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Accurate, fully-automated NMR spectral profiling for metabolomics.

Ravanbakhsh S, Liu P, Bjorndahl TC, Bjordahl TC, Mandal R, Grant JR, Wilson M, Eisner R, Sinelnikov I, Hu X, Luchinat C, Greiner R, Wishart DS - PLoS ONE (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computing Science, University of Alberta, Edmonton, AB, Canada; Alberta Innovates Center for Machine Learning, 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.

No MeSH data available.


Construction of spectral regions.Partitioning of spectrum  into continuous blocks . Here each block is shown with a different shade of blue, below the horizontal axis. The domain of influence of each cluster is also indicated with coloured blocks, where each cluster assumes the same colour in reconstruction  of the spectrum (above horizontal axis).
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pone.0124219.g003: Construction of spectral regions.Partitioning of spectrum into continuous blocks . Here each block is shown with a different shade of blue, below the horizontal axis. The domain of influence of each cluster is also indicated with coloured blocks, where each cluster assumes the same colour in reconstruction of the spectrum (above horizontal axis).

Mentions: BAYESIL “factors” the spectrum and the loss function into a set of inter-related regions and functions. Two characteristics of the NMR spectra make this factorization possible: 1) each shift is over only a small range (typically a window of ±0.025 PPM); and 2) as the height of a (Lorentzian) peak diminishes quickly from its center, each peak and therefore each cluster can only “influence” a small interval. BAYESIL partition the spectrum into disjoint contiguous regions, such that every point in each region involves exactly the same subset of clusters. Fig 3 shows the division of a part of human serum NMR spectrum into regions; blocks in different shades of blue.


Accurate, fully-automated NMR spectral profiling for metabolomics.

Ravanbakhsh S, Liu P, Bjorndahl TC, Bjordahl TC, Mandal R, Grant JR, Wilson M, Eisner R, Sinelnikov I, Hu X, Luchinat C, Greiner R, Wishart DS - PLoS ONE (2015)

Construction of spectral regions.Partitioning of spectrum  into continuous blocks . Here each block is shown with a different shade of blue, below the horizontal axis. The domain of influence of each cluster is also indicated with coloured blocks, where each cluster assumes the same colour in reconstruction  of the spectrum (above horizontal axis).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4446368&req=5

pone.0124219.g003: Construction of spectral regions.Partitioning of spectrum into continuous blocks . Here each block is shown with a different shade of blue, below the horizontal axis. The domain of influence of each cluster is also indicated with coloured blocks, where each cluster assumes the same colour in reconstruction of the spectrum (above horizontal axis).
Mentions: BAYESIL “factors” the spectrum and the loss function into a set of inter-related regions and functions. Two characteristics of the NMR spectra make this factorization possible: 1) each shift is over only a small range (typically a window of ±0.025 PPM); and 2) as the height of a (Lorentzian) peak diminishes quickly from its center, each peak and therefore each cluster can only “influence” a small interval. BAYESIL partition the spectrum into disjoint contiguous regions, such that every point in each region involves exactly the same subset of clusters. Fig 3 shows the division of a part of human serum NMR spectrum into regions; blocks in different shades of blue.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computing Science, University of Alberta, Edmonton, AB, Canada; Alberta Innovates Center for Machine Learning, 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.

No MeSH data available.