Performance Evaluation of SpliceAI for the Prediction of Splicing of Variants.

Neurofibromatosis type 1, characterized by neurofibromas and café-au-lait macules, is one of the most common genetic disorders caused by pathogenic variants. Because of the high proportion of splicing mutations in , identifying variants that alter splicing may be an essential issue for laboratories. Here, we investigated the sensitivity and specificity of SpliceAI, a recently introduced splicing prediction algorithm in conjunction with other tools. We evaluated 285 variants identified from 653 patients. The effect on variants on splicing alteration was confirmed by complementary DNA sequencing followed by genomic DNA sequencing. For prediction of splicing effects, we used SpliceAI, MaxEntScan (MES), and Splice Site Finder-like (SSF). The sensitivity and specificity of SpliceAI were 94.5% and 94.3%, respectively, with a cut-off value of Δ Score > 0.22. The area under the curve of SpliceAI was 0.975 ( < 0.0001). Combined analysis of MES/SSF showed a sensitivity of 83.6% and specificity of 82.5%. The concordance rate between SpliceAI and MES/SSF was 84.2%. SpliceAI showed better performance for the prediction of splicing alteration for variants compared with MES/SSF. As a convenient web-based tool, SpliceAI may be helpful in clinical laboratories conducting DNA-based sequencing.