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Inferring transcriptional logic from multiple dynamic experiments
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Minas, Giorgos, Jenkins, Dafyd J., Rand, D. A. (David A.) and Finkenstädt, Bärbel (2017) Inferring transcriptional logic from multiple dynamic experiments. Bioinformatics, 33 (21). pp. 3437-3444. doi:10.1093/bioinformatics/btx407 ISSN 1367-4803.
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Official URL: http://doi.org/10.1093/bioinformatics/btx407
Abstract
The availability of more data of dynamic gene expression under multiple experimental conditions provides new information that makes the key goal of identifying not only the transcriptional regulators of a gene but also the underlying logical structure attainable. We propose a novel method for inferring transcriptional regulation using a simple, yet biologically interpretable, model to find the logic by which a set of candidate genes and their associated transcription factors (TFs) regulate the transcriptional process of a gene of interest. Our dynamic model links the mRNA transcription rate of the target gene to the activation states of the TFs assuming that these interactions are consistent across multiple experiments and over time. A trans-dimensional Markov Chain Monte Carlo (MCMC) algorithm is used to efficiently sample the regulatory logic under different combinations of parents and rank the estimated models by their posterior probabilities. We demonstrate and compare our methodology with other methods using simulation examples and apply it to a study of transcriptional regulation of selected target genes of Arabidopsis Thaliana from microarray time series data obtained under multiple biotic stresses. We show that our method is able to detect complex regulatory interactions that are consistent under multiple experimental conditions. Programs are written in MATLAB and Statistics Toolbox Release 2016b, The MathWorks, Inc., Natick, Massachusetts, United States and are available on GitHub https://github.com/giorgosminas/TRS . giorgos.minas@warwick.ac.uk , B.F.Finkenstadt@warwick.ac.uk. Supplementary data are available at Bioinformatics online.
Item Type: | Journal Article | |||||||||||||||
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Subjects: | Q Science > QH Natural history > QH426 Genetics | |||||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | |||||||||||||||
SWORD Depositor: | Library Publications Router | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Genetic transcription -- Regulation -- Mathematical models | |||||||||||||||
Journal or Publication Title: | Bioinformatics | |||||||||||||||
Publisher: | Oxford University Press | |||||||||||||||
ISSN: | 1367-4803 | |||||||||||||||
Official Date: | 1 November 2017 | |||||||||||||||
Dates: |
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Volume: | 33 | |||||||||||||||
Number: | 21 | |||||||||||||||
Page Range: | pp. 3437-3444 | |||||||||||||||
DOI: | 10.1093/bioinformatics/btx407 | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Reuse Statement (publisher, data, author rights): | ** From PubMed via Jisc Publications Router. ** History: ** received: 30-11-2016 ** accepted: 19-06-2017 | |||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
Date of first compliant deposit: | 6 December 2017 | |||||||||||||||
Date of first compliant Open Access: | 6 December 2017 | |||||||||||||||
RIOXX Funder/Project Grant: |
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