What 1.3 Billion Sequencing Reads Actually Tell You
Depth buys you resolution. Only design and reproducibility buy you a conclusion.
A large read count is easy to report and easy to misread. Depth improves the precision of an expression estimate, but it does nothing for confounding, batch structure, or a sampling scheme that never varied the factor you care about.
The work that decides whether a dataset means anything happens before alignment: which vines, which stages, which season, how many biological replicates, and what will be held constant so that the contrast under test is the only thing moving.
After that, the value comes from discipline. A pipeline written in Snakemake, pinned software versions, and a single command that reproduces every figure make the result something another scientist can check rather than something they have to trust.
The output that survives review is rarely the longest list of differentially expressed genes. It is the small set of changes that hold up across stages, across seasons, and across a reanalysis somebody else ran.