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Bioinformatics. 2012 Jul 1;28(13):1705-13. doi: 10.1093/bioinformatics/bts194. Epub 2012 May 15.

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

Author information

1
Computational and Statistical Analytics Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA. lars.kangas@pnnl.gov

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.

PMID:
22592377
PMCID:
PMC3381961
DOI:
10.1093/bioinformatics/bts194
[Indexed for MEDLINE]
Free PMC Article
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