Protein identification problem from a Bayesian point of view.
We present a generic Bayesian framework for the peptide and protein identification in proteomics, and provide a unified interpretation for the database searching and thede novopeptide sequencing approaches that are used in peptide identification. We describe several probabilistic graphical models and a variety of prior distributions that can be incorporated into the Bayesian framework to model different types of prior information, such as the known protein sequences, the known protein abundances, the peptide precursor masses, the estimated peptide retention time and the peptide detectabilities. Various applications of the Bayesian framework are discussed theoretically, including its application to the identification of peptides containing mutations and post-translational modifications.
School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA.
Yong Fuga
YF
Li
Department of Chemistry, Indiana University, Bloomington, IN 47406, USA.
Randy J
RJ
Arnold
School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA.
Predrag
P
Radivojac
School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA.
Haixu
H
Tang
Bayesian methods; Mass spectrometry; Protein identification; Shotgun proteomics