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4 Vaquerizas, J.M. et al. (2005) GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data. Nucleic Acids Res. 33, W616–W620 5 Ramonell, K. et al. (2005) Loss-of-function mutations in chitin responsive genes show increased susceptibility to the powdery mildew pathogen Erysiphe cichoracearum. Plant Physiol. 138, 1027–1036 6 Gachon, C.M. et al. (2005) Transcriptional co-regulation of secondary metabolism enzymes in Arabidopsis: functional and evolutionary implications. Plant Mol. Biol. 58, 229–245
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7 Brown, D.M. et al. (2005) Identification of novel genes in Arabidopsis involved in secondary cell wall formation using expression profiling and reverse genetics. Plant Cell 17, 2281–2295 8 Persson, S. et al. (2005) Identification of genes required for cellulose synthesis by regression analysis of public microarray data sets. Proc. Natl. Acad. Sci. U. S. A. 102, 8633–8638 9 Leung, Y.F. and Cavalieri, D. (2003) Fundamentals of cDNA microarray data analysis. Trends Genet. 19, 649–659 1360-1385/$ - see front matter Q 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.tplants.2006.05.009
Microarray data analysis made easy Gerold J.M. Beckers and Uwe Conrath Plant Biochemistry & Molecular Biology Unit, Department of Plant Physiology, RWTH Aachen University, 52056 Aachen, Germany
DNA microarrays are valuable tools for analyzing global gene expression. Because of the increasing popularity and the large volume of data produced, tools for facile microarray data analysis are essential. FiRe, a recently introduced computer program, has now solved the seemingly insuperable discrepancy between simplicity and evaluation of DNA microarray data. The program is available as a macro for the popular Microsoftw Office Excel software and is user-friendly, interactive, versatile and platform-independent, paving the way for a further push in the evaluation of DNA microarrays.
with FiRe can compare lists of genes, bundle data from different spreadsheets, link candidate genes to available Internet sources, and produce heat maps for simple visualization of DNA microarray data. Moreover, FiRe works in real-time within the environment of Microsoftw Office Excel, requires no special technical knowledge, analyzes data from any type of microarray, handles huge datasets (up to 65 000 probes according to the producers) and can be run on the PC and Mac versions of Microsoftw Office Excel. Finally, FiRe is accessible to the public (http://www.unifr.ch/plantbio/FiRe/main.html) and, thus, will probably be swiftly taken up by the research community.
Yet more software for DNA microarray analysis – why? DNA microarrays provide us with the unique opportunity to identify candidate genes with important roles in defined physiological processes [1]. Over the past few years, various algorithms have been created to facilitate the analysis of the data produced in DNA microarray expression studies [2–5]. Unfortunately, these programs often require complex statistical analysis and sophisticated user interfaces, which scare off the unassisted biologist with little experience in bioinformatics. To overcome this problem, Jean-Pierre Me´traux and associates have created FiRe (Find Regulons), a user-friendly program for identifying candidate genes from DNA microarray data that execute a defined pattern of expression in a given physiological condition [6]. Although the identification of candidate genes is based solely on their clear-cut fold-change in expression, the advantages of FiRe are obvious. The program performs as a macro in the popular Microsoftw Office Excel software, which makes it extremely user-friendly. FiRe can even aggregate data from different experiments and highlight genes with expression ratios higher or lower than user-defined threshold levels. In addition, other macros associated
Experience with test-driving FiRe For a test run of FiRe we followed the coherent instructions given in the FiRe User’s Guide that comes with the program and takes only w30 min to read through. At first, data for analysis were uploaded from the Arabidopsis Information Resource (TAIR) website (http://www.arabidopsis.org). The data were created in an experiment performed by Xinnian Dong’s group (Duke University, NC, USA): one half of an Arabidopsis leaf was infiltrated with Pseudomonas syringae; the other half of the leaf was collected over a time course to identify genes that play a role in establishing systemic acquired disease resistance. The data comprise 32 slides (16 replicate sets) provided as WinZipw files. We also compared DNA microarray data from an experiment in which the switch from housekeeping to pathogen defense metabolism had been monitored in Arabidopsis [7]. The content of all slides must first be opened in Microsoftw Office Excel. This is a time-consuming issue that is common to all programs for the analysis of DNA microarray data. At this stage, one just needs to know how to move the mouse and click (Figure 1). After data upload, check that all files containing the data to be analyzed are open before running the macro. When data upload is complete, the user can immediately get started
Corresponding author: Conrath, U. (
[email protected]). Available online 16 June 2006 www.sciencedirect.com
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missing IDs are not a problem – FiRe accepts data without IDs for an infinite number of experiments. It even has the facility to create, within the Microsoftw Office Excel environment, so-called ‘heat maps’ that can easily be exported as pictures to almost any program supporting desktop digital imaging. Using FiRe, cross-comparison of data from within the same experiment or results from diverse experiments performed by different laboratories was enjoyable. It was fun applying the salient features that this smart program offers. Acknowledgements We thank Nick Schlaich for providing DNA microarray data and Muris Korkaric for the cartoon drawing. Work in our laboratory is funded by BASF, BASF Plant Science, Bayer and the Heinrich Hertz Foundation.
References
Figure 1. ‘.you just have to know how to move the mouse and click.’.
with setting criteria such as fold increase in gene expression over control. Data analysis using FiRe is simple, extremely versatile, straight-forward and mostly selfexplanatory, so there is no need to comment on this. In addition to the above-mentioned features, the user can easily exclude a defined dataset from the analysis and even disregard the expression level of certain treatments or time points. However, FiRe not only allows the user to merge lists containing different expression patterns of a particular gene but also enables lists of genes with the same, or at least similar, expression pattern to be compared. Furthermore, the program connects html links to a user-generated list of candidate genes so that databases hosted on the Internet are just one mouse click away. Another plus is that
1 Schena, M. et al. (1995) Quantitative monitoring of gene expression patterns with a complimentary DNA microarray. Science 270, 467–470 2 Breitling, R. et al. (2004) Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett. 573, 83–92 3 Hsiao, A. et al. (2005) VAMPIRE microarray suite: a web-based platform for the interpretation of gene expression data. Nucleic Acids Res. 33, W627–W632 4 Tusher, V.G. et al. (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. U. S. A. 98, 5116–5121 5 Vaquerizas, J.M. et al. (2005) GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data. Nucleic Acids Res. 33, W616–W620 6 Garcion, C. et al. (2006) FiRe and microarrays: a fast answer to burning questions. Trends Plant Sci. 11, DOI: 10.1016/j.tplants.2006.05.009 7 Scheideler, M. et al. (2002) Monitoring the switch from housekeeping to pathogen defense metabolism in Arabidopsis thaliana using cDNA arrays. J. Biol. Chem. 277, 10555–10561 1360-1385/$ - see front matter Q 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.tplants.2006.05.010
Plant Science-related Gordon conferences in 2006 Mitochondria & Chloroplasts 13-18 August 2006 Magdalen College Oxford, UK http://www.grc.uri.edu/programs/2006/mitochon.htm Plant & Fungal Cytoskeleton 20-25 August 2006 Proctor Academy Andover, NH, USA http://www.grc.uri.edu/programs/2006/plantfun.htm Salt and Water Stress in Plants 3-8 September 2006 Oxford, UK http://www.grc.uri.edu/programs/2006/salt.htm www.sciencedirect.com
