Lin, HaiHargreaves, Katherine A.Li, RudongReiter, Jill L.Wang, YueMort, MatthewCooper, David N.Zhou, YaoqiZhang, ChiEadon, Michael T.Dolan, M. EileenIpe, JosephSkaar, Todd C.Liu, Yunlong2020-03-182020-03-182019-11-28Lin, H., Hargreaves, K. A., Li, R., Reiter, J. L., Wang, Y., Mort, M., ... & Dolan, M. E. (2019). RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants. Genome biology, 20(1), 1-16. 10.1186/s13059-019-1847-41474-760Xhttps://hdl.handle.net/1805/22364Single 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.en-USAttribution 4.0 InternationalIntronSingle nucleotide polymorphismRNA splicingComputational biologyBioinformaticsDisease pathogenesisRandom forestPrediction modelHigh-throughput screening assayArticle