Requirements

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

• Introduction to Galaxy Analyses

• Sequence analysis
  • Quality Control: slides slides - tutorial hands-on
  • Mapping: slides slides - tutorial hands-on

Tip: press P to view the presenter notes

What is RNA sequencing?

RNA

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

Credit: Thomas Shafee

RNA sequencing

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

Credit: Thomas Shafee (adapted)

Where does my data come from?

Zang and Mortazavi, Nature, 2012

Principle of RNA sequencing

Korf, Nat Met, 2013

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)

Benefits of RNA sequencing

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)

How to analyze RNA seq data for RNA quantification?

RNA quantification

Pepke et al, Nat Met, 2009

Overview of the Data Processing

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

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

Annotation of RNA-Seq reads

Simple mapping on a reference genome? More challenging

Credit: Rgocs

Annotation of RNA-Seq reads

3 main strategies for annotations

• Transcriptome mapping
• Genome mapping
• De novo transcriptome assembly and annotation

Transcriptome mapping

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

Genome mapping

Splice-aware read alignment

Detection of novel genes and isoforms

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

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, ...

De novo transcriptome assembly

No need for a reference genome ...

1. Assembly into transcripts
2. Map reads back

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?

Differential Expression Analysis

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

Differential Expression Analysis: Normalization

Make the expression levels comparable across

• By Features: genes, isoforms
• By Samples
• Methods
  • FPKM/RPKM (Cufflinks/Cuffdiff)
  • TMM (edgeR)
  • DESeq2 (DESeq2)

"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

Impact of sequencing depth and number of replicates

Conesa et al, Genome Biol, 2016

Recommendation: At least 3 biological replicates

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

Related tutorials

Thank you!

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

Bérénice Batut Anika Erxleben Markus Wolfien

This material is licensed under the Creative Commons Attribution 4.0 International License.