Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data

Abstract

Abstract Gene transcription mediated by RNA polymerase II (pol-II) is a key step in gene expression. The dynamics of pol-II moving along the transcribed region in uence the rate and timing of gene expression. In this work we present a probabilistic model of transcription dynamics which is tted to pol-II occupancy time course data measured using ChIP-Seq. The model can be used to estimate transcription speed and to infer the temporal pol-II activity pro le at the gene promoter. Model parameters are estimated using either maximum likelihood estimation or via Bayesian inference using Markov chain Monte Carlo sampling. The Bayesian approach provides con dence intervals for parameter estimates and allows the use of priors that capture domain knowledge, e.g. the expected range of transcription speeds, based on previous experiments. The model describes the movement of pol-II down the gene body and can be used to identify the time of induction for transcriptionally engaged genes. By clustering the inferred promoter activity time pro les, we are able to determine which genes respond quickly to stimuli and group genes that share activity pro les and may therefore be co-regulated. We apply our methodology to biological data obtained using ChIP-seq to measure pol-II occupancy genome-wide when MCF-7 human breast cancer cells are treated with estradiol (E2). The transcription speeds we obtain agree with those obtained previously for smaller numbers of genes with the advantage that our approach can be applied genomewide. We validate the biological signi cance of the pol-II promoter activity clusters by investigating cluster-speci c transcription factor binding patterns and determining canonical pathway enrichment. We nd that rapidly induced genes are enriched for both estrogen receptor alpha (ER ) and FOXA1 binding in their proximal promoter regions.