Nucleic Acids Research, 2006, Vol. 34, Web Server issue W489 number of tools that make use of gene functional annotations to try to understand the global changes in gene expression in microarray experiments (48), but probably one of the most complete packages in this respect is the Babelomics suite (11,49). This suite of programs for functional annotation of genome-scale experiments has undergone a deep modiﬁcation described in detail elsewhere (49). In brief, Babelomics can now compare two groups of genes and test simultaneously for the signiﬁcant over-abundance of diverse biological themes such as GO terms, KEGG pathways, Interpro motifs, Swis- sprot keywords, Transfac motifs, CisRed motifs, relative abundance in tissues and bioentities extracted from PubMed, with the proper multiple testing adjustment. This is carried out by the FatiGO+ module, the evolution of the FatiGO program (50). Additionally there are two modules designed to search for functionally related blocks of genes that are co-ordinately over- or under-expressed using both the FatiScan (51) or the GSEA (52) algorithms. Despite its general scope (Babelomics is not restricted to microarrays but applicable to any type of large-scale experi- ment), and the possibility of being used alone as an independ- ent resource, the Babelomics suite has been fully integrated into GEPAS. Modules of gene selection (T-rex) or class pre- diction (tnasas) can submit the genes selected as relevant to the FatiGO+ module for testing against the rest of genes. Like- whise, the modules for clustering (hierarchical, k-means, SOM, SOTA) through their cluster’ viewers or through CAAT, can submit the genes within the selected cluster to be tested against the rest of genes. Similar operation can be performed from within ISACGH, with the genes contained in the selected chromosomal region. Moreover, arrangements of genes can be sent from T-rex to the FatiScan to test blocks of functionally related genes tha are co-ordinately over- or under- expressed. Sets of arrays can also be submitted to GSEA with the same purpose. ARRAY-CGH Genetic aberrations, which are the molecular basis of many diseases, have classically been studied through CGH. The introduction of microarray-based CGH methods (array-CGH) has revolutionized this methodology in terms of resolution and throughput (31,53) but, at the same time, has generated a need for new algorithms and software for dealing with this type of data. We have included in GEPAS a new module, ISACGH, which completely replaces the old viewer InSilicoCGH (14). ISACGH includes two new and efﬁcient methods for accurate estimation of genomic copy number from array-CGH hybrid- ization data, integrated into a web-based system that allows, for the ﬁrst time, the combined study of gene expression and genomic copy number. Several visualization options offer a convenient representation of the results. Moreover, the link to the Babelomics (11,49) tools allows, for the ﬁrst time in a tool of this type, the production of functional annotations (using different relevant biological information such as gene onto- logy, pathways, etc.) for the detected chromosomal regions of interest (ampliﬁed or deleted). We use the DAS technology (Distributed Annotation System; see http://www.biodas.org/), that allows a remote mapping of information (our predictions) from a server (our server) to a client (Ensembl), to represent the ISACGH predictions and data onto the Ensembl chromosomal coordinates. ISACGH generically maps data onto their chromosomal coordinates. So, beyond to map genomic hybridisations any other data can be mapped. Thus CAAT can send to ISACGH groups of co-expressing genes, which might be useful for deﬁning regions of increased gene expression, also known as RIDGES (54). Polymorphisms affecting gene expression Although the study of regulatory polymorphisms is not new, there has been a recent revival of interest in them mainly because of the availability of high-throughput data and meth- odologies that allows their characterisation (55). The corres- ponding GEPAS modules (CAAT, tnasas and T-rex) have a unique feature in this regard: the possibility of connecting the genes found to be regulated in a microarray experiment to possible regulatory SNPs in such genes. In particular, clustering and gene selection methods can be connected to the PupaSuite (15–17). DISCUSSION GEPAS is a long-term project that aims to provide the scient- iﬁc community with an advanced set of tools for microarray data analysis, without renouncing to an easy and intuitive use. It has been running uninterruptedly for more than four years and has grown to include more tools as new algorithms were introduced in the microarray data analysis arena (12–14). The GEPAS team has intended to deliver a coherent set of state-of- the-art and widely established algorithms, running away from building a simple collection of as-much-as-possible tools. Actually, any new tool included is the response to a new or emerging requirement requested by our users. As the Func- tional Genomics node of the Spanish Institute of Bioinform- atics (INB; http://www.inab.org) and being part of the Spanish Network of Cancer Centers (RTICCC; http://www.rticcc.org) we have a direct contact with researchers from which we get much of the feedback necessary to build up a useful tool. GEPAS, integrated with the Babelomics suite (11,49), pro- vides the tools for performing the most common analyses of microarray data. Moreover, it has been conceived as a workﬂow that helps the user to carry out a series of consec- utive steps of analysis with simple mouse clicks. GEPAS has been designed to take full advantage of the properties of the web: connectivity, cross-platform functionality and remote usage. Its modular architecture allows easy implementation of new tools and facilitates the connectivity of GEPAS from and to other web-based tools. The user of GEPAS ranges from the experimentalist with not much experience in bioinformatics and no deep statistical skills, interested only in data analysis, to the bioinformatician that invokes some of the tools remotely for different purposes. GEPAS is running in a high-end cluster (with 20 dedicated AMD Opteron CPUs at 2.4 GHz) with a large amount of RAM (6 GB). This allows to use tools (e.g. normalization tools are highly RAM-consuming) that usually are beyond the capab- ilities of the hardware available to many end users. In addition, there is a teaching programme related to GEPAS (see http://bioinfo.cipf.es/docus/courses/courses.