Here we describe a set of enhanced data processing and filtering methods to 
improve significance and coverage of phosphopeptide identifications by mass 
spectrometry. We demonstrate that for samples of limited complexity, 
spectra-based estimation of false discovery rates will lead to overprediction of 
confidently identified phosphorylated peptides due to a bias caused by multiple 
fragmentation of highly abundant peptide species. We further provide evidence 
that fragmentation of abundant peptides at the tails of their chromatographic 
peaks is a major source for false positive peptide matches and that overall 
confidence in phosphopeptide identifications can be improved by a 
chromatographic peak-based aggregation scheme, intensity rank-based neutral loss 
and optimized mass error filters. When replicate runs of a standard sample were 
performed using different fragmentation techniques on an Orbitrap mass 
spectrometer we observed improvements of 7-31% in phosphopeptide coverage 
depending on the fragmentation method and the desired false discovery rate.