Nucleic Acids Research Advance Access originally published online on September 26, 2006
Nucleic Acids Research 2006 34(18):e124; doi:10.1093/nar/gkl694
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Nucleic Acids Research, 2006, Vol. 34, No. 18 e124
© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Identifying functional gene sets from hierarchically clustered expression data: map of abiotic stress regulated genes in Arabidopsis thaliana
1 Institute of Biotechnology PO Box 56 (Viikinkaari 5), FIN-00014, Helsinki, Finland 2 Department of Biological and Environmental Sciences, Division of Genetics, University of Helsinki PO Box 56 (Viikinkaari 5), FIN-00014, Helsinki, Finland
*To whom correspondence should be addressed. Tel:+358 9 19159115; Fax:+358 9 19159079; Email: liisa.holm{at}helsinki.fi
Received April 28, 2006. Revised September 8, 2006. Accepted September 9, 2006.
We present MultiGO, a web-enabled tool for the identification of biologically relevant gene sets from hierarchically clustered gene expression trees (http://ekhidna.biocenter.helsinki.fi/poxo/multigo). High-throughput gene expression measuring techniques, such as microarrays, are nowadays often used to monitor the expression of thousands of genes. Since these experiments can produce overwhelming amounts of data, computational methods that assist the data analysis and interpretation are essential. MultiGO is a tool that automatically extracts the biological information for multiple clusters and determines their biological relevance, and hence facilitates the interpretation of the data. Since the entire expression tree is analysed, MultiGO is guaranteed to report all clusters that share a common enriched biological function, as defined by Gene Ontology annotations. The tool also identifies a plausible cluster set, which represents the key biological functions affected by the experiment. The performance is demonstrated by analysing drought-, cold- and abscisic acid-related expression data sets from Arabidopsis thaliana. The analysis not only identified known biological functions, but also brought into focus the less established connections to defense-related gene clusters. Thus, in comparison to analyses of manually selected gene lists, the systematic analysis of every cluster can reveal unexpected biological phenomena and produce much more comprehensive biological insights to the experiment of interest.
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