title: Identification and quantification of metabolites in (1)H NMR spectra by Bayesian model selection.
abstract: BACKGROUND
  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 (1)H 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.
  
  
  BACKGROUND
  The R package BQuant is available at: http://www.stat.purdue.edu/~ovitek/BQuant-Web/.
authors.affiliation: Department of Statistics, Purdue University, West Lafayette, IN 47907, USA. zhengc@purdue.edu
authors.forename: Cheng
authors.initials: C
authors.lastname: Zheng
authors.forename: Shucha
authors.initials: S
authors.lastname: Zhang
authors.forename: Susanne
authors.initials: S
authors.lastname: Ragg
authors.forename: Daniel
authors.initials: D
authors.lastname: Raftery
authors.forename: Olga
authors.initials: O
authors.lastname: Vitek
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008297:Male; D055432:Metabolomics / Q000379:methods*; D015233:Models, Statistical