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Data processing for GC-TOF-MS data <br … Data processing for GC-TOF-MS data <br />
Nonprocessed MS data from GC-TOF-MS analysis were exported in NetCDF format generated by chromatography processing- and mass spectral deconvolution software (Leco ChromaTOF version 3.22; LECO, St. Joseph, MI, USA) to MATLAB 6.5 or MATLAB2011b (Mathworks, Natick, MA, USA) for the performance of all data-pretreatment procedures, e.g. smoothing, alignment, time-window setting H-MCR, and RDA (Jonsson P, et al. (2006) Predictive metabolite profiling applying hierarchical multivariate curve resolution to GC-MS data--a potential tool for multi-parametric diagnosis. J Proteome Res 5(6):1407-1414.). 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 their RIs, a comparison of the reference mass-spectra with the Golm Metabolome Database (GMD) released from CSB.DB (Kopka J, et al. (2005) GMD@CSB.DB: the Golm Metabolome Database. Bioinformatics 21(8):1635-1638), and our in-house spectral library. The metabolites were identified by comparison with RIs from the library databases (GMD and our own library) and the RIs of authentic standards. The metabolites were defined as annotated metabolites after comparison with the mass spectra and the RIs from these two libraries. The data matrix was normalized using the CCMN algorithm for further analysis (Redestig H, et al. (2009) Compensation for systematic cross-contribution improves normalization of mass spectrometry based metabolomics data. Anal Chem 81(19):7974-7980).<br /> . Anal Chem 81(19):7974-7980).<br />
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