A bayesian approach to protein inference problem in shotgun proteomics

J Comput Biol. 2009 Aug;16(8):1183-93. doi: 10.1089/cmb.2009.0018.


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

MeSH terms

  • Algorithms*
  • Databases, Protein
  • Models, Statistical
  • Proteomics / methods*
  • Sequence Analysis, Protein / methods*