<figure><img src="image_1.png" /></figure>

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# Methods for plant genome assembly

Camille Rustenholz

► To cite this version:

Camille Rustenholz. Methods for plant genome assembly. Evolutionary genomics for plant models and crops, CIBIO, May 2022, Vairão, Portugal. hal-05282928

HAL Id: hal-05282928

https://hal.inrae.fr/hal-05282928v1

Submitted on 25 Sep 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Methods for plant genome assembly

## Second session: Methods in plant genomics

Faculty

of life sciences

University of Strasbourg

INRAE

Camille Rustenholz (camille.rustenholz@inrae.fr), 10.05.2022

<figure><img src="image_3.png" /></figure>
## Outline

1. Definitions and history of genome sequencing

2. The sequencing technologies applied to genome assembly

3. Direct usages of genome assemblies

4. Towards the other « omics »
## Outline

1. Definitions and history of genome sequencing

2. The sequencing technologies applied to genome assembly

3. Direct usages of genome assemblies

4. Towards the other « omics »
Definition of genomics

Genomics studies the functioning of an organism, an organ, a tissue, a cell **on the scale of the whole genome**, instead of being limited to the scale of a single gene.

(Wikipedia)
More definitions...

* **Genome**: all of the genetic material of a species encoded in its DNA (except certain RNA viruses). It is composed of coding and non-coding sequences.
And more definitions...

* **Sequencing:** determination of the nucleotide succession order for a given DNA fragment.

* **Assembly:** consists of aligning and/or merging DNA or RNA fragments from a longer sequence in order to reconstruct the original sequence.

* **Annotation:** 1) locate genes and coding regions ; 2) identify or predict their biological function.

(Wikipedia)
Sequence assembly

Reconstruction of the original sequence from the
reads generated by the sequencer.

"The problem of assembly can be compared to that of reconstructing the text of a book from several copies of it, previously shredded into small pieces."
# Sequencing and genome assembly

<figure><img src="image_4.png" /></figure>

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Sequencing and genome assembly

<figure><img src="image_9.png" /></figure>

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<figure><img src="image_16.png" /></figure>

Sequencing

<figure><img src="image_17.png" /></figure>

CCGAAGT GTCAAA AT CGAGGTCC ATCGAGGTCC TTCCCG
AAGTCAAA ATCGA ATCGAAGTCAAA CGAGGT
GGTTCCCGAA CAAA
# Sequencing and genome assembly

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

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<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

## Sequencing

<figure><img src="image_26.png" /></figure>

CCGAAGT GTCAAA AT CGAGG

ATCGAGGTTC AT TTCCCG

AAGTCAAA ATCGA

TCCCGAAGTCAAA CGAGGT

GGTTCCCGAA CAAA

## Assembly

<figure><img src="image_27.png" /></figure>

ATCGAGGTTC AAGTCAAA

AT GGTTCCCGAA CAAA

ATCGA TCCCGAAGTCAAA

TTCCCG GTCAAA

ATCGAGG CCGAAGT

CGAGGT

ATCGAGGTCCCGAAGTCAAA
Structural annotation – location of words / genes

<figure><img src="image_28.png" /></figure>

Hello, wie
geht es dir
heute ?

TGAGAAAAATTGCAAAAAATAAA
AAAGAAGATATTTCATTACAAC
AAGGAAGCAAATAAGATATATAC
TTATATTTAAAGAGGAAGAAGA
AGAAGAATGGATGATGAAGAAGA
AACATAAGAAGCGTACCAAGAAT
CAGTTGAGTACTCAAACAGCTTTC
CTTTAGTGGAGAAGACAATCAAA
GCAACCTCTGCATCACACAAGAC
CGAGATCTCATGAGCTTTCTTCAG
CAACCCAGTCCTCCTCTGGAGA
AGGTGACTTGGCGATTGATCTTG
TTCTCTATGCGCTTCAGCTGAACC
CTACCTCTTCCCATCTTTTGGTTTT
GCGTTTTTTTTTTTTTCGATTGTTT
Structural annotation – location of words / genes

mqzieurhgvibezanliqugaoubnrvmaibroyaghfioman
mgiivazgiahalloeuévihaoewieghpauhvbnjmnecoih
qzvgehtughbaoemuesbrhuhgohrugçeiuhvbieubviez
vbehrvuoehrvegtbvheiuhqrygigeirhdireoruhvgpeuh
nvoeuhvpeiurbnheutevlioeuhvpeauirhvnezuorhgei
urhvnqekdnfvodhrgpaehgnudfhfvieuhrvnkejhvnib
dfhuvnekuhgnliauhvbdfnvlqiju
Structural annotation – location of words / genes

mqzieurhgvibezanliqugaoubnrvmaibroyaghfioman
mgiivazgiahalloeuévihaoewieghpauhvbnjmnecoih
qzvgehtughbaoemuesbrhuhgohrugçeiuhvbieubviez
vbehrvuoehrvegtbvheiuhqrygigeirhdireoruhvgpeuh
nvoeuhvpeiurbnheutevlioeuhvpeauirhvnezuorhgei
urhvnqekdnfvodhrgpaehgnudfhfvieuhrvnkejhvnib
dfhuvnekuhgnliauhvbdfnvlqiju
Structural annotation – location of words / genes

mqzieurhgvibezanliqugaoubnrvmaibroyaghfioman
mgiivazgia**ollaheuévihaoewieghpauhvbnjmnecoih
qzv**thegughbaoemuesbrhuhgohrugçeiuhvbieubviez
vbehrvuoehrvegtbvheiuhqrygigeirh**rideoruhvgpeuh
nvoeuhvpeiurbn**etuehvlioeuhvpeauirhvnezuorhgei
urhvnqekdnfvodhrgpaehgnudfhfvieuhrvnkejhvnib
dfhuvnekuhgnliauhvbdfnvlqiju
Structural annotation – location of words / genes

TGAGAAAAATTGCAAAAAATAAAAAAGAAGATATTTCATTACAACAAGGAAGC
AAATAAGATATATACTTATATTTTAAAGAGGAAGAAGAAGAAGAATGGATGATG
AAGAAGAAACATAAGAAGCGTACCAAGAATCAGTTGAGTACTCAAACAGCTTTC
CTTTAGTGGAGAAGACAATCAAAGCAACCTCTGCATCACACAAGACCGAGATCT
CATGAGCTTTCTTCAGCAACCCAGTCCTCCTCTTGAGAAGGTGACTTGGCGAT
TGATCTTGTTCTCTATGCGCTTCAGCTGAACCCTACCTCTTCCCATCTTTTGGTTT
TGCGTTTTTTTTTTTTCGATTGTTTTGTTATTTTTATGTATTTTTTTTCTTAGAAC
CCAAACAGAAAACAGCGGAGAAAATTAAACCCAAAAGAAAGGGAAAGAGAATT
CCAAAAAAGAAAACAAAAACAAAAACAAAGAGCAGTTGAAGTAGATAGTTCGA
TTAAAAAAAAGCTTAAAGGGGTTTCCTTGTAGTTTCCAAATGGTGATGTATTTTG
GTGAGAGTTGTGTGGAAGAGAGAATGGTCATTTATAGAAGGAAAGGAAAAAGA
GGCATATTGATGAAAAGTGGTATTGGAGGGTTGGATGGAGGGATGGATGGTGT
GGTAAAACCAGAGTGGTTTTTAAGAATTTGTTGTCGAGTTGTGATTGGCGGAGG
GTACTGTACAGTCGTTACTGTCTGGTGTAGGAGCCACAAGTTTTTAGGCATTTTA
GGCATCAGTAGATGTTATTGTCAGCCCTTGTACGGCCACTGCAGTCTTCTCACTT
CCATTGACGGCGCTTCTGATCATCACTATCCCTTTCTCCTTCTTTTCCTTTAATCT
CTTTTTCTTATTCTTAATCATTTACGTGTGACTCCCTCTCTGAATTATTAACGTCG
ATTACTGTGACTTGACATGCCCCACTC
Structural annotation – location of words / genes

