Phylogenetics: Large Scale Maximum Likelihood Analyses
|EEB 349: Phylogenetics|
|This lab explores two programs (GARLI and RAxML) designed specifically for maximum likelihood analyses on a large scale (hundreds of taxa).|
|Warning: Paul is still working on this lab, so some instructions may be woefully wrong until this warning disappears! (After that, some things may still be wrong, but hopefully not woefully wrong.)|
- 1 The data
- 2 Part A: Starting a GARLI run on the cluster
- 2.1 Preparing the GARLI control file
- 2.2 The tip of the GARLI iceberg
- 2.3 Log into the cluster
- 2.4 Create a folder and a script for the run
- 2.5 Submit the job
- 2.6 Files produced by GARLI
- 3 Part B: Starting a RAxML run on the cluster
- 4 Computing the RAxML majority rule consensus tree using PAUP*
- 5 Comparing GARLI and RAxML
The databryophytes are yellow, ferns are green, gymnosperms are blue and angiosperms are pink. This history of green plants shows several key innovations: embryos are what gave the first bryophytes an edge over aquatic algae on land; branched sporophytes and vascular tissues allowed the first ferns to grow taller and disperse more spores compared to their bryophyte ancestors; seeds and pollen were the big inventions that led to the rise of gymnosperms; and of course flowers allowed efficient pollination by insects and led the the diversification of the angiosperms.
Part A: Starting a GARLI run on the cluster
GARLI (Genetic Algorithm for Rapid Likelihood Inference) is a program written by Derrick Zwickl for estimating the phylogeny using maximum likelihood, and is currently one of the best programs to use if you have a large problem (i.e. many taxa). GARLI now (as of version 2.01) gives you considerable choice in substitution models: GTR[+I][+G] or codon models for nucleotides, plus several choices for amino acids. The genetic algorithm (or GA, for short) search strategy used by GARLI is like other heuristic search strategies in that it cannot guarantee that the optimal tree will be found. Thus, as with all heuristic searches, it is a good idea to run GARLI several times (using different pseudorandom number seeds) to see if there is any variation in the estimated tree. By default, GARLI will conduct two independent searches. If you have a multicore processor (newer Intel-based Macs and PCs are duo-core), GARLI can take advantage of this and use all of your CPUs simultaneously.
Today you will run GARLI on the cluster for a dataset with 50 taxa. This is not a particularly large problem, but has the advantage that you will be able to analyze it several times using both GARLI and RAxML within a lab period. Instead of each of us running GARLI several times, we will each run it once and compare notes at the end of the lab.
Preparing the GARLI control file
Like many programs, GARLI uses a control file to specify the settings it will use during a run. Most of the default settings are fine, but you will need to change a few of them before running GARLI.
Obtain a copy of the control file
The first step is to obtain a copy of the GARLI default control file. Go to the GARLI download page and download a version of GARLI appropriate for your platform (Mac or Windows). The only reason you are downloading GARLI is to obtain a copy of the default control file; you should use the cluster for all your GARLI runs.
Once you have downloaded and unpacked GARLI on your computer, make a copy of the garli.conf.nuc.defaultSettings file and rename the copy garli.conf, then open it in your text editor.
You will only need to change four lines.
Specify the data file name (note the capital L)
datafname = rbcL50.nex
Specify the prefix for output files
ofprefix = 50
The ofprefix is used by GARLI to begin the name of all output files. I usually use something different than the data file name here. That way, if you eventually want to delete all of the various files that GARLI creates, you can just say rm -f 50* ("remove all files beginning "50", the -f means "force" i.e. don't ask if it is ok, just do it) without wiping out your data file as well! (Sounds like the voice of experience, doesn't it?!)
Specify no invariable sites
invariantsites = none
This will cause GARLI to use the GTR+G model rather than the GTR+I+G model, which will facilitate comparisons with RAxML.
Do only one search replicate
searchreps = 1
Save the garli.conf file when you have made these changes.
