genome-wide localization of protein-dna binding and histone modification by a bayesian change-point method with chip-seq data全基因组定位protein-dna绑定和组蛋白修饰的贝叶斯与chip-seq数据变异点的方法.pdfVIP

Genome-Wide Localization of Protein-DNA Binding and Histone Modification by a Bayesian Change-Point Method with ChIP-seq Data Haipeng Xing1.*, Yifan Mo1,2., Will Liao1,2., Michael Q. Zhang2,3¤ 1 Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, United States of America, 2 Computational Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America, 3 Bioinformatics Division, Tsinghua University, Beijing, China Abstract Next-generation sequencing (NGS) technologies have matured considerably since their introduction and a focus has been placed on developing sophisticated analytical tools to deal with the amassing volumes of data. Chromatin immunoprecipitation sequencing (ChIP-seq), a major application of NGS, is a widely adopted technique for examining protein-DNA interactions and is commonly used to investigate epigenetic signatures of diffuse histone marks. These datasets have notoriously high variance and subtle levels of enrichment across large expanses, making them exceedingly difficult to define. Windows-based, heuristic models and finite-state hidden Markov models (HMMs) have been used with some success in analyzing ChIP-seq data but with lingering limitations. To improve the ability to detect broad regions of enrichment, we developed a stochastic Bayesian Change-Point (BCP) method, which addresses some of these unresolved issues. BCP makes use of recent advances in infinite-state HMMs by obtaining explicit formulas for posterior means of read densities. These posterior means can be used to categorize the genome into enriched and unenriched segments, as is customarily done, or examined for more detailed relationships since the underlying subpeaks are preserved rather than simplified into a binary classification. BCP