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.
Описание
A community-maintained standard library of population genetic models | eLife
%0 Journal Article
%1 adrion2020stdpopsim
%A Adrion, Jeffrey R
%A Cole, Christopher B
%A Dukler, Noah
%A Galloway, Jared G
%A Gladstein, Ariella L
%A Gower, Graham
%A Kyriazis, Christopher C
%A Ragsdale, Aaron P
%A Tsambos, Georgia
%A Baumdicker, Franz
%A Carlson, Jedidiah
%A Cartwright, Reed A
%A Durvasula, Arun
%A Gronau, Ilan
%A Kim, Bernard Y
%A McKenzie, Patrick
%A Messer, Philipp W
%A Noskova, Ekaterina
%A Vecchyo, Diego Ortega Del
%A Racimo, Fernando
%A Struck, Travis J
%A Gravel, Simon
%A Gutenkunst, Ryan N
%A Lohmueller, Kirk E
%A Ralph, Peter L
%A Schrider, Daniel R
%A Siepel, Adam
%A Kelleher, Jerome
%A Kern, Andrew D
%D 2020
%I eLife Sciences Publications, Ltd
%J eLife
%K demographic_inference methods myown simulation stdpopsim
%R 10.7554/elife.54967
%T A community-maintained standard library of population genetic models
%U https://doi.org/10.7554%2Felife.54967
%V 9
%X 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.
@article{adrion2020stdpopsim,
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.},
added-at = {2020-07-04T18:59:49.000+0200},
author = {Adrion, Jeffrey R and Cole, Christopher B and Dukler, Noah and Galloway, Jared G and Gladstein, Ariella L and Gower, Graham and Kyriazis, Christopher C and Ragsdale, Aaron P and Tsambos, Georgia and Baumdicker, Franz and Carlson, Jedidiah and Cartwright, Reed A and Durvasula, Arun and Gronau, Ilan and Kim, Bernard Y and McKenzie, Patrick and Messer, Philipp W and Noskova, Ekaterina and Vecchyo, Diego Ortega Del and Racimo, Fernando and Struck, Travis J and Gravel, Simon and Gutenkunst, Ryan N and Lohmueller, Kirk E and Ralph, Peter L and Schrider, Daniel R and Siepel, Adam and Kelleher, Jerome and Kern, Andrew D},
biburl = {https://www.bibsonomy.org/bibtex/23d3bd57bfe0beb082f87d6205c3a3e2b/peter.ralph},
description = {A community-maintained standard library of population genetic models | eLife},
doi = {10.7554/elife.54967},
interhash = {5a2763ece7e4da22e349c51b14e84aca},
intrahash = {3d3bd57bfe0beb082f87d6205c3a3e2b},
journal = {{eLife}},
keywords = {demographic_inference methods myown simulation stdpopsim},
month = jun,
publisher = {{eLife} Sciences Publications, Ltd},
timestamp = {2020-07-04T18:59:49.000+0200},
title = {A community-maintained standard library of population genetic models},
url = {https://doi.org/10.7554%2Felife.54967},
volume = 9,
year = 2020
}