In silico identification software (ISIS): a machine learning approach to tandem mass spectral identification of lipids

作者：Lars J. Kangas, Thomas Metz, Giorgis Isaac, Brian T. Schrom, Bojana Ginovska, Lu‐Ning Wang, Li Tan, Robert R. Lewis, John Holmes Miller · 发表于：Bioinformatics · 年份：2012 · DOI：10.1093/bioinformatics/bts194 · 被引用次数：70 · 研究领域：Metabolomics and Mass Spectrometry Studies、Advanced Chemical Sensor Technologies、Microbial Metabolic Engineering and Bioproduction

MOTIVATION: 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 (ISIS: in silico identification software) and its implementation are presented and show great promise in generating in silico spectra of lipids 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. RESULTS: A preliminary test of the algorithm with 45 lipids from a subset of lipid classes shows both high sensitivity and specificity.