A Bayesian Approach to Protein Inference Problem in Shotgun Proteomics

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.

• Type: Conference
• Year: 2008
• Where: RECOMB
• Authors: Yong Fuga Li, Randy J. Arnold, Yixue Li, Predrag Radivojac, Quanhu Sheng, Haixu Tang
• Keywords: Complex Synthetic Protein, Computational Biology, Protein Inference Problem, RECOMB 2008, Rigorious Probabilistic Solution