Machine learned -based visualization of the diversity of vine genomes worldwide and in Armenia using SOMmelier

Abstract

In this study three major issues have been addressed: Firstly, the diversity of grapevine accessions worldwide and particularly in Armenia, a small country located in the largely volcanic Armenian Highlands, is incredibly rich in cultivated and especially wild grapes; secondly, the information hidden in their (whole) genomes, e.g., about the domestication history of grapevine over the last 11,000 years and phenotypic traits such as cultivar utilization and a putative resistance against powdery mildew, and, thirdly machine learning methods to extract and to visualize this information in an easy to percept way. We shortly describe the Self Origanizing Maps (SOM) portrayal method called "SOMmelier" (as the vine-genome "waiter") and illustrate its power by applying it to whole genome data of hundreds of grapevine accessions. We also give a short outlook on possible future directions of machine learning in grapevine transcriptomics and ampelography.

Full text

Machine learned-based visualization of the diversity of grapevine
genomes worldwide and in Armenia using SOMmelier
Kristina Magaryan1,2, Maria Nikogհosyan3,4, Anush Baloyan3, Hripsime Gasoyan3, Emma Hovhannisyan3, Levon Galstyan3,
Tomas Konecny3, Arsen Arakelyan4, and Hans Binder3,5
1Research Group of Plant Genomics, Institute of Molecular Biology of National Academy of Sciences RA, Yerevan 0014, Armenia
2Department of Genetics and Cytology, Yerevan State University, Yerevan 0025, Armenia
3Armenian Bioinformatics Institute (ABI), Yerevan 0014, Armenia
4Bioinformatics Group, Institute of Molecular Biology Institute of National Academy of Sciences RA, Yerevan 0014, Armenia, Yerevan
0014, Armenia
5Interdisciplinary Centre for Bioinformatics, University of Leipzig, 04107 Leipzig, Germany
Abstract. In the proposed study three major issues have been addressed: Firstly, the diversity of grapevine
accessions worldwide and particularly in Armenia, a small country located in the largely volcanic Armenian
Highlands, is incredibly rich in cultivated and especially wild grapes; secondly, the information hidden in their
(whole) genomes, e.g., about the domestication history of grapevine over the last 11,000 years and phenotypic traits
such as cultivar utilization and a putative resistance against powdery mildew, and, thirdly machine learning methods
to extract and to visualize this information in an easy to percept way. We shortly describe the Self Origanizing Maps
(SOM) portrayal method called “SOMmelier” (as the vine-genome “waiter”) and illustrate its power by applying it to
whole genome data of hundreds of grapevine accessions. We also give a short outlook on possible future directions of
machine learning in grapevine transcriptomics and ampelogaphy.
1 Introduction
The grapevine is one of the earliest domesticated fruit
crops and has been widely cultivated and prized for its
fruit and wine. According to the recent study [1] the
roots of domestication were found deep in the
Pleistocene, ending almost 11.5 thousand years ago (ya)
in the region, where Armenian Highland is existed.
Armenia is considered an ancient origin of grapevine
domestication and wine-making, which is confirmed by
remains of wild and cultivated grapes and wine-
producing facilities found at archaeological sites of the
country. The diverse climatic conditions, unique
geography and existence of wild grapes were the main
drivers in the formation of extensive diversity of
cultivated varieties and the promotion of wine-
making [2].
In the recent decade whole genome studies of
grapevine genetic resources using high-throughput
sequencing technologies have generated novel knowledge
about the evolution of vine traits, genetic diversity,
phylogenetic relatedness and historical origin, phenotype
associations and migration paths of the vines. There has
been a rapid growth in the quality and quantity of data for
grapevine genomes, but methods to interrogate this data
are limited. At the same time, machine learning and
artificial intelligence methods are revolutionising data
analysis. Presented research applied machine learned-
based visualization and analysis of grapevine genomic
data by SOMmelier method to gain a greater
understanding of grapevine genomes, their diversity,
function and evolution [3].
Self-organizing neural networks mainly referred to as
self-organizing maps (SOMs) were introduced by T.
