Data mining and influential analysis of gene expression data for plant resistance genes identification in tomato (Solanum lycopersicum)

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dc.citation.issue2
dc.citation.volume17
dc.contributor.authorTorres-Aviles F
dc.contributor.authorRomeo JS
dc.contributor.authorLopez-Kleine L
dc.date.available15/03/2014
dc.date.issued2014-03
dc.description.abstractBackground Molecular mechanisms of plant–pathogen interactions have been studied thoroughly but much about them is still unknown. A better understanding of these mechanisms and the detection of new resistance genes can improve crop production and food supply. Extracting this knowledge from available genomic data is a challenging task. Results Here, we evaluate the usefulness of clustering, data-mining and regression to identify potential new resistance genes. Three types of analyses were conducted separately over two conditions, tomatoes inoculated with Phytophthora infestans and not inoculated tomatoes. Predictions for 10 new resistance genes obtained by all applied methods were selected as being the most reliable and are therefore reported as potential resistance genes. Conclusion Application of different statistical analyses to detect potential resistance genes reliably has shown to conduct interesting results that improve knowledge on molecular mechanisms of plant resistance to pathogens.
dc.description.publication-statusPublished
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000334441800001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=c5bb3b2499afac691c2e3c1a83ef6fef
dc.identifier.citationELECTRONIC JOURNAL OF BIOTECHNOLOGY, 2014, 17 (2)
dc.identifier.doi10.1016/j.ejbt.2014.01.003
dc.identifier.elements-id364112
dc.identifier.harvestedMassey_Dark
dc.identifier.issn0717-3458
dc.identifier.urihttps://hdl.handle.net/10179/13271
dc.publisherElsevier
dc.relation.isPartOfELECTRONIC JOURNAL OF BIOTECHNOLOGY
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S0717345814000141?via=ihub
dc.subjectClassification
dc.subjectData mining
dc.subjectFunctional gene prediction
dc.subjectGEE models
dc.subjectGene expression data
dc.subjectPlant immunity genes
dc.subject.anzsrc06 Biological Sciences
dc.subject.anzsrc08 Information and Computing Sciences
dc.subject.anzsrc10 Technology
dc.titleData mining and influential analysis of gene expression data for plant resistance genes identification in tomato (Solanum lycopersicum)
dc.typeJournal article
pubs.notesNot known
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Health
pubs.organisational-group/Massey University/College of Health/SHORE/Te Ropu Whariki Research Centre
pubs.organisational-group/Massey University/College of Health/SHORE/Te Ropu Whariki Research Centre/SHORE Centre
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