W490 Nucleic Acids Research, 2006, Vol. 34, Web Server issue html) with on-line tutorials that can be freely used (http:// bioinfo.cipf.es/docus/courses/on-line.html). Although other alternatives are available for microarray data analysis, there is no other similar resource over the web with the number of possibilities offered by GEPAS. ACKNOWLEDGEMENTS We are deeply acknowledged to our external advisors Sandrine Dudoit, Yves Moreau and John Quackenbush for their comments and support. This work is supported by grants from Fundacio´ La Caixa, Fundacio´ n BBVA, MEC BIO2005-01078 and NRC Canada-SEPOCT Spain. The Functional Genomics node (INB) is supported by Genoma Espa n˜a. Funding to pay the Open Access publication charges for this article was provided by Genoma Espan˜ a. Conflict of interest statement. None declared. REFERENCES 1. Eisen,M.B., Spellman,P.T., Brown,P.O. and Botstein,D. (1998) Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA, 95, 14863–14868. 2. Perou,C.M., Jeffrey,S.S., van de Rijn,M., Rees,C.A., Eisen,M.B., Ross,D.T., Pergamenschikov,A., Williams,C.F., Zhu,S.X., Lee,J.C. et al. (1999) Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc. Natl Acad. Sci. USA, 96, 9212–9217. 3. Ge,H., Walhout,A.J. and Vidal,M. (2003) Integrating ‘omic’ information: a bridge between genomics and systems biology. Trends Genet., 19, 551–560. 4. Benjamini,Y. and Yekutieli,D. (2001) The control of false discovery rate in multiple testing under dependency. Ann. Stat., 29, 1165–1188. 5. Storey,J.D. and Tibshirani,R. (2003) Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA, 100, 9440–9445. 6. van’t Veer,L.J., Dai,H., van de Vijver,M.J., He,Y.D., Hart,A.A., Mao,M., Peterse,H.L., van der Kooy,K., Marton M.J., Witteveen,A.T. et al. (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature, 415, 530–536. 7. Simon,R. (2005) Roadmap for developing and validating therapeutically relevant genomic classifiers. J. Clin. Oncol., 23, 7332–7341. 8. Moreau,Y., Aerts,S., De Moor,B., De Strooper,B. and Dabrowski,M. (2003) Comparison and meta-analysis of microarray data: from the bench to the computer desk. Trends Genet., 19, 570–577. 9. Bammler,T., Beyer,R.P., Bhattacharya,S., Boorman,G.A., Boyles,A., Bradford,B.U., Bumgarner,R.E., Bushel,P.R., Chaturvedi,K., Choi,D. et al. (2005) Standardizing global gene expression analysis between laboratories and across platforms. Nature Methods, 2, 351–356. 10. Al-Shahrour,F. and Dopazo,J. (2005) In Azuaje,F. and Dopazo,J. (eds), Data analysis and visualization in genomics and proteomics. Wiley, West Sussex, UK, pp. 99–112. 11. Al-Shahrour,F., Minguez,P., Vaquerizas,J.M., Conde,L. and Dopazo,J. (2005) BABELOMICS: a suite of web tools for functional annotation and analysis of groups of genes in high-throughput experiments. Nucleic Acids Res., 33, W460–W464. 12. Herrero,J., Al-Shahrour,F., Diaz-Uriarte,R., Mateos,A., Vaquerizas,J.M., Santoyo,J. and Dopazo,J. (2003) GEPAS: A web-based resource for microarray gene expression data analysis. Nucleic Acids Res., 31, 3461–3467. 13. Herrero,J., Vaquerizas,J.M., Al-Shahrour,F., Conde,L., Mateos,A., Diaz-Uriarte,J.S. and Dopazo,J. (2004) New challenges in gene expression data analysis and the extended GEPAS. Nucleic Acids Res., 32, W485–W491. 14. Vaquerizas,J.M., Conde,L., Yankilevich,P., Cabezon,A., Minguez,P., Diaz-Uriarte,R., Al-Shahrour,F., Herrero,J. and Dopazo,J. (2005) GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data. Nucleic Acids Res., 33, W616–W620. 15. Conde,L., Vaquerizas,J., Dopazo,H., Arbiza,L., Reumers,J., Rousseau,F., Schymkowitz,J. and Dopazo,J. (2006) PupaSuite: finding functional SNPs for large-scale genotyping purposes. Nucleic Acids Res., in press. 