AI Insight
Researchers developed BUDDY, a web-based platform that streamlines the process of studying human genetic Variants of Uncertain Significance (VUS) using yeast as a model organism. The platform integrates multiple bioinformatic tools, databases, and an AI-assisted design feature to help researchers quickly assess whether specific human genetic variants can be effectively modeled in Saccharomyces cerevisiae. Testing showed that BUDDY significantly reduced preparation time for experienced biologists and enabled a first-year medical student without bioinformatics training to independently design a variant modeling study for Lynch syndrome-associated VUS.
Why it matters
This tool addresses a major bottleneck in clinical genetics by making functional validation of uncertain genetic variants more accessible and efficient. By lowering technical barriers, BUDDY could accelerate variant classification for patient diagnosis and expand research opportunities in educational settings where bioinformatics expertise or resources are limited.
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⚠️ Preprint – Noch nicht peer-reviewed
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The advent of next-generation sequencing has transformed clinical genetics, accelerating the identification of human genetic variants. However, the labor-intensive process of functional validation of Variants of Uncertain Significance (VUS) remains a significant diagnostic barrier. While expressing humanized genes in Saccharomyces cerevisiae is a common approach for modeling missense variants with functional assays, using native yeast orthologs provides a more streamlined approach that preserves species-specific interactions within multisubunit complexes. However, taking this approach relies on a fragmented workflow that requires bioinformatic experience. To address this issue, we developed the Bioinformatic Utility for Diagnostic Discovery in Yeast (BUDDY), an integrated web platform designed to streamline variant analysis by connecting bioinformatic tools and databases including ClinVar. Features include amino acid alignments, a protein structure viewer that displays a superposition of yeast and human proteins, pathogenicity prediction data, CRISPR-Cas9 reagent design, and yeast phenotypes to provide potential functional assays. The platform also contains an artificial intelligence (AI)-assisted experimental design feature that evaluates the utility of modeling specific variant and gene combinations in yeast and can assist with phenotype selection. BUDDY significantly accelerated the bioinformatic preparation time needed for ortholog variant modeling by an experienced biologist. Additionally, without prior bioinformatics training, a first-year medical student used BUDDY to develop a self-directed project and proposed a study of five Lynch syndrome-associated VUS using a forward mutation rate assay. BUDDY is designed to accelerate the investigation of human variants in yeast and has a high potential utility for use in resource-constrained settings such as course-based undergraduate research experiences (CUREs).