Peptizer, a tool for assessing false positive peptide identifications and manually validating selected results.
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.
Helsens
Kenny
K
Department of Medical Protein Research, VIB, B-9000 Ghent, Belgium.
Timmerman
Evy
E
Vandekerckhove
Joël
J
Gevaert
Kris
K
Martens
Lennart
L
D001331:Automation; D005189:False Positive Reactions; D006801:Humans; D020014:K562 Cells; D010455:Peptides / Q000032:analysis*; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D012984:Software; D014584:User-Computer Interface
D010455:Peptides