Identification of Sugar-Related Genes in Sugar Beet (Beta vulgaris) Through Comparative Genomics and Machine Learning Approaches

Despite sugar beet's importance as a major sucrose source, comprehensive identification of genes underlying sugar metabolism remains incomplete. We developed an integrated approach combining comparative genomics with advanced machine learning to systematically catalog sugar-related genes in the Beta vulgaris genome. Analysis of the EL10 reference genome identified 18,223 high-quality protein-coding genes, of which 91.6% showed orthology to Arabidopsis thaliana proteins. Traditional keyword-based screening identified 310 sugar-related genes, of which 286 exhibited high-confidence orthology (E-value < 1×10⁻⁵⁰) to Arabidopsis proteins. To overcome limitations of keyword approaches, we implemented zero-shot classification using transformer-based Sentence-BERT embeddings to identify genes through semantic similarity to sugar-related concepts, independent of explicit nomenclature. This machine learning strategy identified 1,999 candidate genes, including 1,736 novel candidates absent from keyword results—an 85% expansion of the sugar gene catalog. Despite this novelty, 84.8% of keyword-identified genes were also detected by machine learning, validating the approach. Multiple high-confidence predictions corresponded to experimentally validated genes in published studies. This framework establishes transformer-based semantic analysis as a powerful complement to traditional annotation, with broad applicability for functional gene discovery in crop genomics.