# What can we expect with the new version of the reference genome?

Camille Rustenholz, Bianca Frommer

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

## To cite this version:

Camille Rustenholz, Bianca Frommer. What can we expect with the new version of the reference genome?. Thrid Annual Meeting COST Integrape, COST Integrape, Jun 2021, Online, France. 62 p. hal-05282824

HAL Id: hal-05282824

https://hal.inrae.fr/hal-05282824v1

Submitted on 25 Sep 2025

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What can we expect with the
new version of the reference
genome?

**Camille Rustenholz** (Grapevine Genomics, INRAE Colmar, France) and
**Bianca Frommer** (Genetics & Genomics of Plants, CeBiTec, Bielefeld University, Germany)

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COST Action
CA 17111
INTEGRAPE
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# Assembly of PN40024.v4

Camille Rustenholz

Grapevine Genomics, INRAE Colmar, France

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RÉPUBLIQUE
FRANÇAISE

Liberté
Egalité
Fraternité

INRAE

Université

de Strasbourg

<figure><img src="image_6.png" /></figure>

COST Action
CA 17111
INTEGRAPE
## What is a reference genome?

* Top-quality assembly (completeness, accuracy, contiguity)

* Top-quality annotation (completeness, accuracy)

* An anchor for structural and functional genomics knowledge

* Web-interfaces / tools available

* Meet the gold-standards of FAIRness

## How is a reference genome used?

* Resequencing analyses

* SNP calling

* Transcriptomic analyses
<figure><img src="image_7.png" /></figure>

ELSEVIER

<figure><img src="image_8.png" /></figure>

Data in Brief

A new version of the grapevine reference genome assembly (12X.v2) and of its annotation (VCost.v3)

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

CrossMark

A. Canaguier<sup>a,b</sup>, J. Grimplet<sup>c</sup>, G. Di Gaspero<sup>d</sup>, S. Scalabrin<sup>d</sup>, E. Duchêne<sup>e</sup>, N. Choisne<sup>f</sup>, N. Mohellibi<sup>f</sup>, C. Guichard<sup>a,g</sup>, S. Rombauts<sup>b,i</sup>, I. Le Clainche<sup>a,b</sup>, A. Bérard<sup>b</sup>, A. Chauveau<sup>b</sup>, R. Bounon<sup>a,b</sup>, C. Rustenholz<sup>g</sup>, M. Morgante<sup>d</sup>, M.-C. Le Paslier<sup>b</sup>, D. Brunel<sup>b</sup>, A.-F. Adam-Blondon<sup>a,f,*</sup>

## 12X.2 assembly

* Contigs : 14,642

* Scaffolds : 2,059 (> 2kb)

* Mapped scaffolds : 366 (coverage of the genome : 95%)

## 12X.0 assembly Available in 2009

* Contigs : 14,642

* Scaffolds : 2,059 (> 2kb)

* Mapped scaffolds : 211 (coverage of the genome : 91.2%)

## 8X assembly Jaillon et al., Nature, 2007

* Contigs : 19,577

* Scaffolds : 3,514

* Mapped ultracontigs : 191 (coverage of the genome : 68.9%)

* No sequencing effort since 2009

* High quality Sanger contigs

* Improved scaffolding using genetic maps
# High quality reference genome but...

* 15 Mb of Ns (3.1% of scaffold size, chr17 = 18.7Mb)

* chrUn = 27,389,308 bp (4th largest "chromosome")

* Heterozygous regions badly resolved

* Evidences of missing genes

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chr7

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Same maps as Canaguier et al.,
2017 + Ri x Gw GBS map
~ 6,000 markers
<figure><img src="image_29.png" /></figure>

