Center for Molecular and Biomolecular InformaticsThe Center for Molecular and Biomolecular Informatics (CMBI) does research and education, and provides services in bioinformatics and cheminformatics.
Mission & vision
Our mission is:
to add value to personal health data by their translation into integrative knowledge and actionable information.
- CMBI develops bioinformatics approaches that contribute to the understanding of disease mechanisms, personalized therapies and interventions, and a learning health care system
- CMBI is committed to the reusability of their data, tools, and services
- CMBI provides bioinformatics researchers in Radboudumc with a platform for exchange of knowledge and expertise
- CMBI contributes to the education of BMW, MLW, and MMD students so that they can apply and understand the principles behind (existing) bioinformatics tools
Researchers at the CMBI contribute to several courses at both the Faculty of Science and the Medical Faculty. We provide courses in Structural Bioinformatics, Comparative Genomics, Data Analysis, and programming courses such as Java. We specifically focus on the Molecular Life Science students, Biomedical Sciences students and participants in the master Molecular Mechanisms of Disease, although some of our courses can be chosen by Biology, Chemistry or Medical students as well.
Our mission is to provide a basic understanding in Bioinformatic principles for bachelor students as this was shown to be beneficial for those who want to pursue a career in Life Sciences. Follow-up courses are available for those who want to gain greater insight in our field.
For MLS/Biology students it is even possible to follow the B-track by choosing a combination of (master) Bioinformatics courses and internships at Bioinformatics departments.
Our researchers also teach in special interest courses and summer schools here at the Radboudumc, the Radboud University and elsewhere.
For more information, you can contact dr. Hanka Venselaar, education coordinator at the CMBI.
Both bachelor and master students from studies such as Molecular Life Sciences, Biomedical Sciences, Chemistry and MMD are welcome. In general, we are flexible in terms of internship length, type of internship and type of research.
Below you can find a few examples of internships possiblities:
Title: Deconvolution of gene expression data from synovial biopsies of rheumatoid arthritis patients
Description: Analysis of gene expression in bulk tissues using RNA sequencing provides important information to understand the processes involved in disease development and progression. However, many tissues are heterogeneous and contain a mix of different cell types. This makes it difficult to identify gene expression levels specific for various cell type, since the expression level of each individual gene can differ between cell types. Disease development and progression or the response to drug therapy is often accompanied by changes in cell composition. For example, an increase of malignant cells and tumor infiltrating cells compared with surrounding cells is an indicator of tumor growth and of the clinical outcome for patients. Thus, the cellular composition of a tissue is important for the understanding of many biological processes.
Rheumatoid arthritis (RA) is a disease characterized by inflammation of multiple joints, which, if not treated, leads to irreversible joint damage. During joint inflammation cells from the immune system infiltrate in the synovial membrane, leading to the formation of so-called pannus tissue. Synovial biopsies taken from the inflamed joints of RA patients are mainly composed of synovial fibroblasts, macrophages, neutrophils, and lymphocytes. Using publicly available RNAseq datasets, as well as single-cell sequencing data, you will elucidate the constituent cell types and their proportions in synovial biopsies from RA patients by computational deconvolution of gene expression data. Deconvolution is the identification of properties and the relative abundance of components from a mixture. This project will be carried out at the Department of Biomolecular Chemistry in close collaboration with the Centre for Molecular and Biomolecular Informatics with supervision from both departments.
MSc student project
Preferred background: Bioinformatics, Computational Biology
Programming skills: work with large datasets, knowledge of R or Python is required
Anticipated start: ASAP
Length: 0.5-1 year
Title: Improving HOPE by comparing Variant Effect Predictions
Description: HOPE is our own in-house server for the prediction of mutational effects. This server is specifically aimed at the medical scientist and produces a report that is clear and understandable for everyone without a background in structural bioinformatics. HOPE has been running for several years and is being used by researchers all over the world. In order to keep this server up to date we perform small tests in which we select mutations that were described in high-end journals such as The American Journal of Human Genetics and Nature Genetics. We compare the effect predictions made in these article with those made by our HOPE server (and eventually also with predictions made by other widely-used automatic online servers). The results should be used to improve the predictions made by HOPE.
Preferred background: MLS/BMS, evt BIO
Length: flexible, between 1-6 months.
Programming skills: not necessary
Title: Protein Protein Contact Predictions
Description: Proteins can hardly ever function on their own. Many proteins are found in homo- and heteromeric complexes, and many others are found in a transient complex at least once during their lifecycle. Interactions made by these proteins can be disturbed by mutations, which often lead a non-functional protein complex and eventually a disease. Therefore, it would be interesting to predict the effects of mutations that occur on protein surfaces. However, information about interacting residues on surfaces is still scarce and only a few protein protein interaction servers exist.
In collaboration with a research group in Amsterdam, we have linked their PPI-prediction server SERENDIP to a script that visualizes the predictions in a YASARA scene. A possible student project would be to test and analyse these predictions.
Preferred background: MLS/BMS/BIO/MMD
Length: at least 2 months
Programming skills: basics
Title: Consensus Variant Effect Prediction
In order to correctly treat patients, it is beneficial to understand the molecular base of their disease or syndrome. Mutations that occur in the coding sequence of proteins might have effect on ligand binding, membrane anchoring, general stability/folding, interactions with other proteins, etc. Nowadays, several online servers that can analyse these effects exist. One of them is our own HOPE server, a server that can analyse the protein structure in detail and will provide an extensive report readable for the medical scientist. Other predictions servers often provide a result that contains only a binair answer (damaging or not), or a value (0 to 10). HOPE's results could be strengthened by combining its textual report with a consensus prediction that would be a combination of these other servers.
Preferred background: master MLS/Informatics/Data science
Length: 0.5-1 year
Programming skills: Good
Title: Timing and localization of gene expression in the DM1 locus to advance our understanding of the brain phenotype of myotonic dystrophyOur aim is to obtain a deeper understanding of the expression of the genes in the DM1 locus (Fig. 1), the DMPK and DM1-AS transcripts in particular, during brain development, their regional expression patterns in the brain and their expression levels in different cell types present in the brain. These expression patterns can then subsequently be related to the brain abnormalities observed in DM1. In analogy to published work on DMD3, you will be using public large-scale gene expression resources from human and mice, including the Allen Brain Atlas, the FANTOM5 study and the 10x genomics mouse brain single cell dataset. You will be analyzing, comparing and interpreting the expression signatures present in these different resources.
Programming skills: An ambition to work with large datasets is required and basic programming skills (R, python) are strongly preferred.
Preferred background: MMD student project
Anticipated start: end 2018/beginning 2019