PAUP Quick Start Tutorial

The following hands-on tutorial provides a very brief overview of the basic usage of PAUP* 4.0. The tutorial will take you step-by-step through an analysis of one of the sample data files included on your distribution disk and also available on the world wide web at http://paup.csit.fsu.edu/data/primate-mtDNA-interleaved.nex . This tutorial was designed for people with no prior experience using PAUP*. If you are already familiar with PAUP* then you will probably wish to skip this tutorial. We assume that users are familiar with basic phylogenetic terminology and operating system specific issues. As you become more experienced using PAUP* 4.0, you will discover that there are many alternative ways to execute the operations described below. For obvious reasons, we have chosen not to describe all the possibilities in this tutorial; however, we encourage you to explore other menu and command-line options as your time permits.

The Windows interface is almost entirely command-line driven. Some menu functions are available in the Windows interface; however, these functions mostly include file and edit operations. This tutorial will use both menu options and command-line syntax to demonstrate the different environments under which PAUP* may be run.

Throughout this tutorial we follow several typographical conventions. First, menus, menu items, and items contained in dialog boxes or elsewhere on the screen are given in a bold san serif font. For example, the text File > Open means click "File" from the main menu and then select "Open" from the menu items under "File." Second, text that is intended to be typed by the user at the command-line prompt or into a dialog box is given in a plain fixed-width font. For example, the instructions "Type: weights 2:1stpos" mean that everything after "Type:" should be entered exactly as it appears. Finally, interface specific instructions are offset and bulleted, whereas all other text pertains to all of the PAUP* interfaces.

Starting PAUP*

1 Examine the data file

Double-click the PAUP application icon.

PAUP* will automatically launch the open dialog box when it is first started.

Select the file named primate-mtDNA-interleaved.nex in the Sample NEXUS data folder.
Open the file in PAUP*’s editor by changing the initial mode from Execute to Edit and click Open/Execute

In the editor, scroll through the sample file. Notice that the file is divided into blocks of text, delimited by the words "begin" and "end". The word following "begin" defines the block-type. In this example, the following types of blocks are used: taxa, characters, assumptions, and paup. There are, however, numerous other NEXUS block-types. In fact, one of the advantages of the NEXUS format is that applications will simply skip over blocks that they do not recognize. For a more detailed discussion of the NEXUS format see Maddison, et al. (1997). For this example, you will not need to modify the original sample file.

2 Execute the data file

Close the sample file and do the following:

Select File > Execute "primate-mtDNA-interleaved.nex"

After executing the sample file, PAUP* will display comments and some general information about the data. For this example, the source of the data set is given, followed by a section reporting the dimensions of the data matrix, the type of data, etc. As of yet, no analyses have been conducted; PAUP* has simply processed the data and is now waiting to be told what to do next.

Logging results

Ordinarily, you will want to log the results of a PAUP* session to a disk file to have a record of the results of your analyses.

1 Start logging

Select File > Log Output to Disk...

Under Filename: type practice.log and click Start

2 Stop logging

Logging can be started and stopped anytime during your PAUP* session. To stop logging do the following:

Select File > Log Output to Disk...

Summarizing the data

Now that the data matrix has been processed, you can use PAUP* to obtain basic summary information about the data set. To start, you will display information about the characters included in sample data set.

Type: cstatus;

click Execute.

PAUP* will display a summary of the current character status (i.e., types, weights, etc.). Remember, if logging was turned on, the summary information displayed to your screen will also be saved to the log file. You may also choose to display a summary of the taxa (tstatus), the entire data matrix (showmatrix), and more.

Managing the data

PAUP* provides several ways to restrict analyses to a subset of the taxa and characters included in a data matrix. For example, the sample data set includes protein coding and non-coding regions of primate mitochondrial DNA. Suppose we wish to analyze only the coding regions of the data. The characters belonging to these regions have already been identified in the sample file using the charsets command. Character sets simplify certain procedures by allowing you to refer to a group of characters by a single name. You will start by excluding all characters in the data set except for the coding regions.