TGAGAAAAATTGCAAAAAATAAAAAAGAAGATATTTCATTACAACAAGGAAGC
AAATAAGATATATACTTATATTTAAAGAGGAAGAAGAAGAAGAATGGATGATG
AAGAAGAAACATAAGAAGCGTACCAAGAATCAGTTGAGTACTCAAACAGCTTTC
CTTTAGTGGAGAAGACAATCAAAGCAACCTCTGCATCACACAAGACCGAGATCT
CATGAGCTTTCTTCAGCAACCCAGTCCTCCTCTTGAGAAGGTGACTTGGCGAT
TGATCTTGTTCTCTATGCGCTTCAGCTGAACCCTACCTCTTCCCATCTTTTGGTTT
TGCGTTTTTTTTTTTTCGATTGTTTTGTTATTTTTATGTATTTTTTTTCTTAGAAC
CCAAACAGAAAACAGCGGAGAAAATTAAACCCAAAAGAAAGGGAAAGAGAATT
CCAAAAAAGAAAACAAAAACAAAAACAAAGAGCAGTTGAAGTAGATAGTTCGA
TTAAAAAAAAGCTTAAAGGGGTTTCCTTGTAGTTTCCAAATGGTGATGTATTTTG
GTGAGAGTTGTGTGGAAGAGAGAATGGTCATTTATAGAAGGAAAGGAAAAAGA
GGCATATTGATGAAAAGTGGTATTGGAGGGTTGGATGGAGGGATGGATGGTGT
GGTAAAACCAGAGTGGTTTTTAAGAATTTGTTGTCGAGTTGTGATTGGCGGAGG
GTACTGTACAGTCGTTACTGTCTGGTGTAGGAGCCACAAGTTTTTAGGCATTTTA
GGCATCAGTAGATGTTATTGTCAGCCCTTGTACGGCCACTGCAGTCTTCTCACTT
CCATTGACGGCGCTTCTGATCATCACTATCCCTTTCTCCTTCTTTTCCTTTAATCT
CTTTTTCTTATTCTTAATCATTTACGTGTGACTCCCTCTCTGAATTATTAACGTCG
ATTACTGTGACTTGACATGCCCCACTC
Functional annotation – the meaning of the
words / the function of the genes

<figure><img src="image_29.png" /></figure>

Hello, wie
geht es dir
heute ?

Hello, how are you
today?

TGAGAAAAATTGCAAAAAATAAA
AAAGAAGATATTTCATTACAAC
AAGGAAGCAAATAAGATATAC
TTATATTTAAAGAGGAAGAAGA
AGAAGAATGGATGATGAAGAAGA
AACATAAGAAGCGTACCAAGAAT
CAGTTGAGTACTCAAACAGCTTTC
CTTTAGTGGAGAAGACAATCAAA
GCAACCTCTGCATCACACAAGAC
CGAGATCTCATGAGCTTTCTTCAG
CAACCCAGTCCTCCTCTGGAGA
AGGTGACTTGGCGATTGATCTTG
TTCTCTATGCGCTTCAGCTGAACC
CTACCTCTTCCCATCTTTTGGTTTT
GCGTTTTTTTTTTTTTCGATTGTTT

Fragment of *apetala1* involved in
floral identity
# Definition of depth and coverage

* Coverage: Proportion of a region with aligned reads

* Depth: Number of reads that align to a given base

<figure><img src="image_30.png" /></figure>
# Definition of depth and coverage

* Coverage: Proportion of a region with aligned reads

* Depth: Number of reads that align to a given base

<figure><img src="image_31.png" /></figure>
The different scales of assembly

<figure><img src="image_32.png" /></figure>
Pre-genomic period

* 1953: DNA structure by Watson and Crick

<figure><img src="image_33.png" /></figure>

* 1956: Sequencing protocol for protein sequencing (Sanger)

* 1977: Sequencing protocol for DNA (Sanger), ΦX174 bacteriophage (5 386 nt)

* 1980s: Low-throughput sequencing (one gene at the time) because manual

<figure><img src="image_34.png" /></figure>
The beginnings of genomics

* 1987: First automatic sequencers

* 1995: Sequencing of the 1st prokaryote genome, *Haemophilus influenzae* (1,83 Mb)

* 1996: Sequencing of the 1st eukaryote genome, *Saccharomyces cerevisiae* (12 Mb)

* 2001: Announcement of human genome sequencing

<figure><img src="image_35.png" /></figure>
# Plant genomes completely sequenced and assembled

<figure><img src="image_36.png" /></figure>
# Ten Thousand Plant Genome Project

The Ten Thousand Plant Genome Project (10KP) aims to sequence the genomes of 10,000 plants through extensive global cooperation, comprehensive resource collection, and systematic scientific design and research to promote biodiversity, understand plant evolution, and ecological protection and related important basic scientific research and for the development of agricultural applications.

Plants are the core component of the earth's biodiversity and ecosystems, the basis for human survival and development, and the fundamental guarantee for the sustainable development of human society and the economy. Tens of thousands of plants are a treasure trove of resources that are necessary for the sustainable development of the human economy and society. Understanding the systemic evolution and biodiversity of plants at the genomic level would be helpful for a comprehensive understanding of the life mysteries of plants themselves, and is essential to the protection of plant ecosystems and the development and utilization of plant resources. Currently, the diversity of plant genome is still largely unknown, so far only around 300 plant species around the world have been published, mainly the model plant and crop species. A large number of wild species resources still remain unexplored.

The Ten Thousand Plant Genome Project (10KP) was jointly initiated by BGI-Shenzhen (Institute of Life Sciences) and several authoritative experts in the field of botany during the 19th International Botany Conference in 2017.

As an important step in understanding the tree of life and plant genome variation, the program provides new opportunities for studying biological processes and solving basic research problems. It will improve our understanding of plant genome diversity and also provide plant scientists with interdisciplinary comparisons. Analyses would provide important genetic resources. At the same time, the project will expand the species sequencing to every genus apart from crops and model plants, thus providing extremely important genetic information data resources for the plant science community.

At present, the project has completed sequencing and assembly of nearly 200 plant genomes, and plans to complete the sequencing of nearly 1,000 plant genomes (including varieties) in the next four years.

<figure><img src="image_37.png" /></figure>

https://en.genomics.c
n/en-project-dzwyjs-
6184.html
## Outline

1. Definitions and history of genome sequencing

2. The sequencing technologies applied to genome assembly

3. Direct usages of genome assemblies

4. Towards the other « omics »
The sequencing: the yields increase, the costs
decrease

<figure><img src="image_38.png" /></figure>

https://www.sciencenews.org/article/gene-sequencing-future-here
Overview of sequencing technologies (2016!)

