Predicting Splicing from Primary Sequence with Deep Learning

Highlights

• SpliceAI, a 32-layer deep neural network, predicts splicing from a pre-mRNA sequence

• 75% of predicted cryptic splice variants validate on RNA-seq

• Cryptic splicing may yield ∼10% of pathogenic variants in neurodevelopmental disorders

• Cryptic splice variants frequently give rise to alternative splicing

Summary

The splicing of pre-mRNAs into mature transcripts is remarkable for its precision, but the mechanisms by which the cellular machinery achieves such specificity are incompletely understood. Here, we describe a deep neural network that accurately predicts splice junctions from an arbitrary pre-mRNA transcript sequence, enabling precise prediction of noncoding genetic variants that cause cryptic splicing. Synonymous and intronic mutations with predicted splice-altering consequence validate at a high rate on RNA-seq and are strongly deleterious in the human population. De novo mutations with predicted splice-altering consequence are significantly enriched in patients with autism and intellectual disability compared to healthy controls and validate against RNA-seq in 21 out of 28 of these patients. We estimate that 9%–11% of pathogenic mutations in patients with rare genetic disorders are caused by this previously underappreciated class of disease variation.