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.