期刊
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
卷 106, 期 495, 页码 891-903出版社
AMER STATISTICAL ASSOC
DOI: 10.1198/jasa.2011.ap09706
关键词
GC content; Mappability; Mixture model; Negative binomial regression; Next generation sequencing
资金
- NIH [HG03747]
- NSF [DMS004597]
- Morgridge Institute Research support for Computation and Informatics in Biology and Medicine
Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) has revolutionalized experiments for genome-wide profiling of DNA-binding proteins, histone modifications, and nucleosome occupancy. As the cost of sequencing is decreasing, many researchers are switching from microarray-based technologies (ChIP-chip) to ChIP-Seq for genome-wide study of transcriptional regulation. Despite its increasing and well-deserved popularity, there is little work that investigates and accounts for sources of biases in the ChIP-Seq technology. These biases typically arise from both the standard preprocessing protocol and the underlying DNA sequence of the generated data. We study data from a naked DNA sequencing experiment, which sequences noncross-linked DNA after deproteinizing and shearing, to understand factors affecting background distribution of data generated in a ChIP-Seq experiment. We introduce a background model that accounts for apparent sources of biases such as mappability and GC content and develop a flexible mixture model named MOSAiCS for detecting peaks in both one- and two-sample analyses of ChIP-Seq data. We illustrate that our model fits observed ChIP-Seq data well and further demonstrate advantages of MOSAiCS over commonly used tools for ChIP-Seq data analysis with several case studies. This article has supplementary material online.
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