W486–W491 Nucleic Acids Research, 2006, Vol. 34, Web Server issue doi:10.1093/nar/gkl197 Next station in microarray data analysis: GEPAS David Montaner1,2, Joaqu´ın Ta´ rraga1,2, Jaime Huerta-Cepas1,2, Jordi Burguet1, Juan M. Vaquerizas 1 , Luc´ıa Conde 1 , Pablo Minguez1, Javier Vera3, Sach Mukherjee 4 , Joan Valls5, Miguel A. G. Pujana 5 , Eva Alloza1, Javier Herrero6, Fa´ tima Al-Shahrour1 and Joaqu´ın Dopazo1,2,* 1Bioinformatics Department, Centro de Investigacio´ n Pr´ıncipe Felipe (CIPF), Autopista del Saler 16, E46013, Valencia, Spain, 2Functional Genomics Node, INB, CIPF, Autopista del Saler 16, E46013, Valencia, Spain, 3INB—BSC, Jordi Girona 29, Edifici Nexus II, E-08034 Barcelona, Spain, 4Pattern Analysis and Machine Learning Group, Department of Engineering Science University of Oxford, Oxford OX1 2JD, UK, 5Translational Research Laboratory, Catalan Institute of Oncology, Institut d’Investigacio´ Biome` dica de Bellvitge, L’Hospitalet, 08907 Barcelona, Spain and 6Ensembl Team, EMBL-EBI, Hinxton, Cambridge, UK Received February 14, 2006; Revised and Accepted March 21, 2006 ABSTRACT The Gene Expression Profile Analysis Suite (GEPAS) has been running for more than four years. During this time it has evolved to keep pace with the new interests and trends in the still changing world of microarray data analysis. GEPAS has been designed to provide an intuitive although powerful web-based interface that offers diverse analysis options from the early step of preprocessing (normalization of Affymetrix and two-colour microarray experiments and other preprocessing options), to the final step of the functional annotation of the experiment (using Gene Ontology, pathways, PubMed abstracts etc.), and include different possibilities for cluster- ing, gene selection, class prediction and array- comparative genomic hybridization management. GEPAS is extensively used by researchers of many countries and its records indicate an average usage rate of 400 experiments per day. The web-based pipe- line for microarray gene expression data, GEPAS, is available at http://www.gepas.org. INTRODUCTION It is quite common that the introduction of a new technology is accompanied by claims and promises which on many occa- sions cannot be fulﬁlled. This hype is then followed by a wave of disappointment against the technology. Fortunately, as it is reaching a certain degree of maturity, DNA microarray technologies do not seem to have followed this fate. During an initial period, DNA microarray publications were dealing with issues such as reproducibility and sensitivity. Many clas- sical microarray papers dating from the late nineties were mere proof-of-principle experiments (1,2), in which only cluster analysis was applied. Later, sensitivity became a main concern as a natural reaction against quite liberal interpretations of microarray experiments made by some researchers, such as the fold criteria to select differentially expressed genes. It was soon obvious that genome-scale experiments should be care- fully analysed because many apparent associations happened merely by chance (3). In this context, different methods for the adjustment of P-values, which are considered standard today, started to be extensively used (4,5). More recently the use of microarrays as predictors of clinical outcomes (6), despite not being free of criticisms (7), fuelled the use of the methodology because of its practical implications. There are still some concerns with the cross-platform coherence of results but it seems clear that intra-platform reproducibility is high (8) and, despite the fact that gene-by-gene results are not always the same, the biological themes emerging from the different plat- forms are increasingly consistent (9). That points to the importance of the interpretation of experiments in terms of their biological implications instead of a mere comparison of lists of genes (10,11). Keeping a pace with the trends mentioned above, Gene Expression Proﬁle Analysis Suite (GEPAS) has been growing during the last 4 years. In the ﬁrst release it was more oriented towards clustering and data preprocessing (12). Successive releases showed a package more oriented towards gene selec- tion, class prediction and the functional annotation of experi- ments (13,14). The version presented here include several new *To whom correspondence should be addressed. Tel: +34 963289680; Fax: +34 963289701; Email: jdopazo@cipf.es 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. 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