poolHelper: An R package to help in designing Pool‐Seq studies

Abstract

Abstract Next‐generation sequencing of pooled samples (Pool‐seq) is an important tool in population genomics and molecular ecology. In Pool‐seq, the relative number of reads with an allele reflects the allele frequencies in the sample. However, unequal individual contributions to the pool and sequencing errors can lead to inaccurate allele frequency estimates, influencing downstream analysis. When designing Pool‐seq studies, researchers need to decide the pool size (number of individuals) and average depth of coverage (sequencing effort). An efficient sampling design should maximise the accuracy of allele frequency estimates while minimising the sequencing effort. We describe a novel tool to simulate single nucleotide polymorphism (SNP) data using coalescent theory and account for sources of uncertainty in Pool‐seq. We introduce an R package, poolHelper, enabling users to simulate Pool‐seq data under different combinations of average depth of coverage and pool size, accounting for unequal individual contributions and sequencing errors, modelled by adjustable parameters. The mean absolute error is computed by comparing the sample allele frequencies obtained based on individual genotypes with the frequency estimates obtained with Pool‐seq. poolHelper enables users to simulate multiple combinations of pooling errors, average depth of coverage, pool sizes and number of pools to assess how they influence the error of sample allele frequencies and expected heterozygosity. Using simulations under a single population model, we illustrate that increasing the depth of coverage does not necessarily lead to more accurate estimates, reinforcing that finding the best Pool‐seq study design is not straightforward. Moreover, we show that simulations can be used to identify different combinations of parameters with similarly low mean absolute errors. This can help users to define an effective sampling design by using those combinations of parameters that minimise the sequencing effort. The poolHelper package provides tools for performing simulations with different combinations of parameters (e.g. pool size, depth of coverage, unequal individual contribution) before sampling and generating data, allowing users to define sampling schemes based on simulations. This allows researchers to focus on the best sampling scheme to answer their research questions. poolHelper is comprehensively documented with examples to guide effective use.

Keywords

• experimental design
• open source
• Pool‐seq
• R package
• simulations