A Bayesian Approach to Protein Inference Problem in Shotgun Proteomics

  • Yong Fuga Li
  • Randy J. Arnold
  • Yixue Li
  • Predrag Radivojac
  • Quanhu Sheng
  • Haixu Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4955)

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yong Fuga Li
    • 1
  • Randy J. Arnold
    • 2
  • Yixue Li
    • 3
  • Predrag Radivojac
    • 1
  • Quanhu Sheng
    • 1
    • 3
  • Haixu Tang
    • 1
  1. 1.School of InformaticsIndiana UniversityBloomingtonUSA
  2. 2.Department of ChemistryIndiana UniversityBloomingtonUSA
  3. 3.Key Lab of Systems Biology, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina

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