About This Course
RNA-seq measures gene expression across the whole transcriptome and is one of the most widely used NGS experiments in biology. This course teaches a complete, reproducible RNA-seq workflow on real public data.
You go from raw reads to biological answers: quality control, alignment with HISAT2 or quantification with Salmon, building a count matrix, differential expression with DESeq2, visualization, and functional enrichment with GO and KEGG. A short module also covers microarray data in GEO.
Who this course is for
- MSc and PhD students working on gene expression
- Researchers who have RNA-seq data and want to analyse it themselves
- Bioinformatics learners moving from beginner to research-level work
Prerequisites
Introduction to NGS and Sequencing QC (or equivalent). Basic R is helpful; the R you need is taught in the course.
Final project
Compare control and treatment samples from a public dataset, identify differentially expressed genes and interpret the enriched pathways in a short report.
