Every cell has the same genome, but not every gene is active at once. Transcriptomics measures which genes are switched on and how strongly. RNA-seq does this by sequencing the RNA in a sample, so you can compare gene expression between conditions such as healthy and diseased tissue, or treated and untreated cells.
You'll start with how RNA-seq works and what its data looks like, including why the results come as read counts per gene. Then you'll learn how DESeq2, the most widely used tool for this job, normalizes those counts and tests which genes change.
Finally, you'll follow hands-on tutorials in R that take a count table through the whole analysis and produce the plots used in papers. This is a good place to use the R skills from earlier in the course. Once you've finished, move on to Protein Structure & Phylogenetics.
A plain-language introduction to how RNA-seq data is produced and analysed.
A look at the inner workings of DESeq2, the popular tool for differential expression analysis of RNA-seq data.
A hands-on project that reproduces a differential gene expression analysis using DESeq2 and data from the Gene Expression Atlas.
A beginner-friendly walkthrough of differential gene expression analysis with DESeq2 in R.