A community-maintained standard library of population genetic models
- University of Oregon, United States
- University of Oxford, United Kingdom
- Cold Spring Harbor Laboratory, United States
- University of North Carolina at Chapel Hill, United States
- The University of Adelaide, Australia
- University of California, Los Angeles, United States
- McGill University, Canada
- University of Melbourne, Australia
- University of Freiburg, Germany
- University of Washington, United States
- Arizona State University, United States
- IDC Herzliya, Israel
- Stanford University, United States
- Columbia University, United States
- Cornell University, United States
- ITMO University, Russian Federation
- National Autonomous University of Mexico, Mexico
- University of Copenhagen, Denmark
- University of Arizona, United States
Abstract
The explosion in population genomic data demands ever more complex modes of analysis, and increasingly these analyses depend on sophisticated simulations. Re-cent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.
Data availability
All resources are available from https://github.com/popsim-consortium/stdpopsim
Article and author information
Author details
Christopher B Cole
Funding
National Institute of General Medical Sciences (R35GM119856)
- Christopher C Kyriazis
- Kirk E Lohmueller
National Institute of General Medical Sciences (R01GM117241)
- Jeffrey R Adrion
- Andrew D Kern
National Institute of General Medical Sciences (R01GM127348)
- Travis J Struck
- Ryan N Gutenkunst
National Institute of General Medical Sciences (R00HG008696)
- Ariella L Gladstein
- Daniel R Schrider
National Institute of General Medical Sciences (R35GM127070)
- Noah Dukler
- Adam Siepel
National Human Genome Research Institute (R01HG010346)
- Noah Dukler
- Adam Siepel
Villum Fonden (00025300)
- Graham Gower
- Fernando Racimo
UC MEXUS-CONACYT
- Diego Ortega Del Vecchyo
Robertson Foundation
- Jerome Kelleher
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Graham Coop, University of California, Davis, United States
Version history
- Received: January 7, 2020
- Accepted: June 15, 2020
- Accepted Manuscript published: June 23, 2020 (version 1)
- Accepted Manuscript updated: June 25, 2020 (version 2)
- Version of Record published: August 19, 2020 (version 3)
Copyright
© 2020, Adrion et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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A community-maintained standard library of population genetic models
eLife 9:e54967.
https://doi.org/10.7554/eLife.54967
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