High throughput technologies as DNA microarrays generate a huge amount of data which are difficult to interpret. Biologists require easy to use tools to analyse them and explore new scientific hypothesis. We propose a visual data mining tool to extract the useful genomic information buried in gene lists generated by differential expression studies. We compare the genomic distribution which is observed within the gene list to the expected distribution which is estimated from public genomic databases. An algorithm of research and a statistical test allow reliable and optimal results. GExMap helps identifying genomic regions which are enriched in genes whose differential expression is of potential interest for target diseases. This software is freely available and easily customized. Since sources are frequently updated it offers tools for updates at any moment GExMap is usable by any commercially and public available microarray platforms. Furthemore, GExMap helps in interpretation by showing an ordered list for each Gene Ontologies.

       GExMap is designed to be an easy to use software with visual and intuitive results interpretation. We have chosen to develop the software with R language to allow total compatibility with every Operating System. ENSEMBL2 was choosen as a reference identifier for genes, meaning that GExMap requires a pre-processing step to match and replace most of common user’s identifiers types (Unigene, Affymetrix, Agilent ...) by ENSEMBL identifiers. The choice of a reference genome used for this pre-processing step allows compatibility with several data bases and microarray identifiers. In the same step, GExMap creates a new table with all ENSEMBL available information. GExMap is an open source project. Furthermore its sources structure and simple annotation facilitate customisation.

       Biological interpretation of bioinformatics results remains an ongoing and challenging task. Scientists need to access to all type of information buried into biological databases. From the microarray data, GExMap allows to get the observed genomic distribution of the regulated genes and to statistically compare it to expected genomic distribution. Statistical analysis of relative genomic distribution is potentially powerful and informative approach to interpret microarray data. GExMap brings an easy access to this new type of information to biologists.