Informatics for Metabolomics
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 th
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Informatics for Metabolomics Kanthida Kusonmano, Wanwipa Vongsangnak, and Pramote Chumnanpuen
Abstract 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. Keywords Data acquisition and analysis • Informatics Metabolomics • Metabolite biomarkers • Data mining
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K. Kusonmano Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut’s University of Technology Thonburi, Bangkhuntien, Bangkok 10150, Thailand W. Vongsangnak • P. Chumnanpuen (*) Department of Zoology, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand Computational Biomodelling Laboratory for Agricultural Science and Technology, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand e-mail: [email protected] © Springer Science+Business Media Singapore 2016 B. Shen et al. (eds.), Translational Biomedical Informatics, Advances in Experimental Medicine and Biology 939, DOI 10.1007/978-981-10-1503-8_5
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Introduction
The word “metabolism” comes from the Greek word “metabole´” which means transformation or change, while the word “metabolome” is commonly defined as the measuring metabolites in a biological system. The study of metabolome usually relies on two different approaches, which are targeted and nontargeted metabolomics (or metabolite profiles). The targeted metabolomics focuses on the quantification of known compound, while the nontargeted approach aims for screening the patterns
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