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.
Highlights
Mass spectrometry (MS) is a key analytical technology for detecting and identifying small biomolecules such as metabolites [1,2,3]
For multiple MS data, Sheldon et al [155] used precursor ion fingerprints (PIF) and spectral trees for finding similar compounds and utilized previously characterized ion structures for the structural elucidation of the unknown compounds. Mass spectral classifiers Another natural approach to deal with mass spectra of compounds that cannot be found in a spectral library, is to find patterns in the fragmentation spectra of reference compounds, and to use the detected patterns for the automated interpretation of the unidentified spectrum
No computational de novo method is able to elucidate the structure of a metabolite solely from mass spectral data
Summary
Mass spectrometry (MS) is a key analytical technology for detecting and identifying small biomolecules such as metabolites [1,2,3]. For multiple MS data, Sheldon et al [155] used precursor ion fingerprints (PIF) and spectral trees for finding similar compounds and utilized previously characterized ion structures for the structural elucidation of the unknown compounds Mass spectral classifiers Another natural approach to deal with mass spectra of compounds that cannot be found in a spectral library, is to find patterns in the fragmentation spectra of reference compounds, and to use the detected patterns for the automated interpretation of the unidentified spectrum. Hummel et al [167] used structural features to subdivide the Golm Metabolome Database into several classes They proposed a decision tree-based prediction of the most frequent substructures, based on mass spectral features and retention index information, for classification of unknown metabolites into different compound classes. Another reason is that some of the packages are developed for special experimental setups or instruments, and have to be adapted for other data, what makes an independent validation difficult
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