Recent advances in glycoinformatic platforms for glycomics and glycoproteomics

•Recent advancements in glycoinformatics tools to support analysis of glycan and glycopeptide data sets are summarised.•Review summarises the growth of machine learning applications to facilitate the prediction of glycosylation sites.•Review provides an update on international efforts to improve data interoperability in the glycosciences. Protein glycosylation is the most complex and prevalent post-translation modification in terms of the number of proteins modified and the diversity generated. To understand the functional roles of glycoproteins it is important to gain an insight into the repertoire of oligosaccharides present. The comparison and relative quantitation of glycoforms combined with site-specific identification and occupancy are necessary steps in this direction. Computational platforms have continued to mature assisting researchers with the interpretation of such glycomics and glycoproteomics data sets, but frequently support dedicated workflows and users rely on the manual interpretation of data to gain insights into the glycoproteome. The growth of site-specific knowledge has also led to the implementation of machine-learning algorithms to predict glycosylation which is now being integrated into glycoproteomics pipelines. This short review describes commercial and open-access databases and software with an emphasis on those that are actively maintained and designed to support current analytical workflows.