TY - JOUR

T1 - A community-maintained standard library of population genetic models

AU - Adrion, Jeffrey R.

AU - Cole, Christopher B.

AU - Dukler, Noah

AU - Galloway, Jared G.

AU - Gladstein, Ariella L.

AU - Gower, Graham

AU - Kyriazis, Christopher C.

AU - Ragsdale, Aaron P.

AU - Tsambos, Georgia

AU - Baumdicker, Franz

AU - Carlson, Jedidiah

AU - Cartwright, Reed A.

AU - Durvasula, Arun

AU - Gronau, Ilan

AU - Kim, Bernard Y.

AU - McKenzie, Patrick

AU - Messer, Philipp W.

AU - Noskova, Ekaterina

AU - Ortega-Del Vecchyo, Diego

AU - Racimo, Fernando

AU - Struck, Travis J.

AU - Gravel, Simon

AU - Gutenkunst, Ryan N.

AU - Lohmueller, Kirk E.

AU - Ralph, Peter L.

AU - Schrider, Daniel R.

AU - Siepel, Adam

AU - Kelleher, Jerome

AU - Kern, Andrew D.

PY - 2020

Y1 - 2020

N2 - The explosion in population genomic data demands ever more complex modes of analysis, and increasingly, these analyses depend on sophisticated simulations. Recent 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.

AB - The explosion in population genomic data demands ever more complex modes of analysis, and increasingly, these analyses depend on sophisticated simulations. Recent 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.

KW - GENOME

KW - INFERENCE

KW - HISTORY

KW - SIZE

KW - MUTATIONS

KW - EVOLUTION

KW - ROBUST

U2 - 10.7554/eLife.54967

DO - 10.7554/eLife.54967

M3 - Journal article

C2 - 32573438

VL - 9

JO - eLife

JF - eLife

SN - 2050-084X

M1 - 54967

ER -