Coalescent simulation in continuous space
Author(s) -
Jerome Kelleher,
Nick Barton,
Alison Etheridge
Publication year - 2013
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btt067
Subject(s) - coalescent theory , python (programming language) , computer science , algorithm , population , theoretical computer science , programming language , biology , biochemistry , demography , sociology , gene , phylogenetic tree
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 generalized version of this algorithm as a freely available Python module.
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