Halloran John T, Bilmes Jeff A, Noble William S
Department of Electrical Engineering, University of Washington , Seattle 98195, Washington, United States.
Department of Genome Sciences, University of Washington , Seattle 98195, Washington, United States.
J Proteome Res. 2016 Aug 5;15(8):2749-59. doi: 10.1021/acs.jproteome.6b00290. Epub 2016 Jul 22.
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 .
质谱分析中的一个核心问题是，对于每个观察到的串联质谱，识别出相应的生成肽段。我们提出了一种动态贝叶斯网络（DBN）工具包，通过机器学习方法解决这一问题。该工具包的核心是一个用于快速识别的DBN（DRIP），它可以从高置信度的肽段-谱匹配（PSM）集合中进行训练。DRIP的评分函数使用高斯分布来考虑碎片离子匹配，而不是固定的碎片离子容差，并且通过考虑所有可能的比对，直至使用束剪枝算法控制的阈值，来找到理论谱和观察谱之间的最优比对。该函数不仅能产生一流的数据库搜索准确性，还可用于生成显著提升Percolator后处理器性能的特征。DRIP软件基于一个通用的DBN工具包（GMTK）构建，从而为用户特定的推理任务提供了多种选项，并便于在未来工作中对DRIP模型进行轻松修改。DRIP用Python和C++实现，可在http://melodi-lab.github.io/dripToolkit上根据Apache许可获取。
