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GSR: Simulator - Easypop
| Attribute | Value |
|---|---|
| Title | Easypop |
| Short Description | EASYPOP is an individual based model intended to simulate datasets under a very broad range of conditions |
| Long Description | EASYPOP can simulate haploid, diploid or haplodiploid data. For diploids there is the choice between hermaphrodites or sexuals. For hermaphrodites, the proportion of clonal reproduction and selfing can be chosen, whereas for sexuals, complex breeding structures can be simulated (e.g. monogamy with a given proportion of extra-pair matings). The number of individuals can be selected for each population and dispersal is sex-specific. There are various migration models such as two-dimensional stepping stone or hierarchical island model. In addition there is an isolation-by-distance option which works with the coordinates of the populations on any number of dimensions. There are also several mutation models implemented, which are particularly oriented on the simulation of microsatellite loci. Genotypes are real multilocus, (i.e. there are not independent replicates for each locus). All mutation parameters can be set individually for each locus. EASYPOP is able to handle very large simulations on standard personal computers and is limited only by the memory of the machine. The computer code has been optimized for maximum speed. This allows running very large simulations on personal computers in a reasonable amount of time. In order to fit to analytical xpectations in particular for variances, the functions implemented in EASYPOP are probabilistic and not deterministic. In other words, the simulations rely on the genertation of random numbers. |
| Version | 2.0.1 |
| Project Started | 2000 |
| Last Release | 17 years, 10 months ago |
| Homepage | https://www.unil.ch/dee/en/home/menuinst/open-positions-and-public-resources/softwares--dataset/softwares/easypop.html |
| Citations | Balloux F, EASYPOP (version 1.7): a computer program for population genetics simulations., J Hered, 05-01-2001 [ Abstract, cited in PMC ] |
| GSR Certification | Accessibility |
| Last evaluated | 07-12-2018 (2102 days ago) |
| Author verification | The basic description provided was derived from a website or publications by the GSR team and has not yet been verified by the simulation author. To modify this entry or add more information, propose changes to this simulator. |
| Attribute Category | Attribute |
|---|---|
| Target | |
| Type of Simulated Data | Genotype at Genetic Markers, |
| Variations | Biallelic Marker, |
| Simulation Method | Forward-time, |
| Input | |
| Data Type | |
| File format | Fstat, Genepop, |
| Output | |
| Data Type | Genotype or Sequence, Diversity Measures, |
| Sequencing Reads | Other, |
| File Format | Arlequin, Fstat, Genepop, |
| Sample Type | |
| Phenotype | |
| Trait Type | |
| Determinants | |
| Evolutionary Features | |
| Demographic | |
| Population Size Changes | Constant Size, |
| Gene Flow | Stepping Stone Models, Island Models, |
| Spatiality | |
| Life Cycle | |
| Mating System | Random Mating, Monogamous, Polygamous, Haplodiploid, |
| Fecundity | |
| Natural Selection | |
| Determinant | |
| Models | |
| Recombination | Uniform, Varying Recombination Rates, |
| Mutation Models | k-Allele Model, Stepwise Mutation Model, |
| Events Allowed | Varying Demographic Features, |
| Other | |
| Interface | Command-line, |
| Development | |
| Tested Platforms | Windows, Mac OS X, |
| Language | C or C++, |
| License | GNU Public License, |
| GSR Certification | Documentation, Application, |
Number of Primary Citations: 1
Number of Non-Primary Citations: 8
The following 8 publications are selected examples of applications that used Easypop.
2017
Pelletier A, Obbard ME, Harnden M, McConnell S, Howe EJ, Burrows FG, White BN, Kyle CJ, Determining causes of genetic isolation in a large carnivore (Ursus americanus) population to direct contemporary conservation measures., PLoS One, 02-24-2017 [Abstract]
Kuismin MO, Ahlinder J, Sillanpӓӓ MJ, CONE: Community Oriented Network Estimation Is a Versatile Framework for Inferring Population Structure in Large-Scale Sequencing Data., G3 (Bethesda), 10-05-2017 [Abstract]
2016
Gracianne C, Jan PL, Fournet S, Olivier E, Arnaud JF, Porte C, Bardou-Valette S, Denis MC, Petit EJ, Temporal sampling helps unravel the genetic structure of naturally occurring populations of a phytoparasitic nematode. 2. Separating the relative effects of gene flow and genetic drift., Evol Appl, 07-22-2016 [Abstract]
Rojas-Hernandez N, Veliz D, Riveros MP, Fuentes JP, Pardo LM, Highly Connected Populations and Temporal Stability in Allelic Frequencies of a Harvested Crab from the Southern Pacific Coast., PLoS One, 11-04-2016 [Abstract]
2015
Guildea C, Hitchen Y, Duffy R, Dias PJ, Ledger JM, Snow M, Kennington WJ, Introgression threatens the survival of the critically endangered freshwater crayfish Cherax tenuimanus (Decapoda: Parastacidae) in the wild., PLoS One, 03-23-2015 [Abstract]
Parreira BR, Chikhi L, On some genetic consequences of social structure, mating systems, dispersal, and sampling., Proc Natl Acad Sci U S A, 06-30-2015 [Abstract]
Momigliano P, Harcourt R, Robbins WD, Stow A, Connectivity in grey reef sharks (Carcharhinus amblyrhynchos) determined using empirical and simulated genetic data., Sci Rep, 08-28-2015 [Abstract]
Kvistad L, Ingwersen D, Pavlova A, Bull JK, Sunnucks P, Very Low Population Structure in a Highly Mobile and Wide-Ranging Endangered Bird Species., PLoS One, 12-09-2015 [Abstract]