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| Progress in Mass Spectrometry-based MetabolomicsData Analysis Techniques |
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| DOI：10.16768/j.issn.1004-874X.2022.11.011 |
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| Abstract: |
| Metabolomics technique, as an important part of systems biology, aims to identify and quantify all
endogenous small molecule metabolites in organisms at certain condition. The continuous iteration of mass spectrometry and
nuclear magnetic resonance system facilitate great progress in metabolomics technologies. Among them, mass spectrometry
and related metabolomic techniques have been the most widely used due to their ability to detect thousands of metabolites in
biological fluids, cells and tissues simultaneously, without complex pre-processing steps for sample preparation. Therefore,
the development of tools for mass spectrometry-based metabolomics data analysis has been a hot topic in metabolomics research in the past decade. In this review, we systematically summarized the research progress in four main aspects of gas/
liquid chromatography tandem mass spectrometry (GC/LC-MS)-based metabolomics data analysis, including metabolomics data
preprocessing, statistical analysis of metabolomics data, metabolic pathway enrichment analysis, and identification of unknown
metabolites. We mainly introduced the commonly used analysis strategies and software related with MS-based metabolomic data
analysis; and highlighted the cutting-edge innovation about molecular networking-, artificial intelligence-and databases-based
metabolite identification. Finally we gave a brief future perspective about MS-based metabolomic data analysis, and believe
that new developed strategies, which integrate the known biochemical reactions, molecular networking tools, and genetic loci
information regulating the metabolite biosynthesis, will promote the number and accuracy of identified metabolites. This review
will provide new ideas for deeper exploration of new methods for metabolomic data analysis and biological significance from
metabolomic data. |
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