This hands-on workshop will introduce users of the R software environment to the specific skills and applications used in the analysis of RNA-Seq gene expression data.
The workshop will start with an introduction to the tools and workflows of RNA-Seq analysis using the Galaxy bioinformatics platform before moving onto R for more advanced differential gene expression analysis. Practical exercises will include quality control and normalisation of data, differential gene expression analysis, and linking of data to external resources for more in depth interpretation.
Recommended Participants
Biologists and bioinformaticians wishing to use R for RNA expression analysis. Prior expertise with R and the command line interface is required, to a level equivalent of that provided by the QFAB workshop “Introduction to R”.
Learning Objectives
- Reading and Parsing FastQ files
- Carry out standard QC tests on RNA-Seq datasets
- Use the limma-voom R package to produce lists of differentially expressed genes
- Use of Biomart for annotation of transcripts
- Use of GoStats to perform enrichment analysis
Syllabus
- Pre-processing and quality control of RNA-Seq data
- Mapping and quantification of read data
- Identification of differentially-expressed genes
- Annotation of transcripts
- Systems biology interpretation of gene lists using pathway enrichment analysis
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