Linking Your Research Question to Relevant Metabolite Features in Untargeted Metabolomics Profiles

Asst. Prof Justin J.J. van der Hooft
Computational Metabolomics at Bioinformatics Group at Wageningen University
Part of the
Benelux Webinar: “Metabolomics - how to make the most of your data”
Title
Linking your research question to relevant metabolite features in untargeted metabolomics profiling
Abstract
Metabolomics has often been coined as the ultimate phenotyping tool. Indeed, a full understanding of chemical profiles of complex biological extracts would lead to a deeper biochemical understanding of many biological processes. Technological advances made analytical chemical analysis more sensitive than ever before; however, as a result, prioritising the relevant metabolite features that link to your research question becomes increasingly difficult.
In this presentation, I will highlight recent advances in computational metabolomics made by my group and others that use networking and machine learning strategies to overcome this challenge. I will introduce FERMO that links phenotypic read-outs to metabolomics profiles in an automatic and reproducible manner to allow for bioactivity-based prediction. Furthermore, I will introduce msFeaST, that combines statistical analyses with feature sets constructed based on machine learning-based embeddings. As both these approaches make use of the experimental design and available metadata thereof, they allow the user to link their research question with the data during the processing and analysis steps. Hence, untargeted metabolomics data analysis will become easier as more time can be spent on the relevant metabolite features.
I will finish off with a call to action: sharing is caring! I expect that the presented methodological developments will advance our understanding of the role of metabolites and their complex molecular interactions that underpin growth, development, and health.
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