%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 }