+ - 0:00:00
Notes for current slide

Presenter notes contain extra information which might be useful if you intend to use these slides for teaching.

Press P again to switch presenter notes off

Press C to create a new window where the same presentation will be displayed. This window is linked to the main window. Changing slides on one will cause the slide to change on the other.

Useful when presenting.

Notes for next slide



Introduction to Transcriptomics



last_modification Updated:   purlPURL: gxy.io/GTN:S00094

Tip: press P to view the presenter notes | arrow-keys Use arrow keys to move between slides
1 / 27

Presenter notes contain extra information which might be useful if you intend to use these slides for teaching.

Press P again to switch presenter notes off

Press C to create a new window where the same presentation will be displayed. This window is linked to the main window. Changing slides on one will cause the slide to change on the other.

Useful when presenting.

Requirements

Before diving into this slide deck, we recommend you to have a look at:

2 / 27

What is RNA sequencing?

3 / 27

RNA

The structure of a eukaryotic protein-coding gene

  • Transcribed form of the DNA
  • Active state of the DNA

Credit: Thomas Shafee

4 / 27

RNA sequencing

Summary of RNA-Seq principle. In vivo transcription, pre-mRNA, intron splicing all rpoduce a mature mRNA. In vitro this is fragmented into RNA fragments, reverse transcribed into double stranded cDNA and then sequenced.

  • RNA quantification at single base resolution
  • Cost efficient analysis of the whole transcriptome in a high-throughput manner

Credit: Thomas Shafee (adapted)

5 / 27

Where does my data come from?

select a cell population and extract total RNA is shown at the top. Small RNA are size selected by PAGE or kit, an adapter ligated, and converted to cDNA. Or poly(a) selects ribosome minus, and those mRNA are fragmented, and converted to cDNA. In both cases the cDNA becomes a library for sequencing.

Zang and Mortazavi, Nature, 2012

6 / 27

Principle of RNA sequencing

Cartoon showing a magazine stand labelled transcriptome, and a person saying "I'll take all of them". These are run through a shredder, before hundreds of people attempt to re-assemble, and the person hands the professor a poorly assembled magazine.

Korf, Nat Met, 2013

7 / 27

Challenges of RNA sequencing

  • Different origin for the sample RNA and the reference genome
  • Presence of incompletely processed RNAs or transcriptional background noise
  • Sequencing biases (e.g. PCR library preparation)
8 / 27

Benefits of RNA sequencing

A word cloud highlighting words like benefits, novel, sensitivity.

9 / 27

2 main research applications for RNA-Seq

  • Transcript discovery

    Which RNA molecules are in my sample?

    Novel isoforms and alternative splicing, Non-coding RNAs, Single nucleotide variations, Fusion genes

  • RNA quantification

    What is the concentration of RNAs?

    Absolute gene expression (within sample), Differential expression (between biological samples)

10 / 27

How to analyze RNA seq data for RNA quantification?

11 / 27

RNA quantification

Select RNA fraction of interest (polyA, ribo-minus, and others), these are fragmented and reverse transcribed before sequencing and mapping onto the genome and quantification.

Pepke et al, Nat Met, 2009

12 / 27

Overview of the Data Processing

Control and treatment files goes through QC, annotation, and rad counting to produce sets of count tables. Then differential expression analysis is computed.

  • No available standardized workflow
  • Multiple possible best practices for every dataset
13 / 27

Data Pre-processing

  1. Adapter clipping to trim the sequencing adapters
  2. Quality trimming to remove wrongly called and low quality bases

See NGS Quality control

14 / 27

Annotation of RNA-Seq reads

Simple mapping on a reference genome? More challenging

A cartoon of a pre-mRNA, intro and exons. These map to an mRNA and short reads are shown piled up against the mRNA. The short read is splity by intron when aligning to a reference genome.

Credit: Rgocs

15 / 27

Annotation of RNA-Seq reads

3 main strategies for annotations

  • Transcriptome mapping
  • Genome mapping
  • De novo transcriptome assembly and annotation
16 / 27

Transcriptome mapping

Cartoon of multiple exons collapsed, and paired end reads being shown as easy to align.

See NGS Mapping

  • Need reliable gene models
  • No detection of novel genes

Figures by Ernest Turro, EMBO Practical Course on Analysis of HTS Data, 2012

17 / 27

Genome mapping

Splice-aware read alignment

The same cartoon again, but now it is shown split up by introns, and one of the paired end reads is split across three exons, so it is hard to align.

Detection of novel genes and isoforms

Figures by Ernest Turro, EMBO Practical Course on Analysis of HTS Data, 2012

18 / 27

Transcriptome and Genome mapping

Needed

  • Reference genome/transcriptome in FASTA
  • Annotations of known genes, ... in GTF

Where to find?

  • Joint projects to produce and maintain annotations on selected organisms: EMBL-EBI, UCSC, RefSeq, Ensembl, ...
19 / 27

De novo transcriptome assembly

No need for a reference genome ...

  1. Assembly into transcripts
  2. Map reads back
20 / 27

Quantification

What is the expression level of the genomic features?

  • Counting the number of reads per features: Easy!!
  • Challenges
    • How to handle multi-mapped reads (i.e. reads with multiple alignments)?
    • How to distinguish between different isoforms?
      • At gene level?
      • At transcript level?
      • At exon level?
21 / 27

Differential Expression Analysis

Three conditions are created and multiple transcriptomics sequenced into reads and mapped and compared.

Account for variability of expression across biological replicates
with the help of counts

22 / 27

Differential Expression Analysis: Normalization

Make the expression levels comparable across

  • By Features: genes, isoforms
  • By Samples
  • Methods

"Only the DESeq and TMM normalization methods are robust to the presence of different library sizes and widely different library compositions..." - Dillies et al., Brief Bioinf, 2013

23 / 27

Impact of sequencing depth and number of replicates

Image of a table from a paper. The recommendation is at least three biological replicates to accurately detect changes. 3 replicates will give you an 87% chance of detecting a 2-fold change, but only a 17% chance of detecting a 1.25 fold change.

Conesa et al, Genome Biol, 2016

Recommendation: At least 3 biological replicates

24 / 27
  • Number of replicates has greater effect on DE detection accuracy than sequencing depth (more replicates = increased statistical power)
  • DE detection of lowly expressed genes is very sensitive to number of reads and replication
  • DE detection of highly expressed genes possible already at low sequencing depth

Visualization

  • Integrative Genomics Viewer (IGV) or Trackster

    Visualization of the aligned BAM files

  • Sashimi plots

    Quantitative visualization of read coverage along exons and splice junctions

  • CummeRbund

    Visualization package for Cufflinks high-throughput sequencing data

25 / 27
26 / 27

Thank You!

This material is the result of a collaborative work. Thanks to the Galaxy Training Network and all the contributors!

Galaxy Training Network

Tutorial Content is licensed under Creative Commons Attribution 4.0 International License.

27 / 27

Requirements

Before diving into this slide deck, we recommend you to have a look at:

2 / 27
Paused

Help

Keyboard shortcuts

, , Pg Up, k Go to previous slide
, , Pg Dn, Space, j Go to next slide
Home Go to first slide
End Go to last slide
Number + Return Go to specific slide
b / m / f Toggle blackout / mirrored / fullscreen mode
c Clone slideshow
p Toggle presenter mode
t Restart the presentation timer
?, h Toggle this help
Esc Back to slideshow