RegSNPs-intron

RegSNPs-intron predicts the pathogenic impact of intronic single nucleotide variants (iSNVs) in human genomes by integrating RNA splicing, protein structural characteristics, and evolutionary conservation to prioritize variants for disease-related follow-up.

Key Features:

• Random forest classifier: Employs a random forest classifier trained on datasets comprising known pathogenic and neutral intronic SNVs to estimate pathogenic likelihood.
• Feature integration: Integrates multiple genomic features including RNA splicing patterns, protein structural characteristics, and evolutionary conservation data for comprehensive assessment.
• Experimental validation: Prediction performance has been validated using high-throughput functional assays, specifically ASSET-seq (ASsay for Splicing using ExonTrap and sequencing).
• Annotation compatibility: Compatible with ANNOVAR (version 2016Feb01 or later) for integration into annotation workflows.

Scientific Applications:

• Variant prioritization: Prioritizes intronic SNVs for further investigation into disease pathogenesis.
• Selection for functional assays: Guides selection of iSNVs for high-throughput functional validation such as ASSET-seq and other experimental follow-up.

Methodology:

Uses a random forest classifier trained on known pathogenic and neutral intronic SNV datasets and integrates RNA splicing patterns, protein structural characteristics, and evolutionary conservation features.