The R package MetabQ was designed to facilitate the extraction of quantitative data directly from data files generated by the GC-MS machine. MetabQ requires a default MetabQ.settings csv-file and consists of four functions: settings(), relative(), corr() and quant(). The default settings file contains data used by MetabQ for the quantification approach. Function settings() operates as an input function to provide the script with required information and generates the spreadsheet as *_lib.csv file which contains the list of identified metabolites and their analytical parameters. Function relative() operates as a data extraction function, resulting in the *_auto.csv file containing abundances of identified metabolites. Additionally, this function generates graphical files of metabolite peaks. Corr() function can be used for manual correction of the time window, in case the top of the metabolite peak is out of time frame window. This function replaces the graphical file using new settings and generates a new data file with name by addition of _corr. This step can be run as many times as required to correct the data. The quant() function performs the calculations according to the quantitative approach described below. As a result, the script generates the final spreadsheet with absolute metabolite concentration in mg/L.
PAPi is a hypothesis generator tool for metabolomics data-sets. PAPi is a R package that uses KEGG database coupled to metabolomics data-sets to calculate what we have called "Activity Score" (AS) of metabolic pathways. The AS is a prediction of the flux or activity of each metabolic pathway when cells are submitted to different experimental conditions. More details are available in our publication:
Aggio, R. M. B.; Ruggiero, K.; and Villas-Bôas, S.G. 2010. Pathway Activity Profiling (PAPi): from the metabolite profile to the metabolic pathway activity. Bioinformatics 26: 2969–2976.
Metab is a R package created to process metabolomics data-sets in a high-throughput way. Metab works coupled to the Automated Mass Spectral Deconvolution and Identification System (AMDIS) to calculate peak intensities of identified and non-identified compounds analyzed by GC-MS. Metab contains seven functions designed to calculate peak intensities and normalize data-sets by internal standard, biomass and uncultured medium. On top of that, Metab is able to quickly apply t-test or Anova in a friendly manner. The pipeline used by Metab considerably reduces the time spent on processing metabolomics data-sets.