Chapter title |
Informatics for Metabolomics.
|
---|---|
Chapter number | 5 |
Book title |
Translational Biomedical Informatics
|
Published in |
Advances in experimental medicine and biology, November 2016
|
DOI | 10.1007/978-981-10-1503-8_5 |
Pubmed ID | |
Book ISBNs |
978-9-81-101502-1, 978-9-81-101503-8
|
Authors |
Kanthida Kusonmano, Wanwipa Vongsangnak, Pramote Chumnanpuen |
Editors |
Bairong Shen, Haixu Tang, Xiaoqian Jiang |
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. |
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Australia | 2 | 17% |
Spain | 1 | 8% |
Germany | 1 | 8% |
United States | 1 | 8% |
India | 1 | 8% |
Unknown | 6 | 50% |
Demographic breakdown
Type | Count | As % |
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Scientists | 6 | 50% |
Members of the public | 5 | 42% |
Science communicators (journalists, bloggers, editors) | 1 | 8% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Germany | 1 | 1% |
Canada | 1 | 1% |
Australia | 1 | 1% |
Unknown | 83 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 14 | 16% |
Student > Bachelor | 14 | 16% |
Student > Ph. D. Student | 11 | 13% |
Student > Master | 7 | 8% |
Student > Doctoral Student | 6 | 7% |
Other | 19 | 22% |
Unknown | 15 | 17% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 15 | 17% |
Agricultural and Biological Sciences | 14 | 16% |
Medicine and Dentistry | 9 | 10% |
Chemistry | 8 | 9% |
Computer Science | 7 | 8% |
Other | 13 | 15% |
Unknown | 20 | 23% |