Journal
PLOS ONE
Volume 10, Issue 5, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0124219
Keywords
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Categories
Funding
- Alberta Innovates-Health Solutions
- Alberta/Pfizer Translational Research Fund
- Metabolomics Innovation Centre by Genome Canada
- Genome Alberta
- Natural Sciences and Engineering Research Council of Canada
- Canadian Institutes of Health Research
- Alberta Innovates Technology Futures scholarship
- Queen Elizabeth II graduate scholarship
- Alberta Innovates Centre for Machine Learning
- CIHR [111062]
- Alberta Innovates [201201143] Funding Source: researchfish
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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 H-1 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 (similar to 90% correct identification and similar to 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.
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