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- Title
In silico identification software (ISIS): a machine learning approach to tandem mass spectral identification of lipids.
- Authors
Kangas, Lars J.; Metz, Thomas O.; Isaac, Giorgis; Schrom, Brian T.; Ginovska-Pangovska, Bojana; Wang, Luning; Tan, Li; Lewis, Robert R.; Miller, John H.
- Abstract
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.Contact: lars.kangas@pnnl.govSupplementary information: Supplementary data are available at Bioinformatics online.
- Subjects
SYSTEM identification; COMPUTER software; MACHINE learning; TANDEM mass spectrometry; LIQUID chromatography-mass spectrometry; COMPUTER algorithms; SENSITIVITY analysis
- Publication
Bioinformatics, 2012, Vol 28, Issue 13, p1705
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/bts194