Kohonen in the beginning of 1980’s, who presented them
as “a new, effective software tool for the visualization of
high-dimensional data” [4]. The methods has been
further developed into a molecular portrayal method
complemented by comprehensive downstream analysis
options including different visualization options,
knowledge mining and feature selection tasks [5]. It has
been applied mainly to different omics data in the human
disease context (see, e.g., [6,7]) and recently was applied
to a collection of SNP vine genome data [3]. Here we
shortly introduce the method, illustrate its power by
applying it to worldwide grapevine genomes to
reconstruct dissemination of viticulture, discuss the
impact of wild and cultivated grapevines collected in
Armenia and finally present first results of whole
genome analyses using SOMmelier of Armenian
grapevine gene pool.
BIO Web of Conferences 68, 01009 (2023) https://doi.org/10.1051/bioconf/20236801009
44th World Congress of Vine and Wine
© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution
License 4.0 (https://creativecommons.org/licenses/by/4.0/).

2 Machine learning of Vine genomes
Modern high throughput technologies such as genome
sequencing revolutionized molecular life sciences. A
typical genome-wide data consists of up to millions of
items (e.g. single nucleotide polymorphisms, SNPs) for
each of the hundreds to thousands of samples serving as
input for bioinformatics downstream analysis (Fig. 1).
Visualization-based analysis and knowledge mining is a
well proven but difficult to realize concept for genomic
data because of their size and complexity. We make use
of self-organizing maps (SOM), a clustering method
which was always developed more than forty years
ago [4]. Our SOM-“portrayal” approach uses a very high
number of (micro-)clusters largely exceeding the number
of relevant dimensions of variation of the data [8].
Particularly, for genomic data of vine the input matrix of
size ~108-104 is reduced to ~103-104 micro-clusters, also
called meta-SNPs, because each of them clusters similar
SNP-profiles across the vine accessions under study
together. SOM clustering uses an iterative, “machine-
learning” algorithm to achieve a specific, two-dimensions
similarity topology of the clusters. Namely, it arranges
them in a two-dimensional array under the condition that
neighbouring micro-clusters are more similar (in terms of
the Euclidian distance) than distant ones. The clustered
Figure 1. SOM portrayal of grapevine genomes (see text).
data are then visualized for each sample by colouring
each pixel of the two-dimensional array by the eMAF-
value of the meta-SNP, e.g. using a red to blue colour
scale. Because of the self-organizing properties of the
algorithm the obtained colour patterns exhibit blue and
red spot-like regions. They visualize the intrinsic co-
variance structure of the data which is specific for each
particular genome and can be interpreted as the individual
genomic portraits for each of the accessions. Virtually
they label each accession by an “fingerprint”-image. The
portraits are mutually comparable because the SNPs in
each of the micro-clusters are identical across one SOM.
Further, the portraits are interpretable in terms of
biological functions, e.g., by applying previous
knowledge about functional aspects of the genome and
enrichment techniques.
In the scope of proposed study we have analyzed
microarray SNP-data of grapevine accession collected
around the world and whole genome sequencing data of
Armenian wild and cultivated grapevines taken from [9]
and [1], respectively.
3 Genetic footprints of grapevine
cultivation
First, we analysed grapevine genetic SNP- and phenotype
data of cultivars collected around the world. The data
were taken from [9] and consists of 783 grapevine
samples originating from 41 countries in nine geographic
regions ranging from Middle Asia to Iberia in the “Old
World” and including also New World accessions (see
legend in Fig. 2). We calculated country-wise SOM
portraits, which reveal overall a high genetic diversity,
where, however, portraits of countries from the same
region often resemble each other. One finds similarities
between the portraits for neighbouring countries from
Georgia, via Russia, Ukraine and Moldova, towards
Balkan into the west direction and from Georgia and
Armenia via Iran towards Tadjikistan, Uzbekistan and
Afghanistan in the Middle Asia region. Moreover, the
textures alter in a systematic way between the regions,
e.g., from the east (EMCA, MFEA) to the west (BALK,
WCEU, ITAP and IBER), as visualized by the ‘metro-
net’ lines linking similar country portraits (Fig. 2).
Another route is directed from the Caucasus via Lebanon,
Israel towards North Africa (MAGH) and Iberian
Peninsula (IBER). The South Caucasus is also linked via
Anatolia (Turkey), Cyprus and Greece with the Balkan.
