RMet: An automated R based software for analyzing GC-MS and GC×GC-MS untargeted metabolomic data

Moayedpour, S ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1016/j.chemolab.2019.103866
  3. Publisher: Elsevier B.V , 2019
  4. Abstract:
  5. Gas chromatography-mass spectrometry (GC-MS) and comprehensive two-dimensional gas chromatography-mass spectrometry (GC×GC-MS) are powerful techniques for measurement of all metabolites in complex metabolic samples. However, analyzing GC-MS and especially GC×GC-MS metabolomic data is a major challenge to the researchers in the field of metabolomics mainly due to the complexity and large data size. In this regard, an automated R based software entitled RMet has been developed to overcome the challenges in the metabolomic analysis workflow of GC-MS and GC×GC-MS data sets. Additionally, it is able to facilitate the complex process of extracting reliable and useful biological information from these data sets. Moreover, RMet can greatly accelerate the time-consuming data analysis process of large GC-MS and GC×GC-MS datasets by the means of modern chemometric methods. In fact, RMet transforms raw GC-MS and GC×GC-MS data files into the elution profiles and mass spectra of important (significantly affected metabolites) which can be imported into NIST MS search software for the final identification of these metabolites. To show the performance of the developed software, large GC×GC-MS data sets of a previously reported environmental metabolomics study on lettuce samples exposed to contaminants of emerging concerns (CECs) were analyzed by RMet. The procedure for analyzing GC-MS metabolic data with RMet is as same as GC×GC-MS data sets but some steps can be skipped due to the lower size of GC-MS data sets. The software, its manual, sample data sets and source code are freely available on https://github.com/SUTChemometricsGroup/RMet. © 2019 Elsevier B.V
  6. Keywords:
  7. GC-MS ; GC×GC ; R language ; Chemometric analysis ; Chemometrics ; Data analysis ; Elution ; Language ; Lettuce ; Mass fragmentography ; Metabolomics ; Nonhuman ; Software
  8. Source: Chemometrics and Intelligent Laboratory Systems ; Volume 194 , 2019 ; 01697439 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0169743919303818