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Article: Identification of biomarkers for genotyping Aspergilli using non-linear methods for clustering and classification

TitleIdentification of biomarkers for genotyping Aspergilli using non-linear methods for clustering and classification
Authors
Issue Date2008
PublisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcbioinformatics/
Citation
Bmc Bioinformatics, 2008, v. 9 How to Cite?
AbstractBackground: In the present investigation, we have used an exhaustive metabolite profiling approach to search for biomarkers in recombinant Aspergillus nidulans (mutants that produce the 6- methyl salicylic acid polyketide molecule) for application in metabolic engineering. Results: More than 450 metabolites were detected and subsequently used in the analysis. Our approach consists of two analytical steps of the metabolic profiling data, an initial non-linear unsupervised analysis with Self-Organizing Maps (SOM) to identify similarities and differences among the metabolic profiles of the studied strains, followed by a second, supervised analysis for training a classifier based on the selected biomarkers. Our analysis identified seven putative biomarkers that were able to cluster the samples according to their genotype. A Support Vector Machine was subsequently employed to construct a predictive model based on the seven biomarkers, capable of distinguishing correctly 14 out of the 16 samples of the different A. nidulans strains. Conclusion: Our study demonstrates that it is possible to use metabolite profiling for the classification of filamentous fungi as well as for the identification of metabolic engineering targets and draws the attention towards the development of a common database for storage of metabolomics data. © 2008 Kouskoumvekaki et al; licensee BioMed Central Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/181248
ISSN
2015 Impact Factor: 2.435
2015 SCImago Journal Rankings: 1.722
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorKouskoumvekaki, Ien_US
dc.contributor.authorYang, Zen_US
dc.contributor.authorJónsdóttir, SOen_US
dc.contributor.authorOlsson, Len_US
dc.contributor.authorPanagiotou, Gen_US
dc.date.accessioned2013-02-21T02:03:28Z-
dc.date.available2013-02-21T02:03:28Z-
dc.date.issued2008en_US
dc.identifier.citationBmc Bioinformatics, 2008, v. 9en_US
dc.identifier.issn1471-2105en_US
dc.identifier.urihttp://hdl.handle.net/10722/181248-
dc.description.abstractBackground: In the present investigation, we have used an exhaustive metabolite profiling approach to search for biomarkers in recombinant Aspergillus nidulans (mutants that produce the 6- methyl salicylic acid polyketide molecule) for application in metabolic engineering. Results: More than 450 metabolites were detected and subsequently used in the analysis. Our approach consists of two analytical steps of the metabolic profiling data, an initial non-linear unsupervised analysis with Self-Organizing Maps (SOM) to identify similarities and differences among the metabolic profiles of the studied strains, followed by a second, supervised analysis for training a classifier based on the selected biomarkers. Our analysis identified seven putative biomarkers that were able to cluster the samples according to their genotype. A Support Vector Machine was subsequently employed to construct a predictive model based on the seven biomarkers, capable of distinguishing correctly 14 out of the 16 samples of the different A. nidulans strains. Conclusion: Our study demonstrates that it is possible to use metabolite profiling for the classification of filamentous fungi as well as for the identification of metabolic engineering targets and draws the attention towards the development of a common database for storage of metabolomics data. © 2008 Kouskoumvekaki et al; licensee BioMed Central Ltd.en_US
dc.languageengen_US
dc.publisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcbioinformatics/en_US
dc.relation.ispartofBMC Bioinformaticsen_US
dc.titleIdentification of biomarkers for genotyping Aspergilli using non-linear methods for clustering and classificationen_US
dc.typeArticleen_US
dc.identifier.emailPanagiotou, G: gipa@hku.hken_US
dc.identifier.authorityPanagiotou, G=rp01725en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1186/1471-2105-9-59en_US
dc.identifier.pmid18226195-
dc.identifier.scopuseid_2-s2.0-39749170516en_US
dc.identifier.volume9en_US
dc.identifier.isiWOS:000253686900002-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridKouskoumvekaki, I=6602787035en_US
dc.identifier.scopusauthoridYang, Z=7405434139en_US
dc.identifier.scopusauthoridJónsdóttir, SO=35566503500en_US
dc.identifier.scopusauthoridOlsson, L=7203077540en_US
dc.identifier.scopusauthoridPanagiotou, G=8566179700en_US

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