A plugin for the Ensembl Variant Effect Predictor that uses MaxEntScan to predict variant spliceogenicity

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Assessing genetic variant pathogenicity is crucial for understanding Mendelian diseases.
• Spliceogenic variants can lead to non-functional proteins, increasing disease risk.
• Current tools often fail to detect spliceogenicity outside canonical splice sites, overlooking potential disease-causing variants.

Purpose of the Study:

• To develop and present a plugin for Ensembl Variant Effect Predictor (VEP) to enhance spliceogenicity assessment.
• To extend variant annotation capabilities for intronic and exonic regions impacting mRNA splicing.
• To improve the prediction of disease-causing potential for genetic variants.

Main Methods:

• Developed a VEP plugin integrating MaxEntScan for splice site prediction using a maximum entropy model.
• Implemented a sliding window algorithm to predict splice site loss or gain for any variant overlapping a transcript feature.
• Validated the plugin's predictions against two mRNA splicing datasets, including cancer-susceptibility genes.

Main Results:

• The VEP plugin effectively predicts splice site alterations caused by genetic variants.
• The tool enhances the assessment of spliceogenicity for variants located outside traditional splice sites.
• Demonstrated the plugin's utility in analyzing variants within cancer-susceptibility genes.

Conclusions:

• The developed VEP plugin offers a valuable tool for predicting spliceogenic variants and their impact on protein function.
• This advancement improves the pathogenicity assessment of genetic variants, particularly those affecting mRNA splicing.
• The plugin aids in identifying potential disease-causing variants that might be missed by existing annotation methods.