# PHYLIP

## Phylogeny Inference Package

### Version 3.6

### July, 2004

## by Joseph Felsenstein

Department of Genome Sciences and Department of Biology University of Washington Box 357730 Seattle, WA 98195-7730 USA

## E-mail address: `joe @ gs.washington.edu`

## Contents of This Document

Contents of This Document
A Brief Description of the Programs
Copyright Notice for PHYLIP
The Documentation Files and How to Read Them
What The Programs Do
Running the Programs
A word about input files
Running the programs on a Windows machine
Running the programs on a Macintosh with Mac OS 8 or 9
Running the programs on a Macintosh with Mac OS X
Running the programs on a Unix or Linux system
Running the programs in MSDOS
Running the programs in background or under control of a command file
Preparing Input Files
Input and output files
Data file format
The Menu
The Output File
The Tree File
The Options and How To Invoke Them
Common options in the menu
The `U` (User tree) option
The `G` (Global) option
The `J` (Jumble) option
The `O` (Outgroup) option
The `T` (Threshold) option
The `M` (Multiple data sets) option
The `W` (Weights) option
The option to write out the trees into a tree file
The (`0`) terminal type option
The Algorithm for Constructing Trees
Local rearrangements
Global rearrangements
Multiple jumbles
Saving multiple tied trees
Strategy for finding the best tree
A Warning on Interpreting Results
Relative Speed of Different Programs and Machines
Relative speed of the different programs
Speed with different numbers of species
Relative speed of different machines
General Comments on Adapting the Package to Different Computer Systems
Compiling the programs
Unix and Linux
Macintosh
Compiling with Metrowerks Codewarrior
Compiling with GCC on Mac OS X with X Windows
Creating the Metrowerks project file.
On Windows systems
Compiling with Microsoft Visual C++
Compiling with Borland C++
Compiling with Metrowerks Codewarrior for Windows
Compiling with Cygnus Gnu C++
VMS VAX systems
Parallel computers
Other computer systems
Frequently Asked Questions
How to make it do various things
Background information needed:
Questions about distribution and citation:
Questions about documentation
Additional Frequently Asked Questions, or: "Why didn't it occur to you to ...
(Fortunately) obsolete questions
New Features in This Version
Coming Attractions, Future Plans
Endorsements
From the pages of Cladistics
... in the pages of other journals:
... and in the comments made by users when they register:
References for the Documentation Files
Credits
Other Phylogeny Programs Available Elsewhere
PAUP*
MacClade
MrBayes
MEGA
PAML
TREE-PUZZLE
DAMBE
Hennig86
RnA
NONA
TNT
How You Can Help Me
In Case of Trouble

## A Brief Description of the Programs

`PHYLIP`, the Phylogeny Inference Package, is a package of programs for inferring phylogenies (evolutionary trees). It has been distributed since 1980, and has over 15,000 registered users, making it the most widely distributed package of phylogeny programs. It is available free, from its web site:

`http://evolution.gs.washington.edu/phylip.html`

`PHYLIP` is available as source code in C, and also as executables for some common computer systems. It can infer phylogenies by parsimony, compatibility, distance matrix methods, and likelihood. It can also compute consensus trees, compute distances between trees, draw trees, resample data sets by bootstrapping or jackknifing, edit trees, and compute distance matrices. It can handle data that are nucleotide sequences, protein sequences, gene frequencies, restriction sites, restriction fragments, distances, discrete characters, and continuous characters.

## Copyright Notice for PHYLIP The following copyright notice is intended to cover all source code, all documentation, and all executable programs of the PHYLIP package. © Copyright 1980-2004. University of Washington and Joseph Felsenstein. All rights reserved. Permission is granted to reproduce, perform, and modify these programs and documentation files. Permission is granted to distribute or provide access to these programs provided that this copyright notice is not removed, the programs are not integrated with or called by any product or service that generates revenue, and that your distribution of these documentation files and programs are free. Any modified versions of these materials that are distributed or accessible shall indicate that they are based on these program. Institutions of higher education are granted permission to distribute this material to their students and staff for a fee to recover distribution costs. Permission requests for any other distribution of this program should be directed to `license @ u.washington.edu` .

## The Documentation Files and How to Read Them

`PHYLIP` comes with an extensive set of documentation files. These include the main documentation file (this one), which you should read fairly completely. In addition there are files for groups of programs, including ones for the molecular sequence programs, the distance matrix programs, the gene frequency and continuous characters programs, the discrete characters programs, and the tree drawing programs. Finally, each program has its own documentation file. References for the documentation files are all gathered together in this main documentation file. A good strategy is to:

1. Read this main documentation file.
2. Tentatively decide which programs are of interest to you.
3. Read the documentation files for the groups of programs that contain those.
4. Read the documentation files for those individual programs.

There is an excellent guide to using PHYLIP 3.6 also available. It was written by Jarno Tuimala of the Center for Scientific Computing in Espoo, Finland and is available as a PDF here.

## What The Programs Do

Here is a short description of each of the programs. For more detailed discussion you should definitely read the documentation file for the individual program and the documentation file for the group of programs it is in. In this list the name of each program is a link which will take you to the documentation file for that program. Note that there is no program in the PHYLIP package called PHYLIP.

PROTPARS
  Estimates phylogenies from protein sequences (input using the standard one-letter code for amino acids) using the parsimony method, in a variant which counts only those nucleotide changes that change the amino acid, on the assumption that silent changes are more easily accomplished.
DNAPARS
  Estimates phylogenies by the parsimony method using nucleic acid sequences. Allows use the full IUB ambiguity codes, and estimates ancestral nucleotide states. Gaps treated as a fifth nucleotide state. It can also fo transversion parsimony. Can cope with multifurcations, reconstruct ancestral states, use 0/1 character weights, and infer branch lengths.
DNAMOVE
  Interactive construction of phylogenies from nucleic acid sequences, with their evaluation by parsimony and compatibility and the display of reconstructed ancestral bases. This can be used to find parsimony or compatibility estimates by hand.
DNAPENNY
  Finds all most parsimonious phylogenies for nucleic acid sequences by branch-and-bound search. This may not be practical (depending on the data) for more than 10-11 species or so.
DNACOMP
  Estimates phylogenies from nucleic acid sequence data using the compatibility criterion, which searches for the largest number of sites which could have all states (nucleotides) uniquely evolved on the same tree. Compatibility is particularly appropriate when sites vary greatly in their rates of evolution, but we do not know in advance which are the less reliable ones.
DNAINVAR
  For nucleic acid sequence data on four species, computes Lake's and Cavender's phylogenetic invariants, which test alternative tree topologies. The program also tabulates the frequencies of occurrence of the different nucleotide patterns. Lake's invariants are the method which he calls "evolutionary parsimony".
DNAML
  Estimates phylogenies from nucleotide sequences by maximum likelihood. The model employed allows for unequal expected frequencies of the four nucleotides, for unequal rates of transitions and transversions, and for different (prespecified) rates of change in different categories of sites, and also use of a Hidden Markov model of rates, with the program inferring which sites have which rates. This also allows gamma-distribution and gamma-plus-invariant sites distributions of rates across sites.
DNAMLK
  Same as DNAML but assumes a molecular clock. The use of the two programs together permits a likelihood ratio test of the molecular clock hypothesis to be made.
PROML
  Estimates phylogenies from protein amino acid sequences by maximum likelihood. The PAM, JTT, or PMB models can be employed, and also use of a Hidden Markov model of rates, with the program inferring which sites have which rates. This also allows gamma-distribution and gamma-plus-invariant sites distributions of rates across sites. It also allows different rates of change at known sites.
PROMLK
  Same as PROML but assumes a molecular clock. The use of the two programs together permits a likelihood ratio test of the molecular clock hypothesis to be made.
DNADIST
  Computes four different distances between species from nucleic acid sequences. The distances can then be used in the distance matrix programs. The distances are the Jukes-Cantor formula, one based on Kimura's 2- parameter method, the F84 model used in DNAML, and the LogDet distance. The distances can also be corrected for gamma-distributed and gamma-plus-invariant-sites-distributed rates of change in different sites. Rates of evolution can vary among sites in a prespecified way, and also according to a Hidden Markov model. The program can also make a table of percentage similarity among sequences.
PROTDIST
  Computes a distance measure for protein sequences, using maximum likelihood estimates based on the Dayhoff PAM matrix, the JTT matrix model, the PBM model, Kimura's 1983 approximation to these, or a model based on the genetic code plus a constraint on changing to a different category of amino acid. The distances can also be corrected for gamma-distributed and gamma-plus-invariant-sites-distributed rates of change in different sites. Rates of evolution can vary among sites in a prespecified way, and also according to a Hidden Markov model. The program can also make a table of percentage similarity among sequences. The distances can be used in the distance matrix programs.
RESTDIST
  Distances calculated from restriction sites data or restriction fragments data. The restriction sites option is the one to use to also make distances for RAPDs or AFLPs.
RESTML
  Estimation of phylogenies by maximum likelihood using restriction sites data (not restriction fragments but presence/absence of individual sites). It employs the Jukes-Cantor symmetrical model of nucleotide change, which does not allow for differences of rate between transitions and transversions. This program is very slow.
SEQBOOT
  Reads in a data set, and produces multiple data sets from it by bootstrap resampling. Since most programs in the current version of the package allow processing of multiple data sets, this can be used together with the consensus tree program CONSENSE to do bootstrap (or delete-half-jackknife) analyses with most of the methods in this package. This program also allows the Archie/Faith technique of permutation of species within characters. It can also rewrite a data set to convert it from between the PHYLIP Interleaved and Sequential forms, and into a preliminary version of a new XML sequence alignment format which is under development and which is described in the SEQBOOT documentation web page.
FITCH
  Estimates phylogenies from distance matrix data under the "additive tree model" according to which the distances are expected to equal the sums of branch lengths between the species. Uses the Fitch-Margoliash criterion and some related least squares criteria, or the Minimum Evolution distance matrix method. Does not assume an evolutionary clock. This program will be useful with distances computed from molecular sequences, restriction sites or fragments distances, with DNA hybridization measurements, and with genetic distances computed from gene frequencies.
KITSCH
  Estimates phylogenies from distance matrix data under the "ultrametric" model which is the same as the additive tree model except that an evolutionary clock is assumed. The Fitch-Margoliash criterion and other least squares criteria, or the Minimum Evolution criterion are possible. This program will be useful with distances computed from molecular sequences, restriction sites or fragments distances, with distances from DNA hybridization measurements, and with genetic distances computed from gene frequencies.
NEIGHBOR
  An implementation by Mary Kuhner and John Yamato of Saitou and Nei's "Neighbor Joining Method," and of the UPGMA (Average Linkage clustering) method. Neighbor Joining is a distance matrix method producing an unrooted tree without the assumption of a clock. UPGMA does assume a clock. The branch lengths are not optimized by the least squares criterion but the methods are very fast and thus can handle much larger data sets.
CONTML
  Estimates phylogenies from gene frequency data by maximum likelihood under a model in which all divergence is due to genetic drift in the absence of new mutations. Does not assume a molecular clock. An alternative method of analyzing this data is to compute Nei's genetic distance and use one of the distance matrix programs. This program can also do maximum likelihood analysis of continuous characters that evolve by a Brownian Motion model, but it assumes that the characters evolve at equal rates and in an uncorrelated fashion, so that it does not take into account the usual correlations of characters.
GENDIST
  Computes one of three different genetic distance formulas from gene frequency data. The formulas are Nei's genetic distance, the Cavalli-Sforza chord measure, and the genetic distance of Reynolds et. al. The former is appropriate for data in which new mutations occur in an infinite isoalleles neutral mutation model, the latter two for a model without mutation and with pure genetic drift. The distances are written to a file in a format appropriate for input to the distance matrix programs.
CONTRAST
  Reads a tree from a tree file, and a data set with continuous characters data, and produces the independent contrasts for those characters, for use in any multivariate statistics package. Will also produce covariances, regressions and correlations between characters for those contrasts. Can also correct for within-species sampling variation when individual phenotypes are available within a population.
PARS
  Multistate discrete-characters parsimony method. Up to 8 states (as well as "`?`") are allowed. Cannot do Camin-Sokal or Dollo Parsimony. Can cope with multifurcations, reconstruct ancestral states, use character weights, and infer branch lengths.
MIX
  Estimates phylogenies by some parsimony methods for discrete character data with two states (0 and 1). Allows use of the Wagner parsimony method, the Camin-Sokal parsimony method, or arbitrary mixtures of these. Also reconstructs ancestral states and allows weighting of characters (does not infer branch lengths).
MOVE
  Interactive construction of phylogenies from discrete character data with two states (0 and 1). Evaluates parsimony and compatibility criteria for those phylogenies and displays reconstructed states throughout the tree. This can be used to find parsimony or compatibility estimates by hand.
PENNY
  Finds all most parsimonious phylogenies for discrete-character data with two states, for the Wagner, Camin-Sokal, and mixed parsimony criteria using the branch-and-bound method of exact search. May be impractical (depending on the data) for more than 10-11 species.
DOLLOP
  Estimates phylogenies by the Dollo or polymorphism parsimony criteria for discrete character data with two states (0 and 1). Also reconstructs ancestral states and allows weighting of characters. Dollo parsimony is particularly appropriate for restriction sites data; with ancestor states specified as unknown it may be appropriate for restriction fragments data.
DOLMOVE
  Interactive construction of phylogenies from discrete character data with two states (0 and 1) using the Dollo or polymorphism parsimony criteria. Evaluates parsimony and compatibility criteria for those phylogenies and displays reconstructed states throughout the tree. This can be used to find parsimony or compatibility estimates by hand.
DOLPENNY
  Finds all most parsimonious phylogenies for discrete-character data with two states, for the Dollo or polymorphism parsimony criteria using the branch-and-bound method of exact search. May be impractical (depending on the data) for more than 10-11 species.
CLIQUE
  Finds the largest clique of mutually compatible characters, and the phylogeny which they recommend, for discrete character data with two states. The largest clique (or all cliques within a given size range of the largest one) are found by a very fast branch and bound search method. The method does not allow for missing data. For such cases the `T` (Threshold) option of PARS or MIX may be a useful alternative. Compatibility methods are particular useful when some characters are of poor quality and the rest of good quality, but when it is not known in advance which ones are which.
FACTOR
  Takes discrete multistate data with character state trees and produces the corresponding data set with two states (0 and 1). Written by Christopher Meacham. This program was formerly used to accomodate multistate characters in MIX, but this is less necessary now that PARS is available.
DRAWGRAM
  Plots rooted phylogenies, cladograms, circular trees and phenograms in a wide variety of user-controllable formats. The program is interactive and allows previewing of the tree on PC, Macintosh, or X Windows screens, or on Tektronix or Digital graphics terminals. Final output can be to a file formatted for one of the drawing programs, for a ray-tracing or VRML browser, or one at can be sent to a laser printer (such as Postscript or PCL-compatible printers), on graphics screens or terminals, on pen plotters or on dot matrix printers capable of graphics.
DRAWTREE
  Similar to DRAWGRAM but plots unrooted phylogenies.
TREEDIST
  Computes the Branch Score distance between trees, which allows for differences in tree topology and which also makes use of branch lengths. Also computes the Robinson-Foulds symmetric difference distance between trees, which allows for differences in tree topology but does not use branch lengths.
CONSENSE
  Computes consensus trees by the majority-rule consensus tree method, which also allows one to easily find the strict consensus tree. Is not able to compute the Adams consensus tree. Trees are input in a tree file in standard nested-parenthesis notation, which is produced by many of the tree estimation programs in the package. This program can be used as the final step in doing bootstrap analyses for many of the methods in the package.
RETREE
  Reads in a tree (with branch lengths if necessary) and allows you to reroot the tree, to flip branches, to change species names and branch lengths, and then write the result out. Can be used to convert between rooted and unrooted trees, and to write the tree into a preliminary version of a new XML tree file format which is under development and which is described in the RETREE documentation web page.

## Running the Programs

This section assumes that you have obtained PHYLIP as compiled executables (for Windows, Mac OS 9, Mac OS X or Linux), or have obtained the source code and compiled it yourself (for Linux, Unix, or OpenVMS). For machines for which compiled executables are available, there will usually be no need for you to have a compiler or compile the programs yourself. This section describes how to run the programs. Later in this document we will discuss how to download and install PHYLIP (in case you are reading this without yet having done that). Normally you will only read your copy of this document after downloading and installing PHYLIP.

### A word about input files.

For all of these types of machines, it is important to have the input files for the programs (typically data files) prepared in advance. They can be prepared in any editor, but it is important that they be saved in Text Only ("flat ASCII") format, not in the format that word processors such as Microsoft Word want to write. It is up to you to read the PHYLIP documentation files which describe the files formats that are needed. There is a partial description in the next section of this document. The input files can also be obtained by running a program that produces output files in PHYLIP format (some of these programs do, and so do programs by others such as sequence alignment programs such as ClustalW and sequence format conversion programs such as Readseq). There is not any input file editor available in any program in PHYLIP (you should not simply start running one of the programs and then expect to click a mouse somewhere to start creating a data file).

When they start running, the programs look first for input files with particular names (such as `infile`, `treefile`, `intree`, or `fontfile`). Exactly which file names they look for varies a bit from program to program, and you should read the documentation file for the particular program to find out. If you have files with those names the programs will use them and not ask you for the file name. If they do not find files of those names, the programs will say that they cannot find a file of that name, and ask you to type in the file name. For example, if DnaML looks for the file `infile` and does not find one of that name, it prints the message:

`dnaml: can't find input file "infile" Please enter a new file name>`

This does not mean that an error has occurred. All you need to do is to type in the name of the file.

The program looks for the input files in the same folder that the program is in (a folder is the same thing as a "directory"). In Windows, Mac OS X, Linux, or Unix, if you are asked for the file name you can type in the path to the file, as part of the name (thus, if the file is in the folder containing the current folder, you can type in a file name such as `../myfile.dna`). If you do not know what a "folder" is, or what "above" means, then you are a member of the new generation who just clicks the mouse and assumes that a list of file names will magically appear. (Typically members of this generation have no idea where the files are on their system, and accumulate enormous amounts of unnecessary clutter in their file systems.) In this case you should ask someone to explain folders to you.

### Running the programs on a Windows machine.

Double-click on the icon for the program. A window should open with a menu in it. Further dialog with the program occurs by typing on the keyboard in response to what you see in the window. The programs can be interrupted either by typing Control-C (which means to press down on the `Ctrl` key while typing the letter `C`), or by using the mouse to open the `File` menu in the upper-left corner of the program's window area and then select `Quit`. Other than this, most PHYLIP programs make no use of the mouse. The tree-drawing programs Drawtree and Drawgram do allow use of the mouse to select some options.

The programs open a window for their menus. This window may be too small for your tastes. They can be resized by tugging on the lower-right corner of the window. In addition, the font may be too small. On most versions of Windows, you can click on the small icon symbol at the upper-left corner of the window, and choose the `Properties` menu choice there. One of its options is to change the font and size of the print. I prefer large font sizes such as 16x12. Some versions of Windows wuch as Windows XP also ask you if you want to apply this choice to all such windows, not just to the current one.

The programs can also be run in a Command window under Windows, in much the same way as they were under the MSDOS operating system, which is what the Command window emulates. Type the name of the program in lower-case letters (such as `dnaml`). To interrupt the program while it is running, type Control-C (which means to press down on the `Ctrl` key while typing the letter `C`).

### Running the programs on a Macintosh with Mac OS 8 or 9

Double-click on the icon for the program. A window should open. Further dialog with the program occurs by typing on the keyboard in response to what you see in the window. The programs can be interrupted by using the mouse to open the `File` menu in the upper-left corner of the program's window area and then select `Quit`. Alternatively, you can use the Command-Q key combination.

When you use Quit, the program will ask you whether you want to save a file whose name is the program name (often followed by `.out` -- for example, if you are using DNAML it will ask you if you want to save file `Dnaml.out`. This file is simply a record of everything that displayed on the program window, and you usually will not want to save it. Pressing the `Enter` key or selecting the Do Not Save button with the mouse will keep this from being saved.

If you encounter memory limitations on a Mac OS 8 or 9 Macintosh, and determine that this is not due to a problem with the format of the input file, as it often will be, you may be able to solve it by raising the limits of the stack and heap sizes of the program. To do this click on the program and then select `Get Info` from the Finder `File` menu. This will open a window which can be made to show the memory limits of the program. These can be changed by selecting them and typing in larger numbers. This may relieve nagging memory problems. If it does not, consult your local documentation and suspect problems with your input file format.

### Running the programs on a Macintosh with Mac OS X

We have provided a Mac OS X version of the executables. The programs can be run by clicking on their icons. The executables open a window in which the menus appear and in which responses can be typed. As with the Mac OS 8 and Mac OS 9 executables, the programs can be interrupted by using the mouse to open the `File` menu in the upper-left corner of the program's window area and then select `Quit`. Alternatively, you can use the Command-Q key combination.

Also, as with the Mac OS 8 and Mac OS 9 executables, When you use Quit, the program will ask you whether you want to save a file whose name is the program name (often followed by `.out` -- for example, if you are using DNAML it will ask you if you want to save file `Dnaml.out`. This file is simply a record of everything that displayed on the program window, and you usually will not want to save it. Pressing the `Enter` key or selecting the Do Not Save button with the mouse will keep this from being saved.

