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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Autor*in:	Martinez Ricardo [verfasserIn] Pasquier Nicolas [verfasserIn] Collard Martine [verfasserIn] Pasquier Claude [verfasserIn] Lopez-Perez Lucero [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2006

Schlagwörter:	microarray ontology co-expression genes and functional annotations

Übergeordnetes Werk:	In: Journal of Integrative Bioinformatics - De Gruyter, 2018, 3(2006), 2, Seite 188-198
Übergeordnetes Werk:	volume:3 ; year:2006 ; number:2 ; pages:188-198

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1515/jib-2006-37

Katalog-ID:	DOAJ067264573

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authorswithroles_txt_mv	Martinez Ricardo @@aut@@ Pasquier Nicolas @@aut@@ Collard Martine @@aut@@ Pasquier Claude @@aut@@ Lopez-Perez Lucero @@aut@@
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callnumber-first	T - Technology
author	Martinez Ricardo
spellingShingle	Martinez Ricardo misc TP248.13-248.65 misc microarray misc ontology misc co-expression misc genes and functional annotations misc Biotechnology Co-expressed gene groups analysis (CGGA): An automatic tool for the interpretation of microarray experiments
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topic_title	TP248.13-248.65 Co-expressed gene groups analysis (CGGA): An automatic tool for the interpretation of microarray experiments microarray ontology co-expression genes and functional annotations
topic	misc TP248.13-248.65 misc microarray misc ontology misc co-expression misc genes and functional annotations misc Biotechnology
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title	Co-expressed gene groups analysis (CGGA): An automatic tool for the interpretation of microarray experiments
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title_full	Co-expressed gene groups analysis (CGGA): An automatic tool for the interpretation of microarray experiments
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title_sort	co-expressed gene groups analysis (cgga): an automatic tool for the interpretation of microarray experiments
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title_auth	Co-expressed gene groups analysis (CGGA): An automatic tool for the interpretation of microarray experiments
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 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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Co-expressed gene groups analysis (CGGA): An automatic tool for the interpretation of microarray experiments
url	https://doi.org/10.1515/jib-2006-37 https://doaj.org/article/a896633738094e76b3adb8f8074c4237 https://doaj.org/toc/1613-4516
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Co-expressed gene groups analysis (CGGA): An automatic tool for the interpretation of microarray experiments

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