Splicing is a process which modifies mRNA after transcription. It allows for introns to be removed and exons joined together to form mature mRNA, ready for translation into protein. These motifs provide signals to allow for correct splicing to occur. The specific splicing of a gene can be easily affected by mutations in the sequence surrounding the splice site junction, leading to alternate splicing and thus adversely affecting the translated protein.

In-silico splice site prediction tools can be used to predict the effect of a genetic variant on splicing. A large number of prediction tools are currently available, however these have not been formally assessed and may give divergent results. This analysis aims to provide an assessment of the performance of these algorithms in the prediction of splicing-related variant pathogenicity. It will also assess the scope of the splice-site prediction tools to ensure that they can be used in the most appropriate way. The analysis will allow scientists to use splice site prediction tools in the prediction of pathogenesis with more confidence.