Variant Ranker

Variant Ranker prioritizes and annotates coding and non-coding genetic variants by integrating multiple prediction algorithms, conservation metrics, allelic frequencies, clinical data, and ANNOVAR annotations to identify candidate causal variants from high-throughput genomic data.

Key Features:

• Ranking and Annotation: Integrates information from multiple sources to prioritize variants based on deleteriousness, novelty, and existing annotations.
• Algorithmic Integration: Aggregates scores from prediction algorithms, conservation metrics, allelic frequencies, clinical data, and open-source annotations via ANNOVAR.
• Customizable Weights: Encodes user-specified weights for different information types into the ranking algorithm to tailor prioritization.
• Genotype Filtering and Data Management: Performs genotype filtering for case-control samples, supports filtration of large datasets, and accommodates different models of inheritance.
• Functional Enrichment Analysis: Conducts downstream functional enrichment using network-based analysis to identify gene clusters associated with pathways, gene ontology terms, and disease categories.
• Network Visualization: Uses network visualization to reveal clusters of genes spanning biological functions and disease associations.

Scientific Applications:

• Causal gene identification: Prioritizes candidate causal genes and variants from genomic datasets.
• Evaluation on empirical and simulated data: Has been applied to both real and synthetic datasets to rank candidate genes.
• Sequencing study support: Applicable to diverse high-throughput sequencing data for variant discovery and prioritization.

Methodology:

Aggregates scores from prediction algorithms, conservation metrics, allelic frequencies, clinical data, and ANNOVAR annotations; encodes customizable weights into the ranking algorithm; applies genotype filtering for case-control samples and supports dataset filtration by inheritance model; performs network-based functional enrichment to identify gene clusters across pathways, gene ontology terms, and disease categories.