Phylogenetics: APE Lab

	EEB 349: Phylogenetics
	This lab is an introduction to some of the capabilities of APE, a phylogenetic analysis package written for the R language. You may want to review the R Primer lab if you've already forgotten everything you learned about R.

Installing APE and apTreeshape

APE is a package largely written and maintained by Emmanuel Paradis, who has written a very nice book explaining in detail how to use APE. APE is designed to be used inside the R programming language, to which you were introduced earlier in the semester (see Phylogenetics: R Primer). apTreeshape is a different R package (written by Nicolas Bortolussi et al.) that we will use today.

To install APE and apTreeshape, start R and type the following at the R command prompt:

> install.packages("ape")
> install.packages("apTreeshape")

Assuming you are connected to the internet, R should locate these packages and install them for you. After they are installed, you will need to load them into R in order to use them (note that no quotes are used this time):

> library(ape)
> library(apTreeshape)

You should never again need to issue the install.packages command for APE and apTreeshape, but you will need to use the library command to load them whenever you want to use them.

Reading in trees from a file

Download this tree file and save it as a file named yule.tre in a new folder somewhere on your computer. Tell R where this folder is using the setwd (set working directory) command. For example, I created a folder named apelab on my desktop, so I typed this to make that folder my working directory:

> setwd("/Users/plewis/Desktop/apelab")

Now you should be able to read in the tree using this ape command:

> t <- read.nexus("yule.tre")

You can plot the tree using this ape command:

> plot(t)

About this tree

This tree was obtained using PAUP from 10,000 nucleotide sites simulated in Phycas from a Yule tree (which was also generated by Phycas). The model used to generated the simulated data (HKY model, kappa = 4, base frequencies = 0.3 A, 0.2 C, 0.2 G, and 0.3 T, no rate heterogeneity) was also used in the analysis by PAUP (the final ML tree was made ultrametric by enforcing the clock constraint). I analyzed this data in BEAST for part of a lecture. See this PDF file for details.