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

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• 作者：John T. Halloran ; Jeff A. Bilmes ; William S. Noble
• 刊名：Journal of Proteome Research
• 出版年：2016
• 出版时间：August 5, 2016
• 年：2016
• 卷：15
• 期：8
• 页码：2749-2759
• 全文大小：623K
• 年卷期：0
• ISSN：1535-3907

文摘

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.