ZipHiC: a novel Bayesian framework to identify enriched interactions and experimental biases in Hi-C data | Zendy

Itunu G. Osuntoki | Zendy; Andrew Harrison | Zendy; Hongsheng Dai | Zendy; Yanchun Bao | Zendy; Nicolae Radu Zabet | Zendy

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ZipHiC: a novel Bayesian framework to identify enriched interactions and experimental biases in Hi-C data

Author(s) -

Itunu G. Osuntoki,

Andrew Harrison,

Hongsheng Dai,

Yanchun Bao,

Nicolae Radu Zabet

Publication year - 2022

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btac387

Subject(s) - computer science , bayesian probability , scripting language , approximate bayesian computation , computation , statistical model , field (mathematics) , data mining , pattern recognition (psychology) , artificial intelligence , algorithm , mathematics , inference , pure mathematics , operating system

Several computational and statistical methods have been developed to analyze data generated through the 3C-based methods, especially the Hi-C. Most of the existing methods do not account for dependency in Hi-C data.

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