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

Ciira Maina, Antti Honkela, Filomena Matarese, Korbinian Grote, Hendrik G. Stunnenberg, George Reid, Neil D. Lawrence, Magnus Rattray
PLoS Computat Biol, 10(5), 2014.

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 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 (ER) and FOXA1 binding in their proximal promoter regions.

Cite this Paper

BibTeX
@Article{Maina-inference14, title = {Inference of {RNA} Polymerase {II} Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data}, author = {Maina, Ciira and Honkela, Antti and Matarese, Filomena and Grote, Korbinian and Stunnenberg, Hendrik G. and Reid, George and Lawrence, Neil D. and Rattray, Magnus}, journal = {PLoS Computat Biol}, year = {2014}, volume = {10}, number = {5}, doi = {10.1371/journal.pcbi.1003598}, pdf = {https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003598&type=printable}, url = {http://inverseprobability.com/publications/inference-of-rna-polymerase-ii-transcription-dynamics-from-chromatin-immunoprecipitation-time-course-data.html}, 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 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 (ER) and FOXA1 binding in their proximal promoter regions. } }
Endnote
%0 Journal Article %T Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data %A Ciira Maina %A Antti Honkela %A Filomena Matarese %A Korbinian Grote %A Hendrik G. Stunnenberg %A George Reid %A Neil D. Lawrence %A Magnus Rattray %J PLoS Computat Biol %D 2014 %F Maina-inference14 %R 10.1371/journal.pcbi.1003598 %U http://inverseprobability.com/publications/inference-of-rna-polymerase-ii-transcription-dynamics-from-chromatin-immunoprecipitation-time-course-data.html %V 10 %N 5 %X 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 (ER) and FOXA1 binding in their proximal promoter regions.
RIS
TY - JOUR TI - Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data AU - Ciira Maina AU - Antti Honkela AU - Filomena Matarese AU - Korbinian Grote AU - Hendrik G. Stunnenberg AU - George Reid AU - Neil D. Lawrence AU - Magnus Rattray DA - 2014/05/14 ID - Maina-inference14 VL - 10 IS - 5 DO - 10.1371/journal.pcbi.1003598 L1 - https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003598&type=printable UR - http://inverseprobability.com/publications/inference-of-rna-polymerase-ii-transcription-dynamics-from-chromatin-immunoprecipitation-time-course-data.html AB - 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 (ER) and FOXA1 binding in their proximal promoter regions. ER -
APA
Maina, C., Honkela, A., Matarese, F., Grote, K., Stunnenberg, H.G., Reid, G., Lawrence, N.D. & Rattray, M.. (2014). Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data. PLoS Computat Biol 10(5) doi:10.1371/journal.pcbi.1003598 Available from http://inverseprobability.com/publications/inference-of-rna-polymerase-ii-transcription-dynamics-from-chromatin-immunoprecipitation-time-course-data.html.

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