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Molecular & Cellular Proteomics 7:2364-2372, 2008.
© 2008 by The American Society for Biochemistry and Molecular Biology, Inc.

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Research

Peptizer, a Tool for Assessing False Positive Peptide Identifications and Manually Validating Selected Results^*,S

Kenny Helsens^,,¶, Evy Timmerman^,, Joël Vandekerckhove^,, Kris Gevaert^,,|| and Lennart Martens^,,**,

From the Department of Medical Protein Research, VIB, B-9000 Ghent, Belgium, Department of Biochemistry, Ghent University, B-9000 Ghent, Belgium, and ^** European Molecular Biology Laboratory Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom

False positive peptide identifications are a major concern in the field of peptidecentric, mass spectrometry-driven gel-free proteomics. They occur in regions where the score distributions of true positives and true negatives overlap. Removal of these false positive identifications necessarily involves a trade-off between sensitivity and specificity. Existing postprocessing tools typically rely on a fixed or semifixed set of assumptions in their attempts to optimize both the sensitivity and the specificity of peptide and protein identification using MS/MS spectra. Because of the expanding diversity in available proteomics technologies, however, these postprocessing tools often struggle to adapt to emerging technology-specific peculiarity. Here we present a novel tool named Peptizer that solves this adaptability issue by making use of pluggable assumptions. This research-oriented postprocessing tool also includes a graphical user interface to perform efficient manual validation of suspect identifications for optimal sensitivity recovery. Peptizer is open source software under the Apache2 license and is written in Java.

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