Counting on Illumina transition: Why RNASeq compared to Gene Expression Arrays?

Seminar – “Counting on Illumina: Transition”
The switch from expression arrays continues to gather pace. Since the seminal publication describing the
application of NGS technology to gene expression analysis in 2008 (Mortazavi 2008, Nat. Methods) the
community has continued to develop innovative methods to study RNA biology, including single-cell and
ribosomal profiling. But arguably the most powerful benefit of RNA-seq is its unbiased nature…
Key Benefits or RNA-Seq:

  • Unbiased survey of transcription with the ability to identify novel
    transcripts & isoforms
  • Single-base resolution defines transcriptional boundaries & can reveal
    underlying sequence variation
  • None of the background hybridisation issues with promiscuous probebinding
    when using arrays
  • A larger dynamic range (typically 5-6 logs versus 3-4 for microarrays)
    Despite the advances in methodology and the benefits described above it has become apparent there are
    still some aspects of the technique that remain unclear. Therefore in this seminar we will review the
    principles of RNA-seq and discuss some key aspects of experimental design before deconstructing a
    number of myths (using extensive and recent peer-reviewed data) including:
    Myth 1: “You need 200M reads to get meaningful data”
    Fact 2: There are indeed experimental designs that may require 200M reads or more, but to
    get data comparable to a generic 3’-array 10-20M single-end (SE) reads of 50bp
    can suffice.
    Myth 2: “RNA-seq lacks reproducibly”
    Fact 2: This claim is likely linked to Myth 1, as increasing read depth is often viewed as the
    best approach to improve statistical output. However both technical and biological
    replicates are highly reproducible, with sample size a major influence.
    Myth 3: “Data analysis is too complex”
    Fact 3: Data analysis pipelines differ from array-based methods,
    but Illumina’s BaseSpace Sequence Hub offers cloud-based
    storage & analysis with a host of informatics tools wrapped
    into a simple, graphical user interface. Data can also be
    analysed in R-studio via Illumina’s BaseMount tool, or
    downloaded for entry into in-house pipelines.
    So please join us as we take an end-to-end deep dive into this key application:
  • Overview of NGS & Illumina Technology
  • Principles of RNA-Seq, including experimental design
  • Extensive Library Preparation options for RNA-Seq
  • Data Analysis using BaseSpace Sequencing Hub