TranSVPath: A TabTransformer-Based Model for Predicting the Pathogenicity of Structural Variants

Genomic structural variants are recognized as critical molecular factors contributing to various major diseases, including cancer and genetic disorders. However, accurately determining whether a variant can cause a disease in clinical settings is extremely challenging due to limited sample sizes, the diversity of variant types, and the complexity of the mechanisms linking variants to diseases. Existing computational tools attempt to predict the pathogenic effects of these variants, but they often consider only single-layer biological data, limiting their ability to comprehensively explain the functional impacts of the variants. To address this, we propose TranSVPath, a pathogenicity scoring tool for structural variants based on the Transformer framework. TranSVPath provides a more comprehensive biological annotation of structural variations by integrating multi-dimensional data, including overlap with specific genomic regions, single nucleotide variant deleteriousness scores, phylogenetic conservation scores, Mendelian clinical application pathogenicity scores, and gene function loss. Additionally, TranSVPath employs a variant of the Transformer architecture tailored to this integrated data. Its attention mechanism captures key biological features, facilitating molecular-level interpretation of the pathogenic mechanisms of variants. Consequently, TranSVPath can accurately predict the pathogenicity of deletions, insertions, tandem duplications, inversions, and microsatellite insertions. Evaluations on large-scale human structural variant datasets demonstrate that TranSVPath outperforms state-of-the-art algorithms across relevant metrics.