eDiVA

eDiVA prioritizes pathogenic coding and splicing single-nucleotide variants and small insertions/deletions from whole-exome sequencing (WES) of families and parent-child trios to identify causal variants in rare Mendelian diseases.

Key Features:

• Automated annotation and prioritization: Streamlines variant annotation and prioritization with emphasis on coding and splicing SNVs and small indels.
• Next-generation sequencing integration: Integrates WES data analysis with comprehensive functional annotation to facilitate causal variant identification.
• Machine learning-based pathogenicity prediction: Employs a machine learning predictor that leverages genomic and evolutionary signatures to classify variant pathogenicity.
• Clinical information incorporation: Incorporates disease phenotype and mode of inheritance to refine variant prioritization.
• Optimized for familial studies: Tailored for familial genetic disease analyses, including parent-child trios, to address low diagnostic rates in WES studies.
• Benchmarking performance: Demonstrated detection rate and precision that match or exceed state-of-the-art competitors.

Scientific Applications:

• Rare Mendelian disease discovery: Pinpoints rare causal mutations from family- or trio-based WES data without requiring linkage analysis.
• Clinical variant prioritization: Ranks candidate pathogenic variants for clinical genetics and diagnostic investigations in familial cases.

Methodology:

Performs next-generation sequencing (WES) data analysis, comprehensive functional annotation, machine-learning based pathogenicity prediction using genomic and evolutionary signatures, incorporation of clinical phenotype and inheritance mode, and causal variant prioritization optimized for familial and trio analyses focusing on coding and splicing SNVs and small indels.