An introduction to get started in genome assembly and annotation

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# An introduction to get started in genome assembly and annotation

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<a href="/training-material/hall-of-fame/lleroi/" class="contributor-badge contributor-lleroi"><img src="/training-material/assets/images/orcid.png" alt="orcid logo"/><img src="https://avatars.githubusercontent.com/lleroi?s=27" alt="Avatar">Laura Leroi</a>

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<div class="footnote" style="bottom: 4 em;"><i class="far fa-calendar" aria-hidden="true"></i><span class="visually-hidden">last_modification</span> Updated: May 17, 2022</div>

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## Requirements

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

- [Introduction to Galaxy Analyses](/training-material/topics/introduction)

- [Sequence analysis](/training-material/topics/sequence-analysis)
    - Quality Control: [<i class="fab fa-slideshare" aria-hidden="true"></i><span class="visually-hidden">slides</span> slides](/training-material/topics/sequence-analysis/tutorials/quality-control/slides.html) - [<i class="fas fa-laptop" aria-hidden="true"></i><span class="visually-hidden">tutorial</span> hands-on](/training-material/topics/sequence-analysis/tutorials/quality-control/tutorial.html)

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### <i class="far fa-question-circle" aria-hidden="true"></i><span class="visually-hidden">question</span> Questions

- What are the guidelines before starting a Genome Assembly and Annotation project?

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# Steps before starting a genome project

.left[
- Step 1: Build a wide community for the project if possible

- Step 2: Gather information about the target genome

- Step 3: Select the best possible DNA source and extraction

- Step 5: Choose an appropriate sequencing technology

- Step 6: Check the computational resources and requirements
]

---

### Build a wide community for the project (*if it's possible*)

.left[
The aim of a genome project is to **sequence the entire target genome** for a wide **range of genomics applications**.
]

.left[
**Analyses**, **reanalyses** and **integration** of genomic and other phenotype information require:
]

- **Facilities:** Wet lab, sequencing, bioinformatics,…
- **Personnel:** Skill intensive
- **Software:** Knowledge intensive

.left[
<i class="fas fa-exclamation-triangle" aria-hidden="true"></i><span class="visually-hidden">warning</span> Data storage, maintenance, transfer, and analysis costs will also likely remain substantial and represent an increasing proportion of overall sequencing costs in the future.
]

---

### Genome information: Genome size

.pull-left[
**How to collect informations?**

- Flux cytometry
- Databases:
  - Fungi: [http://www.zbi.ee/fungalgenomesize](http://www.zbi.ee/fungalgenomesize)
  - Animals: [http://www.genomesize.com](http://www.genomesize.com)
  - Plants: [http://data.kew.org/cvalues ](http://data.kew.org/cvalues)
- Bibliography
]

.pull-right[
.image-100[
  ![variation in estimated genome sizes in base pairs](../../images/genomes-size.png)
]]

.footnote[https://commons.wikimedia.org/w/index.php?curid=19537795]

---

### Genome information: GC content

.pull-left[
**Why?**

.left[
Sequencing is not random!
GC and AT rich regions are under-represented.
]

**How to solve?**
- Chemistry quirks
- Increase the sequencing depth
- Technologies combination (both long and short reads)
]

.pull-right[
.image-100[
  ![Sequencing coverage by GC content](../../images/coverage-x-GC.png)
]]

.footnote[Chaisson et al. Genetic variation and the de novo assembly of human genomes. Nat Rev Genet 16, 627–640 (2015).]

---

### Genome information: Ploidy level

.pull-right[
.image-55[
  ![Heterozigous genotype](../../images/ploidy.png)
]]

.pull-left[

**Ploidy (N):**

Number of sets of chromosomes in a cell

| Organism                  | Ploidy    |
|:--------------------------|:---------:|
| Bacteria                  | 1N        |
| Human, mouse, rat         | 2N        |
| Amphibians (*Xenopus*)    | 2N to 12N |
| Plants (wheat)            | 6N        |
| **Autopolyploid**         | .         |
| **Hybrids**               | .         |

]

**Higher ploidy -> harder to assemble => Increase of sequencing depth**

.footnote[Daniel Hartl. Essential Genetics: A Genomics Perspective. Jones & Bartlett Learning. p. 177. ISBN 978-0-7637-7364-9. (2011).]

