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 | 3 years, 1 month 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, 03-20-2021 [ Abstract, cited in PMC ] |
GSR Certification | Accessibility |
Last evaluated | 09-28-2022 (541 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, 03-14-2023 [Abstract]
Riley R, Mathieson I, Mathieson S, Interpreting Generative Adversarial Networks to Infer Natural Selection from Genetic Data., bioRxiv, 07-09-2023 [Abstract]