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.

中文翻译：

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RegSNPs-内含子：预测内含子单核苷酸变异致病影响的计算框架

内含子区域中的单核苷酸变异 (SNV) 尚未对其致病潜力进行系统研究。使用已知的致病性和中性内含子 SNV (iSNV) 作为训练数据，我们开发了基于随机森林分类器的 RegSNPs-内含子算法，该分类器集成了 RNA 剪接、蛋白质结构和进化保守特征。RegSNPs-内含子在评估 iSNVs 的致病影响方面表现出优异的性能。使用称为 ASSET-seq（使用 ExonTrap 和测序进行剪接的 ASsay）的高通量功能报告基因检测，我们评估了 RegSNPs-内含子预测对剪接结果的影响。RegSNPs-内含子和 ASSET-seq 共同实现了 iSNVs 疾病发病机制的有效优先排序。