Annotation of Specialized Metabolites from High-Throughput and High-Resolution Mass Spectrometry Metabolomics.

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ID: 82888
2020
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Abstract
High-throughput mass spectrometry (MS) metabolomics profiling of highly complex samples allows the comprehensive detection of hundreds to thousands of metabolites under a given condition and point in time and produces information-rich data sets on known and unknown metabolites. One of the main challenges is the identification and annotation of metabolites from these complex data sets since the number of authentic standards available for specialized metabolites is far lower than an account for the number of mass spectral features. Previously, we reported two novel tools, MetNet and MetCirc, for putative annotation and structural prediction on unknown metabolites using known metabolites as baits. MetNet employs differences between m/z values of MS1 features, which correspond to metabolic transformations, and statistical associations, while MetCirc uses MS/MS features as input and calculates similarity scores of aligned spectra between features to guide the annotation of metabolites. Here, we showcase the use of MetNet and MetCirc to putatively annotate metabolites and provide detailed instructions as to how those can be used. While our case studies are from plants, the tools find equal utility in studies on bacterial, fungal, or mammalian xenobiotic samples.
Reference Key
naake2020annotationmethods Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Naake, Thomas;Gaquerel, Emmanuel;Fernie, Alisdair R;
Journal methods in molecular biology (clifton, nj)
Year 2020
DOI
10.1007/978-1-0716-0239-3_12
URL
Keywords
annotation molecular networking metabolic modification plant metabolite specialized metabolite unknown metabolite

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