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Kusonmano K, Vongsangnak W, Chumnanpuen P. Informatics for Metabolomics. Adv Exp Med Biol. 2016;939:91-115doi: 10.1007/978-981-10-1503-8_5.
Kusonmano, K., Vongsangnak, W., & Chumnanpuen, P. (2016). Informatics for Metabolomics. Advances in experimental medicine and biology, 93991-115. https://doi.org/10.1007/978-981-10-1503-8_5
Kusonmano, Kanthida, et al. "Informatics for Metabolomics." Advances in experimental medicine and biology vol. 939 (2016): 91-115. doi: https://doi.org/10.1007/978-981-10-1503-8_5
Kusonmano K, Vongsangnak W, Chumnanpuen P. Informatics for Metabolomics. Adv Exp Med Biol. 2016;939:91-115. doi: 10.1007/978-981-10-1503-8_5. PMID: 27807745.
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Adv Exp Med Biol. 2016;939:91-115. doi: 10.1007/978-981-10-1503-8_5.

Informatics for Metabolomics.

Advances in experimental medicine and biology

Kanthida Kusonmano, Wanwipa Vongsangnak, Pramote Chumnanpuen

Affiliations

  1. Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi, Bangkhuntien, Bangkok, 10150, Thailand.
  2. Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand.
  3. Computational Biomodelling Laboratory for Agricultural Science and Technology, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand.
  4. Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand. [email protected].
  5. Computational Biomodelling Laboratory for Agricultural Science and Technology, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand. [email protected].

PMID: 27807745 DOI: 10.1007/978-981-10-1503-8_5

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; Data mining; Informatics techniques; Metabolite biomarkers; Metabolomics

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