German Conference on Bioinformatics 2019

The German Conference on Bioinformatics (GCB) is an annual, international conference devoted to all areas of bioinformatics and meant as a platform for the whole bioinformatics community. Recent meetings attracted a multinational audience with 250 – 300 participants each year.
In 2019, the conference focuses on bringing physicians, bioinformatics & medical informatics together and aims to showcase applications and opportunities beyond. Spearheading scientists will be presenting along with young researchers and industry representatives. Workshops will provide opportunities for hands-on experience.

The upcoming GCB will be held at the German Cancer Research Center in Heidelberg. The first day 16 September is reserved for workshops and satellite meetings. The main conference will take place from September 17-19. The schedule will allow for fly-in on Monday and fly-out on Thursday or Friday.

CSAMA 2019 – Statistical Data Analysis for Genome-Scale Biology

CSAMA 2019 (17th edition)
Statistical Data Analysis for Genome Scale Biology
Bressanone-Brixen, Italy (South Tyrol Alps)
July 21-26, 2019


  • Vincent J. Carey, Harvard Medical School
  • Laurent Gatto, University of Cambridge
  • Robert Gentleman, 23andMe, Mountain View
  • Wolfgang Huber, European Molecular Biology Laboratory (EMBL), Heidelberg
  • Martin Morgan, Roswell Park Comprehensive Cancer Center, Buffalo
  • Johannes Rainer, European Academy of Bozen (EURAC)
  • Charlotte Soneson, University of Zurich
  • Levi Waldron, CUNY School of Public Health at Hunter College, New York

Teaching Assistants:

  • Simone Bell, EMBL, Heidelberg
  • Lori Shepherd, RPCCC, Buffalo
  • Mike L. Smith, EMBL, Heidelberg

The one-week intensive course Statistical Data Analysis for Genome-Scale Biology teaches statistical and computational analysis of multi-omics studies in biology and biomedicine. It covers the underlying theory and state of the art (the morning lectures) and practical hands-on exercises based on the R / Bioconductor environment (the afternoon labs). At the end of the course, you should be able to run analysis workflows on your own (multi-)omic data, adapt and combine different tools, and make informed and scientifically sound choices about analysis strategies.

Topics include:

  • Introduction to R and Bioconductor
  • The elements of statistics: hypothesis testing, multiple testing, regression, regularization, clustering and classification, parallelization and performance (machine learning), visualisation
  • RNA-Seq data analysis
  • Computing with sequences and genomic intervals
  • Working with annotation – genes, genomic features, variants, transcripts and proteins
  • Gene set enrichment analysis
  • Mass spec proteomics and metabolomics
  • Basis of microbiome analysis
  • Experimental design, batch effects and confounding
  • Reproducible research and workflow authoring with R markdown
  • Package development, version control and developer tools (incl. git, github, RStudio)
  • Working with large data: performance parallelisation and cloud computing

The course consists of

  • morning lectures: 20 x 45 minutes: Monday to Friday 8:30h – 12:00h
  • 4 practical computer tutorials in the afternoons (13:30h – 16:30h) on Monday, Tuesday, Thursday and Friday

Visit the course’s website at: