Protein identification problem from a Bayesian point of view

We present a generic Bayesian framework for the pep-tide and protein identification in proteomics, and provide a unified interpretation for the database searching and the de novo peptide sequencing approaches that are used in pep-tide identification. We describe several probabilistic graph-ical models and a variety of prior distributions that can be incorporated into the Bayesian framework to model differ-ent types of prior information, such as the known protein sequences, the known protein abundances, the peptide pre-cursor masses, the estimated peptide retention time and the peptide detectabilities. Various applications of the Bayesian framework are discussed theoretically, including its applica-tion to the identification of peptides containing mutations and post-translational modifications