A community-maintained standard library of population genetic models
- University of Oregon, United States
- University of Oxford, United Kingdom
- Cold Spring Harbor Laboratory, United States
- University of North Carolina at Chapel Hill, United States
- University of Copenhagen, Denmark
- University of California, Los Angeles, United States
- McGill University, Canada
- University of Melbourne, Australia
- University of Freiburg, Germany
- University of Washington, United States
- Arizona State University, United States
- IDC Herzliya, Israel
- Stanford University, United States
- Columbia University, United States
- Cornell University, United States
- ITMO University, Russian Federation
- National Autonomous University of Mexico, Mexico
- 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
Christopher B Cole
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.
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
-
- 6,539
- views
-
- 663
- downloads
-
- 147
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
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)
A community-maintained standard library of population genetic models
eLife 9:e54967.
https://doi.org/10.7554/eLife.54967
Further reading
-
- Computational and Systems Biology
Plasmid construction is central to life science research, and sequence verification is arguably its costliest step. Long-read sequencing has emerged as a competitor to Sanger sequencing, with the principal benefit that whole plasmids can be sequenced in a single run. Nevertheless, the current cost of nanopore sequencing is still prohibitive for routine sequencing during plasmid construction. We develop a computational approach termed Simple Algorithm for Very Efficient Multiplexing of Oxford Nanopore Experiments for You (SAVEMONEY) that guides researchers to mix multiple plasmids and subsequently computationally de-mixes the resultant sequences. SAVEMONEY defines optimal mixtures in a pre-survey step, and following sequencing, executes a post-analysis workflow involving sequence classification, alignment, and consensus determination. By using Bayesian analysis with prior probability of expected plasmid construction error rate, high-confidence sequences can be obtained for each plasmid in the mixture. Plasmids differing by as little as two bases can be mixed as a single sample for nanopore sequencing, and routine multiplexing of even six plasmids per 180 reads can still maintain high accuracy of consensus sequencing. SAVEMONEY should further democratize whole-plasmid sequencing by nanopore and related technologies, driving down the effective cost of whole-plasmid sequencing to lower than that of a single Sanger sequencing run.
-
- Biochemistry and Chemical Biology
- Computational and Systems Biology
Protein–protein interactions are fundamental to understanding the molecular functions and regulation of proteins. Despite the availability of extensive databases, many interactions remain uncharacterized due to the labor-intensive nature of experimental validation. In this study, we utilized the AlphaFold2 program to predict interactions among proteins localized in the nuage, a germline-specific non-membrane organelle essential for piRNA biogenesis in Drosophila. We screened 20 nuage proteins for 1:1 interactions and predicted dimer structures. Among these, five represented novel interaction candidates. Three pairs, including Spn-E_Squ, were verified by co-immunoprecipitation. Disruption of the salt bridges at the Spn-E_Squ interface confirmed their functional importance, underscoring the predictive model’s accuracy. We extended our analysis to include interactions between three representative nuage components—Vas, Squ, and Tej—and approximately 430 oogenesis-related proteins. Co-immunoprecipitation verified interactions for three pairs: Mei-W68_Squ, CSN3_Squ, and Pka-C1_Tej. Furthermore, we screened the majority of Drosophila proteins (~12,000) for potential interaction with the Piwi protein, a central player in the piRNA pathway, identifying 164 pairs as potential binding partners. This in silico approach not only efficiently identifies potential interaction partners but also significantly bridges the gap by facilitating the integration of bioinformatics and experimental biology.
