Inference of RNA polymerase II transcription dynamics from chromatin immunoprecipitation time course data.
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 influence the rate and timing of gene expression. In this work, we present a probabilistic model of transcription dynamics which is fitted 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 profile 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 confidence 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 profiles, we are able to determine which genes respond quickly to stimuli and group genes that share activity profiles 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 genome-wide. We validate the biological significance of the pol-II promoter activity clusters by investigating cluster-specific transcription factor binding patterns and determining canonical pathway enrichment. We find that rapidly induced genes are enriched for both estrogen receptor alpha (ERα) and FOXA1 binding in their proximal promoter regions.
Department of Electrical and Electronic Engineering, Dedan Kimathi University of Technology, Nyeri, Kenya.
Ciira
C
wa Maina
Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.
Antti
A
Honkela
Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands.
Filomena
F
Matarese
Genomatix Software GmbH, Muenchen, Germany.
Korbinian
K
Grote
Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands.
Hendrik G
HG
Stunnenberg
Institute for Molecular Biology, Mainz, Germany.
George
G
Reid
Department of Computer Science, University of Sheffield, Sheffield, United Kingdom.
Neil D
ND
Lawrence
Faculty of Life Sciences, University of Manchester, Manchester, United Kingdom.
Magnus
M
Rattray
D000818:Animals; D047369:Chromatin Immunoprecipitation / Q000379:methods*; D003198:Computer Simulation; D012321:DNA-Directed RNA Polymerases / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D011401:Promoter Regions, Genetic / Q000235:genetics*; D011485:Protein Binding; D014158:Transcription, Genetic / Q000235:genetics*; D015533:Transcriptional Activation / Q000235:genetics*
D012321:DNA-Directed RNA Polymerases