<figure><img src="image_39.png" /></figure>
# Evolution of sequencing technologies

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<table><thead><tr><th>Sequencers</th><th>1st generation :<br>Sanger 3730xl<br>(~ 2000)</th><th>2nd generation :<br>Illumina HiSeq 2000<br>(~ 2006)</th><th>3rd generation :<br>Pacific Biosciences RS II<br>(~ 2012)</th></tr></thead><tbody><tr><th scope="row">Method</th><td></td><td></td><td></td></tr><tr><th scope="row">Read length</th><td></td><td></td><td></td></tr><tr><th scope="row">Error rate</th><td></td><td></td><td></td></tr><tr><th scope="row">Amount of data<br>produced at once</th><td></td><td></td><td></td></tr><tr><th scope="row">Cost for 10<sup>9</sup> nt</th><td></td><td></td><td></td></tr></tbody></table>

Adapted from http://data-science-sequencing.github.io/lectures/lecture1/
# Evolution of sequencing technologies

<figure><img src="image_43.png" /></figure>

<figure><img src="image_44.png" /></figure>

<figure><img src="image_45.png" /></figure>

<table><thead><tr><th>Sequencers</th><th>1st generation :<br>Sanger 3730xl<br>(~ 2000)</th><th>2nd generation :<br>Illumina HiSeq 2000<br>(~ 2006)</th><th>3rd generation :<br>Pacific Biosciences RS II<br>(~ 2012)</th></tr></thead><tbody><tr><th scope="row">Method</th><td colspan="3">Terminaison with<br>dideoxynucleotides</td></tr><tr><th scope="row">Read length</th><td colspan="3">400-900 nt</td></tr><tr><th scope="row">Error rate</th><td colspan="3">0,001%</td></tr><tr><th scope="row">Amount of data<br>produced at once</th><td colspan="3">10<sup>5</sup> nt</td></tr><tr><th scope="row">Cost for 10<sup>9</sup> nt</th><td colspan="3">2 000 000 €</td></tr></tbody></table>

Adapted from http://data-science-sequencing.github.io/lectures/lecture1/
# Sanger sequencing

https://www.youtube.com/watch?v=e2G5zx-OJIw

<figure><img src="image_46.png" /></figure>
Improvements in the basic technique over time

<figure><img src="image_47.png" /><figcaption>②</figcaption></figure>

<figure><img src="image_48.png" /><figcaption>④
Extension by DNA polymerase
Termination when ddNTP added</figcaption></figure>

<figure><img src="image_49.png" /></figure>

* Radioactive ddNTP, gel separation

* Fluorescent ddNTPs, gel separation

* Fluorescent ddNTPs, capillary separation
# Two strategies for sequencing / assembling a genome

## WGS

### Whole Genome Shotgun
“Random” method

<figure><img src="image_50.png" /></figure>
## Whole Genome Shotgun – “Random” method

- Build a 500 million piece puzzle (for grapevine)...

- ... with 5,000 million pieces (depth 10x = 10 times the same mixed puzzle)!

<figure><img src="image_51.png" /></figure>
"Whole Genome Shotgun" strategy

<figure><img src="image_52.png" /></figure>

ACGATCGATCGATCGTAATTTATAGCATGCTAGCTACTGACGGGCTTTTACGGCGTTAGATATATATCGATCGATCGATGCTATATAGCGTGACTGATCGTAGCTGTAGCTAGCTGTAGCTAGCT

Genomic Sequence
Assembly method called OLC

Sequencing

<figure><img src="image_53.png" /></figure>
Assembly method called OLC - Overlap

<figure><img src="image_54.png" /></figure>

Alignment
Assembly method called OLC - Layout

<figure><img src="image_55.png" /></figure>

Identification
of the most
informative
sequences
Assembly method called OLC - Layout

<figure><img src="image_56.png" /></figure>

Identification
and error
correction
Assembly method called OLC - Consensus

<figure><img src="image_57.png" /></figure>
# Two strategies for sequencing / assembling a genome

## WGS

### Whole Genome Shotgun
“Random” method

<figure><img src="image_58.png" /></figure>

## BAC-based
“Organized” method

<figure><img src="image_59.png" /></figure>
BAC-based – “Organised” method

<figure><img src="image_60.png" /></figure>

Build a 500 million
piece puzzle (for
grapevine)...

... with 5,000 small
puzzles of 150,000
pieces each (depth
1,5x)!
Subcloning of ~150kb inserts in BAC plasmides

<figure><img src="image_61.png" /></figure>

**BAC** = Bacterial artificial chromosome
Bacterial plasmide designed for large DNA inserts (80-350kb)
<figure><img src="image_62.png" /></figure>

<figure><img src="image_63.png" /></figure>
Minimal Tiling Path (MTP)

<figure><img src="image_64.png" /></figure>
Minimal Tiling Path (MTP)

<figure><img src="image_65.png" /></figure>

12 BAC-
inserts to
sequence
Each BAC-insert is shotgun sequenced

<figure><img src="image_66.png" /></figure>
BAC-based – “Organised” method

<figure><img src="image_67.png" /></figure>

Each selected BAC-insert is a
sub-puzzle whose position in
the large puzzle is known.
# « Sanger » assembled plant genomes

## WGS

<figure><img src="image_68.png" /></figure>

## BAC-based

<figure><img src="image_69.png" /></figure>
# Evolution of sequencing technologies

<figure><img src="image_70.png" /></figure>

<figure><img src="image_71.png" /></figure>

<figure><img src="image_72.png" /></figure>

<table><thead><tr><th>Sequencers</th><th>1st generation :<br>Sanger 3730xl<br>(~ 2000)</th><th>2nd generation :<br>Illumina HiSeq 2000<br>(~ 2006)</th><th>3rd generation :<br>Pacific Biosciences RS II<br>(~ 2012)</th></tr></thead><tbody><tr><th>Method</th><td>Terminaison with<br>dideoxynucleotides</td><td>Sequencing by<br>synthesis with a<br>polymerase</td><td></td></tr><tr><th>Read length</th><td>400-900 nt</td><td>100 nt</td><td></td></tr><tr><th>Error rate</th><td>0,001%</td><td>0,1%</td><td></td></tr><tr><th>Amount of data<br>produced at once</th><td>10<sup>5</sup> nt</td><td>10<sup>12</sup> nt</td><td></td></tr><tr><th>Cost for 10<sup>9</sup> nt</th><td>2 000 000 €</td><td>80 €</td><td></td></tr></tbody></table>

Adapted from http://data-science-sequencing.github.io/lectures/lecture1/
10¹² nt / Illumina run
10¹² nt / Illumina run

270 millions A4 pages

<figure><img src="image_73.png" /></figure>

3700 characters
10¹² nt / Illumina run

270 millions A4 pages

<figure><img src="image_74.png" /></figure>

3700 characters

<figure><img src="image_75.png" /></figure>

135 millions A4 pages
double-sided

<figure><img src="image_76.png" /></figure>
10¹² nt / Illumina run

<figure><img src="image_77.png" /></figure>

3700 characters

270 millions A4 pages

<figure><img src="image_78.png" /></figure>

135 millions A4 pages
double-sided

<figure><img src="image_79.png" /></figure>

A stack of paper
13,5 km high!!!

