Nucleic Acids Research, 2006, Vol. 34, Web Server issue W487 modules, some of which are new while other ones constitute already available tools completely rewritten including new functionalities. GEPAS is not a simple web server, but it con- stitutes one of the largest resources for integrated microarray data available over the web. It has been working for more than four years having by the end of year 2005 an average of 400 experiments analysed per day summing up over all of their modules. GEPAS is used by researches worldwide as can be seen in the usage map, where all the sessions are mapped to its geographic location (http://bioinfo.cipf.es/access_map/map. html). It also offers on-line tutorials that can be used in courses. In the new version (3.0) we present new modules for the normalization of Affymetrix experiments, for differ- ential gene expression, for the evaluation of cluster quality and another module for array-comparative genomic hybridization (Array-CGH) data management. Also, another conceptual novelty is the connection of GEPAS to the PupaSuite tools (15–17), which offers the possibility of analysing polymorph- isms at the light of the results of the gene expression analysis. GENERAL OVERVIEW GEPAS aims to tackle the most common problems in micro- array data analysis in a simple but rigorous way. Thus, after an essential step of normalization, there are different ‘work- ﬂows’, or sequences of steps, that can be followed, depending on the aim of the experiment: class discovery, differential gene expression, class prediction or genomic copy number estima- tion, just to cite the most common objectives of microarray experiments. Class discovery, either in genes or in experi- ments, is achieved by using clustering methods. GEPAS includes some commonly used clustering methods such as hierarchical clustering (18), SOTA (19,20), SOM (21), K-means (22) and SOM-Tree (23). The evaluation of cluster quality, a scarcely addressed issue, has been implemented here in the Cluster Accuracy Analysis Tool (CAAT) module (see below). Differential gene expression implies ﬁnding genes with signiﬁcant differences in expression between two or more classes, related to a continuous experimental factor (e.g. the concentration of a metabolite) or to survival data. A new, more complete module for differential gene expression is presented in this new version of GEPAS (see below). The module Tnasas for class prediction implements different classiﬁers, such as diagonal linear discriminant analysis (DLDA) (24), nearest neighbour (NN) (25), support vector machines (SVM) (26), random forest (27) and shrunken centroids (PAM) (28) of known efﬁciency as class predictors using microarray data (24). Cross-validation error is calculated in a way to avoid the well-known selection bias problem (29,30). See Tnasas help (http://tnasas.bioinfo.cipf.es/cgi- bin/docs/tnasashelp) for a more detailed description of the methods and error estimation strategy. Array-CGH (31) can be analysed through the module ISACGH that allows predict- ing copy number, relating these values to gene expression and performing functional annotation through the babelomics (11) suite. Finally, functional annotation is carried out with the babelomics suite which can be used either as an independent suite or as an integrated part of the GEPAS. Figure 1 illus- trates, following the metaphor of a subway line, the intercon- nections of the different tools in the GEPAS environment. NORMALIZATION AND PREPROCESSING GEPAS now implements normalization facilities for both two- colours and Affymetrix arrays. DNMAD (32) module performs normalization in two-colour arrays using print-tip loess (33) with a number of different options. DNMAD can input Genepix (Axon instruments) GPR ﬁles. The module expresso normalizes Affymetrix CEL ﬁles using standard Bioconductor (34) tools; in particular the package affy (35). Besides its friendly web interface we provide the user with the speed and above all the physical memory available in our server. More information can be found in the corresponding tutorial web pages (http://bioinfo.cipf.es/docus/courses/on-line.html). In addition, the preprocessor (36) module performs some preprocessing of the data (log-transformations, standardiza- tions, imputation of missing values and so on). CLUSTERING AND CLUSTER QUALITY ESTIMATION Despite the fact that clustering is one of the most popular— albeit often improperly used (30)—methodologies in the ana- lysis of microarray data there are very few alternatives for the estimation of the quality of the results found. We have included a module, CAAT, which provides many options for the visualization and intuitive manipulation of hierarchical and non-hierarchical clustering results. Many visualization modes, browsing options and cluster extraction possibilities are cur- rently available. Moreover, CAAT provides some descriptive measures about each partition (average proﬁles, standard devi- ation proﬁles, inter and intra-cluster distances) as well as a global estimation of cluster quality by the silhouette method (37), which performs well, in noisy situations, such as micro- array analysis (38). CAAT submits data to other tools such as the Babelomics (11) functional annotation suite or to ISACGH (Figure 1). There is more detailed information in the CAAT documenta- tion (http://bioinfo.cipf.es/docus/courses/on-line.html). DIFFERENTIAL GENE EXPRESSION This version of GEPAS includes new methods for differential gene expression analysis under different conditions. The old module pomelo has been replaced by the new module T-rex (Tools for RElevant gene seleXion) which is much faster and offers new tests for different situations. T-rex distinguishes among four conceptually different testing cases: Finding genes differentially expressed between two discrete classes (e.g. case/control and so on). A number of authors (39,40) have found that the classical t-statistic, which was widely used in early work on the analysis of differential expression, can be highly unreliable for microarray data. Problems arise mainly as a consequence of statistical issues relating to the SD term in the denominator of the t-statistic. For example, many non-differentially expressed genes may by chance have small observed SDs, which may cause these genes to be erroneously selected. GEPAS now also imple- ments different new tests: The t-test, which is still available.