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Sample Set Information

Title Covering chemical diversity of genetically-modified tomatoes using metabolomics for objective substantial equivalence assessment
Description We propose using multiple analytical platforms for the direct acquisition of an interpretable data set of estimable chemical diversity. As an example, we report an application of our multi-platform approach that assesses the substantial equivalence of tomatoes over-expressing the taste-modifying protein miraculin. In combination, the chosen platforms detected compounds that represent 86% of the estimated chemical diversity of the metabolites listed in the LycoCyc database. Following a proof-of-safety approach, we show that w92% had an acceptable range of variation while simultaneously indicating a reproducible transformation-related metabolic signature. We conclude that multi-platform metabolomics is an approach that is both sensitive and robust and that it constitutes a good starting point for characterizing genetically modified organisms.
Authors Miyako Kusano, Henning Redestig, Tadayoshi Hirai, Akira Oikawa, Fumio Matsuda, Atsushi Fukushima, Masanori Arita, Shin Watanabe, Megumu Yano, Kyoko Hiwasa-Tanase, Hiroshi Ezura, Kazuki Saito
Reference Kusano M et al. (2011) PLOS ONE 6: e16989

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The raw data files are available at DROP Met web site in PRIMe database of RIKEN.

Data Analysis Details Information

Title Leco ChromaTOF, MATLAB, H-MCR, NIST mass spectral search program
Description Nonprocessed MS data from GC-TOF/MS analysis were exported in NetCDF format generated by chromatography processing and mass spectral deconvolutionsoftware, Leco ChromaTOF version 3.22 (LECO, St. Joseph, MI, USA) to MATLAB 6.5 (Mathworks, Natick, MA, USA), where all data pretreatment procedures, such as smoothing, alignment, time-window setting, and H-MCR, were carried out (Jonsson et al. 2006). The resolved MS spectra were matched against reference mass spectra using the NIST mass spectral search program for the NIST/EPA/NIH mass spectral library (version 2.0) and our custom software for peak annotation written in JAVA. Peaks were identified or annotated based on RIs and the reference mass spectra comparison to the Golm Metabolome Database (GMD) released from CSB.DB1 (Kopka et al. 2005)] and our in-house spectral library. The metabolites were identified by comparison with RIs from the library databases (GMD and our own library) and with those of authentic standards, and the metabolites were defined as annotated metabolites on comparison with mass spectra and RIs from these two libraries.

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