BATMAN—an R package for the automated quantification of metabolites from nuclear magnetic resonance spectra using a Bayesian model

• Published in: Bioinformatics Vol. 28; no. 15; pp. 2088 - 2090
• Main Authors: Hao, Jie, Astle, William, De Iorio, Maria, Ebbels, Timothy M D
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.08.2012
• Subjects: Algorithms; Bayes Theorem; Bayesian analysis; Biological; Biological and medical sciences; Computational Biology - methods; Estimates; Fittings; Fundamental and applied biological sciences. Psychology; General aspects; Magnetic Resonance Spectroscopy - methods; Markov Chains; Mathematical models; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Metabolites; Metabolomics - methods; Monte Carlo Method; Nuclear magnetic resonance; Software; Spectra; Automation; Models; Nuclear magnetic resonance; Metabolite; Quantitative analysis
• ISSN: 1367-4803; 1367-4811; 1367-4811; 1460-2059
• DOI: 10.1093/bioinformatics/bts308

Motivation: Nuclear Magnetic Resonance (NMR) spectra are widely used in metabolomics to obtain metabolite profiles in complex biological mixtures. Common methods used to assign and estimate concentrations of metabolites involve either an expert manual peak fitting or extra pre-processing steps, such as peak alignment and binning. Peak fitting is very time consuming and is subject to human error. Conversely, alignment and binning can introduce artefacts and limit immediate biological interpretation of models. Results: We present the Bayesian automated metabolite analyser for NMR spectra (BATMAN), an R package that deconvolutes peaks from one-dimensional NMR spectra, automatically assigns them to specific metabolites from a target list and obtains concentration estimates. The Bayesian model incorporates information on characteristic peak patterns of metabolites and is able to account for shifts in the position of peaks commonly seen in NMR spectra of biological samples. It applies a Markov chain Monte Carlo algorithm to sample from a joint posterior distribution of the model parameters and obtains concentration estimates with reduced error compared with conventional numerical integration and comparable to manual deconvolution by experienced spectroscopists. Availability and implementation:  http://www1.imperial.ac.uk/medicine/people/t.ebbels/ Contact:  t.ebbels@imperial.ac.uk