Title |
Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives
|
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Published in |
Metabolomics, November 2010
|
DOI | 10.1007/s11306-010-0254-3 |
Pubmed ID | |
Authors |
Maud M. Koek, Renger H. Jellema, Jan van der Greef, Albert C. Tas, Thomas Hankemeier |
Abstract |
Metabolomics involves the unbiased quantitative and qualitative analysis of the complete set of metabolites present in cells, body fluids and tissues (the metabolome). By analyzing differences between metabolomes using biostatistics (multivariate data analysis; pattern recognition), metabolites relevant to a specific phenotypic characteristic can be identified. However, the reliability of the analytical data is a prerequisite for correct biological interpretation in metabolomics analysis. In this review the challenges in quantitative metabolomics analysis with regards to analytical as well as data preprocessing steps are discussed. Recommendations are given on how to optimize and validate comprehensive silylation-based methods from sample extraction and derivatization up to data preprocessing and how to perform quality control during metabolomics studies. The current state of method validation and data preprocessing methods used in published literature are discussed and a perspective on the future research necessary to obtain accurate quantitative data from comprehensive GC-MS data is provided. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Unknown | 3 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 67% |
Science communicators (journalists, bloggers, editors) | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Brazil | 7 | 1% |
Netherlands | 3 | <1% |
United States | 3 | <1% |
South Africa | 2 | <1% |
India | 2 | <1% |
Belgium | 2 | <1% |
Denmark | 2 | <1% |
United Kingdom | 1 | <1% |
Slovakia | 1 | <1% |
Other | 3 | <1% |
Unknown | 571 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 142 | 24% |
Researcher | 105 | 18% |
Student > Master | 83 | 14% |
Student > Bachelor | 53 | 9% |
Student > Doctoral Student | 30 | 5% |
Other | 105 | 18% |
Unknown | 79 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 168 | 28% |
Chemistry | 137 | 23% |
Biochemistry, Genetics and Molecular Biology | 68 | 11% |
Medicine and Dentistry | 27 | 5% |
Engineering | 25 | 4% |
Other | 67 | 11% |
Unknown | 105 | 18% |