Integrated data management and validation platform for phosphorylated tandem mass spectrometry data.

TitleIntegrated data management and validation platform for phosphorylated tandem mass spectrometry data.
Publication TypeJournal Article
Year of Publication2010
AuthorsLahesmaa-Korpinen, A-M, Carlson, SM, White, FM, Hautaniemi, S
Date Published2010 Oct
KeywordsAlgorithms, Artificial Intelligence, Databases, Protein, Phosphopeptides, Phosphorylation, Proteome, Proteomics, Tandem Mass Spectrometry

MS/MS is a widely used method for proteome-wide analysis of protein expression and PTMs. The thousands of MS/MS spectra produced from a single experiment pose a major challenge for downstream analysis. Standard programs, such as MASCOT, provide peptide assignments for many of the spectra, including identification of PTM sites, but these results are plagued by false-positive identifications. In phosphoproteomic experiments, only a single peptide assignment is typically available to support identification of each phosphorylation site, and hence minimizing false positives is critical. Thus, tedious manual validation is often required to increase confidence in the spectral assignments. We have developed phoMSVal, an open-source platform for managing MS/MS data and automatically validating identified phosphopeptides. We tested five classification algorithms with 17 extracted features to separate correct peptide assignments from incorrect ones using over 2600 manually curated spectra. The naïve Bayes algorithm was among the best classifiers with an AUC value of 97% and PPV of 97% for phosphotyrosine data. This classifier required only three features to achieve a 76% decrease in false positives as compared with MASCOT while retaining 97% of true positives. This algorithm was able to classify an independent phosphoserine/threonine data set with AUC value of 93% and PPV of 91%, demonstrating the applicability of this method for all types of phospho-MS/MS data. PhoMSVal is available at

Alternate JournalProteomics
PubMed ID20827731
PubMed Central IDPMC3017393
Grant ListR01 CA118705-02 / CA / NCI NIH HHS / United States
R01-CA118705 / CA / NCI NIH HHS / United States
U54-CA11297 / CA / NCI NIH HHS / United States