A Bayesian Approach to Protein Inference Problem in Shotgun Proteomics
A Bayesian Approach to Protein Inference Problem in Shotgun Proteomics
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DOI：
10.1089/cmb.2009.0018
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发表时间：
2009-08-01
影响因子：
1.7
通讯作者：
Tang, Haixu
中科院分区：
文献类型：
--
作者：
Li, Yong Fuga;Arnold, Randy J.;Tang, Haixu
蛋白质推断问题代表了鸟枪式蛋白质组学的一个主要挑战。在这篇文章中，我们描述了一种新的贝叶斯方法来解决这一挑战，将预测的多肽检测能力作为多肽识别的先验概率。我们提出了一个严谨的蛋白质推理概率模型，并给出了这个问题的实用算法解决方案。我们使用一种复杂的合成蛋白质混合物来测试我们的方法，并获得了令人振奋的结果。
The protein inference problem represents a major challenge in shotgun proteomics. In this article, we describe a novel Bayesian approach to address this challenge by incorporating the predicted peptide detectabilities as the prior probabilities of peptide identification. We propose a rigorious probabilistic model for protein inference and provide practical algoritmic solutions to this problem. We used a complex synthetic protein mixture to test our method and obtained promising results.