| Literature DB >> 27702775 |
Benjamin C Haller1, Philipp W Messer1.
Abstract
Modern population genomic datasets hold immense promise for revealing the evolutionary processes operating in natural populations, but a crucial prerequisite for this goal is the ability to model realistic evolutionary scenarios and predict their expected patterns in genomic data. To that end, we present SLiM 2: an evolutionary simulation framework that combines a powerful, fast engine for forward population genetic simulations with the capability of modeling a wide variety of complex evolutionary scenarios. SLiM achieves this flexibility through scriptability, which provides control over most aspects of the simulated evolutionary scenarios with a simple R-like scripting language called Eidos. An example SLiM simulation is presented to illustrate the power of this approach. SLiM 2 also includes a graphical user interface for simulation construction, interactive runtime control, and dynamic visualization of simulation output, facilitating easy and fast model development with quick prototyping and visual debugging. We conclude with a performance comparison between SLiM and two other popular forward genetic simulation packages.Entities:
Keywords: ecological modeling; evolutionary modeling; forward genetic simulation; population genomics; software
Mesh:
Year: 2016 PMID: 27702775 DOI: 10.1093/molbev/msw211
Source DB: PubMed Journal: Mol Biol Evol ISSN: 0737-4038 Impact factor: 16.240