The tip of the GARLI iceberg
As you can see from the number of entries in the control file, we are not going to learn all there is to know about GARLI in one lab session. One major omission is any discussion about bootstrapping, which is very easy to do in GARLI: just set bootstrapreps to some number other than 0 (e.g. 100) in your garli.conf file. I encourage you to download and read the excellent GARLI manual, especially if you want to use amino acid or codon models.
Log into the cluster
Log into the cluster using the command:
Go back to the Phylogenetics: Bioinformatics Cluster lab if you've forgotten some details.
Create a folder and a script for the run
Create a directory named garliraxml inside your home directory and use your favorite file transfer method (scp, psftp, Fugu, FileZilla, etc.) to get garli.conf into that directory.
Now download the data file into the garliraxml directory:
curl http://hydrodictyon.eeb.uconn.edu/eeb5349/rbcL50.nex > rbcL50.nex
Finally, create the script file you will hand to the qsub command to start the run. Use the nano editor to create a file named gogarli inside the garliraxml directory with the following contents:
#$ -cwd #$ -S /bin/bash #$ -o out.txt #$ -e err.txt #$ -m ea #$ -M firstname.lastname@example.org #$ -N jobname garli garli.conf
The lines starting with #$ represent commands that qsub understands. Any line starting with a hash (#) is interpreted as a comment by all linux/unix interpreters, but the extra dollar sign ($) immediately after the hash is used by the qsub program as a flag, telling it that what follows should be interpreted as a command. All of these could be entered on the qsub command line as well, but it is to forget something unless you put it in a script like this.
The command #$ -cwd tells qsub to put output files in the current working directory, the directory in which it was invoked (which will be the garliraxml directory).
The command #$ -S /bin/bash tells qsub to use the bash program to interpret the script. You have already been using the bash program; it interprets the commands you type when you are logged into the cluster. This qsub command just tells qsub to use the same program to interpret commands in the gogarli file.
The command #$ -o out.txt tells qsub to save any output into the file out.txt.
The command #$ -e err.txt tells qsub to save any error messages into the file err.txt.
The command #$ -m ea tells qsub to send you an email when your job ends (e) or aborts (a). The command #$ -M email@example.com tells qsub what your email address is (be sure to specify your own email address here). This is an extremely helpful feature! This feature may not work if you provide a non-uconn email address (feel free to try your gmail address and let me know if it works).
Finally, the -N command provides a job name that qsub will use to identify your job. This is entirely optional, but I find it very helpful to give short names to my runs so that when I get the email I know which run has finished (most useful if you have several runs going simultaneously). Be sure to replace "jobname" with something more meaningful (keep your job name simple - no embedded spaces or punctuation, and you will find that it is better to use 6 characters or fewer, as longer job names get truncated in qstat output).
The last line starts garli.
Submit the job
Here is the command to start the job:
You should issue this command from inside the garliraxml directory, which should contain 3 files: gogarli, rbcL50.nex and garli.conf.
Check progress every few minutes using the qstat command. This run will take about 4 minutes. If you get bored, you can use this command to see the tail end of the log file that GARLI creates automatically:
The tail command is like the cat command except that it only shows you the last few lines of the file (which often is just what you need). If you need to see more lines at the end of the file, you can specify the number using the -n option:
tail -n 100 50.log00.log
The above command will show the last 100 lines.
Files produced by GARLI
After your run finishes, you should find these files in your garliraxml folder. Download them to your laptop and view them to answer the questions:
This file saves the output that would have been displayed had you been running GARLI on your laptop. (Hover over the phrase "Paul's result" below to see what values I saw in my 50.screen.log file; your run may differ because we all started with a different seed.)
This file shows the best log-likelihood at periodic intervals throughout the run. It would be useful if you wanted to plot the progress of the run either as a function of time or generation.
This is a NEXUS tree file that can be opened in FigTree, TreeView, PAUP*, or a number of other phylogenetic programs. Try using FigTree to open it. The best place to root it is on the branch leading to Nephroselmis. In FigTree, click this branch and use the Reroot tool to change the rooting. I also find that trees look better if you click the Order nodes checkbox, which is inside the Trees tab on the left side panel of FigTree.
