Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography–mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data and, finally, to identify and interpret the compounds that have emerged as interesting.

中文翻译：

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“notame”：非靶向 LC-MS 代谢分析的工作流程。


代谢组学分析会生成大量数据，需要涉及分析、生物化学和生物信息学专业知识的全面工作流程，以便提供连贯且高质量的数据，从而能够发现稳健且具有生物学意义的代谢结果。在这篇协议文章中，我们介绍 notame，一种利用液相色谱-质谱分析的非靶向代谢分析方法的分析工作流程。我们概述了我们通常用于分析营养代谢组学数据的实验室方案和统计方法。本文分为三个主要部分：第一和第二部分介绍代谢组学研究的背景和研究设计，第三部分详细描述用于产生、预处理和统计分析代谢组学数据的主要方法和方案的步骤最后，识别并解释那些有趣的化合物。