PoPoolation: A Toolbox for Population Genetic Analysis of Next Generation Sequencing Data from Pooled Individuals

TLDR

Recent statistical analyses suggest that sequencing pooled samples is a cost‑effective way to estimate genome‑wide population genetic parameters. The study introduces PoPoolation, a toolbox for analyzing pooled sequencing data, and evaluates how mapping algorithms, sequencing errors, and read coverage affect parameter estimates. PoPoolation estimates θWatterson, θπ, and Tajima’s D while correcting for pooling bias and sequencing errors, offers sliding‑window visualizations, and is implemented in Perl and R using standard data formats. Source code is available at http://code.google.com/p/popoolation/.

Abstract

Recent statistical analyses suggest that sequencing of pooled samples provides a cost effective approach to determine genome-wide population genetic parameters. Here we introduce PoPoolation, a toolbox specifically designed for the population genetic analysis of sequence data from pooled individuals. PoPoolation calculates estimates of θ(Watterson), θ(π), and Tajima's D that account for the bias introduced by pooling and sequencing errors, as well as divergence between species. Results of genome-wide analyses can be graphically displayed in a sliding window plot. PoPoolation is written in Perl and R and it builds on commonly used data formats. Its source code can be downloaded from http://code.google.com/p/popoolation/. Furthermore, we evaluate the influence of mapping algorithms, sequencing errors, and read coverage on the accuracy of population genetic parameter estimates from pooled data.