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

摘要: We present a mixture model-based analysis for identifying differences in the distribution of RNA polymerase II (Pol II) transcribed regions, measured using ChIP-seq (chromatin immunoprecipitation following massively parallel sequencing technology). The statistical model assumes that number Pol II-targeted sequences contained within each genomic region follows Poisson distribution. A was then developed to distinguish binding changes an empirical approach and expectation-maximization (EM) algorithm estimation inference. In order achieve global maximum M-step, particle swarm optimization (PSO) implemented. applied this data generated from hormone-dependent MCF7 breast cancer cells antiestrogen-resistant before after treatment with 17β-estradiol (E2). determined cells, ~9.9% (2527) genes showed significant E2 treatment. However, only ~0.7% (172) displayed E2-treated cells. These results show can be used analyze data.