On Mac OS X systems the Mac OS 8 or 9 executables can also be run using the Classic environment included with it which allows executables for earlier versions of Mac OS to run.

If your Mac OS X system has the X windows windowing system installed, you could also make excutables that will work with that. See below, under "Compiling with GCC on Mac OS X with X Windows" for instructions on how to do this.

One problem we have often encountered using Mac OS X is that it is easy for data files to have the wrong kind of characters at the ends of their lines. They may have carriage-return (ASCII/ISO 13 or control-M) characters at the ends of their lines when they should instead have the Unix newline character (ASCII/ISO 10 or control-J) there. This can happen with files transferred from other operating systems or files produced in some word processors. It results in segmentation-fault or memory errors. If you encounter these, check this possibility carefully.

### Running the programs on a Unix or Linux system.

Type the name of the program in lower-case letters (such as `dnaml`). To interrupt the program while it is running, type Control-C (which means to press down on the `Ctrl` key while typing the letter `C`).

On some systems you may need to type `./` before the program name, so that in the above case it would be `./dnaml`. This is mostly needed if the users PATH does not include their current directory, something which is often done as a security precaution.

### Running the programs in background or under control of a command file

In running the programs, you may sometimes want to put them in background so you can proceed with other work. On systems with a windowing environment they can be put in their own window, and commands like the Unix and Linux `nice` command used to make them have lower priority so that they do not interfere with interactive applications in other windows. This part of the discussion will assume either a Windows system or a Unix or Linux system. I will note when the commands work on one of these systems but not the other. Running jobs in background on Mac OS 8 or 9 systems is an arcane art into whose mysteries I have not been initiated (or perhaps no one has been initiated). However, Mac OS X is actually Unix (surprise! surprise!) and you can run PHYLIP programs in background on any Mac OS X system by simply following the instructions for Unix, using a terminal window to do so if necessary.

If there is no windowing environment, on a Unix or Linux system you will want to use an ampersand (`&`) after the command file name when invoking it to put the job in the background. You will have to put all the responses to the interactive menu of the program into a file and tell the background job to take its input from that file.

On Windows systems there is no `&` or `nice` command but input and output redirection and command files work fine in a Commmand window. A command file can either be invoked by clicking on its icon or by typing its name from a Command window. The a file of commands must have a name ending in `.bat`, such as `foofile.bat`. You can run the batch file from a Command window by typing its name (such as `foofile`) without the `.bat`.

For example: suppose you want to run DNAPARS in a background, taking its input data from a file called `sequences.dat`, putting its interactive output to file called `screenout`, and using a file called `input` as the place to store the interactive input. The file `input` need only contain two lines:

    sequences.dat
    Y

which is what you would have typed to run the program interactively, in response to the program's request for an input file name if it did not find a file named `infile`, in in response the the menu.

To run the program in background, in Unix or Linux you would simply give the command:

`dnapars < input > screenout &`

These run the program with input responses coming from `input` and interactive output being put into file `screenout`. The usual output file and tree file will also be created by this run (keep that in mind as if you run any other PHYLIP program from the same directory while this one is running in background you may overwrite the output file from one program with that from the other!).

If you wanted to give the program lower priority, so that it would not interfere with other work, and you have Berkeley Unix type job control facilities in your Unix or Linux (and you usually do), you can use the `nice` command:

`nice +10 dnapars < input > screenout &`

which lowers the priority of the run. To also time the run and put the timing at the end of `screenout`, you can do this:

`nice +10 ( time dnapars < input ) >& screenout &`

which I will not attempt to explain.

On Unix or Linux systems you may also want to explore putting the interactive output into the null file `/dev/null` so as to not be bothered with it (but then you cannot look at it to see why something went wrong). If you have problems with creating output files that are too large, you may want to explore carefully the turning off of options in the programs you run.

If you are doing several runs in one, as for example when you do a bootstrap analysis using SEQBOOT, DNAPARS (say), and CONSENSE, you can use an editor to create a "command file" with these commands:

    seqboot < input1 > screenout
    mv outfile infile
    dnapars < input2 >> screenout
    mv outtree intree
    consense < input3 >> screenout

This is the Unix or Linux version -- in the Windows version, the renaming of files and the appending of output to the file `screenout` is handled differently.

On Unix or Linux the command file might be named something like `foofile`, and on Windows systems might be named `foofile.bat`.

On Unix or Linux the command file must be given execute permission by using the command `chmod +x foofile` followed by the command `rehash`. The job that `foofile` describes can be run in background on Unix or Linux by giving the command

`foofile &`

On Windows systems it can be run by clicking on the icon of the command file. Its icon will have a little gear symbol.

Note that you must also have the interactive input commands for SEQBOOT (including the random number seed), DNAPARS, and CONSENSE in the separate files `input1`, `input2`, and `input3`. Note also that when PHYLIP programs attempt to open a new output file (such as `outfile`, `outtree`, or `plotfile`, if they see a file of that name already in existence they will ask you if you want to overwrite it, and offer alternatives including writing to another file, appending information to that file, or quitting the program without writing to the file. This means that in writing batch files it is important to know whether there will be a prompt of this sort. You must know in advance whether the file will exist. You may want to put in your batch file a command that tests for the existence of a pre-existing output file and if so, removes it. You might even want to put in a command that creates a file of that name, so that you can be sure it is there! Either way, you will then know whether to put into your file of keyboard responses the proper response to the inquiry about overwriting that output file.

## Preparing Input Files

The input files for PHYLIP programs must be prepared separately - there is no data editor within PHYLIP. You can use a word processor (or text editor) to prepare them yourself, or you can use a program that produces a PHYLIP-format output. Sequence alignment programs such as ClustalW commonly have an option to produce PHYLIP files as output, and some other phylogeny programs, such as MacClade and TreeView, are capable of producing a PHYLIP-format file.

The format of the input files is discussed below, and you should also read the other PHYLIP documentation relevant to the particular type of data that you are using, and the particular programs you want to run, as there will be more details there.

It is very important that the input files be in "Text Only" or "flat ASCII" format. This means that they contain only printable ASCII/ISO characters, and not any unprintable characters. Many word processors such as Microsoft Word save their files in a format that contains unprintable characters, unless you tell them not to. For Microsoft Word you can select `Save As` from its `File` menu, and choose `Text Only` as the file format. This can also be done in WordPad utility in Windows . Other word processors will have equivalent options. Text editors such as the `vi` and `emacs` editors on Unix and Linux, Windows Notepad, the `SimpleText` editor in Mac OS, or the `pico` editor that comes with the `pine` mailer program, produce their files in Text Only format and should not cause any trouble.

### Input and output files

For most of the PHYLIP programs, information comes from a series of input files, and ends up in a series of output files:

                       -------------------
                      |                   |
    infile ---------> |                   |
                      |                   |
    intree ---------> |                   | -----------> outfile
                      |                   |
    weights --------> |      program      | -----------> outtree
                      |                   |
    categories -----> |                   | -----------> plotfile
                      |                   |
    fontfile -------> |                   |
                      |                   |
                       -------------------

The programs interact with the user by presenting a menu. Aside from the user's choices from the menu, they read all other input from files. These files have default names. The program will try to find a file of that name - if it does not, it will ask the user to supply the name of that file. Input data such as DNA sequences comes from a file whose default name is `infile`. If the user supplies a tree, this is in a file whose default name is `intree`. Values of weights for the characters are in `weights`, and the tree plotting program need some digitized fonts which are supplied in `fontfile` (all these are default names).

For example, if DnaML looks for the file `infile` and does not find one of that name, it prints the message:

`dnaml: can't find input file "infile" Please enter a new file name>`

This simply means that it wants you to type in the name of the input file.

### Data file format

I have tried to adhere to a rather stereotyped input and output format. For the parsimony, compatibility and maximum likelihood programs, excluding the distance matrix methods, the simplest version of the input data file looks something like this:

       6   13
    Archaeopt CGATGCTTAC CGC
    HesperorniCGTTACTCGT TGT
    BaluchitheTAATGTTAAT TGT
    B. virginiTAATGTTCGT TGT
    BrontosaurCAAAACCCAT CAT
    B.subtilisGGCAGCCAAT CAC

The first line of the input file contains the number of species and the number of characters (in this case sites). These are in free format, separated by blanks. The information for each species follows, starting with a ten-character species name (which can include blanks and some punctuation marks), and continuing with the characters for that species. The name should be on the same line as the first character of the data for that species. (I will use the term "species" for the tips of the trees, recognizing that in some cases these will actually be populations or individual gene sequences).

The name should be ten characters in length, filled out to the full ten characters by blanks if shorter. Any printable ASCII/ISO character is allowed in the name, except for parentheses ("`(`" and "`)`"), square brackets ("`[`" and "`]`"), colon ("`:`"), semicolon ("`;`") and comma ("`,`"). If you forget to extend the names to ten characters in length by blanks, the program will get out of synchronization with the contents of the data file, and an error message will result.

Note that Tab characters count as only one character in the species names. Their inclusion can cause trouble. The name will appear to you to be filled out to a full ten characters, but it may be shorter than that to the program. If you make the data file in a word processor such as Word, you would be well-advised to make sure the names contain no Tab characters. You can check for their presence by advancing through the names with your cursor keys, and looking for sudden jumps of two or more characters. It is best to fill the names out with blanks, not Tabs.

In the discrete-character programs, DNA sequence programs and protein sequence programs the characters are each a single letter or digit, sometimes separated by blanks. In the continuous-characters programs they are real numbers with decimal points, separated by blanks:

`Latimeria 2.03 3.457 100.2 0.0 -3.7`

The conventions about continuing the data beyond one line per species are different between the molecular sequence programs and the others. The molecular sequence programs can take the data in "aligned" or "interleaved" format, in which we first have some lines giving the first part of each of the sequences, then some lines giving the next part of each, and so on. Thus the sequences might look like this:

        6   39
    Archaeopt CGATGCTTAC CGCCGATGCT
    HesperorniCGTTACTCGT TGTCGTTACT
    BaluchitheTAATGTTAAT TGTTAATGTT
    B. virginiTAATGTTCGT TGTTAATGTT
    BrontosaurCAAAACCCAT CATCAAAACC
    B.subtilisGGCAGCCAAT CACGGCAGCC

    TACCGCCGAT GCTTACCGC
    CGTTGTCGTT ACTCGTTGT
    AATTGTTAAT GTTAATTGT
    CGTTGTTAAT GTTCGTTGT
    CATCATCAAA ACCCATCAT
    AATCACGGCA GCCAATCAC

Note that in these sequences we have a blank every ten sites to make them easier to read: any such blanks are allowed. The blank line which separates the two groups of lines (the ones containing sites 1-20 and ones containing sites 21-39) may or may not be present, but if it is, it should be a line of zero length and not contain any extra blank characters (this is because of a limitation of the current versions of the programs). It is important that the number of sites in each group be the same for all species (i.e., it will not be possible to run the programs successfully if the first species line contains 20 bases, but the first line for the second species contains 21 bases).

Alternatively, an option can be selected in the menu to take the data in "sequential" format, with all of the data for the first species, then all of the characters for the next species, and so on. This is also the way that the discrete characters programs and the gene frequencies and quantitative characters programs want to read the data. They do not allow the interleaved format.

In the sequential format, the character data can run on to a new line at any time (except in the middle of a species name or, in the case of continuous character and distance matrix programs where you cannot go to a new line in the middle of a real number). Thus it is legal to have:

`Archaeopt 001100
1101
`

or even:

`Archaeopt
0011001101
`

though note that the full ten characters of the species name must then be present: in the above case there must be a blank after the "t". In all cases it is possible to put internal blanks between any of the character values, so that

`Archaeopt 0011001101 0111011100`

is allowed.

Note that you can convert molecular sequence data between the interleaved and the sequential data formats by using the Rewrite option of the J menu item in SEQBOOT.

If you make an error in the format of the input file, the programs can sometimes detect that they have been fed an illegal character or illegal numerical value and issue an error message such as `BAD CHARACTER STATE:`, often printing out the bad value, and sometimes the number of the species and character in which it occurred. The program will then stop shortly after. One of the things which can lead to a bad value is the omission of something earlier in the file, or the insertion of something superfluous, which cause the reading of the file to get out of synchronization. The program then starts reading things it didn't expect, and concludes that they are in error. So if you see this error message, you may also want to look for the earlier problem that may have led to the program becoming confused about what it is reading.

Some options are described below, but you should also read the documentation for the groups of the programs and for the individual programs.

### The Menu

The menu is straightforward. It typically looks like this (this one is for DNAPARS):

    DNA parsimony algorithm, version 3.6

    Setting for this run:
      U                 Search for best tree?  Yes
      S                        Search option?  More thorough search
      V              Number of trees to save?  10000
      J   Randomize input order of sequences?  No. Use input order
      O                        Outgroup root?  No, use as outgroup species  1
      T              Use Threshold parsimony?  No, use ordinary parsimony
      N           Use Transversion parsimony?  No, count all steps
      W                       Sites weighted?  No
      M           Analyze multiple data sets?  No
      I          Input sequences interleaved?  Yes
      0   Terminal type (IBM PC, ANSI, none)?  ANSI
      1    Print out the data at start of run  No
      2  Print indications of progress of run  Yes
      3                        Print out tree  Yes
      4          Print out steps in each site  No
      5  Print sequences at all nodes of tree  No
      6       Write out trees onto tree file?  Yes

      Y to accept these or type the letter for one to change

If you want to accept the default settings (they are shown in the above case) you can simply type `Y` followed by pressing on the `Enter` key. If you want to change any of the options, you should type the letter shown to the left of its entry in the menu. For example, to set a threshold type `T`. Lower-case letters will also work. For many of the options the program will ask for supplementary information, such as the value of the threshold.

Note the `Terminal type` entry, which you will find on all menus. It allows you to specify which type of terminal your screen is. The options are an IBM PC screen, an ANSI standard terminal, or `none`. Choosing zero (`0`) toggles among these three options in cyclical order, changing each time the `0` option is chosen. If one of them is right for your terminal the screen will be cleared before the menu is displayed. If none works, the `none` option should probably be chosen. The programs should start with a terminal option appropriate for your computer, but if they do not, you can change the terminal type manually. This is particularly important in program RETREE where a tree is displayed on the screen - if the terminal type is set to the wrong value, the tree can look very strange.

The other numbered options control which information the program will display on your screen or on the output files. The option to `Print indications of progress of run` will show information such as the names of the species as they are successively added to the tree, and the progress of rearrangements. You will usually want to see these as reassurance that the program is running and to help you estimate how long it will take. But if you are running the program "in background" as can be done on multitasking and multiuser systems, and do not have the program running in its own window, you may want to turn this option off so that it does not disturb your use of the computer while the program is running. Note also menu option 3, "Print out tree". This can be useful when you are running many data sets, and will be using the resulting trees from the output tree file. It may be helpful to turn off the printing out of the trees in that case, particularly if those files would be too big.

## The Output File

Most of the programs write their output onto a file called (usually) `outfile`, and a representation of the trees found onto a file called `outtree`.

The exact contents of the output file vary from program to program and also depend on which menu options you have selected. For many programs, if you select all possible output information, the output will consist of (1) the name of the program and its version number, (2) some of the input information printed out, and (3) a series of phylogenies, some with associated information indicating how much change there was in each character or on each part of the tree. A typical rooted tree looks like this:

                                         +-------------------Gibbon
            +----------------------------2
            !                            !      +------------------Orang
            !                            +------4
            !                                   !  +---------Gorilla
      +-----3                                   +--6
      !     !                                      !    +---------Chimp
      !     !                                      +----5
    --1     !                                           +-----Human
      !     !
      !     +-----------------------------------------------Mouse
      !
      +------------------------------------------------Bovine

The interpretation of the tree is fairly straightforward: it "grows" from left to right. The numbers at the forks are arbitrary and are used (if present) merely to identify the forks. For many of the programs the tree produced is unrooted. Rooted and unrooted trees are printed in nearly the same form, but the unrooted ones are accompanied by the warning message:

`remember: this is an unrooted tree!`

to indicate that this is an unrooted tree and to warn against taking the position of its root too seriously. Mathematicians still call an unrooted tree a tree, though some systematists unfortunately use the term "network" for an unrooted tree. This conflicts with standard mathematical usage, which reserves the name "network" for a completely different kind of graph). The root of this tree could be anywhere, say on the line leading immediately to `Mouse`. As an exercise, see if you can tell whether the following tree is or is not a different one from the above:

                 +-----------------------------------------------Mouse
                 !
       +---------4                                   +------------------Orang
       !         !                            +------3
       !         !                            !      !       +---------Chimp
    ---6         +----------------------------1      !  +----2
       !                                      !      +--5    +-----Human
       !                                      !         !
       !                                      !         +---------Gorilla
       !                                      !
       !                                      +-------------------Gibbon
       !
       +-------------------------------------------Bovine

       remember: this is an unrooted tree!

(it is not different). It is important also to realize that the lengths of the segments of the printed tree may not be significant: some may actually represent branches of zero length, in the sense that there is no evidence that those branches are nonzero in length. Some of the diagrams of trees attempt to print branches approximately proportional to estimated branch lengths, while in others the lengths are purely conventional and are presented just to make the topology visible. You will have to look closely at the documentation that accompanies each program to see what it presents and what is known about the lengths of the branches on the tree. The above tree attempts to represent branch lengths approximately in the diagram. But even in those cases, some of the smaller branches are likely to be artificially lengthened to make the tree topology clearer. Here is what a tree from DNAPARS looks like, when no attempt is made to make the lengths of branches in the diagram proportional to estimated branch lengths:

                     +--Human
                  +--5
               +--4  +--Chimp
               !  !
            +--3  +-----Gorilla
            !  !
         +--2  +--------Orang
         !  !
      +--1  +-----------Gibbon
      !  !
    --6  +--------------Mouse
      !
      +-----------------Bovine

      remember: this is an unrooted tree!

When a tree has branch lengths, it will be accompanied by a table showing for each branch the numbers (or names) of the nodes at each end of the branch, and the length of that branch. For the first tree shown above, the corresponding table is:

     Between        And            Length      Approx. Confidence Limits
     -------        ---            ------      ------- ---------- ------

        1          Bovine            0.90216     (  0.50346,     1.30086) **
        1          Mouse             0.79240     (  0.42191,     1.16297) **
        1             2              0.48553     (  0.16602,     0.80496) **
        2             3              0.12113     (     zero,     0.24676) *
        3             4              0.04895     (     zero,     0.12668)
        4             5              0.07459     (  0.00735,     0.14180) **
        5          Human             0.10563     (  0.04234,     0.16889) **
        5          Chimp             0.17158     (  0.09765,     0.24553) **
        4          Gorilla           0.15266     (  0.07468,     0.23069) **
        3          Orang             0.30368     (  0.18735,     0.41999) **
        2          Gibbon            0.33636     (  0.19264,     0.48009) **

          *  = significantly positive, P < 0.05
          ** = significantly positive, P < 0.01

Ignoring the asterisks and the approximate confidence limits, which will be described in the documentation file for DNAML, we can see that the table gives a more precise idea of what the lengths of all the branches are. Similar tables exist in distance matrix and likelihood programs, as well as in the parsimony programs DNAPARS and PARS.

Some of the parsimony programs in the package can print out a table of the number of steps that different characters (or sites) require on the tree. This table may not be obvious at first. A typical example looks like this:

     steps in each site:
             0   1   2   3   4   5   6   7   8   9
         *-----------------------------------------
        0!       2   2   2   2   1   1   2   2   1
       10!   1   2   3   1   1   1   1   1   1   2
       20!   1   2   2   1   2   2   1   1   1   2
       30!   1   2   1   1   1   2   1   3   1   1
       40!   1

The numbers across the top and down the side indicate which site is being referred to. Thus site 23 is column "3" of row "20" and has 1 step in this case.

There are many other kinds of information that can appear in the output file, They vary from program to program, and we leave their description to the documentation files for the specific programs.

## The Tree File

In output from most programs, a representation of the tree is also written into the tree file `outtree`. The tree is specified by nested pairs of parentheses, enclosing names and separated by commas. We will describe how this works below. If there are any blanks in the names, these must be replaced by the underscore character "`_`". Trailing blanks in the name may be omitted. The pattern of the parentheses indicates the pattern of the tree by having each pair of parentheses enclose all the members of a monophyletic group. The tree file could look like this:

`((Mouse,Bovine),(Gibbon,(Orang,(Gorilla,(Chimp,Human)))));`

In this tree the first fork separates the lineage leading to `Mouse` and `Bovine` from the lineage leading to the rest. Within the latter group there is a fork separating `Gibbon` from the rest, and so on. The entire tree is enclosed in an outermost pair of parentheses. The tree ends with a semicolon. In some programs such as DNAML, FITCH, and CONTML, the tree will be unrooted. An unrooted tree should have its bottommost fork have a three-way split, with three groups separated by two commas:

`(A,(B,(C,D)),(E,F));`

Here the three groups at the bottom node are `A`, `(B,C,D)`, and `(E,F)`. The single three-way split corresponds to one of the interior nodes of the unrooted tree (it can be any interior node of the tree). The remaining forks are encountered as you move out from that first node. In newer programs, some are able to tolerate these other forks being multifurcations (multi-way splits). You should check the documentation files for the particular programs you are using to see in which of these forms you can expect the user tree to be in. Note that many of the programs that actually estimate an unrooted tree (such as DNAPARS) produce trees in the treefile in rooted form! This is done for reasons of arbitrary internal bookkeeping. The placement of the root is arbitrary. We are working toward having all programs be able to read all trees, whether rooted or unrooted, multifurcating or bifurcating, and having them do the right thing with them. But this is a long-term goal and it is not yet achieved.

