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Sample Set Information

ID TSE1303
Title Exploring molecular backgrounds of quality traits in rice by predictive models based on high-coverage metabolomics
Description BACKGROUND:

Increasing awareness of limitations to natural resources has set high expectations for plant science to deliver efficient crops with increased yields, improved stress tolerance, and tailored composition. Collections of representative varieties are a valuable resource for compiling broad breeding germplasms that can satisfy these diverse needs.

Here we show that the untargeted high-coverage metabolomic characterization of such core collections is a powerful approach for studying the molecular backgrounds of quality traits and for constructing predictive metabolome-trait models. We profiled the metabolic composition of kernels from field-grown plants of the rice diversity research set using 4 complementary analytical platforms. We found that the metabolite profiles were correlated with both the overall population structure and fine-grained genetic diversity. Multivariate regression analysis showed that 10 of the 17 studied quality traits could be predicted from the metabolic composition independently of the population structure. Furthermore, the model of amylose ratio could be validated using external varieties grown in an independent experiment.

Our results demonstrate the utility of metabolomics for linking traits with quantitative molecular data. This opens up new opportunities for trait prediction and construction of tailored germplasms to support modern plant breeding.

Authors Redestig H, Kusano M, Ebana K, Kobayashi M, Oikawa A, Okazaki Y, Matsuda F, Arita M, Fujita N, Saito K
Reference BMC Syst Biol. 2011 Oct 28;5:176.

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Data Analysis Details Information

Title Data processing (IT-MS)
Description Peak picking and peak alignment were performed by Profiling Solution (version (Shimadzu, Kyoto, Japan) from original data file (.lcd). The parameters of Profiling Solution were as follows: ion m/z tolerance, 20 mDa; Ion RT tolerance, 0.1 min; Ion intensity threshold, 2e4; detect isomer valley, 20%; allow some ion without isotope peak, ON; time range for processing, 0–20 min. The data matrix exported from Profiling Solution was normalized based on the intensity of [M+H]+ of 1,2-dioctanoyl-sn-glycero-3-phosphocholine. Peak height of individual lipid molecules were calculated based on the m/z values of their molecular-related ions or fragment ions. Annotation process was performed based on theoretical m/z values of each possible glycerolipid species in plants using in-house Perl script.

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