poolne_estim

poolne_estim implements a Markov Chain Monte Carlo (MCMC) algorithm to sample posterior distributions of parameters in a Bayesian hierarchical model and to estimate the effective diploid size of DNA pools from Pool-Seq SNP allele count data.

Key Features:

• Bayesian Hierarchical Model: Employs a Bayesian hierarchical model to represent SNP allele frequencies and variability in pool contributions.
• MCMC Sampling: Uses Markov Chain Monte Carlo (MCMC) algorithms to draw posterior samples of model parameters.
• Effective Pool Size Estimation: Directly estimates the effective diploid size of DNA pools from sequencing-derived SNP allele counts to account for unequal individual contributions.
• Pool-Seq and Molecular Barcodes: Operates on Pool-Seq SNP allele count data and supports analyses of pools or replicates identified by molecular barcodes.
• Accommodation of Unequal Pooling: Models unequal contributions of individuals within pools through hierarchical parameterization.
• Empirical Demonstration: Has been applied to restriction site-associated DNA (RAD) sequencing datasets, including studies on Thaumetopoea pityocampa.
• Comparative Assessment: Enables comparison of allele frequency estimates between pooled and individual-based NGS data across varying sequencing depths and error rates.

Scientific Applications:

• Population Genetics: Supports genome-wide analyses of SNP allele frequencies across populations to study patterns of genetic variation.
• Comparative Genomics: Facilitates comparisons of genetic diversity within and among species by contrasting pooled and individual sequencing datasets.
• Experimental Design Evaluation: Assists assessment of the impact of sampling strategies, sequencing depth, and error rates on allele frequency estimation accuracy.
• Empirical Studies: Applicable to RAD-seq analyses such as SNP studies in the pine processionary moth (Thaumetopoea pityocampa).

Methodology:

Mathematical derivations relate SNP allele counts from DNA pools to allele frequency estimates; a Bayesian hierarchical model directly estimates effective pool size and accommodates unequal pooling contributions; an MCMC algorithm samples posterior distributions of the model parameters.