Spectral Deconvolution for Gas Chromatography Mass Spectrometry-Based Metabolomics: Current Status and Future Perspectives. Computational and Structural Biotechnol Journal. 2013 Jun 28;4:e201301013. eCollection 2013. Du X, Zeisel SH.
Department of Bioinformatics, University of North Carolina at Charlotte, Charlotte, NC; Nutrition Research Institute, University of North Carolina at Chapel Hill, NC Research Campus, Kannapolis, NC.
Mass spectrometry coupled to gas chromatography (GC-MS) has been widely applied in the field of metabolomics. Success of this application has benefited greatly from computational workflows that process the complex raw mass spectrometry data and extract the qualitative and quantitative information of metabolites. Among the computational algorithms within a workflow, deconvolution is critical since it reconstructs a pure mass spectrum for each component that the mass spectrometer observes. Based on the pure spectrum, the corresponding component can be eventually identified and quantified. Deconvolution is challenging due to the existence of co-elution. In this review, we focus on progress that has been made in the development of deconvolution algorithms and provide thoughts on future developments that will expand the application of GC-MS in metabolomics.