Vulnerable exons, like ACADM exon 5, are highly dependent on maintaining a correct balance between splicing enhancers and silencers.

It is now widely accepted that aberrant splicing of constitutive exons is often caused by mutations affecting cis-acting splicing regulatory elements, but there is a misconception that all exons have an equal dependency on splicing regulatory elements and thus a similar susceptibility to aberrant splicing. We investigated exonic mutations in ACADM exon 5 to experimentally examine their effect on splicing and found that 7 out of 11 tested mutations affected exon inclusion, demonstrating that this constitutive exon is particularly vulnerable to exonic splicing mutations. Employing ACADM exon 5 and 6 as models, we demonstrate that the balance between splicing enhancers and silencers, flanking intron length, and flanking splice site strength are important factors that determine exon definition and splicing efficiency of the exon in question.

DeepCLIP: predicting the effect of mutations on protein-RNA binding with deep learning.

Nucleotide variants can cause functional changes by altering protein-RNA binding in various ways that are not easy to predict. This can affect processes such as splicing, nuclear shuttling, and stability of the transcript. Therefore, correct modeling of protein-RNA binding is critical when predicting the effects of sequence variations.