2 Day Workshop – RNA-Seq Analysis With Galaxy

Our 2-Day Hands-On RNA-Seq Analysis Workshop is designed to take you from raw sequencing files to meaningful biological insights — all within the user-friendly Galaxy platform.
By the end of the workshop, you’ll confidently perform every key step of an RNA-Seq workflow, including quality control, alignment, statistical analysis, and visualization.

1. Data Upload & Quality Control

Learn how to import raw RNA-Seq data (FASTQ files) into the Galaxy environment and ensure data integrity using FastQC and MultiQC. You’ll explore how to interpret key QC metrics such as base quality, GC content, and adapter contamination — the foundation for all downstream analyses.

2. Read Alignment to Reference Genome

Understand how sequencing reads are aligned to a reference genome using HISAT2, one of the most efficient RNA-Seq aligners. You’ll gain practical insight into alignment principles, mapping quality, and how to interpret alignment statistics for accurate gene expression analysis.

3. Gene Expression Quantification

Move from aligned reads to numerical expression data using FeatureCounts. You’ll learn how to count reads per gene or transcript and prepare a well-structured count matrix — the critical input for differential expression studies.

4. Differential Expression Analysis

Dive into statistical modeling using DESeq2, the gold standard for identifying differentially expressed genes. You’ll understand how to detect genes with significant expression changes between experimental conditions and interpret results with confidence.

5. Visualization of Results

Turn data into insight with powerful visualizations. You’ll generate volcano plots, PCA plots, and heatmaps to explore clustering patterns and highlight key expression changes. These visual tools help communicate results effectively in reports and publications.

6. Functional Enrichment Analysis

Go beyond the gene list — discover the biology behind it. You’ll perform Gene Ontology (GO) and KEGG Pathway enrichment analyses using GOSeq, linking differentially expressed genes to biological processes and pathways to understand their functional roles.