Description Target Audience Prerequisites Outline

Course Description

Biological research generates huge amounts of next-generation sequencing (NGS) data, including RNA-seq. Scientists must have specialized skills and knowledge to effectively collect, analyze, and interpret this data. While most bioinformatics tools require significant programming knowledge to use, which presents a barrier to new bioinformaticians without a computational background, the Galaxy platform allows researchers to perform analyses using a graphical interface.

Through lectures and hands-on labs, this 3-day French-language CBW course will provide participants with a comprehensive understanding of the fundamental principles of RNA-seq analysis and the skills to perform this analysis themselves without needing to learn the command line. Each lecture-lab module is designed as a distinct unit and all materials will be available open-access after the workshop concludes. Participants will work through a real case scenario, starting from a publication containing RNA-seq data, moving to quality control, mapping, gene quantification, differential analysis, and functional enrichment using gold standard tools (fastq-dump, FASTQC, fastp, STAR, featurecounts, DESEQ2, g:Profiler). We will also open a Bring your own data session, giving the opportunity to talk with experts about their NGS projects.

Course Objectives

By the conclusion of the workshop, participants will have practical experience and skills in:

Understanding library preparation protocols for RNA-seq and experimental designs
Using Galaxy to perform bioinformatics analysis, share content, manage projects, and produce workflow
Downloading publicly available data from GEO/SRA
Assessing quality of RNA-seq data and perform read trimming
Understanding reference genome and transcriptome annotations
Mapping RNA-seq data to a reference genome with STAR
Visualizing RNA-seq alignments with IGV
Estimating gene expression with featurecounts
Performing differential expression analysis with DESEQ2
Performing and interpreting principal component analysis (PCA) and hierarchical clustering for outlier detection
Performing enrichment analysis with g:Profiler

Target Audience

Graduates, postgraduates, post-doctoral researchers, PIs, physicians whose research involves next-generation (and more specifically RNA-seq) data. Open to all public health, hospitals, academia, industry, or government affiliations.

Prerequisites

You will require your own laptop computer. Minimum requirements: 1024×768 screen resolution, 1.5GHz CPU, 2GB RAM, 10GB free disk space, recent versions of Windows, Mac OS X or Linux (Most computers purchased in the past 3-4 years likely meet these requirements).

This workshop requires participants to complete pre-workshop tasks and readings.

Course Outline

Module 1 Lecture: Introduction to RNA-seq

Introduction to NGS data
- Diversity of protocols
- Library preparation
Introduction to RNA-seq
- Experimental design

Module 1 Lab Practical

Introduction to Galaxy (live demo)
Upload data from computer or database
Download/share data
Datatypes
Explore available tools
Manage History
Download raw RNA-seq data from a publication
Perform and interpret data QC from raw data
Trimming

Module 2 Lecture: RNA-seq pipelines and file formats

Pipeline for basic analysis: differential expression analysis
Alignment vs assembly vs pseudo-alignment
How to choose a reference genome/annotation
Mapping with STAR and salmon
Post-alignment QC

Module 2 Lab Practical

Alignment
RNA-seq alignment visualization with IGV
Alignment QC
Predict read strandness for quantification
Gene quantification with featurecounts

Module 3 Lecture: RNA-seq differential analysis

Data normalization
Differential expression with DESeq2
Introduction to functional characterization

Module 3 Lab Practical

Differential expression with DESeq2
- Outlier detection (PCA) and batch correction
- How to define the list of DEGs
- Data representation (volcano plot, heatmap, correlation matrix…)
Principle of functional characterization
- g:Profiler with Gene Ontology, KEGG and Reactome
- Pathview

Module 4: Introduction to Integrative Assignment and Bring-Your-Own Data portion

Participants will work through an integrative assignment that brings together all principles discussed in the workshop with new datasets. They will also have the opportunity to discuss their own research projects and datasets with both the faculty team and other participants.