A Bayesian Approach to Protein Inference Problem in Shotgun Proteomics

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• 作者：Yong Fuga Li ; Randy J. Arnold ; Yixue Li ; Predrag Radivojac ; Quanhu Sheng ; et al.
• 刊名：Lecture Notes in Computer Science
• 出版年：2008
• 出版时间：2008
• 年：2008
• 卷：4955
• 期：1
• 页码：167-180
• 全文大小：610.9 KB

文摘

The protein inference problem represents a major challenge in shotgun proteomics. Here we describe a novel Bayesian approach to address this challenge that incorporates the predicted peptide detectabilities as the prior probabilities of peptide identification. Our model removes some unrealistic assumptions used in previous approaches and provides a rigorious probabilistic solution to this problem. We used a complex synthetic protein mixture to test our method, and obtained promising results.