Title |
Combining peak- and chromatogram-based retention time alignment algorithms for multiple chromatography-mass spectrometry datasets
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Published in |
BMC Bioinformatics, August 2012
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DOI | 10.1186/1471-2105-13-214 |
Pubmed ID | |
Authors |
Nils Hoffmann, Matthias Keck, Heiko Neuweger, Mathias Wilhelm, Petra Högy, Karsten Niehaus, Jens Stoye |
Abstract |
Modern analytical methods in biology and chemistry use separation techniques coupled to sensitive detectors, such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). These hyphenated methods provide high-dimensional data. Comparing such data manually to find corresponding signals is a laborious task, as each experiment usually consists of thousands of individual scans, each containing hundreds or even thousands of distinct signals. In order to allow for successful identification of metabolites or proteins within such data, especially in the context of metabolomics and proteomics, an accurate alignment and matching of corresponding features between two or more experiments is required. Such a matching algorithm should capture fluctuations in the chromatographic system which lead to non-linear distortions on the time axis, as well as systematic changes in recorded intensities. Many different algorithms for the retention time alignment of GC-MS and LC-MS data have been proposed and published, but all of them focus either on aligning previously extracted peak features or on aligning and comparing the complete raw data containing all available features. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Scientists | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Spain | 2 | 2% |
Austria | 1 | <1% |
Brazil | 1 | <1% |
Germany | 1 | <1% |
United Kingdom | 1 | <1% |
South Africa | 1 | <1% |
Russia | 1 | <1% |
United States | 1 | <1% |
Unknown | 93 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 25 | 25% |
Researcher | 24 | 24% |
Student > Master | 11 | 11% |
Student > Doctoral Student | 7 | 7% |
Student > Bachelor | 4 | 4% |
Other | 17 | 17% |
Unknown | 14 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 33 | 32% |
Chemistry | 22 | 22% |
Computer Science | 9 | 9% |
Biochemistry, Genetics and Molecular Biology | 8 | 8% |
Engineering | 5 | 5% |
Other | 8 | 8% |
Unknown | 17 | 17% |