Evidence map›Paper›PMID 26017271›Full record

ArticlePloS one2015

Accurate, fully-automated NMR spectral profiling for metabolomics.

Siamak Ravanbakhsh, Philip Liu, Trent C Bjorndahl, Trent C Bjordahl, Rupasri Mandal, Jason R Grant, Michael Wilson, Roman Eisner, Igor Sinelnikov, Xiaoyu Hu and 3 more

Erratum issuedAbstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 163 papers.

0numbers the graph read from it
0cells of the map it votes in
163citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

163 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. UntargetedTranslational psychiatry · 2026
    Article
  9. Article
  10. Article
  11. Article
  12. Metabolite Fraction Libraries for Quantitative NMR Metabolomics.bioRxiv : the preprint server for biology · 2025
    Article
  13. Observational
  14. Article
  15. Article
  16. Review
  17. Article
  18. Review
  19. Review
  20. Article

103 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Siamak RavanbakhshDepartment of Computing Science, University of Alberta, Edmonton, AB, Canada; Alberta Innovates Center for Machine Learning, Edmonton, AB, Canada.
Philip LiuDepartment of Computing Science, University of Alberta, Edmonton, AB, Canada; Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada.
Trent C Bjorndahl
Trent C BjordahlDepartment of Computing Science, University of Alberta, Edmonton, AB, Canada; Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada.
Rupasri MandalDepartment of Computing Science, University of Alberta, Edmonton, AB, Canada; Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada.
Jason R GrantDepartment of Computing Science, University of Alberta, Edmonton, AB, Canada.
Michael WilsonDepartment of Computing Science, University of Alberta, Edmonton, AB, Canada.
Roman EisnerDepartment of Computing Science, University of Alberta, Edmonton, AB, Canada.
Igor SinelnikovDepartment of Biological Sciences, University of Alberta, Edmonton, AB, Canada.
Xiaoyu HuFiorgen Foundation, 50019 Sesto Fiorentino, Florence, Italy.
Claudio LuchinatCentro Risonanze Magnetiche, University of Florence, Florence, Italy.
Russell GreinerDepartment of Computing Science, University of Alberta, Edmonton, AB, Canada; Alberta Innovates Center for Machine Learning, Edmonton, AB, Canada.
David S WishartDepartment 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.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Magnetic Resonance ImagingAlgorithmsMetabolomics

Identifiers

PMID26017271
PMCPMC4446368

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.