University of Washington - Department of Statistics
Tandem mass spectrometry has become a leading technology for protein identification. Much research has been done to automate the task of matching spectra to peptides.
In this study, we propose a probabilistic sequencing algorithm. It includes a probabilistic network to model the chemistry in the generation of theoretical spectrum, a pair hidden markov model to match theoretical spectrum and observed spectrum, and a probabilistic score function to rank the candidate sequences.