A tool for easy-to-use demographic inference

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  • License: GNU General Public License v3 or later (GPLv3+) (GNU GPLv3+)
  • Author: Ekaterina Noskova
  • Tags demographic history , demographic inference , evolution , population genetics
  • Requires: Python >=3.6
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GADMA

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Welcome to GADMA v2!

GADMA implements methods for automatic inference of the joint demographic history of multiple populations from the genetic data.

GADMA is a command-line tool. Basic pipeline presents a series of launches of the global search algorithm followed by the local search optimization.

GADMA provides two types of demographic inference: 1) for user-specified model of demographic history or a custom model, 2) automatic inference for the model with specified structure (up to three populations, see more here).

GADMA provides choice of several engines of demographic inference. This list will be extended in the future. Available engines and maximum number of supported populations for custom model:

  • ∂a∂i (up to 5 populations)
  • moments (up to 5 populations)
  • momi2 (up to ∞ populations)
  • momentsLD - extenstion of moments (up to 5 populations)

More information about engines see here.

GADMA features various optimization methods (global and local search algorithms) which may be used for any general optimization problem.

Two global search algorithms are supported in GADMA:

  • Genetic algorithm — the most common choice of optimization,
  • Bayesian optimization — for demographic inference with time-consuming evaluations, e.g. for four and five populations using moments or ∂a∂i.

GADMA is developed in Computer Technologies laboratory at ITMO University under the supervision of Vladimir Ulyantsev and Pavel Dobrynin. The principal maintainer is Ekaterina Noskova (ekaterina.e.noskova@gmail.com)

GADMA is now of version 2! See Changelog.

Documentation

Please see documentation for more information including installation instructions, usage, examples and API.

Getting help

F.A.Q.

Please don't be afraid to contact me for different problems and offers via email ekaterina.e.noskova@gmail.com. I will be glad to answer all questions.

Also you are always welcome to create an issue on the GitHub page of GADMA with your question.

Citations

Please see full list of citations in documentation.

If you use GADMA in your research please cite:

Ekaterina Noskova, Vladimir Ulyantsev, Klaus-Peter Koepfli, Stephen J O’Brien, Pavel Dobrynin, GADMA: Genetic algorithm for inferring demographic history of multiple populations from allele frequency spectrum data, GigaScience, Volume 9, Issue 3, March 2020, giaa005, https://doi.org/10.1093/gigascience/giaa005

If you use GADMA2 in your research please cite:

Ekaterina Noskova, Nikita Abramov, Stanislav Iliutkin, Anton Sidorin, Pavel Dobrynin, and Vladimir Ulyantsev, GADMA2: more efficient and flexible demographic inference from genetic data, GigaScience, Volume 12, August 2023, giad059, https://doi.org/10.1093/gigascience/giad059

If you use Bayesian optimization please cite:

Ekaterina Noskova and Viacheslav Borovitskiy, Bayesian optimization for demographic inference, G3 Genes|Genomes|Genetics, Volume 13, Issue 7, July 2023, jkad080, https://doi.org/10.1093/g3journal/jkad080

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  • License: GNU General Public License v3 or later (GPLv3+) (GNU GPLv3+)
  • Author: Ekaterina Noskova
  • Tags demographic history , demographic inference , evolution , population genetics
  • Requires: Python >=3.6
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