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RulNet: a web-oriented platform for regulatory network inference, application to wheat -omics data

dc.rights.licenseopenen_US
dc.contributor.authorVINCENT, Jonathan
dc.contributor.authorMARTRES, Pierre
dc.contributor.authorGOURIOU, Benjamin
dc.contributor.authorGRAND RAVEL, Catherine
hal.structure.identifierEcophysiologie et Génomique Fonctionnelle de la Vigne [UMR EGFV]
dc.contributor.authorDAI, Zhanwu
IDREF: 22822473X
dc.contributor.authorPETIT, Jean-Marc
dc.contributor.authorPAILLOUX, Marie
dc.date.accessioned2020-03-22T20:16:17Z
dc.date.available2020-03-22T20:16:17Z
dc.date.issued2015
dc.identifier.issn1932-6203en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/3919
dc.description.abstractEnWith the increasing amount of -omics data available, a particular effort has to be made to provide suitable analysis tools. A major challenge is that of unraveling the molecular regulatory networks from massive and heterogeneous datasets. Here we describe RulNet, a web-oriented platform dedicated to the inference and analysis of regulatory networks from qualitative and quantitative -omics data by means of rule discovery. Queries for rule discovery can be written in an extended form of the RQL query language, which has a syntax similar to SQL. RulNet also offers users interactive features that progressively adjust and refine the inferred networks. In this paper, we present a functional characterization of RulNet and compare inferred networks with correlation-based approaches. The performance of RulNet has been evaluated using the three benchmark datasets used for the transcriptional network inference challenge DREAM5. Overall, RulNet performed as well as the best methods that participated in this challenge and it was shown to behave more consistently when compared across the three datasets. Finally, we assessed the suitability of RulNet to analyze experimental -omics data and to infer regulatory networks involved in the response to nitrogen and sulfur supply in wheat (Triticum aestivum L.) grains. The results highlight putative actors governing the response to nitrogen and sulfur supply in wheat grains. We evaluate the main characteristics and features of RulNet as an all-in-one solution for RN inference, visualization and editing. Using simple yet powerful RulNet queries allowed RNs involved in the adaptation of wheat grain to N and S supply to be discovered. We demonstrate the effectiveness and suitability of RulNet as a platform for the analysis of RNs involving different types of -omics data. The results are promising since they are consistent with what was previously established by the scientific community.
dc.language.isoENen_US
dc.subjectPlateforme informatique
dc.title.enRulNet: a web-oriented platform for regulatory network inference, application to wheat -omics data
dc.typeArticle de revueen_US
dc.identifier.doi10.1371/journal.pone.0127127
dc.subject.halSciences du Vivant [q-bio]/Biologie végétaleen_US
bordeaux.journalPLoS ONEen_US
bordeaux.volume10en_US
bordeaux.hal.laboratoriesEcophysiologie et Génomique Fonctionnelle de la Vigne (EGFV) - UMR 1287en_US
bordeaux.issue5en_US
bordeaux.institutionBordeaux Sciences Agroen_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-03170463
hal.version1
hal.date.transferred2021-03-16T10:11:12Z
hal.exporttrue
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