<table><thead><tr><th>SCAF-FOLDS</th><th>Nb scaf</th><th>Min size</th><th>Avg size</th><th>Median size</th><th>L50</th><th>N50</th><th>Max size</th><th>Sum</th><th>Nb Ns</th></tr></thead><tbody><tr><td>PN12Xv2</td><td>2,059</td><td>2,001</td><td>235,641</td><td>5,041</td><td>41</td><td>3,426,264</td><td>13,101,952</td><td>485,185,630</td><td>14,976,411</td></tr></tbody></table>
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<table><thead><tr><th>SCAF-FOLDS</th><th>Nb scaf</th><th>Min size</th><th>Avg size</th><th>Median size</th><th>L50</th><th>N50</th><th>Max size</th><th>Sum</th><th>Nb Ns</th></tr></thead><tbody><tr><td>PN12Xv2</td><td>2,059</td><td>2,001</td><td>235,641</td><td>5,041</td><td>41</td><td>3,426,264</td><td>13,101,952</td><td>485,185,630</td><td>14,976,411</td></tr><tr><td>PN40024. v4</td><td>640</td><td>542</td><td>741,572</td><td>20,468.5</td><td>25</td><td>6,499,194</td><td>15,229,578</td><td>474,606,666</td><td>1,755,062</td></tr></tbody></table>
Improvements

* 15 Mb of Ns (3.1% of scaffold size, chr17 = 18.7Mb) → **1.8 Mb (0.4%)**

* chrUn = 27,389,308 bp (4th largest "chromosome") → **12,511,639 bp (-54%)**

* Heterozygous regions badly resolved → **Improved**

* Evidences of missing genes → **grapevine is a flowering plant!**

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chr07

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chr07
New PacBio regions

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* 7,640 new regions (>100bp)

* Total size 24 Mb (5%)

* Average size 3,152 bp

* Median size 558 bp

* Maximal size 327 kb
Error rate and colocalisation with PacBio regions

<figure><img src="image_35.png" /></figure>

Accuracy =
Q36 = 99.97%

(computed with Merqury)
Heterozygous regions

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Heterozygous regions

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# PN40024.v4, overall better than PN12Xv2

* Contiguity

* Less scaffolds but longer

* Completeness

* Less Ns

* Better completeness of the chromosomes (better anchoring)

* Better completeness of heterozygous regions

* New regions assembled

* PN40024 Illumina reads against PN40024.v4: 91.2% properly paired mapped reads (87.8% with PN12Xv2)

* Accuracy

* PN40024.v4: Q36 = 99.97%

* PN12XV2: Q37.4 = 99.98%
<figure><img src="image_39.png" /><figcaption>PN40024.v4, the best for resequencing alignments</figcaption></figure>

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What can we expect with the new
version of the reference genome?

More informative sequences, less missing
genes, better scaffolding, improved alignment
performance

Error rate in PacBio regions
<figure><img src="image_41.png" /></figure>

The real parent of PN40024

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Is Pinot Noir is the real parent of PN40024?

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# Tracking of Pinot Noir haplotypes in PN40024

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* If Pinot Noir is the real parent

* we should find a Pinot Noir haplotype whichever the PN40024 genomic region.

* we should not find homozygous SNPs between Pinot Noir and PN40024.
Pinot Noir is not the real parent of PN40024

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

* Large regions with homozygous SNPs

* Only half of the PN40024 genome carries a Pinot Noir haplotype
PN40024 is a mixture of Pinot Noir and Schiava grossa haplotypes

<figure><img src="image_46.png" /></figure>
PN40024 originated from a selfing of Helfensteiner

* Helfensteiner =
  Pinot Noir x
  Schiava Grossa

* PN40024 only
carries
Helfensteiner
haplotypes

<figure><img src="image_47.png" /></figure>
Proportion of Pinot Noir and Schiava Grossa haplotypes
in PN40024

<figure><img src="image_48.png" /></figure>

Schiava-specific = 41%

Pinot-specific = 27%

"Common" regions = 32%

Schiava-specific + common/2 = 57%

Pinot-specific + common/2 = 43%
# Gene Annotation of PN40024.v4

## Version PN40024.v4.1

## Bianca Frommer

Genetics & Genomics of Plants, CeBiTec, Bielefeld University, Germany

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CeBiTec
Center for Biotechnology

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UNIVERSITÄT
BIELEFELD

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Faculty of Biology

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UNIVERSITÀ
di VERONA

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COST Action
CA 17111
INTEGRAPE
# Why is gene annotation needed? - Differential Gene Expression Analysis

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Created with BioRender.com

## RNA-Seq reads treatment

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Genome
sequence

Gene 1

Gene 2

Gene 3

## RNA-Seq reads control

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# What is structural gene annotation?

