W488 Nucleic Acids Research, 2006, Vol. 34, Web Server issue Figure 1. Map of GEPAS functionalities as a subway line. Data (Affimetrix, two-colour or raw) are introduced from the left side and pass through the preprocessor. Then different types of analyses can be performed: gene selection (T-rex) in different situations (two or more classes, correlation or survival; see text for details) or class discovery (Tnasas) are two types of supervised analyses. Array-CGH data can be analysed through the red line ISACGH. Unsupervised analysis can also be performed using different methods. CAAT allows to map co-expressed genes on their chromosomal coordinates allowing the study of RIDGES (54). All the tools end up in Babelomics (11), that allows for two different types of analysis: comparison of two sets of genes of analysis or blocks of functionally related genes. An empirical Bayes methodology that allows fitting hier- archical mixture models to identify differentially expressed genes (41). One of the advantages of this meth- odology is that it fits a global model taking into account all genes in the dataset. A novel test for the analysis of microarray data by com- bining inference for differential expression and variabil- ity (CLEAR-test) (J. Valls, M. Grau, X. Sole, P. Hernandez, D. Montaner, J. Dopazo, M. A. Peinado, G. Capella, M. A. G. Pujana and V. Moreno, manuscript submitted). Most tests evaluate differential expression by using estimated variability, but no inference is made in terms of the variability itself. CLEAR-test evaluates both whether genes show large fold changes and whether their variability is high. A data-adaptive approach to the analysis of differential expression, in which an effective test statistic is learned directly from microarray data. This approach has been shown to ameliorate many of the problems associated with both the t-statistic and simple moderated statistics like SAM (42), and to produce good results under a range of conditions (43). Finding genes differentially expressed between more than two classes (e.g. different types of cancers and so on) Together with the classical ANOVA methodology we make available the same CLEAR test mentioned above (41). While the mathematical treatment of this kind of data is similar to that of two classes, in our tools, we separate the case when more than two classes are available because of its different conceptual implications. Finding genes whose expression is correlated to a contin- uous variable (e.g. the level of a metabolite). Regression analysis of gene expression on any numerical independent variable has been implemented. C routines have been com- piled for the particular architecture of our computers in order to achieve the maximal speed. Estimates of Pearson’s and Spearman’s correlation coefficients as well as P-values for testing the hypothesis of no correlation can also be obtained with T-rex. Finding genes whose expression is related to survival times. GEPAS uses C routines to estimate a Cox proportional hazards regression model (44). Right censored data are allowed as well as replicates in the survival times. Censoring variables should be provided by the researcher together with survival times that may be replicated. When appropriate, P-values adjusted for multiple testing are provided. Three methodologies are implemented. One of them controls the FWER (family-wise error rate) (45) while the others control the FDR (false discovery rate) (46). Our imple- mentations make use of the p.adjust function in the stats R package and the qvalues package (47) from Bioconductor. FUNCTIONAL ANNOTATION Functional annotation of the experiments gives clues to the researcher for the interpretation of the experiment. There are a