Modeling peptide fragmentation with dynamic Bayesian networks for peptide identification.
Modeling peptide fragmentation with dynamic Bayesian networks for peptide identification.
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DOI：
10.1093/bioinformatics/btn189
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发表时间：
2008-07-01
期刊：
影响因子：
--
通讯作者：
Noble WS
中科院分区：
文献类型：
--
作者：
Klammer AA;Reynolds SM;Bilmes JA;MacCoss MJ;Noble WS
动机：串联质谱仪(MS/MS)是从复杂混合物中鉴定蛋白质的一项不可或缺的技术。蛋白质被消化成多肽，然后在质谱仪中通过它们的碎裂模式进行鉴定。因此，MS/MS蛋白质鉴定的核心依赖于多肽碎片的相对可预测性。不幸的是，肽片段是复杂的，并且没有被完全理解，并且所理解的东西并不总是被肽识别算法所利用。结果：采用混合动态贝叶斯网络(DBN)/支持向量机(SVM)方法来解决这两个问题。我们训练了一组关于高置信度肽谱匹配的DBN。这些DBN，统称为激流，组成了多肽碎片化学的概率模型。对Riptie了解到的分布的检查可以确定新的趋势，例如在多肽裂解位点C-Term到疏水残基的普遍a离子碎裂。此外，激肽可以用来产生似然分数，表明给定的肽谱匹配是否正确。这样的分数的矢量由支持向量机评估，该支持向量机产生用于肽识别的最终分数。与其他最先进的MS/MS识别算法相比，以这种方式使用Riptie可以提高识别率，以1%的错误发现率将阳性识别的数量增加多达12%。可获得性：根据作者的要求，可以获得Python和C源代码。精心策划的培训集可在http://noble.gs.washington.edu/proj/intense/.上找到图形模型工具包(GMTK)可在http://ssli.ee.washington.edu/bilmes/gmtk.免费获得联系人：noble@gs.washington.edu
Motivation: Tandem mass spectrometry (MS/MS) is an indispensable technology for identification of proteins from complex mixtures. Proteins are digested to peptides that are then identified by their fragmentation patterns in the mass spectrometer. Thus, at its core, MS/MS protein identification relies on the relative predictability of peptide fragmentation. Unfortunately, peptide fragmentation is complex and not fully understood, and what is understood is not always exploited by peptide identification algorithms. Results: We use a hybrid dynamic Bayesian network (DBN)/support vector machine (SVM) approach to address these two problems. We train a set of DBNs on high-confidence peptide-spectrum matches. These DBNs, known collectively as Riptide, comprise a probabilistic model of peptide fragmentation chemistry. Examination of the distributions learned by Riptide allows identification of new trends, such as prevalent a-ion fragmentation at peptide cleavage sites C-term to hydrophobic residues. In addition, Riptide can be used to produce likelihood scores that indicate whether a given peptide-spectrum match is correct. A vector of such scores is evaluated by an SVM, which produces a final score to be used in peptide identification. Using Riptide in this way yields improved discrimination when compared to other state-of-the-art MS/MS identification algorithms, increasing the number of positive identifications by as much as 12% at a 1% false discovery rate. Availability: Python and C source code are available upon request from the authors. The curated training sets are available at http://noble.gs.washington.edu/proj/intense/. The Graphical Model Tool Kit (GMTK) is freely available at http://ssli.ee.washington.edu/bilmes/gmtk. Contact:noble@gs.washington.edu
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影响因子：
46.9
作者：
Washburn, MP;Wolters, D;Yates, JR
通讯作者：
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作者：
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通讯作者：
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