A community-maintained standard library of population genetic models.

Autor:	Adrion JR; Department of Biology and Institute of Ecology and Evolution, University of Oregon, Eugene, United States., Cole CB; Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom., Dukler N; Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, United States., Galloway JG; Department of Biology and Institute of Ecology and Evolution, University of Oregon, Eugene, United States., Gladstein AL; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, United States., Gower G; Lundbeck GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark., Kyriazis CC; Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, United States., Ragsdale AP; Department of Human Genetics, McGill University, Montreal, Canada., Tsambos G; Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia., Baumdicker F; Department of Mathematical Stochastics, University of Freiburg, Freiburg, Germany., Carlson J; Department of Genome Sciences, University of Washington, Seattle, United States., Cartwright RA; The Biodesign Institute and The School of Life Sciences, Arizona State University, Tempe, United States., Durvasula A; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States., Gronau I; The Efi Arazi School of Computer Science, Herzliya Interdisciplinary Center, Herzliya, Israel., Kim BY; Department of Biology, Stanford University, Stanford, United States., McKenzie P; Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, United States., Messer PW; Department of Computational BiologyCornell University, Ithaca, United States., Noskova E; Computer Technologies Laboratory, ITMO University, Saint Petersburg, Russian Federation., Ortega-Del Vecchyo D; International Laboratory for Human Genome Research, National Autonomous University of Mexico, Juriquilla, Mexico., Racimo F; Lundbeck GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark., Struck TJ; Departmentof Molecular and Cellular Biology, University of Arizona, Tucson, United States., Gravel S; Department of Human Genetics, McGill University, Montreal, Canada., Gutenkunst RN; Departmentof Molecular and Cellular Biology, University of Arizona, Tucson, United States., Lohmueller KE; Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, United States.; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States., Ralph PL; Department of Biology and Institute of Ecology and Evolution, University of Oregon, Eugene, United States.; Department of Mathematics, University of Oregon, Eugene, United States., Schrider DR; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, United States., Siepel A; Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, United States., Kelleher J; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom., Kern AD; Department of Biology and Institute of Ecology and Evolution, University of Oregon, Eugene, United States.
Jazyk:	angličtina
Zdroj:	ELife [Elife] 2020 Jun 23; Vol. 9. Date of Electronic Publication: 2020 Jun 23.
DOI:	10.7554/eLife.54967
Abstrakt:	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. Competing Interests: JA, CC, ND, JG, AG, GG, CK, AR, GT, FB, JC, RC, AD, IG, BK, PM, EN, DO, FR, TS, SG, RG, KL, PR, DS, AS, JK, AK No competing interests declared, PM Reviewing editor, eLife (© 2020, Adrion et al.)
Databáze:	MEDLINE
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