GSE138130
RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants
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 shows 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. Overall design: Sequencing of RNA products (amplified using PCR) generated from an Exontrap plasmid loaded with a fragment containing a part of real exon and intron harboring the reference or alternative allele of an intronic variant. Counts of spliced products with respect to a set of test variants were documented, in five replicates for each of three cell lines.