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Wigwams : identifying gene modules co-regulated across multiple biological conditions
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Polański, Krzysztof , Rhodes, Johanna, Hills, Claire E., Zhang, Peijun, Jenkins, Dafyd J., Kiddle, Steven J., Jironkin, Aleksey, Beynon, Jim, Buchanan-Wollaston, Vicky, Ott, Sascha and Denby, Katherine J. (2014) Wigwams : identifying gene modules co-regulated across multiple biological conditions. Bioinformatics, Volume 30 (Number 7). pp. 962-970. doi:10.1093/bioinformatics/btt728 ISSN 1367-4803.
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WRAP_Polanski_Bioinformatics-2014-Polanski-962-70.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution. Download (1008Kb) | Preview |
Official URL: http://dx.doi.org/10.1093/bioinformatics/btt728
Abstract
Motivation: Identification of modules of co-regulated genes is a crucial first step towards dissecting the regulatory circuitry underlying biological processes. Co-regulated genes are likely to reveal themselves by showing tight co-expression, e.g. high correlation of expression profiles across multiple time series datasets. However, numbers of up- or downregulated genes are often large, making it difficult to discriminate between dependent co-expression resulting from co-regulation and independent co-expression. Furthermore, modules of co-regulated genes may only show tight co-expression across a subset of the time series, i.e. show condition-dependent regulation.
Results: Wigwams is a simple and efficient method to identify gene modules showing evidence for co-regulation in multiple time series of gene expression data. Wigwams analyzes similarities of gene expression patterns within each time series (condition) and directly tests the dependence or independence of these across different conditions. The expression pattern of each gene in each subset of conditions is tested statistically as a potential signature of a condition-dependent regulatory mechanism regulating multiple genes. Wigwams does not require particular time points and can process datasets that are on different time scales. Differential expression relative to control conditions can be taken into account. The output is succinct and non-redundant, enabling gene network reconstruction to be focused on those gene modules and combinations of conditions that show evidence for shared regulatory mechanisms. Wigwams was run using six Arabidopsis time series expression datasets, producing a set of biologically significant modules spanning different combinations of conditions.
Availability and implementation: A Matlab implementation of Wigwams, complete with graphical user interfaces and documentation, is available at: warwick.ac.uk/wigwams.
Item Type: | Journal Article | ||||||||||
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Subjects: | Q Science > QH Natural history > QH426 Genetics | ||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) Faculty of Science, Engineering and Medicine > Research Centres > Warwick Systems Biology Centre |
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Library of Congress Subject Headings (LCSH): | Genetic regulation | ||||||||||
Journal or Publication Title: | Bioinformatics | ||||||||||
Publisher: | Oxford University Press | ||||||||||
ISSN: | 1367-4803 | ||||||||||
Official Date: | 1 April 2014 | ||||||||||
Dates: |
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Volume: | Volume 30 | ||||||||||
Number: | Number 7 | ||||||||||
Page Range: | pp. 962-970 | ||||||||||
DOI: | 10.1093/bioinformatics/btt728 | ||||||||||
Status: | Peer Reviewed | ||||||||||
Publication Status: | Published | ||||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||||
Date of first compliant deposit: | 27 December 2015 | ||||||||||
Date of first compliant Open Access: | 27 December 2015 | ||||||||||
RIOXX Funder/Project Grant: |
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