One of the most common problems encountered while deciphering results from expression profiling experiments is in relating differential expression of genes to molecular functions and cellular processes. A second important problem is that of comparing experiments performed by different labs using different microarray platforms, or even unrelated techniques. Gene Ontology (GO) is now used to describe biological features, since GO terms are associated with genes, to overcome the apparent distance between expression profiles and biological comprehension. Here we describe the development, implementation and use of GOAL (Gene Ontology Automated Lexicon), a web-based application for the identification of functions and processes regulated in microarray and SAGE (serial analysis of gene expression) experiments. We applied GOAL to a range of experimental datasets related to different biological problems, including cancer and the cell cycle. By using GOAL, reported and novel relevant processes were identified in a number of experiments by our collaborators and by us. Different datasets could also be compared with each other to define conserved functional modules. GOAL allows a seamless and high-level analysis of expression profiles and is implemented as a free WWW resource (http://microarrays.unife.it).
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