A sequence-based, deep learning model accurately predicts RNA splicing branchpoints

Experimental detection of RNA splicing branchpoints, the nucleotide serving as the nucleophile in the first catalytic step of splicing, is difficult. To date, annotations exist for only 16-21% of 3 splice sites in the human genome and even these limited annotations have been shown to be plagued by noise. We develop a sequence-only, deep learning based branchpoint predictor, LaBranchoR, which we conclude predicts a correct branchpoint for over 90% of 3 splice sites genome-wide. Our predicted branchpoints show large agreement with trends observed in the raw data, but analysis of conservation signatures and overlap with pathogenic variants reveal that our predicted branchpoints are generally more reliable than the raw data itself. We use our predicted branchpoints to identify a sequence element upstream of branchpoints consistent with extended U2 snRNA base pairing, show an association between weak branchpoints and alternative splicing, and explore the effects of variants on branchpoints.