For programs that infer branch lengths, these are given in the trees in the tree file as real numbers following a colon, and placed immediately after the group descended from that branch. Here is a typical tree with branch lengths:

`((cat:47.14069,(weasel:18.87953,((dog:25.46154,(raccoon:19.19959,
bear:6.80041):0.84600):3.87382,(sea_lion:11.99700,
seal:12.00300):7.52973):2.09461):20.59201):25.0,monkey:75.85931);`

Note that the tree may continue to a new line at any time except in the middle of a name or the middle of a branch length, although in trees written to the tree file this will only be done after a comma.

These representations of trees are a subset of the standard adopted on 24 June 1986 at the annual meetings of the Society for the Study of Evolution by an informal committee (its final session in Newick's lobster restaurant - hence its name, the Newick standard) consisting of Wayne Maddison (author of MacClade), David Swofford (PAUP), F. James Rohlf (NTSYS-PC), Chris Meacham (COMPROB and the original PHYLIP tree drawing programs), James Archie, William H.E. Day, and me. This standard is a generalization of PHYLIP's format, itself based on a well-known representation of trees in terms of parenthesis patterns which is due to the famous mathematician Arthur Cayley, and which has been around for over a century. The standard is now employed by most phylogeny computer programs but unfortunately has yet to be decribed in a formal published description. Other descriptions by me and by Gary Olsen can be accessed using the Web at:

`http://evolution.gs.washington.edu/phylip/newicktree.html`

## The Options and How To Invoke Them

Most of the programs allow various options that alter the amount of information the program is provided or what is done with the information. Options are selected in the menu.

### Common options in the menu

A number of the options from the menu, the `U` (User tree), `G` (Global), `J` (Jumble), `O` (Outgroup), `W` (Weights), `T` (Threshold), `M` (multiple data sets), and the tree output options, are used so widely that it is best to discuss them in this document.

The `U` (User tree) option. This option toggles between the default setting, which allows the program to search for the best tree, and the User tree setting, which reads a tree or trees ("user trees") from the input tree file and evaluates them. The input tree file's default name is `intree`. In many cases the programs will also tolerate having the trees be preceded by a line giving the number of trees:

    ((Alligator,Bear),((Cow,(Dog,Elephant)),Ferret));
    ((Alligator,Bear),(((Cow,Dog),Elephant),Ferret));
    ((Alligator,Bear),((Cow,Dog),(Elephant,Ferret)));

An initial line with the number of trees was formerly required, but this now can be omitted. Some programs require rooted trees, some unrooted trees, and some can handle multifurcating trees. You should read the documentation for the particular program to find out which it requires. Program RETREE can be used to convert trees among these forms (on saving a tree from RETREE, you are asked whether you want it to be rooted or unrooted).

In using the user tree option, check the pattern of parentheses carefully. The programs do not always detect whether the tree makes sense, and if it does not there will probably be a crash (hopefully, but not inevitably, with an error message indicating the nature of the problem). Trees written out by programs are typically in the proper form.

The `G` (Global) option. In the programs which construct trees (except for NEIGHBOR, the "...PENNY" programs and CLIQUE, and of course the "...MOVE" programs where you construct the trees yourself), after all species have been added to the tree a rearrangements phase ensues. In most of these programs the rearrangements are automatically global, which in this case means that subtrees will be removed from the tree and put back on in all possible ways so as to have a better chance of finding a better tree. Since this can be time consuming (it roughly triples the time taken for a run) it is left as an option in some of the programs, specifically CONTML, FITCH, and DNAML. In these programs the G menu option toggles between the default of local rearrangement and global rearrangement. The rearrangements are explained more below.

The `J` (Jumble) option. In most of the tree construction programs (except for the "...PENNY" programs and CLIQUE), the exact details of the search of different trees depend on the order of input of species. In these programs `J` option enables you to tell the program to use a random number generator to choose the input order of species. This option is toggled on and off by selecting option `J` in the menu. The program will then prompt you for a "seed" for the random number generator. The seed should be an integer between 1 and 32767, and should of form 4n+1, which means that it must give a remainder of 1 when divided by 4. This can be judged by looking at the last two digits of the number. Each different seed leads to a different sequence of addition of species. By simply changing the random number seed and re-running the programs one can look for other, and better trees. If the seed entered is not odd, the program will not proceed, but will prompt for another seed.

The Jumble option also causes the program to ask you how many times you want to restart the process. If you answer 10, the program will try ten different orders of species in constructing the trees, and the results printed out will reflect this entire search process (that is, the best trees found among all 10 runs will be printed out, not the best trees from each individual run).

Some people have asked what are good values of the random number seed. The random number seed is used to start a process of choosing "random" (actually pseudorandom) numbers, which behave as if they were unpredictably randomly chosen between 0 and 232-1 (which is 4,294,967,296). You could put in the number 133 and find that the next random number was 1,876,973,009. As they are effectively unpredictable, there is no such thing as a choice that is better than any other, provided that the numbers are of the form 4n+1. However if you re-use a random number seed, the sequence of random numbers that result will be the same as before, resulting in exactly the same series of choices, which may not be what you want.

The `O` (Outgroup) option. This specifies which species is to be used to root the tree by having it become the outgroup. This option is toggled on and off by choosing `O` in the menu (the alphabetic character `O`, not the digit `0`). When it is on, the program will then prompt for the number of the outgroup (the species being taken in the numerical order that they occur in the input file). Responding by typing `6` and then an `Enter` character indicates that the sixth species in the data is the outgroup. Outgroup-rooting will not be attempted if the data have already established a root for the tree from some other consideration, and may not be if it is a user-defined tree, despite your invoking the option. Thus programs such as DOLLOP that produce only rooted trees do not allow the Outgroup option. It is also not available in KITSCH, DNAMLK, or CLIQUE. When it is used, the tree as printed out is still listed as being an unrooted tree, though the outgroup is connected to the bottommost node so that it is easy to visually convert the tree into rooted form.

The `T` (Threshold) option. This sets a threshold forn the parsimony programs such that if the number of steps counted in a character is higher than the threshold, it will be taken to be the threshold value rather than the actual number of steps. The default is a threshold so high that it will never be surpassed (in which case the steps whill simply be counted). The `T` menu option toggles on and off asking the user to supply a threshold. The use of thresholds to obtain methods intermediate between parsimony and compatibility methods is described in my 1981b paper. When the T option is in force, the program will prompt for the numerical threshold value. This will be a positive real number greater than 1. In programs MIX, MOVE, PENNY, PROTPARS, DNAPARS, DNAMOVE, and DNAPENNY, do not use threshold values less than or equal to 1.0, as they have no meaning and lead to a tree which depends only on considerations such as the input order of species and not at all on the character state data! In programs DOLLOP, DOLMOVE, and DOLPENNY the threshold should never be 0.0 or less, for the same reason. The `T` option is an important and underutilized one: it is, for example, the only way in this package (except for program DNACOMP) to do a compatibility analysis when there are missing data. It is a method of de-weighting characters that evolve rapidly. I wish more people were aware of its properties.

The `M` (Multiple data sets) option. In menu programs there is an `M` menu option which allows one to toggle on the multiple data sets option. The program will ask you how many data sets it should expect. The data sets have the same format as the first data set. Here is a (very small) input file with two five-species data sets:

          5    6
    Alpha     CCACCA
    Beta      CCAAAA
    Gamma     CAACCA
    Delta     AACAAC
    Epsilon   AACCCA
    5    6
    Alpha     CACACA
    Beta      CCAACC
    Gamma     CAACAC
    Delta     GCCTGG
    Epsilon   TGCAAT

The main use of this option will be to allow all of the methods in these programs to be bootstrapped. Using the program SEQBOOT one can take any DNA, protein, restriction sites, gene frequency or binary character data set and make multiple data sets by bootstrapping. Trees can be produced for all of these using the `M` option. They will be written on the tree output file if that option is left in force. Then the program CONSENSE can be used with that tree file as its input file. The result is a majority rule consensus tree which can be used to make confidence intervals. The present version of the package allows, with the use of SEQBOOT and CONSENSE and the M option, bootstrapping of many of the methods in the package.

Programs DNAML, DNAPARS and PARS can also take multiple weights instead of multiple data sets. They can then do bootstrapping by reading in one data set, together with a file of weights that show how the characters (or sites) are reweighted in each bootstrap sample. Thus a site that is omitted in a bootstrap sample has effectively been given weight 0, while a site that has been duplicated has effectively been given weight 2. SEQBOOT has a menu selection to produce the file of weights information automatically, instead of producing a file of multiple data sets. It can be renamed and used as the input weights file.

The `W` (Weights) option. This signals the program that, in addition to the data set, you want to read in a series of weights that tell how many times each character is to be counted. If the weight for a character is zero (`0`) then that character is in effect to be omitted when the tree is evaluated. If it is (`1`) the character is to be counted once. Some programs allow weights greater than 1 as well. These have the effect that the character is counted as if it were present that many times, so that a weight of 4 means that the character is counted 4 times. The values 0-9 give weights 0 through 9, and the values A-Z give weights 10 through 35. By use of the weights we can give overwhelming weight to some characters, and drop others from the analysis. In the molecular sequence programs only two values of the weights, 0 or 1 are allowed.

The weights are used to analyze subsets of the characters, and also can be used for resampling of the data as in bootstrap and jackknife resampling. For those programs that allow weights to be greater than 1, they can also be used to emphasize information from some characters more strongly than others. Of course, you must have some rationale for doing this.

The weights are provided as a sequence of digits. Thus they might be

`10011111100010100011110001100`

The weights are to be provided in an input file whose default name is `weights`. The weights in it are a simple string of digits. Blanks in the weightfile are skipped over and ignored, and the weights can continue to a new line. In programs such as SEQBOOT that can also output a file of weights, the input weights have a default file name of `inweights`, and the output file name has a default file name of `outweights`.

Weights can be used to analyze different subsets of characters (by weighting the rest as zero). Alternatively, in the discrete characters programs they can be used to force a certain group to appear on the phylogeny (in effect confining consideration to only phylogenies containing that group). This is done by adding an imaginary character that has `1`'s for the members of the group, and `0`'s for all the other species. That imaginary character is then given the highest weight possible: the result will be that any phylogeny that does not contain that group will be penalized by such a heavy amount that it will not (except in the most unusual circumstances) be considered. Of course, the new character brings extra steps to the tree, but the number of these can be calculated in advance and subtracted out of the total when reporting the results. This use of weights is an important one, and one sadly ignored by many users who could profit from it. In the case of molecular sequences we cannot use weights this way, so that to force a given group to appear we have to add a large extra segment of sites to the molecule, with (say) A's for that group and C's for every other species.

The option to write out the trees into a tree file. This specifies that you want the program to write out the tree not only on its usual output, but also onto a file in nested-parenthesis notation (as described above). This option is sufficiently useful that it is turned on by default in all programs that allow it. You can optionally turn it off if you wish, by typing the appropriate number from the menu (it varies from program to program). This option is useful for creating tree files that can be directly read into the programs, including the consensus tree and tree distance programs, and the tree plotting programs.

The output tree file has a default name of `outtree`.

The (`0`) terminal type option . (This is the digit `0`, not the alphabetic character `O`). The program will default to one particular assumption about your terminal (ANSI in the case of Linux, Unix, or Mac OS X, none in the case of Mac OS 9, and IBM PC in the case of Windows). You can alternatively select it to be either an IBM PC, or nothing. This affects the ability of the programs to clear the screen when they display their menus, and the graphics characters used to display trees in the programs DNAMOVE, MOVE, DOLMOVE, and RETREE. In the case of Windows, the screen will clear properly with either the IBM PC or the ANSI settings, but the graphics characters needed by MOVE, DNAMOVE, DOLMOVE, or RETREE will display correctly only with the IBM PC setting.

## The Algorithm for Constructing Trees

All of the programs except FACTOR, DNADIST, GENDIST, DNAINVAR, SEQBOOT, CONTRAST, RETREE, and the plotting and consensus tree programs act to construct an estimate of a phylogeny. MOVE, DOLMOVE, and DNAMOVE let you construct it yourself by hand. All of the rest but NEIGHBOR, the "...PENNY" programs and CLIQUE make use of a common approach involving additions and rearrangements. They are trying to minimize or maximize some quantity over the space of all possible evolutionary trees. Each program contains a part that, given the topology of the tree, evaluates the quantity that is being minimized or maximized. The straightforward approach would be to evaluate all possible tree topologies one after another and pick the one which, according to the criterion being used, is best. This would not be possible for more than a small number of species, since the number of possible tree topologies is enormous. A review of the literature on the counting of evolutionary trees will be found one of my papers (Felsenstein, 1978a) and in my book (Felsenstein, 2004, chapter 3).

Since we cannot search all topologies, these programs are not guaranteed to always find the best tree, although they seem to do quite well in practice. The strategy they employ is as follows: the species are taken in the order in which they appear in the input file. The first two (in some programs the first three) are taken and a tree constructed containing only those. There is only one possible topology for this tree. Then the next species is taken, and we consider where it might be added to the tree. If the initial tree is (say) a rooted tree with two species and we want the resulting three-species tree to be a bifurcating tree, there are only three places where we could add the third species. Each of these is tried, and each time the resulting tree is evaluated according to the criterion. The best one is chosen to be the basis for further operations. Now we consider adding the fourth species, again at each of the five possible places that would result in a bifurcating tree. Again, the best of these is accepted. This is usually known as the Sequential Addition strategy.

### Local rearrangements

The process continues in this manner, with one important exception. After each species is added, and before the next is added, a number of rearrangements of the tree are tried, in an effort to improve it. The algorithms move through the tree, making all possible local rearrangements of the tree. A local rearrangement involves an internal segment of the tree in the following manner. Each internal segment of the tree is of this form (where T1, T2, and T3 are subtrees - parts of the tree that can contain further forks and tips):

                T1      T2       T3
                 \      /        /
                  \    /        /
                   \  /        /
                    \/        /
                     *       /
                      *     /
                       *   /
                        * /
                         *
                         !
                         !

the segment we are discussing being indicated by the asterisks. A local rearrangement consists of switching the subtrees T1 and T3 or T2 and T3, so as to obtain one of the following:

              T3       T2      T1            T1       T3      T2
               \       /       /              \       /       /
                \     /       /                \     /       /
                 \   /       /                  \   /       /
                  \ /       /                    \ /       /
                   \       /                      \       /
                    \     /                        \     /
                     \   /                          \   /
                      \ /                            \ /
                       !                              !
                       !                              !
                       !                              !

Each time a local rearrangement is successful in finding a better tree, the new arrangement is accepted. The phase of local rearrangements does not end until the program can traverse the entire tree, attempting local rearrangements, without finding any that improve the tree.

This strategy of adding species and making local rearrangements will look at about (n-1)x(2n-3) different topologies, though if rearrangements are frequently successful the number may be larger. I have been describing the strategy when rooted trees are being considered. For unrooted trees there is a precisely similar strategy, though the first tree constructed may be a three-species tree and the rearrangements may not start until after the addition of the fifth species.

These local rearrangements have come to be called Nearest Neighbor Interchanges (NNIs) in the phylogeny literature.

Though we are not guaranteed to have found the best tree topology, we are guaranteed that no nearby topology (i. e. none accessible by a single local rearrangement) is better. In this sense we have reached a local optimum of our criterion. Note that the whole process is dependent on the order in which the species are present in the input file. We can try to find a different and better solution by reordering the species in the input file and running the program again (or, more easily, by using the `J` option). If none of these attempts finds a better solution, then we have some indication that we may have found the best topology, though we can never be certain of this.

Note also that a new topology is never accepted unless it is better than the previous one, so that the rearrangement process can never fall into an endless loop. This is also the way ties in our criterion are resolved, namely by sticking with the tree found first. However, the tree construction programs other than CLIQUE, CONTML, FITCH, and DNAML do keep a record of all trees found that are tied with the best one found. This gives you some immediate idea of which parts of the tree can be altered without affecting the quality of the result.

### Global rearrangements

A feature of most of the programs, such as PROTPARS, DNAPARS, DNACOMP, DNAML, DNAMLK, RESTML, KITSCH, FITCH, CONTML, MIX, and DOLLOP, is "global" optimization of the tree. In four of these (CONTML, FITCH, DNAML and DNAMLK) this is an option, `G`. In the others it automatically applies. When it is present there is an additional stage to the search for the best tree. Each possible subtree is removed from the tree from the tree and added back in all possible places. This process continues until all subtrees can be removed and added again without any improvement in the tree. The purpose of this extra rearrangement is to make it less likely that one or more a species gets "stuck" in a suboptimal region of the space of all possible trees. The use of global optimization results in approximately a tripling (3 x ) of the run-time, which is why I have left it as an option in some of the slower programs.

What PHYLIP calls "global" rearrangements are more properly called SPR (subtree pruning and regrafting) by Swofford et. al. (1996) as distinct from the NNI (nearest neighbor interchange) rearrangements that PHYLIP also uses, and the TBR (tree bisection and reconnection) rearrangements that it does not use. My book (Felsenstein, 2004, chapter 4) contains a review of work on these and other rearrangements and search methods.

The programs doing global optimization print out a dot "`.`" after each group is removed and re-added to the tree, to give the user some sign that the rearrangements are proceeding. A new line of dots is started whenever a new round of global rearrangements is started following an improvement in the tree. On the line before the dots are printed there is printed a bar of the form "!---------------!" to show how many dots to expect. The dots will not be printed out at a uniform rate, but the later dots, which represent removal of larger groups from the tree and trying them consequently in fewer places, will print out more quickly. With some compilers each row of dots may not be printed out until it is complete.

It should be noted that PENNY, DOLPENNY, DNAPENNY and CLIQUE use a more sophisticated strategy of "depth-first search" with a "branch and bound" search method that guarantees that all of the best trees will be found. In the case of PENNY, DOLPENNY and DNAPENNY there can be a considerable sacrifice of computer time if the number of species is greater than about ten: it is a matter for you to consider whether it is worth it for you to guarantee finding all the most parsimonious trees, and that depends on how much free computer time you have! CLIQUE finds all largest cliques, and does so without undue burning of computer time. Although all of these problems that have been investigated fall into the category of "NP-hard" problems that in effect do not have a rapid solution, the cases that cause this trouble for the largest-cliques algorithm in CLIQUE apparently are not biologically realistic and do not occur in actual data.

### Multiple jumbles

As just mentioned, for most of these programs the search depends on the order in which the species are entered into the tree. Using the `J` (Jumble) option you can supply a random number seed which will allow the program to put the species in in a random order. Jumbling can be done multiple times. For example, if you tell the program to do it 10 times, it will go through the tree-building process 10 times, each with a different random order of adding species. It will keep a record of the trees tied for best over the whole process. In other words, it does not just record the best trees from each of the 10 runs, but records the best ones overall. Of course this is slow, taking 10 times longer than a single run. But it does give us a much greater chance of finding all of the most parsimonious trees. In the terminology of Maddison (1991) it can find different "islands" of trees. The present algorithms do not guarantee us to find all trees in a given "island" from a single run, so multiple runs also help explore those "islands" that are found.

### Saving multiple tied trees

For the parsimony and compatibility programs, one can have a perfect tie between two or more trees. In these programs these trees are all saved. For the newer parsimony programs such as DNAPARS and PARS, global rearrangement is carried out on all of these tied trees. This can be turned off in the menu.

For trees with criteria which are real numbers, such as the distance matrix programs FITCH and KITSCH, and the likelihood programs DNAML, DNAMLK, CONTML, and RESTML, it is difficult to get an exact tie between trees. Consequently these programs save only the single best tree (even though the others may be only a tiny bit worse).

### Strategy for finding the best tree

In practice, it is advisable to use the Jumble option to evaluate many different orderings of the input species. It is advisable to use the Jumble option and specify that it be done many times (as many as different orderings of the input species). (This is usually not necessary when bootstrapping, though the programs will then default to doing it once to avoid artifacts caused by the order in which species are added to the tree.)

People who want a magic "black box" program whose results they do not have to question (or think about) often are upset that these programs give results that are dependent on the order in which the species are entered in the data. To me this property is an advantage, for it permits you to try different searches for better trees, simply by varying the input order of species. If you do not use the multiple Jumble option, but do multiple individual runs instead, you can easily decide which to pay most attention to - the one or ones that are best according to the criterion employed (for example, with parsimony, the one out of the runs that results in the tree with the fewest changes).

In practice, in a single run, it usually seems best to put species that are likely to be sources of confusion in the topology last, as by the time they are added the arrangement of the earlier species will have stabilized into a good configuration, and then the last few species will by fitted into that topology. There will be less chance this way of a poor initial topology that would affect all subsequent parts of the search. However, a variety of arrangements of the input order of species should be tried, as can be done if the `J` option is used, and no species should be kept in a fixed place in the order of input. Note that the results of the "...PENNY" programs and CLIQUE are not sensitive to the input order of species, and NEIGHBOR is only slightly sensistive to it, so that multiple Jumbling is not possible with those programs. Note also that with global search, which is standard in many programs and in others is an option, each group (including each individual species) will be removed and re-added in all possible positions, so that a species causing confusion will have more chance of moving to a new location than it would without global rearrangement.

