Open AccessZipHiC: 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.