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代谢组学分析指南:一种生物信息学工作流程。

Guide to Metabolomics Analysis: A Bioinformatics Workflow.

作者信息

Chen Yang, Li En-Min, Xu Li-Yan

机构信息

The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, China.

Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, China.

出版信息

Metabolites. 2022 Apr 15;12(4):357. doi: 10.3390/metabo12040357.

Abstract

Metabolomics is an emerging field that quantifies numerous metabolites systematically. The key purpose of metabolomics is to identify the metabolites corresponding to each biological phenotype, and then provide an analysis of the mechanisms involved. Although metabolomics is important to understand the involved biological phenomena, the approach's ability to obtain an exhaustive description of the processes is limited. Thus, an analysis-integrated metabolomics, transcriptomics, proteomics, and other omics approach is recommended. Such integration of different omics data requires specialized statistical and bioinformatics software. This review focuses on the steps involved in metabolomics research and summarizes several main tools for metabolomics analyses. We also outline the most abnormal metabolic pathways in several cancers and diseases, and discuss the importance of multi-omics integration algorithms. Overall, our goal is to summarize the current metabolomics analysis workflow and its main analysis software to provide useful insights for researchers to establish a preferable pipeline of metabolomics or multi-omics analysis.

摘要

代谢组学是一个新兴领域,它系统地对众多代谢物进行定量分析。代谢组学的关键目的是识别与每种生物学表型相对应的代谢物,然后对其中涉及的机制进行分析。尽管代谢组学对于理解所涉及的生物学现象很重要,但该方法全面描述这些过程的能力是有限的。因此,建议采用整合代谢组学、转录组学、蛋白质组学及其他组学方法的分析方法。这种不同组学数据的整合需要专门的统计和生物信息学软件。本综述着重介绍代谢组学研究中涉及的步骤,并总结了几种代谢组学分析的主要工具。我们还概述了几种癌症和疾病中最异常的代谢途径,并讨论了多组学整合算法的重要性。总体而言,我们的目标是总结当前的代谢组学分析工作流程及其主要分析软件,以便为研究人员建立更优的代谢组学或多组学分析流程提供有用的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/126e/9032224/cf0a453fd89e/metabolites-12-00357-g001.jpg

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