Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations.

Autor:	Lauterbur ME; Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, United States., Cavassim MIA; Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, United States., Gladstein AL; Embark Veterinary, Inc, Boston, United States., Gower G; Section for Molecular Ecology and Evolution, Globe Institute, University of Copenhagen, Copenhagen, Denmark., Pope NS; Institute of Ecology and Evolution, University of Oregon, Eugene, United States., Tsambos G; School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia., Adrion J; Institute of Ecology and Evolution, University of Oregon, Eugene, United States.; Ancestry DNA, San Francisco, United States., Belsare S; Institute of Ecology and Evolution, University of Oregon, Eugene, United States., Biddanda A; 54Gene, Inc, Washington, United States., Caudill V; Institute of Ecology and Evolution, University of Oregon, Eugene, United States., Cury J; Universite Paris-Saclay, CNRS, INRIA, Laboratoire Interdisciplinaire des Sciences du Numerique, Orsay, France., Echevarria I; School of Life Sciences, University of Glasgow, Glasgow, United Kingdom., Haller BC; Department of Computational Biology, Cornell University, Ithaca, United States., Hasan AR; Department of Cell and Systems Biology, University of Toronto, Toronto, Canada.; Department of Biology, University of Toronto Mississauga, Mississauga, Canada., Huang X; Department of Evolutionary Anthropology, University of Vienna, Vienna, Austria.; Human Evolution and Archaeological Sciences (HEAS), University of Vienna, Vienna, Austria., Iasi LNM; Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany., Noskova E; Computer Technologies Laboratory, ITMO University, St Petersburg, Russian Federation., Obsteter J; Agricultural Institute of Slovenia, Department of Animal Science, Ljubljana, Slovenia., Pavinato VAC; Entomology Department, The Ohio State University, Wooster, United States., Pearson A; Department of Genetics, University of Cambridge, Cambridge, United Kingdom.; Department of Zoology, University of Cambridge, Cambridge, United Kingdom., Peede D; Department of Ecology, Evolution, and Organismal Biology, Brown University, Providence, United States.; Center for Computational Molecular Biology, Brown University, Providence, United States., Perez MF; Department of Genetics and Evolution, Federal University of Sao Carlos, Sao Carlos, Brazil., Rodrigues MF; Institute of Ecology and Evolution, University of Oregon, Eugene, United States., Smith CCR; Institute of Ecology and Evolution, University of Oregon, Eugene, United States., Spence JP; Department of Genetics, Stanford University School of Medicine, Stanford, United States., Teterina A; Institute of Ecology and Evolution, University of Oregon, Eugene, United States., Tittes S; Institute of Ecology and Evolution, University of Oregon, Eugene, United States., Unneberg P; Department of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Uppsala University, Uppsala, Sweden., Vazquez JM; Department of Integrative Biology, University of California, Berkeley, Berkeley, United States., Waples RK; Department of Biostatistics, University of Washington, Seattle, United States., Wohns AW; Broad Institute of MIT and Harvard, Cambridge, United States., Wong Y; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom., Baumdicker F; Cluster of Excellence - Controlling Microbes to Fight Infections, Eberhard Karls Universit¨at Tubingen, Tubingen, Germany., Cartwright RA; School of Life Sciences and The Biodesign Institute, Arizona State University, Tempe, United States., Gorjanc G; The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom., Gutenkunst RN; Department of Molecular and Cellular Biology, University of Arizona, Tucson, United States., Kelleher J; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom., Kern AD; Institute of Ecology and Evolution, University of Oregon, Eugene, United States., Ragsdale AP; Department of Integrative Biology, University of Wisconsin-Madison, Madison, United States., Ralph PL; 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., Gronau I; Efi Arazi School of Computer Science, Reichman University, Herzliya, Israel.
Jazyk:	angličtina
Zdroj:	ELife [Elife] 2023 Jun 21; Vol. 12. Date of Electronic Publication: 2023 Jun 21.
DOI:	10.7554/eLife.84874
Abstrakt:	Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic datasets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than threefold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed the best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone. Competing Interests: ML, MC, GG, NP, GT, SB, VC, JC, IE, BH, AH, XH, LI, EN, JO, VP, AP, DP, MP, MR, CS, JS, AT, ST, PU, JV, RW, AW, YW, FB, RC, GG, RG, JK, AK, AR, PR, DS, IG No competing interests declared, AG is an employee of Embark Veterinary, Inc. The author declares that no other competing interests exist, JA is an employee of Ancestry DNA. The author declares that no other competing interests exist, AB is an employee of 54Gene, Inc. The author declares that no other competing interests exist (© 2023, Lauterbur et al.)
Databáze:	MEDLINE
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