  Motivation: 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 which deconvolutes peaks from
1-dimensional NMR spectra, automatically assigns them to specific metabolites
from a target list and obtains concentration estimates. The Bayesian model
incorporates information on charac-teristic 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 (MCMC)
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:
http://www1.imperial.ac.uk/medicine/people/t.ebbels/ Contact:
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