Empirical prediction of variant-activated cryptic splice donors using population-based RNA-Seq data

Genetic variants affecting the consensus splicing motifs can alter binding of spliceosomal components and induce mis-splicing. Here, the authors develop a method, showing that ranking the most common recurring mis-splicing events in public RNA-Seq data can predict the activation of cryptic-donors. Predicting which cryptic-donors may be activated by a splicing variant in patient DNA is notoriously difficult. Through analysis of 5145 cryptic-donors (versus 86,963 decoy-donors not used; any GT or GC), we define an empirical method predicting cryptic-donor activation with 87% sensitivity and 95% specificity. Strength (according to four algorithms) and proximity to the annotated-donor appear important determinants of cryptic-donor activation. However, other factors such as splicing regulatory elements, which are difficult to identify, play an important role and are likely responsible for current prediction inaccuracies. We find that the most frequently recurring natural mis-splicing events at each exon-intron junction, summarised over 40,233 RNA-sequencing samples (40K-RNA), predict with accuracy which cryptic-donor will be activated in rare disease. 40K-RNA provides an accurate, evidence-based method to predict variant-activated cryptic-donors in genetic disorders, assisting pathology consideration of possible consequences of a variant for the encoded protein and RNA diagnostic testing strategies.

Automated summary

This study demonstrates a method for predicting cryptic donor activation caused by genetic splicing variants, with an empirical method defined by analyzing a large amount of RNA-Seq data, revealing the important determinants of cryptic donor activation.