METABOLITE IN SILICO IDENTIFICATION SOFTWARE (METISIS): A MACHINE LEARNING APPROACH TO TANDEM MASS SPECTRAL IDENTIFICATION OF METABOLITES
Kangas, Lars Juhani
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Liquid chromatography-mass spectrometry-based metabolomics has gained importance in the life sciences, yet it is not supported by software tools for high throughput identification of metabolites based on their fragmentation spectra. An algorithm (MetISIS: metabolite in silico identification software) and its implementation are presented and show great promise in generating in silico spectra of metabolites for the purpose of structural identification. Instead of using chemical reaction rate equations or rules-based fragmentation libraries, the algorithm uses machine learning to find accurate bond cleavage rates in a mass spectrometer employing collision-induced dissociation tandem mass spectrometry. A preliminary test of the algorithm and a comparison to another algorithm with 45 lipids shows both high sensitivity and specificity.