• Gießen

Anika Erxleben-Eggenhofer (University of Freiburg, European Galaxy Team, de.NBI RBC)
Jochen Blom, Sven Griep, Oliver Rupp (Justus Liebig University Giessen, de.NBI BIGI)
Fabienne Thelen, Patrick Barth (Justus Liebig University Giessen, RTG2355)

18-19 March 2024

Justus-Liebig-University Giessen, Heinrich-Buff-Ring 58, Room 0024a

Galaxy is a worldwide open source project with the European Galaxy Server being the biggest instance in Europe with more than 85,000 users. The Freiburg Galaxy Team is hosting this server in Freiburg. Through Galaxy as a gateway, we are offering free access to a huge computational cloud infrastructure, databases and 3,200 bioinformatics tools which can be used by a graphical user interface instead of command-line. There is no need for programming or informatics skills - you just need a web browser (e.g. chrome or firefox).

We will have demonstrations and work together on detailed E-learning step-by-step-instructions of the Galaxy Training Material.

Monday, 18.03.2024, 09:00 – 16:00:
Introduction to Galaxy Analyses


  • How to use Galaxy?
  • How to get from peak regions to a list of gene names?


  • Familiarize yourself with the basics of Galaxy
  • Learn how to obtain data from external sources
  • Learn how to run tools
  • Learn how histories work
  • Learn how to create a workflow
  • Learn how to share your work

Tuesday, 19.03.2024, 09:00 – 16:00: 
Reference-based RNA-Seq data analysis


  • What are the steps to process RNA-Seq data?
  • How to identify differentially expressed genes across multiple experimental conditions?
  • What are the biological functions impacted by the differential expression of genes?


  • Check a sequence quality report generated by FastQC for RNA-Seq data
  • Explain the principle and specificity of mapping of RNA-Seq data to an eukaryotic reference genome
  • Select and run a state of the art mapping tool for RNA-Seq data
  • Evaluate the quality of mapping results
  • Describe the process to estimate the library strandness
  • Estimate the number of reads per genes
  • Explain the count normalization to perform before sample comparison
  • Construct and run a differential gene expression analysis
  • Analyze the DESeq2 output to identify, annotate and visualize differentially expressed genes
  • Perform a gene ontology enrichment analysis
  • Perform and visualize an enrichment analysis for KEGG pathways

Learning goals:
See above 

- You do not need to bring your laptop, we have desktop computers there. You need to use your university computer accounts.
- Register to the European Galaxy Server

Galaxy, RNAseq


Contact & Registration:
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