Daniel A. Polasky

The paper evaluates photon- and electron-based fragmentation methods for glycopeptide analysis using Omnitrap-LCMS, finding UVPD and EID provide detailed structural information comparable to traditional methods.

This paper introduces MBG, a method to expand glycopeptide identification in glycoproteomics by leveraging MS1 signals from displaced glycopeptides, improving detection of low-abundant and modified glycans.

The paper introduces a new detailed mass offset search method in MSFragger to improve the interpretation of complex post-translational modifications.

The paper introduces a computational workflow for faster and more sensitive analysis of FPOP data, achieving 10-fold speed improvement and 50% more modified peptide spectra identification.

MSFragger-Labile improves the analysis of labile post-translational modifications by incorporating modification-specific fragment ions into mass spectrometry searches.

The paper develops a method to automatically detect diagnostic spectral features for post-translational modifications in mass spectrometry data, improving PTM identification and localization rates.

The paper introduces a method to accurately identify glycan compositions in glycopeptide spectra and control false discovery rates, integrating it into a computational pipeline for glycoproteomics analysis.

MSFragger-Glyco improves glycopeptide identification in N- and O-glycoproteomics data, increasing glycoPSMs and revealing new glycan trends.

The paper introduces MSFragger and IonQuant for fast analysis of timsTOF PASEF data, showing improved peptide identification and quantification compared to existing tools.