In the western part of Europe, portraits from Spain show
similarities with Northern African countries (MAGH),
and only partly with French and German portraits, which,
in turn, show similarity links via Switzerland, Austria and
the Czech Republic towards Balkan. Mexican cultivars
resemble Spanish ones according to their SNP-portraits
while cultivars from USA, Australia and Argentina, on
average, reflect more similarities with grapevines from
MFEA and EMCA.
A recent very large study on the whole genomes of
more than 2,000 V.v.subsp. vinifera and about 1,000
V.v.subsp. sylvestris disentangles history of grape
domestication and dissemination much more in
detail [1]. Accordingly, originally grapevine was
cultivated separately in an Caucasian and in an Western
Asian cultivation centre around 11,000 ya (years ago) and
afterwards cultivated grapevine accessions were
disseminated across the Old world mainly by Neolithic
farmers. Overall dissemination stages refer to six clusters
of cultivated grapes (CG1-6), where CG2 refers to the
Caucasian origin (blue arrow in Fig. 2), while CG1 and
CG3-6 refer to the Western Asian origin (red arrows).
The regional similarity relations of the SOM portraits
strikingly agree with these CG-clusters.
2
BIO Web of Conferences 68, 01009 (2023) https://doi.org/10.1051/bioconf/20236801009
44th World Congress of Vine and Wine

Figure 2. Metro map of distribution of cultivated grapes
collected from 41 countries around the Mediterranean Sea.
Country-wise SOM-portraits are linked by lines according to
mutual similarity relations (see [3] for details). The map
resembles the dissemination routes of cultivated grapes which
originated from two areas of primary cultivation in the
Caucasus (blue, CG2) and near East (red, CG1) around 11,000
years ago [1]. CG1 distributed towards the east and the west in
the following thousand years as indicated (ya…years ago) and
mainly constituted the diversity of grapes observed nowadays.
The CG-clusters well agree with the regions included in the
ellipses.
The metro-map in (Fig. 2) illustrates country- and
region-wise similarities between the grapevine genomes
as visualized using their SOM portraits. A
complementary perspective in gene-space can be
obtained by summarizing the SOM portraits into a
genetic overview landscape of grapevine cultivars
(Fig. 3). It shows (red) mountain ranges which refer to
correlated clusters of SNPs with increased excess minor
allele frequencies (eMAF) in the portraits of certain
geographic regions as indicated by the flags. Blue
coloured areas refer to genomic regions of low eMAF
values on the average. Interestingly, the topology of the
genetic eMAF-landscape resembles the geographic
topology around the Mediterranean and the Black Sea
areas including the Caucasus. Namely, the eMAF-
‘mountains’ order cultivars from the Caucasus along a
northern route’ via Balkan towards Western Europe and
along a southern route via Palestine and Maghreb towards
the Iberian Peninsula. A central ‘blue valley’ referring to
predominantly low eMAF-values separates both routes. It
can be interpreted geographically as the Mediterranean
and Black Sea areas, which constitute areas of reduced
genetic exchange. Interestingly, the large barrier is found
between grapes from the Iberian Peninsula and Western
Europe (France, Italy), while the street of Gibraltar
appears only as a small sidearm of the central ‘genetic’
valley, thus indicating a relatively moderate genetic
barrier between North Africa and Iberia. Another
moderate genetic barrier is found between grapes from
the Balkan and Western Europe (Germany, Switzerland
and Italy). According to these barriers, cultivars divide
into four major groups on the coarsest level of
classification, namely Western Europe and Italian grapes,
Iberian grapes and vine cultivars from Eastern and
Maghreb regions, which strikingly agree with the CG6,
CG5, CG4 (and partly CG2) and CG1 groups,
respectively. Detailed inspection of the mountain range of
‘eastern’ grapes reveals fine internal structure of valleys
separating, e.g., Armenian from Georgian grapes and
vines from Anatolia and Greece from Balkan ones.
Additionally, we visualize grape utilization in terms of
phenotype maps which associate table, wine and double
usage with different geographic regions (Fig. 3, part
below). Grapes for fresh consumption (table vines)
predominate in Asia and North African areas, while wine
utilization is found mostly in Western Europe. Overall
SOM portrayal of grapevine genomes illustrates the
specifics of individual grapevine accessions, similarities
between them in the historical context of vine cultivation
and dissemination as wells as phenotypic traits such as
grapevine utilization.