16. Conde,L., Vaquerizas,J.M., Ferrer-Costa,C., de la Cruz,X., Orozco,M. and Dopazo,J. (2005) PupasView: a visual tool for selecting suitable SNPs, with putative pathological effect in genes, for genotyping purposes. Nucleic Acids Res., 33, W501–W505. 17. Conde,L., Vaquerizas,J.M., Santoyo,J., Al-Shahrour,F., Ruiz-Llorente,S., Robledo,M. and Dopazo,J. (2004) PupaSNP Finder: a web tool for finding SNPs with putative effect at transcriptional level. Nucleic Acids Res., 32, W242–W248. 18. Sneath,P. and Sokal,R. (1973) Numerical Taxonomy. W.H. Freeman, San Francisco. 19. Dopazo,J. and Carazo,J.M. (1997) Phylogenetic reconstruction using an unsupervised growing neural network that adopts the topology of a phylogenetic tree. J. Mol. Evol., 44, 226–233. 20. Herrero,J., Valencia,A. and Dopazo,J. (2001) A hierarchical unsupervised growing neural network for clustering gene expression patterns. Bioinformatics, 17, 126–136. 21. Kohonen,T. (1997) Self-organizing maps. Springer-Verlag, Berlin. 22. Hartigan,J. and Wong,M. (1979) A k-means clustering algorithm. Appl. Stat., 28, 100–108. 23. Herrero,J. and Dopazo,J. (2002) Combining hierarchical clustering and self-organizing maps for exploratory analysis of gene expression patterns. J. Proteome Res., 1, 467–470. 24. Dudoit,S., Fridlyand,J. and Speed,T. (2002) Comparison of discrimination methods for the classification of tumors using gene expression data. J. Am. Stat. Assoc., 97, 77–87. 25. Ripley,B. (1996) Pattern recognition and neural networks. Cambridge University Press, Cambridge. 26. Vapnik,V. (1998) Statistical Learning Theory. Wiley, NY. 27. Breiman,L. (2001) Random forests. Machine Learning, 45, 5–32. 28. Tibshirani,R., Hastie,T., Narasimhan,B. and Chu,G. (2002) Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc. Natl Acad. Sci. USA, 99, 6567–6572. 29. Ambroise,C. and McLachlan,G.J. (2002) Selection bias in gene extraction on the basis of microarray gene-expression data. Proc. Natl Acad. Sci. USA, 99, 6562–6566. 30. Simon,R., Radmacher,M.D., Dobbin,K. and McShane,L.M. (2003) Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J. Natl Cancer Inst., 95, 14–18. 31. Mantripragada,K.K., Buckley,P.G., de Stahl,T.D. and Dumanski,J.P. (2004) Genomic microarrays in the spotlight. Trends Genet., 20, 87–94. 32. Vaquerizas,J.M., Dopazo,J. and Diaz-Uriarte,R. (2004) DNMAD: web-based diagnosis and normalization for microarray data. Bioinformatics, 20, 3656–3658. 33. Smyth,G., Yang,Y. and Speed,T. (2003) In Brownstein,M. and Khodursky,A. (eds), Functional Genomics: Methods and Protocols. Humana Press, Totowa, NJ, Vol. 224, pp. 111–136. 34. Gentleman,R.C., Carey,V.J., Bates,D.M., Bolstad,B., Dettling,M., Dudoit,S., Ellis,B., Gautier,L., Ge,Y., Gentry,J. et al. (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol., 5, R80. 35. Gautier,L., Cope,L., Bolstad,B.M. and Irizarry,R.A. (2004) affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics, 20, 307–315. 36. Herrero,J., Diaz-Uriarte,R. and Dopazo,J. (2003) Gene expression data preprocessing. Bioinformatics, 19, 655–656. 37. Rousseeuw,P. (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math., 20, 53–65. 38. Azuaje,F. (2002) A cluster validity framework for genome expression data. Bioinformatics, 18, 319–320. 39. Baldi,P. and Long,A.D. (2001) A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics, 17, 509–519. 40. Cui,X. and Churchill,G.A. (2003) Statistical tests for differential expression in cDNA microarray experiments. Genome Biol., 4, 210. 41. Kendziorski,C.M., Newton,M.A., Lan,H. and Gould,M.N. (2003) On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles. Stat. Med., 22, 3899–3914.