He Chao, Zhang Michael Q, Wang Xiaowo
MOE Key Laboratory of Bioinformatics and Bioinformatics Division, Center for Synthetic and System Biology, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China and.
Department of Molecular and Cell Biology, Center for Systems Biology, The University of Texas, Richardson, TX 75080-3021, USA MOE Key Laboratory of Bioinformatics and Bioinformatics Division, Center for Synthetic and System Biology, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China and.
Bioinformatics. 2015 Dec 1;31(23):3832-4. doi: 10.1093/bioinformatics/btv445. Epub 2015 Jul 31.
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.
http://bioinfo.au.tsinghua.edu.cn/member/xwwang/MICCusage
michael.zhang@utdallas.edu or xwwang@tsinghua.edu.cn.
Supplementary data are available at Bioinformatics online.
染色质相互作用分析技术（ChIA-PET）正迅速成为一种重要的实验方法，用于高分辨率检测染色质远程相互作用。在此，我们展示了基于模型的ChIA-PET数据相互作用调用（MICC），这是一个易于使用的R包，用于从ChIA-PET测序数据中检测染色质相互作用。通过应用贝叶斯混合模型系统地去除随机连接和随机碰撞噪声，在相同的错误发现率下，MICC能够以比现有方法显著更高的灵敏度识别染色质相互作用。
http://bioinfo.au.tsinghua.edu.cn/member/xwwang/MICCusage
michael.zhang@utdallas.edu或xwwang@tsinghua.edu.cn。
补充数据可在《生物信息学》在线获取。
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