Areas of expertise
Dr. de Ridder is Principle Investigator / Associate Professor in the Center for Molecular Medicine of the UMC Utrecht. He obtained his Master’s degree in Electrical Engineering from the Delft University of Technology (TUD) in 2005 (cum laude). Subsequently, he started a PhD to work on pathway discovery in insertional mutagenesis data. This work was carried out in the Delft Bioinformatics lab (Reinders group, TUD) and at the Netherlands Cancer Institute (Wessels group). Dr. de Ridder was an Assistant Professor in the Delft Bioinformatics Lab from 2010 to 2016, after which he moved into his current position at the UMC Utrecht.
In the de Ridder lab we create and apply innovative data science methods to advance our understanding of disease biology. Our research efforts are always inspired by a biological question and typically deal with big data, such as large-scale genomics and epigenomics datasets. As a result, much of the research floats on machine learning and data integration algorithms. We also heavily rely on high-performance computing and statistics.
Typical projects of the lab include the integration of genome conformation measurements (4C and Hi-C) with genome-wide screening data, annotation and prioritization of non-coding variations (SVs and SNVs) and interpretable classification models for patient cohort data.
Non-coding variants obtained by whole genome sequencing in pancreatic and colon cancer (obtained from the Cosmic database) overlaid on a human Hi-C network representing the 3D conformational architecture of the genome. Colors indicate variant enrichment. Some focal points of enrichment can be observed pointing to the existence of 3D mutation hotspots specific for pancreatic cancer.
Research program / group
Genome conformation. The genome is not a straight line. We are developing computational strategies to exploit measurements of the genome conformation in the analysis of genomics data. To this end, we build graph-based data integration strategies and exploit large-scale epigenomics datasets. Recently, we have shown that cancer-causing mutations in the mouse genome are co-localized in 3D hotspots and linked to known cancer genes through long-range chromatin interactions . Together with the de Laat lab (Hubrecht Institute), we are working on designing the computational methods to detect multi-way interactions in the 3D genome.
Non-coding mutations. We work on analytical and computational frameworks that lead to fast, cost-efficient and comprehensive detection and annotation of structural variations in cancer genomes. We particularly focus on previously neglected variations occurring in unexplored regions of the cancer genome - the non-coding genome. With these methods we aim to provide an important component in future genome-first-based clinical decision making for cancer patients and drive discovery of novel cancer genes and mechanism from modern day whole genome sequencing data.
Interpretable machine learning. In this research line we aim to unravel biological mechanisms by investigating how and why trained prediction models fit the data. For instance, we create methods to identify robust genesets or pathways that differentiate between breast cancer subtypes or cancer treatment. To this end, we employ machine learning models that can exploit existing biological knowledge, such as network- and pathway-based classifiers .
Data integration methods. To answer modern biological questions often a systems approach is required, wherein multiple genome-wide measurements interrogating multiple biological phenomena need to be integrated. To enable this, we investigate data integration methodologies, in particular those that exploit graphs and graph-mining. For instance, we developed so called scale-aware graph-topological measures  that enable rich descriptions of network architecture and used this to describe DNA-DNA contact maps in the brain .
Group members De Ridder Lab
- Adrien Melquiond, postdoc
- Alexandra Danyi, PhD student
- Amin Allahyar, guest researcher
- Arnold Kuzniar, eScience engineer
- Buys de Barbanson, PhD student
- Flip Mulder, researcher
- Joanna von Berg, PhD student
- Joachim Kutzera, postdoc
- Joanna Wolthuis, PhD student
- Joske Ubels, PhD student
- Liting Chen, PhD student
- Luca Santuari, postdoc
- Marleen Nieboer, PhD student
- Mamun Rashid, external PhD student
- Myrthe Jager, postdoc
- Roy Straver, postdoc
- Sonja Georgievska, eScience engineer
- Stefania Magnusdottir, postdoc
Members UMC Utrecht Bioinformatics Expertise Core (UBEC)
- Ies Nijman, doordinator Bioinformatics Expertice Core
- Jasmin Böhmer, data steward
- Flip Mulder, researcher
- Roy Straver, postdoc
- Tilman Schaefers, bioinformatician
Overview of the FERAL algorithm for network-based outcome prediction in breast cancer
-  Babaei S., et al. , de Ridder J. 3D hotspots of recurrent retroviral insertions reveal long-range interactions with cancer genes. Nature Comm. 2015
-  Babaei, S., et al. , de Ridder J.*, Reinders M.* Multi-scale chromatin interactions are predictive for spatial co-expression patterns in the mouse cortex. PLoS Comp. Biol. 2015
-  Allahyar A., de Ridder J. FERAL: network-based classifier with application to breast cancer outcome prediction. Bioinformatics. 2015
-  Hulsman M., Dimitrakopoulos C., de Ridder J. Scale-space measures for graph topology link protein network architecture to function, Bioinformatics, 2014
-  Akhtar W, et al. de Ridder J, …, van Lohuizen M, van Steensel B. Chromatin Position Effects Assayed by Thousands of Reporters Integrated in Parallel. Cell. 2013