Progress in Mass Spectrometry-based Metabolomics Data Analysis Techniques

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.

Keywords

• metabolomics
• mass spectrometry
• data preprocessing
• database
• metabolite identification
• molecular network algorithm