False Discovery Rate Analysis for Glycopeptide Identification
Abstract
Tandem mass spectrometry (MS/MS) is the key technology for glycopeptide identification in high-throughput large-scale glycoproteomics. Estimation of false discovery rates (FDR) is essential for evaluating the quality of the MS/MS-based identification software tools. Although numerous glycopeptide identification tools have been recently proposed, there have been few widely accepted approaches for glycopeptide FDR analysis due to the great structural diversity of glycans. The target-decoy search strategy is currently the most common method for FDR estimation of peptide-spectral matches. In this study, we constructed decoy glycan databases by various methods and compared the FDR from the database search scores produced by each decoy glycan database. Furthermore, we employed a mixture model that facilitates distinguishing between correct and incorrect identifications among the database search score distribution for a better comparison of different decoy glycan database constructions.