## A Warning on Interpreting Results

Probably the most important thing to keep in mind while running any of the parsimony or compatibility programs is not to overinterpret the result. Many users treat the set of most parsimonious trees as if it were a confidence interval. If a group appears in all of the most parsimonious trees then they treat it as well established. Unfortunately the confidence interval on phylogenies appears to be much larger than the set of all most parsimonious trees (Felsenstein, 1985b). Likewise, variation of result among different methods will not be a good indicator of the size of the confidence interval. Consider a simple data set in which, out of 100 binary characters, 51 recommend the unrooted tree `((A,B),(C,D))` and 49 the tree `((A,D),(B,C))`. Many different methods will all give the same result on such a data set: they will estimate the tree as `((A,B),(C,D))`. Nevertheless it is clear that the 51:49 margin by which this tree is favored is not statistically significantly different from 50:50. So consistency among different methods is a poor guide to statistical significance.

## Relative Speed of Different
Programs and Machines

### Relative speed of the different programs

C compilers differ in efficiency of the code they generate, and some deal with some features of the language better than with others. Thus a program which is unusually fast on one computer may be unusually slow on another. Nevertheless, as a rough guide to relative execution speeds, I have tested the programs on three data sets, each of which has 10 species and 40 characters. The first is an imaginary one in which all characters are compatible - ("The Willi Hennig Memorial Data Set" as J. S. Farris once called ones like it). The second is the binary recoded form of the fossil horses data set of Camin and Sokal (1965). The third data set has data that is completely random: 10 species and 20 characters that have a 50% chance that each character state is `0` or `1` (or `A` or `G`). The data sets thus range from a completely compatible one in which there is no homoplasy (paralellism or convergence), through the horses data set, which requires 29 steps where the possible minimum number would be 20, to the random data set, which requires 49 steps. We can thus see how this increasing messiness of the data affects running times. The three data sets have all had 20 sites of `A`'s added to the end of each sequence, so as to prevent likelihood or distance matrix programs from having infinite branch lengths (the test data sets used for timing previous versions of PHYLIP were the same except that they lacked these 20 extra sites).

Here are the nucleotide sequence versions of the three data sets:

        10   40
    A         CACACACAAAAAAAAAAACAAAAAAAAAAAAAAAAAAAAA
    B         CACACAACAAAAAAAAAACAAAAAAAAAAAAAAAAAAAAA
    C         CACAACAAAAAAAAAAAACAAAAAAAAAAAAAAAAAAAAA
    D         CAACAAAACAAAAAAAAACAAAAAAAAAAAAAAAAAAAAA
    E         CAACAAAAACAAAAAAAACAAAAAAAAAAAAAAAAAAAAA
    F         ACAAAAAAAACACACAAAACAAAAAAAAAAAAAAAAAAAA
    G         ACAAAAAAAACACAACAAACAAAAAAAAAAAAAAAAAAAA
    H         ACAAAAAAAACAACAAAAACAAAAAAAAAAAAAAAAAAAA
    I         ACAAAAAAAAACAAAACAACAAAAAAAAAAAAAAAAAAAA
    J         ACAAAAAAAAACAAAAACACAAAAAAAAAAAAAAAAAAAA

        10   40
    MesohippusAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
    HypohippusAAACCCCCCCAAAAAAAAACAAAAAAAAAAAAAAAAAAAA
    ArchaeohipCAAAAAAAAAAAAAAAACACAAAAAAAAAAAAAAAAAAAA
    ParahippusCAAACAACAACAAAAAAAACAAAAAAAAAAAAAAAAAAAA
    MerychippuCCAACCACCACCCCACACCCAAAAAAAAAAAAAAAAAAAA
    M. secunduCCAACCACCACCCACACCCCAAAAAAAAAAAAAAAAAAAA
    Nannipus  CCAACCACAACCCCACACCCAAAAAAAAAAAAAAAAAAAA
    NeohippariCCAACCCCCCCCCCACACCCAAAAAAAAAAAAAAAAAAAA
    Calippus  CCAACCACAACCCACACCCCAAAAAAAAAAAAAAAAAAAA
    PliohippusCCCACCCCCCCCCACACCCCAAAAAAAAAAAAAAAAAAAA

        10   40
    A         CACACAACCAAACAAACCACAAAAAAAAAAAAAAAAAAAA
    B         AAACCACACACACAAACCCAAAAAAAAAAAAAAAAAAAAA
    C         ACAAAACCAAACCACCCACAAAAAAAAAAAAAAAAAAAAA
    D         AAAAACACAACACACCAAACAAAAAAAAAAAAAAAAAAAA
    E         AAACAACCACACACAACCAAAAAAAAAAAAAAAAAAAAAA
    F         CCCAAACACCCCCAAAAAACAAAAAAAAAAAAAAAAAAAA
    G         ACACCCCCACACCCACCAACAAAAAAAAAAAAAAAAAAAA
    H         AAAACAACAACCACCCCACCAAAAAAAAAAAAAAAAAAAA
    I         ACACAACAACACAAACAACCAAAAAAAAAAAAAAAAAAAA
    J         CCAAAAACACCCAACCCAACAAAAAAAAAAAAAAAAAAAA

Here are the timings of many of the version 3.6 programs on these three data sets as run after being compiled by Gnu C (version 3.2) and run on an AMD Athlon XP 2200+ computer under Linux.

| Hennigian Data | Horses Data | Random Data
PROTPARS | 0.00500 | 0.00670 | 0.01289
DNAPARS | 0.01050 | 0.00940 | 0.00980
DNAPENNY | 0.01400 | 0.00860 | 1.71100
DNACOMP | 0.00240 | 0.00250 | 0.00590
DNAML | 0.17749 | 0.23970 | 0.21350
DNAMLK | 0.21740 | 0.19450 | 0.24400
PROML | 1.3527 | 3.2085 | 2.0055
PROMLK | 3.3567 | 8.6078 | 4.4886
DNAINVAR | 0.00020 | 0.00020 | 0.00020
DNADIST | 0.00140 | 0.00080 | 0.00150
PROTDIST | 0.09220 | 0.09210 | 0.09310
RESTML | 0.14560 | 0.28810 | 0.21540
RESTDIST | 0.00110 | 0.00090 | 0.00080
FITCH | 0.00760 | 0.01280 | 0.00880
KITSCH | 0.00180 | 0.00260 | 0.00280
NEIGHBOR | 0.00020 | 0.00050 | 0.00050
CONTML | 0.01310 | 0.01500 | 0.01780
GENDIST | 0.00070 | 0.00070 | 0.00070
PARS | 0.00780 | 0.00610 | 0.02930
MIX | 0.00360 | 0.00410 | 0.00610
PENNY | 0.00190 | 0.00470 | 0.8060
DOLLOP | 0.00480 | 0.00450 | 0.00820
DOLPENNY | 0.00200 | 0.01060 | 1.1270
CLIQUE | 0.00100 | 0.00070 | 0.00130

In all cases the programs were run under the default options with optimized compiler switches (`-03 -fomit-frame-pointer`), except as specified here. The data sets used for the discrete characters programs have `0`'s and `1`'s instead of `A`'s and `C`'s. For CONTML the `A`'s and `C`'s were made into `0.0`'s and `1.0`'s and considered as 40 2-allele loci. For the distance programs 10 x 10 distance matrices were computed from the three data sets. For the restriction sites programs `A` and `C` were changed into `+` and `-`. It does not make much sense to benchmark MOVE, DOLMOVE, or DNAMOVE, although when there are many characters and many species the response time after each alteration of the tree should be proportional to the product of the number of species and the number of characters. For DNAML, DNAMLK, and DNADIST the frequencies of the four bases were set to be equal rather than determined empirically as is the default. For RESTML the number of enzymes was set to 1.

In most cases, the benchmark was made more accurate by analyzing 100 data sets using the `M` (Multiple data sets) option and dividing the resulting time by 100. Times were determined as user times using the Linux `time` command. Several patterns will be apparent from this. The algorithms (MIX, DOLLOP, CONTML, FITCH, KITSCH, PROTPARS, DNAPARS, DNACOMP, and DNAML, DNAMLK, RESTML) that use the above-described addition strategy have run times that do not depend strongly on the messiness of the data. The only exception to this is that if a data set such as the Random data requires extra rounds of global rearrangements it takes longer. The programs differ greatly in run time: the protein likelihood programs PROML and PROMLK were very slow, and the other likelihood programs RESTML, DNAML and CONTML are slower than the rest of the programs. The protein sequence parsimony program, which has to do a considerable amount of bookkeeping to keep track of which amino acids can mutate to each other, is also relatively slow.

Another class of algorithms includes PENNY, DOLPENNY, DNAPENNY and CLIQUE. These are branch-and-bound methods: in principle they should have execution times that rise exponentially with the number of species and/or characters, and they might be much more sensitive to messy data. This is apparent with PENNY, DOLPENNY, and DNAPENNY, which go from being reasonably fast with clean data to very slow with messy data. DOLPENNY is particularly slow on messy data - this is because this algorithm cannot make use of some of the lower-bound calculations that are possible with DNAPENNY and PENNY. CLIQUE is very fast on all data sets. Although in theory it should bog down if the number of cliques in the data is very large, that does not happen with random data, which in fact has few cliques and those small ones. Apparently the "worst-case" data sets that cause exponential run time are much rarer for CLIQUE than for the other branch-and-bound methods.

NEIGHBOR is quite fast compared to FITCH and KITSCH, and should make it possible to run much larger cases, although the results are expected to be a bit rougher than with those programs.

### Speed with different numbers of species

How will the speed depend on the number of species and the number of characters? For the sequential-addition algorithms, the speed should be proportional to somewhere between the cube of the number of species and the square of the number of species, and to the number of characters. Thus a case that has, instead of 10 species and 20 characters, 20 species and 50 characters would take (in the cubic case) 2 x 2 x 2 x 2.5 = 20 times as long. This implies that cases with more than 20 species will be slow, and cases with more than 40 species very slow. This places a premium on working on small subproblems rather than just dumping a whole large data set into the programs.

An exception to these rules will be some of the DNA programs that use an aliasing device to save execution time. In these programs execution time will not necessarily increase proportional to the number of sites, as sites that show the same pattern of nucleotides will be detected as identical and the calculations for them will be done only once, which does not lead to more execution time. This is particularly likely to happen with few species and many sites, or with data sets that have small amounts of evolutionary divergence.

For programs FITCH and KITSCH, the distance matrix is square, so that when we double the number of species we also double the number of "characters", so that running times will go up as the fourth power of the number of species rather than the third power. Thus a 20-species case with FITCH is expected to run sixteen times more slowly than a 10-species case.

For programs like PENNY and CLIQUE the run times will rise faster than the cube of the number of species (in fact, they can rise faster than any power since these algorithms are not guaranteed to work in polynomial time). In practice, PENNY will frequently bog down above 11 species, while CLIQUE easily deals with larger numbers.

For NEIGHBOR the speed should vary only as the cube of the number of species, so a case twice as large will take only eight times as long. This will make it an attractive alternative to FITCH and KITSCH for large data sets.

Suggestion: If you are unsure of how long a program will take, try it first on a few species, then work your way up until you get a feel for the speed and for what size programs you can afford to run.

Execution time is not the most important criterion for a program, particularly as computer time gets much cheaper than your time or a programmer's time. With workstations on which background jobs can be run all night, execution speed is not overwhelmingly relevant. Some of us have been conditioned by an earlier era of computing to consider execution speed paramount. But ease of use, ease of adaptation to your computer system, and ease of modification are much more important in practice, and in these respects I think these programs are adequate. Only if you are engaged in 1960's style mainframe computing, or if you have very large amounts of data is minimization of execution time paramount. If you spent six months getting your data, it may not be overwhelmingly important whether your run takes 10 seconds or 10 hours.

Nevertheless it would have been nice to have made the programs faster. The present speeds are a compromise between speed and effectiveness: by making them slower and trying more rearrangements in the trees, or by enumerating all possible trees, I could have made the programs more likely to find the best tree. By trying fewer rearrangements I could have speeded them up, but at the cost of finding worse trees. I could also have speeded them up by writing critical sections in assembly language, but this would have sacrificed ease of distribution to new computer systems. There are also some options included in these programs that make it harder to adopt some of the economies of bookkeeping that make other programs faster. However to some extent I have simply made the decision not to spend time trying to speed up program bookkeeping when there were new likelihood and statistical methods to be developed.

### Relative speed of different machines

It is interesting to compare different machines using DNAPARS as the standard task. One can rate a machine on the DNAPARS benchmark by summing the times for all three of the data sets. Here are relative total timings over all three data sets (done with various versions of DNAPARS) for some machines, taking an AMD Athlon 1.2 GHz computer running Linux with gcc as the standard. Benchmarks from versions 3.4 and 3.5 of the program are also included (respectively the Pascal and C versions whose timings are in parentheses). They are compared only with each other and are scaled to the rest of the timings using the joint runs on the 386SX and the Pentium MMX 266. This use of separate standards is necessary not because of different languages but because different versions of the package are being compared. Thus, the "Time" is the ratio of the Total to that for the Pentium, adjusted by the scalings of machines using 3.4 and 3.5 when appropriate. The Relative Speed is the reciprocal of the Time.

Machine | Operating System | Compiler | Total | Time | Relative Speed
Toshiba T1100+ | MSDOS | Turbo Pascal 3.01A | (269) | 10542 | 0.00009486
Apple Mac Plus | Mac OS | Lightspeed Pascal 2 | (175.84) | 6891 | 0.00014511
Toshiba T1100+ | MSDOS | Turbo Pascal 5.0 | (162) | 6349 | 0.00015750
Macintosh Classic | Mac OS | Think Pascal 3 | (160) | 6271 | 0.00015947
Macintosh Classic | Mac OS | Think C | (43.0) | 4771 | 0.0002096
IBM PS2/60 | MSDOS | Turbo Pascal 5.0 | (58.76) | 2303 | 0.0004343
80286 (12 Mhz) | MSDOS | Turbo Pascal 5.0 | (47.09) | 1845.4 | 0.0005419
Apple Mac IIcx | Mac OS | Think Pascal 3 | (42) | 1645.5 | 0.0006077
Apple Mac SE/30 | Mac OS | Think Pascal 3 | (42) | 1645.6 | 0.0006077
Apple Mac IIcx | Mac OS | Lightspeed Pascal 2 | (39.84) | 1561.6 | 0.0006404
Apple Mac IIcx | Mac OS | Lightspeed Pascal 2# | (39.69) | 1555.0 | 0.00006431
Zenith Z386 (16MHz) | MSDOS | Turbo Pascal 5.0 | (38.27) | 1539.0 | 0.0006498
Macintosh SE/30 | Mac OS | Think C | (13.6) | 1508.4 | 0.0006630
386SX (16 MHz) | MSDOS | Turbo Pascal 6.0 | (34) | 1333.6 | 0.0007498
386SX (16 MHz) | MSDOS | Microsoft Quick C | (12.01) | 1333.6 | 0.0007499
Sequent-S81 | DYNIX | Silicon Valley Pascal | (13.0) | 509.0 | 0.0019646
VAX 11/785 | Unix | Berkeley Pascal | (11.9) | 466.3 | 0.002144
80486-33 | MSDOS | Turbo Pascal 6.0 | (11.46) | 449.0 | 0.02227
Sun 3/60 | SunOS | Sun C | (3.93) | 435.7 | 0.002295
NeXT Cube (68030) | Mach | Gnu C | (2.608) | 289.3 | 0.003456
Sequent S-81 | DYNIX | Sequent Symmetry C | (2.604) | 288.9 | 0.003461
VAXstation 3500 | Unix | Berkeley Pascal | (7.3) | 286.5 | 0.003491
Sequent S-81 | DYNIX | Berkeley Pascal | (5.6) | 219.5 | 0.004557
Unisys 7000/40 | Unix | Berkeley Pascal | (5.24) | 205.3 | 0.004870
VAX 8600 | VMS | DEC VAX Pascal | (3.96) | 155.23 | 0.006442
Sun SPARC IPX | SunOS | Gnu C version 2.1 | (1.28) | 142.04 | 0.007040
VAX 6000-530 | VMS | DEC C | (0.858) | 95.14 | 0.010511
VAXstation 4000 | VMS | DEC C | (0.809) | 89.81 | 0.011135
IBM RS/6000 540 | AIX | XLP Pascal | (2.276) | 89.14 | 0.011219
NeXTstation(040/25) | Mach | Gnu C | (0.75) | 83.15 | 0.012027
Sun SPARC IPX | SunOS | Sun C | (0.68) | 75.43 | 0.01326
486DX (33 MHz) | Linux | Gnu C # | (0.63) | 69.95 | 0.01430
Sun SPARCstation-1 | Unix | Sun Pascal | (1.7) | 66.62 | 0.01501
DECstation 5000/200 | Unix | DEC Ultrix C | (0.45) | 49.97 | 0.02001
Sun SPARC 1+ | SunOS | Sun C | (0.40) | 44.37 | 0.02254
DECstation 3100 | Unix | DEC Ultrix Pascal | (0.77) | 30.11 | 0.03321
IBM 3090-300E | AIX | Metaware High C | (0.27) | 29.98 | 0.03336
DECstation 5000/125 | Unix | DEC Ultrix C | (0.267) | 29.58 | 0.03381
DECstation 5000/200 | Unix | DEC Ultrix C | (0.256) | 28.38 | 0.03524
Sun SPARC 4/50 | SunOS | Sun C | (0.249) | 27.62 | 0.03621
DEC 3000/400 AXP | Unix | DEC C | (0.224) | 24.85 | 0.04024
DECstation 5000/240 | Unix | DEC Ultrix C | (0.1889) | 20.96 | 0.04771
SGI Iris R4000 | Unix | SGI C | (0.184) | 20.41 | 0.04898
IBM 3090-300E | VM | Pascal VS | (0.464) | 18.12 | 0.05519
DECstation 5000/200 | Unix | DEC Ultrix Pascal | (0.39) | 15.188 | 0.06583
Pentium 120 | Linux | Gnu C | 1.848 | 11.953 | 0.08366
Pentium Pro 180 | Linux | Gnu C | 1.009 | 6.527 | 0.1532
Pentium 266 MMX | Linux | Gnu C (PHYLIP 3.5) | (0.054) | 5.996 | 0.1668
Pentium 266 MMX | Linux | Gnu C | 0.927 | 5.996 | 0.1668
Pentium 200 | Linux | Gnu C | 0.853 | 5.517 | 0.1812
SGI PowerChallenge | Irix | Gnu C | 0.844 | 5.459 | 0.1832
DEC Alpha 400 4/233 | DUNIX | Digital C (cc -fast) | 0.730 | 4.722 | 0.2118
Pentium II 500 | Linux | Gnu C | 0.368 | 2.380 | 0.4201
Dual 448/633 MHz Pentiums | Linux | gcc | 0.3069 | 1.985 | 0.5037
Sun Ultra 10 | Solaris 8 | gcc | 0.25848 | 1.672 | 0.5981
Macintosh G3 300 MHz | Mac OS X | Gnu C (-O 3) | 0.2330 | 1.5071 | 0.6635
Compaq/Digital Alpha 500au | DUNIX | Digital C (cc -fast) | 0.167 | 1.080 | 0.9257
AMD Athlon 1.2 GHz | Linux | gcc | 0.1546 | 1.0 | 1.0
Intel Pentium 4 2.26 GHz | Windows XP | Cygwin gcc | 0.1078 | 0.6973 | 1.434
Pentium 4 1700 MHz | Linux | Gnu C | 0.10730 | 0.6940 | 1.441
Macintosh G4 1.2GHz | Mac OS X | Gnu C (-O 3) | 0.0582 | 0.3765 | 2.656

This benchmark not only reflects integer performance of these machines (as DNAPARS has few floating-point operations) but also the efficiency of the compilers. Some of the machines (the DEC 3000/400 AXP and the IBM RS/6000, in particular) are much faster than this benchmark would indicate. The numerical programs benchmark below gives them a fairer test. The Compaq/Digital Alpha 500au times are exaggerated because, although their compiles are optimized for that processor, some of the Pentium compiles are not similarly optimized.

Note that parallel machines like the Sequent and the SGI PowerChallenge are not really as slow as indicated by the data here, as these runs did nothing to take advantage of their parallelism.

These benchmarks have now extended over 17 years, and in the DNAPARS benchmark they extend over a range of over 28,000-fold in speed! The experience of our laboratory, which seems typical, is that computer power grows by a factor of about 1.85 per year. This is roughly consistent with these benchmarks.

For a picture of speeds for a more numerically intensive program, here are benchmarks using DNAML, with an AMD Athlon 1.2 GHz Linux system as the standard. Some of the timings, the ones in parentheses, are using PHYLIP version 3.5, and those are compared to that version run on the Pentium 266. Runs using the PHYLIP 3.4 Pascal version are adjusted using the 386SX timings where both were run. Numbers are total run times (total user time in the case of Unix) over all three data sets.

