Bioinformatics for Genomics





9-13 September 2019


Edinburgh Genomics, Peter Wilson Building, The King's Buildings, The University of Edinburgh, Edinburgh, Scotland, UK


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:


You can cancel up to one month before the workshop and receive a refund minus 30% for administration.


Contact our training team



This is a week-long workshop that combines our popular ‘Linux for Genomics’, ‘R for Genomics’, and ‘Introduction to RNA-seq Data Analysis’, taught by Edinburgh Genomics’ Bioinformaticians. By the end of the five days, you will be: comfortable on the Linux command line; able to view, filter and manipulate large text files; able to write pipelines to perform certain bioinformatics tasks; familiar with and know and use the basics of R; confident using several data QC and processing tools; able to generate gene counts and differential expression statistics; and prepared to put it all together with visualization, interpretation, and gene set analysis.




  • Tim Booth (Bioinformatician/Programmer)
  • Urmi Trivedi (Bioinformatician)
  • Frances Turner (Bioinformatician)
  • Nathan Medd (Training and Outreach Manager)

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, NGS (in particular RNA-seq) technology in their research and want to learn how to process and analyse NGS data.


Covered topics

  • Linux for Genomics
    • The shell and commands
    • Getting help
    • Files and directories
    • Navigating the file system
    • File management
    • Permissions
    • Accessing files
    • Downloading remote files
    • Zipping and unzipping files
    • Pipes and redirects
    • Filtering / manipulating file content
    • Shell scripts
    • Process management
    • Command-line tools for genomics (seqtk, bioawk, samtools, bedtools, tabix)
  • R for Genomics
    • R fundamentals
    • Iteration and data structures (Functions, loops, and 'apply')
    • Working with genomics data structures (GRanges)
    • Accessing genomic resouces (bioconductor)
    • Visualisation (ggplot2)
  • Introduction to RNAseq Data Analysis (and some of the software tools covered)
    • 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, Salmon)
    • Differential expression analysis (R, RStudio, edgeR, rtracklayer, ggplot2, pheatmap)
    • Functional analysis (GSEABase)