Benchmarking deep learning splice prediction tools using functional splice assays.

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.

Riepe TV ，Khan M ，Roosing S ，Cremers FPM ，'t Hoen PAC ... -  《-》

Identification of splice defects due to noncanonical splice site or deep-intronic variants in ABCA4.

Pathogenic variants in the ATP-binding cassette transporter A4 (ABCA4) gene cause a continuum of retinal disease phenotypes, including Stargardt disease. Noncanonical splice site (NCSS) and deep-intronic variants constitute a large fraction of disease-causing alleles, defining the functional consequences of which remains a challenge. We aimed to determine the effect on splicing of nine previously reported or unpublished NCSS variants, one near exon splice variant and nine deep-intronic variants in ABCA4, using in vitro splice assays in human embryonic kidney 293T cells. Reverse transcription-polymerase chain reaction and Sanger sequence analysis revealed splicing defects for 12 out of 19 variants. Four deep-intronic variants create pseudoexons or elongate the upstream exon. Furthermore, eight NCSS variants cause a partial deletion or skipping of one or more exons in messenger RNAs. Among the 12 variants, nine lead to premature stop codons and predicted truncated ABCA4 proteins. At least two deep-intronic variants affect splice enhancer and silencer motifs and, therefore, these conserved sequences should be carefully evaluated when predicting the outcome of NCSS and deep-intronic variants.

Fadaie Z ，Khan M ，Del Pozo-Valero M ，Cornelis SS ，Ayuso C ，Cremers FPM ，Roosing S ，The Abca Study Group ... -  《-》

Comparison of In Silico Tools for Splice-Altering Variant Prediction Using Established Spliceogenic Variants: An End-User's Point of View.

Assessing the impact of variants of unknown significance on splicing has become a critical issue and a bottleneck, especially with the widespread implementation of whole-genome or exome sequencing. Although multiple in silico tools are available, the interpretation and application of these tools are difficult and practical guidelines are still lacking. A streamlined decision-making process can facilitate the downstream RNA analysis in a more efficient manner. Therefore, we evaluated the performance of 8 in silico tools (Splice Site Finder, MaxEntScan, Splice-site prediction by neural network, GeneSplicer, Human Splicing Finder, SpliceAI, Splicing Predictions in Consensus Elements, and SpliceRover) using 114 NF1 spliceogenic variants, experimentally validated at the mRNA level. The change in the predicted score incurred by the variant of the nearest wild-type splice site was analyzed, and for type II, III, and IV splice variants, the change in the prediction score of de novo or cryptic splice site was also analyzed. SpliceAI and SpliceRover, tools based on deep learning, outperformed all other tools, with AUCs of 0.972 and 0.924, respectively. For de novo and cryptic splice sites, SpliceAI outperformed all other tools and showed a sensitivity of 95.7% at an optimal cut-off of 0.02 score change. Our results show that deep learning algorithms, especially those of SpliceAI, are validated at a significantly higher rate than other in silico tools for clinically relevant NF1 variants. This suggests that deep learning algorithms outperform traditional probabilistic approaches and classical machine learning tools in predicting the de novo and cryptic splice sites.

Jang W ，Park J ，Chae H ，Kim M ... -  《-》

Benchmarking splice variant prediction algorithms using massively parallel splicing assays.

Smith C ，Kitzman JO 《GENOME BIOLOGY》

A deep intronic TCTN2 variant activating a cryptic exon predicted by SpliceRover in a patient with Joubert syndrome.

The recent introduction of genome sequencing in genetic analysis has led to the identification of pathogenic variants located in deep introns. Recently, several new tools have emerged to predict the impact of variants on splicing. Here, we present a Japanese boy of Joubert syndrome with biallelic TCTN2 variants. Exome sequencing identified only a heterozygous maternal nonsense TCTN2 variant (NM_024809.5:c.916C >T, p.(Gln306Ter)). Subsequent genome sequencing identified a deep intronic variant (c.1033+423G>A) inherited from his father. The machine learning algorithms SpliceAI, Squirls, and Pangolin were unable to predict alterations in splicing by the c.1033+423G>A variant. SpliceRover, a tool for splice site prediction using FASTA sequence, was able to detect a cryptic exon which was 85-bp away from the variant and within the inverted Alu sequence while SpliceRover scores for these splice sites showed slight increase (donor) or decrease (acceptor) between the reference and mutant sequences. RNA sequencing and RT-PCR using urinary cells confirmed inclusion of the cryptic exon. The patient showed major symptoms of TCTN2-related disorders such as developmental delay, dysmorphic facial features and polydactyly. He also showed uncommon features such as retinal dystrophy, exotropia, abnormal pattern of respiration, and periventricular heterotopia, confirming these as one of features of TCTN2-related disorders. Our study highlights usefulness of genome sequencing and RNA sequencing using urinary cells for molecular diagnosis of genetic disorders and suggests that database of cryptic splice sites predicted in introns by SpliceRover using the reference sequences can be helpful in extracting candidate variants from large numbers of intronic variants in genome sequencing.

Hiraide T ，Shimizu K ，Okumura Y ，Miyamoto S ，Nakashima M ，Ogata T ，Saitsu H ... -  《-》