Spectrum Identification using a Dynamic Bayesian Network Model of Tandem Mass Spectra
Ajit P. Singh, John Halloran, Jeff Bilmes and William Stafford Noble
Uncertainty in Artificial Intelligence: Proceedings of the Twenty-Eighth Conference. Aug. 15-17, 2012. pp. 775-784.
Abstract
Shotgun proteomics is a high-throughput technology used to identify unknown proteins in a complex mixture. At the heart of this process is a prediction task, the spectrum identification problem, in which each fragmentation spectrum produced by a shotgun proteomics experiment must be mapped to the peptide (protein subsequence) which generated the spectrum. We propose a new algorithm for spectrum identification, based on dynamic Bayesian networks, which significantly out-performs the de-facto standard tools for this task: SEQUEST and Mascot.
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