Alternative splicing, the mechanism by which intronic sequences are excised from pre-mRNAs to produce mature mRNA, affects >95% of human protein-coding genes and is a major driver of human disease states. The spliceosome, a protein-RNA complex responsible for splicing pre-mRNA, identifies candidate splice sites partly through the recognition of characteristic sequence motifs at exon-intron junctions. Deep learning models that predict the presence of splice sites from pre-mRNA sequence have achieved breakthrough performance relative to previous machine-learning techniques, and these models have improved our ability to identify pathogenic genetic variants that alter splicing. We show that, while overall performance measures from these models suggest near-perfect performance, substantial gaps in prediction remain, including the identification of splice sites with low usage rates and tissue-specific splice sites. We leverage one of the largest paired RNA and genotyping datasets used to date to train a novel splicing model optimized for a specific cell type, human airway epithelial cells. We trained a dilated convolutional neural network on data from cultured airway epithelial cells from 100 donors, and showed that this model outperforms current state-of-the-art models on splice site identification and splice site usage quantification, including on multiple tissues not included in the model training data. We present the most comprehensive evaluation of state-of-the-art splicing models published to date, revealing reasonable performance across models for genetic variant effect prediction, along with important performance gaps and insights into directions for future model development.
Runyan, M., Gupta, S., Leshaem, Y., Geller-McGrath, D., Liu, C., Saferali, A., Dy, J., Radivojac, P., Tesfaigzi, Y., Castaldi, P., Paul, A.
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