Co-expressed Gene Groups Analysis (CGGA): An Automatic Tool for the Interpretation of Microarray Experiments

Ricardo Martinez 1 Nicolas Pasquier 1, * Claude Pasquier 2 Martine Collard 1 Lucero Lopez-Perez 3
* Auteur correspondant
1 I3S - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis
2 IBV - Institut de Biologie Valrose
3 ODYSSEE - Computer and biological vision
DI-ENS - Département d'informatique de l'École normale supérieure, CRISAM - Inria Sophia Antipolis - Méditerranée , ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, ENPC - École des Ponts ParisTech
Abstract : Microarray technology produces vast amounts of data by measuring simultaneously the expression levels of thousands of genes under hundreds of biological conditions. Nowadays, one of the principal challenges in bioinformatics is the interpretation of this large amount of data using different sources of information. We have developed a novel data analysis method named CGGA (Co-expressed Gene Groups Analysis) that automatically finds groups of genes that are functionally enriched, i.e. have the same functional annotations, and are co-expressed. CGGA automatically integrates the information of microarrays, i.e. gene expression profiles, with the functional annotations of the genes obtained by the genome-wide information sources such as Gene Ontology. By applying CGGA to well-known microarray experiments, we have identified the principal functionally enriched and co-expressed gene groups, and we have shown that this approach enhances and accelerates the interpretation of DNA microarray experiments. CGGA program is available at http://www.i3s.unice.fr/~rmartine/CGGA
Keywords : Clustering Data Mining Genes and Functional Annotations Microarray Ontology Co-expression
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Article dans une revue
Journal of Integrative Bioinformatics, Informationsmanagement in der Biotechnologie e.V. (IMBio e.V.), 2006, 3 (12), pp.1-12
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Ricardo Martinez, Nicolas Pasquier, Claude Pasquier, Martine Collard, Lucero Lopez-Perez. Co-expressed Gene Groups Analysis (CGGA): An Automatic Tool for the Interpretation of Microarray Experiments. Journal of Integrative Bioinformatics, Informationsmanagement in der Biotechnologie e.V. (IMBio e.V.), 2006, 3 (12), pp.1-12. 〈hal-00172501〉

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