Chapter 13 RAxML

13.1 Objectives

By the end of this week, you will:

  • Have RAxML installed
  • Be able to do an analysis with likelihood with various models
  • Understand partitioning
  • Be able to use a variety of character types

RAxML (Stamatakis, 2014) is a very popular program for inferring phylogenies using likelihood, though there are many others. It is notable for being able to infer trees for tens of thousands of species or more. New versions can use DNA, amino acid, SNP, and/or morphological characters.

13.2 Install RAxML

To begin, install RAxML. Follow the instructions in Step 1 of For the fewest issues, just do make -f Makefile.gcc on the command line (not in R) to compile the basic vanilla version. For actual work, you’ll likely find the versions with SSE3 and/or PTHREADS will work faster. On a Mac (Linux is similar; RAxML has binaries), the easiest way to get use this would be:

git clone
cd standard-RAxML
make -f Makefile.gcc

If compiling went correctly, you should see a line like

gcc  -o raxmlHPC axml.o  optimizeModel.o multiple.o searchAlgo.o topologies.o parsePartitions.o treeIO.o models.o bipartitionList.o rapidBootstrap.o evaluatePartialGenericSpecial.o evaluateGenericSpecial.o newviewGenericSpecial.o makenewzGenericSpecial.o   classify.o fastDNAparsimony.o fastSearch.o leaveDropping.o rmqs.o rogueEPA.o ancestralStates.o  mem_alloc.o  eigen.o -lm

Now you need to put the program in a path. This is where your computer looks for programs to run. If you type a program name, like ls or raxmlHPC, your computer checks the folders indicated in the path for a program of this name; when it finds one, it runs that. You can see your path by typing echo $PATH. If you want to run a program, like the newly compiled raxmlHPC, you have two options: you can specify where it is each time you want to run it, or you can put it in a folder in your existing path. The former becomes a pain, so I’d recommend the latter. /usr/bin is in your path, but this is reserved for programs your computer needs to run – don’t mess with it. I’d suggest putting it in /usr/local/bin. To do this, type

sudo cp raxmlHPC /usr/local/bin/raxmlHPC

sudo means superuser do. It’s a very powerful command. Generally, typing on the command line you can delete files that are important to you, but it’s hard to utterly destroy your computer; with superuser abilities, you could delete key files.

Sudo sandwich from xkcd

Sudo sandwich from xkcd

Ok, so we now have RAxML installed. To run it, you could use the very handy ips package to call it from R, but it doesn’t have an interface to all of the relevant commands. Instead, we’re going to just create some commands to run ourselves.

First, we need sample data sets. We will be using ones, modified somewhat, from this tutorial. The original files are here but the modified ones are in the repository for this PhyloMeth exercise in the /inst/extdata folder.

Until now, we’ve seen NEXUS files, which can include data blocks. RAxML uses Phylip-formatted files instead, which are simpler: a line that has the number of taxa and the number of sites, followed by one line per taxon with the taxon name, a space, and then the characters (though there could be interleaving).

13.4 DNA

Most phylogenetic analyses for extant organisms use sequence data. This is often presented as DNA, though sometimes the data are translated to amino acids instead. Usually sequences from multiple genes are concatenated. There are a wide range of models available for sequence evolution. For DNA, the most popular remains general time reversible (GTR): a model that allows for a different transition rate between every pair of nucleotides, subject to the constraint that the rate from nucleotide i to j is the same as the rate from j to i. Different sites evolve at different rates (think of the sites coding for the active site of an enzyme versus those in an intron that has little to no functional purpose). One way to model this heterogeneity is with a gamma distribution: the likelihood is evaluated using several different rates for that site (Yang 1995). One can also apply partitions: allow different sections of the data to have different rates. This is commonly done to allow first, second, and third codon positions to have different rates, or to allow different genes to have different models of evolution. This can offer dramatic improvements in the fit of a model to the data; it is especially important when dealing with gappy data, such as cases where one gene is present for all taxa but another gene has ben sampled for only a subset of taxa.

Do InferDNATreeWithBootstrappingAndPartitions_exercise() in the homework. Once this is done, install this homework library into R. From the folder containing the homework:

R CMD INSTALL LikelihoodTrees

Then, in R:

results <- InferDNATreeWithBootstrappingAndPartitions_exercise()

Though you may have to include other arguments (especially input.path).

You can plot your final tree:

plot.phylo(results$, show.node.label=TRUE)

This shows the branch lengths of the best ML tree and the bootstrap proportions. This is from a non-parametric bootstrap (Felsenstein 1985): the columns of data are sampled with replacement and then a tree search is redone. The more times a bipartition (an edge) on a tree is recovered, the more confidence we have in it (but this is not the same as the probability of it being true). Note one common error: the numbers reported, and in this case shown at nodes, are not properties of a node or a clade: they are bipartitions: taxa A, C, E fall attach (perhaps through other nodes) to one end of an edge, and taxa B, D, E, F, G, H are attached to the other end.