Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP- Purpan, UPS, 180 chemin de Tournefeuille St-Martin-du-Touch, BP 3, Toulouse Cedex, 31931, France.
Metabolomics and Proteomics Core, Helmholtz Zentrum München, 85764, Neuherberg, Germany.
Metabolomics. 2024 Sep 21;20(5):103. doi: 10.1007/s11306-024-02167-2.
Metabolomics, the systematic analysis of small molecules in a given biological system, emerged as a powerful tool for different research questions. Newer, better, and faster methods have increased the coverage of metabolites that can be detected and identified in a shorter amount of time, generating highly dense datasets. While technology for metabolomics is still advancing, another rapidly growing field is metabolomics data analysis including metabolite identification. Within the next years, there will be a high demand for bioinformaticians and data scientists capable of analyzing metabolomics data as well as chemists capable of using in-silico tools for metabolite identification. However, metabolomics is often not included in bioinformatics curricula, nor does analytical chemistry address the challenges associated with advanced in-silico tools.
In this educational review, we briefly summarize some key concepts and pitfalls we have encountered in a collaboration between a bioinformatician (originally not trained for metabolomics) and an analytical chemist. We identified that many misunderstandings arise from differences in knowledge about metabolite annotation and identification, and the proper use of bioinformatics approaches for these tasks. We hope that this article helps other bioinformaticians (as well as other scientists) entering the field of metabolomics bioinformatics, especially for metabolite identification, to quickly learn the necessary concepts for a successful collaboration with analytical chemists.
We summarize important concepts related to LC-MS/MS based non-targeted metabolomics and compare them with other data types bioinformaticians are potentially familiar with. Drawing these parallels will help foster the learning of key aspects of metabolomics.
代谢组学是一种在给定生物系统中系统分析小分子的方法，它已成为解决不同研究问题的有力工具。更新、更好、更快的方法提高了可以在更短的时间内检测和鉴定的代谢物的覆盖率，从而生成了高度密集的数据集。虽然代谢组学技术仍在不断发展，但另一个快速发展的领域是代谢组学数据分析，包括代谢物鉴定。在未来几年内，将需要大量能够分析代谢组学数据的生物信息学家和数据科学家，以及能够使用计算机模拟工具进行代谢物鉴定的化学家。然而，代谢组学通常不包括在生物信息学课程中，分析化学也没有解决与高级计算机模拟工具相关的挑战。
在生物信息学家（最初未接受过代谢组学培训）和分析化学家之间的合作中，我们简要总结了我们遇到的一些关键概念和陷阱。我们发现，许多误解源于对代谢物注释和鉴定以及生物信息学方法在这些任务中的正确使用的知识差异。我们希望本文能帮助其他进入代谢组学生物信息学领域的生物信息学家（以及其他科学家），特别是对于代谢物鉴定，能够快速学习与分析化学家成功合作所需的概念。
我们总结了与基于 LC-MS/MS 的非靶向代谢组学相关的重要概念，并将其与生物信息学家可能熟悉的其他数据类型进行了比较。绘制这些平行线将有助于促进对代谢组学关键方面的学习。
