Research
Research
The field of mass spectrometry continues to evolve at a rapid pace, and as it does so, it increasingly takes on aspects of data science alongside analytics. Driven by methodological changes that become ever more application-oriented, mass spectrometry has moved beyond the traditional aim to simply catalogue analytes in a sample. As a result, the field is in constant need of advanced informatics approaches to make sense of the acquired data. Indeed, these informatics solutions have become such an essential part of this field that it finds itself frequently limited by available algorithms and computational solutions, rather than by available instruments or samples.
The CompOmics group therefore set itself the goal to address this fundamental issue, by seeking out the most challenging areas of mass spectrometry data analysis, and providing cutting edge algorithms and machine learning models to tackle these challenges.
This overall focus has led us to pioneer fast and accurate machine learning based models to predict analyte behaviour, not only in the mass spectrometer but also in the upstream chromatography and sample preparation. We are in turn using these highly performant models to create entirely new approaches to solve long-standing issues, which include the large-scale reprocessing of extremely large amounts of public proteomics data, to extract new knowledge from these existing data, and to explore the reliable detection of complex analytes in samples, such as modified peptides in proteomics data, and to provide optimized solutions for a range of metabolite analytics challenges.
The CompOmics group is also dedicated to disseminating its research as widely as possible, which has led to the development of a large suite of production-grade software tools and online knowledgebases, which can be found on GitHub.