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Objective: Analyze how precursor and fragment mass tolerance affect the number of true positives and false positives. Introduction: Mass spectrometry coupled to database searching is a powerful and popular protein identification tool. A typical shotgun proteomics experiment begins with degrading intact proteins into peptides. The peptide mixture then undergoes LC-MS/MS analysis, and the resulting experimental spectra are compared to theoretical spectra derived from protein, cDNA, or EST databases. Successful database searching is dependent on database size, post-translational modifications, and precursor and fragment ion m/z tolerance. Method: A standard protein set was made containing 62 verified T. cruzi recombinant proteins spiked into an E. coli lysate. This mixture was digested then analyzed by LC-MS/MS using an LTQ-Orbitrap. Resulting spectra were searched against forward, reverse, and concatenated databases using Sequest, Mascot, and X!Tandem. Peptide probabilities were calculated using ProteinProphet, and peptide false discovery rates (FDR's) were calculated by using ProteoIQ. It is necessary to use a standardized protein mixture to determine the number of true positives (T. cruzi proteins) and false positives (random proteins) found as a function of m/z search tolerance. Preliminary Results: At a 95% probability, more true positives are discovered as ion precursor mass accuracy is increased; however, more false positives are also discovered and at a higher rate. For example, as mass accuracy is increased from +/−1000ppm to +/−20ppm, the number of spectra corresponding to true positives increases by 50% while the number for false positives increases by 380%. Using a 5% FDR filter with the same mass accuracy change yields a 37% increase in true positive matches, while leaving the number of false positives unchanged. Conclusions: FDR filtering can result in more successful data validation than probability filtering when performing high resolution mass spectrometry.