Introduction to Bulk RNAseq analysis

Content

This course is an introduction for how to approach bulk RNAseq data, starting from the sequencing reads.

It will provide an overview of the fundamentals of RNAseq analysis, including:

• read preprocessing,
• data normalization,
• data exploration with PCAs and heatmaps,
• performing differential expression analysis and annotation of the differentially expressed genes.

Participants will also learn how to evaluate confounding and batch effects in the data.

The course will further touch upon laboratory protocols, library preparation, and experimental design of RNA sequencing experiments, especially about how they influence downstream bioinformatic analysis. 

Learning Outcomes

A student who has met the objectives of the course will be able to: 

• Gain insight into how to design an RNA-seq experiment 
• Preprocess sequencing reads 
• Analyze bulk RNAseq data using the R package DESeq2 
• Know best practices for performing Differential Expression Analysis 
• Annotate and interpret their results 

Requirements

• Working knowledge of the command line, R language, RStudio and Rmarkdown is mandatory.
  • You could be interested in the course From Excel to R
• Basic knowledge of RNA sequencing technology. 
• Basic knowledge of data science concepts such as principal component analysis, clustering and statistical testing.