Overview
Teaching: 180 min
Exercises: 0 minQuestions
How do I use PAUP to build a parsimony tree?
Objectives
Explain how parsimony works.
Explain two statistical tests of clade support.
Perform parsimony analyses using PAUP on LONI
Note to self: Discuss homeworks.
Today, we will use PAUP* (Phylogenetic Analysis Using Parsimony [*and other methods]) to estimate a phylogeny using the maximum parsimony optimality criterion. We will also practice performing bootstrap and jackknife quantification of uncertainty. In the process, we will also see some of the basics of how phylogenetic software will read data and understand models.
A Note on Software Installs on LONI: After we’re done with a piece of software, if you can’t see yourself using it again, feel free to delete it. I will make a note otherwise if I need you to retain it for future use.
Log in to LONI, and remain in your home directory. We will load PAUP via a provided module.
module load paup
Now, move into your work directory. Go into the class repository and pull my changes (I have added several data files).
Exit the class repository. Make a folder for today’s lab called “PaupLab”. Make Data, Results, and Scripts subdirectories inside of it. Enter the Data directory. Copy the data from class repository. The following is a fill in the blank - figure out how you get from your data directory to the classroom data dir.
cp _____/SELUSys2018/data/PAUPlab/primate-mtDNA.nex .
Now, we will start an interactive session, since we’ll be performing some sort of intense computations:
qsub -I -V -A loni_selu_sys -q single -l nodes=1:ppn=1,walltime=3:00:00
Paup is one of the older phylogenetics software packages available, and Dave Swofford has put considerable effort into ensuring that the main mechanisms and models are correct. Many of us who do other software development use PAUP as a check on our own calculations.
PAUP reads in data in the Nexus format. Go ahead and use nano to open the primate dataset.
Next, we will open PAUP:
paup
You should get something like the below:
P A U P *
Portable version 4.0b10 for Unix
Sat Feb 17 16:04:16 2018
-----------------------------NOTICE-----------------------------
This is a beta-test version. Please report any crashes,
apparent calculation errors, or other anomalous results.
There are no restrictions on publication of results obtained
with this version, but you should check the WWW site
frequently for bug announcements and/or updated versions.
See the README file on the distribution media for details.
----------------------------------------------------------------
paup>
Next, we will load in the data file, using the execute command. Execute is kind of PAUP’s catch-all for loading in data.
execute Data/primate-mtDNA.nex
Let’s have a look at the screen output. What information does it tell us?
Some other useful commands are:
cstatus
tstatus
showmatrix
showdist
log file="mylogfile"
Try each command. What is it telling you? If any of them are still unclear, you can add a question mark after the command to get more info.
First, we will compute a parsimony tree using the command all trees:
alltrees;
This will take a moment. Let’s talk about parsimony, and the alltrees command. What is that command doing? What is parsimony doing? [go to board exercise]
Look at the best tree with the command showtree:
showtree;
Are we surprised by the length of the tree, given the number of characters?
Next, we will try heuristic searching:
hsearch
Note that a heuristic is being applied. What is a heuristic?
Did it make a difference to the score of the best tree? Why?
Let’s try some data that are too large for an exhaustive search:
execute Data/BardenClean.nex
This is some of my research data. Try to execute alltrees now. What does this message mean? Now try the heuristic search.
What is the score of the best tree you found __. How many best trees were there? _____ Try one of the alternative swap measures (either NNI or SPR).
hsearch swap= ______
What was the score of your best tree? When would you expect the swap algorithm to matter strongly?
Now, save some trees:
savetrees from=1 to=1 file=Results/tree1.tre;
savetrees from=2 to=2 file=Results/tree1.tre;
Download them. How are these trees different? Why is PAUP saving multiple trees?
To confirm our intuition, let’s score them all under parsimony:
pscore 1-121
This will print the number of parsimony steps on each tree. Does this confirm what you thought before?
We can summarize the trees with a consensus tree:
contree all / treefile=Results/contree.tre;
If you look at the help for the consensus tree function, you see that there are several options for how to compute the consensus tree. The default is strict: the tree which contains only groups found in all trees. Semistrict is similar, containing all compatible groups. This tree may contain clades that are not present in all trees, but are not contradicted by them either. Majority Rule is a common display setting, showing groups that are present in more than 50% of trees. An Adams Consensus Tree will place taxa that vary in their placement in the most inclusive sport within the group.
Try a couple of these tree types. How do they differ in terms of their tree representation?
We will now bootstrap our data. The bootstrap analysis generates a number of matrices (in our case, 100) from the original data matrix. Every taxon is represented in every matrix. Columns, however, are drawn, with replacement up until the original size of the matrix is obtained (i.e., the matrices are resampled with replacement). This process randomizes which characters are in the matrix, and measures repeatability. Typically more than 100 bootstraps will be run, and will generate support values for a node that run from 0-100.
Reload the primate data. Now, begin a bootstrap run:
bootstrap nreps=100 search=heuristic /addseq=random;
Save the tree:
savetrees from=1 to=1 savebootp=nodelabels file=output/bootstrap.tre;
The jackknife is similar to the bootstrap, but is performed without replacement. Therefore, each dataset is smaller.
jackknife nreps=100 search=heuristic /addseq=random;
Why would this be useful?
savetrees from=1 to=1 savebootp=nodelabels file=output/jackknife.tre;
We’ll continue in PAUP, but begin thinking about how to use different types of step matrices.
Key Points