Automated Analysis of Large-Scale NMR Data Generates Metabolomic Signatures and Links Them to Candidate Metabolites.
- Khalili B Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.
- Tomasoni M Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.
- Mattei M Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.
- Mallol Parera R Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.
- Sonmez R Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.
- Krefl D Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.
- Rueedi R Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.
- Bergmann S Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.
- 2019-07-19
Published in:
- Journal of proteome research. - 2019
1D NMR automated analysis
ISA
NMR spectroscopy
STOCSY
metabolite identification
modular analysis
pseudoquantification
untargeted metabolomics
Algorithms
Body Fluids
Databases, Factual
Humans
Metabolome
Metabolomics
Nuclear Magnetic Resonance, Biomolecular
Principal Component Analysis
Proteins
English
Identification of metabolites in large-scale 1H 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.
- Language
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- English
- Open access status
- hybrid
- Identifiers
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- DOI 10.1021/acs.jproteome.9b00295
- PMID 31318216
- Persistent URL
- https://sonar.ch/global/documents/20388
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