Detecting differentially expressed genes with RNA-seq 11.9.2019
This workshop introduces the participants to RNA-seq data analysis methods, tools and file formats. It covers the whole workflow from quality control and alignment to quantification and differential gene expression analysis. While this course focuses on differential gene expression analysis (DGE), it also discusses the pros and cons of differential transcript expression (DTE) and differential transcript usage (DTU) analysis.
The workshop consists of lectures and practical exercises. The free and user-friendly Chipster software is used in the exercises, so no previous knowledge of Unix or R is required, and the workshop is thus suitable for everybody. The course data and ready-made example sessions are available on Chipster server, where anybody can log in as guest.
You will learn how to
-check the quality of reads with FastQC
-infer strandedness with RseQC
-align RNA-seq reads to the reference genome with HISAT2 and STAR
-perform alignment level quality control using RseQC
-quantify expression by counting reads per genes using HTSeq
-check the experiment level quality with PCA plots and heatmaps
-analyze differential expression with DESeq2 and edgeR
-take multiple factors (including batch effects) into account in differential expression analysis
Scientific topics: RNA-Seq