RNA-seq Analysis

This sequence provides a detailed explanation of the process of RNA-seq analysis and teaches you to the skills to carry out your own analyses. You will explore real RNA-seq data from the fruit-fly midgut. You will learn how to use several different R packages for RNA-seq analysis, as well as learning how to prepare and reformat your data so that it can be analyzed by these programs.



Required packages:

  • Standard R packages:
    • learnr
    • tidyverse
    • caTools
    • bitops
  • BioConductor packages:
    • DESeq2
    • clusterProfiler
    • org.Dm.eg.db


For each tutorial, download the file and open it using RStudio. You may need to right-click the link and choose “Download File”. Make sure you save it as a .Rmd file - some browsers like to save it as a text file by adding .txt to the end of the file name.

Introduction to RNA-seq

  1. Compare and contrast the genome (DNA) and transcriptome (RNA)
  2. Explain what is being measured by RNA-seq
  3. Describe the steps involved in RNA-seq

Explore Read Count Data (HTSeq files)

  1. Understand the columns and rows of an HTSeq file
  2. Use R to generate basic statistics for HTSeq files
  3. Create and analyze histograms from HTSeq files

Calculate Differential Expression with DESeq2

  1. Calculate differential expression statistics using DESeq2
  2. Extract DESeq2 results and reformat them to be more useful for future analysis
  3. Understand the columns and rows of DESeq2 results
  4. Use R to extract results for a single gene of interest
  5. Use R to create a list of significantly different genes for different significance cutoffs.

More coming soon!

Check back soon for more tutorials teaching the nitty-gritty details of analyzing RNA-seq data!