* Identification of gene locations and its coding regions

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* Alternative Splicing: More than one transcript per gene

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# And functional gene annotation?

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

Photosynthesis

<figure><img src="image_62.png" /></figure>

Defense
response

<figure><img src="image_63.png" /></figure>

RNA
processing

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Cell wall
organisation

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

Wound
healing

<figure><img src="image_66.png" /></figure>

Protein
Phosphorylation

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

Amino acid
metabolism

## What is the function of the gene?
# Challenges of (Vitis) gene annotation

* An annotation can only be as good as the genome assembly

* Quality directly influences the annotation

* InDels can change the reading-frame

* SNP errors can mask the real start/stop-codon or lead to false prior start/stop-codons

* High repeat content

* Clustered genes (e.g. resistance genes)
# Annotation Pipeline

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

Tested and improved
on PN40024 12X.v2
genome sequence
# Annotation Pipeline

<figure><img src="image_69.png" /></figure>
# Publicly available Evidence Data

* *Vitis vinifera* paired-end RNA-Seq data from IGA & SRA

* Stranded & unstranded data

* Tissues: Leaves, Berries, Flowers, Flower Buds

* Conditions:

* Trichoderma-induced *Plasmopara viticola* resistance (leaves)

* Different development stages

* Protein sequences

* *Arabidopsis thaliana* Sequences (UniProt/SwissProt)

* Eudicotyledon Sequences (UniProt/SwissProt, OrthoDB10 v1)

* Vitales Sequences (UniProt/SwissProt)
# Annotation Pipeline

<figure><img src="image_70.png" /></figure>
# Gene Prediction Programs

* GeneID

* Using publicly available *Vitis vinifera* parameter set

* SNAP

* HMM training using transcriptome assemblies, protein data & MAKER

* 2 training rounds

* glimmerHMM

* Training with PsiCLASS transcripts

* BRAKER2 with RNA-Seq & Protein Data

* GeneMark-ETP+ training and gene prediction

* AUGUSTUS training and gene prediction

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

## Data Combination with EvidenceModeler
# Combining the Evidences - Evidence Modeler

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# Combining the Evidences - Evidence Modeler

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# The filtering...

* Keeping Genes only supported by *ab initio* predictions:

* If supported by at least 2/4 gene prediction programs

* If complete (Start & Stop codon present)

* If gene length >=300 bp

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

Marked as **good Gene Models**

* Else discarded

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

Marked as **ab initio Gene Models**

<figure><img src="image_76.png" /></figure>

**Ab initio Genes** recovered if UniProt/SwissProt DB hit OR NCBI non-redundant (nr) protein DB hit with e-value <1e-6
# The filtering...

Genes supported by evidence data or VCost.v3 gene models (at least one):

* Discarded if not complete & length <300 bp

? Marked as **short Gene Models**

* Else considered as good

? Marked as **good Gene Models**

<table><thead><tr><th>Dataset</th><th>PN40024.v4.1</th></tr></thead><tbody><tr><td><strong>Good Gene Models</strong><br><em>ab initio</em> Models with hit<br>against</td><td>29,910</td></tr><tr><td>UniProt</td><td>+1,616</td></tr><tr><td>nr DB</td><td>+3,671</td></tr><tr><td><strong>Ab initio Gene Models</strong></td><td>10,187</td></tr><tr><td><strong>Short Gene Models</strong></td><td>21</td></tr></tbody></table>
# Annotation Pipeline

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

<table><thead><tr><th></th><th>VCost.v3</th><th>PN40024.v4.1</th></tr></thead><tbody><tr><td>Genes</td><td>42,336</td><td>37,731</td></tr><tr><td>Protein-coding</td><td>41,733</td><td>35,197</td></tr><tr><td>ALT protein-coding</td><td></td><td>1,809</td></tr><tr><td>tRNAs</td><td>18</td><td>725</td></tr><tr><td>Transcripts</td><td>46,511</td><td>43,296</td></tr><tr><td>Exons</td><td>207,903</td><td>220,540</td></tr><tr><td>CDS</td><td>198,674</td><td>210,760</td></tr><tr><td>5' UTR</td><td>22,407</td><td>23,736</td></tr><tr><td>3' UTR</td><td>20,566</td><td>21,791</td></tr></tbody></table>

