In our group, we develop approaches and tools to support structural and functional annotation in untargeted metabolomics workflows. To do this together in a responsible and respective way, we held the following shared team values in high regard: open-mindedness, pragmatism, and collegiality. Doing so, we strive to create the environment and atmosphere in which each team member can equally contribute and commit in a fair manner, and where we feel accountable for our contributions. We will encourage each group member to develop at their own pace and regularly flourish. As a group, we support, encourage, and apply open science following the FAIR principles as much as possible. This applies both to the data we use and the scripts and code we produce.

We aim to create tools that will enable biochemical interpretation of spectral data. This will enable biochemical interpretation of spectral data obtained from complex metabolite mixtures through structural and functional annotations. This will depend on finding out: i) which structural information is encoded in metabolomics data; ii) how novel chemistry can be recognised in spectral data, and iii) how to effectively identify relevant metabolite groups in metabolomics profiles of complex metabolite mixtures?

The Van der Hooft Group develops computational metabolomics approaches inspired by two other fields - that of natural language processing (NLP) and genomics. For example, Justin has pioneered the use of topic modeling and word embedding NLP algorithms to discover substructures and structural relationships in metabolomics profiles.