PopPsiSeqR: Process and Visualize Evolve & Resequence Experiments

Description

Handle data from evolve and resequence experiments. Measured allele frequencies (e.g., from variants called from high-throughput sequencing data) are compared using an update of the PsiSeq algorithm (Earley, Eric and Corbin Jones (2011) doi:10.1534/genetics.111.129445). Functions for saving and loading important files are also included, as well as functions for basic data visualization.

Author(s)

Maintainer: Charles Soeder csoeder@email.unc.edu [copyright holder]

See Also

Useful links:

• https://github.com/csoeder/PopPsiSeq

• https://github.com/csoeder/PopPsiSeqR

• Report bugs at https://github.com/csoeder/PopPsiSeqR/issues

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling 'rhs(lhs)'.

Save the shifted frequencies

Description

Save the shifted frequencies

Usage

export.freqshft(frequency_shifts, output_file)

Arguments

Value

nothing

Examples

merged_frequencies.filename <- system.file("extdata",
"merged_frequencies.example_data.tbl", package = "PopPsiSeqR")
frequencies.bg <- import.freqtbl(merged_frequencies.filename)
frequency_shifts.bg <- freqShifter(frequencies.bg)
export.freqshft(frequency_shifts.bg , tempfile())

Frequency Shift Calculator

Description

This function accepts a GRanges object containing allele frequencies from two parental populations and an offspring population. It then polarizes each variant site and calculates how the offspring has shifted from equilibrium.

Usage

freqShifter(freqbed_in)

Arguments

Value

per-site PopPsiSeq frequency shifts

polarization of alleles

At each variant site, either the selected parent or the backcrossed parent might have the alternate allele at a higher frequency than the other (sites in which they have the same allele frequency are not informative and are assumed to have been filtered out). To regularize the data, each site was independently polarized, which is to say, the alternate and reference alleles were reassigned ad hoc to make the selected parent population have the higher frequency.

putting the offspring in context

At each site, the offspring's allele frequency is compared to the hypothetical equilibrium frequency expected by simply averaging the parents' frequencies. This is reported as the mean_oriented_shift; also reported is the distance to fixation in each direction (max_oriented_shift, min_oriented_shift), and the difference between parental allele frequencies (AF_difference)

Examples

merged_frequencies.filename <- system.file("extdata",
"merged_frequencies.example_data.tbl", package = "PopPsiSeqR")
frequencies.bg <- import.freqtbl(merged_frequencies.filename)
frequency_shifts.bg <- freqShifter(frequencies.bg)

Load Merged Allele Frequency Table

Description

This function accepts as input a path to a BED file containing allele frequency and returns a GRanges object ready for the freqShifter function.

Usage

import.freqtbl(freqtbl_filename)

Arguments

Value

frequency table as bedgraph

input format

This function accepts as input a path to a file in an extended BED6+ format; specifically,

'chrom start end name score strand reference_allele alternate_allele selected_parent_count selected_parent_allele_frequency backcrossed_parent_count backcrossed_parent_allele_frequency offspring_count offspring_allele_frequency'

eg,

'chr2L 8517 8518 0 0 + G A 8 0 16 0.25 8 0.25'

Some of these fields (name, score, strand, reference_allele, alternate_allele, selected_parent_count, backcrossed_parent_count, offspring_count) are required as placeholders but not used in the current PopPsiSeq algorithm This format is the output of joining and filtering the output of vcftools' –freq output; see vignette for details

Examples

merged_frequencies.filename <- system.file("extdata",
"merged_frequencies.example_data.tbl", package = "PopPsiSeqR")
frequencies.bg <- import.freqtbl(merged_frequencies.filename)

Load Smoothed Frequency Shift

Description

Load Smoothed Frequency Shift

Usage

import.smvshift(filename, selected_parent = "sim", backcrossed_parent = "sec")

Arguments

Value

loaded data as a bedgraph

Examples

windowed_shifts.filename <- system.file("extdata",
"windowed_shifts.example_data.bed", package = "PopPsiSeqR")
windowed_shifts.bg <- import.smvshift(windowed_shifts.filename)

Subtractor

Description

Subtractor

Usage

subTractor(
  data_one,
  data_two,
  treament_name = "pseudoparent",
  field = "avg_simward_AFshift",
  hoarder = FALSE
)

Arguments

Value

GRanges of the difference

Examples

 lab_sechellia.filename <- system.file("extdata",
"wild_sechellia.example_data.bed", package = "PopPsiSeqR")
lab.bg <- import.smvshift(lab_sechellia.filename)
lab.bg$sechellia <- "lab"
wild_sechellia.filename <- system.file("extdata",
"lab_sechellia.example_data.bed", package = "PopPsiSeqR")
wild.bg <- import.smvshift(wild_sechellia.filename)
wild.bg$sechellia <- "wild"
sub.traction <- subTractor(lab.bg, wild.bg ,treament_name = "sechellia")

Data displayer

Description

Data displayer

Usage

windowedFrequencyShift.plotter(
  windowed_shift,
  selected_parent = "sim",
  backcrossed_parent = "sec",
  contigs = c("chr2L", "chr2R", "chr3L", "chr3R"),
  main_title = "popPsiSeq results",
  ref_gen = "droSim1",
  primary_aesthetic = ggplot2::aes(),
  envelope_aesthetic = ggplot2::aes(),
  ancestral_aesthetic = ggplot2::aes()
)

Arguments

Value

a ggbio plot object

Examples

windowed_shifts.filename <- system.file("extdata",
"windowed_shifts.example_data.bed", package = "PopPsiSeqR")
windowed_shifts.bg <- import.smvshift(windowed_shifts.filename)
windowedFrequencyShift.plotter(windowed_shifts.bg)