RNA-seq Data Analysis workshop, Dec 12 - 15

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.

Instructors

  • Frances Turner (Bioinformatician, Edinburgh Genomics)
  • Heleen De Weerd (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.

Requirements

  • 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 working knowledge of R at the level of Edinburgh Genomics R for Biologists workshop. (If you are unsure of how these relate to your coding skills please check the above course pages and look at topics covered)

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)
  • Estimating gene count (featureCounts)
  • Differential expression analysis (R, RStudio, edgeR, rtracklayer, ggplot2, pheatmap)
  • Functional analysis (GSEABase)

Related Links 

Edinburgh Genomics