EGGS: Empirical Genotype Generalizer for Samples

Area of Science:

• Population Genetics
• Bioinformatics
• Computational Biology

Background:

• Genotype data often contains missing values, complicating population genetic analyses.
• Existing tools may lack flexibility in simulating various genetic scenarios and data formats.

Purpose of the Study:

• Introduce Empirical Genotype Generalizer for Samples (EGGS), a novel software tool.
• Address the challenge of missing genotype data and provide advanced simulation capabilities.

Main Methods:

• EGGS accepts empirical genotypes with missing data, replicating missing genotype distributions.
• The tool simulates deamination, sequencing errors, and creates pseudohaploids.
• EGGS facilitates conversion between Variant Call Format (VCF), ms-style replicates, and EIGENSTRAT/ANCESTRYMAP formats.

Main Results:

• EGGS effectively handles missing genotype data by replicating distributions within empirical segments.
• The software supports diverse data manipulations including phasing, polarization, and error simulation.
• EGGS is capable of producing VCF files for non-biallelic sites in diploid samples.

Conclusions:

• EGGS provides a flexible and comprehensive solution for managing and simulating genotype data in population genetics.
• The tool enhances the analysis of genetic data with missing values and diverse simulation needs.
• Availability of source code and executables promotes wider adoption and reproducibility in the field.