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A Fragmentation Event Model for Peptide Identification by Mass Spectrometry

  • Yu Lin
  • Yantao Qiao
  • Shiwei Sun
  • Chungong Yu
  • Gongjin Dong
  • Dongbo Bu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4955)

Abstract

We present in this paper a novel fragmentation event model for peptide identification by tandem mass spectrometry. Most current peptide identification techniques suffer from the inaccuracies in the predicted theoretical spectrum, which is due to insufficient understanding of the ion generation process, especially the b/y ratio puzzle.

  To overcome this difficulty, we propose a novel fragmentation event model, which is based on the abundance of fragmentation events rather than ion intensities. Experimental results demonstrate that this model helps improve database searching methods. On LTQ data set, when we control the false-positive rate to be 5%, our fragmentation event model has a significantly higher true positive rate (0.83) than SEQUEST (0.73). Comparison with Mascot exhibits similar results, which means that our model can effectively identify the false positive peptide-spectrum pairs reported by SEQUEST and Mascot.

This fragmentation event model can also be used to solve the problem of missing peak encountered by De Novo methods. To our knowledge, this is the first time the fragmentation preference for peptide bonds is used to overcome the missing-peak difficulty.

Availability: http://www.bioinfo.org.cn/MSMS/.

Keywords

True Positive Rate Relative Entropy Tandem Mass Spectrum Theoretical Spectrum Fragmentation Event 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yu Lin
    • 1
  • Yantao Qiao
    • 1
  • Shiwei Sun
    • 1
  • Chungong Yu
    • 1
  • Gongjin Dong
    • 1
  • Dongbo Bu
    • 1
    • 2
  1. 1.Bioinformatics Group, Key Lab of Intelligent Software Systems, Institute of Computing TechnologyChinese Academy of SciencesChina
  2. 2.Bioinformatics LabUniversity of WaterlooOntarioCanada

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