Material for the course RNA-seq data analysis with Chipster

Keywords

RNA-Seq, FASTQ, QC, Pre-processing, Alignment, BAM, Expression-estimation, Feature-summarisation, Differential-expression, Statistical-model, Exploratory-analysis

Authors

• Eija Korpelainen (@eija), [email protected]

Type

• Lecture
• Practical

Description

This material covers the whole RNA-seq data analysis pipeline, from quality control of raw reads to differential expression analysis.

Aims

Performing RNA-seq analysis, Being able to choose an appropriate strategy, Recognizing the challenges and pitfalls, Recognizing and troubleshooting issues with the data, Recognizing the importance of experimental design

Prerequisites

• As the user-friendly Chipster software is used in the exercises, no command line or R experience is required.

Target audience

The course is suitable for any researcher interested in learning RNA-seq data analysis.

Learning objectives

• Applying FastQC quality control software and interpreting the output
• Performing possible preprocessing steps with PRINSEQ software
• Differentiating genome and trancriptome alignment
• Selecting the appropriate alignment tool
• Recognizing the challenges and pitfalls in alignment
• Producing alignment with TopHat
• Interpreting the aligner output
• Being able to visualise alignments
• Applying RseQC software for alignment level QC and interpreting the output
• Producing a table of counts with HTSeq software
• Identifying confounding effects with PCA and MDS plots and taking necessary action
• Recognizing the need for normalization
• Performing DE analysis with edgeR and DESeq2 and interpreting the output
• Understanding and performing multifactor analysis
• Operating Chipster software

Materials

• Slides for lectures on RNA-seq data analysis
• Practicals on RNA-seq data analysis using the free Chipster software

Data

• The datasets for the exercises are available on Chipster server as example sessions. Two datasets are used:
• Raw reads from human hESC1 and GM12878 cells produced by the ENCODE project
• Table of per-gene read counts from an experiment by Brooks et al which studied the effect of RNAi knockdown of the splicing factor Pasilla in Drosophila melanogaster. The read counts were obtained from the pasilla Bioconductor package.

Timing

The lecture and practicals can be performed in one day.

Content stability

The content is updated approximately every 3 months.

Technical requirements

• Chipster software v3.3 or later

Literature references

• Suitable reading includes the book RNA-seq data analysis - practical approach