Journal article
Demes: a standard format for demographic models
- Abstract:
- Understanding the demographic history of populations is a key goal in population genetics, and with improving methods and data, ever more complex models are being proposed and tested. Demographic models of current interest typically consist of a set of discrete populations, their sizes and growth rates, and continuous and pulse migrations between those populations over a number of epochs, which can require dozens of parameters to fully describe. There is currently no standard format to define such models, significantly hampering progress in the field. In particular, the important task of translating the model descriptions in published work into input suitable for population genetic simulators is labor intensive and error prone. We propose the Demes data model and file format, built on widely used technologies, to alleviate these issues. Demes provide a well-defined and unambiguous model of populations and their properties that is straightforward to implement in software, and a text file format that is designed for simplicity and clarity. We provide thoroughly tested implementations of Demes parsers in multiple languages including Python and C, and showcase initial support in several simulators and inference methods. An introduction to the file format and a detailed specification are available at https://popsim-consortium.github.io/demes-spec-docs/.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 1.7MB, Terms of use)
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- Publisher copy:
- 10.1093/genetics/iyac131
Authors
- Publisher:
- Oxford University Press
- Journal:
- Genetics More from this journal
- Volume:
- 222
- Issue:
- 3
- Article number:
- iyac131
- Publication date:
- 2022-09-29
- Acceptance date:
- 2022-08-23
- DOI:
- EISSN:
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1943-2631
- ISSN:
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0016-6731
- Language:
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English
- Keywords:
- Pubs id:
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1278549
- Local pid:
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pubs:1278549
- Deposit date:
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2022-09-12
- ARK identifier:
Terms of use
- Copyright holder:
- Gower et al.
- Copyright date:
- 2022
- Rights statement:
- © The Author(s) 2022. Published by Oxford University Press on behalf of Genetics Society of America. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
- Licence:
- CC Attribution (CC BY)
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