Figure 3. The SOM genetic landscape reflects the
dissemination paths of cultivated grapes from near east and the
Caucasus towards Western Europe and Iberian Peninsula. Grape
utilization maps of the grape show preferential wine making in
Western Europe, Iberia and Balkan, table grape usage in West
and East Asia and double usage in Maghreb and Iberia.
4 Diversity of wild and cultivated
grapevines in Armenia
4.1 Armenian gene pool of V. sylvestris
Armenia is an important origin of grapevine
domestication, located in the dual domestications centre
of grapevine evolution governed by endemic wild
grapevines [1]. The country is characterized by a high
diversity of cultivated (Vitis vinifera L. subsp.vinifera)
and wild (Vitis vinifera L. subsp. sylvestris) grapevines.
The country has played a leading role in the centuries-
lasting history of grapevine cultivation in the Near East.
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44th World Congress of Vine and Wine

Varying climatic conditions and the existence of wild
grapes lead to the formation and promotion of viticulture
and winemaking, as evidenced by nearly 450
autochthonous varieties [2]. Hundreds of unique
indigenous cultivars are still preserved in old vineyards
and abandoned gardens, though most of them are
threatened by extinction. Wild grapes, thriving along
riverbanks, climbing the rocks and embracing the trees
can be found in Vayots Dzor, Tavush, Lori, Syunik
provinces and in Artsakh.
The understanding of the importance of the protection
and conservation of genetic resources of the grapevine
wild ancestor Vitis vinifera L. subsp. sylvestris is very
high for several reasons. Nowadays, the wild grape
population has become relict due to several forms of
human disturbance such as habitat destruction and
fragmentation, irregular management of the natural
environment, pathogen spread, which has increased in the
last decades, and a demanding reproductive
strategy [10].
Studies on wild grapes reinforced in parallel with the
advanced molecular technologies, the ultimate goals of
preserving its biodiversity, clarifying its taxonomic status
and identifying traits of interest for the breeding program.
The study of genetic relationships among the two
subspecies of Vitis vinifera evidenced genetic relatedness
between wild and cultivated grapes in Armenia
(Margaryan et al, 2023, accepted for publication in
VITIS). The applied hierarchical and non-hierarchical
clustering methods differentiated between sylvestris and
vinifera, but also demonstrated existence of gene flow
between the wild and cultivated grapevines through
overlaps and presence of admixed ancestry values (see
also below). High levels of genetic diversity
demonstrated by the effective number of alleles and
richness of private and new alleles, mirrored the
existence of significant diversity both within and between
the subspecies suggesting that Armenia is an important
centre of grape biodiversity.
4.2 Diversity as seen by microsatellite markers
The knowledge of genetic diversity and relatedness
among grapevine varieties and wild plants is important to
recognize gene pool. One of the major goals more than
10 years for the Group of Plant Genomics at IMB NAS
RA was the large-scale research to evaluate the level and
relationships of existing genetic diversity of grapes across
Armenia, aiming to identify genotypes that could provide
genetic insights into the Armenian grapevine germplasm
structure. It was confirmed that Armenian grapevine
germplasm is a blend of different genotypes, exhibiting a
high level of differentiation, resulting in higher-than-
expected levels of heterozygosity. This is often observed
in woody perennial crops where varieties are selected for
their vigor and crop performance, indirectly endorsing
high levels of heterozygosity.
Prospections in traditional viticulture regions across
Armenia provided insights in the huge grapevine genetic
diversity existing in the country. A combination of
nuclear microsatellite markers and ampelography proved
useful to determine the identity of collected samples
recovered from old vineyards and home gardens.
Synonyms, homonyms, alternative spellings, and
misnomers were clarified. Well-identified and referenced
grape genetic resources are a prerequisite for its
utilization and the management of germplasm
repositories.
The high number of alleles, included also rare and
new alleles, high observed and effective heterozygosity
values, and presence of female APT3-allele 366, which is
absent in western European cultivars, illustrate the huge
diversity of Armenian germplasm. Presumably, these
findings are related to recurrent introgression of Vitis
sylvestris into the cultivated compartment during
domestication events. Instability of grapevine cultivars
also was detected, showing three and in some cases also
four alleles at one locus.
