Automated Analysis of Large-Scale NMR Data Generates Metabolomic Signatures and Links Them to Candidate Metabolites.

Détails

ID Serval
serval:BIB_FEEE642E4FEC
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Automated Analysis of Large-Scale NMR Data Generates Metabolomic Signatures and Links Them to Candidate Metabolites.
Périodique
Journal of proteome research
Auteur(s)
Khalili B., Tomasoni M., Mattei M., Mallol Parera R., Sonmez R., Krefl D., Rueedi R., Bergmann S.
ISSN
1535-3907 (Electronic)
ISSN-L
1535-3893
Statut éditorial
Publié
Date de publication
06/09/2019
Peer-reviewed
Oui
Volume
18
Numéro
9
Pages
3360-3368
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: ppublish
Résumé
Identification of metabolites in large-scale <sup>1</sup> H NMR data from human biofluids remains challenging due to the complexity of the spectra and their sensitivity to pH and ionic concentrations. In this work, we tested the capacity of three analysis tools to extract metabolite signatures from 968 NMR profiles of human urine samples. Specifically, we studied sets of covarying features derived from principal component analysis (PCA), the iterative signature algorithm (ISA), and averaged correlation profiles (ACP), a new method we devised inspired by the STOCSY approach. We used our previously developed metabomatching method to match the sets generated by these algorithms to NMR spectra of individual metabolites available in public databases. On the basis of the number and quality of the matches, we concluded that ISA and ACP can robustly identify ten and nine metabolites, respectively, half of which were shared, while PCA did not produce any signatures with robust matches.
Mots-clé
1D NMR automated analysis, ISA, NMR spectroscopy, STOCSY, metabolite identification, modular analysis, pseudoquantification, untargeted metabolomics
Pubmed
Web of science
Création de la notice
04/08/2019 16:25
Dernière modification de la notice
16/10/2019 6:12
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