Haplotype network inference software tools | Population genetics data analysis
Haplotype networks are used in the analysis of population genetic data to visualise genealogical relationships at the intraspecific level, as well as to make inference about biogeography and history of populations.
Allows population genetics analysis using haplotype networks. PopART primary function is the inference and visualization of genetic relationships among intraspecific sequences. The software includes implementations of minimum spanning, median-joining and TCS network methods and provides a framework for the implementation and distribution of new methods. It also provides basic statistics that are useful for population genetics analyses, as well as tools for visualizing the geographical distribution of genetic data using a map-based interface.
An R package dedicated to the multivariate analysis of genetic markers. adegenet extends the ade4 package of multivariate methods by implementing formal classes and functions to manipulate and analyse genetic markers. adegenet provides tools for the analysis of genome-wide SNP data using standard personal computers. Data can be imported from common population genetics software and exported to other software and R packages.
A software package integrating several basic and advanced methods for population genetics data analysis, like the computation of standard genetic diversity indices, the estimation of allele and haplotype frequencies, tests of departure from linkage equilibrium, departure from selective neutrality and demographic equilibrium, estimation or parameters from past population expansions, and thorough analyses of population subdivision under the AMOVA framework. The main innovations of the new version include enhanced outputs in XML format, the possibility to embed graphics displaying computation results directly into output files, and the implementation of a new method to detect loci under selection from genome scans.
Investigates allozyme and molecular population genetic data. TFPGA offers a standalone toolkit gathering: (i) assortment of statistical methods such as descriptive or F-statistics; (ii) sets of tests including Hardy-Weinberg equilibrium or Mantel tests; and (iii) methods for data analysis allowing users to compute hierarchical data sets, dominant or codominant markers.
A cross-platform package for population genetic analyses that runs within Microsoft Excel. GenAlEx offers analysis of diploid codominant, haploid and binary genetic loci and DNA sequences. Both frequency-based (F-statistics, heterozygosity, HWE, population assignment, relatedness) and distance-based (AMOVA, PCoA, Mantel tests, multivariate spatial autocorrelation) analyses are provided. New features include calculation of new estimators of population structure: G'(ST), G''(ST), Jost's D(est) and F'(ST) through AMOVA, Shannon Information analysis, linkage disequilibrium analysis for biallelic data and novel heterogeneity tests for spatial autocorrelation analysis. Export to more than 30 other data formats is provided. Teaching tutorials and expanded step-by-step output options are included.
A package for the analysis of population genetic data. pegas provides functions for standard population genetic methods, as well as low-level functions for developing new methods. The flexible and efficient graphical capabilities of R are used for plotting haplotype networks as well as for other functionalities. pegas emphasizes the need to further develop an integrated-modular approach for software dedicated to the analysis of population genetic data.
A population genomics package for the R software environment (a de facto standard for statistical analyses). PopGenome can efficiently process genome-scale data as well as large sets of individual loci. PopGenome offers a wide range of diverse population genetics analyses, including neutrality tests as well as statistics for population differentiation, linkage disequilibrium, and recombination. PopGenome is linked to Hudson's MS and Ewing's MSMS programs to assess statistical significance based on coalescent simulations. PopGenome's integration in R facilitates effortless and reproducible downstream analyses as well as the production of publication-quality graphics.