Bulk RNASeq: from counts to differential expression
Bulk RNASeq: from counts to differential expression
9 March 2026, 09h00-16h00
16 March 2026, 09h00-16h00
19 March 2026, 09h00-12h30
General context
Understanding differential gene expression is essential for interpreting RNA sequencing data in modern life sciences. This introductory course is designed for researchers who work with bulk RNASeq data and need to move from raw counts to meaningful biological insights.
Through hands-on sessions, participants will learn how to process count files, apply statistical methods in R, and troubleshoot common issues. By the end of the training, attendees will be equipped to identify differentially expressed genes and confidently analyze their own datasets using widely adopted tools and workflows.
- Explain the principles of bulk RNASeq data processing and differential expression analysis.
- Identify appropriate tools for generating count files, starting from FASTQ files.
- Perform differential expression analysis in R using count data from different quantification tools.
- Interpret output tables and plots to draw biologically meaningful conclusions from differential expression results.
- Troubleshoot common issues encountered during RNASeq data analysis workflows.
- Experience with R programming. If you have never worked in R, start with our course Basic statistics in R.
- Processing FASTQ files using Galaxy: FASTQC, Trimmomatic, STAR, htseq-counts, FeatureCounts, Salmon, Kallisto, Qualimap, RSEQC
- Identification of DE using Bioconductor: DESeq2 + other packages like tximeta (script for EdgeR is provided but not demonstrated)
- Visualization of results using R: ggplot2, pheatmap,
- Mapping of IDs to Gene symbols using Bioconductor: AnnotationDbi
Trainers
Janick Mathys
Janick tries to help VIB scientists analyze their data by offering bioinformatics training and support. Next to organizing trainings, creating e-learning courses and teaching statistics, R, Python, Linux, HPC, bulk and single cell RNA-Seq analysis, she consults scientists and develops pipelines for omics analyses. Before joining VIB, she worked as a post-doc at KULeuven, doing research on transcriptomics and transcription regulation. She coordinated the Master of Bioinformatics program of KULeuven and taught the course on Biological Databases.