---

### Genome information: Heterozygosity level

.pull-left[
.left[
**Heterozygous:** Locus-specific,  diploid (2N) organism has two different alleles of a particular gene at the same locus
]]

.pull-right[
.image-100[
  ![Heterozigous genotype](../../images/heterozygous.jpg)
]]

.left[
**Heterozygosity** is a metric used to denote the probability an individual will be heterozygous for a particular allele
]

**Higher heterozygosity -> harder to assemble => Increase of sequencing depth**

.footnote[https://www.genome.gov/genetics-glossary/heterozygous]

---

### Genome information: Heterozygosity level

.image-125[
  ![Concepts in phased assemblies](../../images/phased-assemblies.png)
]

.footnote[Heng Li’s blog: lh3.github.io/2021/04/17/concepts-in-phased-assemblies]

---

### Genome information: Complexity aka repeats elements

.left[
**It is impossible to resolve repeats of length L unless you have reads longer than L**
]

Most common source of assembly errors:

.pull-left[
.image-65[
  ![Collapsed consensus from repeat copies](../../images/collapsed-consensus.png)
]]

.pull-right[
.image-65[
  ![Collapsed, excision and rearrangement consensus](../../images/excision-rearrangement-consensus.png)
]]

---

### Genome information: Others

- **Karyotype:** chromosome number
- **Sex chromosome system:** None, XY, ZW, UV,…
- **Purity:** possible presence of contaminants and/or symbionts?
- Is there any other **useful data (NCBI, SRA, ENA, etc)** that could improve my assembly?

---

### Genome information: Tips

.pull-left[
- **Flux cytometry** :  genome size and ploidy level
- **k-mer frequency from Illumina reads** : genome size, ploidy level, GC content, heterozygosity and even repeats composition!
]

.pull-right[
.image-20[
  ![GenomeScope Profile](../../images/kmers-frequency.png)
]]

---

### The best possible DNA

.left[
Select the best possible DNA source and extraction.
The extraction of **high-quality DNA** is the **most important** aspect of a successful genome project

The **lack of a good starting material** will limit the choice of sequencing technology and will **affect the quality of obtained data**
]

---

### The best possible DNA: Chemical purity of DNA

.left[
**Sample-related contaminants:**
- Polysaccharides
- Proteoglycans
- Proteins
- Secondary metabolites
- Polyphenols
- Humic acids
- Pigments
- Etc,...

All these contaminants **can impair the efficacy of library preparation** in any technology, especially true **for PCR-free libraries** (both PacBio and ONT)
]

---

### The best possible DNA: Quantity of DNA

.left[
Different technologies require different amount of DNA:
- Illumina and 10x > 3 ng
- BioNano > 200 ng
- ONT > 1 μg
- Dovetail (Hic) > 5 μg
- PacBio > 15 μg
]

---

### The best possible DNA: Structural integrity of DNA

.left[
High Molecular Weight (**HMW**) for Nanopore/PacBio (obtained mainly from fresh material)
]

---

### The best possible DNA: Tips

.left[
- Many *DNA extraction protocol* available for a lot of species/taxa (VGP, Darwin Tree of Life, Nanopore, PacBio, etc)
- **Keep DNA samples** from the same individual in case of library preparation/sequencing fail, need more coverage, new sequencing technology, etc
- Use a **single individual** and sequence a **haploid, highly inbred diploid** organism, or **isogenic** individual
]

---

### Appropriate sequencing technology

.left[
Mostly **dependant on the quantity and quality of DNA and the cost** but many parameters need to be considered prior to running an NGS experiment:

- Short versus long reads or both
- Read length
- Read quality/error rate
- Genome read coverage/depth
- Library preparation
- Downstream applications
]

---

### Appropriate sequencing technology: Primary assembly

.left[
- **Illumina:** short reads (up to 2x250bp) with high quality reads. Sequencing bias with AT/GC rich regions
- **IonTorrent:** short reads (up to 500bp) with medium quality reads
- **Nanopore:** long reads (average ~15kbp) with low quality reads. Errors are not randomly distributed!
- **PacBio:**
  - CLR: long reads (average ~20kbp) with low quality reads
  - HiFi: long reads (average ~15kbp) with high quality reads
]

---

### Appropriate sequencing technology: Scaffolding

.left[
- **HiC:** single or double **restriction enzyme fragmentation.** Need huge amount of coverage.
- **Omni-C: sequence-independent endonuclease**
]

---

### Appropriate sequencing technology: Short vs long reads

.pull-left[
Depending on the sequencing technology reads length and sequencing depth are different.
- More sequencing depth but shorter reads
- Longer reads but less sequencing depth
]

.pull-rigth[
.image-40[
  ![Gigabase per run per read length](../../images/gigabase-read-length.png)
]]

