scripts used in δaδi

Diffusion Approximations for Demographic Inference (δaδi, v.1.7.0; Gutenkunst et al., 2009) was used to estimate demographic parameters under models of pairwise divergence. We tested eight models of divergence (Fig. S1): A) no divergence (neutral, populations never diverge); B) split with no migrati...

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description Diffusion Approximations for Demographic Inference (δaδi, v.1.7.0; Gutenkunst et al., 2009) was used to estimate demographic parameters under models of pairwise divergence. We tested eight models of divergence (Fig. S1): A) no divergence (neutral, populations never diverge); B) split with no migration (divergence without gene flow); C) split with migration (divergence with gene flow that is bidirectionally symmetric, 1 migration parameter); D) split with bidirectional migration (divergence with gene flow that is bidirectionally asymmetric, 2 migration parameters); E) split with exponential population growth, no migration; F) split with exponential population growth and migration; G) secondary contact with migration (1 migration parameter); and H) secondary contact with bidirectional migration (2 migration parameters).The neutral, split with migration, and exponential population growth models are provided in the δaδi file Demographics2D.py (as snm, splitmig, and IM, respectively). The models split with no migration, and split with exponential growth no migration are versions of the splitmig and IM models with the migration parameters set to zero. The split with bidirectional gene flow model is a custom script that is a derivative splitmig to examine asymmetric gene flow. The secondary contact model with one migration parameters (symmetric gene flow) is from Rougemont et al. (2017), and the secondary contact model with two migration parameters is a derivative of that model to account for potential asymmetry in gene flow
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We tested eight models of divergence (Fig. S1): A) no divergence (neutral, populations never diverge); B) split with no migration (divergence without gene flow); C) split with migration (divergence with gene flow that is bidirectionally symmetric, 1 migration parameter); D) split with bidirectional migration (divergence with gene flow that is bidirectionally asymmetric, 2 migration parameters); E) split with exponential population growth, no migration; F) split with exponential population growth and migration; G) secondary contact with migration (1 migration parameter); and H) secondary contact with bidirectional migration (2 migration parameters).The neutral, split with migration, and exponential population growth models are provided in the δaδi file Demographics2D.py (as snm, splitmig, and IM, respectively). The models split with no migration, and split with exponential growth no migration are versions of the splitmig and IM models with the migration parameters set to zero. 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We tested eight models of divergence (Fig. S1): A) no divergence (neutral, populations never diverge); B) split with no migration (divergence without gene flow); C) split with migration (divergence with gene flow that is bidirectionally symmetric, 1 migration parameter); D) split with bidirectional migration (divergence with gene flow that is bidirectionally asymmetric, 2 migration parameters); E) split with exponential population growth, no migration; F) split with exponential population growth and migration; G) secondary contact with migration (1 migration parameter); and H) secondary contact with bidirectional migration (2 migration parameters).The neutral, split with migration, and exponential population growth models are provided in the δaδi file Demographics2D.py (as snm, splitmig, and IM, respectively). The models split with no migration, and split with exponential growth no migration are versions of the splitmig and IM models with the migration parameters set to zero. The split with bidirectional gene flow model is a custom script that is a derivative splitmig to examine asymmetric gene flow. The secondary contact model with one migration parameters (symmetric gene flow) is from Rougemont et al. (2017), and the secondary contact model with two migration parameters is a derivative of that model to account for potential asymmetry in gene flow</abstract><pub>figshare</pub><doi>10.6084/m9.figshare.14327252</doi><oa>free_for_read</oa></addata></record>
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subjects Bioinformatics
Computational Biology
Evolutionary Biology
FOS: Biological sciences
FOS: Computer and information sciences
Genomics
title scripts used in δaδi
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