<figure><img src="image_80.png" /></figure>

1 µm thickness

<figure><img src="image_81.png" /></figure>
10¹² nt / Illumina run

<figure><img src="image_82.png" /></figure>

3700 characters

270 millions A4 pages

313 human cells

<figure><img src="image_83.png" /></figure>

3,2 × 10⁹ nt

135 millions A4 pages
double-sided

156 diploid human cells

<figure><img src="image_84.png" /></figure>

<figure><img src="image_85.png" /></figure>

A stack of paper
13,5 km high!!!

A cell pileup
3 mm high!!!

<figure><img src="image_86.png" /></figure>

1 µm thickness

<figure><img src="image_87.png" /></figure>

20 µm diameter
Illumina sequencing

https://www.youtube.com/watch?annotation_id=a
nnotation_1533942809&feature=iv&src vid=HMyC
qWhwB8E&v=fCd6B5HRaZ8

<figure><img src="image_88.png" /></figure>

<figure><img src="image_89.png" /></figure>
## Illumina sequencers

<table><thead><tr><th></th><th>iSeq 100 System</th><th>MiniSeq System</th><th>MiSeq Series O</th><th>NextSeq Series O</th><th>HiSeq 4000 System</th><th>HiSeq X Series‡</th><th>NovaSeq 6000 System</th></tr></thead><tbody><tr><th>Run Time</th><td>9-17.5 hrs</td><td>4-24 hours</td><td>4-55 hours</td><td>12-30 hours</td><td>&lt; 1-3.5 days</td><td>&lt; 3 days</td><td>~13 - 38 hours (dual SP flow cells)<br/>~13-25 hours (dual S1 flow cells)<br/>~16-36 hours (dual S2 flow cells)<br/>~44 hours (dual S4 flow cells)</td></tr><tr><th>Maximum Output</th><td>1.2 Gb</td><td>7.5 Gb</td><td>15 Gb</td><td>120 Gb</td><td>1500 Gb</td><td>1800 Gb</td><td>8000 Gb</td></tr><tr><th>Maximum Reads Per Run</th><td>4 million</td><td>25 million</td><td>25 million †</td><td>400 million</td><td>5 billion</td><td>6 billion</td><td>20 billion</td></tr><tr><th>Maximum Read Length</th><td>2 x 150 bp</td><td>2 x 150 bp</td><td>2 x 300 bp</td><td>2 x 150 bp</td><td>2 x 150 bp</td><td>2 x 150 bp</td><td>2 x 250**</td></tr></tbody></table>

Large range of throughput up to ultra-high throughput but sequences between 50 and 300nt

<figure><img src="image_90.png" /></figure>

Sanger-type assembly is impossible with so many and so short sequences
Complicated and fragmented assembly

<figure><img src="image_91.png" /></figure>

<figure><img src="image_92.png" /></figure>

<table><thead><tr><th></th><th>1st generation<br>Sanger WGS<br>PN40024</th><th>2nd generation<br>Illumina<br>Sultanine</th></tr></thead><tbody><tr><td>Sequenced depth</td><td>12</td><td>327</td></tr><tr><td>Reads number</td><td>~ 12 000</td><td>~ 1 500 000</td></tr><tr><td>Number of assembled<br>scaffolds</td><td>2 059</td><td>17 951</td></tr></tbody></table>
# Applications of Illumina sequencing

## Popular Applications & Methods

- Large Whole-Genome Sequencing (human, plant, animal)

- Small Whole-Genome Sequencing (microbe, virus)

- Exome Sequencing

- Targeted Gene Sequencing (amplicon, gene panel)

- Whole-Transcriptome Sequencing

- Gene Expression Profiling with mRNA-Seq

- miRNA & Small RNA Analysis

- DNA-Protein Interaction Analysis

- Methylation Sequencing

- Shotgun Metagenomics
BGI in China

<figure><img src="image_93.png" /></figure>

<figure><img src="image_94.png" /></figure>

<figure><img src="image_95.png" /></figure>
# Evolution of sequencing technologies

<figure><img src="image_96.png" /></figure>

<figure><img src="image_97.png" /></figure>

<figure><img src="image_98.png" /></figure>

<table><thead><tr><th>Sequencers</th><th>1<sup>st</sup> generation :<br>Sanger 3730xl<br>(~ 2000)</th><th>2<sup>nd</sup> generation :<br>Illumina HiSeq 2000<br>(~ 2006)</th><th>3<sup>rd</sup> generation :<br>Pacific Biosciences RS II<br>(~ 2012)</th></tr><tr><th>Method</th><th>Terminaison with<br>dideoxynucleotides</th><th>Sequencing by<br>synthesis with a<br>polymerase</th><th>Real-time single<br>molecule</th></tr></thead><tbody><tr><th scope="row">Read length</th><td>400-900 nt</td><td>100 nt</td><td>10 000 nt</td></tr><tr><th scope="row">Error rate</th><td>0,001%</td><td>0,1%</td><td>15%</td></tr><tr><th scope="row">Amount of data<br>produced at once</th><td>10<sup>5</sup> nt</td><td>10<sup>12</sup> nt</td><td>10<sup>10</sup> nt</td></tr><tr><th scope="row">Cost for 10<sup>9</sup> nt</th><td>2 000 000 €</td><td>80 €</td><td>1 000 €</td></tr></tbody></table>

Adapted from http://data-science-sequencing.github.io/lectures/lecture1/
Pacific Biosciences (PacBio) sequencing

https://www.youtube.com/watch?v=v8p4ph2MAvI

https://www.youtube.com/watch?time&v=WMZmGoouhwU&time=1

<figure><img src="image_99.png" /></figure>

<figure><img src="image_100.png" /></figure>
Nanopore sequencing – No synthesis!

https://www.youtube.com/watch?v=Eg-Rm5AoZGw

<figure><img src="image_101.png" /></figure>
Base calling: PacBio versus Nanopore

<figure><img src="image_102.png" /></figure>

<figure><img src="image_103.png" /></figure>

<figure><img src="image_104.png" /></figure>

<figure><img src="image_105.png" /></figure>
Use of PacBio reads according to depth

<figure><img src="image_106.png" /></figure>
Use of PacBio reads according to depth

100% Illumina
assembly + PacBio
Scaffolding

PacBio/Illumina
hybrid assembly

100 % PacBio
assembly

<figure><img src="image_107.png" /></figure>
Error correction with the sequencing depth (>50x)

Raw
reads

<figure><img src="image_108.png" /></figure>

Corrected
reads

<figure><img src="image_109.png" /></figure>
Hierarchical Genome Assembly Process (HGAP)
adapted to large and repeated genomes

* Identification of the longest reads

* Correction with the depth (>50X)

* Assembly using the longest corrected reads (~Sanger)

<figure><img src="image_110.png" /></figure>
Use of PacBio reads according to depth

100% Illumina
assembly + PacBio
Scaffolding

PacBio/Illumina
hybrid assembly

100 % PacBio
assembly

<figure><img src="image_111.png" /></figure>
Illumina to correct PacBio

<figure><img src="image_112.png" /></figure>

PacBio reads PacBio with
errors (black bars)

Alignment of Illumina reads

PacBio reads correction

Assembly using the corrected
PacBio reads and resolving of
the repeat regions
New PacBio technology: Sequel 2

