Single nucleotide variants (SNVs) in intronic regions have yet to be systematically investigated for their disease-causing potential. Using known pathogenic and neutral intronic SNVs (iSNVs) as training data, we develop the RegSNPs-intron algorithm based on a random forest classifier that integrates RNA splicing, protein structure, and evolutionary conservation features. RegSNPs-intron showed excellent performance in evaluating the pathogenic impacts of iSNVs. Using a high-throughput functional reporter assay called ASSET-seq (ASsay for Splicing using ExonTrap and sequencing), we evaluate the impact of RegSNPs-intron predictions on splicing outcome. Together, RegSNPs-intron and ASSET-seq enable effective prioritization of iSNVs for disease pathogenesis.
CITATION STYLE
Lin, H., Hargreaves, K. A., Li, R., Reiter, J. L., Wang, Y., Mort, M., … Liu, Y. (2019). RegSNPs-intron: A computational framework for predicting pathogenic impact of intronic single nucleotide variants. Genome Biology, 20(1). https://doi.org/10.1186/s13059-019-1847-4
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