Bayesian Hierarchical Model for Protein Identifications.

ABSTRACT: In proteomics, identification of proteins from complex mixtures of proteins extracted from biological samples is an important problem. Among the experimental technologies, Mass-Spectrometry (MS) is the most popular one. Protein identification from MS data typically relies on a "two-step" procedure of identifying the peptide first followed by the separate protein identification procedure next. In this setup, the interdependence of peptides and proteins are neglected resulting in relatively inaccurate protein identification. In this article, we propose a Markov chain Monte Carlo (MCMC) based Bayesian hierarchical model, a first of its kind in protein identification, which integrates the two steps and performs joint analysis of proteins and peptides using posterior probabilities. We remove th

SUBMITTER: Mitra R 

PROVIDER: S-EPMC6519717 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature