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Probability-based pattern recognition and statistical framework for randomization: modeling tandem mass

Jian Feng1, Daniel Q Naiman, Bret Cooper

  • 1Department of Applied Mathematics and Statistics, The Johns Hopkins University, Baltimore, Maryland, USA.

Bioinformatics (Oxford, England)
|May 19, 2007
PubMed
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This study introduces a new pattern recognition algorithm to create decoy databases for proteomics. This method improves the accuracy of false positive rate estimation in tandem mass spectrum/peptide sequence matching compared to traditional reverse database searching.

Area of Science:

  • Bioinformatics
  • Proteomics

Background:

  • Reverse database searching is standard in proteomics for controlling false matches.
  • However, reversed sequences lack natural patterns, challenging standard search algorithms.

Purpose of the Study:

  • To develop a novel method for estimating false positive rates in proteomics.
  • To improve upon the limitations of reverse database searching.

Main Methods:

  • Designed an unsupervised pattern recognition algorithm to detect patterns in large sequence datasets.
  • Generated decoy databases using Monte Carlo sampling based on detected patterns.
  • Searched decoy databases to predict false positive rates for spectrum/peptide matches.

Main Results:

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  • The new method provides a more accurate estimation of false positive identification rates.
  • This approach is independent of instrumentation, search software, and sample type.
  • Outperforms the conventional reverse database searching method in accuracy.

    • Conclusions:

    • The developed pattern detection algorithm offers a superior alternative for false positive rate estimation in proteomics.
    • The algorithm has potential applications in sequence analysis for biology and cryptology.