SVhound

SVhound predicts genomic regions likely to harbor unidentified structural variation alleles using population-scale VCF datasets and probabilistic assessments to quantify hidden SV diversity.

Key Features:

• Population-Level Analysis: Operates on population-scale variant call format (VCF) datasets to analyze structural variation distribution across samples.
• Probabilistic Assessment: Computes likelihoods of hidden SV alleles in defined genomic regions using probabilistic analysis.
• Predictive Capability: Forecasts regions likely to contain additional, previously unidentified SV alleles based on existing population SV data.
• Validation against Full Datasets: Demonstrated high correlation with full datasets (average r=0.7136 across 2,800 tests) when validated against the 1000 Genomes Project (1KGP).
• Cross-Species Applicability: Applied to non-human SV call sets, including rhesus macaque (Macaca mulatta), demonstrating applicability beyond human data.
• Parameter Optimization: Supports adjustment of parameters to optimize predictive performance for specific datasets and research objectives.

Scientific Applications:

• Evolutionary Biology: Characterizing how novel SV alleles contribute to species evolution and lineage-specific variation.
• Population Genetics: Exploring the full spectrum of structural variation within and between populations to quantify genetic diversity.
• Disease Association Studies: Identifying candidate structural variants that may be linked to disease but have been previously undetected.

Methodology:

Analyzes population-level VCF datasets of known structural variants; performs probabilistic calculations to estimate the likelihood of hidden SV alleles in genomic regions; validates predictions using subsets of comprehensive datasets such as the 1000 Genomes Project (1KGP).