A Bayesian Approach to Protein Inference Problem in Shotgun Proteomics

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 rigorous probabilistic model for protein inference and provide practical algorithmic solutions to this problem. We used a complex synthetic protein mixture to test our method and obtained promising results