RNA-seq using Galaxy

This hands-on workshop provides a beginner-friendly introduction to RNA sequencing (RNA-Seq) and its bioinformatics analysis using the Galaxy platform. Participants will learn the fundamentals of RNA-Seq, including how it is used to study gene expression and transcriptome dynamics across different biological conditions. The workshop will guide attendees through key analysis steps such as quality control of raw reads, alignment to a reference genome, quantification of gene expression, and differential expression analysis—all within Galaxy’s intuitive, web-based interface. Designed for those with experience in coding or Galaxy, this session offers both theoretical background and practical skills to help participants confidently perform RNA-Seq analysis in their own research. 

Who Should Attend 

This workshop is tailored for students and researchers in the life sciences who already have experience using the Galaxy platform and are looking to deepen their understanding of RNA-Seq analysis. It is particularly suited for those working in fields such as genomics, molecular biology, or microbiology who want to build on their existing Galaxy skills through hands-on practice with RNA-Seq workflows. Familiarity with the Galaxy interface is assumed, allowing the workshop to focus on more advanced aspects of RNA-Seq, including quality control, alignment, expression quantification, and differential expression analysis using sequencing data. 

Learning Outcomes 

By the end of this workshop, participants will: 

• Gain an understanding of RNA-Seq analysis using Galaxy Australia 
• Become familiar with the Galaxy platform and tools used for RNA-seq pipeline 
• Perform Quality check on RNA-seq and Sequence Alignment 
• Perform differential expression gene analysis using Galaxy Australia 
• Develop the ability to analyse and interpret RNA-seq results. 

Workshop Topics 

• Quality Control of short reads 
• Align short reads to reference genome  
• Convert reads to count 
• Create a raw count table 
• Convert count to genes 
• Differential expressed genes analysis.