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Bioinformatics Advance Access published online on May 6, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth295
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received February 11, 2004
Revised March 31, 2004
Accepted April 21, 2004

Article

THEA: Ontology driven analysis of microarray data

C. Pasquier 1*, F. Girardot 1, K. Jevardat de Fombelle 1, R. Christen 1

1 Institute of Signaling, Developmental Biology and Cancer Research, Laboratory of Virtual Biology, CNRS UMR 6543 Centre de Biochimie, Nice, 06108 Cedex 02, France

* To whom correspondence should be addressed. E-mail: claude.pasquier{at}unice.fr.


  Abstract

Motivation: Microarray technology makes it possible to measure thousands of variables and to compare their values under hundreds of conditions. Once microarray data are quantified, normalized and classified, the analysis phase is essentially a manual and subjective task based on visual inspection of classes in the light of the vast amount of information available. Currently, data interpretation clearly constitutes the bottleneck of such analyses and there is an obvious need for tools able to fill the gap between data processed with mathematical methods and existing biological knowledge.

Results: THEA (Tools for High-throughput Experiments Analysis) is an integrated information processing system allowing convenient handling of data. It allows to automatically annotate data issued from classification systems with selected biological information coming from a knowledge base and either to manually search and browse through these annotations or to automatically generate meaningfull generalizations according to statistical criteria (data mining).

Availability: The software is available on the web site: http://thea.unice.fr/

Supplementary Information: Supplementary tables as well as files containing the biological data used in this publication can be downloaded from our website: http://bioinfo.unice.fr/publications/thea_article/


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