distAngsd

distAngsd estimates genetic distances between two individuals from diploid next-generation sequencing (NGS) data using probabilistic models.

Key Features:

• Pairwise distance estimation: Estimates genetic distances and phylogenetic relationships between two individuals from diploid NGS data.
• Probabilistic methods (distAngsd-geno and distAngsd-nuc): distAngsd-geno and distAngsd-nuc are probabilistic approaches that estimate genetic distances by leveraging the full information in NGS data.
• Genotype uncertainty modeling: The methods explicitly account for uncertainties in genotype calling inherent to diploid sequencing data.
• Noisy data support: Designed to handle noisy diploid sequencing data where haploid assumptions and ambiguity characters are inadequate.
• Robustness at low depth and high error rates: Demonstrates accurate and stable statistical behavior even at very low sequencing depth and high sequencing error rates.
• Simulation-based validation: Extensive simulations have been used to demonstrate superior accuracy and more stable behavior compared to existing methods for estimating genetic distances.

Scientific Applications:

• Phylogenetic inference: Useful in phylogenetic studies requiring accurate pairwise genetic distance estimates between individuals from diploid NGS data.
• Genomic and population studies: Applicable to population genetics, conservation biology, and studies of species evolution using noisy diploid NGS datasets.

Methodology:

Implements probabilistic models (distAngsd-geno and distAngsd-nuc) that integrate the full spectrum of information from sequencing data, including uncertainties in genotype calling, and uses extensive simulations for performance evaluation.