Technologies used in modern life sciences change rapidly. These technologies produce data that are steadily growing and usually become more complex over time. To analyse these data requires sound knowledge of the technologies and computational tools as well as significant resources in high-performance computing systems. However, very often researchers have neither the time or knowledge, nor the resources to conduct the analysis of the vast data. We have a high performance compute cluster and a data storage system that we use for our own research, collaborations and service projects. In addition to IBU’s own resources, the University of Bern has a partnership with Vital-IT that provides access to their large computation and storage infrastructure.

SynaptiX “The Systems Biology of Forgetting“ – a SystemsX RTD project

This collaborative research programme (with groups from Fribourg, Bern and Reno, US) systematically investigate the biology of forgetting in Drosophila in an interdisciplinary fashion by combining theory, behavioural experiments and genetic engineering, transcriptomics, and super-resolution microscopy. So far, little is known regarding the active processes that underlie forgetting. We will perform an in-depth transcriptome analysis of neuronal cells with manipulated neural function of the mushroom bodies (the fly's memory centre) and define data-driven RNA-network models as an attempt to understand the biological pathways involved in forgetting.

More information on the website.

Biochemical pathway modelling of bacterial strains used in dairy production (with Agrososcope Liebefeld-Posieux ALP)

ALP has a very large collection of bacterial strains dating back to the 60’s of the last century. The project ALP/IBU wants to shed light on the evolution of these bacteria and we have started to completely sequence 500 strains that are used in cheese production using 2nd and 3rd generation sequencing technologies. We are improving and developing methods to close the circular bacterial genomes as far as possible. Using this information and biochemical properties measured in the wet lab, we want to build models to predict biochemical properties of these strains.

SetRank: an advanced Gene Set Enrichment Analysis (GSEA) algorithm

SetRank can overcome many of the shortcomings of other GSEA methods. It does not depend on applying arbitrary p-value cut-offs on data and eliminates a high proportion of false positive hits. The key principle of SetRank is that it discards gene sets that initially have been found as significant, in case their significance was based solely on parts of genes also involved in other processes. This approach ensures that SetRank returns very specific and reliable results. The interpretation of results is further aided by creating a gene set network which visualizes the relations between the significant gene sets.

More information about our research


The interpretation of results is aided by creating a gene set network which visualizes the relations between the significant gene sets.