Last week I ran a one-day workshop on RNA-seq data analysis in the UVA Health Sciences Library. I set up an AWS public EC2 image with all the necessary software installed. Participants logged into AWS, launched the image, and we kicked off the morning session with an introduction to the Unix shell (taught by Jessica Bonnie, a biostatistician here in our genomics group, and a fellow Software Carpentry instructor). I followed with a walkthrough of using FastQC for quality assessment, FASTX toolkit for trimming, TopHat for alignment, and featureCounts to summarize gene expression read counts at the gene level. I started the afternoon session started with an introduction to R, followed by a tutorial on analyzing the count data we generated in the first part using DESeq2 in R.
All of the rendered course material is available here. The source code used to generate this material is all on available on GitHub (go read my post on collaborative lesson development, if you haven't already). Much of the introductory Unix lesson material was adapted from the Software Carpentry and Data Carpentry projects.
I wrote a more thorough blog post about how the course went here on the Software Carpentry blog.
I also compiled a PDF of all the course materials, available on Figshare: http://dx.doi.org/10.6084/m9.figshare.1247658.