Start with high-quality
double stranded DNA

<figure><img src="image_113.png" /></figure>

<figure><img src="image_114.png" /></figure>

<figure><img src="image_115.png" /></figure>

<figure><img src="image_116.png" /></figure>

<figure><img src="image_117.png" /></figure>

Ligate SMRTbell
adapters and size select

Anneal primers and
bind DNA polymerase

Circularized DNA
is sequenced in
repeated passes

The polymerase reads
are trimmed of adapters
to yield subreads

Consensus is called
from subreads

<figure><img src="image_118.png" /></figure>

**HiFi READ**
(>99% accuracy)
HiFi reads = Sanger-quality long reads

<figure><img src="image_119.png" /></figure>
# Lastest assembly metrics on a grapevine variety with HiFi reads and HiFiasm software

<figure><img src="image_120.png" /></figure>

<table><thead><tr><th></th><th>Number of contigs</th><th>Average length (bp)</th><th>Maximal length (bp)</th><th>N50 (bp)</th><th>Total assembly size (bp)</th></tr></thead><tbody><tr><td>Haplotype1</td><td>451</td><td>1,143,742</td><td>37,765,902</td><td>24,568,132</td><td>515,827,745</td></tr><tr><td>Haplotype2</td><td>278</td><td>1,840,590</td><td>33,044,252</td><td>24,883,300</td><td>511,684,216</td></tr></tbody></table>

## BUSCO Assessment Results

<figure><img src="image_121.png" /></figure>

<figure><img src="image_122.png" /></figure>

98.2% Complete BUSCOs (C)

94.6% Complete and single-copy BUSCOs (S)

3.7% Complete and duplicated BUSCOs (D)

0.9% Fragmented BUSCOs (F)

0.8% Missing BUSCOs (M)
# Eleven chromosomes from telomere-to-telomere!

<figure><img src="image_123.png" /></figure>

PN40024.v4

chr01 : 1 contig

chr02 : 1 contig

chr03 : 2 contigs

chr04 : 1 contig

chr05 : 2 contigs

chr06 : 1 contig

chr07 : 1 contig

chr08 : 1 contig

chr09 : 1 contig

chr10 : 2 contigs

chr11 : 3 contigs

chr12 : 1 contig

chr13 : 3 contigs

chr14 : 2 contigs

chr15 : 6 contigs

chr16 : 1 contig

chr17 : 3 contigs

chr18 : 1 contig

chr19 : 1 contig
THE FALCON UNZIP PROCESS

<figure><img src="image_124.png" /></figure>
# Evolution of sequencing technologies

<figure><img src="image_125.png" /></figure>

<figure><img src="image_126.png" /></figure>

<figure><img src="image_127.png" /></figure>

<table><thead><tr><th>Sequencers</th><th>1<sup>st</sup> generation :<br>Sanger 3730xl<br>(~ 2000)</th><th>2<sup>nd</sup> generation :<br>Illumina HiSeq 2000<br>(~ 2006)</th><th>3<sup>rd</sup> generation :<br>Pacific Biosciences RS II<br>(~ 2012)</th></tr><tr><th>Method</th><th>Terminaison with<br>dideoxynucleotides</th><th>Sequencing by<br>synthesis with a<br>polymerase</th><th>Real-time single<br>molecule</th></tr></thead><tbody><tr><td>Read length</td><td>400-900 nt</td><td>100 nt ↗</td><td>10 000 nt ↗</td></tr><tr><td>Error rate</td><td>0,001%</td><td>0,1%</td><td>15% ↗ ↗</td></tr><tr><td>Amount of data<br>produced at once</td><td>10<sup>5</sup> nt</td><td>10<sup>12</sup> nt ↗ ↗</td><td>10<sup>10</sup> nt ↗</td></tr><tr><td>Cost for 10<sup>9</sup> nt</td><td>2 000 000 € ↗</td><td>80 € ↗</td><td>1 000 € ↗</td></tr></tbody></table>

Adapted from http://data-science-sequencing.github.io/lectures/lecture1/
Sequencing technologies and ease to assemble genomes

<figure><img src="image_128.png" /></figure>
Sequencing technologies and ease to assemble genomes

<figure><img src="image_129.png" /></figure>

Michael and VanBuren, Curr Op Plant Biol, 2020
## Outline

1. Definitions and history of genome sequencing

2. The sequencing technologies applied to genome assembly

3. Direct usages of genome assemblies

4. Towards the other « omics »
# Structural genomics (genome organisation)

* Genomics in the strict sense (study of genomes)

* Analysis of the organisation of a genome

* Identification of the elements that constitute it
Characteristics of the grapevine genome

- 2n = 38 chromosomes (19 pairs)

- 475 Mb

- Heterozygous (1 SNP / 100bp)

<figure><img src="image_130.png" /></figure>

<figure><img src="image_131.png" /></figure>

**Vitis vinifera**
475 Mb
but 2 x 475Mb to
assemble!!!
# The grapevine genome sequence suggests ancestral hexaploidization in major angiosperm phyla

The French-Italian Public Consortium for Grapevine Genome Characterization*

* PN40024 = genotype derived from *Pinot noir* Helfensteiner after 9 successive selfing (93% homozygous)

* 4th plant genome to be sequenced, assembled and annotated

* Sanger, WGS 8x
<figure><img src="image_132.png" /></figure>

<figure><img src="image_133.png" /></figure>

<figure><img src="image_134.png" /></figure>

<figure><img src="image_135.png" /></figure>

<figure><img src="image_136.png" /></figure>

<figure><img src="image_137.png" /></figure>

<figure><img src="image_138.png" /></figure>

<figure><img src="image_139.png" /></figure>

<figure><img src="image_140.png" /></figure>
PN40024 genome organisation across versions

<table><thead><tr><th></th><th>8X<br>Jaillon</th><th>12X<br>URGI</th><th>12Xv2<br>Canaguier</th><th>PN40024.v4<br>Integrape</th></tr></thead><tbody><tr><td>Contigs</td><td>19,577</td><td>14,642</td><td>14,642</td><td>-</td></tr><tr><td>Scaffolds</td><td>3,514</td><td>2,059</td><td>2,059</td><td>640</td></tr><tr><td>% completeness of<br>pseudomolecules</td><td>68.9%</td><td>91.2%</td><td>94.6%<br>(2.5% Ns)</td><td>95.3%<br>(0.3% Ns)</td></tr><tr><td>Annotated genes</td><td>30,434</td><td>29,971</td><td>42,336</td><td>37,731</td></tr><tr><td>% repeats</td><td>41.4%</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>

Jaillon et al., Nature, 2007

URGI website: http://urgi.versailles.inra.fr/Species/Vitis/Data-Sequences/Genome-sequences

Canaguier et al., 2017

Integrape website: https://integrape.eu/resources/genes-genomes/genome-accessions/
<figure><img src="image_141.png" /><figcaption>Grapevine = palaeo-hexaploid</figcaption></figure>

<figure><img src="image_142.png" /></figure>

<figure><img src="image_143.png" /></figure>
<figure><img src="image_144.png" /><figcaption>Grapevine = palaeo-hexaploid</figcaption></figure>

