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- W2740432823 abstract "Structural and functional genomics are useful approaches to better understand the biological process regarding the genetic composition of individuals and its expression at the phenotypic level. Numerous data are produced in studies of this area of research, multivariate statistics providing powerful tools for reducing their dimensionality. The general objective of this research was to apply principal component analysis (PCA) and multivariate analysis of variance (MANOVA) to analyse the proteomics of the tomato ripening and its association with agronomic quantitative traits. A PCA was applied as a non classic exploratory way to the binary data presence / absence of a given polypeptide in mature green, breaker and red ripe tomato fruits from F2 plants. To verify results from PCA, a hierarquical cluster analysis was performed by UPGMA to the same data. Advantage of PCA is that the relative contribution of a given polypeptide for grouping can be measured. Then, polypeptides mostly contributing for grouping were introduced as the source of variation in a one-way MANOVA to detect association with quantitative traits, which were the dependent variables. Polypeptides mostly contributing to general variability were efficiently identified by PCA, while MANOVA verified that one of them is a putative molecular marker of the quantitative traits." @default.
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- W2740432823 date "2010-01-01" @default.
- W2740432823 modified "2023-09-25" @default.
- W2740432823 title "A Multivariate Approach to the Proteomics of Tomato Fruit Ripening" @default.
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