Susanne Bornelöv "sblab"

bioinformatics, (epi)genomics, machine learning, piRNAs, codon usage


"Research is to see what everybody else has seen and think what nobody has thought"

Perhaps inspired by this quote, we are exploring new ways to leverage existing data in order to gain new biological insights. In the age of big data, with petabytes of sequencing data being generated annually and deposited into public repositories, this presents both an exciting opportunity and a significant challenge.

Our team specialises in large-scale data analysis and machine learning techniques to better understand gene regulation and genome evolution. We are currently focused on the role of codon usage and how codon-level information impacts these processes. By analysing ribosome profiling data and similar technologies, we are able to study ribosome occupancy at single-codon levels. Additionally, we are investigating PIWI-interacting RNAs (piRNAs), a class of small non-coding RNAs that guide PIWI proteins to recognise and silence transposable elements in animals germ cells. This work is conducted together with the small RNA team in the Hannon lab. While our primary focus is on these areas, we are also interested in broader topics such as genome organization and epigenetic mechanisms.


We are grateful to the CRUK CI and Greg Hannon for hosting us. Currently, our work on codon usage is funded by an 8-year Wellcome Career Development Award awarded to Susanne Bornelöv.