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LegumeGRN: a gene regulatory network prediction server for functional and comparative studies

2013-07-03Journal article Open access
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dc.contributor.authorWang, Mingyi
dc.contributor.authorVerdier, Jerome
dc.contributor.authorBenedito, Vagner A
dc.contributor.authorTang, Yuhong
dc.contributor.authorMurray, Jeremy D
dc.contributor.authorGe, Yinbing
dc.contributor.authorBecker, Jörg D
dc.contributor.authorCarvalho, Helena
dc.contributor.authorRogers, Christian
dc.contributor.authorUdvardi, Michael
dc.contributor.authorHe, Ji
dc.date.accessioned2015-11-10T17:02:03Z
dc.date.available2015-11-10T17:02:03Z
dc.date.issued2013-07-03
dc.description.abstractBuilding accurate gene regulatory networks (GRNs) from high-throughput gene expression data is a long-standing challenge. However, with the emergence of new algorithms combined with the increase of transcriptomic data availability, it is now reachable. To help biologists to investigate gene regulatory relationships, we developed a web-based computational service to build, analyze and visualize GRNs that govern various biological processes. The web server is preloaded with all available Affymetrix GeneChip-based transcriptomic and annotation data from the three model legume species, i.e., Medicago truncatula, Lotus japonicus and Glycine max. Users can also upload their own transcriptomic and transcription factor datasets from any other species/organisms to analyze their in-house experiments. Users are able to select which experiments, genes and algorithms they will consider to perform their GRN analysis. To achieve this flexibility and improve prediction performance, we have implemented multiple mainstream GRN prediction algorithms including co-expression, Graphical Gaussian Models (GGMs), Context Likelihood of Relatedness (CLR), and parallelized versions of TIGRESS and GENIE3. Besides these existing algorithms, we also proposed a parallel Bayesian network learning algorithm, which can infer causal relationships (i.e., directionality of interaction) and scale up to several thousands of genes. Moreover, this web server also provides tools to allow integrative and comparative analysis between predicted GRNs obtained from different algorithms or experiments, as well as comparisons between legume species. The web site is available at http://legumegrn.noble.org.pt_PT
dc.description.sponsorshipOklahoma Center for The Advancement of Science and Technology: (OCAST Grant No. PSB11-031).pt_PT
dc.identifier.citationWang M, Verdier J, Benedito VA, Tang Y, Murray JD, Ge Y, et al. (2013) LegumeGRN: A Gene Regulatory Network Prediction Server for Functional and Comparative Studies. PLoS ONE 8(7): e67434. doi:10.1371/journal.pone.0067434pt_PT
dc.identifier.doi10.1371/journal.pone.0067434pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.7/487
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherPLOSpt_PT
dc.relation.publisherversionhttp://journals.plos.org/plosone/article?id=10.1371/journal.pone.0067434pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectMachine learning algorithmspt_PT
dc.subjectGene expressionpt_PT
dc.subjectLegumespt_PT
dc.subjectGene regulatory networkspt_PT
dc.subjectMedicagopt_PT
dc.subjectTranscriptome analysispt_PT
dc.subjectRegulonspt_PT
dc.subjectGene regulationpt_PT
dc.titleLegumeGRN: a gene regulatory network prediction server for functional and comparative studiespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage7pt_PT
oaire.citation.issue7pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titlePLOS Onept_PT
oaire.citation.volume8pt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

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