The files required to reproduce these analyses are included in the R package directory on installation, and can be downloaded from Github.

If you have RStudio, you can open the R Markdown file used to generate this document (vignettes/Conduct-analyses.Rmd) to run the R scripts that will copy all necessary files and begin analyses on your behalf.

You will need to specify some paths for automatic downloading:

# Directory in which to install MrBayes
BAYES_DIR <- "C:/Research/MrBayes"

# Directory in which to conduct parsimony analysis
HOME <- "C:/Research/oreilly" # Must not end in a trailing '/'

# GitHub remote
INST_ROOT <- "https://raw.githubusercontent.com/ms609/OReillyEtAl2016/master/inst/"

Bayesian analysis

bayesgen.pl — Generate MrBayes files

A Perl script to execute analysis using Markov models in MrBayes.

The script reads the datasets of O’Reilly et al. [1], appends a MrBayes block to the Nexus files, and executes a MrBayes run, saving the consensus trees and preparing them for analysis in R.

Before running the script:

MRBAYES_RELEASE <- "https://github.com/NBISweden/MrBayes/releases/download/v3.2.6/MrBayes-3.2.6_WIN32_x64.zip"
zipFile <- tempfile(fileext='.zip')
download.file(MRBAYES_RELEASE, destfile=zipFile, method='auto', mode='wb')
unzip(zipFile, c('MrBayes/mrbayes_x64.exe', 'MrBayes/mrbayes_x86.exe'), 
      exdir=BAYES_DIR, junkpaths=TRUE)
file.remove(zipFile)
tempFile <- tempfile(fileext='.zip')
download.file("https://datadryad.org/bitstream/handle/10255/dryad.112089/oreilly2016matrices.zip", tempFile)
unzip(tempFile, exdir=paste0(HOME, '/Matrices'))
file.remove(tempFile)
  • Copy mrbayesblock.nex to the Matrices directory, and bayesgen.pl to the root MrBayes directory. Modify the latter files to specify the path to MrBayes (default: C:/Research/MrBayes/) and path to extracted matrices (default: C:/Research/oreilly/Matrices)
download.file(paste0(INST_ROOT, "analysis-bayesian/mrbayesblock.nex"),
              paste0(HOME, '/Matrices/mrbayesblock.nex'))
              
bayesGenPath <- paste0(BAYES_DIR, '/bayesgen.pl')
download.file(paste0(INST_ROOT, "analysis-bayesian/bayesgen.pl"), bayesGenPath)
bayesGen <- readLines(bayesGenPath)
bayesGen[5] <- paste0('$dir = "', HOME, '/Matrices";')
bayesGen[6] <- paste0('$bayes_dir = "', BAYES_DIR, '";')
writeLines(bayesGen, bayesGenPath)

t2nexPath <- paste0(BAYES_DIR, '/t2nex.pl')
download.file(paste0(INST_ROOT, "analysis-bayesian/t2nex.pl"), t2nexPath)
t2nex <- readLines(t2nexPath)
t2nex[2] <- paste0('$dir = "', HOME, '/Matrices";')
writeLines(t2nex, t2nexPath)

  • Perform the analyses by executing bayesgen.pl. (Once Perl is installed, you can just double-click the file.)

  • Once the analyses are complete, copy all files ending .run#.nex to C:/Research/oreilly/MrBayes.

Parsimony analysis

mptgen.pl — Generate parsimony trees in TNT

A Perl script to generate most parsimonious trees by parsimony search in TNT.

The script generates TNT scripts to perform parsimony analysis on each of the O_Reilly et al. datasets, under equal and implied weights, with and without suboptimal trees. It then executes these scripts and converts the output into a format suitable for analysis in R.

Before running the script, you’ll need an installation of Perl. Strawberry Perl works on MS Windows.

Then:

  • Create a local directory (default: C:/Research/oreilly) with two subdirectories entitled Matrices, and Trees. Then, within the new Trees directory, create the further subdirectories eq.100, eq.350, eq.1000, k2.100 (and .350, .1000), and so forth for k3, k5, k10, k20 and k200.
N_CHAR <- c(100, 350, 1000)
sapply(paste0(HOME, '/', c('', 'Matrices', 'Trees')), dir.create)
sapply(N_CHAR, function (nChar) 
  sapply(paste0(HOME, '/Trees/', 
                c('eq', 'k2', 'k3', 'k5', 'k10', 'k20', 'k200'),
                '.', nChar), dir.create, showWarnings=FALSE))
zipFile <- paste0(HOME, '/TNT.ZIP')
# This is the Windows path; use the appropriate path for your operating system
download.file("http://www.lillo.org.ar/phylogeny/tnt/ZIPCHTNT.ZIP", 
              destfile=zipFile, method='auto', mode='wb')
unzip(zipFile, 'tnt.exe', exdir=HOME)
file.remove(zipFile)
  • Copy mptgen.pl and (optinally) tnt2nex.pl into this root directory, updating each file so its variable $dir corresponds to the appropriate path.
    tnt2nex.pl translates TNT output into NEXUS format and may be useful if you wish to perform further analysis of TNT output. This will be performed automatically if you uncomment the final line of mptgen.pl.
tnt2nexPath <-  paste0(HOME, '/tnt2nex.pl')
mptgenPath <-  paste0(HOME, '/mptgen.pl')

download.file(paste0(INST_ROOT, "analysis-parsimony/tnt2nex.pl"), tnt2nexPath)
tnt2nex <- readLines(tnt2nexPath)
tnt2nex[3] <- paste0('$dir = "', HOME, '";')
writeLines(tnt2nex, tnt2nexPath)

download.file(paste0(INST_ROOT, "analysis-parsimony/mptgen.pl"), mptgenPath)
mptgen <- readLines(mptgenPath)
mptgen[3] <- paste0('$dir = "', HOME, '";')
writeLines(mptgen, mptgenPath)
  • Copy the file template.run into the tntscript subdirectory.
download.file(paste0(INST_ROOT, "analysis-parsimony/tnt_template.run"), 
              paste0(HOME, '/tnt_template.run'))
tempFile <- tempfile(fileext='.zip')
download.file("https://datadryad.org/bitstream/handle/10255/dryad.112089/oreilly2016matrices.zip", tempFile)
unzip(tempFile, exdir=paste0(HOME, '/Matrices'))
## Optionally: 
file.remove(tempFile)
  • Perform the analyses by executing mptgen.pl. (Once Perl is installed, you can just double-click the file.)

Data analysis

Once the data have ben gneerated, they can be analysed using [https://github.com/ms609/OReillyEtAl2016/blob/master/data-raw/GenerateData.Rmd].

References

1. O’Reilly JE, Puttick MN, Parry L, Tanner AR, Tarver JE, Fleming J, Pisani D, Donoghue PCJ. 2016 Bayesian methods outperform parsimony but at the expense of precision in the estimation of phylogeny from discrete morphological data. Biology Letters 12, 20160081. (doi:10.1098/rsbl.2016.0081)

2. O’Reilly JE, Puttick MN, Parry L, Tanner AR, Tarver JE, Fleming J, Pisani D, Donoghue PCJ. 2016 Data from: Bayesian methods outperform parsimony but at the expense of precision in the estimation of phylogeny from discrete morphological data. Dryad Digital Repository, doi:10.5061/dryad.10qf3. (doi:10.5061/dryad.10qf3)