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

Weixing Feng, Yunlong Liu, Jiejun Wu, Kenneth P. Nephew, Tim H.M. Huang, Lang Li

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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 17β-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 Poisson mixture model can be used to analyze ChIP-seq data.

Original languageEnglish (US)
Article numberS23
JournalBMC genomics
Volume9
Issue numberSUPPL. 2
DOIs
StatePublished - Sep 16 2008

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ASJC Scopus subject areas

  • Biotechnology
  • Genetics

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