1 Exclude characters

Type: include coding/only;

2 Delete taxa

You will also restrict your analyses to all but five species of hominoids and three other primates species used as the outgroup taxa. The five hominoids (Homo sapiens, Pan, Gorilla, Pongo, and Hylobates) have already been identified in the sample file using the taxset command. In the same way that charset allows you to refer to a group of characters by a single name, taxset allows you to refer to a group of taxa by a single name.

Type: undelete hominoids lemur_catta macaca_fuscata saimiri_sciureus/only;

Notice that spaces in taxon names must be replaced with an "_" (underscore character) or enclosed in single quotes when entered at the command-line. Also, PAUP* does not pay attention to the character case in taxa labels. Finally, be aware that when you exclude characters or delete taxa using the exclude and delete commands respectively (or the menu equivalents) you do not actually modify the data file. That is, the next time you execute the sample data set all of the characters and taxa will be included.

Defining assumptions

Before you begin an analysis there is a good chance that you know something about the characters in your data matrix, which might suggest that the characters should be differentially weighted. For example, we know that substitutions at the first codon position generally occur less frequently than substitutions at third positions. The simple explanation for this is that substitutions at first position codons usually result in an amino acid substitution; whereas, third-position changes can occur without changing the amino acid translation. You will incorporate this information into the following analysis by applying a higher weight to substitutions occurring at first position codons. Codon positions have already been identified in the sample file using the charset command.

1 Add character weights

Type: weights 2:1stpos;

2 Set character types

By default, PAUP* considers all transformation costs to be equal. In this section, you will invoke a character type that will assign a higher weight to transversions than to transitions. More specifically, we will assume that transversions, changes from a purine (A or G) to pyrimidine (C or T), are two times the cost of transitions, changes from a purine to a purine and pyrimidine to a pyrimidine. One way to incorporate this assumption into the analysis is to set up a transition/transversion "step matrix”. Such a step matrix has already been defined in the sample file. To apply the transformation cost to all of the characters currently being considered, do the following:

Type: ctype 2_1:all;

3 Save current assumptions

Up to this point you have excluded characters, deleted taxa, weighted characters, and defined character transformation types. If for some reason you had to abandon your analyses and close PAUP*, you would have to select all of the menu options or repeat the commands previously entered to get back to where you are now. One way to avoid this potentially time-consuming task is to save your assumptions to a file that can be recalled at a later time.

Type: saveassum file=tutorial.dat;

4 Recall assumptions

Restart PAUP* and execute the file primate-mtDNA-interleaved.nex as you did in the beginning of the tutorial. Do the following to recall the previous set of assumptions:

Select File : Open... and select tutorial.dat
Change the Initial mode from Edit to Execute and click Execute

You should now be back to where you started. To be sure the assumptions are in effect issue the command cstatus from the command-line. You should get the following output:

Character-status summary:

Current optimality criterion = parsimony

205 characters are excluded

Of the remaining 693 included characters:

All characters are of user-defined type "2 1"

462 characters have weight 1

231 characters have weight 2

296 characters are constant

155 variable characters are parsimony-uninformative

Number of (included) parsimony-informative characters = 242

Searching for Trees

1 Define optimality criterion

PAUP* 4.0 has the advantage of being able to analyze data using several different optimality criteria; parsimony, likelihood, and distance. Several chapters in this manual and a plethora of published literature are devoted to comparing the performance of optimality criteria. Rather than spend time here discussing the relative merits of the available optimality criteria, we will just say that each criterion has its strengths and limitations. To begin with, you will use the default criterion, maximum parsimony, to search for optimal trees. Later in this tutorial you will search under the other criteria.

Type: set criterion=parsimony;

2 Define search strategy

PAUP* provides two basic classes of methods for searching for optimal trees; exact and heuristic. Exact methods guarantee to find the optimal tree(s) but may require prohibitive amounts of computer time for medium to large-sized data sets. Heuristic methods do not guarantee optimality but generally require far less computer time. Even though the current data set is relatively small, you will start by conducting a heuristic search.

