Title |
Computational mass spectrometry for small molecules
|
---|---|
Published in |
Journal of Cheminformatics, March 2013
|
DOI | 10.1186/1758-2946-5-12 |
Pubmed ID | |
Authors |
Kerstin Scheubert, Franziska Hufsky, Sebastian Böcker |
Abstract |
: The identification of small molecules from mass spectrometry (MS) data remains a major challenge in the interpretation of MS data. This review covers the computational aspects of identifying small molecules, from the identification of a compound searching a reference spectral library, to the structural elucidation of unknowns. In detail, we describe the basic principles and pitfalls of searching mass spectral reference libraries. Determining the molecular formula of the compound can serve as a basis for subsequent structural elucidation; consequently, we cover different methods for molecular formula identification, focussing on isotope pattern analysis. We then discuss automated methods to deal with mass spectra of compounds that are not present in spectral libraries, and provide an insight into de novo analysis of fragmentation spectra using fragmentation trees. In addition, this review shortly covers the reconstruction of metabolic networks using MS data. Finally, we list available software for different steps of the analysis pipeline. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Spain | 2 | 33% |
Germany | 1 | 17% |
India | 1 | 17% |
Unknown | 2 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 50% |
Scientists | 2 | 33% |
Science communicators (journalists, bloggers, editors) | 1 | 17% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Russia | 3 | 1% |
United Kingdom | 3 | 1% |
Germany | 2 | <1% |
United States | 2 | <1% |
France | 1 | <1% |
Australia | 1 | <1% |
Colombia | 1 | <1% |
Portugal | 1 | <1% |
Czechia | 1 | <1% |
Other | 6 | 2% |
Unknown | 224 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 72 | 29% |
Researcher | 51 | 21% |
Student > Master | 22 | 9% |
Student > Bachelor | 19 | 8% |
Professor > Associate Professor | 12 | 5% |
Other | 35 | 14% |
Unknown | 34 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Chemistry | 71 | 29% |
Agricultural and Biological Sciences | 53 | 22% |
Biochemistry, Genetics and Molecular Biology | 23 | 9% |
Computer Science | 15 | 6% |
Engineering | 9 | 4% |
Other | 34 | 14% |
Unknown | 40 | 16% |