SE168:/

Sample Set Information

ID	TSE1330
Title	Automation of chemical assignment for identifying molecular formula of S-containing metabolites by combining metabolomics and chemoinformatics with 34S labeling
Description	Introduction Sulfur-containing metabolites (S-metabolites) in organisms including plants have unique benefits to humans. So far, few analytical methods have explored such metabolites. Objectives We aimed to develop an automatic chemically assigning platform by metabolomics and chemoinformatics with 34S labeling to identify the molecular formula of S-metabolites. Results We identified 35 molecular formulae for known S-metabolites and characterized 72 for unknown. Chemoinformatics required around 1.5 min to analyze a pair of the non-labeled and 34S-labeled data of the organ. Conclusion In this study, we developed an automation platform for automatically identifying the presence of S-metabolites. We identified the molecular formula of known S-metabolites, which are accessible in free databases, together with that of unknown. This analytical method did not focus on identifying the structure of S-metabolites, but on the automatic identification of their molecular formula.
Authors	Nakabayashi, R., Tsugawa, H.,Mori, T. and Saito, K.
Reference	Metabolomics, November 2016, 12:168, DOI: 10.1007/s11306-016-1115-5
Comment

http://link.springer.com/article/10.1007/s11306-016-1115-5

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• SE168 : [ Automation of chemical assignment for identifying molecular formula of S-containing metabolites by combining metabolomics and chemoinformatics with 34S labeling ]
  • S01 : [ Arabidopsis thaliana (ecotype Columbia-0) ]
    • M1 : [ FTICR-MS (Direct infusion) ]
      • D01 : [ Data analysis ]
  • MS01 : [ FTICR-MS (Direct infusion) ]
  • DS1 : [ Data analysis ]