ThetaMater: Bayesian estimation of population size parameter θ from genomic data

Area of Science:

• Population genetics
• Bioinformatics
• Computational biology

Background:

• Estimating population size (θ) is crucial for understanding species' evolutionary history and conservation status.
• Genomic data offers a powerful resource for demographic inference, but requires specialized analytical tools.
• Existing methods for θ estimation may lack scalability or efficiency when applied to large genomic datasets.

Purpose of the Study:

• To introduce ThetaMater, an open-source R package designed for the Bayesian estimation of the population size parameter θ.
• To provide researchers with an efficient and scalable computational tool for analyzing genomic data in population genetics.

Main Methods:

• Development of an R package, ThetaMater, implementing Bayesian estimation algorithms.
• Utilization of genomic data for demographic parameter inference.
• Focus on efficient and scalable computation for large datasets.

Main Results:

• ThetaMater provides a suite of functions for robust θ estimation.
• The package is designed for efficient and scalable application to genomic datasets.
• Open-source availability facilitates widespread adoption and further development.

Conclusions:

• ThetaMater offers a valuable new resource for population geneticists and evolutionary biologists.
• The package enhances the ability to estimate population size parameters from genomic data.
• Open-source development promotes reproducibility and accessibility in bioinformatics research.