<figure><img src="image_145.png" /></figure>

<figure><img src="image_146.png" /></figure>

<figure><img src="image_147.png" /></figure>
<figure><img src="image_148.png" /><figcaption>Grapevine = palaeo-hexaploid</figcaption></figure>

<figure><img src="image_149.png" /></figure>

<figure><img src="image_150.png" /></figure>

<figure><img src="image_151.png" /></figure>
<figure><img src="image_152.png" /><figcaption>Grapevine = palaeo-hexaploid</figcaption></figure>

<figure><img src="image_153.png" /></figure>

<figure><img src="image_154.png" /></figure>

<figure><img src="image_155.png" /></figure>
<figure><img src="image_156.png" /><figcaption>Grapevine = palaeo-hexaploid</figcaption></figure>
# Several large expansions of gene families with roles in secondary metabolism

Martin et al. BMC Plant Biology 2010, 10:226
http://www.biomedcentral.com/1471-2229/10/226

151 TPS genes

BMC
Plant Biology

## RESEARCH ARTICLE

Open Access

Functional Annotation, Genome Organization and Phylogeny of the Grapevine (*Vitis vinifera*)
Terpene Synthase Gene Family Based on Genome Assembly, FLcDNA Cloning, and Enzyme Assays

Diane M Martin¹,²*, Sébastien Aubourg³†, Marina B Schouwey⁴, Laurent Daviet⁴, Michel Schalk⁴, Omid Toub ¹,², Steven T Lund², Jörg Bohlmann¹*

<figure><img src="image_157.png" /></figure>

PLOS

ONE

579 CYP450 genes

## RESEARCH ARTICLE

Annotation, classification, genomic organization and expression of the *Vitis vinifera* CYPome

Tina Ilc¹, Gautier Arista², Raquel Tavares³, Nicolas Navrot¹, Eric Duchêne², Amandine Velt², Frédéric Choulet⁴, Etienne Paux⁴, Marc Fischer², David R. Nelson⁵, Philippe Hugueney², Danièle Werck-Reichhart¹, Camille Rustenholz²*

## Aromas

Vannozzi et al. BMC Plant Biology 2012, 12:130
http://www.biomedcentral.com/1471-2229/12/130

48 STS genes

BMC
Plant Biology

## RESEARCH ARTICLE

Open Access

Genome-wide analysis of the grapevine stilbene synthase multigenic family: genomic organization and expression profiles upon biotic and abiotic stresses

Alessandro Vannozzi¹,²*, Ian B Dry³, Marianna Fasoli⁴, Sara Zenoni⁴ and Margherita Lucchin¹,²

GCAT
TACG
GCAT

genes

829 NLR genes

## Communication

NLGenomeSweeper: A Tool for Genome-Wide NBS-LRR Resistance Gene Identification

Nicholas Toda¹, Camille Rustenholz², Agnès Baud³, Marie-Christine Le Paslier¹, Joelle Amselem³, Didier Merdinoglu²* and Patricia Faivre-Rampant ¹,*

## Defense / resistance
# Structural genomics (genome organisation)

* Genomics in the strict sense (study of genomes)

* Identification of the elements that constitute it

* Number of genes

* % repeats

* Gene families

* Analysis of the organisation of a genome

* Polyploidy

* Location of the genes / repeats
Functional genomics

* Description of genes (and proteins) functions and interactions at the scale of the whole genome.

* Gene expression

* Link genes to phenotypes
# Integrated Network Analysis Identifies Fight-Club Nodes as a Class of Hubs Encompassing Key Putative Switch Genes That Induce Major Transcriptome Reprogramming during Grapevine Development<sup>1,2</sup>

Maria Concetta Palumbo,<sup>a,1</sup> Sara Zenoni,<sup>b,1</sup> Marianna Fasoli,<sup>b</sup> Mélanie Massonnet,<sup>b</sup> Lorenzo Farina,<sup>c</sup>

Filippo Castiglione,<sup>a</sup> Mario Pezzotti,<sup>b</sup> and Paola Paci<sup>d,e,2</sup>

# Ripening Transcriptomic Program in Red and White Grapevine Varieties Correlates with Berry Skin Anthocyanin Accumulation<sup>1</sup>

Mélanie Massonnet,<sup>a,2</sup> Marianna Fasoli,<sup>a,3</sup> Giovanni Battista Tornielli,<sup>a</sup> Mario Altieri,<sup>a</sup> Marco Sandri,<sup>a</sup> Paola Zuccolotto,<sup>b</sup> Paola Paci,<sup>c,d</sup> Massimo Gardiman,<sup>e</sup> Sara Zenoni,<sup>a,4</sup> and Mario Pezzotti<sup>a</sup>

ORCID IDs: 0000-0003-1101-8129 (M.F.); 0000-0001-5027-0269 (G.B.T.); 0000-0001-9647-7943 (M.A.); 0000-0002-1422-5695 (M.S.); 0000-0003-4399-7018 (P.Z.); 0000-0003-4541-1201 (M.G.); 0000-0002-0496-8161 (S.Z.); 0000-0002-7430-6147 (M.P.).

<footnote><sup>a</sup>Institute for Computing Applications “Mauro Picone,” National Research Council, 00185 Rome, Italy</footnote>

<footnote><sup>b</sup>Dipartimento di Bio</footnote>

<footnote><sup>c</sup>Department of Com</footnote>

<footnote><sup>d</sup>Institute for System</footnote>

<footnote><sup>e</sup>SysBio Centre for S</footnote>

<footnote><sup>a</sup>Department of Biotechnology, University of Verona, 37134 Verona, Italy</footnote>

<footnote><sup>b</sup>Big & Open Data Innovation Laboratory, University of Brescia, 25123 Brescia, Italy</footnote>

<footnote><sup>c</sup>Institute for Systems Analysis and Computer Science Antonio Ruberti, National Research Council, 00185 Rome, Italy</footnote>

<footnote><sup>d</sup>SysBio Centre for Systems Biology, 00185 Rome, Italy</footnote>

<footnote><sup>e</sup>CREA-VE, 31015 Conegliano, Italy</footnote>
<figure><img src="image_158.png" /><figcaption>Comparison of gene expression in red and white berries at 4 developmental stages</figcaption></figure>

Massonnet et al., Plant Physiol, 2017
<figure><img src="image_159.png" /></figure>
<figure><img src="image_160.png" /></figure>
<figure><img src="image_161.png" /></figure>

Identify the key
genes in
anthocyanins
biosynthesis

Massonnet et al., Plant Physiol, 2017
# GREAT (GRape Expression ATlas)

<figure><img src="image_162.png" /></figure>

More than 2,000 RNA-Seq samples analyzed, normalized and comparable among each other

<figure><img src="image_163.png" /></figure>

https://great.colmar.inrae.fr

To request access, please send an email to contact-GREAT-colmar@inrae.fr

Assembly

University of Strasbourg
Compare any genes in any conditions

<figure><img src="image_164.png" /><figcaption>Heatmap on genes expression (RPKM).</figcaption></figure>
Identify genes with similar expression profiles

<figure><img src="image_165.png" /></figure>

<figure><img src="image_166.png" /></figure>

<figure><img src="image_167.png" /></figure>

<figure><img src="image_168.png" /></figure>
# Performs Differential Gene Expression analysis

Number of genes differentially expressed in each comparison.

