slendr: a framework for spatio-temporal population genomic simulations on geographic landscapesuse asterix (*) to get italics

Martin Petr, Benjamin C. Haller, Peter L. Ralph, Fernando RacimoPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"

2023

<p style="text-align: justify;">One of the goals of population genetics is to understand how evolutionary forces shape patterns of genetic variation over time. However, because populations evolve across both time and space, most evolutionary processes also have an important spatial component, acting through phenomena such as isolation by distance, local mate choice, or uneven distribution of resources. This spatial dimension is often neglected, partly due to the lack of tools specifically designed for building and evaluating complex spatio-temporal population genetic models. To address this methodological gap, we present a new framework for simulating spatially-explicit genomic data, implemented in a new R package called <em>slendr</em> (<a href="http://www.slendr.net" target="_blank" rel="noopener">www.slendr.net</a>), which leverages a SLiM simulation back-end script bundled with the package. With this framework, the users can programmatically and visually encode spatial population ranges and their temporal dynamics (i.e., population displacements, expansions, and contractions) either on real Earth landscapes or on abstract custom maps, and schedule splits and gene-flow events between populations using a straightforward declarative language. Additionally, <em>slendr</em> can simulate data from traditional, non-spatial models, either with SLiM or using an alternative built-in coalescent <em>msprime</em> back end. Together with its R-idiomatic interface to the <em>tskit</em> library for tree-sequence processing and analysis, <em>slendr</em> opens up the possibility of performing efficient, reproducible simulations of spatio-temporal genomic data entirely within the R environment, leveraging its wealth of libraries for geospatial data analysis, statistics, and visualization. Here, we present the design of the <em>slendr</em> R package and demonstrate its features on several practical example workflows.</p>

https://www.slendr.net/articles/vignette-09-paper.html, https://github.com/bodkan/slendrYou should fill this box only if you chose 'Scripts were used to obtain or analyze the results'. URL must start with http:// or https://

https://github.com/bodkan/slendrYou should fill this box only if you chose 'Codes have been used in this study'. URL must start with http:// or https://

population genetics, simulation, tree sequence, spatial data analysis, bioinformatics

NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).

Bioinformatics & Computational Biology, Evolutionary Theory, Phylogeography & Biogeography, Population Genetics / Genomics

Matthew Osmond (mm.osmond@utoronto.ca), Anthony Wilder Wohns (awohns@broadinstitute.org), Alan Rogers (u0028949@utah.edu), Stephan Schiffels (stephan_schiffels@eva.mpg.de) No need for them to be recommenders of PCIEvolBiol. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct

2022-09-14 12:57:56

Emiliano Trucchi