From: owner-finbionet-members@helsinki.fi <owner-finbionet-members@helsinki.fi> On Behalf Of Tienhaara, Anita M H
Sent: maanantai 28. maaliskuuta 2022 11.21
To: finbionet-members@helsinki.fi
Subject: Analysis courses on QuantSeq 3' data and full length RNA-seq data at CSC

Dear all,

Registration is now open for two Zoom courses on the analysis of bulk RNA-seq data. The courses are described briefly here below and in more detail at https://ssl.eventilla.com/bulkrnaseq2022. In the exercises we use analysis tools via the free and user-friendly Chipster software, so no previous knowledge of Unix or R is required, and the courses are thus suitable for everybody.

1. Analysis of QuantSeq RNA-seq data with Chipster 8.4.2022 at 9:00-12:00
This course focuses on the analysis of QuantSeq FWD UMI 3' RNA-seq data, which requires specific preprocessing steps. The topics include how to

- detect UMI, TATA and polyA readthrough and adapters with MultiQC
- extract UMIs and store them in read names using 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. If you are not familiar with these more general analysis steps, you might like to take course 2 instead (see below).

2. Analysis of bulk RNA-seq data with Chipster 6.-8.4.2022 at 9:00-12:00 each day
This course covers both full length and 3' RNA-seq data. In addition to the topics mentioned for the course 1, you will learn how to:

- check the quality of reads with MultiQC
- remove bad quality data with Trimmomatic
- infer strandedness with RseQC
- efficient analysis: how to assign paired FASTQ files to samples (allows aligning all the samples with one click)
- 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
- produce heatmaps of differentially expressed genes
- share analysis with a colleague

Should you have any questions, please don't hesitate to email chipster@csc.fi.

Best regards,

Eija

Eija Korpelainen, Ph.D
Product Manager, ELIXIR-FI Training Coordinator
CSC - IT Centre for Science
P.O.Box 405, 02101 Espoo, Finland
Phone +358 50 381 9726
eija.korpelainen@csc.fi