Abstract
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.
Highlights
False positive peptide identifications are a major concern in the field of peptidecentric, mass spectrometry-driven gel-free proteomics
The most commonly applied method is based on sequence database searching by database search engines such as SEQUEST [4], Mascot [5], X!Tandem [6], Virtual Expert Mass Spectrometrist [7], or Open Mass Spectrometry Search Algorithm [8]
Peptizer was developed as a postprocessing tool aimed at separating true and false positive peptide identifications in a highly configurable manner without relying on any built-in assumptions
Summary
MS/MS Data—The MS/MS spectra used in this study have been published previously [24]. Full experimental details are provided in the supplemental information. Fractions of 4 min wide were collected and treated with 2,4,6-trinitrobenzenesulfonic acid Such modified primary fractions were loaded for the secondary COFRADIC run wherein the ␣-amino-blocked peptides, which show no altered chromatographic properties, are collected. To estimate the false positive distribution we performed Mascot searches against a concatenated decoy database as described previously [29]. The aggregator used summed all votes together and marked the peptide identification as suspicious if the result was equal to or greater than 2 (or when an agent with veto rights declines)
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