Tuber proteome comparison of five potato varieties by principal component analysis

Journal of the Science of Food and Agriculture
Carla S de MelloAna Carolina Maisonnave Arisi

Abstract

Data analysis of omics data should be performed by multivariate analysis such as Principal Component Analysis (PCA). The way data is clustered in PCA is of major importance to develop some classification systems based on multivariate analysis, such as Soft Independent Modeling of Class Analogy (SIMCA). In a previous study, a one-class classifier based on SIMCA was built using microarray data from a set of potatoes. The PCA grouped the transcriptomic data according to varieties. The present work aimed to use PCA to verify the clustering of the proteomic profiles for same potato varieties. Proteomic profiles of five potato varieties (Biogold, Fontane, Innovator, Lady Rosetta and Maris Piper) were evaluated by two-dimensional gel electrophoresis (2-DE) performed on two IPG strip lengths: 13 and 24 cm, both under pH range of 4-7. For each strip length, two gels were prepared from each variety; in total there were 10 gels per analysis. For 13 cm strips, 199-320 spots were detected per gel, and for 24 cm strips, 365-684 spots. All four PCAs performed with these datasets presented clear grouping of samples according to the varieties. The data presented here showed that PCA was applicable for proteomic analysis of potato and it was abl...Continue Reading

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Citations

Mar 25, 2017·Journal of the Science of Food and Agriculture·Mônia Stremel AzevedoAna Carolina Oliveira Costa
May 23, 2017·Journal of Proteomics·Arindam GhatakRavi Gupta
Aug 5, 2017·Journal of Agricultural and Food Chemistry·Gabriela Barbosa RossiAna Carolina Maisonnave Arisi

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