ENT

ENT performs genotype phasing by entropy minimization to infer haplotypes at scale for genomic and population studies.

Key Features:

• Entropy minimization algorithm: Uses entropy minimization to identify haplotype configurations from genotype data.
• Scalability: Processes extensive genotype datasets with substantially lower computational cost than many traditional algorithms.
• Pedigree-aware phasing: Phases both unrelated and related genotypes from complex pedigrees.
• Accuracy: Achieves phasing accuracy comparable to leading existing methods and near-optimal performance on evaluated datasets.
• Data types: Operates on SNP genotype data from diploid autosomal chromosomes.
• Validation on real and simulated data: Evaluated using real and simulated datasets, including HapMap-scale studies.

Scientific Applications:

• Haplotype inference from SNP genotypes: Produces phased haplotypes from genotype data in diploid organisms.
• Disease association studies: Provides phased haplotypes that enhance statistical power for disease association analyses.
• Population genetics and mapping: Enables large-scale analyses of SNP variability and haplotype structure in population-scale projects such as HapMap.

Methodology:

Phases genotypes by minimizing haplotype configuration entropy (entropy minimization) and has been evaluated on real and simulated datasets.