dr. JJJ (Justin) van der Hooft

After obtaining his MSc in Molecular Sciences at Wageningen University, Justin took up a PhD position in analytical biochemistry where he developed methods for systematic metabolite identification and annotation with both mass spectrometry and NMR techniques. In particular, he focused on mass spectrometry fragmentation approaches to gather more structural information on measured molecules. In 2012 Justin obtained his PhD and he then moved to Glasgow where he worked on several postdoc projects including epicatechin bioavailability, drug screening, and bacterial metabolomics. It was also in Glasgow where he set up a collaboration with Dr Simon Rogers and the concept of MS2LDA to discover substructures in tandem mass spectrometry data was borne. In September 2017 Justin moved back to Wageningen to start on a joint postdoc between Prof. Pieter Dorrestein’s group at UCSD and Dr Marnix Medema’s group at WUR with the focus on combining genome and metabolome mining. In May 2018, he started on a 3-year grant from the Netherlands eScience Center that further expands the work on metabolome mining with the aim to accelerate deciphering of complex metabolic mixtures as well as the combination with genome mining for structural annotation of natural products.

During this time, Justin has been an active member of the Metabolomics Society. He was part of the founding Early-Careers Members Network (EMN) committee and chaired the committee in the lead-up to Metabolomics2016 in Dublin. Furthermore, Justin was elected into the Board of Directors during 2016 - 2020. He is still part of the Strategy Task Group and the Metabolite Identification Task Group – something which is close to his heart.

In 2020, Justin started to build his own group as assistant professor in computational metabolomics in Wageningen. His research vision is to close the gap between what we can see in metabolomics and what we can actually learn from it. 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? He will develop computational metabolomics approaches inspired by two other fields - that of natural language processing (NLP) and genomics. For example, he has pioneered the use of topic modeling and word embedding NLP algorithms to discover substructures and structural relationships in metabolomics profiles. He will use the plant root microbiome and human food metabolome as prime applications since they represent complex metabolite mixtures full of yet unknown metabolic matter that once elucidated will boost our insights in molecular mechanisms underpinning the regulation of growth, development, and health.

Core qualities: motivated, communicative, analytical thinking, and project-oriented.

Core skills: spectroscopist, data analysis, mass spectrometry fragmentation, structural elucidation, metabolomics.

Links:

iOMEGA project: https://github.com/iomega

MS2LDA tool: http://www.ms2lda.org

matchms package: https://github.com/matchms/matchms