The problem. Standard differential-expression asks whether a gene goes up or down. But regulation often happens at finer grain: a splicing factor can shift which exons are included while leaving total transcript output roughly flat. Gene-level counting is blind to that.

The idea. Brooks and colleagues knocked down the splicing regulator pasilla in Drosophila and used RNA-seq to map differential exon usage — testing each exon’s inclusion relative to its gene, rather than summing the gene. This exposed a conserved regulatory map of alternative splicing that gene-level analysis would have missed entirely.

Why it matters. The pasilla dataset became the canonical example for DEXSeq and exon-level analysis, so it’s one I’ll meet the moment I move beyond gene-level DESeq2 in aml_rnaseq_nf. Conceptually it’s a useful widening of the lens: the same negative-binomial machinery I already use, re-pointed from genes to exons. The statistics carry over; the question changes.

Verdict. Foundational for splicing analysis and quietly ubiquitous as a teaching dataset. Read it to internalise that “no change in expression” and “no change in isoform” are different claims — and that my current pipeline only answers the first.