| 黄文洁 1
，吴绍文 1
，刘 蕊 2
，孔 谦 1
，晏石娟 1.基于质谱的代谢组学数据分析技术研究进展[J].广东农业科学,2022,49(11):96-109 |
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基于质谱的代谢组学数据分析技术研究进展 |
| Progress in Mass Spectrometry-based MetabolomicsData Analysis Techniques |
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| DOI：10.16768/j.issn.1004-874X.2022.11.011 |
| 中文关键词: 代谢组 质谱 数据预处理 数据库 代谢物鉴定 分子网络算法 |
| 英文关键词: metabolomics mass spectrometry data preprocessing database metabolite identification molecular network
algorithm |
| 基金项目:高水平农科院建设 - 科技创新战略专项（202205，R2020PY-JX019）；2022 年梅州特色现代农业产
业人才振兴计划“揭榜挂帅”项目 - 梅州柚木质化阻断技术研究项目；广东省农业科学院“十四五”农业优势产业
学科团队建设项目（202114TD） |
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| 摘要点击次数: 1243 |
| 全文下载次数: 1535 |
| 中文摘要: |
| 代谢组学是系统生物学研究的重要组成部分，是一种对特定条件下生物体内所有内源性小分子代
谢物进行全面定性和定量分析的技术。质谱和核磁共振系统的不断更新迭代推进了代谢组学技术的迅猛发展，
其中质谱技术因其能同时检测出数千个生物流体、细胞和组织中的代谢物，且所需前处理步骤简单，已发展为
当前代谢组学研究中应用最广泛的技术，开发基于质谱的代谢组学数据分析方法也因此成为过去 10 年代谢组学
研究的热点领域。对基于 GC-MS 和 LC-MS 的代谢组学数据预处理、代谢组学数据统计分析、代谢途径富集分析，
以及未知代谢物鉴定 4 个方向取得的研究进展进行系统总结，详细介绍常用的数据分析策略和分析软件；并重
点综述了包括基于数据库、分子网络算法、人工智能算法等未知代谢物鉴定的前沿方法，最后展望了基于质谱
的代谢组学数据分析的未来发展方向，在已知生化反应和分子网络分析的基础上再整合代谢物合成的遗传位点
等信息，有望进一步提高代谢物的鉴定数量和准确度。全面综述基于质谱的代谢组学数据分析技术，将为开发
新的代谢组学分析方法和挖掘代谢组学数据的生物学意义提供重要的参考和思路。 |
| 英文摘要: |
| 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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