
Abstract Summary: ChIA-PET is rapidly emerging as an important experimental approach to detect chromatin long-range interactions at high resolution. Here, we present Model based Interaction Calling from ChIA-PET data (MICC), an easy-to-use R package to detect chromatin interactions from ChIA-PET sequencing data. By applying a Bayesian mixture model to systematically remove random ligation and random collision noise, MICC could identify chromatin interactions with a significantly higher sensitivity than existing methods at the same false discovery rate. Availability and implementation: http://bioinfo.au.tsinghua.edu.cn/member/xwwang/MICCusage Contact: michael.zhang@utdallas.edu or xwwang@tsinghua.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.
Chromatin Immunoprecipitation, Models, Statistical, Bayes Theorem, Sequence Analysis, DNA, Applications Notes, Chromatin, Humans, Computer Simulation, Programming Languages, Algorithms
Chromatin Immunoprecipitation, Models, Statistical, Bayes Theorem, Sequence Analysis, DNA, Applications Notes, Chromatin, Humans, Computer Simulation, Programming Languages, Algorithms
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