Abstract
Background Large-scale identification of metabolites is key to elucidating and modeling metabolism at the systems level. Advances in metabolomics technologies, particularly ultrahigh resolution mass spectrometry enable comprehensive and rapid analysis of metabolites, which is impractical to achieve by conventional methods. However, a significant barrier to meaningful data interpretation is the identification of a wide range of metabolites including unknowns and the determination of their role(s) in various metabolic networks. Our recent development of chemoselective (CS) probes to tag metabolite functional groups provides additional structural constraints for metabolite identification, but remains limited by the lack of functional groupresolved metabolite databases.
Highlights
Large-scale identification of metabolites is key to elucidating and modeling metabolism at the systems level
Materials and methods We have developed a novel algorithm to allow for the rapid detection of functional groups within existing metabolite databases such as KEGG Ligand and the Human Metabolome Database in order to create functional group resolved versions of both databases
These databases will allow for combined molecular formula and functional group queries to aid in metabolite identification based on accurate mass information without a priori knowledge
Summary
Large-scale identification of metabolites is key to elucidating and modeling metabolism at the systems level. Development of large-scale metabolite identification methods for metabolomics From UT–KBRIN Bioinformatics Summit 2014 Cadiz, KY, USA.
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