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Bioinformatics Advance Access originally published online on May 17, 2007
Bioinformatics 2007 23(17):2210-2217; doi:10.1093/bioinformatics/btm267
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Probability-based pattern recognition and statistical framework for randomization: modeling tandem mass spectrum/peptide sequence false match frequencies

Jian Feng 1, Daniel Q. Naiman 1 and Bret Cooper 2,*

1Department of Applied Mathematics and Statistics, The Johns Hopkins University, Baltimore, and 2Soybean Genomics and Improvement Laboratory, USDA-ARS, Beltsville, Maryland, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: In proteomics, reverse database searching is used to control the false match frequency for tandem mass spectrum/peptide sequence matches, but reversal creates sequences devoid of patterns that usually challenge database-search software.

Results: We designed an unsupervised pattern recognition algorithm for detecting patterns with various lengths from large sequence datasets. The patterns found in a protein sequence database were used to create decoy databases using a Monte Carlo sampling algorithm. Searching these decoy databases led to the prediction of false positive rates for spectrum/peptide sequence matches. We show examples where this method, independent of instrumentation, database-search software and samples, provides better estimation of false positive identification rates than a prevailing reverse database searching method. The pattern detection algorithm can also be used to analyze sequences for other purposes in biology or cryptology.

Availability: On request from the authors.

Contact: Bret.Cooper{at}ars.usda.gov

Supplementary information: http://bioinformatics.psb.ugent.be/

Associate Editor: Limsoon Wong

Received on March 6, 2007; revised on April 9, 2007; accepted on May 9, 2007

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