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PopGenome (version 2.7.5)

An Efficient Swiss Army Knife for Population Genomic Analyses

Description

Provides efficient tools for population genomics data analysis, able to process individual loci, large sets of loci, or whole genomes. PopGenome not only implements a wide range of population genetics statistics, but also facilitates the easy implementation of new algorithms by other researchers. PopGenome is optimized for speed via the seamless integration of C code.

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Version

Install

install.packages('PopGenome')

Monthly Downloads

302

Version

2.7.5

License

GPL-3

Maintainer

Bastian Pfeifer

Last Published

January 31st, 2020

Functions in PopGenome (2.7.5)

Achaz.stats-methods

Achaz statistic

Prints the codon table which is used in the PopGenome framework

VCF_split_into_scaffolds

Split a VCF file into multiple scaffold-VCFs

detail.stats-methods

Several statistics

calc.R2-methods

Linkage statistics (R2, P-value, Distance)

GFF_split_into_scaffolds

Split a GFF file into multiple scaffold-GFFs

diversity.stats-methods

calc.fixed.shared-methods

Fixed and shared polymorphisms

diversity.stats.between-methods

create.PopGenome.method

Integration of own functions into the PopGenome-framework

Reading large FASTA alignments

get.individuals-methods

Print the names/IDs of individuals

concatenate.regions

Concatenate regions

neutrality.stats-methods

Neutrality Statistics

Reading tabixed VCF files (an interface to WhopGenome)

Read output data from MS and MSMS

concatenate.classes

Concatenate GENOME classes

Read data in .SNP format

Read SNP data in tabixed VCF format

set.populations-methods

Define populations

Annotation info

set.ref.positions-methods

Set reference positions for SNP data

Loading a PopGenome session

getBayes-methods

Get values for BayeScanR

get.status-methods

State of calculations

get.feature.names

Feature informations and GFF-attributes

Smoothed line-plot for multiple populations

get.codons-methods

Detailed information about the nature of codon changes

split_data_into_GFF_attributes

Split the data into GFF attributes

recomb.stats-methods

Recombination statistics

split_data_into_GFF_features

Split the data into GFF features

count.unknowns-methods

Calculate missing nucleotide frequencies

weighted.jackknife-methods

Weighted Jackknife

mult.linkage.stats-methods

Multilocus linkage statistics

GFF file (subdirectory "data")

jack.knife.transform

Jacknife Transformation

FASTA file (subdirectory "data")

get.biallelic.matrix-methods

Get the biallelic matrix

region.as.fasta-methods

Extract a region and write it to a FASTA file

introgression.stats-methods

Introgression statistics

Read SNP data from the HapMap consortium

linkage.stats-methods

Linkage Disequilibrium

Read alignments and calculate summary data

set.outgroup-methods

Define an outgroup

set.synnonsyn-methods

Set synonymous positions for SNP data

sweeps.stats-methods

Selective Sweeps

set.filter-methods

Setting filter to the analysis

sliding.window.transform-methods

Sliding Window Transformation

Save the "GENOME" object of a PopGenome session

splitting.data-methods

Split data into subsites

.SNP file (variant call data from 1001 Arabidopsis Genomes project)

show.slots-methods

Show Slots of class GENOME

test.params-class

Set parameters for coalescent simulations with Hudson's MS and Ewing's MSMS.

VCF file (subdirectory "data")

F_ST.stats-methods

An R implementation of BayeScan (Foll \& Gagiotti 2008)

Coalescent simulation with or without selection

PG_plot.biallelic.matrix-methods

Plot the biallelic matrix

Get the simulated MS/MSMS statistics

F_ST.stats.2-methods

Fixation Index (2)

McDonald-Kreitman Test (McDonald \& Kreitman 1991)