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

Martinez, Ricardo ; Pasquier, Nicolas ; Collard, Martine ; Pasquier, Claude ; Lopez-Perez, Lucero

Journal of Integrative Bioinformatics - JIB (ISSN 1613-4516)



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


Beteiligte Einrichtung: Technische Fakultät, Arbeitsgruppen der Informatik
DDC-Sachgruppe: Datenverarbeitung, Informatik

Zitat-Vorschlag:
Martinez, Ricardo ; Pasquier, Nicolas ; Collard, Martine ; Pasquier, Claude ; Lopez-Perez, Lucero  (2006)  Co-expressed gene group analysis (CGGA): An automatic tool for the interpretation of microarray experiments. Journal of Integrative Bioinformatics - JIB (ISSN 1613-4516), 3(2), 2006. Special Issue: 3rd Integrative Bioinformatics Workshop, Harpenden, United Kingdom, 2

Online-Journal: http://journal.imbio.de/index.php?paper_id=37
URL: http://biecoll.ub.uni-bielefeld.de/volltexte/2007/218

Also published by Shaker:
Ralf Hofestädt, Thoralf Töpel (eds.). Integrative Bioinformatics -
Yearbook 2006. Shaker, 2007.


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 Letzte Änderung: 15.2.2011
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