GlycoSLASH: Concurrent Glycopeptide Identification from Multiple Related LC-MS/MS Data Sets by Using Spectral Clustering and Library Searching

Liquid chromatography coupled with tandem mass spectrometry is commonly adopted in large-scale glycoproteomic studies involving hundreds of disease and control samples. The software for glycopeptide identification in such data (e.g., the commercial software Byonic) analyzes the individual data set and does not exploit the redundant spectra of glycopeptides presented in the related data sets. Herein, we present a novel concurrent approach for glycopeptide identification in multiple related glycoproteomic data sets by using spectral clustering and spectral library searching. The evaluation on two large-scale glycoproteomic data sets showed that the concurrent approach can identify 105%-224% more spectra as glycopeptides compared to the glycopeptide identification on individual data sets using Byonic alone. The improvement of glycopeptide identification also enabled the discovery of several potential biomarkers of protein glycosylations in hepatocellular carcinoma patients.

Automated summary

Liquid chromatography coupled with tandem mass spectrometry is commonly used in large-scale glycoproteomic studies. A novel concurrent approach for glycopeptide identification in multiple related data sets is presented using spectral clustering and spectral library searching. Evaluation on two large-scale data sets showed that this approach can identify 105%-224% more spectra as glycopeptides compared to using Byonic alone, enabling the discovery of potential biomarkers in hepatocellular carcinoma.