University of Gothenburg

Education projects

Besides aiding researchers in the analysis of their data, BCF is also invested in teaching. Several of our standard courses were initially developed thanks to the Swedish Foundation for Strategic Research (RIF14–0081) grant. Moreover, we have share our experience in adopting short bioinformatics courses for PhD during the pandemic. We are thankful to all our students during this journey.

Higher education courses at BCF

Contributors: BCF experts

Bioinformatics courses are practical in nature, learning by doing is the best way to understand the concepts and practice running analysis in different interfaces. There is an increasing need for wet lab oriented people to acquire technical skills, and this is challenging for both students and teacher just because our way of thinking is different. Designing an implementing a data analysis course in this setup, require extra time when compared to courses targeting people with a bioinformatics background. Within this project, we have spent resources to develop short and comprehensive courses that subsequently were included in the PhD courses catalogue at the Sahlgrenska Academy.

Online education during the COVID-19 pandemic

BCF contributor(s): Marcela Davila and Sanna Abrahamsson

This study focuses on short bioinformatics-related courses for graduate students at the University of Gothenburg, Sweden, which were originally developed for onsite training. Once adapted as online courses, several modifications in their design were tested to obtain the best fitting learning strategy for the students. To improve the online learning experience, we propose a combination of: 1) short synchronized sessions, 2) extended time for own and group practical work, 3) recorded live lectures and 4) increased opportunities for feedback in several formats.

Proteomics data analysis workflow

BCF contributor(s): Jari Martikainen, Peidi Liu and Marcela Dávila. In collaboration with the Proteomics Core Facility

Proteomics data allow us to identify and quantify proteins in a given sample to understand its intrinsic molecular processes. The bioinformatics analysis of this data has been improved over the years to address common caveats including batch effects, missing values and poor experimental design, among others. Here we exemplify the standard process of analyzing proteomics data and illustrate the deviations from this process when data needs to be merged from different runs (high number of samples).