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2019
Chong, Jasmine; Yamamoto, Mai; Xia, Jianguo
MetaboAnalystR 2.0: From Raw Spectra to Biological Insights Journal Article
In: Metabolites, vol. 9, no. 3, pp. 57, 2019, (Number: 3 Publisher: Multidisciplinary Digital Publishing Institute).
Abstract | Links | BibTeX | Tags: enrichment analysis, global metabolomics, LC-MS, pathway analysis, spectra processing
@article{chong_metaboanalystr_2019,
title = {MetaboAnalystR 2.0: From Raw Spectra to Biological Insights},
author = {Jasmine Chong and Mai Yamamoto and Jianguo Xia},
url = {https://www.mdpi.com/2218-1989/9/3/57},
doi = {10.3390/metabo9030057},
year = {2019},
date = {2019-01-01},
urldate = {2021-05-19},
journal = {Metabolites},
volume = {9},
number = {3},
pages = {57},
abstract = {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.},
note = {Number: 3
Publisher: Multidisciplinary Digital Publishing Institute},
keywords = {enrichment analysis, global metabolomics, LC-MS, pathway analysis, spectra processing},
pubstate = {published},
tppubtype = {article}
}
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.