The last decades, development of high-throughput technologies such as next-generation sequencing, microarrays and mass spectrometry has led to a paradigm shift in biology and life sciences. Where previously one biomolecule (for example, a gene, or a protein) was studied as a single entity, these experimental techniques allow to study a set of biomolecules (for example, the genome or the proteome) in one go and in a limited amount of time. While, at first, experiments were analyzed independently from each other, combining information of several experimental techniques, and correlating them with environmental and/or clinical parameters, is, nowadays, of great interest. As a consequence, novel statistical and computational methodologies for the analysis of multi-omics datasets have to be developed, allowing a multi-dimensional view on complex biological processes.
In this PhD project, we aim at the development of computational methods for the analysis of multi-omics datasets, that are commonly generated by the three biology units of SCK•CEN, which will improve the understanding and valorization of the data, and which will keep the biology units up to date with current developments in biology and bioinformatics.