Bayesian deconvolution and quantification of metabolites in complex 1D NMR spectra using BATMAN

Area of Science:

• Metabolomics
• Nuclear Magnetic Resonance (NMR) Spectroscopy
• Computational Chemistry

Background:

• Data processing of 1D NMR spectra presents a significant challenge in metabolomic studies requiring precise metabolite quantification.
• Existing methods often struggle with the complexity and variability inherent in NMR data from biological mixtures.

Purpose of the Study:

• To introduce a robust protocol for automated metabolite deconvolution and quantification from complex 1D NMR spectra.
• To present the Bayesian Automated Metabolite Analyzer for NMR (BATMAN) R package as a solution for efficient NMR data analysis.

Main Methods:

• Utilizing the BATMAN R package, which models NMR resonances based on user-defined metabolite templates (chemical shifts, J-couplings, relative intensities).
• Implementing flexible modeling that allows for slight peak shifts and variations in peak widths across spectra.
• Employing wavelet-based non-parametric modeling for spectral signals not explained by metabolite templates.
• Developing a comprehensive protocol covering template library setup, parameter optimization, prior information refinement, and quality control.

Main Results:

• BATMAN provides relative concentration estimates for identified metabolites with associated Bayesian uncertainty.
• The package quantifies spectral regions not explained by templates using wavelets.
• Graphical diagnostics facilitate simultaneous quality assessment across multiple spectra.

Conclusions:

• The developed protocol offers an efficient workflow for analyzing large sets of NMR spectra.
• BATMAN is expected to significantly benefit diverse metabolomics research by automating complex data processing.