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Nucleic Acids Research 2006 34(Web Server issue):W504-W509; doi:10.1093/nar/gkl204
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© The Author 2006. Published by Oxford University Press. All rights reserved
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

Article

Arabidopsis Co-expression Tool (ACT): web server tools for microarray-based gene expression analysis

Iain W. Manfield*, Chih-Hung Jen1, John W. Pinney1, Ioannis Michalopoulos1, James R. Bradford1, Philip M. Gilmartin and David R. Westhead1

Centre for Plant Sciences, University of Leeds West Yorkshire, LS2 9JT, UK 1 Institute of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds West Yorkshire, LS2 9JT, UK

*To whom correspondence should be addressed. Tel: +44 113 343 2901; Fax: +44 113 343 3144; Email: i.manfield{at}leeds.ac.uk

Received February 14, 2006. Revised March 22, 2006. Accepted March 22, 2006.

The Arabidopsis Co-expression Tool, ACT, ranks the genes across a large microarray dataset according to how closely their expression follows the expression of a query gene. A database stores pre-calculated co-expression results for ~21 800 genes based on data from over 300 arrays. These results can be corroborated by calculation of co-expression results for user-defined sub-sets of arrays or experiments from the NASC/GARNet array dataset. Clique Finder (CF) identifies groups of genes which are consistently co-expressed with each other across a user-defined co-expression list. The parameters can be altered easily to adjust cluster size and the output examined for optimal inclusion of genes with known biological roles. Alternatively, a Scatter Plot tool displays the correlation coefficients for all genes against two user-selected queries on a scatter plot which can be useful for visual identification of clusters of genes with similar r-values. User-input groups of genes can be highlighted on the scatter plots. Inclusion of genes with known biology in sets of genes identified using CF and Scatter Plot tools allows inferences to be made about the roles of the other genes in the set and both tools can therefore be used to generate short lists of genes for further characterization. ACT is freely available at www.Arabidopsis.leeds.ac.uk/ACT.

Present addresses: Chih-Hung Jen, Genome Research Centre (VYMGC), National Yang-Ming University, 155 Li-Nong Street, Taipei, Taiwan

John W. Pinney, Faculty of Life Sciences, University of Manchester, Oxford Road, Manchester, M13 9PT, UK


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