Show 10 entries

Search:

<table><thead><tr><th>Comparison</th><th>Genes_up_regulated</th><th>Genes_down_regulated</th><th>Actions</th></tr></thead><tbody><tr><td>1 Erysiphe_vs_control</td><td>3929</td><td>4080</td><td>Download complete DE table</td></tr><tr><td>2 L_Carignane_Erysiphe_1DPI_vsControl_L_Carignane_1DPI</td><td>11</td><td>0</td><td>Download complete DE table</td></tr><tr><td>3 L_Carignane_Erysiphe_5DPI_vsControl_L_Carignane_5DPI</td><td>1493</td><td>598</td><td>Download complete DE table</td></tr><tr><td>4 Sativa_Husseine_Erysiphe_1DPI vs Control_Sativa_Husseine_1DPI</td><td>4</td><td>1</td><td>Download complete DE table</td></tr><tr><td>5 Sativa_Husseine_Erysiphe_5DPI vs Control_Sativa_Husseine_5DPI</td><td>1431</td><td>129</td><td>Download complete DE table</td></tr><tr><td>6 Sativa_Karadzhandal_Erysiphe_1DPI vs Control_Sativa_Karadzhandal_1DPI</td><td>84</td><td>1</td><td>Download complete DE table</td></tr><tr><td>7 Sativa_Karadzhandal_Erysiphe_5DPI vs Control_Sativa_Karadzhandal_5DPI</td><td>717</td><td>147</td><td>Download complete DE table</td></tr><tr><td>8 Sativa_Khalchili_Erysiphe_1DPI vs Control_Sativa_Khalchili_1DPI</td><td>2</td><td>0</td><td>Download complete DE table</td></tr><tr><td>9 Sativa_Khalchili_Erysiphe_5DPI vs Control_Sativa_Khalchili_5DPI</td><td>36</td><td>1</td><td>Download complete DE table</td></tr><tr><td>10 Sativa_Late_Vavilov_Erysiphe_1DPI vs Control_Sativa_Late_Vavilov_1DPI</td><td>0</td><td>0</td><td>Download complete DE table</td></tr></tbody></table>

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Download analysis report (PCA, heatmap, ...)
<figure><img src="image_169.png" /></figure>

# Genetic variations of acidity in grape berries are controlled by the interplay between organic acids and potassium

Éric Duchêne¹·D·Vincent Dumas¹·Gisèle Butterlin¹·Nathalie Jaegli¹·Camille Rustenholz¹·Aurélie Chauveau²·
Aurélie Bérard²·Marie Christine Le Paslier²·Isabelle Gaillard³·Didier Merdinoglu¹
## QTL analysis for pH in berries

<figure><img src="image_170.png" /></figure>

<table><thead><tr><th>Map</th><th>Trait(s)</th><th>Sampling</th><th>Chr.</th><th>Significant in dataset...</th><th>Lower limit (bp)</th><th>Upper limit (bp)</th><th>Number of genes</th></tr></thead><tbody><tr><td>Gw</td><td>pH_S2, [Tart], [K<sup>+</sup>]:[Tart]</td><td>S2</td><td>10</td><td>2009, 2015</td><td>1,787,722</td><td>4,601,875</td><td>289</td></tr></tbody></table>

<table><thead><tr><th>Chrom.</th><th>Id</th><th>Position (bp)</th><th>Best hit on UniProtKB/Swiss-Prot</th><th>e-value</th><th>Score</th><th>% Identity</th></tr></thead><tbody><tr><td>10</td><td>VIT_00s0194g00210</td><td>2,364,396...2,365,775</td><td>KCO1_ARATH—Two-pore potassium channel 1</td><td>1.00E-130</td><td>977</td><td>56.2</td></tr><tr><td></td><td>VIT_00s0194g00250</td><td>2,401,053...2,402,438</td><td>KCO1_ARATH—Two-pore potassium channel 1</td><td>2.20E-132</td><td>988</td><td>56.5</td></tr><tr><td></td><td>VIT_00s0187g00130</td><td>2,086,611...2,089,517</td><td>TDT_ARATH—Tonoplast dicarboxylate transporter</td><td>0</td><td>2107</td><td>76.5</td></tr></tbody></table>
<figure><img src="image_171.png" /></figure>

Check for
updates

# The wild grape genome sequence provides insights into the transition from dioecy to hermaphroditism during grape domestication

Hélène Badouin¹*†, Amandine Velt²†, François Gindraud¹, Timothée Flutre³, Vincent Dumas², Sonia Vautrin⁴,
William Marande⁴, Jonathan Corbi¹, Erika Sallet⁵, Jérémy Ganofsky¹, Sylvain Santoni⁶, Dominique Guyot⁷,
Eugenia Ricciardelli⁸, Kristen Jepsen⁸, Jos Käfer¹, Hélène Berges⁴, Eric Duchêne², Franck Picard¹,
Philippe Hugueney², Raquel Tavares¹, Roberto Bacilieri⁶*†, Camille Rustenholz²*† and Gabriel A. B. Marais¹*† ID
Genome assembly of Vitis vinifera spp sylvestris C1-2

<figure><img src="image_172.png" /></figure>

V. v. sylvestris is the wild ancestor of cultivated grapevine
<figure><img src="image_173.png" /></figure>
<figure><img src="image_174.png" /></figure>

<figure><img src="image_175.png" /></figure>

Male sterility candidate

<figure><img src="image_176.png" /><figcaption>e</figcaption></figure>

<figure><img src="image_177.png" /></figure>

Female sterility candidates

<figure><img src="image_178.png" /><figcaption>f</figcaption></figure>

<figure><img src="image_179.png" /></figure>
Functional genomics

* Description of genes (and proteins) functions and interactions at the scale of the whole genome.

* Gene expression

* Transcriptomics

* Gene networks

* Link genes to phenotypes

* Candidate genes for quantitative genetics studies (QTL / GWAS)
Comparative genomics

* Comparison of genomic features of different organisms

* Genome variations within the same species

* Species evolution
RESEARCH

Open Access

<figure><img src="image_180.png" /></figure>

Check for
updates

# ‘Nebbiolo’ genome assembly allows surveying the occurrence and functional implications of genomic structural variations in grapevines (Vitis vinifera L.)

Simone Maestri¹, Giorgio Gambino², Giulia Lopatriello¹, Andrea Minio³, Irene Perrone², Emanuela Cosentino¹, Barbara Giovannone¹, Luca Marcolungo¹, Massimiliano Alfano¹, Stephane Rombauts⁴,⁵, Dario Cantu³, Marzia Rossato¹, Massimo Delledonne¹* and Luciano Calderón⁶*
<figure><img src="image_181.png" /><figcaption>Number of shared and variety-specific gene families</figcaption></figure>

Maestri et al., BMC Genomics, 2022
**Organism 1**

**Unique**

Dispensable

Dispensable

**Organism 3**

**Unique**

**Core**

**Unique**

**Organism 2**

**Pangenome**
# Vitis Gene Pangenome

Cabernet Sauvignon

Carménère

Chardonnay

Concord (hybrid)

Flame Seedless

Merlot

Zinfandel

*Vitis vinifera* spp. *sylvestris* C1-2

*Vitis vinifera* spp. *sylvestris* DVIT3351.27

## What's coming in Vitis PanGenome Release 3?

Vitis PanGenome R3 is due to be released in May 2022.

