A community-maintained standard library of population genetic models

  1. Jeffrey R Adrion
  2. Christopher B Cole
  3. Noah Dukler
  4. Jared G Galloway
  5. Ariella L Gladstein
  6. Graham Gower
  7. Christopher C Kyriazis
  8. Aaron P Ragsdale
  9. Georgia Tsambos
  10. Franz Baumdicker
  11. Jedidiah Carlson
  12. Reed A Cartwright
  13. Arun Durvasula
  14. Ilan Gronau
  15. Bernard Y Kim
  16. Patrick McKenzie
  17. Philipp W Messer
  18. Ekaterina Noskova
  19. Diego Ortega Del Vecchyo
  20. Fernando Racimo
  21. Travis J Struck
  22. Simon Gravel
  23. Ryan N Gutenkunst
  24. Kirk E Lohmueller
  25. Peter L Ralph
  26. Daniel R Schrider
  27. Adam Siepel
  28. Jerome Kelleher  Is a corresponding author
  29. Andrew D Kern  Is a corresponding author
  1. University of Oregon, United States
  2. University of Oxford, United Kingdom
  3. Cold Spring Harbor Laboratory, United States
  4. University of North Carolina at Chapel Hill, United States
  5. University of Copenhagen, Denmark
  6. University of California, Los Angeles, United States
  7. McGill University, Canada
  8. University of Melbourne, Australia
  9. University of Freiburg, Germany
  10. University of Washington, United States
  11. Arizona State University, United States
  12. IDC Herzliya, Israel
  13. Stanford University, United States
  14. Columbia University, United States
  15. Cornell University, United States
  16. ITMO University, Russian Federation
  17. National Autonomous University of Mexico, Mexico
  18. University of Arizona, United States

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.

Data availability

All resources are available from https://github.com/popsim-consortium/stdpopsim

Article and author information

Author details

  1. Jeffrey R Adrion

    Department of Biology, University of Oregon, Eugene, United States
    Competing interests
    No competing interests declared.
  2. Christopher B Cole

    Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6733-633X
  3. Noah Dukler

    Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8739-8052
  4. Jared G Galloway

    Department of Biology and Institute of Ecology and Evolution, University of Oregon, Eugene, United States
    Competing interests
    No competing interests declared.
  5. Ariella L Gladstein

    Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, United States
    Competing interests
    No competing interests declared.
  6. Graham Gower

    Lundbeck GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6197-3872
  7. Christopher C Kyriazis

    Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    No competing interests declared.
  8. Aaron P Ragsdale

    Human Genetics, McGill University, Montreal, Canada
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0715-3432
  9. Georgia Tsambos

    Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7001-2275
  10. Franz Baumdicker

    Department of Mathematical Stochastics, University of Freiburg, Freiburg, Germany
    Competing interests
    No competing interests declared.
  11. Jedidiah Carlson

    Department of Genome Sciences, University of Washington, Seattle, United States
    Competing interests
    No competing interests declared.
  12. Reed A Cartwright

    The Biodesign Institute and The School of Life Sciences, Arizona State University, Tempe, United States
    Competing interests
    No competing interests declared.
  13. Arun Durvasula

    Department of Human Genetics, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0631-3238
  14. Ilan Gronau

    IDC Herzliya, Herzliya, Israel
    Competing interests
    No competing interests declared.
  15. Bernard Y Kim

    Department of Biology, Stanford University, Stanford, United States
    Competing interests
    No competing interests declared.
  16. Patrick McKenzie

    Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, United States
    Competing interests
    No competing interests declared.
  17. Philipp W Messer

    Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, United States
    Competing interests
    Philipp W Messer, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8453-9377
  18. Ekaterina Noskova

    Computer Technologies Laboratory, ITMO University, Saint Petersburg, Russian Federation
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1168-0497
  19. Diego Ortega Del Vecchyo

    International Laboratory for Human Genome Research, National Autonomous University of Mexico, Juriquilla, Mexico
    Competing interests
    No competing interests declared.
  20. Fernando Racimo

    Lundbeck GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5025-2607
  21. Travis J Struck

    Molecular and Cellular Biology, University of Arizona, Tucson, United States
    Competing interests
    No competing interests declared.
  22. Simon Gravel

    Human Genetics, McGill University, Montreal, Canada
    Competing interests
    No competing interests declared.
  23. Ryan N Gutenkunst

    Molecular and Cellular Biology, University of Arizona, Tucson, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8659-0579
  24. Kirk E Lohmueller

    Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3874-369X
  25. Peter L Ralph

    Institute of Ecology and Evolution, University of Oregon, Eugene, United States
    Competing interests
    No competing interests declared.
  26. Daniel R Schrider

    Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5249-4151
  27. Adam Siepel

    Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
    Competing interests
    No competing interests declared.
  28. Jerome Kelleher

    Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
    For correspondence
    jerome.kelleher@bdi.ox.ac.uk
    Competing interests
    No competing interests declared.
  29. Andrew D Kern

    Institute of Ecology and Evolution, University of Oregon, Eugene, United States
    For correspondence
    adkern@uoregon.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4381-4680

Funding

National Institute of General Medical Sciences (R35GM119856)

  • Christopher C Kyriazis
  • Kirk E Lohmueller

National Institute of General Medical Sciences (R01GM117241)

  • Jeffrey R Adrion
  • Andrew D Kern

National Institute of General Medical Sciences (R01GM127348)