Machine | Operating System | Compiler | Seconds | Time | Relative Speed
386SX 16 Mhz | PCDOS | Turbo Pascal 6 | (7826) | 1027.55 | 0.0009732
386SX 16 Mhz | PCDOS | Quick C | (6549.79) | 1027.55 | 0.0009732
Compudyne 486DX/33 | Linux | Gnu C | (1599.9) | 251.0 | 0.003984
SUN Sparcstation 1+ | SunOS | Sun C | (1402.8) | 220.1 | 0.004543
Everex STEP 386/20 | PCDOS | Turbo Pascal 5.5 | (1440.8) | 189.17 | 0.005286
486DX/33 | PCDOS | Turbo C++ | (1107.2) | 173.70 | 0.005757
Compudyne 486DX/33 | PCDOS | Waterloo C/386 | (1045.78) | 164.07 | 0.006094
Sun SPARCstation IPX | SunOS | Gnu C | (960.2) | 150.64 | 0.006638
NeXTstation(68040/25) | Mach | Gnu C | (916.6) | 143.80 | 0.006954
486DX/33 | PCDOS | Waterloo C/386 | (861.0) | 135.08 | 0.007403
Sun SPARCstation IPX | SunOS | Sun C | (787.7) | 123.58 | 0.008091
486DX/33 | PCDOS | Gnu C | (650.9) | 102.12 | 0.009792
VAX 6000-530 | VMS | DEC C | (637.0) | 99.94 | 0.01001
DECstation 5000/200 | Unix | DEC Ultrix RISC C | (423.3) | 66.41 | 0.01506
IBM 3090-300E | AIX | Metaware High C | (201.8) | 31.65 | 0.03159
Convex C240/1024 | Unix | C | (101.6) | 15.940 | 0.06274
DEC 3000/400 AXP | Unix | DEC C | (98.29) | 15.42 | 0.06485
Pentium 120 | Linux | Gnu C | 25.26 | 19.230 | 0.05200
Pentium Pro 180 | Linux | Gnu C | 18.88 | 14.372 | 0.06957
Pentium 200 | Linux | Gnu C | 16.51 | 12.569 | 0.07956
SGI PowerChallenge | IRIX | Gnu C | 12.446 | 9.475 | 0.10554
DEC Alpha 400 4/233 | Linux | Gnu C (cc -fast) | 8.0418 | 6.122 | 0.16335
Pentium MMX 266 | Linux | Gnu C (PHYLIP 3.5) | (36.15) | 5.671 | 0.17632
Pentium MMX 266 | Linux | Gnu C | 7.45 | 5.671 | 0.17632
Pentium II 500 | Linux | Gnu C | 6.02 | 4.583 | 0.2182
Dual 448/633 MHz Pentiums | Linux | Gnu C | 3.7225 | 2.834 | 0.3529
Sun Ultra 10 | Solaris 8 | Gnu C | 3.7101 | 2.824 | 0.3541
Pentium 4 1.7 GHz | Linux | Gnu C | 2.0668 | 1.5734 | 0.6356
Macintosh G3 300 MHz | Mac OS X | Gnu C (-O 3) | 1.805 | 1.3741 | 0.7278
Intel Pentium 4 2.26 GHz | Windows XP | Cygwin gcc | 1.55457 | 1.1834 | 0.8450
AMD Athlon 1.2 GHz | Linux | Gnu C | 1.3136 | 1.0 | 1.0
Compaq/Digital Alpha 500au | Linux | Gnu C (cc -fast) | 0.9383 | 0.7143 | 1.4000
Macintosh G4 1.2 GHz | Mac OS X | Gnu C (-O 3) | 0.7080 | 0.5390 | 1.8554

As before, the parallel machines such as the Convex and the SGI PowerChallenge were only run using one processor, which does not take into account the gain that could be obtained by parallelizing the programs. The speed of the Compaq/Digital Alpha 500au is exaggerated because it was compiled in a way optimized for its processor, while some of the Pentium compiles were not.

You are invited to send me figures for your machine for inclusion in future tables. Use the data sets above and compute the total times for DNAPARS and for DNAML for the three data sets (setting the frequencies of the four bases to 0.25 each for the DNAML runs). Be sure to tell me the name and version of your compiler, and the version of PHYLIP you tested. If the times are too small to be measured accurately, obtain the times for 10 or 100 data sets (the Multiple data sets option) and divide by 10 or 100.

## General Comments on Adapting
the Package to Different Computer Systems

In the sections following you will find instructions on how to adapt the programs to different computers and compilers. The programs should compile without alteration on most versions of C. They use the "malloc" library or "calloc" function to allocate memory so that the upper limits on how many species or how many sites or characters they can run is set by the system memory available to that memory-allocation function.

In the document file for each program, I have supplied a small input example, and the output it produces, to help you check whether the programs are running properly.

## Compiling the programs

If you have not been able to get executables for PHYLIP, you should be able to make your own. This can be easy under Linux and Unix, but more difficult if you have a Macintosh or a Windows system. If you have the latter, we strongly recommend you download and use the Macintosh and Windows executables that we distribute. If you do that, you will not need to have any compiler or to do any compiling. I get a certain number of inquiries each year from confused users who are not sure what a compiler is but think they need one. After downloading the executables they contact me and complain that they did not find a compiler included in the package, and would I please e-mail them the compiler. What they really need to do is use the executables and forget about compiling them.

Some users may also need to compile the programs in order to modify them. The instructions below will help with this.

I will discuss how to compile PHYLIP using one of a number of widely-used compilers. After these I will comment on compiling PHYLIP on other, less widely-used systems.

### Unix and Linux

In Unix and Linux (which is Unix in all important functional respects, if not in all legal respects) it is often easy to compile PHYLIP yourself. Unix (and Linux) systems generally have a C compiler and have the `make` utility. We distribute with the PHYLIP source code a Unix-compatible `Makefile`.

However, note that some popular Linux distributions do not include a C compiler in their default configuration. For example, in RedHat Linux version 8, the "Personal Workstation" installation that is the default does not include the C compiler or the X Windows libraries needed to compile PHYLIP. These are available, and can be loaded from the CDROMs in the distribution. The following instructions assume that you have the C compiler and X libraries. If you cannot easily configure your system to include them, you should look into using the RedHat RPM binary distribution, mentioned on the PHYLIP 3.6 web page.

As is mentioned below (under Macintoshes) the Mac OS X operating system is a Unix, and if the X windows windowing system is installed, these Unix instructions will work for it.

After you have finished unpacking the Documentation and Source Code archive, you will find that you have created a directory `phylip-3.6` in which there are three subdirectories, called `exe`, `src`, and `doc`. There is also an HTML web page, `phylip.html`. The `exe` directory will be empty, `src` contains the source code files, including the `Makefile`. Directory `doc` contains the documentation files.

Enter the `src` directory. Before you compile, you will want to look at the `Makefile` and see whether you want to alter the compilation command. We have the default C compiler flags set with no flags. If you have modified the programs, you might want to use the debugging flags "-g". On the other hand, if you are trying to make a fast executable using the GCC compiler, you may want to use the one which is "An optimized one for gcc". In either case, remove the "#" before that CFLAGS command, and place it before the CFLAGS command that was previously in use. There are careful instructions on this in the Makefile. Once you have set up the CFLAGS and DFLAGS statements to be the way you want, to compile all the programs just type:

`make install`

You will then see the compiling commands as they happen, with occasional warning messages. If these are warnings, rather than errors, they are not too serious. A typical warning would be like this:

`dnaml.c:1204: warning: static declaration for re_move follows non-static`

After a time the compiler will finish compiling. If you have done a `make install` the system will then move the executables into the `exe` subdirectory and also save space by erasing all the relocatable object files that were produced in the process. You should be left with useable executables in the `exe` directory, and the `src` directory should be as before. To run the executables, go into the `exe` directory and type the program name (say `dnaml`, which you may or may not have to precede by a dot and a slash`./`). The names of the executables will be the same as the names of the C programs, but without the `.c` suffix. Thus `dnaml.c` compiles to make an executable called `dnaml`.

One caution is that our two tree-drawing programs, Drawgram and Drawtree, need to have the Athena Widgets, which are provided with most X Windows installations. If you get error messages saying that some files with "Xaw" in the name cannot be found, this means that either the Athena Widgets are not installed on your system, or that the paths do not find them. If they are there but not found during the compile, a simple change to the DLIBS line in the Makefile may cure the problem.

If you see messages that the compilation could not find "Xlib.h" and other, similar functions, this means that some parts of the X Windows development environment is not installed on your system. You will need to make sure that they are installed properly.

Another is that the usual Linux C compiler is the Gnu GCC compiler. In some Linux systems it is not invoked by the command `cc` but by `gcc`. You would then need to edit the Makefile to reflect this (see below for comments on that process).

A typical Unix or Linux installation would put the directory `phylip-3.6` in `/usr/local`. The name of the executables directory `EXEDIR` could be changed to be `/usr/local/bin`, so that the `make install` command puts the executables there. If the users have `/usr/local/bin` in their paths, the programs would be found when their names are typed. The font files `font1` through `font6` could also be placed there. A batch script containing the lines

          ln -s /usr/local/bin/font1 font1
          ln -s /usr/local/bin/font2 font2
          ln -s /usr/local/bin/font3 font3
          ln -s /usr/local/bin/font4 font4
          ln -s /usr/local/bin/font5 font5
          ln -s /usr/local/bin/font6 font6

could be used to establish links in the user's working directory so that Drawtree and Drawgram would find these font files when users type a name such as `font1` when the program asks them for a font file name. The documentation web pages are in subdirectory `doc` of the main PHYLIP directory, except for one, `phylip.html` which is in the main PHYLIP directory. It has a table of all of the documentation pages, including this one. If users create a bookmark to that page it can be used to access all of the other documentation pages.

To compile just one program, such as DNAML, type:

`make dnaml`

After this compilation, `dnaml` will be in the `src` subdirectory. So will some relocatable object code files that were used to create the executable. These have names ending in `.o` - they can safely be deleted.

If you have problems with the compilation command, you can edit the `Makefile`. It has careful explanations at its front of how you might want to do so. For example, you might want to change the C compiler name `cc` to the name of the Gnu C compiler, `gcc`. This can be done by removing the comment character `#` from the front of one line, and placing it at the front of a nearby line. How to do so should be clear from the material at the beginning of the `Makefile`. We have included sample lines for using the `gcc` compiler and for using the Cygwin Gnu C++ environment on Windows, as well as the default of `cc`.

Some older C compilers (notably the Berkeley C compiler which is included free with some Sun systems) do not adhere to the ANSI C standard (because they were written before it was set down). They have trouble with the function prototypes which are in our programs. We have included an `#ifndef` preprocessor command to eliminate the problem, if you use the switch `-DOLDC` when compiling. Thus with these compilers you need only use this in your C flags (in the Makefile) and compilers such as Berkeley C will cause no trouble.

### Macintosh

Compiling with Metrowerks Codewarrior on Macintosh

We shall assume that you have a recent version of the Metrowerks Codewarrior C++ compiler. This description, and the project files that we provide, assume Codewarrior 8.3. We also assume some familiarity with the use of the Codewarrior compiler and its Integrated Development Environment (IDE). These instructions are for Mac OS 9 or for the Classic environment under Mac OS X, and for native-mode compiling under Mac OS X.

Start with our `src` folder (directory) that contains the C source code files such as `dnaml.c` and also the Codewarrior resource files such as `dnaml.rsrc`, which are provided by us.

We have provided a full set of project files in the self-extracting Macintosh Stuffit archive. Usually users will have no need to compile the programs, but occasionally they may want to change a setting or add a feature. In that case the Metrowerks Codewarrior compiler can be used. We have provided support for compiling the programs in its most recent version, version 8.3. The following discussion will assume that you have obtained and installed the compiler.

You should find in the source code folder `src` a folder called `mac` which contains the Metrowerks Codewarrior compiler "project files" (with names ending in `.proj`, as well as the resource files (which end in `.rsrc` for each program. You can get into this folder, activate the Metrowerks compiler, and open the appropriate project file. To compile the program, simply make sure that the project file is an active window, and type `Command-M` (which is to say, hold down the `Command` key while typing `M`). Alternatively, pull down the `Project` window and select `Make`. The program should then compile, possibly with ignorable warning messages.

Compiling with GCC on Mac OS X with X Windows

On Mac OS X systems you can also use the GCC compiler and X Windows to compile a version of the executables that runs in native mode. To do that, you must have the GCC compiler and the X11 windows development kit materials installed. X Windows is part of the default distribution of Mac OS X for version 10.3 (Panther) and later, but it needs to be installed. For earlier versions of Mac OS X a version of it is available at `http://www.apple.com/macosx/x11/download/` and it is easy to download and install on a Mac OS X system.

Once you have the GCC compiler and the X11 libraries installed, you can use a Terminal window (which you will find available in the Utilities folder in the Applications folder) and compile PHYLIP by treating it as a Unix or Linux application and following the instructions given above under "Unix and Linux". Basically you just get into the folder that contains the PHYLIP source code and type

make install

This uses the ordinary Unix/Linux Makefile, which works well for Mac OS X with the gcc compiler.

Creating the Metrowerks project file.

As said above, for the Metrowerks compiler we have provided you with a complete set of project files. Generally, you will not need to create project files. But if you do want to create them and compile using them yourself, we have instructions here. We will use Dnaml as our example:

1. Start up the Codewarrior IDE integrated development environment.
2. Create a new project file by choosing `New...` on the `File` menu.
3. Type in the project name `dnaml.proj`
4. On the Project menu on the left side of the `New` window, double-click on `Mac OS C Stationery`
5. In the `New project` window that opens, click on the triangle to the left of `Mac OS Classic` (if you are using Mac OS 9) or `Mac OS X Mach-O` (if you are using Mac OS X).
6. Then click on the triangle to the left of `Standard Console`.
7. Click on the menu item `SIOUX-WASTE C Classic` (if you are using Mac OS 9) or `C Console SIOUX Mach-O` (if you are using Mac OS X).
8. Press the `OK` button. After a bit a window `dnaml.proj` will open.
9. Click on the triangle to the left of the `Sources` menu item. A template item called `HelloWorld.c` will open.
10. Select `HelloWorld.c`.
11. Open the `Edit` menu at the top of the Mac screen and select `Clear`. A box will open asking if you want to remove `HelloWorld.c` from the project.
12. Select `OK`.
13. If the `dnaml.c` file came from the self-extracting Macintosh archive that we distribute, it should show a yellow-and-back-striped Metrowerks icon (if not, as when you get it from some other form of our distribution, you may have to pass it through a program like Microsoft Word, making sure to save it as a Text Only file, to get Metrowerks to be able to see it as a potential source code file).
14. Drag the `dnaml.c` file onto the `Sources` item in your `dnaml.proj` window.
15. Drop it onto Sources so that it appears under the `Sources` choice. This may take a few tries -- if it appears above `Sources` grab it and move it again.
16. Now add the other files that must be compiled with `dnaml.c`. These can be identified by looking at our `Makefile` -- for Dnaml they are `seq.c`, `phylip.c`, `seq.h`, and `phylip.h`. Each of them needs to be added to the project file in the same way that `dnaml.c` was.
17. In addition to the files indiciated in the `Makefile`, `macface.c` and `macface.h` (or `interface.c` and `interface.h` if making the project file for drawtree or drawgram) need to be added to the project in the same way as mentioned in the previous step.
18. Drag `dnaml.rsrc` (if you are using Mac OS 9) or `dna.plc` and `dna.icns` (if you are using Mac OS X) into `Sources` in the same way. It doesn't matter whether these appear before or after `dnaml.c`.
19. Go to the `Edit` menu and select the `PPC Std C SIOUX-WASTE Settings` item. A window of that name will then open.
20. Under the `Target` item select `PPC Target` (if you are using Mac OS X). A window will open to the right.
21. Change the name in the `File Name` box to be `dnaml` (if you are using Mac OS 9) or `dnaml.app` (if you are using Mac OS X).
22. If you are using Mac OS 9, change the `????` in the `Creator` box to (say) `PHYD`
23. Change the `Preferred Heap Size` to `1024` (you may also need to have it default to larger than that).
24. Under `Language Settings` in the left-hand menu of the window, select `C/C++ Language`. A window called `C/C++ Language` will open to the immediate right.
25. (If you are using Mac OS X) Change the Prefix File from `MSLCarbonPrefix.h` to `osx_mwcw_prefix.h` (this file should be included with our source code files).
26. Click on `Require Function Prototypes` to deselect that setting.
27. Click on the `Save` button at the lower-right of the project settings window.
28. Close the `PPC Std C SIOUX-WASTE Settings` window using the usual box in the upper-left corner.
29. On your Desktop you should now find a folder `PHYLIP`. If it has a file called `HelloWorld.c` you may want to discard that file.
30. In that `PHYLIP` folder you will find a file `dnaml.proj`.
31. Double-click on that project file. If the Metrowerks is not already open, it should open now.
32. If a window called `Project Messages` opens and there is a complaint in it about access paths being wrong, you should fix these by selecting the `Reset project entry paths` item in the `Project` menu.
33. In the `Project` menu, select `Make`

### Windows systems

Compiling with Microsoft Visual C++

With Microsoft Visual C++, you can compile using a Makefile. We have supplied this in the source code distrubution as `Makefile.msvc`. You will need to preserve the Unix Makefile by renaming `Makefile` to, say, `Makefile.unix`, then make a copy of `Makefile.msvc` and call it `Makefile`. (You may have to change your windows' settings to make the three-letter extensions visible, or you could use the RENAME command in the Command tool).

Setting the path. Before using `nmake` you will need to have the paths set properly. For this, use the Start menu to open Command or a Dos Prompt first. To set the path type

    set MSVC=Path

where Path is where Microsoft Visual Studio is installed (e.g. it might be in `c:\Microsoft Visual Studio`). However the path you type should not have any spaces in it, so that pathname cannot be used. There are sometimes alternative paths that can get you to the same place. In my lab's Windows XP system, the path `C:\msvsnet\Vc7` also leads to the same place, and that pathname could be used. You may have to work out an appropriate pathname yourself. This means that you may have to use a folder's DOS filename. One way to get the DOS filename is to use the command `DIR /X` in a Command window. In general to get a DOS name you take the first six letters of the folder name and follow them by `~1`. For example, `Microsoft Visual Studio` will have a DOS name `Micros~1`, `Program Files` will be `Progra~1`). Depending on what other file are in the folder the DOS name may be the first six letters followed by `~2,~3,~4`, etc... (e.g. `Micros~3` or `Progra~5`). It may take some experimentation to figure it out. With older Versions of Windows (pre-win2000) it may be possible to just right click on the folder icon and select Properties to get the DOS name.

Once you have set MSVC, type

    PATH=%PATH%;%MSVC%\VC98\bin

If you are using Visual C++ 6.0, or type

    PATH=%PATH%;%MSVC%\VC7\bin

if you are using Visual C++ .NET 2002 (or later). Then the Makefile will need to be edited. The line

    MSVCPATH=c:\Micros~1\VC98

will need to be changed so that It points to wherever Microsoft Visual Studio is installed followed by `\VC98` (for Visual Studio 6.0) or `\VC7` (for Visual Studio C++ .NET 2002 or later).

Using the Makefile. The Makefile is invoked using the `nmake` command. If you simply type `nmake` you will get a list of possible `make` commands. For example, to compile a single program such as `Dnaml` but not install it, type `nmake dnaml`. To compile and install all programs type `nmake install`. We have supplied all the support files and icons needed for the compilations. They are in folder `msvc` in the main source code folder.

However, notice that as of latest Microsoft Visual C++ .NET 2002 some of the important compiler optimizations are available only in the Professional edition, which is expensive. For this reason, we have switched to the GCC compiler for producing our distributed Windows executables.

Compiling with Borland C++

Borland C++ can be downloaded for free from Inprise (Borland) (see their site http://www.borland.com/products/downloads/download_cbuilder.html. You should download the compiler as it includes all the utilities needed to compile phylip. It can compile using a Makefile. We have supplied this in the source code distribution as `Makefile.bcc`. You will need to preserve the Unix Makefile by renaming it to, say, `Makefile.unix`, then make a copy of `Makefile.bcc` and call it `Makefile`. The `Makefile` is invoked using the make command.

You will first need to create an `ilink32.cfg` and a `bcc32.cfg` file and put the files into the `phylip-3.6/src` directory. These files are text files and their contents are described in the `readme.txt` that comes with the Borland tools. If the Borland tools are in the default location the contents of `ilink32.cfg` would be.

    -L"c:\Borland\Bcc55\lib"

and the contents of `bcc32.cfg`

    -I"c:\Borland\Bcc55\include"
    -L"c:\Borland\Bcc55\lib"

These files can be created in a text editor such as Notepad or Wordpad.

To invoke the `make` command you will first need to open a command prompt window. Then set the path appropriately. To set the path, type

    set BORLAND=Path

Where "Path" is where Borland is installed, such as `C:\Borland\BCC55`. Then type

    PATH=%PATH%;%BORLAND%\Bin

If you simply type `make` you will get a list of possible make commands. For example, to compile a single program such as Dnaml but not install it, type `make dnaml`. To compile and install all programs type `make install`. We have supplied all the the support files and icons needed for the compilations. They are in folder `bcc` of the main PHYLIP ource code folder. We have had to supply a complete second set of the resource files with names `*.brc` because Borland resource files have a minor incompatibility with Microsoft Visual C++ resource files.

