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

• Published in: Journal of proteome research Vol. 15; no. 8; pp. 2749 - 2759
• Main Authors: Halloran, John T, Bilmes, Jeff A, Noble, William S
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 05.08.2016
• Subjects: Bayes Theorem; Databases, Protein; Machine Learning; Peptides - analysis; Proteomics - methods; Software; Tandem Mass Spectrometry; peptide detection; tandem mass spectrometry; machine learning; Bayesian network
• ISSN: 1535-3893; 1535-3907
• DOI: 10.1021/acs.jproteome.6b00290

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.