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Bioinformatics Advance Access originally published online on November 22, 2006
Bioinformatics 2007 23(3):277-280; doi:10.1093/bioinformatics/btl595
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A predictive model for identifying proteins by a single peptide match

Roger Higdon 1 and Eugene Kolker 1,2,*

1 The BIATECH Institute, Bothell WA 98011, USA
2 Division of Biomedical and Health Informatics, University of Washington Seattle, WA 98195, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Tandem mass-spectrometry of trypsin digests, followed by database searching, is one of the most popular approaches in high-throughput proteomics studies. Peptides are considered identified if they pass certain scoring thresholds. To avoid false positive protein identification, ≥2 unique peptides identified within a single protein are generally recommended. Still, in a typical high-throughput experiment, hundreds of proteins are identified only by a single peptide. We introduce here a method for distinguishing between true and false identifications among single-hit proteins. The approach is based on randomized database searching and usage of logistic regression models with cross-validation. This approach is implemented to analyze three bacterial samples enabling recovery 68–98% of the correct single-hit proteins with an error rate of <2%. This results in a 22–65% increase in number of identified proteins. Identifying true single-hit proteins will lead to discovering many crucial regulators, biomarkers and other low abundance proteins.

Contact: ekolker{at}biatech.org

Supplementary information: Supplementary Data are available at Bioinformatics online.

Associate Editor: Alfonso Valencia

Received on October 11, 2006; revised on November 17, 2006; accepted on November 20, 2006

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