Cutting-edge research

Current research includes the development of methods, algorithms and software and the solving of biological research problems using these tools. Close collaboration with researchers from research groups both inside and outside the university – at other institutions of higher education and with our industrial partners – is key, giving access to experimental data and ensuring we conduct research that is relevant to biological applications.

The group's focus is on developing methods for analysis of different types of high-quantity data representing complete datasets (omics): for example, transcriptomics, proteomics and epigenomics. The most important research areas presently are the identification of biomarkers and the integration of large-scale omics data. We collaborate on bioinformatics studies on stem-cell differentiation, development of stem-cell-based in vitro models for disease modelling and toxicity testing, discovery and evaluation of cancer biomarkers, network modelling, identification of functional disease modules, and epigenetics-based genetic mis-regulation. The practical focus is on analysis of data from large-scale experimental studies using such techniques as microarrays, next-generation sequencing and mass spectrometry.

Primary assignments and goals

• Developing algorithms for analysis and integration of large-scale datasets from transcriptomics, proteomics, and genomics, along with signalling pathways and networks.
• Applying machine learning and other advanced computational methods to analysis of large-scale biological datasets.
• Discovering and statistically evaluating molecular biomarkers for diagnosis, prognosis and classification of disease.
• Applying bioinformatic methods to discover and evaluate biomarkers such application areas as stem-cell differentiation, toxicity testing, disease modelling and cancer development.

Education linked to research

The University of Skövde offers a one-year master's program in bioinformatics that gives students broad specialist competence in the area. The program aims to develop students' ability to solve biological problems, plan and perform analyses of molecular and biomedical data, and critically assess results arrived at from the data. The curriculum includes courses focusing on analysis of large-scale biological data, bioinformatics algorithms, and bioinformatics research and development.

There is particular focus on computer science and statistics, with courses in programming, including the statistical language R. Traditional lectures are mixed with work in the computer labs. Students gain familiarity with computer tools for compiling and analysing data from biological experiments and other research projects. They learn to use such analysis methods as sequency and expression analysis.