PhyloWGA: phylogenetic analyses and interrogation of whole genome alignments (WGAs)

NOTE See the file https://github.com/radamsRHA/PhyloWGA/blob/master/PhyloWGA.pdf for more detailed instructions

Installing R package PhyloWGA from github

The R package PhyloWGA is freely available to download and distribute from github https://github.com/radamsRHA/PhyloWGA/. To install and load PhyloWGA, you must first install the R package devtools, Additionally, make sure the most updated version of R is installed

install.packages("devtools")

Now using devtools we can install PhyloWGA from github:

library(devtools)
install_github("radamsRHA/PhyloWGA")
library(PhyloWGA) # Load package ThetaMater

Additionally, a number of dependencies are required by PhyloWGA, including the following:

library(PhyloWGA)
library(ape)
library("phylotools")

PhyloWGA also requires the following dependencies to be installed and available on your path $PATH:

samtools: http://www.htslib.org (samtools must be in your path)
concatepillar: http://leigh.geek.nz/software.shtml (concaterpillar.py must be in your path, can be found also at github.com/radamsRHA/PhyloWGA/inst/extdata/concaterpillar-1.8a.zip)
iqtree: http://www.iqtree.org (iqtree must be in your path, can be found also at github.com/radamsRHA/PhyloWGA/inst/extdata/iqtree-1.6.12-MacOSX.zip)
raxml:must be version 7.3! (used by concatepillar.py, raxmlHPC must be in your path, can be found also at github.com/radamsRHA/PhyloWGA/inst/extdata/raxmlHPC.zip)

To begin using PhyloWGA try using the examples associated with each function and the file PhyloWGA.pdf provided with the R package.

Example: Phylogenetic analyses along windows of a chromosome alignment

In this first example, we will use the function Chromo.Phylome to infer a chromosome-specific set of gene trees (i.e., "chromo phylome") for the example segment.

First, let's load PhyloWGA and its dependencies and find the path to the example chromosome alignment

################
# Load depends #
################
library(PhyloWGA)
library(ape)
library("phylotools")

#####################################
# Read example chromosome alignment #
#####################################
String.Path_ExampleChromosomeAlignment <-  system.file("extdata", "Example_Chr10.fasta", package="PhyloWGA")

Now, let's use Chromo.Phylome to infer a set of gene trees from this alignment using a very fine scale (window size = step size = 100bp) analyses for this example.Chromo.Phylome will proceed along the alignment in windows of 100bp using a GTR+G model.

####################################
# Conduct chromo phylome inference #
####################################
handle.Chr1_w100_s100_Phylome <- Chromo.Phylome(string.PathParentDir = '~/Desktop/', 
                                                          numeric.WindowSize = 100, 
                                                          numeric.StepSize = 100, 
                                                          string.PathToFastaFile = String.Path_ExampleChromosomeAlignment, 
                                                          string.Commands_iqtree= "-m GTR+G")

After running Chromo.Phylome, check the output directory on the Desktop to see the results, including the file Results_CheckPoint_w100_s100.txt that summarizes the run. Also, the object handle.Chr1_w100_s100_Phylome contains the results of the analysis.

Example: construct a set of statistically-justified and genome-informed supergenes

The function Chromo.Crawl will "crawl" along the input alignment and apply the likelihood ratio test of CONCATEPILLAR to infer model-based supergenes (i.e., genomic windows that have been concated together due to evidence of a shared tree). Let's run Chromo.Crawl on our example dataset:

########################
# Conduct chromo crawl #
########################
handle.Chr1_w100_s100_Crawler <- Chromo.Crawl(string.PathParentDir = '~/Desktop/', 
                         numeric.WindowSize = 100, 
                         numeric.StepSize = 100, 
                         string.PathToFastaFile = String.Path_ExampleChromosomeAlignment, 
                         numeric.NumberOfCores = 2)

The results of Chromo.Crawl will be found on the parent directory placed on the Desktop, and include a directory of supergene alignments (one for each supegene), and a results file that summarizes the analysis Results_CheckPoint_w10000_s10000.txt. These can then be used to further dissect evidence of phylogenetic conflict, and the resulting supergene alignments (found in the subdirectory SupergeneAlignments) can also be used for downstream species tree inference, and so forth.

Example: setting up PhyloWGA for parallel analyses

The function Organize.ParallelPhyloWGA can be used to organize a directory that contains a set of subdirectories, each of which contain a slice of a WGA and an R script for conducting a particular PhyloWGA analyses. Batch scripts can then be run on each subdirectory. For example, one can provide sbatch scripts for each subdirectory for analyses on a cluser.

###############################################
# Organize PhyloWGA for Chromo.Crawl analysis #
###############################################
Organize.ParallelPhyloWGA(numeric.NumberSubsets = 2, 
                          string.PathToFastaFile = String.Path_ExampleChromosomeAlignment, 
                          string.PathParentDir = '~/Desktop/', 
                          string.Analysis = "Chromo.Crawl", 
                          numeric.WindowSize = 1000,
                          numeric.StepSize = 1000, 
                          string.Commands_iqtree = "", 
                          numeric.NumberOfCores = 2)

Example: conducting Chromo.Phylome on a customized set of loci

The function Chromo.Phylome.Custom allows users to infer locus-specific phylogenetic trees given a user-specified matrix of locus coordinates. You can provide a 2-columns matrix with the number of rows equal to the number of loci that will be analyzed. See the arguments matrix.WindowCoordinates = matrix.LocusCoordinates below.

LocusCoordinates <- matrix(nrow = 3, ncol = 2)
LocusCoordinates[1,] <- c(1,500)
LocusCoordinates[2,] <- c(600,1200)
LocusCoordinates[3,] <- c(2000,5000)

Chromo.Phylome.Custom(string.PathParentDir = "~/Desktop/", 
                      string.PathToFastaFile = String.Path_ExampleChromosomeAlignment, 
                      matrix.WindowCoordinates = matrix.LocusCoordinates,
                      string.Commands_iqtree = "")