Biological systems are increasingly being studied in a holistic manner, using omics approaches, to provide quantitative and qualitative descriptions of the diverse collection of cellular components. Among the omics approaches, metabolomics, which deals with the quantitative global profiling of small molecules or metabolites, is being used extensively to explore the dynamic response of living systems, such as organelles, cells, tissues, organs and whole organisms, under diverse physiological and pathological conditions. This technology is now used routinely in a number of applications, including basic and clinical research, agriculture, microbiology, food science, nutrition, pharmaceutical research, environmental science and the development of biofuels. Of the multiple analytical platforms available to perform such analyses, nuclear magnetic resonance and mass spectrometry have come to dominate, owing to the high resolution and large datasets that can be generated with these techniques. The large multidimensional datasets that result from such studies must be processed and analyzed to render this data meaningful. Thus, bioinformatics tools are essential for the efficient processing of huge datasets, the characterization of the detected signals, and to align multiple datasets and their features. This paper provides a state-of-the-art overview of the data processing tools available, and reviews a collection of recent reports on the topic. Data conversion, pre-processing, alignment, normalization and statistical analysis are introduced, with their advantages and disadvantages, and comparisons are made to guide the reader.

人们越来越多地使用组学方法以整体方式研究生物系统，以提供对细胞成分的不同集合的定量和定性描述。在组学方法中，代谢组学处理小分子或代谢物的定量全局分析，被广泛用于探索生命系统（例如细胞器、细胞、组织、器官和整个生物体）在不同生理和条件下的动态响应。病理状况。该技术现已常规用于许多应用，包括基础和临床研究、农业、微生物学、食品科学、营养学、药物研究、环境科学和生物燃料的开发。在可用于执行此类分析的多个分析平台中，核磁共振和质谱已占据主导地位，因为这些技术可以生成高分辨率和大数据集。必须对此类研究产生的大型多维数据集进行处理和分析，以使这些数据有意义。因此，生物信息学工具对于有效处理巨大数据集、表征检测到的信号以及对齐多个数据集及其特征至关重要。本文提供了可用数据处理工具的最新概述，并回顾了有关该主题的最新报告集。介绍了数据转换、预处理、对齐、归一化和统计分析，及其优缺点，并进行比较以指导读者。