BSc and MSc Thesis Subjects of the Bioinformatics Group

On this page you can find an overview of the BSc and MSc thesis topics that are offered by our group. Please contact the project supervisor when you would like to learn more about a specific project.

MSc thesis: In the Bioinformatics group, we offer a wide range of MSc thesis projects, from applied bioinformatics to computational method development. Here is a list of available MSc thesis projects. Besides the fact that these topics can be pursued for a MSc thesis, they can also be pursued as part of a Research Practice. If you consider doing your thesis project in our group, please email the thesis coordinators at

BSc thesis: As a BSc student you will work as an apprentice alongside one of the PhD students or postdocs in the group. You will work on your own research project, closely guided by your supervisor. You will be expected to work with several tools and/or databases, be creative and potentially overcome technical challenges. Below you will find short descriptions of the research projects of our PhDs and Postdocs. In addition you can take a look at the list of MSc thesis projects above. Please contact the thesis coordinators at to discuss your interests.

BSc thesis topics

Meiotic recombination in crops

Roven Fuentes
Meiotic recombination is a fundamental biological process that ensures balanced chromosome distribution and the formation of new allelic combinations. Breeders rely on this mechanism to develop new variety of crops with a collage of different alleles encoding for higher and better yield, tolerance to certain diseases and stresses, and resilience to the outcomes of climate change. Understanding the genomic features that influence the non-uniform distribution of crossovers gives insights on possible barriers or promoters of recombination, aiding introgression hybridization and precision breeding. For example, a large inversion result to unsuccessful chromosome synapse, consequently preventing recombination in the inverted region. We aim to develop a high-throughput and cost-effective approach of profiling crossovers and identify predictive features of crossovers on different crops. This profiling is particularly important for long-generation crops because it may reveal in advance issues like linkage drags or low recombination frequency in regions of interest, saving precious time and allowing breeders to address them earlier. We also plan to develop a system for breeders that predicts the possible landscape of recombination for a specific hybrid cross without the need for actual crossing.

Finding genes for traits using systems genetics

Margi Hartanto
Genome editing promises to revolutionize plant breeding because it allows accurate and efficient modification of genes to improve crop traits. Both for large-scale plant phenotyping and genotyping a range of high-throughput methods are becoming available, but there are no systematic methods to subsequently link the genes to traits, to find the targets for modification. A method potentially capable of this is Quantitative Trait Locus (QTL) analysis, which is used to identify genomic regions affecting a 'continuous' trait (like plant height, or seed size). However, two main issues prevent QTL analysis from being used systematically: first, its low resolution, with identified DNA regions that can span hundreds of genes; and second, its lack of power when dealing with complex traits affected by many genes with possibly small effects. In this project we develop and apply systems genetics approaches to integrate QTL analysis with various kinds of molecular interaction data. By combining gene annotation and genetic variation with gene expression and phenotype measurements, we identify molecular networks underlying plant traits. These serve to identify key regulatory genes and predict the effects of naturally occurring genetic variants. The methods and predictions will be made available in the AraQTL workbench at

Structure/function prediction of lipopeptides

Barbara Terlouw
In nature, microbes such as fungi and bacteria produce a vast range of secondary metabolites to gain a selective advantage over other organisms. I am working on a specific group of metabolites called lipopeptides, and am particularly interested in their antibiotic potential. Several lipopeptide antibiotics have already been discovered, and the immense structural diversity of lipopeptides suggests that there may be many as yet undiscovered lipopeptide antibiotics out there. Lipopeptides are often produced from biosynthetic gene clusters; groups of physically clustered genes that together encode a pipeline responsible for the production of a secondary metabolite.  Unfortunately, it is still difficult to predict the structure and the function of a lipopeptide from the DNA sequence of a biosynthetic gene cluster. Therefore, my research attempts to first predict the structure of a lipopeptide from its biosynthetic gene cluster sequence, and then from the structure infer its function. This will help with the discovery and possibly engineering of novel lipopeptide antibiotics.‚Äč

