Commonly used phylogenetic methods, Monash HPC access and basic bioinformatics



RAxML takes a .fasta file of aligned sequences as input, and computes maximum likelihood trees. As with any file that will be fed into a tree-computing program, remove all brackets, colons and semi-colons from the sequence names.


Installing raxml on your personal computer is optional, because it's available on M3 and our server, and raxml is usually extremely slow. There are two installation options: 1. Via homebrew 2. Go straight here, and follow their steps accordingly. That website gives you the raxml executable to download, simply named raxml, which you can run for (very) small datasets. Unfortunately, this website is not the top hit in Google, nor is it very easy to find from the main RAxML webpage! You'll also have to add this executable to your PATH, or have to run it from whichever folder you're keeping it in.

RAxML on M3

The partial SLURM script below runs a 8-thread process on a single node, using PTHREADs for parrallelization and with SSE enabled (doesn't matter if you don't know what those are):

module load raxml/8.2.9
raxmlHPC-PTHREADS-SSE3 -T 8 -f a -m GTRGAMMA -p 12345 -x 12345 -N 10 -s input_file.fasta -n output_file.txt

The important SLURM #SBATCH parameters to set are:

#SBATCH --ntasks=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=8
#SBATCH --mem-per-cpu=10000

This specifies the job to run as one process (ntask=1), issued to a single node, and requests 8 CPUs from that node. Note that the cpus-per-task corresponds to the -T flag in the RAxML command.

If you're just running the RAxML command above on your own computer:

raxml -T 8 -f a -m GTRGAMMA -p 12345 -x 12345 -N 10 -s input_file.fasta -n output_file.txt

Commonly Specified RAXML Options

To see a full list of options and their explanations, use ramxml -help. - -m <model> - Specifies the nucleotide substitution model to be used. The most common model is GTRGAMMA. - -f <algorithm> - Specifies the tree-computing algorithm to use; this is a bit of a black box. For most cases, we use -f a, to do "rapid Bootstrap analysis and search for best-scoring ML tree in one program run" (from raxml -help), simply because it purports to be the fastest ("rapid"). - -p <random seed> - Sets the random seed for reproducibility. It's good practice, but not essential, to use a prime number, because of technicl reasons. - -s <input_alignment_file> - Specify the input alignment file. RAxML accepts relaxed phy, phy, or fasta. - -N <num_of_alternative_runs> - No. of alternative runs on different starting trees. - -T <num_of_threads> - No. of threads. Make sure to set this at most the number of CPUs you have on your machine, or this will suffer a massive performance drop!

From previous tests (see next section), the command given above has been optimized for speed for most cases. Don't forget to: - Change the number of alternative runs -N as required. Publication-standard is at least 100. - For number of threads -T, anywhere between 4 and 16 is a good number. Note that it's not always the more the merrier - with SSE enabled in particular, the computational cost of managing more and more CPUs will start to exceed any time savings gained from using more CPUs.

Performance Tests

Conducted some speed tests on M3 using 50 randomly selected Flu B Yam HA sequences. Results:

1 process, 1 CPU4:39:193:44:30...
1 process, 4 CPU1:41:551:05:471:04:56
1 process, 8 CPU0:53:320:40:380:59:47
1 process, 16 CPU0:48:430:48:480:59:52

Looks like the best bet is to go with 1-process, 8-CPU with PTHREADS-SSE. As noted in the RAxML documentation, performance does not necessarily decrease as the number of CPUs increases, as communication overhead between CPUs would increase as well. This will differ from dataset to dataset, but hopefully not by much.


  • When performing bootstrap computations, RAxML will return a consensus tree in a format that FigTree doesn't understand; you'll get some kind of "error parsing number" error message. To fix this, open the output file in Text Wrangler, and use regex to replace:


  • I'm not actually sure if the ML and the bootstrap consensus trees are the same tree! They shouldn't be, because, by definition of the word "maximum", there can only be one maximum likelihood tree. The consensus tree is an aggregate of all the computed trees, which will have the strongest bootstrap support.