Internships
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: Opening DOORS
synaptic protein mutations as an inroad to understanding epileptic disease
Project Description:
With the release of the first whole‐brain transcriptomics dataset at single cell resolution, a new era for gene expression analysis begins. The proposed project will be in the lead of this new approach by studying disease-relevant co‐expressed gene networks at an unprecedented level or resolution. The results from bioinformatic analysis will directly lead to the next step: The most disease-related, conserved genes will be targeted with RNA interference knockdown in actual brains. This will utilitze a Drosophila model of epilepsy and can be performed within months of the data analysis study. This allows the research group to identify for instance epilepsy genes that are drug‐resistant at this point. Patients with such diseases could be grouped in trials in the future and they may benefit from a common treatment strategy.
The research project will include analysis of large whole‐brain single cell datasets using mainstream and novel genetic tools in order to develop a better understanding of how in silico analysis can inform biomedical research. It will furthermore explore, and challenge, the evolutionary conservation of important components of the nerve cell machinery. Evolutionary comparisons of human genes to their homologs will shed new light on clinically identified disease genes and newly associated factors.
Required Skillset: Statistics, R, Python, Linux user (terminal) / bash scripting
Preferred: RNA-seq / single cell / co-expression network analysis
Donders Institute and RadboudUMC
Supervisor: Dr. rer. nat. Kevin Lüthy, Prof. Peter-Bram ‘t Hoen
Contact: Kevin.Luthy@radboudumc.nl; Peter-Bram.tHoen@radboudumc.nl
Title: Setting up a computational pipeline for drug identification for Koolen-de Vries Syndrome using transcriptomics data
Koolen-de Vries Syndrome (KdVS) is an intellectual disability syndrome caused by haploinsufficiency of KANSL1. The KANSL1 protein is part of the non-specific lethal (NSL) complex that is involved in regulation of gene expression. How exactly this affects the brain remains unknown. Also, there is no treatment available for KdVS. To study KdVS in vitro, we generated induced pluripotent stem cells (iPSCs) from fibroblasts from KdVS patients and healthy controls. From these iPSCs we generated neurons (iNeurons) and investigated transcriptional changes in these iNeurons using RNA-seq. The transcriptional profiles of KdVS iNeurons will be used to perform a computational screen to identify drug compounds that are predicted to rescue the KdVS phenotype. A large consortium, as part of the LINCS L1000 project, generated transcriptional profiles following drug perturbations of ~20.000 compounds [1]. This large database will be used to compare the KdVS transcriptional profiles against, in order to identify drug compounds that anticorrelate with the gene expression changes observed in KdVS iNeurons as they are predicted to rescue the KdVS phenotype. RNA-seq data is available for other neurodevelopmental disorders (Kleefstra syndrome and MELAS) as well, studied using the same iNeuron model. These iNeurons have been exposed to a drug compound that is known to (partially) rescue the observed phenotype in vitro and/or in vivo, which can be used to validate the computational pipeline that was set up.
The internship project will consist of setting up the computational pipeline to select for drugs of interest based on the transcriptional profiles in KdVS iNeurons. Data analysis will include pre-processing raw RNA-seq data using linux/python, differential expression analysis using the limma R package [2], gene set enrichment analysis using i.e. GSEABenchmarkeR [3], and unbiased and hypothesis-driven drug identification analyses using correlations, overrepresentation analysis or other approaches. Therefore, basic knowledge of R programming and/or python is preferred. This internship project is part of a larger project in which the drugs selected using this pipeline will be tested for their ability to the rescue the KdVS phenotype in iNeurons both on a transcriptional and functional level.
References
1. Subramanian, A., et al., A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell, 2017. 171(6): p. 1437-1452.e17.
2. Ritchie, M.E., et al., limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res, 2015. 43(7): p. e47.
3. Geistlinger, L., et al., Towards a gold standard for benchmarking gene set enrichment analysis. BioRxiv, 2019.
Internship supervisor: Prof. dr. Peter-Bram ‘t Hoen
Daily supervisor: Anouk Verboven, PhD student (anouk.verboven@radboudumc.nl)
Starting date: Between November – February
Title: Exploiting Plasmodium genome variation to understand immune evasion, antigenic diversity and to develop better vaccines.
Plasmodium genomes are being sequenced at a high rate (e.g.https://www.malariagen.net/projects/pf3k), and data can be exploited to better understand the sequence variation. Specific question that we want to answer are: Which proteins do show the highest level of variation and is this variation linked to whether they are e.g. immunogenic, expressed in specific stages in the Plasmodium developmental stage, are exposed to the outside of the cell or are encoded on specific regions of the chromosome. The project combines the gathering of relevant data (e.g. about immunogenicity, or about developmental stage dependent expression of the protein), the analysis of the relationships between those data and the critical thinking about causal relationships versus correlations.
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, or MLW
Anticipated start: end 2019/beginning 2020
Contact: Martijn.Huijnen@radboudumc.nl
Title: Analysis of microbiomes obtained with molecular inversion probe (smMIP) next gen seq and design of novel smMIPS to identify human microbiome compositions.
The metagenomes of bacterial species living in and on humans have uncovered a new angle to predicting and understanding human health and disease.
Single molecule Molecular Inversion Probes (smMIPS) allow us to derive the composition of a metagenome at unprecendented phylogenetic depth at affordable costs, facilitating large scale analyses. Design of smMIPS for large numbers of genomes, using conserved sequences for the probes, and analysis of the results requires understanding of the experimental techniques that are being used, programming skills and biological knowledge to interpret the results: typical skills of a bioinformatician. In the project the student would first analyze the data that we have already obtained about human metagenomes, using in-house developed software, and would then go on to design new smMIPS to increase the number of species and strains that we can obtain.
