SE168:/DS1

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

Data Analysis Details Information

ID	DS1
Title	Data analysis
Description	The MS spectra were recorded using Hystar 4.0 (Bruker Daltonik GmbH, Bremen, Germany) and the data were processed using DataAnalysis 4.2 (Bruker Daltonik GmbH). Internal calibration was performed using the exact mass of the internal standards. Molecular formulae were determined from an in-house database storing metabolite information downloaded on April 1, 2015, from metabolome databases: [(BMDB (http://www.cowmetdb.ca/cgi-bin/browse.cgi) (2697), ChEBI (Hastings et al. 2013) (14,546), DrugBank (Wishart et al. 2006) (5355), ECMDB (Guo et al. 2013) (987), FooDB (http://foodb.ca/) (6441), HMDB (Wishart et al. 2007) (9652), KNApSAcK (Afendi et al. 2012) (13,825), PlantCyc (http://www.plantcyc.org/) (2421), PubChem Classification Browser (Biosystems and Pathways, https://pubchem.ncbi.nlm.nih.gov/classification/#hid=72) (8242), SMPDB (Frolkis et al. 2010) (1187), T3DB (Lim et al. 2010) (1726), UNPD (http://pkuxxj.pku.edu.cn/UNPD/) (32,952), and YMDB (Jewison et al. 2012) (1047)]. The number of molecular formulae downloaded from each database is shown in the parentheses. A personal computer (Windows 10 Intel Xeon (R) CPU E5-2650 v3 (2.3 GHz) with 128 GB RAM) required around 1.5 min to analyze a pair of the non-labeled and 34S-labeled data of a certain organ. The program named SMetSearch can be freely downloaded from the standalone software section of the RIKEN PRIMe database (http://prime.psc.riken.jp/Metabolomics_Software/SmetSearch/index.html).
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