4.3 SOM portrayal of WGS data
In a new, ongoing project at the Armenian Bioinformatics
Institute, a group of young researchers started analysis of
whole genome sequencing (WGS) data of wild and
cultivated grapevines collected across the viticultural
regions of Armenia. This data provided an essential
contribution to the understanding of the evolutionary
history of grapevine [1]. Phylogenetic clustering
separates wild from cultivated grapevines except a small
mixed cluster of both (Fig. 4). SOM analysis generated
individual portrait of the genomes of each Armenian
accession studied. Mean portraits of wild and cultivated
grapevines show mirror symmetrical patterns of a red and
a blue spot indicating antagonistic eMAF patterns which
confirms the separation of accessions in the clustering
tree (Fig. 4, part below).
Figure 4. Phylogenetic cluster tree of Armenian vine accessions
distinguishes v. sylvestris and v. vinifera. SOM portraits of all
accessions are shown in the part below.
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BIO Web of Conferences 68, 01009 (2023) https://doi.org/10.1051/bioconf/20236801009
44th World Congress of Vine and Wine

4.3 Admixture and phenotypic traits
For a closer look we performed admixture analysis
assuming K=2, 3, 4 and 6 genomic fractions (Fig. 5). For
K=2 the genomic fractions divide into a (predominantly)
v. vinifera- (blue colored) and a v. sylvestris (red colored)
ones in agreement with the phylogenetic tree clustering
and SOM analysis (see previous subsection). Hybrid and
feral vines show composite genomes with dominant
vinfera-related fraction. For K=3 and 4 one finds a
binary, continuously varying composition of v. vinifera as
well as a binary composition of v. sylvestris which
however is strongly intermixed with the v. vinifera
component. Admixture for K=6 virtually confirms the
K=4 analysis.
Figure 5. Admixture plots for K=2, 3, 4 and 6 components of
Armenian wild (V. sylvestris) and cultivated (V. vinifera)
accessions. Selected individual genetic portraits are shown on
top of the admixture plot. Part below: Mean portraits over
phenotypic traits “utilization” and “berry skin colour”.
The individual genetic portraits reveal partly parallel
changes of spot patterns and of admixture components
which suggest associations between both views but need
further analysis. For example, the red eMAF spots rotate
in counter clock- and clockwise direction for V. v. subsp.
vinifera and V.v. subsp. sylvestris, respectively, which
indicates their systematic variation along the x-admixture
coordinate, and, in turn, associates with phenotypic traits
such as vine utilization and berry skin colour (see the
colour bars in Fig. 5). Phenotype-stratified mean SOM-
portraits reveal distinct genetic differences between them.
Hence, admixture analysis and SOM portrayal provide
complementary information in terms of composite plots
along the accession axis and of detailed genetic patterns
for of individual accessions, respectively.
4.4 Searching for resistance (R-)genes
Out of the 63 v. sylvestris accessions studied, 21 (33%)
were shown putative resistance against the fungal disease
powdery mildew, which enables to search for R-
associated chromosomal loci by comparing them on
genome-wide scale. GWAS analysis revealed four
chromosomal loci significantly associated with resistance
against powdery mildew (Fig. 6). SNPs on chromosome
13 are located in an intergenic region corresponding to
the previously identified resistance REN1 locus [11]
while the other SNPs suggest possible new R- loci. Mean
SOM portraits of Armenian putative resistant and non-
resistant v. sylvestris accessions show the typical red spot
in the left upper corner with slight differences. For higher
resolution we generated the difference portrait which,
interestingly, shows red R-associated “spots” of increased
eMAF in the right upper half of the map and blue spots of
decreased eMAF in the left lower part. These patterns
suggest a systematic effect of resistance in the vine
genomes, particularly, of increased and reduced MAF,
respectively. Preliminary results show that selected
significant R-SNPs are located in the blue spots of
reduced eMAF in the left part of the difference portrait.
In summary, potential resistant v. sylvestris accessions
provide a rich reservoir for the search of R-genes and the
underlying molecular mechanisms with potential impact
for viticulture. Hereby the combination of GWAS and
SOM-portrayal are useful tools for identifying genomic
R-loci and mechanisms.
Figure 6. Manhattan plot of the results of GWAS on resistant
and non-resistant V. sylvestris from Armenia. Mean portraits
and their difference are shown in the part below.
