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  Application of metabolomics to plant genotype discrimination using statistics and machine learning

Taylor, J., King, R. D., Altmann, T., & Fiehn, O. (2002). Application of metabolomics to plant genotype discrimination using statistics and machine learning. In European Conference on Computational Biology (ECCB 2002) (pp. 241-248).

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-2E68-2 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-2E69-F
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 Creators:
Taylor, J.1, Author
King, R. D.1, Author
Altmann, T.2, Author              
Fiehn, O.3, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Developmental Physiology and Genomics, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753313              
3Metabolomic Analysis, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753345              

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Free keywords: metabolome arabidopsis clustering gas-chromatography biochemical pathways functional genomics mass-spectrometry sugar alcohols identification expression strategy acids
 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.

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Language(s): eng - English
 Dates: 2002
 Publication Status: Published in print
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 Identifiers: ISI: ISI:000178836800033
URI: ://000178836800033 http://bioinformatics.oxfordjournals.org/content/18/suppl_2/S241.full.pdf
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Title: European Conference on Computational Biology (ECCB 2002)
Place of Event: SAARBRUCKEN, GERMANY
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Title: European Conference on Computational Biology (ECCB 2002)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 241 - 248 Identifier: -