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      Metabolite Profiling of Sorghum Seeds of Different Colors from Different Sweet Sorghum Cultivars Using a Widely Targeted Metabolomics Approach

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          Abstract

          Sweet sorghum ( Sorghum bicolor) is one of the most important cereal crops in the world with colorful seeds. To study the diversity and cultivar-specificity of phytochemicals in sweet sorghum seeds, widely targeted metabolomics was used to analyze the metabolic profiles of the white, red, and purple seeds from three sweet sorghum cultivars Z6, Z27, and HC4. We identified 651 metabolites that were divided into 24 categories, including fatty acids, glycerolipids, flavonoids, benzoic acid derivatives, anthocyanins, and nucleotides and its derivatives. Among them, 217 metabolites were selected as significantly differential metabolites which could be related to the seed color by clustering analysis, principal component analysis (PCA), and orthogonal signal correction and partial least squares-discriminant analysis (OPLS-DA). A significant difference was shown between the red seed and purple seed samples, Z27 and HC4, in which 106 were downregulated and 111 were upregulated. The result indicated that 240 metabolites were significantly different, which could be related to the purple color with 58 metabolites downregulated and 182 metabolites upregulated. And 199 metabolites might be involved in the red phenotype with 54 downregulated and 135 upregulated. There were 45 metabolites that were common to all three cultivars, while cyanidin O-malonyl-malonyl hexoside, cyanidin O-acetylhexoside, and cyanidin 3-O-glucosyl-malonylglucoside were significantly upregulated red seeds, which could be the basis for the variety of seed colors. Generally, our findings provide a comprehensive comparison of the metabolites between the three phenotypes of S. bicolor and an interpretation of phenotypic differences from the point of metabolomics.

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          The meaning and use of the area under a receiver operating characteristic (ROC) curve.

          A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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            Metabolomics--the link between genotypes and phenotypes.

            Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms 'transcriptome' and proteome', the set of metabolites synthesized by a biological system constitute its 'metabolome'. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the differences among metabolite target analysis, metabolite profiling, and metabolic fingerprinting are clarified, and terms are defined. Current approaches are examined, and potential applications are summarized with a special emphasis on data mining and mathematical modelling of metabolism.
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              Signature-Discovery Approach for Sample Matching of a Nerve-Agent Precursor Using Liquid Chromatography−Mass Spectrometry, XCMS, and Chemometrics

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                Author and article information

                Contributors
                Journal
                Int J Genomics
                Int J Genomics
                IJG
                International Journal of Genomics
                Hindawi
                2314-436X
                2314-4378
                2020
                4 March 2020
                : 2020
                : 6247429
                Affiliations
                1Agricultural College, Inner Mongolia University for Nationalities, Tongliao, 028000 Inner Mongolia, China
                2Tongliao Academy of Agricultural Science, Tongliao, 028000 Inner Mongolia, China
                3Independent Researcher, Chifeng, 024000 Inner Mongolia, China
                Author notes

                Academic Editor: Margarita Hadzopoulou-Cladaras

                Author information
                https://orcid.org/0000-0002-0400-4223
                Article
                10.1155/2020/6247429
                7073482
                5514a286-88e1-4229-ad68-341a1b7c920a
                Copyright © 2020 Yaxing Zhou et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 26 September 2019
                : 3 December 2019
                : 14 December 2019
                Funding
                Funded by: Inner Mongolia Autonomous Region Educational Science Research “Thirteenth Five-Year Plan” Project-Agro-Specialty Specialty Industry-University-Research Cooperation Education Innovation Research
                Award ID: NGJGH2018095
                Funded by: Inner Mongolia Autonomous Region Education Department Project-Sweet Sorghum×White Shell Sudan Grass Chromosome Doubling and Excellent Germplasm Selection
                Award ID: NJZY17193
                Funded by: Inner Mongolia Science and Technology Innovation Guide Project-Research and Demonstration of High Sugar Hammer High Dancao Breeding
                Funded by: Natural Science Foundation of Inner Mongolia Autonomous Region-QTL Mapping of Sugar Content Traits in Stems and Leaves
                Award ID: 2019MS03076
                Categories
                Research Article

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