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