scholarly journals learnPopGen : An R package for population genetic simulation and numerical analysis

2019 ◽  
Vol 9 (14) ◽  
pp. 7896-7902
Author(s):  
Liam J. Revell
Keyword(s):  
Numerical Analysis ◽  
Population Genetic ◽  
R Package ◽  
Genetic Simulation

scholarly journals Clotho: addressing the scalability of forward time population genetic simulation

2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Patrick P. Putnam ◽  
Philip A. Wilsey ◽  
Ge Zhang
Keyword(s):  
Population Genetic ◽  
Genetic Simulation

scholarly journals A community-maintained standard library of population genetic models

2019 ◽  
Author(s):  
Jeffrey R. Adrion ◽  
Christopher B. Cole ◽  
Noah Dukler ◽  
Jared G. Galloway ◽  
Ariella L. Gladstein ◽  
...  
Keyword(s):  
Population Genetic ◽  
Genomic Data ◽  
Simulation Models ◽  
Easy Access ◽  
Major Barrier ◽  
Simulation Engine ◽  
Complex Models ◽  
Demographic Inference ◽  
Genetic Simulation ◽  
Inference Methods

AbstractThe 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.

scholarly journals admixr—R package for reproducible analyses using ADMIXTOOLS

2019 ◽  
Vol 35 (17) ◽  
pp. 3194-3195 ◽  
Author(s):  
Martin Petr ◽  
Benjamin Vernot ◽  
Janet Kelso
Keyword(s):  
Population Genetic ◽  
R Package ◽  
Supplementary Information ◽  
Command Line ◽  
Software Suite ◽  
Genetic Studies ◽  
Convenient Interface ◽  
Reference Manual ◽  
Population Genetic Analyses ◽  
R Functions

Abstract Summary We present a new R package admixr, which provides a convenient interface for performing reproducible population genetic analyses (f3, D, f4, f4-ratio, qpWave and qpAdm), as implemented by command-line programs in the ADMIXTOOLS software suite. In a traditional ADMIXTOOLS workflow, the user must first generate a set of text configuration files tailored to each individual analysis, often using a combination of shell scripting and manual text editing. The non-tabular output files then need to be parsed to extract values of interest prior to further analyses. Our package simplifies this process by automating all low-level configuration and parsing steps, making analyses as simple as running a single R command. Furthermore, we provide a set of R functions for processing, filtering and manipulating datasets in the EIGENSTRAT format. By unifying all steps of the workflow under a single R framework, this package enables the automation of analytic pipelines, significantly improving the reproducibility of population genetic studies. Availability and implementation The source code of the R package is available under the MIT license. Installation instructions, reference manual and a tutorial can be found on the package website at https://bioinf.eva.mpg.de/admixr. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A community-maintained standard library of population genetic models

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Jeffrey R Adrion ◽  
Christopher B Cole ◽  
Noah Dukler ◽  
Jared G Galloway ◽  
Ariella L Gladstein ◽  
...  
Keyword(s):  
Population Genetic ◽  
Genomic Data ◽  
Simulation Models ◽  
Easy Access ◽  
Major Barrier ◽  
Simulation Engine ◽  
Complex Models ◽  
Demographic Inference ◽  
Genetic Simulation ◽  
Inference Methods

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.

scholarly journals Poppr: an R package for genetic analysis of populations with clonal or partially clonal reproduction

2013 ◽  
Author(s):  
Zhian N Kamvar ◽  
Javier F Tabima ◽  
Niklaus J Grünwald
Keyword(s):  
Genetic Analysis ◽  
Open Source ◽  
Population Genetic ◽  
Genetic Data ◽  
Genotypic Diversity ◽  
R Package ◽  
Bootstrap Support ◽  
Population Genetic Analysis ◽  
Mixed Populations ◽  
Batch Analysis

Many microbial, fungal, or oomcyete populations violate assumptions for population genetic analysis because these populations are clonal or partially clonal. Furthermore, few tools exist that are specifically designed for analyzing data from clonal populations, making analysis difficult and haphazard. We developed the R package poppr providing unique tools for analysis of data from admixed, clonal, and/or mixed populations. Currently, poppr can be used for dominant/codominant and haploid/diploid genetic data. Data can be imported from several formats including GenAlEx formatted text files and can be analyzed on a user-defined hierarchy that includes unlimited levels of subpopulation structure and clone censoring. New functions include calculation of Bruvo’s distance for microsatellites, batch-analysis of the index of association with several indices of genotypic diversity, and graphing including dendrograms with bootstrap support and minimum spanning networks. A manual with documentation and examples is provided. Poppr is open source and major releases are available on CRAN: http://cran.r-project.org/package=poppr. More supporting documentation and tutorials can be found under ‘resources’ at: http://grunwaldlab.cgrb.oregonstate.edu/.

scholarly journals skelesim: an extensible, general framework for population genetic simulation in R

2016 ◽  
Vol 17 (1) ◽  
pp. 101-109 ◽  
Author(s):  
Christian M. Parobek ◽  
Frederick I. Archer ◽  
Michelle E. DePrenger-Levin ◽  
Sean M. Hoban ◽  
Libby Liggins ◽  
...  
Keyword(s):  
Population Genetic ◽  
General Framework ◽  
Genetic Simulation
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