Mekelweg 4, 2628 CD Delft
Prof. dr. ir. M.J.T. Reinders
The Delft Bioinformatics Lab develops innovative models and computer algorithms to further fundamental biological knowledge, and applies these to advance the state-of-the-art in health care and industry. To this end, we closely collaborate with researchers in molecular and cell biology, medicine and microbial systems biology. We focus on data-driven bioinformatics: creating algorithms to infer and exploit simple models of complex interactions, by coupling biological insights and available prior knowledge to high-throughput measurements. Our core expertise is in statistics, optimisation, machine learning and pattern recognition. Major application areas are in systems biology, modeling life at the molecular level; in medicine, trying to understand the biology underlying cancer and aging; and in biotechnology, engineering industrially relevant microorganisms.
- Human / clinical genetics: a.o. applications in human aging, Alzheimer and prenatal screening
- Cancer genomics in mouse and human;Industrial biotechnology: a.o. applications in fungi such as yeast (Saccharomyces cerevisiae), Aspergillus niger and mushrooms
- Fundamental cell biology
- Molecular imaging
- Microbial genomics
Expertise and Track Record
4350098241 Tracking the trigger of rheumotoid arthritis development through microbial DNA in blood with dr. F. Kurreeman (LUMC).
Our uniqueness lies in that we are strong in developing novel algorithms to analyze complex genomic data.
In this paper we sequenced, assembled, annotated and analyzed the genome of Saccharomyces cerevisiae CEN.PK 113-7D which is widely used for metabolic engineering and systems biology research in industry and academia. In addition, single-nucleotide variations (SNV), insertions/deletions (indels) and differences in genome organization compared to the reference strain S. cerevisiae S288C were analyzed.  An algorithm-based topographical biomaterials library to instruct cell fate. Unadkat HV, Hulsman M, Cornelissen K, Papenburg BJ, Truckenmüller RK, Carpenter AE, Wessling M, Post GF, Uetz M, Reinders MJ, Stamatialis D, van Blitterswijk CA, de Boer J. Proc Natl Acad Science U S A. 2011 Oct 4;108(40):16565-70.
This paper studies the effects of material properties on biological performance. In particular it introduces a high throughput device to perform these materiomics measurents. We have done the analyses of these high-throughput measurements of interactions of living cells with nano-scale structures.  Constitutive nuclear lamina-genome interactions are highly conserved and associated with A/T-rich sequence. Meuleman W, Peric-Hupkes D, Kind J, Beaudry JB, Pagie L, Kellis M, Reinders M, Wessels L, van Steensel B. Genome Research 2013 Feb;23(2):270-80.
In this paper we show how one can gain insight in the organization within the cell from measured genome-nuclear lamina interactions using thorough bioinformatics analyses. In particular, it reveals that these interactions are highly conserved.
- Detecting recurrent gene mutation in interaction network context using multi-scale graph diffusion. Babaei S, Hulsman M, Reinders M, de Ridder J. BMC Bioinformatics. 2013 Jan 23;14:29. doi: 10.1186/1471-2105-14-29. In this paper, we show how to identify pathways of frequently mutated genes by exploiting their network neighborhood encoded in the protein-protein interaction network. We introduce a multi-scale diffusion kernel and apply it to a large collection of murine retroviral insertional mutagenesis data. DAGIC (Detecting Aberrant Genes in Interaction Context) is available for download at: http://bioinformatics.tudelft.nl/users/sepideh-babaei
- De novo detection of copy number variation by co-assembly. Nijkamp JF, van den Broek MA, Geertman JM, Reinders MJ, Daran JM, de Ridder D. Bioinformatics. 2012 Dec 15;28(24):3195-202. doi: 10.1093/bioinformatics/bts601. Epub 2012 Oct 9. In this paper we introduce Magnolya which estimates copy number by direct comparison of individual genomes by de novo assembly, rather than through a reference genome. By co-assembly multiple sequencing samples into a 'coloured' graph, the contigs have integer copy numbers, thereby avoiding the need to to segment genomic regions based on depth of coverage. Magnolya is available for download at http://bioinformatics.tudelft.nl/magnolya.
- Inferring combinatorial association logic networks in multimodal genome-wide screens. de Ridder J, Gerrits A, Bot J, de Haan G, Reinders M, Wessels L. Bioinformatics. 2010 Jun 15;26(12):i149-57. In this paper we propose an efficient method to infer combinatorial association logic networks from multiple genome-wide measurements from the same sample. We demonstrate our method on a genetical genomics dataset, in which we search for Boolean combinations of multiple genetic loci that associate with transcript levels. The MATLAB code of the prototype implementation is available upon request (http://bioinformatics.tudelft.nl/)
- Ibidas: Querying flexible data structures to explore heterogeneous bioinformatics data . Marc Hulsman, Jan Bot, Arjen de Vries and Marcel Reinders. Lectures Notes for Bioinformatics 2013; 7970. To resolve handling of large and diverse datasets, which is common in bioinformatics, we propose a query solution which can operate on diversely structured data throughout the whole bioinformatics workflow, rather than just on data available in the data sources. This has been implemented in a system called Ibidas. With Ibidas one can operate on the outputs of algorithms and the contents of files and databases in a unified way that directly structures the data in a format suitable for further analysis. Ibidas is available for download at http://bioinformatics.tudelft.nl/software or https://trac.nbic.nl/ibidas/
- Exploring sequence characteristics related to high-level production of secreted proteins in Aspergillus niger. van den Berg BA, Reinders MJ, Hulsman M, Wu L, Pel HJ, Roubos JA, de Ridder D. PLoS One. 2012;7(10):e45869. doi: 10.1371/journal.pone.0045869. Epub 2012 Oct 1. In this paper we introduce sequence-based machine learning techniques for identifying relevant DNA and protein sequence features that predict over-expression of extracellular proteins by fungi. This is applied on a large set of 600 homologous and nearly 2,000 heterologous fungal genes that were overexpressed in Aspergillus niger. The predictor is available online at http://bioinformatics.tudelft.nl/hipsec; a more generic tool is offered at http://bioinformatics.tudelft.nl/spice. A patent application based on the findings in this paper has been filed.
The group is part of NBIC, NCSB, SB@NL.
Marcel Reinders is scientific director of NBIC.