MetaboAnalystR 2.0: From Raw Spectra to Biological Insights
1
Institute of Parasitology, McGill University, Montreal, QC H3A 0G4, Canada
2
Department of Animal Science, McGill University, Montreal, QC H3A 0G4, Canada
*
Author to whom correspondence should be addressed.
Metabolites 2019, 9(3), 57; https://doi.org/10.3390/metabo9030057
Received: 5 March 2019 / Revised: 20 March 2019 / Accepted: 21 March 2019 / Published: 22 March 2019
(This article belongs to the Special Issue Metabolomics Data Processing and Data Analysis—Current Best Practices)
Global metabolomics based on high-resolution liquid chromatography mass spectrometry (LC-MS) has been increasingly employed in recent large-scale multi-omics studies. Processing and interpretation of these complex metabolomics datasets have become a key challenge in current computational metabolomics. Here, we introduce MetaboAnalystR 2.0 for comprehensive LC-MS data processing, statistical analysis, and functional interpretation. Compared to the previous version, this new release seamlessly integrates XCMS and CAMERA to support raw spectral processing and peak annotation, and also features high-performance implementations of mummichog and GSEA approaches for predictions of pathway activities. The application and utility of the MetaboAnalystR 2.0 workflow were demonstrated using a synthetic benchmark dataset and a clinical dataset. In summary, MetaboAnalystR 2.0 offers a unified and flexible workflow that enables end-to-end analysis of LC-MS metabolomics data within the open-source R environment.
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MDPI and ACS Style
Chong, J.; Yamamoto, M.; Xia, J. MetaboAnalystR 2.0: From Raw Spectra to Biological Insights. Metabolites 2019, 9, 57. https://doi.org/10.3390/metabo9030057
AMA Style
Chong J, Yamamoto M, Xia J. MetaboAnalystR 2.0: From Raw Spectra to Biological Insights. Metabolites. 2019; 9(3):57. https://doi.org/10.3390/metabo9030057
Chicago/Turabian StyleChong, Jasmine; Yamamoto, Mai; Xia, Jianguo. 2019. "MetaboAnalystR 2.0: From Raw Spectra to Biological Insights" Metabolites 9, no. 3: 57. https://doi.org/10.3390/metabo9030057
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