About the course | Intended audience | Prerequisites | Content details
About the course
RNA sequencing (RNA-seq) is widely used to quantify gene expression and investigate biological processes at the transcriptome level. This course provides a practical introduction to the analysis of bulk RNA-seq data, from raw sequencing reads through to biological interpretation of differential expression results.
Participants will learn about quality control, alignment, and quantification of gene expression against a reference transcriptome. The course also covers exploratory data analysis in R using techniques such as principal component analysis and clustering, as well as differential expression analysis using the DESeq2 R/Bioconductor package. In addition, participants will learn how to generate visualisations, including heatmaps, and perform gene set testing to relate differentially expressed genes to biological functions and pathways.
By the end of the course, participants should be able to independently analyse bulk RNA-seq data and critically interpret the results.
Teaching is primarily hands-on, with short presentations and demonstrations introducing the concepts and methods needed to analyse bulk RNA-seq datasets.
Back to top Intended audience
This course is suitable for:
- researchers and students interested in analysing bulk RNA-seq data
- participants who want practical experience with transcriptomics data analysis workflows
- researchers with experience of high-throughput sequencing who wish to develop skills in RNA-seq data analysis
- participants with a working knowledge of UNIX and R
Prerequisites
Participants should have:
- a basic understanding of high-throughput sequencing technologies
- a working knowledge of the UNIX command line (e.g. Introduction to the Unix Command Line)
- a working knowledge of R (e.g. Data analysis in R)
For an overview of high-throughput sequencing technologies, we recommend watching this iBiology video.
The following experience is recommended:
- running analyses on High Performance Computing (HPC) clusters (e.g. Working on HPC Clusters)
Content details
The course covers the following topics:
- Introduction to RNA-seq technologies and experimental design
Introduces RNA sequencing technologies and discusses key considerations when designing RNA-seq experiments, including the advantages and limitations of RNA-seq assays. - Quality control of sequencing data
Covers quality assessment of raw sequencing reads using FastQC and introduces approaches for evaluating the quality of RNA-seq datasets. - Alignment and quantification of gene expression
Introduces methods for alignment and transcript quantification using Salmon and discusses alternative tools and pipelines for estimating gene expression levels. - Exploratory analysis of RNA-seq data
Covers importing RNA-seq data into R and performing exploratory analyses using methods such as principal component analysis and clustering. - Statistical analysis and differential expression
Introduces statistical modelling approaches for bulk RNA-seq data and covers differential expression analysis using the DESeq2 package. - Annotation of RNA-seq results
Introduces annotation resources available in Bioconductor and demonstrates how to associate differential expression results with biological information. - Visualisation of differential expression results
Covers methods for visualising RNA-seq results, including heatmaps and other graphical summaries for communicating findings. - Gene-set testing and biological interpretation
Introduces approaches for identifying over-represented gene sets and pathways among differentially expressed genes and assigning biological meaning to gene lists.