Part B: Starting a RAxML run on the cluster
Another excellent ML program for large problems is RAxML, written by Alexandros Stamatakis. This program is exceptionally fast, and has been used to estimate maximum likelihood trees for 25,000 taxa! Let's run RAxML on the same data as GARLI and compare results.
Preparing the data file
While GARLI reads NEXUS files, RAxML uses a simpler format. It is easy to use the nano editor to make the necessary changes, however. First, make a copy of your rbcL50.nex file:
cp rbcL50.nex rbcL50.dat
Open rbcL50.dat in nano and use Ctrl-k repeatedly to remove these initial lines:
#nexus begin data; dimensions ntax=50 nchar=1314; format datatype=dna gap=- missing=?; matrix
Add a new first line to the file that looks like this:
Now use the down arrow to go to the end of the file and remove the last two lines (again using Ctrl-k):
Save the file using Ctrl-x and you are ready to run RAxML!
The tip of the RAxML iceberg
As with GARLI, RAxML is full of features that we will not have time to explore today. Try typing raxml -h at the linux command prompt to see a brief description of all the available options. The manual does a nice job of explaining all the features so I recommend reading it if you use RAxML for your own data.
Running RAxML on the cluster
Hopefully, you have created the rbcL50.dat file in your garliraxml directory. If not, go ahead and move it there. Then return to your home directory and use nano to create a gorax script file that contains the following:
#$ -cwd #$ -S /bin/bash #$ -o raxout.txt #$ -e raxerr.txt #$ -m ea #$ -M firstname.lastname@example.org #$ -N jobname raxml -p 13579 -N 1 -e 0.00001 -m GTRCAT -s rbcL50.dat -n BASIC
You'll note that this is similar to the gogarli script we created earlier, but please read the explanation below before submitting the run to the cluster.
The qsub command lines are the same except that we specified raxout.txt rather than out.txt and raxerr.txt rather than err.txt (this is so that our RAxML run will not overwrite the files generated by our GARLI run).
The last line invokes raxml and requires the most explanation. First, RAxML does not use a control file like GARLI, so all options must be specified on the command line when it is invoked. Let's take each option in turn:
- -p 13579 provides a pseudorandom number seed to RAxML to use when it generates its starting tree. It is a good idea to specify some number here so that you have the option of exactly recreating the analysis later.
- -N 1 tells RAxML to just perform one search replicate.
- -e 0.00001 sets the precision with which model parameters will be estimated. RAxML will search for better combinations of parameter values until it fails to increase the log-likelihood by more than this amount. Ordinarily, the default value (0.1) is sufficient, but we are making RAxML work harder so that the results are more comparable to GARLI, which does a fairly thorough final parameter optimization.
- -m GTRCAT tells RAxML to use the GTR+CAT model for the search. I will explain in lecture how the CAT model works in RAxML - it is essentially a sites-specific-rates model for rate heterogeneity, but derives the categories and relative rates from the data set itself before the search begins.
- -s rbcL50.dat provides the name of the data file.
- -n BASIC supplies a suffix to be appended to all output file names
Start the run by entering this from within the garliraxml directory (where your rbcL50.dat and gorax files are located):
Bootstrapping with RAxML
After your first RAxML run finishes (probably within a minute), start a second, longer run to perform bootstrapping. Modify the last line of your gorax file as follows:
# raxml -p 13579 -N 1 -e 0.00001 -m GTRCAT -s rbcL50.dat -n BASIC raxml -f a -x 12345 -p 13579 -N 100 -m GTRCAT -s rbcL50.dat -n FULL
Note that I've used a # character to comment out our previous raxml line. (Feel free to simply delete that line if you wish.) Go ahead and start this run using qsub. This one will take longer, but not as long as you might expect (about 10 minutes). It will conduct a bootstrap analysis involving 100 bootstrap replicates (-N 100) using the GTR+CAT model. The -x 12345 specifies a starting seed for the bootstrap resampling. For every 5 bootstrap replicates performed, RAxML will climb uphill on the original dataset starting from the tree estimated for that bootstrap replicate. This provides a series of searches for the maximum likelihood tree starting from different, but reasonable, starting trees. The -f a on the command line sets up this combination of bootstrapping and ML searching.
