SpliceAPP

SpliceAPP predicts RNA splicing errors arising from human mutations using a statistical model trained on massively parallel splicing assay data.

Key Features:

• Splicing Error Prediction: The statistical model accurately annotates splicing defects of near-exon intronic variants, outperforming state-of-the-art predictive tools.
• Variant Upload and Scoring: Accepts genetic variants with genome coordinates and outputs splicing error scores with impact evaluation on nearby splice sites.
• Branch Site Query: Provides branch site information within a region of interest.
• Branchpoint Database: Incorporates branchpoint information derived from experimental data.

Scientific Applications:

• Pathogenic Variant Screening: Screens pathogenic intronic variants to support precision medicine development.
• Splicing Motif Annotation: Facilitates annotation of splicing motifs through predictive modeling.
• Splicing Defect Assessment: Assesses the impact of disease-relevant variants on splicing decisions using regression-based methods to identify determining factors and their weights.

Methodology:

SpliceAPP applies regression-based methods to experimental data from a massively parallel splicing assay assessing 11,191 disease-relevant variants to identify factors determining splicing decisions and their respective weights.