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Network aggregation improves gene function prediction of grapevine gene co-expression networks

Wong, Darren

Description

Aggregation across multiple networks highlights robust co-expression interactions and improves the functional connectivity of grapevine gene co-expression networks. Abstract In recent years, the rapid accumulation of transcriptome datasets from diverse experimental conditions has enabled the widespread use of gene co-expression network (GCN) analysis in plants. In grapevine, GCN analysis has shown great promise for gene function prediction, however, measurable progress is currently lacking....[Show more]

dc.contributor.authorWong, Darren
dc.date.accessioned2020-11-20T04:15:46Z
dc.identifier.issn0167-4412
dc.identifier.urihttp://hdl.handle.net/1885/216224
dc.description.abstractAggregation across multiple networks highlights robust co-expression interactions and improves the functional connectivity of grapevine gene co-expression networks. Abstract In recent years, the rapid accumulation of transcriptome datasets from diverse experimental conditions has enabled the widespread use of gene co-expression network (GCN) analysis in plants. In grapevine, GCN analysis has shown great promise for gene function prediction, however, measurable progress is currently lacking. Using accumulated microarray datasets from the grapevine whole-genome array (33 experiments, 1359 samples), we explored how meta-analysis through aggregation infuences the functional connectivity (performance) of derived networks using guilt-by-association neighbor voting. Two annotation schemes, i.e. MapMan BIN and Pfam, at two sparsity thresholds, i.e. top 100 (stringent) and 300 (relaxed) ranked genes were evaluated. We observed that aggregating across multiple networks improves performance dramatically, with the aggregate outperforming the majority of functional terms across individual networks. Network sparsity and size (i.e. the number of samples and aggregates) were key factors infuencing performance while the choice of annotation scheme had little. Systematic comparison with various state-of-the-art microarray and RNA-seq networks was also performed, however, none outperformed the aggregate microarray network despite having good predictive performance. Repeating these series of tests using a functional enrichment-based performance metric also showed remarkably consistent fndings with guilt-by-association neighbor voting. To demonstrate its functionality, we explore the function and transcriptional regulation of grapevine EXPANSIN genes. We envisage that network aggregation will ofer new and unique opportunities for gene function prediction in future grapevine functional genomics studies. To this end, we make the aggregate networks and associated metadata publicly available at VTC-Agg
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherKluwer Academic Publishers
dc.rights© Springer Nature B.V. 2020
dc.sourcePlant Molecular Biology
dc.subjectVitis vinifera
dc.subjectNetwork aggregation
dc.subjectCo-expression
dc.subjectMeta-analysis
dc.subjectTranscriptome
dc.subjectEXPANSIN
dc.titleNetwork aggregation improves gene function prediction of grapevine gene co-expression networks
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume103
dc.date.issued2020
local.identifier.absfor060702 - Plant Cell and Molecular Biology
local.identifier.ariespublicationa383154xPUB11368
local.publisher.urlhttps://link.springer.com
local.type.statusPublished Version
local.contributor.affiliationWong, Darren, College of Science, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.startpage425
local.bibliographicCitation.lastpage441
local.identifier.doi10.1007/s11103-020-01001-2
local.identifier.absseo820306 - Wine Grapes
dc.date.updated2020-07-19T08:27:26Z
CollectionsANU Research Publications

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