Automatic validation of phosphopeptide identifications from tandem mass spectra.

ABSTRACT: We developed and compared two approaches for automated validation of phosphopeptide tandem mass spectra identified using database searching algorithms. Phosphopeptide identifications were obtained through SEQUEST searches of a protein database appended with its decoy (reversed sequences). Statistical evaluation and iterative searches were employed to create a high-quality data set of phosphopeptides. Automation of postsearch validation was approached by two different strategies. By using statistical multiple testing, we calculate a p value for each tentative peptide phosphorylation. In a second method, we use a support vector machine (SVM; a machine learning algorithm) binary classifier to predict whether a tentative peptide phosphorylation is true. We show good agreement (85%) between pos

SUBMITTER: Lu B 

PROVIDER: S-EPMC2527591 | biostudies-literature | 2007 Feb

REPOSITORIES: biostudies-literature