Programming skills: An ambition to work with large sequence datasets is required and basic programming skills (e.g. python) are strongly preferred.
Anticipated start: summer 2019
Duration: 0.5 year, or more
Contact: Martijn.Huijnen@radboudumc.nl
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
Contact: M.Dunaeva@ncmls.ru.nl; G.Pruijn@ncmls.ru.nl; Peter-Bram.tHoen@radboudumc.nl
Title: Building a metagenomics pipeline for analysis of human microbiome sequencing data.
Background: The world around us is teeming with microscopic life. The natural habitat of these so-called microbiota, for example in the human intestines or on the skin, is called the microbiome. These bacterial consortia are currently analyzed mostly by sequencing-based approaches, either by marker gene sequencing (MGS) metataxonomics, or by whole-genome sequencing (WGS) shotgun metagenomics. The advantage of WGS over MGS is that it provides insight into gene and metabolic function potential of a microbiome because all free DNA in a sample is sequenced, whereas MGS focuses on one gene only (usually the 16S rRNA gene).
Challenge: MGS is relatively cheap and data analysis is straight-forward, and it is still the standard in the field. In contrast, WGS is relatively expensive and very computational intensive. Nevertheless, more and more WGS datasets are being generated, as WGS is slowly becoming more affordable. At the CMBI, we currently lack a dedicated pipeline for analysis of metagenomics data. In order for us to be prepared for future projects we want to invest now in setting-up a WGS analysis pipeline.
Project description: A plethora of tools is currently available for analysis of WGS data. With these existing tools the student will have to build an analysis pipeline for metagenomics data. This means that the student will first have to study in-depth the different options available. Thereafter, a combination of tools will be used to build a comprehensive pipeline. Note that there is no need for the students to write bioinformatics tools or algorithms themselves. Depending on preferences and needs, this pipeline can in time be expanded with other tools and functionalities. Therefore, documentation (e.g. GitHub), portability (e.g. Docker or Jupyter) and structured programming is important in this project. Also, from a QC point-of-view the monitoring and detailed reporting of all pipeline steps is crucial. In final phase of the project, the student will validate his/her pipeline with available metagenomics datasets of different human microbiome niches, as reported in high-quality literature studies. In conclusion, we offer a lot of diversity and challenges for a highly motivated and enthusiastic student, in a flexible, supportive and professional academic environment.
Preferred background: Bioinformatics (BSc: HLO bioinformatics)
Basic programming skills are required (Python, Bash, R).
Anticipated start: summer / fall 2019
Length: at least 5-6 months, can always be prolonged
Contact: Tom.Ederveen@radboudumc.nl; Daniel.Garza@radboudumc.nl
Title: In silico validation of a novel smMIP-based sequencing method for microbiome profiling.
Background: The world around us is teeming with microscopic life. The natural habitat of these so-called microbiota, for example in the human intestines or on the skin, is called the microbiome. These bacterial consortia are currently analyzed mostly by sequencing-based approaches, either by marker gene sequencing (MGS) metataxonomics, or by whole-genome sequencing (WGS) shotgun metagenomics. Recently, a novel molecular / sequencing method called smMIP was developed for profiling of genes with a very high sensitivity. Within the Radboudumc, this smMIP method, for single-molecule molecular inversion probe, is currently being adapted for in-depth and high resolution profiling of microbiota.
Challenge: Recent studies show that smMIP is highly sensitive method for profiling of genes or RNA transcripts. However, its application for studying microbiomes has not been explored yet. Multiple departments within the Radboudumc are currently working together on application and validation of smMIP for microbiota profiling (i.e. dept. of Biochemistry, CMBI Bioinformatics and Dermatology). Pilot sequencing data has been generated, but several other important validations are yet to be performed, one of these is an extensive in silico validation.
Project description: The student will be working with a panel of smMIPs that is designed to target the vaginal microbiome, which is developed in our institute. Using existing smMIP analysis software we want to perform an in silico validation on a published metagenome data set of vaginome samples, to see if theoretical smMIP profiles match those microbiota profiles as found by metagenome analysis of the same data. As metagenomics is not trivial, the student will be working closely together with another student who will be fully dedicated on building an analysis pipeline for metagenomics data. Furthermore, the smMIP software is not build for the here intended purpose of in silico profiling of metagenome sequencing reads, therefore asking creativity from the student in adapting the smMIP bioinformatics tool to our specific needs. In parallel, another pilot study will be performed in which 16S vaginome profiles are compared to those retrieved by the smMIP approach. In conclusion, we offer a lot of diversity and challenges for a highly motivated and enthusiastic student, in a flexible, supportive and professional academic environment.
Preferred background: Bioinformatics (MSc: MLS/BMS/BIO/MMD)
Basic programming skills are required (Python, Bash, R).
Anticipated start: summer / fall 2019
Length: at least 5-6 months, can always be prolonged
Contact: Tom.Ederveen@radboudumc.nl; William.Leenders@radboudumc.nl; Karolina.Andralojc@radboudumc.nl
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
Contact: Hanka.Venselaar@radboudumc.nl
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
Contact: Hanka.Venselaar@radboudumc.nl
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
Contact: Hanka.Venselaar@radboudumc.nl
Title: Timing and localization of gene expression in the DM1 locus to advance our understanding of the brain phenotype of myotonic dystrophy
Our 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
Contact:
Peter-Bram.tHoen@radboudumc.nl