Compiling with Metrowerks Codewarrior for Windows

As with Macintosh systems, Metrowerks Codewarrior requires you to have project files for each program you compile. For Metrowerks Codewarrior for Windows we are not providing the projects themselves, but we are providing projects which have been exported as XML files. To open one of these one cannot just click on File/Open but instead on the menu option File/Import Project. Metrowerks will then ask you for the project name. Type in the name of the program (e.g. dnaml). Once this is done Metrowerks will act like this is a regular project file.

We have supplied a complete set of these XML project files in the source code distribution. They are in folder `metro` of the main source code folder. This is supplied with the source code distribution for Windows (it is not in the source code distributions for other platforms). For Metrowerks Codewarrior for Windows we are not providing the projects themselves, but we are providing projects which have been exported as XML files. To open one of these one cannot just click on File/Open but instead on the menu option File/Import Project. Metrowerks will then ask you for the project name. Type in the name of the program (e.g. dnaml). Once this is done Metrowerks will act like this is a regular project file.

To compile the program pull down the `Project` menu and select `Make`. The program should then compile, possibly with ignorable warning messages.

For the moment we are not giving here the details of how to create these projects yourself -- you usually will not need to, as you have the project files we have supplied.

Compiling with Cygnus Gnu C++

Cygnus Solutions (now a part of Red Hat, Inc.) has adapted the Gnu C compiler to Windows systems and provided an environment, CygWin, which mimics Unix for compiling. This is available for purchase from them, and they also make it available to be downloaded for free. The download is large. To get it, go to their download site at `http://sources.redhat.com/cygwin` and follow the instructions there. To download it you need to download their `setup.exe` program and then it will download the rest when it is run. You will need a lot of disk space for it (about a gigabyte).

When installing Cygwin it is importand to install gcc and make. During the course of the setup program Setup will ask you to select packages. Expand the Devel Category by clicking on it. Scroll down to gcc and click on "skip". "Skip" will change to the current version of gcc. Scroll down to the make package and click on "Skip" again. These two programs are nessessary to install phylip.

Once you have installed the free Cygnus environment and the associated Gnu C compiler on your Windows system, compiling PHYLIP is essentially identical to what one does for Unix or Linux. In PHYLIP's `src` folder, change the name of our Unix `Makefile` to something like `Makefile.unx` (so as to keep it around). There is a special Makefile for the Cygwin compiler called `Makefile.cyg`. Make a copy of it called `Makefile`.

This Makefile should contain a compiling command:

`CC = gcc`

Now enter the Cygwin environment (which you can do using the Windows `Start` menu and its `Programs` menu item. There should be a `Cygnus` menu choice within that submenu, which you can use to start the Cygnus environment. This puts you in an imitation of a Unix shell.

On entering the CygWin environment you will find yourself in one of the folders within the CygWin folder. Change to the folder where the PHYLIP programs have been put, for example by issuing the command

`cd c:/phylip-3.6`
You should then be able to compile PHYLIP by issuing the appropriate make command, such as `make install`. If you have modified one of our source code files such as `dnaml.c`, it would be wise to have saved the original version of it first as, say, `dnaml.c0`. To associate an icon with a program (say DnaML), you need an icon file (say `dna.ico`) which contains the icon in standard format. There should also be a file called `dnaml.rc` which contains the single line:

`dnaml ICON "dna.ico"`

We have provided a folder `icons` in the `src` folder, containing a full set of icons and a full set of resource files (`*.rc`). Our Cygwin Makefile will automatically invoke them.

### VMS VAX systems

We have not tried to compile version 3.6 on an OpenVMS system but the following instructions should work. On the OpenVMS operating system with DEC VAX VMS C the programs will compile without alteration. The commands for compiling a typical program (DNAPARS, which depends on the separately compiled files `phylip.c` and `seq.c`) are:

`$ DEFINE LNK$LIBRARY SYS$LIBRARY:VAXCRTL
$ CC DNAPARS.C
$ CC PHYLIP.C
$ CC SEQ.C
$ LINK DNAPARS,PHYLIP,SEQ
`

Once you use this `$ DEFINE` statement during a given interactive session, you need not repeat it again as the symbol `LNK$LIBRARY` is thereafter properly defined. The compilation process leaves a file `DNAPARS.OBJ` in your directory: this can be discarded. The executable program is named `DNAPARS.EXE`. To run the program one then uses the command:

`$ R DNAPARS`

The compiler defaults to the filenames `INFILE.`, `OUTFILE.`, and `TREEFILE.`. If the input file `INFILE.` does not exist the program will prompt you to type in its name. Note that some commands on VMS such as `TYPE OUTFILE` will fail because the name of the file that it will attempt to type out will be not `OUTFILE.` but `OUTFILE.LIS`. To get it to type the write file you would have to instead issue the command `TYPE OUTFILE.`.

When you are using the interactive previewing feature of DRAWGRAM (or DRAWTREE) on a Tektronix or DEC ReGIS compatible terminal, you will want before running the program to have issued the command:

`$ SET TERM/NOWRAP/ESCAPE`

so that you do not run into trouble from the VMS line length limit of 255 characters or the filtering of escape characters.

To know which files to compile together, look at the entries in the `Makefile`.

VMS systems are rapidly disappearing, so we will not devote much effort to get PHYLIP working on them.

### Parallel computers

As parallel computers become more common, the issue of how to compile PHYLIP for them has become more pressing. People have been compiling PHYLIP for vector machines and parallel machines for many years. We have not made a version for parallel machines because there is still no standard parallel programming environment on such machines (or rather, there are many standards, so that one cannot find one that makes a parallel execution version of PHYLIP widely distributable). However symmetric multiprocessing using the MPI Message Passing Interface is spreading rapidly, and we will probably support it in future versions of PHYLIP.

Although the underlying algorithms of most programs, which treat sites independently, should be amenable to vector and parallel processors, there are details of the code which might best be changed. In certain of the programs (`Dnaml`, `Dnamlk`, `Proml`, `Promlk`) I have put a special comment statement next to the loops in the program where the program will spend most of its time, and which are the places most likely to benefit from parallelization. This comment statement is:

               /* parallelize here */

In particular within these innermost loops of the programs there are often scalar quantities that are used for temporary bookkeeping. These quantities, such as `sum1, sum2, zz, z1, yy, y1, aa, bb, cc, sum,` and `denom` in procedure makenewv of `Dnaml` and similar quantities in procedure nuview) are there to minimize the number of array references. For vectorizing and parallelizing compilers it will be better to replace them by arrays so that processing can occur simultaneously.

If you succeed in making a parallel version of PHYLIP we would like to know how you did it. In particular, if you can prepare a web page which describes how to do it for your computer system, we would like to use material from it in our PHYLIP web pages. Please e-mail it to me. We hope to have a set of pages that give detailed instructions on how to make parallel version of PHYLIP on various kinds of machines. Alternatively, if we were given your modified version of the program we might be able to figure out how to make modifications to our source code to allow users to compile the program in a way which makes those modifications.

### Other computer systems

As you can see from the variety of different systems on which these programs have been successfully run, there are no serious incompatibility problems with most computer systems. PHYLIP in various past Pascal versions has also been compiled on 8080 and Z80 CP/M Systems, Apple II systems running UCSD Pascal, a variety of minicomputer systems such as DEC PDP-11's and HP 1000's, on 1970's era mainframes such as CDC Cyber systems, and so on. In a later era it was also compiled on IBM 370 mainframes, and of course on DOS and Windows systems and on Macintosh systems. We have gradually accumulated experience on a wider variety of C compilers. If you succeed in compiling the C version of PHYLIP on a different machine or a different compiler, I would like to hear the details so that I can consider including the instructions in a future version of this manual.

## Frequently Asked Questions

This set of Frequently Asked Questions, and their answers, is from the PHYLIP web site. A more up-to-date version can be found there, at:

`http://evolution.gs.washington.edu/phylip/faq.html`

### Problems that are encountered

"It doesn't work! It doesn't work!! It says `can't find infile.`"
  Actually, it's working just fine. Many of the programs look for an input file called `infile`, and if one of that name is not present in the current folder, they then ask you to type in the name of the input file. That's all that it's doing. This is done so that you can get the program to read the file without you having to type in its name, by making a copy of your input file and calling it `infile`. If you don't do that, then the program issues this message. It looks alarming, but really all that it is trying to do is to get you to type in the name of the input file. Try giving it the name of the input file.
"The program reads my data file and then says it has a memory allocation error!"
  This is what tends to happen if there is a problem with the format of the data file, so that the programs get confused and think they need to set aside memory for 1,000,000 species or so. The result is a "memory allocation error" (the error message may say that "the function asked for an inappropriate amount of memory"). Check the data file format against the documentation: make sure that the data files have not been saved in the format of your word processor (such as Microsoft Word) but in a "flat ASCII" or "text only" mode. Note that adding memory to your computer is not the way to solve this problem -- you probably have plenty of memory to run the program once the data file is in the correct format.
"On our Mac OS 9 system, larger data files fail to run."
  We have set the memory allowances on the Mac OS 9 executables to be generous, but not too big. You therefore may need to increase them. Use the `Get Info` item on the Finder `File` menu.
"I opened the program but I don't see where to create a data file!"
  The programs (there are more than one) use data files that have been created outside of the program. They do not have any data editor within them. You can create a data file by using an editor, such as Microsoft Word, EMACS, vi, SimpleText, Notepad, etc. But be sure not to save the file in Microsoft Word's own format. It should be saved in Text Only format. You can use the documentation files, including the examples at the end of those files, to figure out the format of the input file. Documentation files such as `main.html`, `sequence.html`, `distance.html` and many others should be consulted. Many users create their data files by having their alignment program (such as ClustalW), output its alignments in PHYLIP format. Many alignment programs have options to do that.
"I ran PHYLIP, and all it did was say it was extracting a bunch of files!"
  There is no executable program named `PHYLIP` in the PHYLIP package! But in some cases (especially the Windows distribution) there is a file called `phylip.exe`. That file is an archive of documentation and source code. Once you have run it and extracted the files in it, so that they are in the folder, running it again will just do the extraction again, which is unnecessary. Similarly for the archive files for the Windows executables, which have names like `phylipwx.exe` and `phylipwy.exe`. They are run only once to extract their contents.
"One program makes an output file and then the next program crashes while reading it!"
  Did you rename the file? If a program makes a file called `outfile`, and then the next program is told to use `outfile` as its input file, terrible things will happen. The second program first opens `outfile` as an output file, thus erasing it. When it then tries to read from this empty `outfile` a psychological crisis ensues. The solution is simply to rename `outfile` before trying to use it as an input file.
"I make a file called infile and then the program can't find it!"
  Let me guess. You are using Windows, right? You made your file in Word or in Notepad or WordPad, right? If you made a file in one of these editors, and saved it, not in Word format, but in Text Only format, then you were doing the right thing. But when you told the operating system to save the file as `infile`, it actually didn't. It saved it as `infile.txt`. Then just to make life harder for you, the operating system is set up by default to not show that three-letter extension to the file name. Next to its icon it will show the name `infile`. So you think, quite reasonably, that there is a file called `infile`. But there isn't a file of that name, so the program, quite reasonably, can't find a file called `infile`. If you want to check what the actual file name is, use the `Properties` menu item of the `File` item on your folder (in Windows versions, anyway). You should be able to get the program to work by telling it that the file name is `infile.txt`.
"Consense gives wierd branch lengths! How do I get more reasonable ones?"
  Consense gives branch lengths which are simply the numbers of replicates that support the branch. This is not a good reflection of how long those branches are estimated to be. The best way to put better branch lengths on a consensus tree is to use it as a User Tree in a program that will estimate branch lengths for it, such as DnaML. You may need to convert it to being an unrooted tree, using Retree, first. If the original program you were using was a program that does not estimate branch lengths, you may instead have to use one that does. You can use a likelihood program, or make some distances between your species (using, for example, DNADIST) and use FITCH to put branch lengths on the user tree. Here is the sequence of steps you should go through:
  1. Take the tree and use Retree to make sure it is Unrooted (just read it into Retree and then save it, specifying Unrooted)
  2. Use the unrooted tree as a User Tree (option `U`) in one of our programs (such as FITCH or DNAML). If you use FITCH, you also first need to use one of the distance programs such as DNADIST to compute a set of distances to serve as its input.
  3. Specify that the branch lengths of the tree are not to be used but should be re-estimated. This is actually the default.
"I looked at the tree printed in the output file `outfile` and it looked wierd. Do I always need to look at it in Drawgram?"
  It's possible you are using the wrong font for looking at the tree in the output file. The tree is drawn with dashes and exclamation points. If a proportional font such as Times Roman or Helvetica is used, the tree lines may not connect. Try selecting the whole tree and setting the font to a fixed-width one such as Courier. You may be astounded how much clearer the tree has become.
"DrawTree (or DrawGram) doesn't work: it can't find the font file!"
  Six font files, called `font1` through `font6`, are distributed with the executables (and with the source code too). The program looks for a copy of one of them called `fontfile`. If you haven't made such a copy called `fontfile` it then asks you for the name of the font file. If they are in the current folder, just type one of `font1` through `font6`. The reason for having the program look for `fontfile` is so that you can copy your favorite font file, call the copy `fontfile`, and then it will be found automatically without you having to type the name of the font file each time.
"Can DrawGram draw a scale beside the tree? Print the branch lengths as numbers?"
  It can't do either of these. Doing so would make the program more complex, and it is not obvious how to fit the branch length numbers into a tree that has many very short internal branches. If you want these scales or numbers, choose an output plot file format (such as Postscript, PICT or PCX) that can be read by a drawing program such as Adobe Illustrator, Freehand, Canvas, CorelDraw, or MacDraw. Then you can add the scales and branch length numbers yourself by hand. Note the menu option in DrawTree and DrawGram that specifies the tree size to be a given number of centimeters per unit branch length.
"How can I get DrawGram or DrawTree to print the bootstrap values next to the branches?"
  When you do bootstrapping and use Consense, it prints the bootstrap values in its output file (both in a table of sets, and on the diagram of the tree which it makes). These are also in the output tree file of Consense. There they are in place of branch lengths. So to get them to be on the output of DrawGram or DrawTree, you must write the tree in the format of a drawing program and use it to put the values in by hand, as mentioned in the answer to the previous question.
"I have an HP laser printer and can't get DrawGram to print on it"
  DRAWGRAM and DRAWTREE produce a plot file (called `plotfile`): they do not send it to the printer. It is up to you to get the plot file to the printer. If you are running Windows this can probably be done with the Command tool and the command `COPY/B PLOTFILE PRN:`, unless your printer is a networked printer. The `/B` is important. If it is omitted the copy command will strip off the highest bit of each byte, which can cause the printing to fail or produce garbage.
"DNAML won't read the treefile that is produced by DNAPARS!"
  That's because the DNAPARS tree file is a rooted tree, and DNAML wants an unrooted tree. Try using Retree to change the file to be an unrooted tree file. Our most recent versions of the programs usually automatically convert a rooted tree into an unrooted one as needed. But the programs such as DNAMLK or DOLLOP that need a rooted tree won't be able to use an unrooted tree.
"In bootstrapping, SEQBOOT makes too large a file"
  If there are 1000 bootstrap replicates, it will make a file 1000 times as long as your original data set. But for many methods there is another way that uses much less file space. You can use SEQBOOT to make a file of multiple sets of weights, and use those together with the original data set to do bootstrapping.
"In bootstrapping, the output file gets too big."
  When running a program such as NEIGHBOR or DNAPARS with multiple data sets (or multiple weights) for purposes of bootstrapping, the output file is usually not needed, as it is the output tree file that is used next. You can use the menu of the program to turn off the writing of trees into the output file. The trees will still be written into the output tree file.
"Why don't your programs correctly read the sequence alignment files produced by ClustalW?"
  They do read them correctly if you make the right kind. Files from ClustalV or ClustalW whose names end in `".aln"` are not in PHYLIP format, but in Clustal's own format which will not work in PHYLIP. You need to find the option to output PHYLIP format files, which ClustalW and ClustalV usually assign the extension `.phy`.
"Why doesn't NEIGHBOR read my DNA sequences correctly?"
  Because it wants to have as input a distance matrix, not sequences. You have to use DNADIST to make the distance matrix first.

  ### How to make it do various things

"How do I bootstrap?"
  The general method of bootstrapping involves running SEQBOOT to make multiple bootstrapped data sets out of your one data set, then running one of the tree-making programs with the Multiple data sets option to analyze them all, then running CONSENSE to make a majority rule consensus tree from the resulting tree file. Read the documentation of SEQBOOT to get further information. Before, only parsimony methods could be bootstrapped. With this new system almost any of the tree-making methods in the package can be bootstrapped. It is somewhat more tedious but you will find it much more rewarding.
"How do I specify a multi-species outgroup with your parsimony programs?"
  It's not a feature but is not too hard to do in many of the programs. In parsimony programs like MIX, for which the W (Weights) and A (Ancestral states) options are available, and weights can be larger than 1, all you need to do is:

  (a) | In MIX, make up an extra character with states 0 for all the outgroups and 1 for all the ingroups. If using DNAPARS the ingroup can have (say) `G` and the outgroup `A`.
  (b) | Assign this character an enormous weight (such as `Z` for 35) using the W option, all other characters getting weight 1, or whatever weight they had before.
  (c) | If it is available, Use the A (Ancestral states) option to designate that for that new character the state found in the outgroup is the ancestral state.
  (d) | In MIX do not use the O (Outgroup) option.
  (e) | After the tree is found, the designated ingroup should have been held together by the fake character. The tree will be rooted somewhere in the outgroup (the program may or may not have a preference for one place in the outgroup over another). Make sure that you subtract from the total number of steps on the tree all steps in the new character.
  In programs like DNAPARS, you cannot use this method as weights of sites cannot be greater than 1. But you do an analogous trick, by adding a largish number of extra sites to the data, with one nucleotide state ("A") for the ingroup and another ("G") for the outgroup. You will then have to use RETREE to manually reroot the tree in the desired place.

"How do I force certain groups to remain monophyletic in your parsimony programs?"
  By the same method as in the previous question, using multiple fake characters, any number of groups of species can be forced to be monophyletic. In MOVE, DOLMOVE, and DNAMOVE you can specify whatever outgroups you want without going to this trouble.
"How can I reroot one of the trees written out by PHYLIP?"
  Use the program RETREE. But keep in mind whether the tree inferred by the original program was already rooted, or whether you are free to reroot it without changing its meaning.
"What do I do about deletions and insertions in my sequences?"
  The molecular sequence programs will accept sequences that have gaps (the "`-`" character). They do various things with them, mostly not optimal. DNAPARS counts "gap" as if it were a fifth nucleotide state (in addition to A, C, G, and T). Each site counts one change when a gap arises or disappears. The disadvantage of this treatment is that a long gap will be overweighted, with one event per gapped site. So a gap of 10 nucleotides will count as being as much evidence as 10 single site nucleotide substitutions. If there are not overlapping gaps, one way to correct this is to recode the first site in the gap as "`-`" but make all the others be "`?`" so the gap only counts as one event. Other programs such as DNAML and DNADIST count gaps as equivalent to unknown nucleotides (or unknown amino acids) on the grounds that we don't know what would be there if something were there. This completely leaves out the information from the presence or absence of the gap itself, but does not bias the gapped sequence to be close to or far from other gapped or ungapped sequences. So it is not necessary to remove gapped regions from your sequences, unless the presence of gaps indicates that the region is badly aligned.
"How can I produce distances for my data set which has 0's and 1's?"
  You can't do it in a simple and general way, for a straightforward reason. Distance methods must correct the distances for superimposed changes. Unless we know specifically how to do this for your particular characters, we cannot accomplish the correction. There are many formulas we could use, but we can't choose among them without much more information. There are issues of superimposed changes, as well as heterogeneity of rates of change in different characters. Thus we have not provided a distance program for 0/1 data. It is up to you to figure out what is an appropriate stochastic model for your data and to find the right distance formulas.
"I have RFLP fragment data: which programs should I use?"
  This is a more difficult question than you may imagine. Here is quick tour of the issues:
  - You can code fragments as 0 and 1 and use a parsimony program. It is not obvious in advance whether 0 or 1 is ancestral, though it is likely that change in one direction is more likely than change in the other for each fragment. One can use either Wagner parsimony (programs `PARS`, `MIX`, `PENNY` or `MOVE`) or use Dollo parsimony (`DOLLOP, DOLPENNY` or `DOLMOVE`) with the ancestral states all set as unknown ("`?`").
  - You can use a distance matrix method using the RFLP distance of Nei and Li (1979). Their restriction fragment distance is available in our program RestDist.
  - You should be very hesitant to bootstrap RFLP's. The individual fragments do not evolve independently: a single nucleotide substitution can eliminate one fragment and create two (or vice versa).
  For restriction sites (rather than fragments) life is a bit easier: they evolve nearly independently so bootstrapping is possible and `RESTML` can be used, as well as restriction sites distances computed in `RESTDIST`. Also directionality of change is less ambiguous when parsimony is used. A more complete tour of the issues for restriction sites and restriction fragments is given in chapter 15 of my book (Felsenstein, 2004).
"Why don't your parsimony programs print out branch lengths?"
  Well, DNAPARS and PARS can. The others have not yet been upgraded to the same level. The longer answer is that it is because there are problems defining the branch lengths. If you look closely at the reconstructions of the states of the hypothetical ancestral nodes for almost any data set and almost any parsimony method you will find some ambiguous states on those nodes. There is then usually an ambiguity as to which branch the change is actually on. Other parsimony programs resolve this in one or another arbitrary fashion, sometimes with the user specifying how (for example, methods that push the changes up the tree as far as possible or down it as far as possible). Our older programs leave it to the user to do this. In DNAPARS and PARS we use an algorithm discovered by Hochbaum and Pathria (1997) (and independently by Wayne Maddison) to compute branch lengths that average over all possible placements of the changes. But these branch lengths, as nice as they are, do not correct for mulitple superimposed changes. Few programs available from others currently correct the branch lengths for multiple changes of state that may have overlain each other. One possible way to get branch lengths with nucleotide sequence data is to take the tree topology that you got, use RETREE to convert it to be unrooted, prepare a distance matrix from your data using DNADIST, and then use FITCH with that tree as User Tree and see what branch lengths it estimates.
"Why can't your programs handle unordered multistate characters?"
  In this 3.6 release there is a program PARS which does parsimony for undordered multistate characters with up to 8 states, plus `?`. The other the discrete characters parsimony programs can only handle two states, `0` and `1`. This is mostly because I have not yet had time to modify them to do so - the modifications would have to be extensive. Ultimately I hope to get these done. If you have four or fewer states and need a feature that is not in PARS, you could recode your states to look like nucleotides and use the parsimony programs in the molecular sequence section of PHYLIP, or you could use one of the excellent parsimony programs produced by others.

