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How to decide which are the most pertinent overly-represented features during gene set enrichment analysis

hal.structure.identifierDepartement of Electrical Engineering-SCD [Leuven] [ESAT-SCD]
hal.structure.identifierCentre de Bioinformatique de Bordeaux [CBIB]
dc.contributor.authorBARRIOT, Roland
hal.structure.identifierCentre de Bioinformatique de Bordeaux [CBIB]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierModels and Algorithms for the Genome [MAGNOME]
dc.contributor.authorSHERMAN, David James
hal.structure.identifierCentre de Bioinformatique de Bordeaux [CBIB]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorDUTOUR, Isabelle
dc.date.accessioned2024-04-15T09:56:14Z
dc.date.available2024-04-15T09:56:14Z
dc.date.issued2007
dc.identifier.issn1471-2105
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/198816
dc.description.abstractEnABSTRACT: BACKGROUND: The search for enriched features has become widely used to characterize a set of genes or proteins. A key aspect of this technique is its ability to identify correlations amongst heterogeneous data such as Gene Ontology annotations, gene expression data and genome location of genes. Despite the rapid growth of available data, very little has been proposed in terms of formalization and optimization. Additionally, current methods mainly ignore the structure of the data which causes results redundancy. For example, when searching for enrichment in GO terms, genes can be annotated with multiple GO terms and should be propagated to the more general terms in the Gene Ontology. Consequently, the gene sets often overlap partially or totally, and this causes the reported enriched GO terms to be both numerous and redundant, hence, overwhelming the researcher with non-pertinent information. This situation is not unique, it arises whenever some hierarchical clustering is performed (e.g. based on the gene expression profiles), the extreme case being when genes that are neighbors on the chromosomes are considered. RESULTS: We present a generic framework to efficiently identify the most pertinent over-represented features in a set of genes. We propose a formal representation of gene sets based on the theory of partially ordered sets (posets), and give a formal definition of target set pertinence. Algorithms and compact representations of target sets are provided for the generation and the evaluation of the pertinent target sets. The relevance of our method is illustrated through the search for enriched GO annotations in the proteins involved in a multiprotein complex. The results obtained demonstrate the gain in terms of pertinence (up to 64% redundancy removed), space requirements (up to 73% less storage) and efficiency (up to 98% less comparisons). CONCLUSIONS: The generic framework presented in this article provides a formal approach to adequately represent available data and efficiently search for pertinent over-represented features in a set of genes or proteins. The formalism and the pertinence definition can be directly used by most of the methods and tools currently available for feature enrichment analysis.
dc.language.isoen
dc.publisherBioMed Central
dc.title.enHow to decide which are the most pertinent overly-represented features during gene set enrichment analysis
dc.typeArticle de revue
dc.identifier.doi10.1186/1471-2105-8-332
dc.subject.halInformatique [cs]/Bio-informatique [q-bio.QM]
dc.subject.halSciences du Vivant [q-bio]/Bio-Informatique, Biologie Systémique [q-bio.QM]
bordeaux.journalBMC Bioinformatics
bordeaux.volume8
bordeaux.hal.laboratoriesLaboratoire Bordelais de Recherche en Informatique (LaBRI) - UMR 5800*
bordeaux.issue1
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierinria-00202721
hal.version1
hal.popularnon
hal.audienceInternationale
dc.subject.itclassification
dc.subject.itgene enrichment
dc.subject.itgene ontology
dc.subject.itdata-mining
hal.origin.linkhttps://hal.archives-ouvertes.fr//inria-00202721v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=BMC%20Bioinformatics&rft.date=2007&rft.volume=8&rft.issue=1&rft.eissn=1471-2105&rft.issn=1471-2105&rft.au=BARRIOT,%20Roland&SHERMAN,%20David%20James&DUTOUR,%20Isabelle&rft.genre=article


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