A major challenge raised by genome-wide exploration of the transcriptome is to assign some biological significance to long lists of differentially expressed genes, but multiple tools are available to achieve that goal. First, clustering analysis can reveal patterns of transcriptional regulation shared by sets of genes, while gene promoter and 3’-untranslated region analysis can identify transcription factors and microRNAs (miRNAs) potentially coordinating the regulation of several transcripts at a time [4-7]. In addition, differential gene expression data sets can be overlaid onto databases integrating current knowledge on protein activity, functional interactions and higher-order relationships between proteins. Available bioinformatics resources include Gene Ontology (GO) classification (biological process, molecular function and cellular component, see [8]) and pathway-mining tools [9-12], which enable to identify networks of co-regulated genes whose products participate in the same biological function or signaling cascade. The use of genome-wide transcriptional profiling, associated with appropriate functional analysis, can therefore contribute to the identification of missing links in the sequence of molecular events leading to alcoholism [13].
