Development and application of a data processing method for food metabolomics analysis†

IF 3 4区生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY

Molecular omics Pub Date : 2023-05-04 DOI:10.1039/D2MO00338D

Yuanluo Lei, Xiaoying Chen, Jiachen Shi, Yuanfa Liu and Yong-Jiang Xu

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Abstract

Food metabolomics is described as the implementation of metabolomics to food systems such as food materials, food processing, and food nutrition. These applications generally create large amounts of data, and although technologies exist to analyze these data and different tools exist for various ecosystems, downstream analysis is still a challenge and the tools are not integrated into a single method. In this article, we developed a data processing method for untargeted LC-MS data in metabolomics, derived from the integration of computational MS tools from OpenMS into the workflow system Konstanz Information Miner (KNIME). This method can analyze raw MS data and produce high-quality visualization. A MS1 spectra-based identification, two MS2 spectra-based identification workflows and a GNPSExport-GNPS workflow are included in this method. Compared with conventional approaches, the results of MS1&MS2 spectra-based identification workflows are combined in this approach via the tolerance of retention times and mass to charge ratios (m/z), which can greatly reduce the rate of false positives in metabolomics datasets. In our example, filtering with the tolerance removed more than 50% of the possible identifications while retaining 90% of the correct identification. The results demonstrated that the developed method is a rapid and reliable method for food metabolomics data processing.

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食品代谢组学分析数据处理方法的开发与应用

食品代谢组学被描述为将代谢组学应用于食品系统，如食品材料、食品加工和食品营养。这些应用程序通常会产生大量数据，尽管存在分析这些数据的技术和不同生态系统的不同工具，但下游分析仍然是一个挑战，并且这些工具没有集成到单一方法中。在本文中，我们开发了一种代谢组学中非靶向LC-MS数据的数据处理方法，该方法来源于将OpenMS的计算质谱工具集成到工作流系统Konstanz Information Miner (KNIME)中。该方法可以分析原始质谱数据并产生高质量的可视化结果。该方法包括一个基于MS1谱的识别流程、两个基于MS2谱的识别流程和一个GNPSExport-GNPS工作流。与传统方法相比，该方法通过保留时间和质量电荷比(m/z)的容限将基于ms1和MS2光谱的鉴定工作流程的结果结合在一起，从而大大降低了代谢组学数据集的假阳性率。在我们的示例中，使用容差进行过滤删除了超过50%的可能标识，同时保留了90%的正确标识。结果表明，该方法是一种快速、可靠的食品代谢组学数据处理方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Molecular omics Biochemistry, Genetics and Molecular Biology-Biochemistry

CiteScore

5.40

自引率

3.40%

发文量

期刊介绍： Molecular Omics publishes high-quality research from across the -omics sciences. Topics include, but are not limited to: -omics studies to gain mechanistic insight into biological processes – for example, determining the mode of action of a drug or the basis of a particular phenotype, such as drought tolerance -omics studies for clinical applications with validation, such as finding biomarkers for diagnostics or potential new drug targets -omics studies looking at the sub-cellular make-up of cells – for example, the subcellular localisation of certain proteins or post-translational modifications or new imaging techniques -studies presenting new methods and tools to support omics studies, including new spectroscopic/chromatographic techniques, chip-based/array technologies and new classification/data analysis techniques. New methods should be proven and demonstrate an advance in the field. Molecular Omics only accepts articles of high importance and interest that provide significant new insight into important chemical or biological problems. This could be fundamental research that significantly increases understanding or research that demonstrates clear functional benefits. Papers reporting new results that could be routinely predicted, do not show a significant improvement over known research, or are of interest only to the specialist in the area are not suitable for publication in Molecular Omics.