What do we do?
In the Statistical Bioinformatics Group, we develop robust data analysis solutions, including new or improved methods, for the analysis of genome-scale data. We develop statistical methods for interpreting data from high-throughput sequencing and other technologies in the context of genome sequencing, gene expression and regulation and analysis of epigenomes. We are largely data- and problem-driven, and ultimately the methods we develop are geared to the characteristics of the technology platform generating the data. We develop publicly available open-source software tools, generally through the Bioconductor project.
Main publications 2017
- Nowicka et al. (2017) CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets. F1000 Research 6 (748).
- Soneson C and Robinson MD (2017) Towards unified quality verification of synthetic count data with countsimQC. Bioinformatics, btx631.
- Sharma et al. (2017) Male sex in houseflies is determined by Mdmd, a paralog of the generic splice factor gene CWC22. Science 356 (6338), 642-645.