LuciPHOr: Algorithm for Phosphorylation Site Localization with False Localization Rate Estimation Using Modified Target-Decoy Approach. Fermin, D., Walmsley, S. J., Gingras, A., Choi, H., & Nesvizhskii, A. I. MOLECULAR & CELLULAR PROTEOMICS, 12(11):3409-3419, NOV, 2013.
doi  abstract   bibtex   
The localization of phosphorylation sites in peptide sequences is a challenging problem in large-scale phosphoproteomics analysis. The intense neutral loss peaks and the coexistence of multiple serine/threonine and/or tyrosine residues are limiting factors for objectively scoring site patterns across thousands of peptides. Various computational approaches for phosphorylation site localization have been proposed, including Ascore, Mascot Delta score, and ProteinProspector, yet few address direct estimation of the false localization rate (FLR) in each experiment. Here we propose LuciPHOr, a modified target-decoy-based approach that uses mass accuracy and peak intensities for site localization scoring and FLR estimation. Accurate estimation of the FLR is a difficult task at the individual-site level because the degree of uncertainty in localization varies significantly across different peptides. LuciPHOr carries out simultaneous localization on all candidate sites in each peptide and estimates the FLR based on the target-decoy framework, where decoy phosphopeptides generated by placing artificial phosphorylation(s) on non-candidate residues compete with the non-decoy phosphopeptides. LuciPHOr also reports approximate site-level confidence scores for all candidate sites as a means to localize additional sites from multiphosphorylated peptides in which localization can be partially achieved. Unlike the existing tools, LuciPHOr is compatible with any search engine output processed through the Trans-Proteomic Pipeline. We evaluated the performance of LuciPHOr in terms of the sensitivity and accuracy of FLR estimates using two synthetic phosphopeptide libraries and a phosphoproteomic dataset generated from complex mouse brain samples.
@article{ ISI:000328816000032,
Author = {Fermin, Damian and Walmsley, Scott J. and Gingras, Anne-Claude and Choi,
   Hyungwon and Nesvizhskii, Alexey I.},
Title = {{LuciPHOr: Algorithm for Phosphorylation Site Localization with False
   Localization Rate Estimation Using Modified Target-Decoy Approach}},
Journal = {{MOLECULAR & CELLULAR PROTEOMICS}},
Year = {{2013}},
Volume = {{12}},
Number = {{11}},
Pages = {{3409-3419}},
Month = {{NOV}},
Abstract = {{The localization of phosphorylation sites in peptide sequences is a
   challenging problem in large-scale phosphoproteomics analysis. The
   intense neutral loss peaks and the coexistence of multiple
   serine/threonine and/or tyrosine residues are limiting factors for
   objectively scoring site patterns across thousands of peptides. Various
   computational approaches for phosphorylation site localization have been
   proposed, including Ascore, Mascot Delta score, and ProteinProspector,
   yet few address direct estimation of the false localization rate (FLR)
   in each experiment. Here we propose LuciPHOr, a modified
   target-decoy-based approach that uses mass accuracy and peak intensities
   for site localization scoring and FLR estimation. Accurate estimation of
   the FLR is a difficult task at the individual-site level because the
   degree of uncertainty in localization varies significantly across
   different peptides. LuciPHOr carries out simultaneous localization on
   all candidate sites in each peptide and estimates the FLR based on the
   target-decoy framework, where decoy phosphopeptides generated by placing
   artificial phosphorylation(s) on non-candidate residues compete with the
   non-decoy phosphopeptides. LuciPHOr also reports approximate site-level
   confidence scores for all candidate sites as a means to localize
   additional sites from multiphosphorylated peptides in which localization
   can be partially achieved. Unlike the existing tools, LuciPHOr is
   compatible with any search engine output processed through the
   Trans-Proteomic Pipeline. We evaluated the performance of LuciPHOr in
   terms of the sensitivity and accuracy of FLR estimates using two
   synthetic phosphopeptide libraries and a phosphoproteomic dataset
   generated from complex mouse brain samples.}},
DOI = {{10.1074/mcp.M113.028928}},
ISSN = {{1535-9476}},
EISSN = {{1535-9484}},
ResearcherID-Numbers = {{Nesvizhskii, Alexey/A-5410-2012
   Gingras, Anne-Claude/E-9982-2010}},
ORCID-Numbers = {{Gingras, Anne-Claude/0000-0002-6090-4437}},
Unique-ID = {{ISI:000328816000032}},
}
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