RT Journal Article
SR Electronic
T1 GADMA2: more efficient and flexible demographic inference from genetic data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.06.14.496083
DO 10.1101/2022.06.14.496083
A1 Ekaterina Noskova
A1 Nikita Abramov
A1 Stanislav Iliutkin
A1 Anton Sidorin
A1 Pavel Dobrynin
A1 Vladimir Ulyantsev
YR 2022
UL http://biorxiv.org/content/early/2022/06/16/2022.06.14.496083.abstract
AB 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 StatementThe authors have declared no competing interest.