Co-expression tools for plant biology: opportunities for hypothesis generation and caveats |
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| Authors: | BJÖ RN USADEL ,TAKESHI OBAYASHI ,MAREK MUTWIL,FEDERICO M. GIORGI,GEORGE W. BASSEL,MIMI TANIMOTO,AMANDA CHOW,DIRK STEINHAUSER,STAFFAN PERSSON,& NICHOLAS J. PROVART |
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| Affiliation: | Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany,;Human Genome Center, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokane-dai, Minato-ku, Tokyo 108-8639, Japan,;Centre for the Analysis of Genome Evolution and Function/Department of Cell &Systems Biology, University of Toronto, 25 Willcocks St., Toronto, Ontario, Canada M5S 3B2 and;Department of Molecular and Cellular Biology, Science Complex, University of Guelph, Guelph, Ontario, Canada N1G 2W1 |
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| Abstract: | Gene co‐expression analysis has emerged in the past 5 years as a powerful tool for gene function prediction. In essence, co‐expression analysis asks the question ‘what are the genes that are co‐expressed, that is, those that show similar expression profiles across many experiments, with my gene of interest?’. Genes that are highly co‐expressed may be involved in the biological process or processes of the query gene. This review describes the tools that are available for performing such analyses, how each of these perform, and also discusses statistical issues including how normalization of gene expression data can influence co‐expression results, calculation of co‐expression scores and P values, and the influence of data sets used for co‐expression analysis. Finally, examples from the literature will be presented, wherein co‐expression has been used to corroborate and discover various aspects of plant biology. |
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| Keywords: | Arabidopsis bioinformatics correlation databases reverse genetics |
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