On this page
Applied High-throughput Analysis
This is an advanced course master of bioinformatics that focuses on the full data- analytical pipelines to (pre)process high throughput omics data. This will include the experimental protocols, as potential sources of variation/bias, image (microarray/sequencing) analysis leading to raw sequence/intensity data, quality control of the raw data, discussion and application of normalization and statistical analysis tools, quality control of data normalization/analysis and gene set/pathway analysis. The different steps will be described and illustrated by means of real examples in the areas of genomics, transcriptomics, and epigenomics. Importantly, the full data analytical pipeline is studied, focusing on conceptual insight rather than the theoretical details of the studied algorithms.
Applied Statistics
This course is tailored for researchers who aim to analyze (biological) data and derive statistically sound conclusions to address their research inquiries. The primary focus is on the accurate application of prevalent statistical methodologies, coupled with the critical interpretation and effective reporting of findings. A comprehensive understanding of the mathematical underpinnings behind these techniques is essential for students to navigate through the course material effectively. The emphasis of this course lies on application of statistical methods, hence an important part of the study time is attributed to performing analysis on an independent base using the statistical software program R.
Bioinformatics
This course on bio-informatics focuses on the algorithmic and computational aspects of biological datamanagement and -exploitation. It complements the students mathematical and computational background and lays the foundation for the bio-informatician.
Biological Databases
The enhanced course on bioinformatics focuses on database design and implementation of biological datasets. Both string based data (sequence) and integer/real data as obtained by genome-wide experiments are integrated in different datamodels. It complements the (bio)informatics background by applying modern application development environments to (relational) database methodologies.
Design Project
In the design project, students work in heterogeneous teams (different background and/or field of study) in order to create innovative solutions for bioinformatics problems. Students can create their own proposals. This course aims to sharpen both technical and intellectual skills in the field of bioinformatics and to apply theoretical knowledge from other courses to practical problems. The project involves different components: project management, requirement analysis, design and implementation, evaluation, testing and documentation. The project proposals are selected with a strong emphasis on valorization.
Genome Analysis
Many recent methods in molecular biology generate huge amounts of data. This course aims at familiarizing students with these data, how to process and statistically analyze them using state-of-the-art methods, and where/how to find or deposit this type of data. Ethical implications, including scientific integrity and sustainability, are discussed. We'll particularly focus on transcriptomics data for in depth analysis (both in theory and practical sessions), yet we'll also touch upon several other omics techniques, data types and analytical considerations.