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MetaboAnalystR 2.0: From Raw Spectra to Biological Insights

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 metabolomic...

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Detalles Bibliográficos
Autores principales: Chong, Jasmine, Yamamoto, Mai, Xia, Jianguo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6468840/
https://www.ncbi.nlm.nih.gov/pubmed/30909447
http://dx.doi.org/10.3390/metabo9030057
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author Chong, Jasmine
Yamamoto, Mai
Xia, Jianguo
author_facet Chong, Jasmine
Yamamoto, Mai
Xia, Jianguo
author_sort Chong, Jasmine
collection PubMed
description 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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spelling pubmed-64688402019-04-22 MetaboAnalystR 2.0: From Raw Spectra to Biological Insights Chong, Jasmine Yamamoto, Mai Xia, Jianguo Metabolites 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. MDPI 2019-03-22 /pmc/articles/PMC6468840/ /pubmed/30909447 http://dx.doi.org/10.3390/metabo9030057 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chong, Jasmine
Yamamoto, Mai
Xia, Jianguo
MetaboAnalystR 2.0: From Raw Spectra to Biological Insights
title MetaboAnalystR 2.0: From Raw Spectra to Biological Insights
title_full MetaboAnalystR 2.0: From Raw Spectra to Biological Insights
title_fullStr MetaboAnalystR 2.0: From Raw Spectra to Biological Insights
title_full_unstemmed MetaboAnalystR 2.0: From Raw Spectra to Biological Insights
title_short MetaboAnalystR 2.0: From Raw Spectra to Biological Insights
title_sort metaboanalystr 2.0: from raw spectra to biological insights
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6468840/
https://www.ncbi.nlm.nih.gov/pubmed/30909447
http://dx.doi.org/10.3390/metabo9030057
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