RNA-Seq Analysis (RNAA02)
Delivered by experienced bioinformaticians and computational biologists specialising in transcriptomics and sequencing data analysis.
Learn how to analyse RNA-Seq data using modern transcriptomic workflows in R.
Delivered online over 4 days, 18:00-21:00 UK time.
RNA sequencing (RNA-Seq) has become one of the most powerful technologies for studying gene expression, enabling researchers to investigate biological processes across a wide range of organisms and experimental systems. This hands-on course provides a practical introduction to RNA-Seq data analysis, covering quality control, differential expression, visualisation, and functional interpretation using reproducible workflows in R.
What you’ll gain
Course format
Who is this course for?
Why take this course?
RNA-Seq has become the standard approach for investigating gene expression across diverse areas of biology, from development and evolution to disease, physiology, agriculture, and conservation. As sequencing technologies continue to advance, researchers are generating increasingly large and complex transcriptomic datasets that require robust statistical analysis and reproducible computational workflows.
This course equips you with the practical skills needed to move confidently from raw sequencing data to biological insight. You'll learn modern RNA-Seq analysis workflows, develop a strong understanding of best practices, and gain the tools to analyse and interpret transcriptomic datasets for a wide range of research applications.
Learn more & enrol
PR Stats course page for RNA-Seq Analysis (RNAA02)
Questions?
Email: [email protected]
Delivered by experienced bioinformaticians and computational biologists specialising in transcriptomics and sequencing data analysis.
Learn how to analyse RNA-Seq data using modern transcriptomic workflows in R.
Delivered online over 4 days, 18:00-21:00 UK time.
RNA sequencing (RNA-Seq) has become one of the most powerful technologies for studying gene expression, enabling researchers to investigate biological processes across a wide range of organisms and experimental systems. This hands-on course provides a practical introduction to RNA-Seq data analysis, covering quality control, differential expression, visualisation, and functional interpretation using reproducible workflows in R.
What you’ll gain
- A strong understanding of RNA-Seq experimental design and transcriptomic workflows
- Practical experience analysing RNA-Seq data in R
- Skills in quality control, read alignment, differential expression analysis, and visualisation
- Understanding of functional enrichment and pathway analysis
- Confidence in interpreting transcriptomic results and communicating biological findings
Course format
- Live, instructor-led online training
- Hands-on coding with real-world RNA-Seq datasets
- Interactive practical exercises throughout
- Strong focus on applied, research-ready workflows
Who is this course for?
- Bioinformaticians and computational biologists
- Molecular biologists and geneticists
- Researchers working with transcriptomics or sequencing data
- PhD students and life scientists
- Anyone interested in modern genomic data analysis
Why take this course?
RNA-Seq has become the standard approach for investigating gene expression across diverse areas of biology, from development and evolution to disease, physiology, agriculture, and conservation. As sequencing technologies continue to advance, researchers are generating increasingly large and complex transcriptomic datasets that require robust statistical analysis and reproducible computational workflows.
This course equips you with the practical skills needed to move confidently from raw sequencing data to biological insight. You'll learn modern RNA-Seq analysis workflows, develop a strong understanding of best practices, and gain the tools to analyse and interpret transcriptomic datasets for a wide range of research applications.
Learn more & enrol
PR Stats course page for RNA-Seq Analysis (RNAA02)
Questions?
Email: [email protected]