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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-6468840 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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