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

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 (1)H NMR spectra. The approach represents the spectra as mixtures of reference profiles from a dat

SUBMITTER: Zheng C 

PROVIDER: S-EPMC3106181 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature