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Bioinformatics Advance Access published online on November 22, 2006
Bioinformatics, 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
Received October 11, 2006
Revised November 17, 2006
Accepted November 20, 2006

Discovery note

A predictive model for identifying proteins by a single peptide match

Roger Higdon 1 and Eugene Kolker 2 *

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

* To whom correspondence should be addressed.
Eugene Kolker, E-mail: ekolker{at}biatech.org


  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, two or more 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%-to-98% of the correct single-hit proteins with an error rate of less than 2%. This results in a 22%-to-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.

Associate Editor: Alfonso Valencia
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