Genome-guided discovery and structure prediction of novel bio-surfactants

Mohammad Alanjary
Surfactants are integral compounds found in cosmetics, industrial cleaners, and food. Mounting pressures to replace synthetically derived surfactants with environmentally friendly, low toxicity, bio-surfactants have steadily grown in various applications (e.g. dispersants used in oil spill cleanup). Lipopeptides are naturally occurring compounds that show great promise for sustainable bio-surfactants and are found in a breath of bacterial species including well-studied Bacillus strains. This project aims to chart the diversity of lipopeptides from bacterial genomes and to further develop structure prediction methods to generate targeted leads with desirable properties. Comparative analysis of the genetic diversity of lipopeptides will also aid re-engineering efforts for effective production at industrial scales and reduce dependence on fossil fuels.

Linking the metabolome and genome

Justin van der Hooft
The central theme of my research is the integration of metabolome and genome mining tools to accelerate and improve functional annotations of biosynthesis genes and specialized molecules. One the one hand, I am working on improving workflows to maximize the structural information obtained from mass spectrometry fragmentation data. On the other hand, I will develop and extend existing workflows that recognize patterns of co-localized genes in predicted biosynthesis genes clusters. Both the metabolomics and genomics workflows focus on the recognition of molecular substructures as building blocks of more complex natural products. As these tools will provide complementary structural data on the specialized molecules produced by microbes, fungi, and plants, I will finally integrate those workflows to boost natural product discovery.

Pangenomic applications for plants and pathogens

Eef Jonkheer
As a result of the advances in NGS sequencing there is a gradual shift from representing a species by a single reference genome sequence to representing it by a pangenome. A pangenome is a data structure containing all genomic variation in a species or population. In my project, we aim to develop pangenomic applications for plants and pathogens to demonstrate the advantages of pangenomes: pangenome-based discoveries and improved efficiency and/or accuracy. We will build upon the existing pangenome framework of PanTools, and exploit its existing features to develop new computational methods for highly efficient comparative analysis of large numbers of related genomes. One line of research focusses on gene-level analyses in large sets of genomes. For example, inferring the evolutionary relationships across thousand bacterial strains or identifying a subset of genes in tomato which contribute to a particular trait like drought tolerance. The other line of research is aimed at discovery and exploration of genome-wide variation, from single-nucleotide polymorphisms to large structural variation.

Protein sequencing

Carlos de Lannoy
The protein content of a cell holds a wealth of information on its nature and function, however high-throughput identification and sequencing of proteins at single-molecule resolution remains an unmet challenge. As was previously the case for nucleic acid sequencing methods, the development of a cost-efficient reliable protein sequencing method would allow major advances in our understanding of biological processes and disease mechanisms.
In a joint effort spanning three universities across The Netherlands, we develop and assess novel approaches to high-throughput protein fingerprinting and sequencing. In one approach, we aim to employ the molecular motor complex ClpXP to walk over a target protein and read out the passing of tagged amino acids. From the observed pattern in passing amino acids we then deduce the identity of the target protein.
Our contributions to this project fall into two categories. First, we perform in silico simulations of newly conceptualized approaches to protein analysis, to assess performance, explain observed molecule behavior and advise on further modifications to methodology. Second, we develop algorithms to automate the recognition of amino acid passing events and match read-out patterns to the identity of the target protein.

Novel Enzymes for Fragrance and Flavour

Janani Durairaj
Terpenoids represent a vast and diverse group of natural compounds produced by many plants which usually have a distinctive smell associated with them. This makes them valuable in natural flavour and fragrance products such as orange flavouring and sandalwood scents. Of late, such compounds are being produced by microbial production platforms. The enzymes responsible for the production of terpenoids, the terpene synthases, are very diverse in sequence and there is a great scope for improving their efficiency and specificity to produce compounds of interest.
This project aims to explore properties and mechanisms of terpene synthases, and improve overall catalytic efficiency as well as specificity for certain terpenoid products using machine learning techniques. Specifically, different features detailing the functionally important motifs or residues in each sequence can be found which are useful for predicting either the product catalyzed, or the catalytic efficiency of the enzyme. Good predictors trained using these features will then allow us to make sequences predicted to form a certain product and test their catalytic activity experimentally.