Probabilistic partitioning methods to find significant patterns in ChIP-Seq data

作     者：Nair, Nishanth Ulhas Kumar, Sunil Moret, Bernard M. E. Bucher, Philipp 

作者机构：Ecole Polytech Fed Lausanne Sch Comp & Commun Sci Lab Computat Biol & Bioinformat CH-1015 Lausanne Switzerland Ecole Polytech Fed Lausanne Sch Life Sci Swiss Inst Expt Canc Res ISREC CH-1015 Lausanne Switzerland Swiss Inst Bioinformat CH-1015 Lausanne Switzerland 

出 版 物：《BIOINFORMATICS》 (生物信息学)

年 卷 期：2014年第30卷第17期

页      面：2406-2413页

核心收录：

学科分类：0710[理学-生物学] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0836[工学-生物工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Swiss National Science Foundation [200021_121710/1, 31003A_125193] Swiss National Science Foundation (SNF) [200021_121710, 31003A_125193] Funding Source: Swiss National Science Foundation (SNF) 

主　　题：SEQUENCE alignment (Bioinformatics) PROBABILITY theory PARALLEL algorithms IMMUNOPRECIPITATION HIGH throughput screening (Drug development) EUCLIDEAN distance EXPECTATION-maximization algorithms 

摘      要：Motivation: We have witnessed an enormous increase in ChIP-Seq data for histone modifications in the past few years. Discovering significant patterns in these data is an important problem for understanding biological mechanisms. Results: We propose probabilistic partitioning methods to discover significant patterns in ChIP-Seq data. Our methods take into account signal magnitude, shape, strand orientation and shifts. We compare our methods with some current methods and demonstrate significant improvements, especially with sparse data. Besides pattern discovery and classification, probabilistic partitioning can serve other purposes in ChIP-Seq data analysis. Specifically, we exemplify its merits in the context of peak finding and partitioning of nucleosome positioning patterns in human promoters.