Peptide-to-protein distribution versus a competition for significance to estimate error rate in blood protein identification.

The simplest model—that authentic tandem mass spectrometry (MS/MS) spectra are no different from noise, random spectra, or false-positive results—may be directly examined by chi-square comparison of the peptide-to-protein distribution. The peptide-to-protein distribution of a set of 4151 redundant blood proteins identified by X!TANDEM indicated that there is a low probability that the authentic data were the same as noise, random spectra, or false-positive correlations (P<0.0001). In contrast, a competition for significance failed to distinguish approximately 90% of authentic blood proteins from those of noise, random spectra, or false-positive results (P<0.01) and apparently incurred a large type II error (false negative). The chi-square test of peptide-to-protein frequency distributions was found to be an efficient means to distinguish authentic data from false-positive results. Frequency-based statistics unambiguously demonstrated that proteins can be identified by liquid chromatography–electrospray ionization-MS/MS from human blood with acceptable confidence. Thus, the chi-square fit of the peptide-to-protein distribution could distinguish authentic data from random or false-positive data, but the score distribution method could not separate real results from false results.