Dataset Information

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 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.

Availability

The R package BQuant is available at: http://www.stat.purdue.edu/~ovitek/BQuant-Web/.

SUBMITTER: Zheng C

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

REPOSITORIES: biostudies-literature

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Publications

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

Zheng Cheng C Zhang Shucha S Ragg Susanne S Raftery Daniel D Vitek Olga O

Bioinformatics (Oxford, England) 20110312 12

<h4>Motivation</h4>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 a ...[more]

PMID: 21398670

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Project description:BackgroundOne-dimensional 1H-NMR spectroscopy is widely used for high-throughput characterization of metabolites in complex biological mixtures. However, the accurate identification of individual compounds is still a challenging task, particularly in spectral regions with higher peak densities. The need for automatic tools to facilitate and further improve the accuracy of such tasks, while using increasingly larger reference spectral libraries becomes a priority of current metabolomics research.ResultsWe introduce a web server application, called MetaboHunter, which can be used for automatic assignment of 1H-NMR spectra of metabolites. MetaboHunter provides methods for automatic metabolite identification based on spectra or peak lists with three different search methods and with possibility for peak drift in a user defined spectral range. The assignment is performed using as reference libraries manually curated data from two major publicly available databases of NMR metabolite standard measurements (HMDB and MMCD). Tests using a variety of synthetic and experimental spectra of single and multi metabolite mixtures show that MetaboHunter is able to identify, in average, more than 80% of detectable metabolites from spectra of synthetic mixtures and more than 50% from spectra corresponding to experimental mixtures. This work also suggests that better scoring functions improve by more than 30% the performance of MetaboHunter's metabolite identification methods.ConclusionsMetaboHunter is a freely accessible, easy to use and user friendly 1H-NMR-based web server application that provides efficient data input and pre-processing, flexible parameter settings, fast and automatic metabolite fingerprinting and results visualization via intuitive plotting and compound peak hit maps. Compared to other published and freely accessible metabolomics tools, MetaboHunter implements three efficient methods to search for metabolites in manually curated data from two reference libraries.

Dataset Information

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

Motivation

Results

Availability

Publications

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

Similar Datasets

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