Identification and quantification of metabolites in 1H NMR spectra by Bayesian model selection

Cheng Zheng, Shucha Zhang, Susanne Ragg, Daniel Raftery, Olga Vitek

Research output: Contribution to journalArticle

64 Scopus citations

Abstract

Motivation: Nuclear magnetic resonance (NMR) spectroscopy is widely used for high-throughput characterization of metabolites in complex biological mixtures. However, accurate interpretation of the spectra in terms of identities and abundances of metabolites can be challenging, in particular in crowded regions with heavy peak overlap. Although a number of computational approaches for this task have recently been proposed, they are not entirely satisfactory in either accuracy or extent of automation. Results: We introduce a probabilistic approach Bayesian Quantification (BQuant), for fully automated database-based identification and quantification of metabolites in local regions of 1H NMR spectra. The approach represents the spectra as mixtures of reference profiles from a database, and infers the identities and the abundances of metabolites by Bayesian model selection. We show using a simulated dataset, a spike-in experiment and a metabolomic investigation of plasma samples that BQuant outperforms the available automated alternatives in accuracy for both identification and quantification.

Original languageEnglish (US)
Article numberbtr118
Pages (from-to)1637-1644
Number of pages8
JournalBioinformatics
Volume27
Issue number12
DOIs
StatePublished - Jun 1 2011

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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