A bayesian approach to protein inference problem in shotgun proteomics

Abstract. 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 detectabil-ities as the prior probabilities of peptide identification. Our model re-moves 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.