Automated Analysis
of Large-Scale NMR Data Generates
Metabolomic Signatures and Links Them to Candidate Metabolites
Version 3 2019-08-23, 14:39
Version 2 2019-08-12, 12:34
Version 1 2019-08-01, 12:51
Posted on 2019-08-23 - 14:39
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.
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Khalili, Bita; Tomasoni, Mattia; Mattei, Mirjam; Mallol Parera, Roger; Sonmez, Reyhan; Krefl, Daniel; et al. (2019). Automated Analysis
of Large-Scale NMR Data Generates
Metabolomic Signatures and Links Them to Candidate Metabolites. ACS Publications. Collection. https://doi.org/10.1021/acs.jproteome.9b00295
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AUTHORS (8)
BK
Bita Khalili
MT
Mattia Tomasoni
MM
Mirjam Mattei
RM
Roger Mallol Parera
RS
Reyhan Sonmez
DK
Daniel Krefl
RR
Rico Rueedi
SB
Sven Bergmann
KEYWORDS
1 H NMR dataurine samplessetLarge-Scale NMR Data Generates Metabolomic SignaturesPCAmetabolite signaturesmetabomatching methodcorrelation profilesCandidate Metabolites IdentificationISA968 NMR profilescomponent analysisanalysis toolscovarying featuresNMR spectraiterative signature algorithmSTOCSY approachAutomated AnalysisACP