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

Naake, T., Gaquerel, E., & Fernie, A. R. (2020). Annotation of Specialized Metabolites from High-Throughput and High Resolution Mass Spectrometry Metabolomics. In S. Li (Ed.), Computational Methods and Data Analysis for Metabolomics (pp. 209-225). New York, NY: Springer US.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0005-8CD0-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-8CD1-1
Genre: Book Chapter

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 Creators:
Naake, T.1, Author              
Gaquerel, Emmanuel2, Author
Fernie, A. R.1, Author              
Affiliations:
1Central Metabolism, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753339              
2External Organizations, ou_persistent22              

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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.

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Language(s): eng - English
 Dates: 2020
 Publication Status: Published in print
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1007/978-1-0716-0239-3_12
BibTex Citekey: Naake2020
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Title: Computational Methods and Data Analysis for Metabolomics
Source Genre: Book
 Creator(s):
Li, Shuzhao, Editor
Affiliations:
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Publ. Info: New York, NY : Springer US
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 209 - 225 Identifier: ISBN: 978-1-0716-0239-3