Abstract

Evolve and Resequencing (E&R) studies allow us to monitor adaptation at the genomic level. By sequencing evolving populations at regular time intervals, E&R studies promise to shed light on some of the major open questions in evolutionary biology such as the repeatability of evolution and the molecular basis of adaptation. However, data interpretation, statistical analysis and the experimental design of E&R studies increasingly require simulations of evolving populations, a task that is difficult to accomplish with existing tools, which may i) be too slow, ii) require substantial reformatting of data, iii) not support an adaptive scenario of interest or iv) not sufficiently capture the biology of the used model organism. Therefore we developed MimicrEE2, a multi-threaded Java program for genome-wide forward simulations of evolving populations. MimicrEE2 enables the convenient usage of available genomic resources, supports biological particulars of model organism frequently used in E&R studies and offers a wide range of different adaptive models (selective sweeps, polygenic adaptation, epistasis). Due to its user-friendly and efficient design MimicrEE2 will facilitate simulations of E&R studies even for small labs with limited bioinformatics expertise or computational resources. Additionally, the scripts provided for executing MimicrEE2 on a computer cluster permit the coverage even of a large parameter space. MimicrEE2 runs on any computer with Java installed. It is distributed under the GPLv3 license at https://sourceforge.net/projects/mimicree2/.

Highlights

  • The Evolve and Resequencing (E&R) approach is a powerful tool for studying adaptation at a genome-wide scale [1, 2]

  • It is of considerable interest to evaluate the power of an experimental design before embarking on a costly E&R study

  • To aid researchers in these tasks we developed MimicrEE2 a tool for fast genome-wide forward simulations of evolving populations

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Summary

Introduction

The Evolve and Resequencing (E&R) approach is a powerful tool for studying adaptation at a genome-wide scale [1, 2]. Simulations have, for example, shown that time-series based test statistics could increase the power to identify selected loci in E&R studies [14, 15].

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