→ 1,572 novel protein-coding genes in PacBio sequences ~4,5%
## Genes in heterozygous Regions

<table><thead><tr><th rowspan="2"></th><th colspan="2">Bases</th><th colspan="2">Genes</th></tr><tr><th>PN40024.v4</th><th>ALT</th><th>PN40024.v4</th><th>ALT</th></tr></thead><tbody><tr><td>chr02</td><td>1,610,271</td><td>1,886,900</td><td>190</td><td>214</td></tr><tr><td>chr03</td><td>288,001</td><td>287,774</td><td>14</td><td>13</td></tr><tr><td>chr04</td><td>1,049,642</td><td>929,781</td><td>123</td><td>122</td></tr><tr><td>chr07</td><td>11,422,405</td><td>10,851,409</td><td>830</td><td>792</td></tr><tr><td>chr10</td><td>5,475,057</td><td>5,100,371</td><td>650</td><td>623</td></tr><tr><td>chr11</td><td>733,078</td><td>630,772</td><td>43</td><td>41</td></tr><tr><td>chr15</td><td>60,730</td><td>52,641</td><td>5</td><td>4</td></tr></tbody><tfoot><tr><td>TOTAL</td><td>20,639,184</td><td>19,739,648</td><td>1,855</td><td>1,809</td></tr></tfoot></table>
BUSCO v5 Analysis

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

2,326 total searched BUSCOs (eudicots_odb10)
# Gene naming - RBHs

Which PN40024.v4.1 gene belongs to which VCost.v3 gene?

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

Reciprocal Best (BLAST) Hits (RBHs)

o Blastp of protein sequences (longest isoform)

o e-value 0.0001 & 70% coverage & identity

o Genes have to appear as blocks (collinearity)

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

~66% were matched

PN40024.v4.1

Vcost.v3

<figure><img src="image_81.png" /></figure>
PN40024.v4.1 Annotation file (gff3)

<table><thead><tr><td></td><td>ID</td><td>Alias</td></tr></thead><tbody><tr><td>Gene</td><td>Vitvi02g01736</td><td>VIT_00s0341g00010,LOC100264826, Vitvi00g00481</td></tr><tr><td>Novel Gene</td><td>Vitvi01g04001</td><td>-</td></tr><tr><td>ALT Gene</td><td>Vitvi02g04116_alt</td><td>-</td></tr><tr><td>tRNA</td><td>To be added</td><td>-</td></tr><tr><td>mRNA</td><td>Vitvi01g04001_t001</td><td>-</td></tr><tr><td>CDS</td><td>Vitvi01g04001_t001.cds1</td><td>-</td></tr><tr><td>Exon</td><td>Vitvi01g04001_t001.exon1</td><td>-</td></tr><tr><td>5' UTR</td><td>Vitvi01g04001_t001.utr5p1</td><td>-</td></tr><tr><td>3' UTR</td><td>Vitvi01g04001_t001.utr3p1</td><td>-</td></tr></tbody></table>
Transcriptomic analysis - Assignment to
annotations

Ripening Transcriptomic Program in Red and White
Grapevine Varieties Correlates with Berry Skin
Anthocyanin Accumulation¹

Mélanie Massonnet,ª,² Marianna Fasoli,ª,³ Giovanni Battista Tornielli,ª Mario Altieri,ª Marco Sandri,ª Paola Zuccolotto,ᵇ Paola Paci,ᶜ,>d Massimo Gardiman,ᶜ Sara Zenoni,ª,⁴ and Mario Pezzottiª
Transcriptomic analysis - Assignment to annotations

