This hands-on course 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 expression analysis. Both whole transcript and QuantSeq 3' UMI data are covered. QuantSeq data analysis involves different preprocessing, so the full session on 8.4.2022 is dedicated to analyzing QuantSeq data and you have the option to register for that day only if you are already familiar with the othe topics (please see the details below).
The course consists of lectures and exercises. The lectures will be pre-recorded, and participants are requested to view the videos prior to the course and test their knowledge with a set of questions. This gives you more time to reflect on the concepts so that you can use the course time more efficiently for discussions and exercises. Note that the lectures specific to QuantSeq data are given during the course on 8.4.2022.
The course takes place at 9-12 Helsinki time (8-11 CET) each day in Zoom.
Prerequisities
In the exercises we use analysis tools embedded in the free and user-friendly Chipster software, so no previous knowledge of Unix or R is required, and the course is thus suitable for everybody who is planning to use RNA-seq.
Content
6.4.2022 at 9-12: Quality control, trimming and alignment
- check the quality of reads with MultiQC
- remove bad quality data with Trimmomatic
- infer strandedness with RseQC
- align RNA-seq reads to the reference genome with HISAT2 and STAR
- efficient analysis: how to assign paired FASTQ files to samples and align all the samples with one click
- perform alignment level quality control using RseQC
7.4.2022 at 9-12: Quantifying expression, experiment level QC, differential expression analysis
- 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
- produce heatmaps of differentially expressed genes
- how to share analysis with a colleague
8.4.2022 at 9-12: QuantSeq data analysis
- MultiQC: how to detect UMI, TATA and polyA readthrough and adapters
- extract UMI with UMI-tools
- remove polyA readthrough, adapters and bad quality ends with BBDuk
- align RNA-seq reads to the reference genome with STAR
- deduplicate alignments using UMI-tools
- note that in the exercises we practise the full workflow, which includes also strandedness inference, quantitation, experiment level QC and differential expression analysis.
Trainers
Eija Korpelainen (CSC), Maria Lehtivaara (CSC)
Course materials
Before the course you will get access to the course videos available in Chipster Youtube channel. Slides and exercises will be shared on during the course.