Exploration of chaos game representation and integrative deep learning approaches for whole-genome sequencing-based grapevine genetic testing.

ABSTRACT:

Motivation

The identification of grapevine species, cultivars, and clones associated with desired traits is an important component of viticulture. True-to-type identification is very challenging for grapevine due to the existence of a large number of cultivars and clones and the historical issues of synonyms and homonyms. DNA-based identification, superior to morphology-based methods, has been used as the current standard true-to-type method for grapevine, but not without shortcomings, such as the limited number of biomarkers and accessibility of services.

Results

To overcome some of the limitations of traditional microsatellite-marker-based genetic testing, we explored a whole-genome-sequencing (WGS)-based approach to achieve the best accuracy at an affordable cost. To addr

SUBMITTER: Vu A 

PROVIDER: S-EPMC12449056 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature