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A temporal switch model for estimating transcriptional activity in gene expression

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Jenkins, Dafyd J., Finkenstädt, Bärbel and Rand, D. A. (David A.) (2013) A temporal switch model for estimating transcriptional activity in gene expression. Bioinformatics, Volume 29 (Number 9). pp. 1158-1165. doi:10.1093/bioinformatics/btt111 ISSN 1367-4803.

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Official URL: http://dx.doi.org/10.1093/bioinformatics/btt111

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Abstract

Motivation: The analysis and mechanistic modelling of time series gene expression data provided by techniques such as microarrays, NanoString, reverse transcription–polymerase chain reaction and advanced sequencing are invaluable for developing an understanding of the variation in key biological processes. We address this by proposing the estimation of a flexible dynamic model, which decouples temporal synthesis and degradation of mRNA and, hence, allows for transcriptional activity to switch between different states.

Results: The model is flexible enough to capture a variety of observed transcriptional dynamics, including oscillatory behaviour, in a way that is compatible with the demands imposed by the quality, time-resolution and quantity of the data. We show that the timing and number of switch events in transcriptional activity can be estimated alongside individual gene mRNA stability with the help of a Bayesian reversible jump Markov chain Monte Carlo algorithm. To demonstrate the methodology, we focus on modelling the wild-type behaviour of a selection of 200 circadian genes of the model plant Arabidopsis thaliana. The results support the idea that using a mechanistic model to identify transcriptional switch points is likely to strongly contribute to efforts in elucidating and understanding key biological processes, such as transcription and degradation.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Q Science > QH Natural history > QH301 Biology
Q Science > QH Natural history > QH426 Genetics
Divisions: Faculty of Science, Engineering and Medicine > Science > Mathematics
Faculty of Science, Engineering and Medicine > Science > Statistics
Library of Congress Subject Headings (LCSH): Gene expression, Genomics , Messenger RNA, Oscillating chemical reactions, Chemical reactions, Bayesian statistical decision theory, Arabidopsis thaliana
Journal or Publication Title: Bioinformatics
Publisher: Oxford University Press
ISSN: 1367-4803
Official Date: 1 May 2013
Dates:
DateEvent
1 May 2013Published
Volume: Volume 29
Number: Number 9
Page Range: pp. 1158-1165
DOI: 10.1093/bioinformatics/btt111
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 1 August 2016
Date of first compliant Open Access: 1 August 2016
Funder: Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC) , Engineering and Physical Sciences Research Council (EPSRC)
Grant number: BB/F005806/1 (BBSRC) ; EP/C544587/1 (EPSRC)

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