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dc.contributor.author Taylor, Janet
dc.contributor.author King, Ross Donald
dc.contributor.author Altmann, Thomas
dc.contributor.author Fiehn, Oliver
dc.date.accessioned 2006-04-25T15:41:06Z
dc.date.available 2006-04-25T15:41:06Z
dc.date.issued 2002
dc.identifier.citation Taylor , J , King , R D , Altmann , T & Fiehn , O 2002 , ' Application of metabolomics to plant genotype discrimination using statistics and machine learning ' pp. 241- . en
dc.identifier.other PURE: 68463
dc.identifier.other dspace: 2160/153
dc.identifier.uri http://hdl.handle.net/2160/153
dc.identifier.uri http://bioinformatics.oxfordjournals.org/cgi/reprint/18/suppl_2/S241 en
dc.description Janet Taylor, Ross D King, Thomas Altmann and Oliver Fiehn (2002). Application of metabolomics to plant genotype discrimination using statistics and machine learning. 1st European Conference on Computational Biology (ECCB). (published as a journal supplement in Bioinformatics 18: S241-S248). en
dc.description.abstract MOTIVATION: Metabolomics is a post genomic technology which seeks to provide a comprehensive profile of all the metabolites present in a biological sample. This complements the mRNA profiles provided by microarrays, and the protein profiles provided by proteomics. To test the power of metabolome analysis we selected the problem of discrimating between related genotypes of ARABIDOPSIS: Specifically, the problem tackled was to discrimate between two background genotypes (Col0 and C24) and, more significantly, the offspring produced by the crossbreeding of these two lines, the progeny (whose genotypes would differ only in their maternally inherited mitichondia and chloroplasts). OVERVIEW: A gas chromotography--mass spectrometry (GCMS) profiling protocol was used to identify 433 metabolites in the samples. The metabolomic profiles were compared using descriptive statistics which indicated that key primary metabolites vary more than other metabolites. We then applied neural networks to discriminate between the genotypes. This showed clearly that the two background lines can be discrimated between each other and their progeny, and indicated that the two progeny lines can also be discriminated. We applied Euclidean hierarchical and Principal Component Analysis (PCA) to help understand the basis of genotype discrimination. PCA indicated that malic acid and citrate are the two most important metabolites for discriminating between the background lines, and glucose and fructose are two most important metabolites for discriminating between the crosses. These results are consistant with genotype differences in mitochondia and chloroplasts. en
dc.language.iso eng
dc.relation.ispartof en
dc.subject Metabolome en
dc.subject Arabidopsis en
dc.subject clustering en
dc.title Application of metabolomics to plant genotype discrimination using statistics and machine learning en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Bioinformatics and Computational Biology Group en
dc.description.status Non peer reviewed en


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