Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays

Antti HonkelaJaakko Peltonen, Hande Topa, Iryna Charapitsa, Filomena Matarese, Korbinian Grote, Hendrik G. Stunnenberg, George Reid, Neil D. LawrenceMagnus Rattray
,  112(42):13115-13120, 2015.

Abstract

Genes with similar transcriptional activation kinetics can display very different temporal mRNA profiles because of differences in transcription time, degradation rate, and RNA-processing kinetics. Recent studies have shown that a splicing-associated RNA production delay can be significant. To investigate this issue more generally, it is useful to develop methods applicable to genome-wide datasets. We introduce a joint model of transcriptional activation and mRNA accumulation that can be used for inference of transcription rate, RNA production delay, and degradation rate given data from high-throughput sequencing time course experiments. We combine a mechanistic differential equation model with a nonparametric statistical modeling approach allowing us to capture a broad range of activation kinetics, and we use Bayesian parameter estimation to quantify the uncertainty in estimates of the kinetic parameters. We apply the model to data from estrogen receptor α activation in the MCF-7 breast cancer cell line. We use RNA polymerase II ChIP-Seq time course data to characterize transcriptional activation and mRNA-Seq time course data to quantify mature transcripts. We find that 11% of genes with a good signal in the data display a delay of more than 20 min between completing transcription and mature mRNA production. The genes displaying these long delays are significantly more likely to be short. We also find a statistical association between high delay and late intron retention in pre-mRNA data, indicating significant splicing-associated production delays in many genes.

Cite this Paper


BibTeX
@InProceedings{pmlr-v-honkela-genome15, title = {Genome-wide modeling of transcription kinetics reveals patterns of {RNA} production delays}, author = {Antti Honkela and Jaakko Peltonen and Hande Topa and Iryna Charapitsa and Filomena Matarese and Korbinian Grote and Hendrik G. Stunnenberg and George Reid and Neil D. Lawrence and Magnus Rattray}, pages = {13115--13120}, year = {}, editor = {}, volume = {112}, number = {42}, pdf = {http://www.pnas.org/content/112/42/13115.full.pdf}, url = {http://inverseprobability.com/publications/honkela-genome15.html}, abstract = {Genes with similar transcriptional activation kinetics can display very different temporal mRNA profiles because of differences in transcription time, degradation rate, and RNA-processing kinetics. Recent studies have shown that a splicing-associated RNA production delay can be significant. To investigate this issue more generally, it is useful to develop methods applicable to genome-wide datasets. We introduce a joint model of transcriptional activation and mRNA accumulation that can be used for inference of transcription rate, RNA production delay, and degradation rate given data from high-throughput sequencing time course experiments. We combine a mechanistic differential equation model with a nonparametric statistical modeling approach allowing us to capture a broad range of activation kinetics, and we use Bayesian parameter estimation to quantify the uncertainty in estimates of the kinetic parameters. We apply the model to data from estrogen receptor α activation in the MCF-7 breast cancer cell line. We use RNA polymerase II ChIP-Seq time course data to characterize transcriptional activation and mRNA-Seq time course data to quantify mature transcripts. We find that 11% of genes with a good signal in the data display a delay of more than 20 min between completing transcription and mature mRNA production. The genes displaying these long delays are significantly more likely to be short. We also find a statistical association between high delay and late intron retention in pre-mRNA data, indicating significant splicing-associated production delays in many genes.} }
Endnote
%0 Conference Paper %T Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays %A Antti Honkela %A Jaakko Peltonen %A Hande Topa %A Iryna Charapitsa %A Filomena Matarese %A Korbinian Grote %A Hendrik G. Stunnenberg %A George Reid %A Neil D. Lawrence %A Magnus Rattray %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-honkela-genome15 %I PMLR %J Proceedings of Machine Learning Research %P 13115--13120 %R 10.1073/pnas.1420404112 %U http://inverseprobability.com %V %N 42 %W PMLR %X Genes with similar transcriptional activation kinetics can display very different temporal mRNA profiles because of differences in transcription time, degradation rate, and RNA-processing kinetics. Recent studies have shown that a splicing-associated RNA production delay can be significant. To investigate this issue more generally, it is useful to develop methods applicable to genome-wide datasets. We introduce a joint model of transcriptional activation and mRNA accumulation that can be used for inference of transcription rate, RNA production delay, and degradation rate given data from high-throughput sequencing time course experiments. We combine a mechanistic differential equation model with a nonparametric statistical modeling approach allowing us to capture a broad range of activation kinetics, and we use Bayesian parameter estimation to quantify the uncertainty in estimates of the kinetic parameters. We apply the model to data from estrogen receptor α activation in the MCF-7 breast cancer cell line. We use RNA polymerase II ChIP-Seq time course data to characterize transcriptional activation and mRNA-Seq time course data to quantify mature transcripts. We find that 11% of genes with a good signal in the data display a delay of more than 20 min between completing transcription and mature mRNA production. The genes displaying these long delays are significantly more likely to be short. We also find a statistical association between high delay and late intron retention in pre-mRNA data, indicating significant splicing-associated production delays in many genes.
RIS
TY - CPAPER TI - Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays AU - Antti Honkela AU - Jaakko Peltonen AU - Hande Topa AU - Iryna Charapitsa AU - Filomena Matarese AU - Korbinian Grote AU - Hendrik G. Stunnenberg AU - George Reid AU - Neil D. Lawrence AU - Magnus Rattray BT - PY - DA - ED - ID - pmlr-v-honkela-genome15 PB - PMLR SP - 13115 DP - PMLR EP - 13120 DO - 10.1073/pnas.1420404112 L1 - http://www.pnas.org/content/112/42/13115.full.pdf UR - http://inverseprobability.com/publications/honkela-genome15.html AB - Genes with similar transcriptional activation kinetics can display very different temporal mRNA profiles because of differences in transcription time, degradation rate, and RNA-processing kinetics. Recent studies have shown that a splicing-associated RNA production delay can be significant. To investigate this issue more generally, it is useful to develop methods applicable to genome-wide datasets. We introduce a joint model of transcriptional activation and mRNA accumulation that can be used for inference of transcription rate, RNA production delay, and degradation rate given data from high-throughput sequencing time course experiments. We combine a mechanistic differential equation model with a nonparametric statistical modeling approach allowing us to capture a broad range of activation kinetics, and we use Bayesian parameter estimation to quantify the uncertainty in estimates of the kinetic parameters. We apply the model to data from estrogen receptor α activation in the MCF-7 breast cancer cell line. We use RNA polymerase II ChIP-Seq time course data to characterize transcriptional activation and mRNA-Seq time course data to quantify mature transcripts. We find that 11% of genes with a good signal in the data display a delay of more than 20 min between completing transcription and mature mRNA production. The genes displaying these long delays are significantly more likely to be short. We also find a statistical association between high delay and late intron retention in pre-mRNA data, indicating significant splicing-associated production delays in many genes. ER -
APA
Honkela, A., Peltonen, J., Topa, H., Charapitsa, I., Matarese, F., Grote, K., Stunnenberg, H.G., Reid, G., Lawrence, N.D. & Rattray, M.. (). Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays. , in PMLR (42):13115-13120

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