.footnote[https://github.com/alexcorm/developments-in-next-generation-sequencing]

---

### Appropriate sequencing technology: Short vs long reads

.pull-left[
Reads accuracy differs depending on the sequencing technology:
- Illumina and PacBio HiFi: more accurate
- ONT and PacBio CLR: less accurate (but longer)
]

.pull-rigth[
.image-40[
  ![Reads accuracy distribution](../../images/reads-accuracy-distribution.png)
]]

---

### Appropriate sequencing technology

.image-100[
  ![Several sequencing technologies](../../images/sequencing-technologies.png)
]

.footnote[Stark, R., Grzelak, M. & Hadfield, J. RNA sequencing: the teenage years. Nat Rev Genetics 20, 631–656 (2019).]

---

### Appropriate sequencing technology: Coverage versus depth

.left[
**Coverage:** fraction of the genome sequenced at least by one read

**Depth:** average number of reads that cover any given region

Intuitively, more reads should increase coverage and depth.
]

.image-40[
  ![Sequencing coverage by GC content](../../images/coverage-x-GC.png)
]

.footnote[Chaisson et al. Genetic variation and the de novo assembly of human genomes. Nat Rev Genet 16, 627–640 (2015).]

---

### Computational resources and requirements

.left[
To succeed you need to have **sufficient compute resources (CPUS, RAM, walltime and storage)**.
- The resource are **different** between:
  - Assembly
  - Annotation
  - Other analysis tools

- For genome **assembly:**
  - Running times and RAM increase with the **type** and **amount of data**
  - More data for large genomes, increase **running time/RAM/Storage**
  - Most of tools **running** on a **single node:** parallelized but not distributed

- For genome **annotation:**
  - Mapping/alignment of external data (RNA-seq, proteins) can be parallelized and distributed
  - Annotation process can be parallelized and distributed
]

---

### Typical sequencing strategies: Bacterial genomes

.left[
- PacBio CLR or Oxford Nanopore reads at 40-50x coverage, self-correction and/or hybrid correction (using Illumina data)
- PacBio HiFi reads at 20-30x coverage
- Illumina 2x250bp paired-end reads from MiSeq
]

---

### Typical sequencing strategies: Larger genomes

.left[
- PacBio CLR or Oxford Nanopore reads at 100x coverage, hybrid correction using Illumina data and scaffolding using HiC or Omni-C
- PacBio HiFi reads at 30x coverage and scaffolding using HiC or Omni-C
- PacBio HiFi reads at 30x coverage, 120x Oxford Nanopore ultra long reads
]

---

## Thank You!

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

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	<tr>
		<td><abbr title="These people wrote the bulk of the tutorial, they may have done the analysis, built the workflow, and wrote the text themselves.">Author(s)</abbr></td>
		<td>
			
<a href="/training-material/hall-of-fame/lleroi/" class="contributor-badge contributor-lleroi"><img src="/training-material/assets/images/orcid.png" alt="orcid logo"/><img src="https://avatars.githubusercontent.com/lleroi?s=27" alt="Avatar">Laura Leroi</a>

</td>
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		<td><abbr title="These people edited the text, either for spelling and grammar, flow, GTN-fit, or other similar editing categories">Editor(s)</abbr></td>
		<td>
			
<a href="/training-material/hall-of-fame/abretaud/" class="contributor-badge contributor-abretaud"><img src="/training-material/assets/images/orcid.png" alt="orcid logo"/><img src="https://avatars.githubusercontent.com/abretaud?s=27" alt="Avatar">Anthony Bretaudeau</a>

<a href="/training-material/hall-of-fame/alexcorm/" class="contributor-badge contributor-alexcorm"><img src="/training-material/assets/images/orcid.png" alt="orcid logo"/><img src="https://avatars.githubusercontent.com/alexcorm?s=27" alt="Avatar">Alexandre Cormier</a>

<a href="/training-material/hall-of-fame/stephanierobin/" class="contributor-badge contributor-stephanierobin"><img src="/training-material/assets/images/orcid.png" alt="orcid logo"/><img src="https://avatars.githubusercontent.com/stephanierobin?s=27" alt="Avatar">Stéphanie Robin</a>

<a href="/training-material/hall-of-fame/r1corre/" class="contributor-badge contributor-r1corre"><img src="/training-material/assets/images/orcid.png" alt="orcid logo"/><img src="https://avatars.githubusercontent.com/r1corre?s=27" alt="Avatar">Erwan Corre</a>

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This material is licensed under the Creative Commons Attribution 4.0 International License</a>.