Files produced by RAxML
This file contains some basic information about the run. Use this file to answer these questions:
This file holds the best tree found. It is not a NEXUS tree file, but simply a tree description; however, FigTree is able to open such files.
This file holds the trees resulting from bootstrapping (also not NEXUS format; one tree description per line). These trees do not have branch lengths. You can open this file in FigTree and use the arrow buttons to move from one to the next.
This file contains the best tree with bootstrap support values embedded in the tree description. Load this tree into FigTree. FigTree will ask you what name you want to use for the support values. Pick a name such as "bootstraps" and click Ok. Once the tree is visible, check Node labels on the left, chooose "bootstraps" (or whatever you named them) from the Display list, and increase the font size so you can see it (ok, you are probably young enough that you can still see the numbers without magnification!).
Computing the RAxML majority rule consensus tree using PAUP*
RAxML performed bootstrapping but did not provide a majority rule consensus tree. RAxML did however create the file RAxML_bootstrap.FULL containing the best tree from each of the 100 bootstrap replicates. It is relatively easy to get PAUP to create the majority rule consensus tree from this file. To do this, create a nexus file named majrule.nex containing this text:
#nexus begin paup; exe rbcL50.nex; gettrees file=RAxML_bootstrap.FULL; contree all / nostrict majrule treefile=raxmlmajrule.tre; end;
PAUP requires that you read in the data file before reading in the trees (so that it has a list of valid taxon names), hence the initial exe command. The gettrees command should be straightforward: this just tells PAUP to read in all the trees in the file RAxML_bootstrap.FULL. The last contree command instructs PAUP to create a majority rule consensus tree (majrule) from the trees it now has in memory, but not to create the strict consensus tree (nostrict) that it would ordinarily do by default. Finally, the treefile command provides the name of a file in which to save the majority rule consensus tree. As always, if you want to see all the options for a command like contree, just put a question mark after the command name in PAUP (and use the help command to get a list of all commands):
paup> contree ?; Usage: ConTree [tree-list] [/ options...] ; Available options: Keyword ------- Option type --------------------- Current setting ---------- strict no|yes no semistrict no|yes no adams no|yes no majRule no|yes yes percent <integer-value> 50 LE50 no|yes no useTreeWts no|yes no grpFreq no|yes yes indices no|yes no showTree no|yes yes treeFile <tree-file-name> <none> saveSupport no|brlens|nodeLabels|Both nodeLabels replace no|yes *no append no|yes *no tCompress no|yes no rootMethod outgroup|lundberg|midpoint outgroup outRoot polytomy|paraphyl|monophyl polytomy forcePolyt no|yes no *Option is nonpersistent
Run the majrule.nex in PAUP as follows (no need to qlogin for this):
and note that the majority rule tree was saved in the file raxmlmajrule.tre.
Comparing GARLI and RAxML
To compare the two programs, use the nano editor to create a tree file named combined.nex containing a trees block with the best tree from both programs and a paup block to compute the likelihoods of these three trees under the GTR+G model. Here I've simply inserted ellipses (...) as placeholders for the actual tree descriptions. It may be easier to construct this tree file on your own laptop (using Notepad++ on Windows or TextWrangler on Mac) and then upload it again to the cluster; use whichever approach is most convenient for you.
#nexus begin paup; exe rbcL50.nex; end; begin trees; utree garli = (...); utree raxml = (...); end; begin paup; set criterion=likelihood; lset nst=6 rmatrix=estimate rates=gamma shape=estimate; lscores all; agree all; end;
Which tree has the best log-likelihood? You will probably find that GARLI's likelihood is slightly different than RAxML, but both will be nearly the same value. Both of these approaches will give different answers if you run them multiple times under different random number seeds, so you should probably do several replicates (or a bootstrap analysis) before making anything too momentous of the results.
The last command given to PAUP was "agree all". This computes an agreement subtree from the results of the three analyses.