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BIO Web of Conferences 68, 01009 (2023) https://doi.org/10.1051/bioconf/20236801009
44th World Congress of Vine and Wine

5 Future aspects of machine learning in
vine science
5.1 Functional genomics in the era of climate
change
Genetics isn’t everything! Biological function is
governed by a bundle of molecular mechanisms under the
effect of environmental condition which includes
genomic regulation via an interplay of different omics
levels including transcriptomics and epigenomics. Hence,
genetic analyses must be complemented by phenotypic
and additional omics data for a deeper understanding of
grape physiology, e.g. to handle environmental stress in
the context of climate change. As an illustrative example
we applied SOM portrayal to transcriptomic data
(RNAseq) extracted from leaves of grapevines
conditioned under different temperature conditions
simulating cold-stress [12]. Different stress conditions
(freeze stress, chill stress, freeze shock) induce distinct
transcriptomic changes relative to the reference “warm”
environmental state for five vine accessions (see Fig. 7,
part above). Their mean SOM-portraits per condition
reveal systematic changes of the transcriptional programs
which can be summarized into a merged transcriptional
landscape (Fig. 7, part below, red and blue areas indicate
over- and under-expression, respectively). The red
overexpression modules of co-regulated genes can be
assigned to different biological functions specifically
activated under the different temperature conditions along
a stress-trajectory (white arrow). Hence, SOM-portrayal
can be applied to different omics data beyond genetics as
a generic clustering, visualization and analysis method
for big and complex data collected for studying plant
physiology under environmental stress.
5.2 Digital ampelography: Learning the shape
“Classical” ampelography generates another type of
complex data with impact for classification of grapevine
accessions based, e.g., on the metrics of their leaves. It
has a long history and can be seen as a sort of “classical”
standard based on leave-shapes. We recently developed a
SOM-learning method to classify human body
shapes [13] which can be viewed as an analogous metric
system based on a series of items per human body.
Application to ampelographic measures opens one option
to handle large-scale leaf data of hundreds of vine
accessions using machine learning. Deep learning of leaf
shapes represents another, very interesting option for
developing and applying digital ampelography techniques
to large collections of grapevine varieties [14]. Here, the
whole shape of a large number leaves is learned for
classification of vine varieties with high accuracy. Digital
ampelography is currently in the proof-of-principle stage
and needs larger consensus data sets for broad
applications. Our contribution will be the systematic
gallery of leave shapes of Armenian accessions as well as
their machine learning using SOM and deep learning
techniques. Furthermore, in a wider sense the individual
genetic SOM portraits of vine accessions as presented in
chapter 4 can be used for deep learning of genetic
patterns for developing a “genetic ampelography”. A
proof of principle study using deep learning on SOM
portraits taken from another application [15] makes its
application to vine genomes promising.
Figure 7. SOM portrayal of vine transcriptomics under
temperature stress. The phylogenetic tree clusters the
transcriptomes extracted from grapevine leaves (five accessions,
three replicates) into disjunct clusters, each related to one of the
four temperature conditions applied to the plants. Each cluster is
characterized by its specific transcriptional state as visualized
by its transcriptomics portrait. Part below: the overview
landscape summarizes the observed modules of overexpressed
genes (red spots), which can be assigned to certain biological
functions using previous knowledge and gene set enrichment
techniques. The white arrow illustrates a “stress trajectory”
pointing from normal, “warm” reference state via cold (chill
and freeze shock stress) towards freeze stress. RNAseq data
were taken from [12].
6 Conclusions
Whole genome data on thousands of grapevine
accessions open novel perspectives in viticulture.
Machine learning and, particularly, SOMelier molecular
portrayal in combination with other bioinformatics
methods offers interesting options for their intuitive
analysis and understanding in terms of mutual similarities
as well as of their functional impact. The detailed study
of the richness of Armenian genetic resources is in the
focus of our research addressing the history of grapevine
cultivation, resistance against fungal diseases and
environmental stress in the context of climate change.
We acknowledge the support given by FAST (Foundation of
Armenian Science and Technology) in the frame of the
ADVANCE program and project 21T-1F076, SC of RA.
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BIO Web of Conferences 68, 01009 (2023) https://doi.org/10.1051/bioconf/20236801009
44th World Congress of Vine and Wine

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BIO Web of Conferences 68, 01009 (2023) https://doi.org/10.1051/bioconf/20236801009
44th World Congress of Vine and Wine