Description
A framework for simulating spatially explicit genomic data which
leverages real cartographic information for programmatic and visual encoding
of spatiotemporal population dynamics on real geographic landscapes. Population
genetic models are then automatically executed by the SLiM software by Haller
et al. (2019) <doi:10.1093/molbev/msy228> behind the scenes, using a custom
built-in simulation SLiM script. Additionally, fully abstract spatial models
not tied to a specific geographic location are supported, and users can also
simulate data from standard, non-spatial, random-mating models. These can be
simulated either with the SLiM built-in back-end script, or using an efficient
coalescent population genetics simulator msprime by Baumdicker et al. (2022)
<doi:10.1093/genetics/iyab229> with a custom-built Python script bundled with the
R package. Simulated genomic data is saved in a tree-sequence format and can be
loaded, manipulated, and summarised using tree-sequence functionality via an R
interface to the Python module tskit by Kelleher et al. (2019)
<doi:10.1038/s41588-019-0483-y>. Complete model configuration, simulation and
analysis pipelines can be therefore constructed without a need to leave the R
environment, eliminating friction between disparate tools for population genetic
simulations and data analysis.