Research interests
As a geneticist I am interested in genetic and phenotypic variation between different individuals. I am especially drawn to high-throughput measuring techniques, such as RNA-sequencing, and metabolomics which result in large datasets. These large datasets enable the discovery of complex interactions by computational biology, bioinformatics, statistics, and data visualization. Working at the NIOO, I have become interested in variation in species composition and abundance between different habitats and changes therein. Combining the experience from previous and ongoing work on the genetics of gene expression with the new possibilities of meta-biome sequencing is currently an appealing challenge.

Systems Genetics/eQTLs
Natural variation in transcript abundance can be found in many organisms, including Arabidopsis and Caenorhabditis elegans (C. elegans) and can be causal for the variation in phenotypes observed between individuals of a species. C. elegans is a nematode widely used as a human model species and used to investigate the link between stress resistance and complex human disease. Whereas Arabidopsis is a widely used model plant for crop species, such as Tomato and Lettuce. With the aid of high-throughput measuring techniques such as RNA-sequencing, and populations of Recombinant Inbred Lines (RILs) and Introgression Lines (ILs), gene regulatory networks can be constructed. Moreover, by eQTL mapping and gene expression data from different environments regulatory loci can be identified and placed into context. This can be used to identify major regulators and modifiers of agriculturally important traits or complex human disease.

Network biology
Interactions and responses in biological systems are often complex. Current technologies enable large scale measurements of these on many different levels. Genes can interact or co-respond on transcript or protein level which could subsequently affect metabolite levels. On a larger scale, organisms, such as bacteria, fungi, plants, and animals interact and respond to each other. To help understand these relations different levels need integration. One way of finding patterns and clusters in multi leveled investigations is network visualizations, such as co-expression, species co-occurrence or species co-response networks. These networks can show large scale complex biological patterns and pinpoint specific future steps of attention.