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Bioinformatics Advance Access published online on May 17, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm267
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

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, Maryland, USA
2Soybean Genomics and Improvement Laboratory, USDA-ARS, Beltsville, Maryland, USA

*To whom correspondence should be addressed. Bret Cooper, E-mail: cooperb{at}ba.ars.usda.gov


  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.

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

Associate Editor: Dr. Limsoon Wong

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

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