Publication | Open Access
P<scp>op</scp>G<scp>en</scp>R<scp>eport</scp>: simplifying basic population genetic analyses in R
636
Citations
32
References
2014
Year
GeneticsSummary UsingGenomic SelectionLandscape GeneticsGenomicsClinical GeneticsPhylogenetic AnalysisGenome-wide Association StudyGenetic AnalysisGenotype-phenotype AssociationMolecular EcologyNew R PackageStatistical ComputingBiostatisticsPublic HealthQuantitative GeneticsStatistical GeneticsGenetic VariationPopulation GeneticsEvolutionary BiologySteep Learning CurvePopulation GenomicsMedicine
R offers reproducible population genetic analyses but its steep learning curve limits accessibility. P op G en R eport is an R package that simplifies population genetics analyses by providing a report‑generating function. The package offers a single‑command function that runs up to 13 predefined analyses, supports data import from spreadsheets, calculates allele distributions, genetic distances, null alleles, allelic richness, spatial autocorrelation, and allows modular extension with user‑defined functions, all integrated into a PDF report. Each analysis outputs figures and tables embedded in a PDF report and also as separate files (figures in multiple formats, tables as CSV).
Summary Using scripting languages such as R to perform population genetic analyses can improve the reproducibility of research, but using R can be challenging for many researchers due to its steep learning curve. P op G en R eport is a new R package that simplifies performing population genetics analyses in R, through the use of a new report‐generating function. The function popgenreport allows users to perform up to 13 pre‐defined and 1 user‐defined analyses through the use of a single command line. Each analysis generates figures and tables that are incorporated into a pdf report and are also made available as individual files (figures are provided in multiple formats, table contents are provided as csv files). The package includes new R functions that simplify the importation of data from a spreadsheet file, examine allele distributions across populations and loci and identify private alleles, determine pairwise individual genetic distances using the methods of Smouse and Peakall ( ) and Kosman and Leonard ( ), respectively, detect the presence of null alleles, calculate allelic richness, and test for spatial autocorrelation in genotypes using the methods of Smouse and Peakall ( ). The package has a modular structure that makes the process of adding new functionality straightforward. To facilitate the addition of user‐designed functions, the package includes a fully customizable module that can be automatically included in the pdf report. To support users not experienced in R, the website ( www.popgenreport.org ) has a tutorial for the package and a downloadable, portable version of the package with LaTeX pre‐configured for the Windows operating system.
| Year | Citations | |
|---|---|---|
Page 1
Page 1