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.

Find out more about the Group’s activities

Main publications 2020

  • Crowell H L et al.
    muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data
    Nature Communications, 10.1038/s41467-020-19894-4
  • Germain P-L et al.
    pipeComp, a general framework for the evaluation of computational pipelines, reveals performant single cell RNA-seq preprocessing tools
    Genome Biology, 10.1186/s13059-020-02136-7
  • Tiberi S and Robinson M D
    BANDITS: Bayesian differential splicing accounting for sample-to-sample variability and mapping uncertainty
    Genome Biology, 10.1186/s13059-020-01967-8

Members

University zurich

Mark Robinson
Statistical Bioinformatics Group
University of Zurich
Group Webpage

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Domain(s) of activity:

  • Genes and genomes
  • Benchmarking
  • Biostatistics
  • Data visualisation
  • Deep sequencing data
  • Functional genomics
  • Next generation sequencing
  • Single-cell biology
  • Training
  • Transcriptomics
  • Workflows

Domain(s) of application:

  • Basic research
  • Medicine and health

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