### New Genomes (Assemblies and/or Annotation)

* *Vitis vinifera* cv. Black Corinth Seedless (seedless Black Corinth or Zante Currant)

* *Vitis vinifera* cv. Black Corinth Seeded (seeded Black Corinth or Zante Currant)

* *Vitis vinifera* cv. Cabernet Franc Cl. 04 (Cabernet Franc)

* *Vitis vinifera* cv. Nebbiolo (Nebbiolo)

* *Vitis vinifera* cv. Riesling (Riesling)

* *Vitis arizonica* (Arizona grape, canyon grape or uva del monte)

* *Muscadinia rotundifolia* cv. Trayshed (Muscadine)
# Gramene Gene Tree – Comparison of multiple gene predictions

<figure><img src="image_182.png" /></figure>

This is the gene tree containing *Vitvi18g04697*.

Mark genes as okay or flag genes that you think might have issues and choose a reason from the drop down menu. When finished, enter your email address and submit.

<figure><img src="image_183.png" /></figure>
RESEARCH ARTICLE

Open Access

<figure><img src="image_184.png" /></figure>

Check for
updates

# The genomic diversification of grapevine clones

Amanda M. Vondras¹, Andrea Minio¹, Barbara Blanco-Ulate¹,², Rosa Figueroa-Balderas¹, Michael A. Penn¹, Yongfeng Zhou³, Danelle Seymour³, Zirou Ye¹, Dingren Liang¹, Lucero K. Espinoza¹, Michael M. Anderson¹, M. Andrew Walker¹, Brandon Gaut³ and Dario Cantu¹*
# Grapevine varieties and clones

<figure><img src="image_185.png" /></figure>

**Variety:** Set of clones from a single individual, originated from a cross (a seed), named by the same name and used for the same purpose.
# Clonal diversity

<figure><img src="image_186.png" /></figure>
Genome assembly of one clone of Zinfandel

<figure><img src="image_187.png" /></figure>

Identification of
variations
between clones
# Genome assembly of one clone of Zinfandel

<figure><img src="image_188.png" /></figure>

Identification of
variations
between clones

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Identification of
variations
between
varieties

Vondras et al., BMC
Genomics, 2019
Comparison of varietal and clonal diversity

Heterozygous variants

Homozygous variants

Vondras et al., BMC Genomics, 2019

<figure><img src="image_190.png" /></figure>
OXFORD

G3
Genes | Genomes | Genetics

G3, 2021, 11(4), jkab033

DOI: 10.1093/g3journal/jkab033

Advance Access Publication Date: 5 February 2021

Genome Report

# Diploid chromosome-scale assembly of the *Muscadinia rotundifolia* genome supports chromosome fusion and disease resistance gene expansion during *Vitis* and *Muscadinia* divergence

Noé Cochetel ID,† Andrea Minio ID,† Mélanie Massonnet ID, Amanda M. Vondras ID, Rosa Figueroa-Balderas ID, and Dario Cantu ID*
# Muscadinia rotundifolia or Vitis rotundifolia

## ♦ Genre Vitis

- Sous-genre Euvitis → 19 pairs of chromosomes

- Sous-genre Muscadinia → 20 pairs of chromosomes!!!

<figure><img src="image_191.png" /></figure>

<figure><img src="image_192.png" /></figure>
<figure><img src="image_193.png" /><figcaption>Genome assembly of *Muscadinia* cv Trayshed</figcaption></figure>
Chr 7 of *Vitis vinifera* is the result of a fusion

C

<figure><img src="image_194.png" /></figure>

<figure><img src="image_195.png" /></figure>
# The population genetics of structural variants in grapevine domestication

Yongfeng Zhou ID¹, Andrea Minio ID², Mélanie Massonnet ID², Edwin Solares ID¹, Yuanda Lv¹, Tengiz Beridze³, Dario Cantu ID²* and Brandon S. Gaut ID¹*
Genetic differentiation between wild and
cultivated grapevines based on SV

<figure><img src="image_196.png" /></figure>
<figure><img src="image_197.png" /><figcaption>4Mb inversion at the berry color locus</figcaption></figure>

<figure><img src="image_198.png" /></figure>

<figure><img src="image_199.png" /></figure>

<figure><img src="image_200.png" /></figure>

<figure><img src="image_201.png" /></figure>

Zhou et al., Nature Plants, 2019
Comparative genomics

* Comparison of genomic features of different organisms

* Genome variations within the same species

* Between varieties

* Between clones

* Species evolution

* Between cultivated and wild → domestication

* Between different species
## Outline

1. Definitions and history of genome sequencing

2. The sequencing technologies applied to genome assembly

3. Direct usages of genome assemblies

4. Towards the other « omics »
Epigenomics

* Study of epigenetic modifications of a genome

* Epigenetics marks: reversible DNA or histone modifications that affect gene expression and genome stability

<figure><img src="image_202.png" /></figure>
<figure><img src="image_203.png" /></figure>

# Global DNA Methylation Patterns Can Play a Role in Defining Terroir in Grapevine (*Vitis vinifera* cv. Shiraz)

Huahan Xie¹,²†, Moumouni Konate¹,²†, Na Sai¹,²,³, Kiflu G. Tesfamicael¹,², Timothy Cavagnaro², Matthew Gilliham²,³, James Breen⁴,⁵, Andrew Metcalfe⁶, John R. Stephen⁷, Roberta De Bei², Cassandra Collins² and Carlos M. R. Lopez¹,²*
# DNA methylation is affected by geographical locations

<figure><img src="image_204.png" /><figcaption>PC-LDA of epigenetic differences</figcaption></figure>
Metabolomics

* Study of the metabolome which represents the complete set of metabolites in a biological cell, tissue, organ or organism, which are the end products of cellular processes.

<figure><img src="image_205.png" /></figure>
<figure><img src="image_206.png" /></figure>

Article

# Severe Stunting Symptoms upon Nepovirus Infection Are Reminiscent of a Chronic Hypersensitive-like Response in a Perennial Woody Fruit Crop

Isabelle R. Martin *,† ID, Emmanuelle Vigne, Amandine Velt, Jean-Michel Hily ‡, Shahinez Garcia, Raymonde Baltenweck ID, Véronique Komar, Camille Rustenholz, Philippe Hugueney, Olivier Lemaire and Corinne Schmitt-Keichinger * ID

> Correlation between transcriptomics and metabolomics to understand the molecular mechanisms involved in grapevine defense against a virus
<figure><img src="image_207.png" /></figure>

METAGENOMICS

<figure><img src="image_208.png" /></figure>
Some ideas for the round table...

1. Genome assemblies are only modeling the true sequences...

2. Structural (and functional) annotation is as crucial as assembly and becomes much more work-intensive...

3. Assembly of the two haplotypes of a heterozygous genome...
<figure><img src="image_209.png" /></figure>

<figure><img src="image_210.png" /></figure>

<figure><img src="image_211.png" /></figure>

<figure><img src="image_212.png" /></figure>

<figure><img src="image_213.png" /></figure>

RÉPUBLIQUE
FRANÇAISE

Liberté
Égalité
Fraternité

INRAE

University

of Strasbourg

Thank you!

<figure><img src="image_214.png" /></figure>

<figure><img src="image_215.png" /></figure>

Camille Rustenholz - camille.rustenholz@inrae.fr