MACHINE LEARNING DRAFTS THE DISSEMINATION LANDSCAPE OF THE VINE GENOME – NEW PUBLICATION AT MDPI SCIENTIFIC PLATFORM

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Our ‘SOMmelier’ approach visualizes essential aspects of grapevine genomes related to geographic distribution, paths of dissemination and vine utilization.

‘SOMmelier’ portrays vine genomes in terms of individualized images. They are intuitive, meaning that they don’t need specialized genetic knowledge for interpretation. Together with vine genome landscapes described here, we propose to use individual genomic portraits as an option to supplement vine cultivar passports as fingerprint characteristics of their genomes. Such fingerprint portraits consider virtually the whole diversity of the vine.

Note also that we here discuss accession portraits mainly on the level of geographic regions as a sort of worked example to support the interpretation of genomes of individual accession portraits.

Our study also demonstrates that bioinformatics methods proven before in analytic tasks on different omics realms, mostly transcriptomics, but also epigenomics, proteomics and human genomics, provide reasonable results if applied to vine cultivars as an example of plant genomes. Our approach thus extends the methods toolbox for plant genetics by providing novel approaches which complement established ones.

Their pros and cons should be evaluated in future applications. The ‘SOMelier ‘method opens the opportunity to process larger genotype data, obtained by, e.g., whole genome sequencing and/or increased number of cultivars included.