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GADMA2: more efficient and flexible demographic inference from genetic data

View ORCID ProfileEkaterina Noskova, View ORCID ProfileNikita Abramov, View ORCID ProfileStanislav Iliutkin, View ORCID ProfileAnton Sidorin, View ORCID ProfilePavel Dobrynin, View ORCID ProfileVladimir Ulyantsev
doi: https://doi.org/10.1101/2022.06.14.496083
Ekaterina Noskova
1Computer Technologies Laboratory, ITMO University
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Nikita Abramov
2HSE University
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Stanislav Iliutkin
1Computer Technologies Laboratory, ITMO University
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Anton Sidorin
3Laboratory of Biochemical Genetics, Department of Genetics and Biotechnology, St. Petersburg State University
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Pavel Dobrynin
1Computer Technologies Laboratory, ITMO University
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Vladimir Ulyantsev
1Computer Technologies Laboratory, ITMO University
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Abstract

Inference of complex demographic histories typically requires parameterized models specified manually by the researcher. With an increased variety of methods and tools, each with its own interface, model specification becomes tedious and error-prone. Moreover, optimization algorithms used to find optimal parameters sometimes turn out to be inefficient. The open source software GADMA addresses these problems, providing automatic demographic inference. It proposes a common interface for several simulation engines and provides global optimization of parameters based on a genetic algorithm. Here, we introduce new features of GADMA2, the second version of the GADMA software. It has renovated core code base, new simulation engines, an updated optimization algorithm, and flexible specification of demographic history parameters. We provide a full overview of GADMA2 enhancements and demonstrate example of their usage.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵† Denotes shared senior authorship, listed alphabetically

  • https://github.com/ctlab/GADMA

  • https://gadma.readthedocs.io

  • https://github.com/noscode/demographic_inference_data

  • https://github.com/noscode/HPO_results_GADMA

  • https://bitbucket.org/noscode/gadma_results

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted June 17, 2022.
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GADMA2: more efficient and flexible demographic inference from genetic data
Ekaterina Noskova, Nikita Abramov, Stanislav Iliutkin, Anton Sidorin, Pavel Dobrynin, Vladimir Ulyantsev
bioRxiv 2022.06.14.496083; doi: https://doi.org/10.1101/2022.06.14.496083
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GADMA2: more efficient and flexible demographic inference from genetic data
Ekaterina Noskova, Nikita Abramov, Stanislav Iliutkin, Anton Sidorin, Pavel Dobrynin, Vladimir Ulyantsev
bioRxiv 2022.06.14.496083; doi: https://doi.org/10.1101/2022.06.14.496083

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