Abstract: Statistical and clustering analyses of gene expression results from high-throughput microarray experiments produce lists of hundreds of genes candidate regulated, or with particular expression profile patterns, in the conditions under study. Independently of the microarray platforms and analysis methods used to identify and cluster differentially expressed genes, the common task any researcher faces is to translate the identified lists of genes into a better understanding of the patho-physiological phenomena involved. To this aim, many biological annotations are available within numerous heterogeneous and widely distributed databases. Although several tools have been developed for annotating lists of genes, most of them do not provide methods to evaluate the relevance of the retrieved annotations for the considered set of genes, or to estimate the functional bias introduced by the gene set present on the specific array used to identify the considered gene list. Lately, few tools have been proposed that use gene annotations provided through the Gene Ontology (GO) [1] controlled vocabularies to enrich lists of genes with biological information. Some of them (e.g. Affymetrix Data Mining Tool, DAVID, FatiGO, GoMiner, MAPPFinder) also present the GO categories more relevant for a given set of genes according to the number of genes of the considered set belonging to a given category, or in relation to their statistical evaluation performed using some basic tests. To extend these functionalities we created GFINDer (i.e. Genome Function INtegrated Discoverer, http://www.medinfopoli.polimi.it/GFINDer/), a web server able to automatically provide large-scale lists of user-classified genes with the statistically significant functional profiles that biologically characterize the different gene classes in a considered gene list.