Fig. 2

Precision-Recall performance of classifying intronic and exonic MFASS variants. Different machine learning models were used to separate splice disrupting variants from those without a splice effect. Shown are all variants in MFASS (a) that were scored by all splice effect predictors, b only exonic and c only intronic variants. Generally, specialized splice effect predictors, such as MMSplice, SPANR, and SpliceAI, perform better than the more general CADD, both on exonic and intronic variants. We observe the best performance by combining MMSplice and SpliceAI with the percent spliced-in (psi) value of the reference allele in a linear combination (MMAIpsi). Such a model however is assay-specific and circular with MFASS class definitions. A new CADD-Splice model, integrating MMSplice and SpliceAI as features, outperforms previous CADD models