Hands-on Practical RNA-Seq Data Analysis With Galaxy

If you’ve ever been curious about understanding the inner workings of RNA-Seq and Next-Generation Sequencing (NGS) technologies, this comprehensive course is tailored specifically for you. Over the course of several engaging sessions, we will take you on a journey through the intricate process of analyzing genetic data.

Course Contents:

Introduction to NGS, RNA-Seq Pipeline & GALAXY

• Introduction to Next Generation Sequencing & RNA-Seq
• RNA-Seq Data Analysis Workflow
• What is Galaxy
• How to Get Started With Galaxy Account

Practical RNA-Seq Differential Gene Expression Analysis

• RNA-Seq Dataset Retrieval
• Quality Control with FastQC
• Pro-processing of the Reads
• Alignment of the Reads Against Reference Genome
• Post Alignment Processing
• Transcript Assembly and Quantification with StringTie
• Differential Gene Expression Analysis with DESEQ2

At the outset of this course, you will lay the foundation for your understanding the fundamental principles of RNA-Seq and NGS. You will grasp the core concepts that underlie this cutting-edge technology, ensuring that you have a solid grasp of the theory behind it.

As we progress, you will transition from theory to practice, gaining practical skills that are essential for working with genetic data. You will learn how to perform crucial Quality Control and Trimming tasks using popular tools like FASTQC, FastP, and Cutadapt. These steps are vital to ensure the reliability of your data before diving into deeper analysis.

Next, you’ll learn about mapping and alignment and using HISAT2 to align sequencing data to a reference genome, and you’ll also learn how to evaluate the efficiency of this alignment. This step is critical for accurately identifying the location of genetic sequences within the genome.

Finally, you’ll explore the world of functional analysis. You’ll use Gene Ontology and KEGG Pathways to interpret the biological significance of your findings, allowing you to extract valuable insights from your RNA-Seq data. This step is crucial for understanding the biological processes and pathways affected by the gene expression changes you’ve identified.

Don’t miss out on this unparalleled opportunity to enhance your bioinformatics skills and make a significant impact in the world of genomics. By the end of this course, you’ll be equipped with the knowledge and practical skills needed to confidently tackle complex RNA-Seq and NGS data analysis projects, making you a valuable asset in the ever-evolving field of genomics research. Join us on this exciting journey of discovery and empowerment!

RNA-Seq without writing code, using Galaxy

Galaxy is a web-based platform that runs standard bioinformatics tools through a browser interface. It exists precisely for researchers who need the analysis but not the command line, and it runs the same underlying tools a scripted pipeline would. This course covers a complete RNA-Seq differential expression analysis in Galaxy in about 3 hours.

What you work through

• RNA-Seq, NGS and Galaxy — how the sequencing works and how Galaxy organises an analysis into a reproducible history.
• Quality control and trimming — FastQC, fastp and Cutadapt, reading the reports and deciding what to trim.
• Mapping with HISAT2 — aligning reads to a reference genome and evaluating whether the alignment worked.
• Differential expression — StringTie and DESeq2 to identify genes that change between conditions.
• Functional analysis — Gene Ontology and KEGG pathway enrichment to interpret the result biologically.

Is Galaxy a compromise?

Not for this. The tools are the same ones a command-line pipeline calls — HISAT2 is HISAT2 whether launched from a terminal or a browser. What you trade is flexibility and scale: Galaxy is excellent for a standard analysis on a normal-sized dataset, and less suited to unusual workflows or very large cohorts where scripting wins. For a researcher who needs a clean, reproducible RNA-Seq result, it is a legitimate choice rather than a lesser one.

A useful side effect is that Galaxy records every step and parameter as a shareable history, which makes reproducing the analysis straightforward — something scripted pipelines often manage worse in practice.

What you can do afterwards

Run a complete RNA-Seq experiment from raw reads to an annotated, enriched gene list, entirely in a browser, and share the exact workflow with a collaborator or reviewer.

Who it suits

Wet-lab researchers with sequencing data and no programming background, students who need a result before they have time to learn Linux, and groups wanting a reproducible standard pipeline without maintaining one. If you later want the command-line route, our Linux and Python RNA-Seq courses cover the same analysis in code.