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Computational mass spectrometry for small molecules
被引:117
|作者:
Scheubert, Kerstin
[1
]
Hufsky, Franziska
[1
,2
]
Boecker, Sebastian
[1
]
机构:
[1] Univ Jena, Chair Bioinformat, Jena, Germany
[2] Max Planck Inst Chem Ecol, Jena, Germany
来源:
关键词:
Mass spectrometry;
Metabolomics;
Spectral library;
Molecular formula identification;
Structure elucidation;
Fragmentation trees;
Networks;
COMPUTER-AIDED INTERPRETATION;
SPECTRAL LIBRARY SEARCH;
INTER-LABORATORY TRANSFERABILITY;
AGGREGATED ISOTOPIC DISTRIBUTION;
MINIMUM REPORTING STANDARDS;
RECOGNITION EXPERT-SYSTEM;
PRODUCT ION SPECTRA;
METABOLITE IDENTIFICATION;
GAS-CHROMATOGRAPHY;
ELEMENTAL COMPOSITIONS;
D O I:
10.1186/1758-2946-5-12
中图分类号:
O6 [化学];
学科分类号:
0703 ;
摘要:
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.
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页数:24
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