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---
layout: introduction_slides
topic_name: RNA-Seq
logo: "GTN"
---
# What is RNA sequencing?
---
### RNA sequencing
RNA
- Transcribed form of the DNA
- Active state of the DNA
RNA sequencing
- RNA quantification at single base resolution
- Cost efficient analysis of the whole transcriptome in a high-throughput manner
---
### Where does my data come from?
<small>[*Zang and Mortazavi, Nature, 2012*](http://www.nature.com/ni/journal/v13/n9/full/ni.2407.html)</small>
---
### Principle of RNA sequencing
<small>[*Korf, Nat Met, 2013*](http://www.nature.com/nmeth/journal/v10/n12/full/nmeth.2735.html)</small>
---
### 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
---
### FASTQ-files
```bash
@4:1:4888:1039:Y
TGAACGCTGTTTCCAAGAAATGCTGGAAGAGGTCGATGGGTGTTATCTCTG
+
IIIHIIIIIIIIIIIIIIIIIIIIIIIIIH!CBCBBBB@=B@A?1@==<@=
```
- 50+ million reads per data set (~15 GB data for one human fastq-file)
- Unique identifier per read
- Nucleotide sequence with single base resolution
- Quality score for each base
---
### 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 gene expression (between biological samples)
- Isoform expression / differential exon usage / alternative splicing
---
## How to analyze RNA seq data for RNA quantification?
---
### RNA quantification
<small>[*Pepke et al, Nat Met, 2009*](http://www.nature.com/nmeth/journal/v6/n11s/full/nmeth.1371.html)</small>
---
### Overview of the Data Processing
- No available standardized workflow
- Multiple possible best practices for every dataset
---
## Data Pre-processing
.image-50[]
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)]
---
## Annotation of RNA-Seq reads
.image-75[]
How do I identify my reads?
---
### 3 main strategies for annotations
<small>
[*Conesa et al, Genome Biol, 2016*](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0881-8)
</small>
---
### Sources of reference annotations
- Joint projects to produce and maintain annotations on selected organisms: EMBL-EBI, UCSC, RefSeq, Ensembl, ...
- Annotations of known genes, repeats, ... in GTF file format
---
### Transcriptome alignment
*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]
---
### Genome alignment
Splice-aware read alignment
Detection of novel genes and isoforms
.footnote[Figures by Ernest Turro, EMBO Practical Course on Analysis of HTS Data, 2012]
---
### *De novo* transcriptome assembly
No need for a reference genome ...
---
## Quantification of transcript level
What is the expression level of the genomic features?
---
### Counting the number of reads per features
Easy!!
But some 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
---
### Differential Expression Analysis
.image-75[]
Account for variability of expression across biological replicates<br>with the help of counts
---
### Normalization
Make the expression levels comparable across
- Features: genes, isoforms
- Libraries: samples
---
### Normalization methods
- [*FPKM/RPKM*](http://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)
Normalize counts *k<sub>ij</sub>* for gene *i* in library *j* by size factor *s<sub>j</sub>*
.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]
---
### Analysis of Differential Gene Expression (DGE)
Idea
- Model the gene counts by negative binomial distribution
- Account for variability of gene expression across biological replicates
---
### Impact of sequencing depth and number of replicates
.image-50[]
<small> [*Conesa et al, Genome Biol, 2016*](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0881-8)</small>
**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
---
### Detection of Alternative Splicing
<small>
[*Hooper, Hum. Genomics, 2014*](http://humgenomics.biomedcentral.com/articles/10.1186/1479-7364-8-3)
</small>
---
### Visualization
- Integrative Genomics Viewer ([*IGV*](http://bib.oxfordjournals.org/content/14/2/178.full?keytype=ref&%2520ijkey=qTgjFwbRBAzRZWC))
Visualization of the aligned BAM files
- [*Sashimi plots*](http://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
---
### Where do I find data?
- Sequencing data which you have prepared by yourself or you have obtained from your colleagues
- Sequence Read Archive - [*SRA*](https://www.ncbi.nlm.nih.gov/sra)
- Gene Expression Omnibus - [*GEO*](https://www.ncbi.nlm.nih.gov/geo/)
- Ensembl - [*e!*](http://www.ensembl.org/info/website/tutorials/sequence.html)
- The Cancer Genome Atlas - [*TCGA*](https://tcga-data.nci.nih.gov/docs/publications/tcga/?)
- ... our tutorial