  ### Background information needed:

"What file format do I use for the sequences?"
"How do I use the programs? I can't find any documentation!"
  These are discussed in the documentation files. Do you have them? If you have a copy of this page you probably do. They are in a separate archive from the executables (they are in the Documentation and Sources archives, which you should definitely fetch). Input file formats are discussed in `main.html`, in `sequence.html`, `distance.html`, `contchar.html`, `discrete.html`, and the documentation files for the individual programs.
"Where can I find out how to infer phylogenies?"
  There are now a few books. For molecular data you could use one of these:

  At the upper-undergraduate level:

  - Graur, D. and W.-H. Li. 2000. Fundamentals of Molecular Evolution. Sinauer Associates, Sunderland, Massachusetts. (or the earlier edition by Li and Graur).
  - Page, R. D. P. and E. C. Holmes. 1998. Molecular Evolution: A Phylogenetic Approach. Blackwell, Oxford.

  and as graduate-level texts:

  - Nei, M. and S. Kumar. 2000. Molecular Evolution and Phylogenetics. Oxford University Press, Oxford.
  - Li, W.-H. 1999. Molecular Evolution. Sinauer Associates, Sunderland, Massachusetts.

  For more mathematically-oriented readers, there is the book

  - Semple, C., and M. Steel. 2003. Phylogenetics. Oxford Lecture Series in Mathematics and Its Applications, volume 24. Oxford University Press, Oxford.

  Best of all is of course my own book on phylogenies, which covers the subject for many data types, at a graduate course level:

  - Felsenstein, J. 2004. Inferring Phylogenies. Sinauer Associates, Sunderland, Massachusetts.

  There are also some recent books that take a more practical hands-on approach, and give some detailed information on how to use programs, including PHYLIP programs. These include:

  - Hall, B. G. 2004. Phylogenetic Trees Made Easy, 2nd edition. Sinauer Associates, Sunderland, Massachusetts.
  - Salemi, M., and A.-M. Vandamme (eds.) 2003. The Phylogenetic Handbook. A Practical Approach to DNA and Protein Phylogeny. Cambridge University Press, Cambridge.

  In addition, one of these three review articles may help:

  - Swofford, D. L., G. J. Olsen, P. J. Waddell, and D. M. Hillis. 1996. Phylogenetic inference. pp. 407-514 in Molecular Systematics, 2nd ed., ed. D. M. Hillis, C. Moritz, and B. K. Mable. Sinauer Associates, Sunderland, Massachusetts.
  - Felsenstein, J. 1988. Phylogenies from molecular sequences: inference and reliability. Annual Review of Genetics 22: 521-565.
  - Felsenstein, J. 1988. Phylogenies and quantitative characters. Annual Review of Ecology and Systematics 19: 445-471.

  A useful article introducing the inference of phylogenies at a more elementary level is:

  - Baldauf, S. L. 2003. Phylogeny for the faint of heart: a tutorial. Trends in Genetics 19: 345-351.

  I have already mentioned above that there is an excellent guide to using PHYLIP 3.6 for molecular analyses available. It is by Jarno Tuimala:

  - Tuimala, J. 2004. A Primer to Phylogenetic Analysis using Phylip Package. 2nd edition. Center for Scientific Computing, Espoo, Finland.
  and it is available as a PDF here.

  ### Questions about distribution and citation:

"If I copied PHYLIP from a friend without you knowing, should I try to keep you from finding out?"
  No. It is to your advantage and mine for you to let me know. If you did not get PHYLIP "officially" from me or from someone authorized by me, but copied a friend's version, you are not in my database of users. You may also have an old version which has since been substantially improved. I don't mind you "bootlegging" PHYLIP (it's free anyway), but you should realize that you may have copied an outdated version. If you are reading this Web page, you can get the latest version just as quickly over Internet. It will help both of us if you get onto my mailing list. If you are on it, then I will give your name to other nearby users when they ask for the names of nearby users, and they are urged to contact you and update your copy. (I benefit by getting a better feel for how many distributions there have been, and having a better mailing list to use to give other users local people to contact). Use the registration form which can be accessed through our web site's registration page.
"How do I make a citation to the PHYLIP package in the paper I am writing?"
  One way is like this:

  Felsenstein, J. 2004. PHYLIP (Phylogeny Inference Package) version 3.6. Distributed by the author. Department of Genome Sciences, University of Washington, Seattle.

  or if the editor for whom you are writing insists that the citation must be to a printed publication, you could cite a notice for version 3.2 published in Cladistics:

  Felsenstein, J. 1989. PHYLIP - Phylogeny Inference Package (Version 3.2). Cladistics 5: 164-166.

  For a while a printed version of the PHYLIP documentation was available and one could cite that. This is no longer true. Other than that, this is difficult, because I have never written a paper announcing PHYLIP! My 1985b paper in Evolution on the bootstrap method contains a one-paragraph Appendix describing the availability of this package, and that can also be cited as a reference for the package, although it was distributed since 1980 while the bootstrap paper is 1985. A paper on PHYLIP is needed mostly to give people something to cite, as word-of-mouth, references in other people's papers, and electronic newsgroup postings have spread the word about PHYLIP's existence quite effectively.

"Can I make copies of PHYLIP available to the students in my class?"
  Generally, yes. Read the Copyright notice near the front of this main documentation page. If you charge money for PHYLIP, or use it in a service for which you charge money, you will need to negotiate a royalty. But you can make it freely available and you do not need to get any special permission from us to do so.
"How many copies of PHYLIP have been distributed?"
  On 27 September, 1996 we reached 5,000 registered installations worldwide. (By now we are well over 15,000 but have lost count for the moment). Of course there are many more people who have got copies from friends. PHYLIP is the most widely distributed phylogeny package. In recent years magnetic tape distribution, diskette distribution and e-mail distribution of PHYLIP have disappeared (as I insist people use the network if they can). But all this has been more than offset by, first, an explosion of distributions by anonymous ftp over Internet, and then a bigger explosion of World Wide Web distributions and registrations (about 8 registrations per day at the moment).

  ### Questions about documentation

"Where can I get a printed version of the PHYLIP documents?"
  For the moment, you can only get a printed version by printing it yourself. For versions 3.1 to 3.3 a printed version was sold by Christopher Meacham and Tom Duncan, then at the University Herbarium of the University of California at Berkeley. But they have had to discontinue this as it was too much work. You should be able to print out the documentation files on almost any printer and make yourself a printed version of whichever of them you need.
"Why have I been dropped from your newsletter mailing list?"
  You haven't. The newsletter was dropped. It simply was too hard to mail it out to such a large mailing list. The last issue of the newsletter was Number 9 in May, 1987. The Listserver News Bulletins that we tried for a while have also been dropped as too hard to keep up to date. I am hoping that our World Wide Web site will take their place.

### Additional Frequently Asked Questions, or: "Why didn't it occur to you to ...

... allow the options to be set on the command line?"
  We could in Unix and Linux, or somewhat differently in Windows. But there are so many options that this would be difficult, especially when the options require additional information to be supplied such as rates of evolution for many categories of sites. You may be asking this question because you want to automate the operation of PHYLIP programs using batch files (command files) to run in background. If that is the issue, see the section of this main documentation page on "Running the programs in background or under control of a command file". It explains how to set the options using input redirection and a file that has the menu responses as keystrokes.
... write these programs in Java?"
  Well, we might. It is not completely clear which of two contenders, C++ and Java, will become more widespread, and which one will gradually fade away. Whichever one is more successful, we will probably want to use for future versions of PHYLIP. As the C compilers that are used to compile PHYLIP are usually also able to compile C++, we will be moving in that direction, but with constant worrying about whether to convert PHYLIP to Java instead.
... forgot about all those inferior systems and just develop PHYLIP for Unix?"
  This is self-answering, since the same people first said I should just develop it for Apple II's, then just for CP/M Z-80's, then just for IBM PCDOS, then just for Macintoshes or for Sun workstations, and then for Windows. If I had listened to them and done any one of these, I would have had a very hard time adapting the package to any of the other ones once these folks changed their mind (and most of them did)!
... write these programs in Pascal?"
  These programs started out in Pascal in 1980. In 1993 we released both Pascal and C versions. The present version (3.6) and future versions will be C-only. I make fewer mistakes in Pascal and do like the language better than C, but C has overtaken Pascal and Pascal compilers are starting to be hard to find on some machines. Also C is a bit better standardized which makes the number of modifications a user has to make to adapt the programs to their system much less.
... write these programs in PROLOG (or Ada, or Modula-2, or SIMULA, or BCPL, or PL/I, or APL, or LISP)?"
  These are all languages I have considered. All have advantages, but they are not really widespread (as are C, C++, and Java).
... include in the package a program to do the Distance Wagner method, (or successive approximations character weighting)?"
  In most cases where I have not included other methods, it is because I decided that they had no substantial advantages over methods that were included (such as the programs FITCH, KITSCH, NEIGHBOR, the `T` option of MIX and DOLLOP, and the "`?`" ancestral states option of the discrete characters parsimony programs).
... include in the package ordination methods and more clustering algorithms?"
  Because this is not a clustering package, it's a package for phylogeny estimation. Those are different tasks with different objectives and mostly different methods. Mary Kuhner and Jon Yamato have, however, included in NEIGHBOR an option for UPGMA clustering, which will be very similar to KITSCH in results.
... include in the package a program to do nucleotide sequence alignment?"
  Well, yes, I should have, and this is scheduled to be in future releases. But multiple sequence alignment programs, in the era after Sankoff, Morel, and Cedergren's 1973 classic paper, need to use substantial computer horsepower to estimate the alignment and the tree together (but see Karl Nicholas's program `GeneDoc` or Ward Wheeler and David Gladstein's `MALIGN`, as well as more approximate methods of tree-based alignment used in `ClustalW`, `TreeAlign`, or `POY`).

### (Fortunately) obsolete questions

(The following four questions, once common, have finally disappeared, I am pleased to report).

#### "Why didn't it occur to you to ...

... let me log in to your computer in Seattle and copy the files out over a phone line?"
  No thanks. It would cost you for a lot of long-distance telephone time, plus a half hour of my time and yours in which I had to explain to you how to log in and do the copying.
... send me a listing of your program?"
  Damn it, it's not "a program", it's 35 programs, in a great many files. What were you thinking of doing, having 1800-line programs typed in by slaves at your end? If you were going to go to all that trouble why not try network transfer? If you have these then you can print out all the listings you want to and add them to the huge stack of printed output in the corner of your office.
... write a magnetic tape in our computer center's favorite format (inverted Lithuanian EBCDIC at 998 bpi)?"
  Because the ANSI standard format is the most widely used one, and even though your computer center may pretend it can't read a tape written this way, if you sniff around you will find a utility to read it. It's just a lot easier for me to let you do that work. If I tried to put the tape into your format, I would probably get it wrong anyway.
... give us a version of these in FORTRAN?"
  Because the programs are far easier to write and debug in C or Pascal, and cannot easily be rewritten into FORTRAN (they make extensive use of recursive calls and of records and pointers). In any case, C is widely available. If you don't have a C compiler or don't know how to use it, you are going to have to learn a language like C or Pascal sooner or later, and the sooner the better.

## New Features in This Version

Version 3.6 has many new features:

- Faster (well, less, slow) likelihood programs.
- The DNA and protein likelihood and distance programs allow for rate variation between sites using a gamma distribution of rates among sites, or using a gamma distribution plus a given fraction of sites which are assumed invariant.
- A new multistate discrete characters parsimony program, PARS, that handles unordered multistate characters.
- The DNAPARS and PARS parsimony programs can infer multifurcating trees, which sensibly reduces the number of tied trees they find.
- A new protein sequence likelihood program, PROML, and also a version, PROMLK which assumes a molecular clock.
- A new restriction sites and restriction fragments distance program, RESTDIST, that can also be used to compute distances for RAPD and AFLP data. It also allows for gamma-distributed rate variation among DNA sites.
- In the DNA likelihood programs, you can now specify different categories of rates of change (such as rates for first, second, and third positions of a coding sequence) and assign them to specific sites. This is in addition to the ability of the program to use the Hidden Markov Model mechanism to allow rates of change to vary across sites in a way that does not ask you to assign which rate goes with which site.
- The input files for many of the programs are now simpler, in that they do not contain options information such as specification of weights and categories. That information is now provided in separate files with default names such as `weights` and `categories`.
- The DNA likelihood programs can now evaluate multifurcating user trees (option `U`).
- All programs that read in user-defined trees now do so from a separate file, whose default name is `intree`, rather than requiring them to be in the input file as before.
- The DNA likelihood programs can infer the sequence at ancestral nodes in the interior of the tree.
- DNAPARS can now do transversion parsimony.
- The bootstrapping program SEQBOOT now can, instead of producing a large file containing multiple data sets, be asked instead to produce a weights file with multiple sets of weights. Many programs in this release can analyze those multiple weights together with the original data set, which saves disk space.
- The bootstrapping program SEQBOOT can pass weights and categories information through to a multiple weights file or a multiple categories file.
- SEQBOOT can also convert sequence files from Interleaved to Sequential form, or back.
- SEQBOOT can convert a PHYLIP molecular sequences or discrete characters morphology data file into the NEXUS format, which is used by a number of other phylogeny programs such as MacClade, MrBayes and PAUP*.
- SEQBOOT can also carry out a number of different methods of permuting the order of characters in a data set. This could be used to carry out the Incongruence Length Difference (or Partition Homogeneity) method of testing homogeneity of data sets.
- SEQBOOT can also write a sequence data file into one version of an XML format for sequence alignments, for use by programs that need XML input (none of the current PHYLIP programs can yet use this format, but it may be useful in the future).
- RETREE can now write tree out into a preliminary version of a new XML tree file format which is in the process of being defined.
- The Kishino-Hasegawa-Templeton (KHT) test which compares user-defined trees (option U) is now joined by the Shimodaira-Hasegawa (SH) test (Shimodaira and Hasegawa, 1999) which corrects for comparisons among multiple tests. This avoids a statistical problem with multiple user trees.
- CONTRAST can now carry out an analysis that takes into account within-species variation, according to a model similar (but not identical) to that introduced by Michael Lynch (1990). This enables analysis of individuals sampled from the species, in a way that properly takes sampling error into account.
- A new program, TREEDIST, computes the Robinson-Foulds symmetric difference distance among trees. This measures the number of branches in the trees that are present in one but not the other. It also can compute the Branch Score distance defined by Kuhner and Felsenstein (1994) which takes branch lengths into account.
- FITCH and KITSCH now have an option to make trees by the minimum evolution distance matrix method.
- The protein parsimony program PROTPARS now allows you to choose among a number of different genetic codes such as mitochondrial codes.
- The consensus tree program CONSENSE can compute the Ml family of consensus tree methods, which generalize the Majority Rule consensus tree method. It can also compute our extended Majority Rule consensus (which is Majority Rule with some additional groups added to resolve the tree more completely), and it can also compute the original Majority Rule consensus tree method which does not add these extra groups. It can also compute the Strict consensus.
- The tree-drawing programs DRAWGRAM and DRAWTREE have a number of new options of kinds of file they can produce, including Windows Bitmap files, files for the Idraw and FIG X windows drawing programs, the POV ray-tracer, and even VRML Virtual Reality Markup Language files that will enable you to wander around the tree using a VRML plugin for your browser, such as Cosmo Player or Cortona.
- DRAWTREE now uses my new Equal Daylight Algorithm to draw unrooted trees. This gives a much better-looking tree. Of course, competing programs such as TREEVIEW and PAUP draw trees that look just as good - because they too have started to use my method (with my encouragement). DRAWTREE also can use another algorithm, the n-body method.
- The tree-drawing programs can now produce trees across multiple pages, which is handy for looking at trees with very large numbers of tips, and for producing giant diagrams by pasting together multiple sheets of paper.

There are many more, lesser features added as well.

## Coming Attractions, Future Plans

There are some obvious deficiencies in this version. Some of these holes will be filled in the next few releases (leading to version 4.0). They include:

1. Obviously we need to start thinking about a more visual mouse/windows interface, but only if that can be used on X windows, Macintoshes, and Windows.
2. Program PENNY and its relatives will improved so as to run faster and find all most parsimonious trees more quickly.
3. An "evolutionary clock" version of CONTML will be done, and the same may also be done for RESTML.
4. We are gradually generalizing the tree structures in the programs to infer multifurcating trees as well as bifurcating ones. We should be able to have any program read any tree and know what to do with it, without the user having to fret about whether an unrooted tree was fed to a program that needs a rooted tree.
5. In general, we need more support for protein sequences, including a codon model of change, allowing for different rates for synonymous and nonsynonymous changes.
6. We also need more support for combining runs from multiple loci, allowing for different rates of evolution at the different loci.
7. We will be expanding our use and production of XML data set files and XML tree files.
8. A program to align molecular sequences on a predefined User Tree may ultimately be included. This will allow alignment and phylogeny reconstruction to procede iteratively by successive runs of two programs, one aligning on a tree and the other finding a better tree based on that alignment. In the shorter run a simple two-sequence alignment program may be included.
9. An interactive "likelihood explorer" for DNA sequences will be written. This will allow, either with or without the assumption of a molecular clock, trees to be varied interactively so that the user can get a much better feel for the shape of the likelihood surface. Likelihood will be able to be plotted against branch lengths for any branch.
10. If possible we will allow use of Hidden Markov Models for correcting for purine/pyrimidine richness variations among species, within the framework of the maximum likelihood programs. That the maximum likelihood programs do not allow for base composition variation is their major limitation at the moment.
11. The Hidden Markov Model (regional rates) option of DNAML and DNAMLK will be generalized to allow for rates at sites to gradually change as one moves along the tree, in an attempt to implement Fitch and Markowitz's (1970) notion of "covarions".
12. A more sophisticated compatibility program should be included, if I can find one.
13. We are economizing on the size of the source code, and enforcing some standardization of it, by putting frequently used routines in separate files which can be linked into various programs. This will enforce a rather complete standardization of our code.
14. We will move our code to an object-oriented language, most likely C++. One could describe the language that version 3.4 was written in as "Pascal", version 3.5 as "Pascal written in C", version 3.6 as "C written in C", and maybe version 4.0 as "C++ written in C" and then 4.1 as "C++ written in C++". At least that scenario is one possibility.

Much of the future development of the package will be in the DNA and protein likelihood programs and the distance matrix programs. This is for several reasons. First, I am more interested in those problems. Second, collection of molecular data is increasing rapidly, and those programs have the most promise for future development for those data.

## Endorsements

Here are some comments people have made in print about PHYLIP. Explanatory material in square brackets is my own. They fall naturally into three groups:

### From the pages of Cladistics:

> "Under no circumstances can we recommend PHYLIP/WAG [their name for the Wagner parsimony option of MIX]."
> Luckow, M. and R. A. Pimentel (1985)

> "PHYLIP has not proven very effective in implementing parsimony (Luckow and Pimentel, 1985)."
> J. Carpenter (1987a)

> "... PHYLIP. This is the computer program where every newsletter concerning it is mostly bug-catching, some of which have been put there by previous corrections. As Platnick (1987) documents, through dint of much labor useful results may be attained with this program, but I would suggest an easier way: FORMAT b:"
> J. Carpenter (1987b)

> "PHYLIP is bug-infested and both less effective and orders of magnitude slower than other programs ...."
> "T. N. Nayenizgani" [J. S. Farris] (1990)

> "Hennig86 [by J. S. Farris] provides such substantial improvements over previously available programs (for both mainframes and microcomputers) that it should now become the tool of choice for practising systematists."
> N. Platnick (1989)

### ... in the pages of other journals:

> "The availability, within PHYLIP of distance, compatibility, maximum likelihood, and generalized `invariants' algorithms (Cavender and Felsenstein, 1987) sets it apart from other packages .... One of the strengths of PHYLIP is its documentation ...."
> Michael J. Sanderson (1990)
> (Sanderson also criticizes PHYLIP for slowness and inflexibility of its parsimony algorithms, and compliments other packages on their strengths).

