2026-10-14T13:35:37Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34046307 2026-10-01T14:21:47Z category_4 category_146 category_7 category_12 category_873 category_45 category_734 category_931 category_132 portal_63 item_type_6 month_year_10_2026

Benchmarking Glycoproteomics Software Using Diagnostic Ion Based Post-Validation for Glycopeptide Identification Hiroaki Sakaue (1694350) Kunio Kawanishi (24554681) Azusa Tomioka (1364766) Chiaki Nagai-Okatani (2818309) Hiroyuki Kaji (505920) Atsushi Kuno (7003) Biochemistry Space Science Medicine Cell Biology Chemical Sciences not elsewhere classified Sociology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Infectious Diseases warranted cautious interpretation sialic acid synthesis rare glycan structures program enabled comprehensive identical data sets highly branched glycans grable ), investigating compared five state improved glycopeptide identification glycopeptide identification advancements diagnostic oxonium ions specific criteria based identify rare neugc grable identified complex glycopeptide identifications even combining software programs y1 ions provides glycopeptide identification specific programs based filtering weaker evidence spectral evidence robustly supported reliable analysis limiting enzyme lack udp k20 cells individual tools independent spectrum identifications retained glycoproteomics software complementary strengths approximately one acetylglucosamine 2 Advancements in glycoproteomics software have improved glycopeptide identification. However, algorithmic differences cause discrepancies in glycopeptide identifications even when identical data sets are used. We compared five state-of-the-art glycoproteomics software programs (Byonic, MSFragger-Glyco, pGlyco3, Glyco-Decipher, and GRable), investigating their ability to identify rare NeuGc- and KDN-containing glycopeptides from BJAB-K20 cells, which lack UDP-N-acetylglucosamine 2-epimerase, the rate-limiting enzyme for sialic acid synthesis. Approximately one-third of the identified glycopeptides were unique to individual tools. Byonic identified the most glycopeptides, whereas Glyco-Decipher and GRable identified complex, highly branched glycans. NeuGc- and KDN-containing glycopeptides were identified by specific programs, highlighting their ability to handle rare glycan structures. To assess the reliability of these identifications, we reanalyzed the MS/MS spectra and stratified all identifications retained under the software-specific criteria based on spectral evidence from diagnostic oxonium ions and Y0/Y1 or b/y ions. Although most were robustly supported, some relied on weaker evidence and warranted cautious interpretation. Leveraging the capabilities of each program enabled comprehensive, reliable analysis of glycopeptides with rare glycan structures. Combining software programs with complementary strengths and systematic postvalidation using diagnostic oxonium ions and Y0/Y1 ions provides an independent spectrum-level confidence assessment beyond conventional FDR-based filtering, improving the reliability and depth of glycopeptide identification. 2026-10-01T00:00:00Z Text Journal contribution 10.1021/acs.jproteome.6c00345.s001 https://figshare.com/articles/journal_contribution/Benchmarking_Glycoproteomics_Software_Using_Diagnostic_Ion_Based_Post-Validation_for_Glycopeptide_Identification/34046307 CC BY-NC 4.0