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J Proteome Res. 2016 Aug 5;15(8):2749-59. doi: 10.1021/acs.jproteome.6b00290. Epub 2016 Jul 22.

Dynamic Bayesian Network for Accurate Detection of Peptides from Tandem Mass Spectra.

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Department of Electrical Engineering, University of Washington , Seattle 98195, Washington, United States.
Department of Genome Sciences, University of Washington , Seattle 98195, Washington, United States.


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 .


Bayesian network; machine learning; peptide detection; tandem mass spectrometry

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