RNA-seq analysis: Fall, 2024
Welcome!
This lesson will introduce you to the basics of gene expression analysis using RNA-Seq (short for RNA sequencing). Due to the considerable progress and constant decreasing costs of RNA-Seq, this technique has became a standard technique in biology.
It is going to be fun and empowering! We will use the shell (covered on the first day of this submodule) and R (covered on the second day of the submodule) to perform our RNA-Seq analyses and visualisations. Before you begin, be sure you are all set up for the lesson. See and complete the Setup section.
Main learning objectives
After completing this lesson, you should be able to:
- Assess the quality of RNA-seq sequencing data (“reads”) using the command-line instructions.
- Align RNA-seq reads to a reference genome using a splice-aware aligner (e.g. STAR).
- Generate a count matrix from the RNA-seq data alignment.
- Perform a QC of your experiment through Principal Component Analysis (PCA) and sample clustering.
- Execute a differential gene expression analysis using R and the DESeq2 package.
- Be able to create key plots: volcano plot, heatmap and clustering of differentially expressed genes.
- Provide a biological interpretation to differentially expressed genes through ORA/GSEA analyses and data integration.
Schedule
| Setup | ||
|---|---|---|
| 09:00 - 10:00 | Introduction & QC | What can I learn by doing this RNA-Seq lesson? What are the tools that I will be using? How do I perform a quality check of my RNA-seq fastq files with FastQC? How can I remove RNA-seq reads of low quality? |
| 10:00 - 11:00 | Aligning | How do I align my reads to a reference genome using STAR and hisat2? |
| 11:00 - 11:45 | Counting | What is a BAM file? How do I determine the number of reads that map within genes? |
| 1:00 - 2:00 | Differential expression | How do I know that my RNA-seq experiment has worked according to my experimental design? What is a Principal Component Analysis (PCA) and how can I use it? What are factor levels and why is it important for different expression analysis? How can I call the genes differentially regulated in response to my experimental design? What is a volcano plot and how can I create one? What is a heatmap and how can it be informative for my comparison of interest? |
| 2:00 - 3:00 | Over-representation analysis | Given a list of differentially expressed genes, how do I search for enriched functions? |
| 3:00 - 3:45 | Gene set enrichment | What is the difference between an over-representation analysis (ORA) and a gene set enrichment analysis (GSEA)? |
Credits
This lesson is heavily based on teaching materials from the Harvard Chan Bioinformatics Core (HBC) in-depth NGS data analysis course and RNA-seq lesson from the ScienceParkStudyGroup.