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 Nephew, Tim H M Huang, Lang Li

Research output: Contribution to journalArticle

27 Citations (Scopus)

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
Article numberS23
JournalBMC Genomics
Volume9
Issue numberSUPPL. 2
DOIs
StatePublished - Sep 16 2008

Fingerprint

RNA Polymerase II
Technology
Estrogen Receptor Modulators
Hormones
Poisson Distribution
Breast Neoplasms
High-Throughput Nucleotide Sequencing
Chromatin Immunoprecipitation
Statistical Models
Genes
Estradiol

ASJC Scopus subject areas

  • Biotechnology
  • Genetics

Cite this

A Poisson mixture model to identify changes in RNA polymerase II binding quantity using high-throughput sequencing technology. / Feng, Weixing; Liu, Yunlong; Wu, Jiejun; Nephew, Kenneth; Huang, Tim H M; Li, Lang.

In: BMC Genomics, Vol. 9, No. SUPPL. 2, S23, 16.09.2008.

Research output: Contribution to journalArticle

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