<table><thead><tr><th rowspan="2">Variety</th><th rowspan="2">Sample BBCH77</th><th colspan="3">12X.v2 - VCost.v3</th><th colspan="2">PN40024.v4.1</th></tr><tr><th>M Seqs</th><th>% Aligned</th><th>% Assigned</th><th>% Aligned</th><th>% Assigned</th></tr></thead><tbody><tr><td rowspan="3">Sangiovese</td><td>SRR1631822</td><td>31.2</td><td>92.1</td><td>87.9</td><td>92.3</td><td>90.3</td></tr><tr><td>SRR1631823</td><td>33.3</td><td>92.2</td><td>88.3</td><td>92.4</td><td>90.9</td></tr><tr><td>SRR1631824</td><td>32.3</td><td>92.3</td><td>88.3</td><td>92.5</td><td>90.9</td></tr><tr><td rowspan="3">Barbera</td><td>SRR1631834</td><td>40.7</td><td>91.4</td><td>87.5</td><td>91.5</td><td>90.2</td></tr><tr><td>SRR1631835</td><td>37.2</td><td>90.8</td><td>87.6</td><td>90.9</td><td>90.2</td></tr><tr><td>SRR1631836</td><td>43.6</td><td>91.4</td><td>87.0</td><td>91.5</td><td>89.6</td></tr><tr><td rowspan="3">Refosco</td><td>SRR1631858</td><td>39.2</td><td>91.6</td><td>88.1</td><td>91.6</td><td>91.1</td></tr><tr><td>SRR1631859</td><td>36.7</td><td>91.8</td><td>87.8</td><td>91.8</td><td>90.8</td></tr><tr><td>SRR1631860</td><td>43.8</td><td>91.2</td><td>88.2</td><td>91.3</td><td>91.1</td></tr></tbody></table>
Transcriptomic analysis - Differential gene expression

<figure><img src="image_82.png" /></figure>
Manual Curation Workshop

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

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

1. PN40024.v4.1 annotation 2

ALT

REF

2. Previous annotation versions 2

CRIBI V1

VCOSTv3

3. Gene catalogue 1

Gene catalogue

4. RNAseq 5

CS IsoSeq max introns 20kb

Mixed samples

Mixed samples - insertsize <= 300 - MQ

>= 40

Mixed samples with multimapping

PN40024 RNAseq on REF only

5. Orthologs 4

Arabidopsis

ORTHODB

SWISSPROT

Vitales

6. SNPs 1

PN40024 Illumina SNPs

7. Others 4

Heterozygous region

N gaps

Repeat regions

Scaffolds

PN40024.v4

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

File

View

Help

0

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

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chr13

chr13.20282362..20285686 (3.33 Kb)

Go

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⊕

20,282,500

20,283,750

20,285,000

User-created Annotations

Scaffolds

REF

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VCOSTv3

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CRIBI V1

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Arabidopsis

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Mixed samples

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# WebApollo - Annotation improvement

<figure><img src="image_98.png" /></figure>
# WebApollo - New Genes

<figure><img src="image_99.png" /></figure>
BUSCOs - manual Curation

Complete BUSCOs
(single + duplicated):

<figure><img src="image_100.png" /></figure>

2,326 total searched BUSCOs (eudicots_odb10)
Other available Tools

<figure><img src="image_101.png" /><figcaption>BLAST sequence server</figcaption></figure>

Query= Vitvi13g04597 (sequence:mRNA) 213 residues [chr13:24226623-24231438 - strand] [peptide]

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

<figure><img src="image_103.png" /></figure>
What can we expect with the new version of
the reference gene annotation?

A more complete gene set, novel genes
(PacBio regions, resolved heterozygous regions),
growing number of manually curated genes,
better suited for transcriptomic analysis

Still unresolved gene regions
# Acknowledgements

**Amandine Velt**

Marie Lahaye

Sophie Blanc

Éric Duchêne

Vincent Dumas

Philippe Hugueney

Jérôme Grimplet

Tomas Matus

David Navarro

**Nicola Vitulo**

Daniela Holtgräwe

Gabriele Di Gaspero

Gabriele Magris

Michele Morgante

Mario Pezzotti

Anne-Marie Digby

Doreen Ware

Marcela Tello-Ruiz

Kapeel Chougule

Nicolas Vigneron

Luis Orduna

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<figure><img src="image_106.png" /></figure>

<figure><img src="image_107.png" /></figure>
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COST Action
CA 17111
INTEGRAPE

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Thank you
for your
attention!

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