Informatics for metabolomics

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Publication Details

Author list: Kusonmano K., Vongsangnak W., Chumnanpuen P.

Publisher: Springer

Publication year: 2016

Journal: Advances in Experimental Medicine and Biology (0065-2598)

Volume number: 939

Start page: 91

End page: 115

Number of pages: 25

ISBN: 978-981-10-1502-1

ISSN: 0065-2598

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994565799&doi=10.1007%2f978-981-10-1503-8_5&partnerID=40&md5=83479ec995943c74e2b97f3905d46b82

Languages: English-Great Britain (EN-GB)

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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. ฉ Springer Science+Business Media Singapore 2016.

Keywords

Data acquisition and analysis, Informatics techniques, Metabolite biomarkers, Metabolomics