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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-throuput manner
---
### Where my data comes 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>
---
### Principle of RNA sequencing <br>Quantification
<small>[*Pepke et al, Nat Met, 2009*](http://www.nature.com/nmeth/journal/v6/n11s/full/nmeth.1371.html)</small>
---
### Challenges of RNA sequencing
- Different origin for the sample RNA and the reference genome
- Presence of incompletely processed RNAs or transcriptional noise
- Sequencing biases (*e.g.* PCR library preparation)
---
### Benefits of RNA sequencing
---
## How to analyze RNA seq data?
---
### 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
---
### Overview of the Data Processing
- No available standardized workflow
- Multiple possible best practices for every dataset
---
### Data Pre-processing
*See [NGS Quality control](../../NGS-QC/slides/index.html)*
1. Adapter clipping to trim the sequencing adapters
2. Quality trimming to remove wrongly called and low quality bases
---
### Genomic Alignment<br>3 strategies
<small>
[*Conesa et al, Genome Biol, 2016*](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0881-8)
</small>
---
### Differential Expression Analysis
<tiny>Account for variability of gene expression across biological replicates<br>with the help of gene counts
</tiny>
---
### Differential Expression Analysis<br>Within-sample Normalization
Neccessary to correct for feature length and library-size effects
- [*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)
---
### Differential Expression Analysis<br>Number of Replicates for DE Genes
<small> [*Conesa et al, Genome Biol, 2016*](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0881-8)</small>
---
### 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 you prepared yourself or you obtained from your colleques
- 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