Single point mutations at both synonymous and non-synonymous positions within exons can have severe effects on gene function through disruption of splicing. Predicting these mutations in silico purely from the genomic sequence is difficult due to an incomplete understanding of the multiple factors that may be responsible. In addition, little is known about which computational prediction approaches, such as those involving exonic splicing enhancers and exonic splicing silencers, are most informative.

We assessed the features of single-nucleotide genomic variants verified to cause exon skipping and compared them to a large set of coding SNPs common in the human population, which are likely to have no effect on splicing. Our findings implicate a number of features important for their ability to discriminate splice-affecting variants, including the naturally occurring density of exonic splicing enhancers and exonic splicing silencers of the exon and intronic environment, extensive changes in the number of predicted exonic splicing enhancers and exonic splicing silencers, proximity to the splice junctions and evolutionary constraint of the region surrounding the variant. By extending this approach to additional datasets, we also identified relevant features of variants that cause increased exon inclusion and ectopic splice site activation.

We identified a number of features that have statistically significant representation among exonic variants that modulate splicing. These analyses highlight putative mechanisms responsible for splicing outcome and emphasize the role of features important for exon definition. We developed a web-tool, Skippy, to score coding variants for these relevant splice-modulating features.