title: Dynamic Bayesian Network for Accurate Detection of Peptides from Tandem Mass Spectra.
abstract: A central problem in mass spectrometry analysis involves identifying, for each observed tandem mass spectrum, the corresponding generating peptide. We present a dynamic Bayesian network (DBN) toolkit that addresses this problem by using a machine learning approach. At the heart of this toolkit is a DBN for Rapid Identification (DRIP), which can be trained from collections of high-confidence peptide-spectrum matches (PSMs). DRIP's score function considers fragment ion matches using Gaussians rather than fixed fragment-ion tolerances and also finds the optimal alignment between the theoretical and observed spectrum by considering all possible alignments, up to a threshold that is controlled using a beam-pruning algorithm. This function not only yields state-of-the art database search accuracy but also can be used to generate features that significantly boost the performance of the Percolator postprocessor. The DRIP software is built upon a general purpose DBN toolkit (GMTK), thereby allowing a wide variety of options for user-specific inference tasks as well as facilitating easy modifications to the DRIP model in future work. DRIP is implemented in Python and C++ and is available under Apache license at http://melodi-lab.github.io/dripToolkit .
authors.affiliation: Department of Electrical Engineering, University of Washington , Seattle 98195, Washington, United States.
authors.forename: John T
authors.initials: JT
authors.lastname: Halloran
authors.affiliation: Department of Electrical Engineering, University of Washington , Seattle 98195, Washington, United States.
authors.forename: Jeff A
authors.initials: JA
authors.lastname: Bilmes
authors.affiliation: Department of Genome Sciences, University of Washington , Seattle 98195, Washington, United States.
authors.forename: William S
authors.initials: WS
authors.lastname: Noble
mesh_terms: D001499:Bayes Theorem; D030562:Databases, Protein; D000069550:Machine Learning; D010455:Peptides / Q000032:analysis*; D040901:Proteomics / Q000379:methods*; D012984:Software; D053719:Tandem Mass Spectrometry
chemical_list: D010455:Peptides
keywords: Bayesian network; machine learning; peptide detection; tandem mass spectrometry