A <it>Poisson </it>mixture model to identify changes in RNA polymerase II binding quantity using high-throughput sequencing technology

• Main Authors: Liu Yunlong, Feng Weixing, Wu Jiejun, Nephew Kenneth P, Huang Tim HM, Li Lang
• Format: Article
• Language: English
• Published: BMC 2008-09-01
• Series: BMC Genomics

<p>Abstract</p> <p>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 <it>Poisson </it>distribution. A <it>Poisson </it>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 17<it>β</it>-estradiol (E2). We determined that in the hormone-dependent cells, ~9.9% (2527) genes showed significant changes in Pol II binding after E2 treatment. However, only ~0.7% (172) genes displayed significant Pol II binding changes in E2-treated antiestrogen-resistant cells. These results show that a <it>Poisson </it>mixture model can be used to analyze ChIP-seq data.</p>