Assigning significance to peptides identified by tandem mass spectrometry using decoy databases


Automated methods for assigning peptides to observed tandem mass spectra typically return a list of peptide−spectrum matches, ranked according to an arbitrary score. In this article, we describe methods for converting these arbitrary scores into more useful statistical significance measures. These methods employ a decoy sequence database as a model of the null hypothesis, and use false discovery rate (FDR) analysis to correct for multiple testing. We first describe a simple FDR inference method and then describe how estimating and taking into account the percentage of incorrectly identified spectra in the entire data set can lead to increased statistical power.

Journal of Proteome Research, 7 (1): 29–34