Graph data science in fungal biotechnology: Opportunities and applications

Fungal biotechnology is crucial for generating high-value enzymes and fermentation products. Despite its industrial importance, major knowledge gaps in understanding fungal genomic variation, phenotypic diversity, and protein function prediction constrain biological innovation. While advancements in sequencing technologies have established data science as an integral component in driving developments in industrial fungal biotechnology, the inherent complexity of fungal genomes and incompatible repositories continue to limit comprehensive characterization of biological relationships and their translation into industrial applications. This review examines recent progress in non-graph methodologies applied to fungal biology. Genome annotation tools uncover genetic variation through homology-based approaches and enable functional annotation of sequence variants. Metric-based methods identify horizontal gene transfer events, while multivariate techniques characterize phenotypic variation across conditions. However, the increasing diversity, scale, and multimodal nature of fungal datasets require more integrative frameworks. Graph data science, a multivariate approach to model complex relationships as networks, offers opportunities to overcome these challenges. We discuss how graph-based methods enhance the detection of genomic structural variation and enable the modeling of molecular interactions. Furthermore, we outline how these approaches facilitate the exploration of complex fungal systems through multi-taxon, reference-free analyses, that integrate evolutionary signals, functional associations, and curated knowledgebases. By surveying available fungal resources and their taxonomic and ecological representations, we identify well-characterized genera, highlight underexplored taxa requiring further data generation, and pinpoint the ecological biases inherent in current sequencing efforts. Collectively, these advancements demonstrate how graph data science can accelerate fungal research and bridge fundamental discoveries and biotechnological applications.