slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes
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 isola...
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2023-12-01
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| Online Access: | https://peercommunityjournal.org/articles/10.24072/pcjournal.354/ |
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| author | Petr, Martin Haller, Benjamin C. Ralph, Peter L. Racimo, Fernando |
| author_facet | Petr, Martin Haller, Benjamin C. Ralph, Peter L. Racimo, Fernando |
| author_sort | Petr, Martin |
| collection | DOAJ |
| description | 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 slendr (www.slendr.net), 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, slendr can simulate data from traditional, non-spatial models, either with SLiM or using an alternative built-in coalescent msprime back end. Together with its R-idiomatic interface to the tskit library for tree-sequence processing and analysis, slendr 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 slendr R package and demonstrate its features on several practical example workflows.
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| format | Article |
| id | doaj-art-29726fceb9b248f4b45645d08f4f9741 |
| institution | Kabale University |
| issn | 2804-3871 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Peer Community In |
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| series | Peer Community Journal |
| spelling | doaj-art-29726fceb9b248f4b45645d08f4f97412025-02-07T10:16:48ZengPeer Community InPeer Community Journal2804-38712023-12-01310.24072/pcjournal.35410.24072/pcjournal.354slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes Petr, Martin0https://orcid.org/0000-0003-4879-8421Haller, Benjamin C.1https://orcid.org/0000-0003-1874-8327Ralph, Peter L.2https://orcid.org/0000-0002-9459-6866Racimo, Fernando3https://orcid.org/0000-0002-5025-2607Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Denmark; Section for Molecular Ecology and Evolution, Globe Institute, University of Copenhagen, DenmarkDepartment of Computational Biology, Cornell University, Ithaca, NY, USAInstitute of Ecology and Evolution, University of Oregon, Eugene, OR, USALundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Denmark; Section for Molecular Ecology and Evolution, Globe Institute, University of Copenhagen, DenmarkOne 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 slendr (www.slendr.net), 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, slendr can simulate data from traditional, non-spatial models, either with SLiM or using an alternative built-in coalescent msprime back end. Together with its R-idiomatic interface to the tskit library for tree-sequence processing and analysis, slendr 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 slendr R package and demonstrate its features on several practical example workflows. https://peercommunityjournal.org/articles/10.24072/pcjournal.354/population genetics; simulation; spatio-temporal analysis; tree sequence; bioinformatics |
| spellingShingle | Petr, Martin Haller, Benjamin C. Ralph, Peter L. Racimo, Fernando slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes Peer Community Journal population genetics; simulation; spatio-temporal analysis; tree sequence; bioinformatics |
| title | slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes
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| title_full | slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes
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| title_fullStr | slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes
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| title_full_unstemmed | slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes
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| title_short | slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes
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| title_sort | slendr a framework for spatio temporal population genomic simulations on geographic landscapes |
| topic | population genetics; simulation; spatio-temporal analysis; tree sequence; bioinformatics |
| url | https://peercommunityjournal.org/articles/10.24072/pcjournal.354/ |
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