MStractor: R Workflow Package for Enhancing Metabolomics Data Pre-Processing and Visualization
| Autor: | Markus Herderich, Natoiya D. R. Lloyd, Jeremy Hack, L Nicolotti |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Computer science Endocrinology Diabetes and Metabolism data analysis Feature extraction Mass spectrometry computer.software_genre Microbiology 01 natural sciences Biochemistry Article 03 medical and health sciences Metabolomics Liquid chromatography–mass spectrometry Molecular Biology pre-processing Data processing R programming language 010401 analytical chemistry R Programming Language LC/MS metabolomics QR1-502 0104 chemical sciences Visualization 030104 developmental biology Workflow Data mining computer |
| Zdroj: | Metabolites Volume 11 Issue 8 Metabolites, Vol 11, Iss 492, p 492 (2021) |
| ISSN: | 2218-1989 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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