Title |
A Poisson mixture model to identify changes in RNA polymerase II binding quantity using high-throughput sequencing technology
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Published in |
BMC Genomics, September 2008
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DOI | 10.1186/1471-2164-9-s2-s23 |
Pubmed ID | |
Authors |
Weixing Feng, Yunlong Liu, Jiejun Wu, Kenneth P Nephew, Tim HM Huang, Lang Li |
Abstract |
We present a mixture model-based analysis for identifying differences in the distribution of RNA polymerase II (Pol II) in transcribed regions, measured using ChIP-seq (chromatin immunoprecipitation following massively parallel sequencing technology). The statistical model assumes that the number of Pol II-targeted sequences contained within each genomic region follows a Poisson distribution. A Poisson mixture model was then developed to distinguish Pol II binding changes in transcribed region using an empirical approach and an expectation-maximization (EM) algorithm developed for estimation and inference. In order to achieve a global maximum in the M-step, a particle swarm optimization (PSO) was implemented. We applied this model to Pol II binding data generated from hormone-dependent MCF7 breast cancer cells and antiestrogen-resistant MCF7 breast cancer cells before and after treatment with 17beta-estradiol (E2). We determined that in the hormone-dependent cells, approximately 9.9% (2527) genes showed significant changes in Pol II binding after E2 treatment. However, only approximately 0.7% (172) genes displayed significant Pol II binding changes in E2-treated antiestrogen-resistant cells. These results show that a Poisson mixture model can be used to analyze ChIP-seq data. |
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