What 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.

Find out more about the Group’s activities


University zurich

Mark Robinson
Statistical Bioinformatics Group
University of Zurich
Group Webpage

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Domains of activity:

  • Genes and genomes
  • Biostatistics
  • DNA Microarrays
  • Epigenetics
  • Experimental biology
  • Immunology
  • Next generation sequencing
  • Oncology
  • Single-cell biology
  • Software engineering
  • Transcriptomics

Domains of application:

  • Medicine and health
  • Basic research