A Bayesian approach to protein inference problem in shotgun proteomics

Authors:
Yong Fuga Li;Randy J. Arnold;Yixue Li;Predrag Radivojac;Quanhu Sheng;Haixu Tang
Affiliations:
School of Informatics, Indiana University, Bloomington, IN;Department of Chemistry, Indiana University, Bloomington, IN;Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China;School of Informatics, Indiana University, Bloomington, IN;School of Informatics, Indiana University, Bloomington, IN and Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China;School of Informatics, Indiana University, Bloomington, IN
Venue:
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Year:
2008

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Abstract

The protein inference problem represents a major challenge in shotgun proteomics. Here we describe a novel Bayesian approach to address this challenge that incorporates the predicted peptide detectabilities as the prior probabilities of peptide identification. Our model removes some unrealistic assumptions used in previous approaches and provides a rigorious probabilistic solution to this problem. We used a complex synthetic protein mixture to test our method, and obtained promising results.