Educators:
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)

Date:
18-19 March 2024

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

Contents:
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

Questions:

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

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

Questions:

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?
Objectives:

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

Prerequisites:
- 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

Keywords:
Galaxy, RNAseq

Tools:
Galaxy