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GSR: Simulator - pg-gan
| Attribute | Value |
|---|---|
| Title | pg-gan |
| Short Description | create realistic simulated data that matches real population genetic data. |
| Long Description | This software can be used to create realistic simulated data that matches real population genetic data. It implements a GAN-based algorithm (Generative Adversarial Network). |
| Project Started | 2021 |
| Last Release | 4 years, 7 months ago |
| Homepage | https://github.com/mathiesonlab/pg-gan |
| Citations | Wang Z, Wang J, Kourakos M, Hoang N, Lee HH, Mathieson I, Mathieson S, Automatic inference of demographic parameters using generative adversarial networks., Mol Ecol Resour, March 20, 2021 [ Abstract, cited in PMC ] |
| GSR Certification | Accessibility |
| Last evaluated | Sept. 28, 2022 (1082 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 | Haploid DNA Sequence, |
| Variations | Biallelic Marker, |
| Simulation Method | Standard Coalescent, Other, |
| Input | |
| Data Type | |
| File format | |
| Output | |
| Data Type | |
| Sequencing Reads | |
| File Format | |
| Sample Type | |
| Phenotype | |
| Trait Type | |
| Determinants | |
| Evolutionary Features | |
| Demographic | |
| Population Size Changes | Exponential Growth or Decline, User Defined, |
| Gene Flow | |
| Spatiality | |
| Life Cycle | |
| Mating System | |
| Fecundity | |
| Natural Selection | |
| Determinant | |
| Models | |
| Recombination | |
| Mutation Models | |
| Events Allowed | |
| Other | |
| Interface | Command-line, |
| Development | |
| Tested Platforms | Windows, Mac OS X, Linux and Unix, |
| Language | Python, |
| License | |
| GSR Certification | Documentation, Application, |
Number of Primary Citations: 1
Number of Non-Primary Citations: 2
The following 2 publications are selected examples of applications that used pg-gan.
2023
Small ST, Costantini C, Sagnon N, Guelbeogo MW, Emrich SJ, Kern AD, Fontaine MC, Besansky NJ, Standing genetic variation and chromosome differences drove rapid ecotype formation in a major malaria mosquito., Proc Natl Acad Sci U S A, March 14, 2023 [Abstract]
Riley R, Mathieson I, Mathieson S, Interpreting Generative Adversarial Networks to Infer Natural Selection from Genetic Data., bioRxiv, July 9, 2023 [Abstract]