Machine learned-based visualization of the diversity of grapevine genomes worldwide and in Armenia using SOMmelier
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 informati...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
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| Series: | BIO Web of Conferences |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/full_html/2023/13/bioconf_oiv2023_01009/bioconf_oiv2023_01009.html |
| _version_ | 1827389683988955136 |
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| author | Magaryan Kristina Nikogհosyan Maria Baloyan Anush Gasoyan Hripsime Hovhannisyan Emma Galstyan Levon Konecny Tomas Arakelyan Arsen Binder Hans |
| author_facet | Magaryan Kristina Nikogհosyan Maria Baloyan Anush Gasoyan Hripsime Hovhannisyan Emma Galstyan Levon Konecny Tomas Arakelyan Arsen Binder Hans |
| author_sort | Magaryan Kristina |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T16:40:26Z |
| format | Article |
| id | doaj.art-b92ac36028934a37a170c50ef595c79f |
| institution | Directory Open Access Journal |
| issn | 2117-4458 |
| language | English |
| last_indexed | 2024-03-08T16:40:26Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | BIO Web of Conferences |
| spelling | doaj.art-b92ac36028934a37a170c50ef595c79f2024-01-05T10:33:19ZengEDP SciencesBIO Web of Conferences2117-44582023-01-01680100910.1051/bioconf/20236801009bioconf_oiv2023_01009Machine learned-based visualization of the diversity of grapevine genomes worldwide and in Armenia using SOMmelierMagaryan Kristina0Nikogհosyan Maria1Baloyan Anush2Gasoyan Hripsime3Hovhannisyan Emma4Galstyan Levon5Konecny Tomas6Arakelyan Arsen7Binder Hans8Research Group of Plant Genomics, Institute of Molecular Biology of National Academy of Sciences RAArmenian Bioinformatics Institute (ABI)Armenian Bioinformatics Institute (ABI)Armenian Bioinformatics Institute (ABI)Armenian Bioinformatics Institute (ABI)Armenian Bioinformatics Institute (ABI)Armenian Bioinformatics Institute (ABI)Bioinformatics Group, Institute of Molecular Biology Institute of National Academy of Sciences RAArmenian Bioinformatics Institute (ABI)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.https://www.bio-conferences.org/articles/bioconf/full_html/2023/13/bioconf_oiv2023_01009/bioconf_oiv2023_01009.html |
| spellingShingle | Magaryan Kristina Nikogհosyan Maria Baloyan Anush Gasoyan Hripsime Hovhannisyan Emma Galstyan Levon Konecny Tomas Arakelyan Arsen Binder Hans Machine learned-based visualization of the diversity of grapevine genomes worldwide and in Armenia using SOMmelier BIO Web of Conferences |
| title | Machine learned-based visualization of the diversity of grapevine genomes worldwide and in Armenia using SOMmelier |
| title_full | Machine learned-based visualization of the diversity of grapevine genomes worldwide and in Armenia using SOMmelier |
| title_fullStr | Machine learned-based visualization of the diversity of grapevine genomes worldwide and in Armenia using SOMmelier |
| title_full_unstemmed | Machine learned-based visualization of the diversity of grapevine genomes worldwide and in Armenia using SOMmelier |
| title_short | Machine learned-based visualization of the diversity of grapevine genomes worldwide and in Armenia using SOMmelier |
| title_sort | machine learned based visualization of the diversity of grapevine genomes worldwide and in armenia using sommelier |
| url | https://www.bio-conferences.org/articles/bioconf/full_html/2023/13/bioconf_oiv2023_01009/bioconf_oiv2023_01009.html |
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