These walkthroughs were written by Aaron Mitchell-Dick. They are built on the vignettes published by the authors of each package — every one is linked below — reorganised into a single continuous workflow rather than a set of separate documents you have to stitch together yourself.
They are intended to be thorough enough that someone with beginner-level knowledge can perform an RNA-Seq analysis from scratch, and they deliberately comment on the things an advanced user knows and forgets to mention: which directory you should be in, why a command failed, what a warning in the output actually means.
There are many other good resources for beginners that require no hard lessons in coding. Galaxy in particular is an excellent free, open-source platform serving the entire bioinformatics community.
Questions, corrections, and requests are welcome: aaronmitchd@gmail.com.
Reuse
Prose and figures on this site are released under CC BY 4.0; the code is released under the MIT licence. Use it, adapt it, teach from it. Attribution is appreciated but a citation to the underlying tools matters more — see below.
Primary citations
If you publish work that used this pipeline, cite the methods:
- Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nature Biotechnology 34, 525–527 (2016).
- Soneson C, Love MI, Robinson MD. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research 4:1521 (2015).
- Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15:550 (2014).