Differential expression analysis with Bulk RNA-seq

This workshop covers the basics of differential expression analysis at the gene level with bulk RNA-seq data, assuming a reference genome and both structural and functional annotation are available. This workflow can apply to model organisms, and others with published genomic resources.

See this site for our workshop schedule and registration link.

See this page for our general short-form workshop approach.

Note

For species where genomic resources are not available, we offer a version of the workshop that includes de novo transcriptome assembly and functional annotation. If a genome is available and neither structural nor functional annotation is available, we offer an independent workshop entirely on genome annotation

Schedule

• Prior to workshop
  • Prior to the synchronous portion of the workshop, attendees complete a self-guided introduction to our high performance computing cluster where they will learn to connect, work at the command line using the BASH shell on a Linux operating system, and submit work using the HPC job scheduler SLURM.
• Day 1
  • Intro to sequencing technologies
  • Intro to RNA-seq data
  • Study design considerations
  • Retrieving data from NCBI
  • QC and Trimming
• Day 2
  • Reference alignment
  • Expression quantification
• Day 3
  • Expression quantification continued.
  • Differential expression analysis
  • Functional annotations
  • Gene set enrichment and over-representation analysis
  • Visualizing RNA-seq data and differential expression results

Data

As an example dataset, we use RNA-seq data from:

Reid, N. M., Proestou, D. A., Clark, B. W., Warren, W. C., Colbourne, J. K., Shaw, J. R., … & Whitehead, A. (2016). The genomic landscape of rapid repeated evolutionary adaptation to toxic pollution in wild fish. Science, 354(6317), 1305-1308.

The subset of data we use forms a 2x2 factorial experiment that allows us to demonstrate how to extract both main effects and interactions.