Research interests

Overall goal
The development of a dedicated bio-informatics platform to structure results of high-throughput experiments directed to pathway identification. This includes the testing of such a platform and providing a user-friendly solution to research groups identifying cellular pathways.
Reaching this goal
R2 is our in house developed web-based bioinformatics platform that enables graph-based analysis of probable targets in the context of (tumor) microarray data (url: Specifically focusing at biologists, R2 aids in the analysis and visualization of microarray data (tumor series, experiments) and their annotated features (such as clinical information). R2 contains an expanding set of analyses which are heavily inter-connected, allowing users to quickly hop from one view (representation of the data) to another. Currently R2 supports all major chip designs for human, mouse and rat and harbors nearly 30.000 microarray samples (divided over more than 250 datasets). Through integration with Cytoscape, the resulting gene lists of this data analysis can be visualized directly as a network.
This provides access to a wealth of plugins to further functionally the network and derive new biologically testable hypotheses. Specifically the causal nature of time-series gene manipulation experiments can be explored in this way.
Public use of the project results
The main research-objective of our group is the fast translation of probable drug targets for childhood tumors from the wet-lab to the clinic. The tools developed are used to determine these targets in a pathway context.


Bioinformatics, Networks, High-throughput data analysis

Research output

  1. Defects in 8-oxo-guanine repair pathway cause high frequency of C > A substitutions in neuroblastoma

    Research output: Contribution to journalArticleAcademicpeer-review

  2. A cancer drug atlas enables synergistic targeting of independent drug vulnerabilities

    Research output: Contribution to journalArticleAcademicpeer-review

  3. The Cytoscape Automation app article collection [version 1; referees: Not peer reviewed]

    Research output: Contribution to journalEditorialAcademicpeer-review

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