| Autor: |
Nicolotti L; The Australian Wine Research Institute, Adelaide, SA 5064, Australia.; Metabolomics Australia, The Australian Wine Research Institute, Adelaide, SA 5064, Australia., Hack J; The Australian Wine Research Institute, Adelaide, SA 5064, Australia.; Metabolomics Australia, The Australian Wine Research Institute, Adelaide, SA 5064, Australia., Herderich M; The Australian Wine Research Institute, Adelaide, SA 5064, Australia.; Metabolomics Australia, The Australian Wine Research Institute, Adelaide, SA 5064, Australia., Lloyd N; The Australian Wine Research Institute, Adelaide, SA 5064, Australia.; Metabolomics Australia, The Australian Wine Research Institute, Adelaide, SA 5064, Australia. |
| Abstrakt: |
Untargeted metabolomics experiments for characterizing complex biological samples, conducted with chromatography/mass spectrometry technology, generate large datasets containing very complex and highly variable information. Many data-processing options are available, however, both commercial and open-source solutions for data processing have limitations, such as vendor platform exclusivity and/or requiring familiarity with diverse programming languages. Data processing of untargeted metabolite data is a particular problem for laboratories that specialize in non-routine mass spectrometry analysis of diverse sample types across humans, animals, plants, fungi, and microorganisms. Here, we present MStractor, an R workflow package developed to streamline and enhance pre-processing of metabolomics mass spectrometry data and visualization. MStractor combines functions for molecular feature extraction with user-friendly dedicated GUIs for chromatographic and mass spectromerty (MS) parameter input, graphical quality-control outputs, and descriptive statistics. MStractor performance was evaluated through a detailed comparison with XCMS Online. The MStractor package is freely available on GitHub at the MetabolomicsSA repository. |
