Kusonmano Kanthida, Vongsangnak Wanwipa, Chumnanpuen Pramote
Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi, Bangkhuntien, Bangkok, 10150, Thailand.
Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand.
Adv Exp Med Biol. 2016;939:91-115. doi: 10.1007/978-981-10-1503-8_5.
Metabolome profiling of biological systems has the powerful ability to provide the biological understanding of their metabolic functional states responding to the environmental factors or other perturbations. Tons of accumulative metabolomics data have thus been established since pre-metabolomics era. This is directly influenced by the high-throughput analytical techniques, especially mass spectrometry (MS)- and nuclear magnetic resonance (NMR)-based techniques. Continuously, the significant numbers of informatics techniques for data processing, statistical analysis, and data mining have been developed. The following tools and databases are advanced for the metabolomics society which provide the useful metabolomics information, e.g., the chemical structures, mass spectrum patterns for peak identification, metabolite profiles, biological functions, dynamic metabolite changes, and biochemical transformations of thousands of small molecules. In this chapter, we aim to introduce overall metabolomics studies from pre- to post-metabolomics era and their impact on society. Directing on post-metabolomics era, we provide a conceptual framework of informatics techniques for metabolomics and show useful examples of techniques, tools, and databases for metabolomics data analysis starting from preprocessing toward functional interpretation. Throughout the framework of informatics techniques for metabolomics provided, it can be further used as a scaffold for translational biomedical research which can thus lead to reveal new metabolite biomarkers, potential metabolic targets, or key metabolic pathways for future disease therapy.
生物系统的代谢组学分析具有强大的能力,能够提供对其代谢功能状态如何响应环境因素或其他扰动的生物学理解。自代谢组学前时代以来,已经建立了大量累积的代谢组学数据。这直接受到高通量分析技术的影响,尤其是基于质谱(MS)和核磁共振(NMR)的技术。持续地,已经开发了大量用于数据处理、统计分析和数据挖掘的信息学技术。以下工具和数据库是为代谢组学领域而开发的,它们提供了有用的代谢组学信息,例如化学结构、用于峰识别的质谱图模式、代谢物谱、生物学功能、动态代谢物变化以及数千种小分子的生化转化。在本章中,我们旨在介绍从代谢组学前时代到后代谢组学时代的整体代谢组学研究及其对社会的影响。针对后代谢组学时代,我们提供了一个代谢组学信息学技术的概念框架,并展示了从预处理到功能解释的代谢组学数据分析技术、工具和数据库的有用示例。在提供的代谢组学信息学技术框架中,它可以进一步用作转化生物医学研究的支架,从而有助于揭示新的代谢物生物标志物、潜在的代谢靶点或未来疾病治疗的关键代谢途径。