Co-expressed gene groups analysis (CGGA): An automatic tool for the interpretation of microarray experiments

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...

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Bibliographic Details
Published in:Journal of integrative bioinformatics 2006-12, Vol.3 (2), p.188-198
Main Authors: Martinez, Ricardo, Pasquier, Nicolas, Collard, Martine, Pasquier, Claude, Lopez-Perez, Lucero
Format: Article
Language:English
Subjects:
Co-expression
Genes and Functional Annotations
Microarray
Ontology
Online Access:Get full text
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Summary: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 wellknown 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.
ISSN:1613-4516
DOI:10.2390/biecoll-jib-2006-37