Kelleher, Jerome ; Barton, Nick HIST Austria ; Etheridge, Alison
Coalescent simulation has become an indispensable tool in population genetics and many complex evolutionary scenarios have been incorporated into the basic algorithm. Despite many years of intense interest in spatial structure, however, there are no available methods to simulate the ancestry of a sample of genes that occupy a spatial continuum. This is mainly due to the severe technical problems encountered by the classical model of isolation by distance. A recently introduced model solves these technical problems and provides a solid theoretical basis for the study of populations evolving in continuous space. We present a detailed algorithm to simulate the coalescent process in this model, and provide an efficient implementation of a generalised version of this algorithm as a freely available Python module.
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Kelleher J, Barton NH, Etheridge A. Coalescent simulation in continuous space. Bioinformatics. 2013;29(7):955-956. doi:10.1093/bioinformatics/btt067
Kelleher, J., Barton, N. H., & Etheridge, A. (2013). Coalescent simulation in continuous space. Bioinformatics, 29(7), 955–956. https://doi.org/10.1093/bioinformatics/btt067
Kelleher, Jerome, Nicholas H Barton, and Alison Etheridge. “Coalescent Simulation in Continuous Space.” Bioinformatics 29, no. 7 (2013): 955–56. https://doi.org/10.1093/bioinformatics/btt067.
J. Kelleher, N. H. Barton, and A. Etheridge, “Coalescent simulation in continuous space,” Bioinformatics, vol. 29, no. 7, pp. 955–956, 2013.
Kelleher J, Barton NH, Etheridge A. 2013. Coalescent simulation in continuous space. Bioinformatics. 29(7), 955–956.
Kelleher, Jerome, et al. “Coalescent Simulation in Continuous Space.” Bioinformatics, vol. 29, no. 7, Oxford University Press, 2013, pp. 955–56, doi:10.1093/bioinformatics/btt067.