  • Travis J Struck
  • Ryan N Gutenkunst

National Institute of General Medical Sciences (R00HG008696)

  • Ariella L Gladstein
  • Daniel R Schrider

National Institute of General Medical Sciences (R35GM127070)

  • Noah Dukler
  • Adam Siepel

National Human Genome Research Institute (R01HG010346)

  • Noah Dukler
  • Adam Siepel

Villum Fonden (00025300)

  • Graham Gower
  • Fernando Racimo

UC MEXUS-CONACYT

  • Diego Ortega Del Vecchyo

Robertson Foundation

  • Jerome Kelleher

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Graham Coop, University of California, Davis, United States

Version history

  1. Received: January 7, 2020
  2. Accepted: June 15, 2020
  3. Accepted Manuscript published: June 23, 2020 (version 1)
  4. Accepted Manuscript updated: June 25, 2020 (version 2)
  5. Version of Record published: August 19, 2020 (version 3)

Copyright

© 2020, Adrion et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 5,997
    Page views
  • 613
    Downloads
  • 77
    Citations

Article citation count generated by polling the highest count across the following sources: PubMed Central, Scopus, Crossref.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Jeffrey R Adrion
  2. Christopher B Cole
  3. Noah Dukler
  4. Jared G Galloway
  5. Ariella L Gladstein
  6. Graham Gower
  7. Christopher C Kyriazis
  8. Aaron P Ragsdale
  9. Georgia Tsambos
  10. Franz Baumdicker
  11. Jedidiah Carlson
  12. Reed A Cartwright
  13. Arun Durvasula
  14. Ilan Gronau
  15. Bernard Y Kim
  16. Patrick McKenzie
  17. Philipp W Messer
  18. Ekaterina Noskova
  19. Diego Ortega Del Vecchyo
  20. Fernando Racimo
  21. Travis J Struck
  22. Simon Gravel
  23. Ryan N Gutenkunst
  24. Kirk E Lohmueller
  25. Peter L Ralph
  26. Daniel R Schrider
  27. Adam Siepel
  28. Jerome Kelleher
  29. Andrew D Kern
(2020)
A community-maintained standard library of population genetic models
eLife 9:e54967.
https://doi.org/10.7554/eLife.54967

Share this article

https://doi.org/10.7554/eLife.54967

Further reading

    1. Cell Biology
    2. Computational and Systems Biology
    Thomas Grandits, Christoph M Augustin ... Alexander Jung
    Research Article

    Computer models of the human ventricular cardiomyocyte action potential (AP) have reached a level of detail and maturity that has led to an increasing number of applications in the pharmaceutical sector. However, interfacing the models with experimental data can become a significant computational burden. To mitigate the computational burden, the present study introduces a neural network (NN) that emulates the AP for given maximum conductances of selected ion channels, pumps, and exchangers. Its applicability in pharmacological studies was tested on synthetic and experimental data. The NN emulator potentially enables massive speed-ups compared to regular simulations and the forward problem (find drugged AP for pharmacological parameters defined as scaling factors of control maximum conductances) on synthetic data could be solved with average root-mean-square errors (RMSE) of 0.47 mV in normal APs and of 14.5 mV in abnormal APs exhibiting early afterdepolarizations (72.5% of the emulated APs were alining with the abnormality, and the substantial majority of the remaining APs demonstrated pronounced proximity). This demonstrates not only very fast and mostly very accurate AP emulations but also the capability of accounting for discontinuities, a major advantage over existing emulation strategies. Furthermore, the inverse problem (find pharmacological parameters for control and drugged APs through optimization) on synthetic data could be solved with high accuracy shown by a maximum RMSE of 0.22 in the estimated pharmacological parameters. However, notable mismatches were observed between pharmacological parameters estimated from experimental data and distributions obtained from the Comprehensive in vitro Proarrhythmia Assay initiative. This reveals larger inaccuracies which can be attributed particularly to the fact that small tissue preparations were studied while the emulator was trained on single cardiomyocyte data. Overall, our study highlights the potential of NN emulators as powerful tool for an increased efficiency in future quantitative systems pharmacology studies.

    1. Computational and Systems Biology
    2. Neuroscience
    Domingos Leite de Castro, Miguel Aroso ... Paulo Aguiar
    Research Article Updated

    Closed-loop neuronal stimulation has a strong therapeutic potential for neurological disorders such as Parkinson’s disease. However, at the moment, standard stimulation protocols rely on continuous open-loop stimulation and the design of adaptive controllers is an active field of research. Delayed feedback control (DFC), a popular method used to control chaotic systems, has been proposed as a closed-loop technique for desynchronisation of neuronal populations but, so far, was only tested in computational studies. We implement DFC for the first time in neuronal populations and access its efficacy in disrupting unwanted neuronal oscillations. To analyse in detail the performance of this activity control algorithm, we used specialised in vitro platforms with high spatiotemporal monitoring/stimulating capabilities. We show that the conventional DFC in fact worsens the neuronal population oscillatory behaviour, which was never reported before. Conversely, we present an improved control algorithm, adaptive DFC (aDFC), which monitors the ongoing oscillation periodicity and self-tunes accordingly. aDFC effectively disrupts collective neuronal oscillations restoring a more physiological state. Overall, these results support aDFC as a better candidate for therapeutic closed-loop brain stimulation.