GlycoDash: automated, visually assisted curation of glycoproteomics datasets for large sample numbers

The challenge of robust and automated glycopeptide quantitation from liquid chromatography-mass spectrometry (LC-MS) data has yet to be adequately addressed by commercial software. Recently, open-source tools like Skyline and LaCyTools have advanced the field of label-free MS¹ level quantitation. Yet, important steps late in the data processing workflow remain manual. Because manual data curation is time-consuming and error-prone, it presents a bottleneck, especially in an era of emerging high-throughput methodologies and increasingly complex analyses such as antigen-specific antibody glycosylation. We addressed this gap by developing GlycoDash, an R Shiny-based interactive web application designed to democratize label-free high-throughput glycoproteomics data analysis. The software comes in at a stage where analytes have been identified and quantified, but whole measurement and individual analyte signals of insufficient quality for quantitation remain and reduce the quality of the overall dataset. GlycoDash focuses on these challenges by incorporating several options for measurement and metadata linking, spectral and analyte curation, normalization, and repeatability assessment, and additionally includes glycosylation trait calculation, data visualization, and reporting capabilities that adhere to FAIR principles. The performance and versatility of GlycoDash were demonstrated across antibody glycoproteomics data of increasing complexity, ranging from relatively simple monoclonal antibody glycosylation analysis to a clinical cohort with over a thousand measurements. In a matter of hours, these large, diverse, and complex datasets were curated and explored. High-quality datasets with integrated metadata ready for final analysis and visualization were obtained. Critical aspects of the curation strategy underlying GlycoDash are discussed. GlycoDash effectively automates and streamlines the curation of glycopeptide quantitation data, addressing a critical need for high-throughput glycoproteomics data analysis. Its robust performance across diverse datasets and its comprehensive feature toolbox significantly enhance both research and clinical applications in glycoproteomics.