Peptide Identification: Refining a Bayesian Stochastic Model

Notwithstanding the challenges associated with different methods of peptide identification, other methods have been explored over the years. The complexity, size and computational challenges of peptide-based data sets calls for more intrusion into this sphere. By relying on the prior information about the average relative abundances of bond cleavages and the prior probability of any specific amino acid sequence, we refine an already developed Bayesian approach in identifying peptides. The likelihood function is improved by adding additional ions to the model and its size is driven by two overall goodness of fit measures. In the face of the complexities associated with our posterior density, a Markov chain Monte Carlo algorithm coupled with simulated annealing is used to simulate candidate choices from the posterior distribution of the peptide sequence, where the peptide with the largest posterior density is estimated as the true peptide