J Comput Biol. 2009 Aug;16(8):1183-93.

A bayesian approach to protein inference problem in shotgun proteomics.

Li YF, Arnold RJ, Li Y, Radivojac P, Sheng Q, Tang H.

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School of Informatics, Indiana University , Bloomington, IN 47408, USA.

Abstract

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.

PMID:: 19645593; [PubMed - indexed for MEDLINE]
PMCID: PMC2799497

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Cited by 3 PubMed Central articles

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