Benchmarking deep learning splice prediction tools using functional splice assays

Abstract

Hereditary disorders are frequently caused by genetic variants that affect pre-messenger RNA splicing. Though genetic variants in the canonical splice motifs are almost always disrupting splicing, the pathogenicity of variants in the noncanonical splice sites (NCSS) and deep intronic (DI) regions are difficult to predict. Multiple splice prediction tools have been developed for this purpose, with the latest tools employing deep learning algorithms. We benchmarked established and deep learning splice prediction tools on published gold standard sets of 71 NCSS and 81 DI variants in the ABCA4 gene and 61 NCSS variants in the MYBPC3 gene with functional assessment in midigene and minigene splice assays. The selection of splice prediction tools included CADD, DSSP, GeneSplicer, MaxEntScan, MMSplice, NNSPLICE, SPIDEX, SpliceAI, SpliceRover, and SpliceSiteFinder-like. The best-performing splice prediction tool for the different variants was SpliceRover for ABCA4 NCSS variants, SpliceAI for ABCA4 DI variants, and the Alamut 3/4 consensus approach (GeneSplicer, MaxEntScacn, NNSPLICE and SpliceSiteFinder-like) for NCSS variants in MYBPC3 based on the area under the receiver operator curve. Overall, the performance in a real-time clinical setting is much more modest than reported by the developers of the tools.

Keywords: ABCA4; MYBPC3; RNA splicing; deep learning; splice prediction tools; variant effect prediction.