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# Introduction to Transcriptomics

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## Requirements

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

- [Introduction to Galaxy Analyses](/archive/2019-11-01/topics/introduction)

- [Sequence analysis](/archive/2019-11-01/topics/sequence-analysis)
    - Quality Control: [<i class="fa fa-slideshare" aria-hidden="true"></i><span class="visually-hidden">slides</span> slides](/archive/2019-11-01/topics/sequence-analysis/tutorials/quality-control/slides.html) - [<i class="fa fa-laptop" aria-hidden="true"></i><span class="visually-hidden">tutorial</span> hands-on](/archive/2019-11-01/topics/sequence-analysis/tutorials/quality-control/tutorial.html)
    - Mapping: [<i class="fa fa-slideshare" aria-hidden="true"></i><span class="visually-hidden">slides</span> slides](/archive/2019-11-01/topics/sequence-analysis/tutorials/mapping/slides.html) - [<i class="fa fa-laptop" aria-hidden="true"></i><span class="visually-hidden">tutorial</span> hands-on](/archive/2019-11-01/topics/sequence-analysis/tutorials/mapping/tutorial.html)

.footnote[Tip: press `P` to view the presenter notes]

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# What is RNA sequencing?

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### RNA

![The structure of a eukaryotic protein-coding gene](../images/Gene_structure_eukaryote_2_annotated.png)

- Transcribed form of the DNA
- Active state of the DNA

.footnote[[Credit: Thomas Shafee](https://en.wikipedia.org/wiki/File:Gene_structure_eukaryote_2_annotated.svg)]

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## RNA sequencing

![Summary of RNA-Seq principle](../images/Summary_of_RNA-Seq.png)

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

.footnote[[Credit: Thomas Shafee (adapted)](https://commons.wikimedia.org/wiki/File:Summary_of_RNA-Seq.svg)]

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### Where does my data come from?

![](../images/RNA_seq_zang2016.png)

.footnote[[*Zang and Mortazavi, Nature, 2012*](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4138050/)]

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### Principle of RNA sequencing

![](../images/korf_2013.jpg)

.footnote[[*Korf, Nat Met, 2013*](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4461013/)]

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### 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)

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### Benefits of RNA sequencing

![](../images/wordcloud.png)

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### 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)

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## How to analyze RNA seq data for RNA quantification?

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### RNA quantification

![](../images/pepke_2009.jpg)

.footnote[[*Pepke et al, Nat Met, 2009*](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4077321/)]

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### Overview of the Data Processing

![](../images/rna_seq_workflow.png)

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

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### Data Pre-processing

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

.footnote[See [NGS Quality control](../../NGS-QC/slides/index.html)]

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### Annotation of RNA-Seq reads

Simple mapping on a reference genome? More challenging

![](../images/RNA-Seq-alignment.png)

.footnote[[Credit: Rgocs](https://en.wikipedia.org/wiki/File:RNA-Seq-alignment.png)]

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### Annotation of RNA-Seq reads

3 main strategies for annotations

- Transcriptome mapping
- Genome mapping
- *De novo* transcriptome assembly and annotation

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### Transcriptome mapping

![](../images/transcriptome_alignment.png)

*See [NGS Mapping](../../NGS-mapping/slides/index.html)*

- Need reliable gene models
- No detection of novel genes

.footnote[Figures by Ernest Turro, EMBO Practical Course on Analysis of HTS Data, 2012]

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### Genome mapping

Splice-aware read alignment

![](../images/genome_alignment.png)

Detection of novel genes and isoforms

.footnote[Figures by Ernest Turro, EMBO Practical Course on Analysis of HTS Data, 2012]

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### 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, ...

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### *De novo* transcriptome assembly

No need for a reference genome ...

1. Assembly into transcripts
2. Map reads back

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### 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?

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### Differential Expression Analysis

.image-75[![](../images/RNA_seq_DEscheme.png)]

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

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### Differential Expression Analysis: Normalization

*Make the expression levels comparable across*

- By Features: genes, isoforms
- By Samples
- Methods
    - [*FPKM/RPKM*](https://www.nature.com/nmeth/journal/v5/n7/abs/nmeth.1226.html) (Cufflinks/Cuffdiff)
    - [*TMM*](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2010-11-3-r25) (edgeR)
    - [*DESeq2*](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8) (DESeq2)

.footnote[*"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]

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### Impact of sequencing depth and number of replicates

.image-75[![](../images/RNA_seq_numreplicates.png)]

.footnote[[*Conesa et al, Genome Biol, 2016*](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0881-8)]

**Recommendation: At least 3 biological replicates**

???

- 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

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### Visualization

- Integrative Genomics Viewer ([*IGV*](https://bib.oxfordjournals.org/content/14/2/178.full?keytype=ref&%2520ijkey=qTgjFwbRBAzRZWC)) or Trackster

Visualization of the aligned BAM files

- [*Sashimi plots*](https://bioinformatics.oxfordjournals.org/content/early/2015/01/21/bioinformatics.btv034)

Quantitative visualization of read coverage along exons and splice junctions

- [*CummeRbund*](http://compbio.mit.edu/cummeRbund/manual_2_0.html)

Visualization package for Cufflinks high-throughput sequencing data

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## Related tutorials

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## Thank you!

This material is the result of a collaborative work. Thanks to the [Galaxy Training Network](https://wiki.galaxyproject.org/Teach/GTN) and all the contributors!

<a href="/archive/2019-11-01/hall-of-fame#bebatut" class="contributor-badge"><img src="https://avatars.githubusercontent.com/bebatut" alt="Bérénice Batut">Bérénice Batut</a>, <a href="/archive/2019-11-01/hall-of-fame#erxleben" class="contributor-badge"><img src="https://avatars.githubusercontent.com/erxleben" alt="Anika Erxleben">Anika Erxleben</a>, <a href="/archive/2019-11-01/hall-of-fame#mwolfien" class="contributor-badge"><img src="https://avatars.githubusercontent.com/mwolfien" alt="Markus Wolfien">Markus Wolfien</a>

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