PIFs

PIFs estimates allele frequencies from pooled and individual next-generation sequencing (NGS) data and quantifies precision under sampling, coverage, and sequencing error scenarios.

Key Features:

• Graphical and numerical outputs: Provides graphical and numerical summaries for detailed analysis and visualization of allele frequency estimates.
• DNA pooling for cost-effective estimation: Leverages pooled-DNA strategies to estimate SNP allele frequencies with accuracy comparable to individual-based analyses while reducing library preparation and sequencing costs.
• Handling unequal pool contributions: Accounts for unequal contributions of individuals within a pooled sample to maintain accurate frequency estimates.
• Effective pool size: Introduces and uses the concept of effective pool size to quantify the impact of unequal contributions on allele frequency estimation.
• Bayesian hierarchical model: Implements a Bayesian hierarchical model that directly estimates effective pool size from the data to improve robustness of frequency estimates.
• Assessment across experimental designs: Evaluates accuracy across varying sampling designs, sequencing depths, and error scenarios.

Scientific Applications:

• Population genetics studies: Enables genome-wide comparisons of genetic variation across multiple populations where individual sequencing is cost-prohibitive.
• NGS data analysis: Supports analysis of both pooled- and individual-based NGS datasets for allele frequency estimation.
• RAD sequencing studies: Has been demonstrated on restriction site-associated DNA (RAD) sequencing datasets, including applications to Thaumetopoea pityocampa (pine processionary moth).

Methodology:

Mathematical derivations for allele-frequency estimation from pooled DNA, including unequal individual contributions, and a Bayesian hierarchical model that directly estimates effective pool size from data.