W642 Nucleic Acids Research, 2005, Vol. 33, Web Server issue Figure 5. ‘GO-term chart’ output. Biological function GO terms calculated to be statistically overrepresented in a user-specified gene list are reported along with the number of genes from the list associated with each term. by the Gene Ontology Consortium. Based on the GO-term composition of all genes on the array used, a p-value is deter- mined using a hypergeometric distribution for the overrepres- entation of each GO term among the speciﬁed gene list. The ‘Gostats’ Bioconductor package was used to implement this method. For more information, see http://Bioconductor.org/ Docs/Papers/2003/Compendium/GOstats.pdf. Output. According to the user-speciﬁed parameters (GO-term type, GO-term speciﬁcity level, minimum number of genes annotated with a certain GO term, p-value threshold), a list of enriched GO terms is generated for the genes provided. For each enriched GO term, the numbers of supporting genes from the list as well as from the entire chip are reported and visu- alized. The counts of annotated and unannotated genes are reported as well. If the gene list corresponds to differentially expressed genes which are supplied with log ratios, the num- bers of over- and underexpressed genes among the regulated genes are presented. To generate a ranking based on statistical signiﬁcance, a p-value is calculated for the overrepresentation of GO-terms based on the hypergeometric distribution. The results are summarized in bar graphs and tables Figure 5 shows such a GO-term bar chart for the experiment comparing human placenta and testis gene expression patterns. Different colors are assigned to up- and downregulated genes. The number of GO terms in the ‘biological function’ category signiﬁcantly enriched in the group of differentially expressed genes is presented. Not surprisingly considering the source of the RNAs, the biological function GO terms ‘spermatogenesis’ (overexpressed in testis) and ‘pregnancy’ (underexpressed in testis) dominate the list. SUMMARY RACE offers an easy to use collection of bioinformatics web tools to analyze DNA microarray data, without requiring any installation or maintenance on the user side. By using various R subroutines and Bioconductor packages, RACE provides users with access to powerful statistical analysis tools without the need for speciﬁc expertise in their use. It offers different users or laboratories the possibility of performing data QC, normalization and analysis in a standardized way, which is likely to lead to more consistent and reproducible results. ACKNOWLEDGEMENTS We thank all the people providing and maintaining the excel- lent open source software on which RACE is based, i.e. Linux, Apache, Perl and R, together with CRAN and Bioconductor. We also thank Olivier Schaad for beta-testing RACE and providing numerous suggestions, Robeto Fabbretti from the Vital-IT Group of the Swiss Institute of Bioinformatics for hosting the RACE server, Otto Hagenbu¨ chle and Johann Weber for comments on the manuscript, and Thierry Sengstag and the members of the DAFL for support. The DNA Array Facility is supported by the Etat de Vaud. Funding to pay the Open Access publication charges for this article was provided by the Etat de Vaud. Conflict of interest statement. None declared. REFERENCES 1. Chi,J.-T., Chang,H.Y., Haraldsen,G., Jahnsen,F.L., Troyanskaya,O.G., Chang,D.S., Wang,Z., Rockson,S.G., van de Rijn,M., Botstein,D. and Brown,P.O. (2003) Endothelial cell diversity revealed by global expression profiling. Proc. Natl Acad. Sci. USA, 100, 10623–10628. 2. Magee,J.A., Abdulkadir,S.A. and Milbrandt,J. (2003) Haploinsufficiency at the Nkx3.1 locus: a paradigm for stochastic, dosage-sensitive gene regulation during tumor initiation. Cancer Cell, 3, 273–283. 3. Roepman,P., Wessels,L.F., Kettelarij,N., Kemmeren,P., Miles,A.J., Lijnzaad,P., Tilanus,M.G., Koole,R., Hordijk,G.J., van der Vliet,P.C. et al. (2005) An expression profile for diagnosis of lymph node metastases from primary head and neck squamous cell carcinomas. Nature Genet., 37, 182–186. 4. Chung,C.H., Bernard,P.S. and Perou,C.M. (2002) Molecular portraits and the family tree of cancer. Nature Genet., 32, 533–540. 5. Gerhold,D.L., Jensen,R.V. and Gullans,S.R. (2002) Better therapeutics through microarrays. Nature Genet., 32 (Suppl.), 547–551. 6. Zhang,W., Morris,Q.D., Chang,R., Shai,O., Bakowski,M.A., Mitsakakis,N., Mohammad,N., Robinson,M.D., Zirngibl,R., Somogyi,E. et al. (2004) The functional landscape of mouse gene expression. J. Biol., 3, 21. 7. Yang,Y.H., Dudoit,S., Luu,P., Lin,D.M., Peng,V., Ngai,J. and Speed,T.P. (2002) Normalization for cDNA normalization data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res., 30, e15. 8. Desagher,S., Severac,D., Lipkin,A., Bernis,C., Ritchie,W., Le Digarcher,A. and Journot,L. (2004) Genes regulated in neurons undergoing transcription-dependent apoptosis belong to signaling pathways rather than the apoptotic machinery. J. Biol. Chem., 280, 5693–5702.