Type: hsearch addseq=random;

Once the search is started, PAUP* will display general information about the options and assumptions being used during the search. If you were logging results, this information would be saved to the log file. When the search completes, PAUP* will display general information about the results of the search.

Click Close to dismiss the search status dialog box

Printing trees

1 Display trees

According to the output on your screen, there is a single tree currently in memory. To display the tree do the following:

Type: showtrees;

2 Describe trees

The showtrees command draws a simple picture of the branching order of the taxa.

Say for example, you want to know something about the branch lengths of the tree. To get a more detailed picture of the tree do the following:

Type: describetrees 1/plot=phylogram brlens=yes;

3 Print low-resolution trees

One way to get a quick paper copy of the tree shown on your display is to print the contents of the display buffer. Printing the display buffer, however, will print the tree as well as everything else output to the screen before the tree was displayed. Therefore, we recommend that you first clear the contents of the display buffer and then display the tree again using the showtrees or describetrees command.
Select File > Print Display Buffer…

/------------------------------------ Lemur catta

| /---- Homo sapiens

| |

| /---------14 /------- Pan

| | \13

| /-----15 \--------- Gorilla

18 | |

| /------------16 \-------------- Pongo

| | |

+---------------17 \-------------- Hylobates

| |

| \-------------------- Macaca fuscata

\----------------------------- Saimiri sciureus

4 Print high-resolution trees

If you are using either the Windows or Portable versions of PAUP* and need to print a high-resolution tree you must use a third party tree-printing software package. The program TreeView by Rod Page can print and save high-resolution trees from NEXUS formatted tree files. TreeView will run on a Macintosh or PC and may be downloaded for free at http://taxonomy.zoology.gla.ac.uk/rod/treeview.html .

Saving results

PAUP* can save trees in several different formats: NEXUS, Freqpars, Phylip, and Hennig86. To save the tree in NEXUS format:

Type: savetrees file=mp.tre;

Setting the optimality criterion to distance

PAUP* provides a wide range of pairwise distant measures, from simple absolute differences to more complicated model-based corrected distances. Pairwise distances can be summarized in a table or used to construct UPGMA and neighbor joining trees. In addition, PAUP* can use the minimum evolution and least-squares functions to evaluate trees under the distance criterion. The following section will introduce you to some of these methods.

1 Set the optimality criterion

Type: set criterion=distance;

2 Display distances

First you will need to choose among the distance measures that PAUP* can calculate. For this tutorial, you will chose the Hasegawa, Kishino, and Yano (1985) distance, which estimates a transition/tranversion ratio and base frequencies.

Type: dset distance=hky85;
Type: showdist;

3 Build a neighbor joining tree

Next, you will construct a neighbor joining tree using the HKY85 distances.

Type: nj;

4 Build a least squares tree

Type: dset objective=lsfit power=2;
Type: hsearch;

Setting the optimality criterion to likelihood

To complete this tutorial, you will search for optimal trees using the maximum likelihood criterion. Under maximum likelihood, an explicit model of nucleotide substitution is used to evaluate trees. Selecting an appropriate model of nucleotide substitution is an important step in a likelihood analysis but is beyond the scope of this tutorial. To save time, we have chosen an appropriate model; however, you are encouraged to see Swofford et al. (1996) for a discussion of model selection under the maximum likelihood criterion. You will use the parsimony tree, saved earlier in this tutorial, to obtain an optimal set of model parameters given the data. Later you will use the same model and set of parameter estimates to search for a maximum likehood tree.

1 Set the optimality criterion

Type: set criterion=likelihood;

2 Evaluate the parsimony tree

We have chosen the Hasegawa, Kishino, and Yano (1985) model of sequence evolution with gamma distributed rates. Given the parsimony topology and the data we will use PAUP* to estimate the optimal transition/tranversion rate ratio, base frequencies, and among-site rate heterogeniety.

