title: Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry.
abstract: We present a peptide-spectrum alignment strategy that employs a dynamic Bayesian network (DBN) for the identification of spectra produced by tandem mass spectrometry (MS/MS). Our method is fundamentally generative in that it models peptide fragmentation in MS/MS as a physical process. The model traverses an observed MS/MS spectrum and a peptide-based theoretical spectrum to calculate the best alignment between the two spectra. Unlike all existing state-of-the-art methods for spectrum identification that we are aware of, our method can learn alignment probabilities given a dataset of high-quality peptide-spectrum pairs. The method, moreover, accounts for noise peaks and absent theoretical peaks in the observed spectrum. We demonstrate that our method outperforms, on a majority of datasets, several widely used, state-of-the-art database search tools for spectrum identification. Furthermore, the proposed approach provides an extensible framework for MS/MS analysis and provides useful information that is not produced by other methods, thanks to its generative structure.
authors.affiliation: Dept. of Electrical Engineering University of Washington Seattle, WA 98195, USA.
authors.forename: John T
authors.initials: JT
authors.lastname: Halloran
authors.affiliation: Dept. of Electrical Engineering University of Washington Seattle, WA 98195, USA.
authors.forename: Jeff A
authors.initials: JA
authors.lastname: Bilmes
authors.affiliation: Department of Genome Sciences University of Washington Seattle, WA 98195.
authors.forename: William S
authors.initials: WS
authors.lastname: Noble