Demes
Demes standardizes the representation of demographic models using a text-based data model to define and exchange population sizes, growth rates, migration patterns, and epochs for population genetic simulation and inference.
Key Features:
- Standardized text file format: A text file format for unambiguous definition and exchange of demographic models.
- Comprehensive model representation: Support for discrete populations with specified sizes, growth rates, migration patterns, and multiple epochs.
- Model translation: Explicitly supports translating published demographic model descriptions into inputs for population genetic simulators.
- Language-specific parsers: Parsers implemented in multiple programming languages such as Python and C to consume the Demes format.
Scientific Applications:
- Simulation of demographic scenarios: Representing demographic histories as simulator-ready models for forward and coalescent simulations.
- Testing demographic hypotheses: Encoding complex demographic hypotheses for simulation-based hypothesis testing.
- Inference of historical population dynamics: Providing standardized model definitions for use with inference methods that require explicit demographic models.
Methodology:
Demes defines a text file format and provides parsers implemented in multiple programming languages such as Python and C to produce representations that integrate with population genetic simulators and inference methods.
Topics
Details
- License:
- MIT
- Tool Type:
- library
- Programming Languages:
- Python, C, Julia
- Added:
- 10/31/2022
- Last Updated:
- 10/31/2022
Operations
Publications
Gower G, Ragsdale AP, Bisschop G, Gutenkunst RN, Hartfield M, Noskova E, Schiffels S, Struck TJ, Kelleher J, Thornton KR. Demes: a standard format for demographic models. Genetics. 2022;222(3). doi:10.1093/genetics/iyac131. PMID:36173327. PMCID:PMC9630982.
PMID: 36173327
Funding: - Villum Fonden Young Investigator award to Fernando Racimo: 00025300
- National Institute of General Medical Sciences of the National Institutes of Health: R01GM127348
- Natural Environment Research Council Independent Research Fellowship: NE/R015686/1
- European Research Council under the European Union’s Horizon 2020 research and innovation program: 851511
- European Research Council (ModelGenomLand: 757648