Evidence map›Paper›PMID 37342968›Full record

ArticleeLife2023

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

M Elise Lauterbur, Maria Izabel A Cavassim, Ariella L Gladstein, Graham Gower, Nathaniel S Pope, Georgia Tsambos, Jeffrey Adrion, Saurabh Belsare, Arjun Biddanda, Victoria Caudill and 32 more

Abstract read
In one paragraph

Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

51 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  7. Recent population history in Swedish cattle breeds.Genetics, selection, evolution : GSE · 2026
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  10. Coalescence and translation: A language model for population genetics.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  11. Article
  12. Review
  13. Neural posterior estimation for population genetics.bioRxiv : the preprint server for biology · 2026
    Article
  14. Article
  15. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

42 authors.

M Elise LauterburDepartment of Ecology and Evolutionary Biology, University of Arizona, Tucson, United States.ORCID 0000-0002-7362-3618
Maria Izabel A Cavassim *Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, United States.ORCID 0000-0001-9726-1431
Ariella L Gladstein *Embark Veterinary, Inc, Boston, United States.ORCID 0000-0001-7735-2336
Graham Gower *Section for Molecular Ecology and Evolution, Globe Institute, University of Copenhagen, Copenhagen, Denmark.ORCID 0000-0002-6197-3872
Nathaniel S Pope *Institute of Ecology and Evolution, University of Oregon, Eugene, United States.
Georgia Tsambos *School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia.ORCID 0000-0001-7001-2275
Jeffrey AdrionInstitute of Ecology and Evolution, University of Oregon, Eugene, United States.ORCID 0000-0003-1021-6000
Saurabh BelsareInstitute of Ecology and Evolution, University of Oregon, Eugene, United States.ORCID 0000-0002-8148-1867
Arjun Biddanda54Gene, Inc, Washington, United States.ORCID 0000-0003-1861-1523
Victoria CaudillInstitute of Ecology and Evolution, University of Oregon, Eugene, United States.ORCID 0000-0002-0577-5513
Jean CuryUniversite Paris-Saclay, CNRS, INRIA, Laboratoire Interdisciplinaire des Sciences du Numerique, Orsay, France.
Ignacio EchevarriaSchool of Life Sciences, University of Glasgow, Glasgow, United Kingdom.
Benjamin C HallerDepartment of Computational Biology, Cornell University, Ithaca, United States.ORCID 0000-0003-1874-8327
Ahmed R HasanDepartment of Cell and Systems Biology, University of Toronto, Toronto, Canada.ORCID 0000-0003-0002-8399
Xin HuangDepartment of Evolutionary Anthropology, University of Vienna, Vienna, Austria.
Leonardo Nicola Martin IasiDepartment of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.
Ekaterina NoskovaComputer Technologies Laboratory, ITMO University, St Petersburg, Russian Federation.ORCID 0000-0003-1168-0497
Jana ObsteterAgricultural Institute of Slovenia, Department of Animal Science, Ljubljana, Slovenia.
Vitor Antonio Correa PavinatoEntomology Department, The Ohio State University, Wooster, United States.ORCID 0000-0003-2483-1207
Alice PearsonDepartment of Genetics, University of Cambridge, Cambridge, United Kingdom.
David PeedeDepartment of Ecology, Evolution, and Organismal Biology, Brown University, Providence, United States.
Manolo F PerezDepartment of Genetics and Evolution, Federal University of Sao Carlos, Sao Carlos, Brazil.
Murillo F RodriguesInstitute of Ecology and Evolution, University of Oregon, Eugene, United States.
Chris C R SmithInstitute of Ecology and Evolution, University of Oregon, Eugene, United States.
Jeffrey P SpenceDepartment of Genetics, Stanford University School of Medicine, Stanford, United States.ORCID 0000-0002-3199-1447
Anastasia TeterinaInstitute of Ecology and Evolution, University of Oregon, Eugene, United States.
Silas TittesInstitute of Ecology and Evolution, University of Oregon, Eugene, United States.ORCID 0000-0003-4697-7434
Per UnnebergDepartment of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
Juan Manuel VazquezDepartment of Integrative Biology, University of California, Berkeley, Berkeley, United States.ORCID 0000-0001-8341-2390
Ryan K WaplesDepartment of Biostatistics, University of Washington, Seattle, United States.
Anthony Wilder WohnsBroad Institute of MIT and Harvard, Cambridge, United States.
Yan WongBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-3536-6411
Franz BaumdickerCluster of Excellence - Controlling Microbes to Fight Infections, Eberhard Karls Universit¨at Tubingen, Tubingen, Germany.
Reed A CartwrightSchool of Life Sciences and The Biodesign Institute, Arizona State University, Tempe, United States.ORCID 0000-0002-0837-9380
Gregor GorjancThe Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom.
Ryan N GutenkunstDepartment of Molecular and Cellular Biology, University of Arizona, Tucson, United States.ORCID 0000-0002-8659-0579
Jerome KelleherBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-7894-5253
Andrew D KernInstitute of Ecology and Evolution, University of Oregon, Eugene, United States.ORCID 0000-0003-4381-4680
Aaron P RagsdaleDepartment of Integrative Biology, University of Wisconsin-Madison, Madison, United States.
Peter L RalphInstitute of Ecology and Evolution, University of Oregon, Eugene, United States.ORCID 0000-0002-9459-6866
Daniel R SchriderDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, United States.ORCID 0000-0001-5249-4151
Ilan GronauEfi Arazi School of Computer Science, Reichman University, Herzliya, Israel.ORCID 0000-0001-8536-4062

Funding

Unraveling the mammalian secretory pathway through systems biology and algorithm developmentR35GM119850 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI LEWIS, NATHAN ENOCH · 2016 to 2025
$4.3M
Population genomics of the selective effects of new mutationsR35GM119856 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LOHMUELLER, KIRK · 2016 to 2025
$3.4M
Deep learning for population geneticsR01HG010774 · NHGRI · UNIVERSITY OF OREGON · PI ANDREW D KERN · 2020 to 2026
$3.2M
Advancing evolutionary genetics through deep learningR35GM138286 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI DANIEL R SCHRIDER · 2020 to 2026
$2.8M
Joint inferences of natural selection between sites and populationsR01GM127348 · NIGMS · UNIVERSITY OF ARIZONA · PI GUTENKUNST, RYAN · 2019 to 2023
$1.7M
Predoctoral Training Program in Biological Data Science at Brown UniversityT32GM128596 · NIGMS · BROWN UNIVERSITY · PI RAMACHANDRAN, SOHINI, SANDSTEDE, BJORN · 2018 to 2022
$1.5M
Biotechnology and Biological Sciences Research Council BBS/E/D/30002275NHGRI NIH HHS R01 HG010774NIGMS NIH HHS R01 GM127348NIGMS NIH HHS R35 GM119850NIGMS NIH HHS R35 GM119856NIGMS NIH HHS R35 GM138286NIGMS NIH HHS T32 GM128596
6 · The paper itself

Abstract

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.

Indexed as

GenomeSoftwareComputer SimulationGenetics, PopulationGenomicsgeneticsgenomicsnoneopen sourcepopulation geneticssimulations

Identifiers

PMID37342968
PMCPMC10328510

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.