SE146:/DS3

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. RESULTS: 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. CONCLUSIONS: 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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PubMed

Data Analysis Details Information

ID	DS3
Title	Data processing (CE-MS)
Description	An original data file (.wiff) was converted to an unique binary file (.kiff) using in-house software (nondisclosure). Peak picking and alignment were performed using the another in-house software (nondisclosure), peaks were picked and aligned among samples automatically. By contrast with the detected m/z and migration time values of standard compounds including internal standards, peaks were annotated automatically using the same software. For normalization, the individual area of the detected peaks was divided by the peak area of the internal reference standards. Based on the calibration curves for standard compounds, peak area values were converted into values corresponding to amounts.
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