)

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