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PLoS One. 2015 May 27;10(5):e0124219. doi: 10.1371/journal.pone.0124219. eCollection 2015.

Accurate, fully-automated NMR spectral profiling for metabolomics.

Author information

1
Department of Computing Science, University of Alberta, Edmonton, AB, Canada; Alberta Innovates Center for Machine Learning, Edmonton, AB, Canada.
2
Department of Computing Science, University of Alberta, Edmonton, AB, Canada; Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada.
3
Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
4
Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada.
5
Fiorgen Foundation, 50019 Sesto Fiorentino, Florence, Italy.
6
Centro Risonanze Magnetiche, University of Florence, Florence, Italy.
7
Department of Computing Science, University of Alberta, Edmonton, AB, Canada; Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada; National Research Council, National Institute for Nanotechnology, Edmonton, AB, Canada.

Abstract

Many diseases cause significant changes to the concentrations of small molecules (a.k.a. metabolites) that appear in a person's biofluids, which means such diseases can often be readily detected from a person's "metabolic profile"-i.e., the list of concentrations of those metabolites. This information can be extracted from a biofluids Nuclear Magnetic Resonance (NMR) spectrum. However, due to its complexity, NMR spectral profiling has remained manual, resulting in slow, expensive and error-prone procedures that have hindered clinical and industrial adoption of metabolomics via NMR. This paper presents a system, BAYESIL, which can quickly, accurately, and autonomously produce a person's metabolic profile. Given a 1D 1H NMR spectrum of a complex biofluid (specifically serum or cerebrospinal fluid), BAYESIL can automatically determine the metabolic profile. This requires first performing several spectral processing steps, then matching the resulting spectrum against a reference compound library, which contains the "signatures" of each relevant metabolite. BAYESIL views spectral matching as an inference problem within a probabilistic graphical model that rapidly approximates the most probable metabolic profile. Our extensive studies on a diverse set of complex mixtures including real biological samples (serum and CSF), defined mixtures and realistic computer generated spectra; involving > 50 compounds, show that BAYESIL can autonomously find the concentration of NMR-detectable metabolites accurately (~ 90% correct identification and ~ 10% quantification error), in less than 5 minutes on a single CPU. These results demonstrate that BAYESIL is the first fully-automatic publicly-accessible system that provides quantitative NMR spectral profiling effectively-with an accuracy on these biofluids that meets or exceeds the performance of trained experts. We anticipate this tool will usher in high-throughput metabolomics and enable a wealth of new applications of NMR in clinical settings. BAYESIL is accessible at http://www.bayesil.ca.

PMID:
26017271
PMCID:
PMC4446368
DOI:
10.1371/journal.pone.0124219
[Indexed for MEDLINE]
Free PMC Article

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