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Nucleic Acids Research 2004 32(Web Server Issue):W492-W499; doi:10.1093/nar/gkh443
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© 2004, the authors
Nucleic Acids Research, Vol. 32, Web Server issue © Oxford University Press 2004; all rights reserved

GOAL: automated Gene Ontology analysis of expression profiles

Stefano Volinia1,2,*, Rita Evangelisti1, Francesca Francioso1, Diego Arcelli3, Massimo Carella2,4 and Paolo Gasparini2,5

1 Laboratory of Functional Genomics and Telethon Facility—Data Mining for Analysis of DNA Microarrays, Department of Morphology and Embryology, Università degli Studi, Via Fossato di Mortara 64/b, 44100 Ferrara, Italy, 2 TIGEM, Telethon Institute of Genetics and Medicine, Naples, Italy, 3 Istituto Dermopatico dell'Immacolata, Rome, Italy, 4 IRCCS CSS Servizio di Genetica Medica, S. Giovanni Rotondo, Foggia, Italy and 5 Dipartimento Di Patologia Generale, Seconda Università, Naples, Italy

* To whom correspondence should be addressed. Tel: +39 0532 291714; Fax: +39 0532 291533; Email: s.volinia{at}unife.it

Received December 23, 2004; Revised and Accepted April 20, 2004

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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