CorrTest

CorrTest tests the hypothesis of independence of evolutionary rates among branches in a phylogeny.

Introduction

CorrTest contains one R fuction: rate.CorrTest

rate.CorrTest(brlen_tree, outgroup, sister.resample = 0, outputFile)

brlen_tree is an object of class "phylo" specifying the branch lengths

outgroup is a vector of character specifying all outgroup tips

sister.resample is the number of sister resamplings. The default value is 0.

outputFile is a character string naming the output file that contains the CorrTest score and p-value

Users need to provide an evolutionary tree with branch lengths in order to run rate.CorrTest. To get a tree with branch lengths, we suggest that you use existing software (e.g., RAxML or MEGA) or functions in phangorn (https://cran.r-project.org/web/packages/phangorn/phangorn.pdf). (See below for examples usage.)

This program will produce the probability that the null hypothesis of rate independency among lineages is rejected, as well as the associated prediction score. When the rate independence is rejected by CorrTest, then autocorrelation parameter can be estimated by using one of the Bayesian analysis software (e.g., MCMCTree).

Note that this program has also been implemented in MEGA X (https://www.megasoftware.net).

Directory Structure

"code" directory contains rate.CorrTest R function.

"example" directory contains an example data and an example code to run rate.CorrTest.

"data" directory contains all empirical data, CorrTest results and Bayesian results in Tao et al. (2018).

Getting Started

To intall rate.CorrTest on your local machine, please follow the steps:

1. Download rate.CorrTest from code directory.
2. In R session, type setwd(<yout folder location>) to change the working directory to be the folder that contains rate.CorrTest function.
3. Type source("rate.CorrTest.R") to activate the funciton.

rate.CorrTest requires 4 external packages: ape, phangorn, stats and R.utils. Install them in advance before using the program. To do so, type the following command inside the R session and follow the instructions to complete the installation:

install.packages("ape")
install.packages("phangorn")
install.packages("stats")
install.packages("R.utils")

To run rate.CorrTest, please install the program follow above steps and then follow the following steps:

setwd("example")
t.ml = read.tree("dosReis_Mammals274_ML.nwk")
out.tip = c("Ornithorhynchus_anatinus", "Zaglossus_bruijni", "Tachyglossus_aculeatus")

rate.CorrTest(brlen_tree = t.ml, outgroup = out.tip, sister.resample = 0, outputFile = "CorrTest.txt")

Note that users need to provide a tree with branch lengths as the input for rate.CorrTest. To get the branch length tree, one can use existing software, such as RAxML and MEGA, or use the existing functions in phangorn using the following steps. If you have more questions about how to estimate branch lengths, please refer to "phangorn" manual (https://cran.r-project.org/web/packages/phangorn/phangorn.pdf).

setwd("example")
seq = read.phyDat("dosReis274_complete.fas", format="fasta", type="DNA")
	
## NOT RUN: To estimate branch lengths based on a fixed topology
topo = read.tree("dosReis_Mammals274_topology.nwk") 
topo = acctran(topo, seq)
fit.fix = pml(topo, data=seq, model="HKY", k=4)
fit.fix = optim.pml(fit.fix, optNni = FALSE, optEdge = TRUE, rearrangements = "none")

## NOT RUN: To estimate topogy and branch lengths together
dm = dist.ml(seq)
treeNJ = NJ(dm)
fit.relax = pml(treeNJ, data=seq, model="HKY", k=4)
fit.relax = optim.pml(fit.relax, optNni = TRUE, optEdge = TRUE, rearrangements = "NNI")
	
## NOT RUN: export the tree 
write.tree(fit.fix$tree, file="treeML_fix_topology.nwk")
write.tree(fit.relax$tree, file="treeML.nwk")

Currently, rate.CorrTest only allows binary trees. To use it with multifurcating trees, convert each polytomy into a series of bifurcations and set the length of the newly created branches to be 0.

If you have more questions, please email cathyqqtao@gmail.com (or qiqing.tao@temple.edu).

Citation

If you use CorrTest from R, please cite: Tao Q, Tamura K, Battistuzzi F, and Kumar S. 2019. A machine learning method for detecting autocorrelation of evolutionary rates in large phylogenies. Mol. Biol. Evol. 36(4):811-824.

If you use CorrTest from MEGA X, please also cite: Kumar S, Stecher G, Li M, Knyaz C, and Tamura K. 2018. MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms. Mol. Biol. Evol. 35:1547-1549.