Fig. 3: Application of splicing prediction on ClinVar database.

a SpTransformer is applied to evaluate the splicing effect of a single nucleotide variant by calculating an ΔSplice score and matching graphical representations. b Examples of two pathogenic mutations in the ClinVar database. SpTransformer successfully predicted splicing changes even far from variants (right panel). Both cases were validated by RT-PCR in previous studies c The distribution of mutations classified by clinical significance within several intervals of ΔSplice scores. As the ΔSplice score increases, the ratio of pathogenic mutations becomes larger. d Distributions of ΔSplice scores of all SNVs, grouped by both pathogenicity in ClinVar database and annotated variant type. The number of SNVs and the proportion of SNVs above/below the cutoff were annotated. The bar chart on the left aggregates the data by rows, while the bar chart at the top tabulates the data by columns. SNVs with alternative pathogenicity annotations (e.g., “conflicting interpretations”) were excluded from the analysis. a Created with BioRender.com, was released under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license.