Dagstuhl-Seminar — Computational Proteomics

In proteomics, novel algorithms are drivers of innovation, enhancing extraction of actionable insights from collected data and thereby advancing the field. We have identified three highly interesting computational challenges (and thus opportunities) that have come to the foreground in recent years: First is the application of machine learning in proteomics, which is already contributing to advancements in data analysis and interpretation in areas such as protein identification, functional annotations, and personalized medicine. Second is the rapidly growing field of glycoproteomics, which links to therapeutics as many therapeutic proteins are glycosylated. The focus here will lie on the characterization of the glycan moieties attached to proteins, the analysis of which poses specific computational challenges. Third is a cross-cutting topic at the interface between proteomics and other omics in the context of translational research. This includes both mass spectrometry and new approaches to generate, interrogate, and integrate data related to proteins, their transcripts and related metabolites. These topics are at the forefront of arising technologies and require novel solutions to emerging, highly complex problems.