Type: gettrees file=mp.tre;
Type: lscores 1 / wts=ignore nst=2 tratio=estimate rates=gamma shape=estimate;

Depending on the computer you are using it may take a few seconds to several minutes for PAUP* to optimize branch lengths and substitution model parameters on the tree currently in memory. When PAUP* finishes it will output the negative log likelihood of the tree topology found by the parsimony search and give the estimated model parameters values.

Tree 1

------------------------

-ln L 5855.26749

Ti/tv:

exp. ratio 7.221505

kappa 15.364896

Shape 0.279401

3 Set likelihood model parameters

Before starting the heuristic search, you will fix the model parameters to those estimated in the previous step. If the options are left to estimate, PAUP* will estimate the parameters on each topology rearrangement made during the heuristic search. Because PAUP* may make thousands of topology rearrangements during a heuristic search, leaving options set to estimate will dramatically increase the time required to complete the search. In general, a more efficient method of estimating model parameters and tree topologies under maximum likelihood is by successively estimating model parameters on novel trees generated by the tree search (Swofford et al. 1996). More specifically, if the topology found under the likelihood criterion differs from that on which the parameters were estimated, then you reestimate parameters on the new topology and search again using the new set of parameters. For this tutorial, you will complete one iteration of estimating parameters on a topology and applying the parameters to a subsequent search. In principle, you would continue until you converged on the same topology.

Type: lset tratio=previous shape=previous;

4 Start the tree search

Type: hsearch addseq=asis;

A small window will pop up, showing you the status of the heuristic search. Again, the time required to complete the search will depend on the computer you are using.

Click Close to dismiss the search status dialog box.

To get a picture of the resulting tree including the branch lengths:

Type: describetrees 1/plot=phylogram brlens=yes;

Submitting commands in batch file

Analyses can also be conducted using a non-interactive batch method. This is especially useful when you know your analyses will require a great deal of time to complete. In the example below, all the instructions required to complete the sample analyses described above are contained in a "paup" block. A set command was added at the beginning of the paup block to suppress the dialog box indicating that the heuristic search has completed and several other warnings. To run the block in batch mode, copy the text given below to a file and save the file in the same directory as the primate-mtDNA-interleaved.nex file. Now execute the file as you did the primate-mtDNA-interleaved.nex file.

Begin paup;

set autoclose=yes warntree=no warnreset=no;

log start file=practice.log replace;

execute primate-mtDNA-interleaved.nex;

cstatus;

include coding/only;

undelete hominoids lemur_catta macaca_fuscata saimiri_sciureus/only;

weight 2:1stpos;

ctype 2_1:all;

set criterion=parsimony;

hsearch addseq=random;

showtrees;

describetrees 1/plot=phylogram brlens=yes;

savetrees file=mp.tre replace;

set criterion=distance;

dset distance=hky85;

showdist;

nj;

dset objective=lsfit power=2;

hsearch;

set criterion=likelihood;

gettrees file=mp.tre;

lscores 1/wts=ignore nst=2 tratio=estimate rates=gamma shape=estimate;

lset tratio=previous shape=previous;

hsearch addseq=asis;

end;

Moving on

This concludes the brief overview of the basic usage of PAUP* 4.0. A list and brief descriptions of all the commands and options available in PAUP* is given in the Command Reference documentation. From here we encourage you to explore your own data sets or some of the other sample data sets included with PAUP*. You can also find information on how to use PAUP* on-line at our web site: http://paup.csit.fsu.edu/

References

Swofford, D. L., G. J. Olsen, P. J. Waddell, and D. M. Hillis. 1996. Phylogenetic inference. Pages 407-514 in D. M. Hillis, C. Moritz, and B. K. Mable (ed.), Molecular Systematics, 2nd edition. (Sinauer Associates: Sunderland, Massachusetts).