Syllabus

This workshop is aimed towards biologists, researchers, computer scientists or data analysts with limited experience in analysing NGS data.

The syllabus for this workshop are as follows.

  • Working on the unix/linux command line
    • Command line navigation and related commands: cd, mkdir, rm, rmdir
    • Commonly used linux tools: cp, mv, tar, less, more, head, tail, nano, grep, top, man
    • Wildcards
    • Ownership and permissions
    • Symbolic links
    • Piping commands
  • Working on remote computing cluster
    • Logging on to HPC
    • Booking resources
    • Job templates, submission and queues
    • Modules
  • Commonly used bioinformatic tools and pipelines
  • Working with integrated genome viewer
  • Variant-calling workflow
    • Mapping reads to the reference genome
    • Variant detection
    • VCF file format
  • RNA-Seq workflow
    • RNA-Seq experimental design and considerations
    • QC, mapping and gene expression counts
    • Differential gene expression analyses
  • Current advances in NGS technologies

After this workshop you should be able to:

  • Describe the basic principles of next generation sequencing.
  • Use the Linux command line interface to manage simple file processing operations, and organise directory structures.
  • Connect to and work on a remote compute cluster.
  • Apply programs in Linux for analysis of NGS data.
  • Summarise the applications of current NGS technologies, including the weakness and strengths of the approaches and when it is appropriate to use which one of them.
  • Explain common NGS file formats.
  • Interpret quality control of NGS reads.
  • Explain the steps involved in variant calling using whole genome sequencing data.
  • Independently perform a basic variant calling workflow on example data.
  • Explain the steps involved in a differential gene expression workflow using RNA seq data.
  • Hands-on experience with handling of raw RNA sequencing data, QC and quantification of gene expression.
  • Conceptual understanding of differential gene expression analysis.