What do we do?
At 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 catered to the characteristics of the technology platform generating the data. We develop publicly-available open-source software tools, generally through the Bioconductor project. The majority of our time is spent on collaborative projects and development of statistical methods with accompanying software. Where needed, we design experiments and collect data to compare the performance of competing methods and platforms.
Main publications 2016
- Comparison of Clustering Methods for High-Dimensional Single-Cell Flow and Mass Cytometry Data. LM Weber, MD Robinson. Cytometry A 89 (12), 1084-1096
- CrispRVariants charts the mutation spectrum of genome engineering experiments. H Lindsay et al., Nature Biotechnology 34 (7), 701-702
- iCOBRA: open, reproducible, standardized and live method benchmarking. C Soneson, MD Robinson. Nature Methods 13 (4), 283