一种用于鸟枪法蛋白质组学中蛋白质推断问题的贝叶斯方法。
A bayesian approach to protein inference problem in shotgun proteomics.
作者信息
Li Yong Fuga, Arnold Randy J, Li Yixue, Radivojac Predrag, Sheng Quanhu, Tang Haixu
机构信息
School of Informatics, Indiana University , Bloomington, IN 47408, USA.
出版信息
J Comput Biol. 2009 Aug;16(8):1183-93. doi: 10.1089/cmb.2009.0018.
Abstract
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.
摘要
蛋白质推断问题是鸟枪法蛋白质组学中的一项重大挑战。在本文中，我们描述了一种新颖的贝叶斯方法，通过将预测的肽段可检测性纳入肽段鉴定的先验概率来应对这一挑战。我们提出了一种用于蛋白质推断的严格概率模型，并为该问题提供了实际的算法解决方案。我们使用复杂的合成蛋白质混合物来测试我们的方法，并获得了有希望的结果。
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