RNA-seq Data Analysis

Registration open soon


16-19 November 2021

9.30 - 15.30 each day




24 for each workshop

You will be contacted by our finance team for full payment. Once payment is made, your place will be confirmed and full details sent by our training team.

Registration fee:

£250 - University of Edinburgh staff/students
£263 - Other university or registered charity staff/students
£325 - Industrial researchers


Contact our training team



RNA sequencing (RNA-seq) is quickly becoming the method of choice for transcriptome profiling. Nevertheless, it is a non-trivial task to transform the vast amount of data obtained with high-throughput sequencers into useful information. Thus, RNA-seq data analysis is still a major bottleneck for most researchers in this field. The ability of correctly interpreting RNA-seq results, as well as knowledge on the intrinsic properties of these data, are essential to avoid incorrect experimental designs and the application of inappropriate analysis methodologies. The aim of this workshop is to familiarise researchers with RNA-seq data and to initiate them in the analysis by providing lectures and practicals on analysis methodologies. In the practicals Illumina-generated sequencing data and various widely used software programs will be used.


"Loved it! I'm now itching to get my hands on my RNA-seq data and analyse it myself." (November 2014)

"I really enjoyed the workshop and I thought it was extremely well organised and taught." (May 2017)



  • Urmi Trivedi (Bioinformatician, Edinburgh Genomics)
  • Frances Turner (Bioinformatician, Edinburgh Genomics)
  • Nathan Medd (Training & Outreach Manager, Edinburgh Genomics)

Workshop format

The workshop consists of presentations and hands-on tutorials.

Who should attend

Graduates, postgraduates, and PIs, who are using, or planning to use, RNA-seq technology in their research and want to learn how to process and analyse RNA-seq data.


  • A general understanding of molecular biology and genomics.
  • A working knowledge of Linux at the level of the Edinburgh Genomics Linux for Genomics workshop.
  • A rudementary knowledge of R. Although we go through some introductory R, it would be very beneficial to have some experience with the R environment before the start of the course. 


Covered topics (and software)

  • Introduction to Next Generation Sequencing
  • Quality control and data pre-processing (FastQC, cutadapt)
  • Mapping to a reference genome (STAR, SAMtools)
  • Visualisation of mapped reads (SAMtools, IGV)
  • Introduction to R (R)
  • Estimating gene count (featureCounts)
  • Differential expression analysis (R, RStudio, edgeR, rtracklayer, ggplot2, pheatmap)
  • Functional analysis (GSEABase)