Informatics for Metabolomics

  • Kanthida Kusonmano
  • Wanwipa Vongsangnak
  • Pramote Chumnanpuen
Chapter
First Online:
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 939)

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 techniques Metabolomics Metabolite biomarkers Data mining 
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Notes

Acknowledgment

We would like to thank the Preproposal Research Fund (grant nos.PRF4/2558 and PRF-PII/59), Faculty of Science, Kasetsart University.

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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Kanthida Kusonmano
    • 1
  • Wanwipa Vongsangnak
    • 2
    • 3
  • Pramote Chumnanpuen
    • 2
    • 3
  1. 1.Bioinformatics and Systems Biology Program, School of Bioresources and TechnologyKing Mongkut’s University of Technology ThonburiBangkokThailand
  2. 2.Department of Zoology, Faculty of ScienceKasetsart UniversityBangkokThailand
  3. 3.Computational Biomodelling Laboratory for Agricultural Science and Technology, Faculty of ScienceKasetsart UniversityBangkokThailand

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