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# Introduction to Genome Annotation

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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)

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

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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

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# Genome Annotation

**Structural Annotation**

Positions of genomic features along the genome

**Functional Annotation**

Assigning functions to those features

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Two parts, structural and function. Structural can come from ab-initio predictions or structural data. Functional annotation often comes from analysis of protein domains or in rare cases from experimental data.

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## Structural Annotation

Types of elements:

- genes
- regulatory regions
- ncRNA
- repeat elements
- pseudogenes and paralogs

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### Structural Annotation: Why?

![](../images/intro-structural-annotation.png)

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### Structural Annotation: Why?

Locate your favorite gene + see what's next to it

Basis for other analysis, e.g.:

- Transcriptomic data (count reads mapping inside exons)
- Variants detection (SNP, indels, …) and their effects
- Epigenomic (ChIPSeq, FAIRESeq, …)

Compare with other species

- Presence/absence/mutations of genes
- Family reduction or expansion
- Structural variants

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### Prokaryotic Genes

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Promoter:

- -35 Region
- TATA Box
- Initiation site (TSS)
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.pull-right[
![](../images/intro-prok1.png)
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Operons:

- Promoter
- Some genes
- A terminator
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![](../images/intro-prok2.png)
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Prokaryotic genes often have a well conserved structure, with a promoter, one or a few genes and a terminator.

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### Eukaryotic Genes

![](../images/intro-eukaryotic-genes.png)

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Things are a little more complicated for eukaryotic: splicing

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## Automatic Structural Annotation

Very difficult problem

- Short, variable, unspecific motifs
- Need data to support predictions

![](../images/intro-tss.png)

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### Evidence

Multiple pieces of evidence

- Alignment of RNASeq reads
- Alignment of EST or transcripts (same species or closely related species)
- Alignment of proteins (closely related species)

.image-50[
![](../images/intro-evidences.png)
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*But* data unavailable for novel or very distant genes, or unexpressed genes

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### *ab initio* Gene Calling

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Predictions using:
- Genome sequence
- Stastical model (specific to organism)

Models:
- Built from training on evidence-based gene calls (2-3 iterations)
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.pull-right.image-90[
![](../images/intro-ab-initio.png)
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### Data Reconcilliation

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- Integration of evidence and *ab initio* predictions
- "Consensus" of multiple sources
- Automated pipelines: Maker, Braker, Pasa, Prokka
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.pull-right.image-90[
![](../images/intro-consensus.png)
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<small>
Source: [Maker tutorial](http://weatherby.genetics.utah.edu/MAKER/wiki/index.php/MAKER_Tutorial_for_WGS_Assembly_and_Annotation_Winter_School_2018)
</small>

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### Evaluation of Evidence: metrics

- Number of genes
- Average number of exons
- Average gene length
- ...

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### Evaluation of Evidence: BUSCO

#### Benchmarking Universal Single-Copy Orthologs

* Sets of genes having single-copy orthologs in all species of a clad (insects, plants, bacteria, ...)
* Genes supposed to be vital for the species
* Expected to be found in a good annotation
* Results:
  * Found genes
  * Fragmented genes
  * Duplicated genes

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### Visualisation of Results

Genome Browsers (JBrowse, UCSC, ...)

![](../images/intro-viz.png)

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### Repeat Elements

- Tranposons, low complexity regions
- Disrupt gene calling
- Prediction pipelines:
  - RepeatMasker
  - REPET
  - RepBase

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### Exotic Elements

- tRNA, rRNA, ncRNA, ...
- Dedicated tools for prediction
  - Aragorn
  - tRNAscan
  - ...

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### Summary

- Difficult
- Never perfect
  - Missing/incomplete genes
  - Split/fused genes
  - Pseudogenes

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## Manual Annotation

- Recruit experts of some gene families
- Manual curation of their favorite genes
  - Better annotation
  - Things to say in the genome paper
- Limits
  - There aren't experts for all genes
  - They can only annotate what is in the sequence
  - Poor assembly ⇒ Poor annotation
  - We need a user-friendly environment

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### Editors

Apollo (based upon JBrowse), Artemis, others

![](../images/intro-apollo.png)

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### Steps

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Annotations steps

- Check structure (exons, introns, start, stop, utr, ...)
- Search for isoforms
- Ensure consistent naming conventions
- Add functional annotations (based on homologies with other species)
]

.pull-right[
![](../images/intro-steps.png)
]

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## Functional Annotation

Collection of information on the function of identified genes
- biological function
- regulations, expressions, ...

Data Sources
- wet lab experiments (reliable but long and expensive)
- manual assignment (cf apollo)
- **automatic assignment**

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### Methods

- similarity search / homology
- pattern search
- orthologies
- synteny to related organisms
- Comparison against databases:
  - GenBank, NR: sequence bank
  - InterPro: pattern library (active sites, protein families, peptide signal ...)

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### Blast

- Blast against NR
  - For each protein (or CDS) of the annotation
  - Find the best xx hits
- Huge database, good chances to have a match
- Risk:
  - Spread of "putative xx protein"
  - Spread of low-evidence annotations

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### InterProScan

- For each protein (or CDS) of the annotation
  - Search for all InterPro patterns
- Many motifs
- Some of them manually curated
- Gene Ontology Terms available for domains

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### Gene Ontology

.pull-left[
Controlled vocabulary to describe:
- molecular function
- biological process
- cellular component
  - e.g.: `GO:0044430` = cytoskeletal part

![](../images/intro-go.png)
]

.pull-right[
![](../images/intro-go-source.png)
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### Blast2GO

- Blast2GO
  - For each protein (or CDS) of the annotation, tag with GO terms
  - Based on Blast and InterProScan results

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### Orthology

- For each annotated gene
  - Search of orthologous genes in related species
  - Search for paralogues

- Bioinformatics method:
  - Blast all against all transcripts
  - Filtering the best hits
  - Clustering
  - OrthoFinder, OrthoMCL, ...

![](../images/intro-ortho.png)

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### Visualisation

- Genomic databases (NCBI, FlyBase, etc.)
- Other sites (Tripal sites)
  - reference data (assembly, annotation, ...)
  - interfaces to visualize this data
  - interfaces for querying
e.g. [bipaa.genouest.org](https://bipaa.genouest.org)

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## Genome Annotation

- Very difficult
- Automatic:
	- Structural:
	  - *Ab initio* methods are improving
	  - EST/RNA-Seq data provides good evidence
	- Functional:
	  - Concrete evidence cost-prohibitive to obtain
	  - Risks of automatically spreading "putative" annotations
- Manual:
	- Slow
	- No expert or evidence available

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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#abretaud" class="contributor-badge"><img src="https://avatars.githubusercontent.com/abretaud" alt="Anthony Bretaudeau">Anthony Bretaudeau</a>, <a href="/archive/2019-11-01/hall-of-fame#erasche" class="contributor-badge"><img src="https://avatars.githubusercontent.com/erasche" alt="Helena Rasche">Helena Rasche</a>

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