> "This package of programs has gradually become a basic necessity to anyone working seriously on various aspects of phylogenetic inference .... The package includes more programs than any other known phylogeny package. But it is not just a collection of cladistic and related programs. The package has great value added to the whole, and for this it is unique and of extreme importance .... its various strengths are in the great array of methods provided ...."
> Bernard R. Baum (1989)

> (note also W. Fink's critical remarks (1986) on version 2.8 of PHYLIP).

### ... and in the comments made by users when they register:

> "a program on phylogeny -- PHYLOGENY INTERFERENCE PACKAGE (PHYLIP). We would therefore like to ask ..."

[names withheld] (in 1994)

> "I am struglling with your clever programs."

[name withheld] (in 1995)

> "I'm famously computer illiterate - I look forward to many frustrating hours trying to run this program"

Desmond Maxwell (in 1998)

> "I am a brave man. PHYLIP is a brave program. We'll do fine together."

Christopher Winchell (in 2000)

> "The Mahabarata of phylogenetics looks better than ever."

Ross Crozier (in 2001)

> "I love phylip. Tastes great and less filling!"

Byron Adams (in 2002)

## References for the Documentation Files

In the documentation files that follow I frequently refer to papers in the literature. In order to centralize the references they are given in this section. If you want to find further papers beyond these, my book (Felsenstein, 2004) lists more than 1,000 further references.

Adams, E. N. 1972. Consensus techniques and the comparison of taxonomic trees. Systematic Zoology 21: 390-397.

Adams, E. N. 1986. N-trees as nestings: complexity, similarity, and consensus. Journal of Classification 3: 299-317.

Archie, J. W. 1989. A randomization test for phylogenetic information in systematic data. Systematic Zoology 38: 239-252.

Barry, D., and J. A. Hartigan. 1987. Statistical analysis of hominoid molecular evolution. Statistical Science 2: 191-210.

Baum, B. R. 1989. PHYLIP: Phylogeny Inference Package. Version 3.2. (Software review). Quarterly Review of Biology 64: 539-541.

Bron, C., and J. Kerbosch. 1973. Algorithm 457: Finding all cliques of an undirected graph. Communications of the Association for Computing Machinery 16: 575-577.

Camin, J. H., and R. R. Sokal. 1965. A method for deducing branching sequences in phylogeny. Evolution 19: 311-326.

Carpenter, J. 1987a. A report on the Society for the Study of Evolution workshop "Computer Programs for Inferring Phylogenies". Cladistics 3: 363-375.

Carpenter, J. 1987b. Cladistics of cladists. Cladistics 3: 363-375.

Cavalli-Sforza, L. L., and A. W. F. Edwards. 1967. Phylogenetic analysis: models and estimation procedures. Evolution 32: 550-570 (also American Journal of Human Genetics 19: 233-257).

Cavender, J. A. and J. Felsenstein. 1987. Invariants of phylogenies in a simple case with discrete states. Journal of Classification 4: 57-71.

Churchill, G.A. 1989. Stochastic models for heterogeneous DNA sequences. Bulletin of Mathematical Biology 51: 79-94.

Conn, E. E. and P. K. Stumpf. 1963. Outlines of Biochemistry. John Wiley and Sons, New York.

Day, W. H. E. 1983. Computationally difficult parsimony problems in phylogenetic systematics. Journal of Theoretical Biology 103: 429-438.

Dayhoff, M. O. and R. V. Eck. 1968. Atlas of Protein Sequence and Structure 1967-1968. National Biomedical Research Foundation, Silver Spring, Maryland.

Dayhoff, M. O., R. M. Schwartz, and B. C. Orcutt. 1979. A model of evolutionary change in proteins. pp. 345-352 in Atlas of Protein Sequence and Structure, volume 5, supplement 3, 1978, ed. M. O. Dayhoff. National Biomedical Research Foundation, Silver Spring, Maryland .

Dayhoff, M. O. 1979. Atlas of Protein Sequence and Structure, Volume 5, Supplement 3, 1978. National Biomedical Research Foundation, Washington, D.C.

DeBry, R. W. and N. A. Slade. 1985. Cladistic analysis of restriction endonuclease cleavage maps within a maximum-likelihood framework. Systematic Zoology 34: 21-34.

Dempster, A. P., N. M. Laird, and D. B. Rubin. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B 39: 1-38.

Eck, R. V., and M. O. Dayhoff. 1966. Atlas of Protein Sequence and Structure 1966. National Biomedical Research Foundation, Silver Spring, Maryland.

Edwards, A. W. F., and L. L. Cavalli-Sforza. 1964. Reconstruction of evolutionary trees. pp. 67-76 in Phenetic and Phylogenetic Classification, ed. V. H. Heywood and J. McNeill. Systematics Association Volume No. 6. Systematics Association, London.

Estabrook, G. F., C. S. Johnson, Jr., and F. R. McMorris. 1976a. A mathematical foundation for the analysis of character compatibility. Mathematical Biosciences 23: 181-187.

Estabrook, G. F., C. S. Johnson, Jr., and F. R. McMorris. 1976b. An algebraic analysis of cladistic characters. Discrete Mathematics 16: 141-147.

Estabrook, G. F., F. R. McMorris, and C. A. Meacham. 1985. Comparison of undirected phylogenetic trees based on subtrees of four evolutionary units. Systematic Zoology 34: 193-200.

Faith, D. P. 1990. Chance marsupial relationships. Nature 345: 393-394.

Faith, D. P. and P. S. Cranston. 1991. Could a cladogram this short have arisen by chance alone?: On permutation tests for cladistic structure. Cladistics 7: 1-28.

Farris, J. S. 1977. Phylogenetic analysis under Dollo's Law. Systematic Zoology 26: 77-88.

Farris, J. S. 1978a. Inferring phylogenetic trees from chromosome inversion data. Systematic Zoology 27: 275-284.

Farris, J. S. 1981. Distance data in phylogenetic analysis. pp. 3-23 in Advances in Cladistics: Proceedings of the first meeting of the Willi Hennig Society, ed. V. A. Funk and D. R. Brooks. New York Botanical Garden, Bronx, New York.

Farris, J. S. 1983. The logical basis of phylogenetic analysis. pp. 1-47 in Advances in Cladistics, Volume 2, Proceedings of the Second Meeting of the Willi Hennig Society. ed. Norman I. Platnick and V. A. Funk. Columbia University Press, New York.

Farris, J. S. 1985. Distance data revisited. Cladistics 1: 67-85.

Farris, J. S. 1986. Distances and statistics. Cladistics 2: 144-157.

Farris, J. S. ["T. N. Nayenizgani"]. 1990. The systematics association enters its golden years (review of Prospects in Systematics, ed. D. Hawksworth). Cladistics 6: 307-314.

Felsenstein, J. 1973a. Maximum likelihood and minimum-steps methods for estimating evolutionary trees from data on discrete characters. Systematic Zoology 22: 240-249.

Felsenstein, J. 1973b. Maximum-likelihood estimation of evolutionary trees from continuous characters. American Journal of Human Genetics 25: 471-492.

Felsenstein, J. 1978a. The number of evolutionary trees. Systematic Zoology 27: 27-33.

Felsenstein, J. 1978b. Cases in which parsimony and compatibility methods will be positively misleading. Systematic Zoology 27: 401-410.

Felsenstein, J. 1979. Alternative methods of phylogenetic inference and their interrelationship. Systematic Zoology 28: 49-62.

Felsenstein, J. 1981a. Evolutionary trees from DNA sequences: a maximum likelihood approach. Journal of Molecular Evolution 17: 368-376.

Felsenstein, J. 1981b. A likelihood approach to character weighting and what it tells us about parsimony and compatibility. Biological Journal of the Linnean Society 16: 183-196.

Felsenstein, J. 1981c. Evolutionary trees from gene frequencies and quantitative characters: finding maximum likelihood estimates. Evolution 35: 1229-1242.

Felsenstein, J. 1982. Numerical methods for inferring evolutionary trees. Quarterly Review of Biology 57: 379-404.

Felsenstein, J. 1983b. Parsimony in systematics: biological and statistical issues. Annual Review of Ecology and Systematics 14: 313-333.

Felsenstein, J. 1984a. Distance methods for inferring phylogenies: a justification. Evolution 38: 16-24.

Felsenstein, J. 1984b. The statistical approach to inferring evolutionary trees and what it tells us about parsimony and compatibility. pp. 169-191 in: Cladistics: Perspectives in the Reconstruction of Evolutionary History, edited by T. Duncan and T. F. Stuessy. Columbia University Press, New York.

Felsenstein, J. 1985a. Confidence limits on phylogenies with a molecular clock. Systematic Zoology 34: 152-161.

Felsenstein, J. 1985b. Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39: 783-791.

Felsenstein, J. 1985c. Phylogenies from gene frequencies: a statistical problem. Systematic Zoology 34: 300-311.

Felsenstein, J. 1985d. Phylogenies and the comparative method. American Naturalist 125: 1-12.

Felsenstein, J. 1986. Distance methods: a reply to Farris. Cladistics 2: 130-144.

Felsenstein, J. and E. Sober. 1986. Parsimony and likelihood: an exchange. Systematic Zoology 35: 617-626.

Felsenstein, J. 1988a. Phylogenies and quantitative characters. Annual Review of Ecology and Systematics 19: 445-471.

Felsenstein, J. 1988b. Phylogenies from molecular sequences: inference and reliability. Annual Review of Genetics 22: 521-565.

Felsenstein, J. 1992. Phylogenies from restriction sites, a maximum likelihood approach. Evolution 46: 159-173.

Felsenstein, J. and G. A. Churchill. 1996. A hidden Markov model approach to variation among sites in rate of evolution Molecular Biology and Evolution 13: 93-104.

Felsenstein, J. 2004. Inferring Phylogenies. Sinauer Associates, Sunderland, Massachusetts.

Fink, W. L. 1986. Microcomputers and phylogenetic analysis. Science 234: 1135-1139.

Fitch, W. M., and E. Markowitz. 1970. An improved method for determining codon variability in a gene and its application to the rate of fixation of mutations in evolution. Biochemical Genetics 4: 579-593.

Fitch, W. M., and E. Margoliash. 1967. Construction of phylogenetic trees. Science 155: 279-284.

Fitch, W. M. 1971. Toward defining the course of evolution: minimum change for a specified tree topology. Systematic Zoology 20: 406-416.

Fitch, W. M. 1975. Toward finding the tree of maximum parsimony. pp. 189-230 in Proceedings of the Eighth International Conference on Numerical Taxonomy, ed. G. F. Estabrook. W. H. Freeman, San Francisco.

Fitch, W. M. and E. Markowitz. 1970. An improved method for determining codon variability and its application to the rate of fixation of mutations in evolution. Biochemical Genetics 4: 579-593.

George, D. G., L. T. Hunt, and W. C. Barker. 1988. Current methods in sequence comparison and analysis. pp. 127-149 in Macromolecular Sequencing and Synthesis, ed. D. H. Schlesinger. Alan R. Liss, New York.

Gilmour, R. 2000. Taxonomic markup language: applying XML to systematic data. Bioinformatics 16: 406-407.

Gomberg, D. 1966. "Bayesian" post-diction in an evolution process. unpublished manuscript: University of Pavia, Italy.

Graham, R. L., and L. R. Foulds. 1982. Unlikelihood that minimal phylogenies for a realistic biological study can be constructed in reasonable computational time. Mathematical Biosciences 60: 133-142.

Hasegawa, M. and T. Yano. 1984a. Maximum likelihood method of phylogenetic inference from DNA sequence data. Bulletin of the Biometric Society of Japan No. 5: 1-7.

Hasegawa, M. and T. Yano. 1984b. Phylogeny and classification of Hominoidea as inferred from DNA sequence data. Proceedings of the Japan Academy 60 B: 389-392.

Hasegawa, M., Y. Iida, T. Yano, F. Takaiwa, and M. Iwabuchi. 1985a. Phylogenetic relationships among eukaryotic kingdoms as inferred from ribosomal RNA sequences. Journal of Molecular Evolution 22: 32-38.

Hasegawa, M., H. Kishino, and T. Yano. 1985b. Dating of the human-ape splitting by a molecular clock of mitochondrial DNA. Journal of Molecular Evolution 22: 160-174.

Hendy, M. D., and D. Penny. 1982. Branch and bound algorithms to determine minimal evolutionary trees. Mathematical Biosciences 59: 277-290.

Higgins, D. G. and P. M. Sharp. 1989. Fast and sensitive multiple sequence alignments on a microcomputer. Computer Applications in the Biological Sciences (CABIOS) 5: 151-153.

Hochbaum, D. S. and A. Pathria. 1997. Path costs in evolutionary tree reconstruction. Journal of Computational Biology 4: 163-175.

Holmquist, R., M. M. Miyamoto, and M. Goodman. 1988. Higher-primate phylogeny - why can't we decide? Molecular Biology and Evolution 5: 201-216.

Inger, R. F. 1967. The development of a phylogeny of frogs. Evolution 21: 369-384.

Jin, L. and M. Nei. 1990. Limitations of the evolutionary parsimony method of phylogenetic analysis. Molecular Biology and Evolution 7: 82-102.

Jones, D. T., W. R. Taylor and J. M. Thornton. 1992. The rapid generation of mutation data matrices from protein sequences. Computer Applications in the Biosciences (CABIOS) 8: 275-282.

Jukes, T. H. and C. R. Cantor. 1969. Evolution of protein molecules. pp. 21-132 in Mammalian Protein Metabolism, ed. H. N. Munro. Academic Press, New York.

Kidd, K. K. and L. A. Sgaramella-Zonta. 1971. Phylogenetic analysis: concepts and methods. American Journal of Human Genetics 23: 235-252.

Kim, J. and M. A. Burgman. 1988. Accuracy of phylogenetic-estimation methods using simulated allele-frequency data. Evolution 42: 596-602.

Kimura, M. 1980. A simple model for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. Journal of Molecular Evolution 16: 111-120.

Kimura, M. 1983. The Neutral Theory of Molecular Evolution. Cambridge University Press, Cambridge.

Kingman, J. F. C. 1982a. The coalescent. Stochastic Processes and Their Applications 13: 235-248.

Kingman, J. F. C. 1982b. On the genealogy of large populations. Journal of Applied Probability 19A: 27-43.

Kishino, H. and M. Hasegawa. 1989. Evaluation of the maximum likelihood estimate of the evolutionary tree topologies from DNA sequence data, and the branching order in Hominoidea. Journal of Molecular Evolution 29: 170-179.

Kluge, A. G., and J. S. Farris. 1969. Quantitative phyletics and the evolution of anurans. Systematic Zoology 18: 1-32.

Kuhner, M. K. and J. Felsenstein. 1994. A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Molecular Biology and Evolution 11: 459-468 (Erratum 12: 525 1995).

Künsch, H. R. 1989. The jackknife and the bootstrap for general stationary observations. Annals of Statistics 17: 1217-1241.

Lake, J. A. 1987. A rate-independent technique for analysis of nucleic acid sequences: evolutionary parsimony. Molecular Biology and Evolution 4: 167-191.

Lake, J. A. 1994. Reconstructing evolutionary trees from DNA and protein sequences: paralinear distances. Proceedings of the Natonal Academy of Sciences, USA 91: 1455-1459.

Le Quesne, W. J. 1969. A method of selection of characters in numerical taxonomy. Systematic Zoology 18: 201-205.

Le Quesne, W. J. 1974. The uniquely evolved character concept and its cladistic application. Systematic Zoology 23: 513-517.

Lewis, H. R., and C. H. Papadimitriou. 1978. The efficiency of algorithms. Scientific American 238: 96-109 (January issue)

Lockhart, P. J., M. A. Steel, M. D. Hendy, and D. Penny. 1994. Recovering evolutionary trees under a more realistic model of sequence evolution. Molecular Biology and Evolution 11: 605-612.

Luckow, M. and D. Pimentel. 1985. An empirical comparison of numerical Wagner computer programs. Cladistics 1: 47-66.

Lynch, M. 1990. Methods for the analysis of comparative data in evolutionary biology. Evolution 45: 1065-1080.

Maddison, D. R. 1991. The discovery and importance of multiple islands of most-parsimonious trees. Systematic Zoology 40: 315-328.

Margush, T. and F. R. McMorris. 1981. Consensus n-trees. Bulletin of Mathematical Biology 43: 239-244.

Nelson, G. 1979. Cladistic analysis and synthesis: principles and definitions, with a historical note on Adanson's Familles des Plantes (1763-1764). Systematic Zoology 28: 1-21.

Nei, M. 1972. Genetic distance between populations. American Naturalist 106: 283-292.

Nei, M. and W.-H. Li. 1979. Mathematical model for studying genetic variation in terms of restriction endonucleases. Proceedings of the National Academy of Sciences, USA 76: 5269-5273.

Page, R. D. M. 1989. Comments on component-compatibility in historical biogeography. Cladistics 5: 167-182.

Penny, D. and M. D. Hendy. 1985. Testing methods of evolutionary tree construction. Cladistics 1: 266-278.

Platnick, N. 1987. An empirical comparison of microcomputer parsimony programs. Cladistics 3: 121-144.

Platnick, N. 1989. An empirical comparison of microcomputer parsimony programs. II. Cladistics 5: 145-161.

Reynolds, J. B., B. S. Weir, and C. C. Cockerham. 1983. Estimation of the coancestry coefficient: basis for a short-term genetic distance. Genetics 105: 767-779.

Robinson, D. F. and L. R. Foulds. 1979. Comparison of weighted labelled trees. pp. 119-126 in Combinatorial Mathematics VI. Proceedings of the Sixth Australian Conference on Combinatorial Mathematics, Armidale, Australia, August, 1978, ed. A. F. Horadam and W. D. Wallis. Lecture Notes in Mathematics, No. 748. Springer-Verlag, Berlin.

Robinson, D. F. and L. R. Foulds. 1981. Comparison of phylogenetic trees. Mathematical Biosciences 53: 131-147.

Rohlf, F. J. and M. C. Wooten. 1988. Evaluation of the restricted maximum likelihood method for estimating phylogenetic trees using simulated allele- frequency data. Evolution 42: 581-595.

Rzhetsky, A., and M. Nei. 1992. Statistical properties of the ordinary least-squares, generalized least-squares, and minimum-evolution methods of phylogenetic inference. Journal of Molecular Evolution 35: 367-375 .

Saitou, N., and M. Nei. 1987. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Molecular Biology and Evolution 4: 406-425.

Sanderson, M. J. 1990. Flexible phylogeny reconstruction: a review of phylogenetic inference packages using parsimony. Systematic Zoology 39: 414-420.

Sankoff, D. D., C. Morel, R. J. Cedergren. 1973. Evolution of 5S RNA and the nonrandomness of base replacement. Nature New Biology 245: 232-234.

Shimodaira, H. and M. Hasegawa. 1999. Multiple comparisons of log-likelihoods with applications to phylogenetic inference. Molecular Biology and Evolution 16: 1114-1116.

Shimodaira, H. 2002. An approximately unbiased test of phylogenetic tree selection. Systematic Biology 51: 492--508.

Sokal, R. R. and P. H. A. Sneath. 1963. Principles of Numerical Taxonomy. W. H. Freeman, San Francisco.

Smouse, P. E. and W.-H. Li. 1987. Likelihood analysis of mitochondrial restriction-cleavage patterns for the human-chimpanzee-gorilla trichotomy. Evolution 41: 1162-1176.

Sober, E. 1983a. Parsimony in systematics: philosophical issues. Annual Review of Ecology and Systematics 14: 335-357.

Sober, E. 1983b. A likelihood justification of parsimony. Cladistics 1: 209-233.

Sober, E. 1988. Reconstructing the Past: Parsimony, Evolution, and Inference. MIT Press, Cambridge, Massachusetts.

Sokal, R. R., and P. H. A. Sneath. 1963. Principles of Numerical Taxonomy. W. H. Freeman, San Francisco.

Steel, M. A., P. J. Lockhart, and D. Penny. 1993. Confidence in evolutionary trees from biological sequence data. Nature 364: 440-442.

Steel, M. A. 1994. Recovering a tree from the Markov leaf colourations it generates under a Markov model. Applied Mathematics Letters 7: 19-23.

Studier, J. A. and K. J. Keppler. 1988. A note on the neighbor-joining algorithm of Saitou and Nei. Molecular Biology and Evolution 5: 729-731.

Swofford, D. L. and G. J. Olsen. 1990. Phylogeny reconstruction. Chapter 11, pages 411-501 in Molecular Systematics, ed. D. M. Hillis and C. Moritz. Sinauer Associates, Sunderland, Massachusetts.

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

Templeton, A. R. 1983. Phylogenetic inference from restriction endonuclease cleavage site maps with particular reference to the evolution of humans and the apes. Evolution 37: 221-244.

Thompson, E. A. 1975. Human Evolutionary Trees. Cambridge University Press, Cambridge.

Veerassamy, S., A. Smith and E. R. M. Tillier. 2003. A transition probability model for amino acid substitutions from Blocks. Journal of Computational Biology 10: 997-1010.

Wu, C. F. J. 1986. Jackknife, bootstrap and other resampling plans in regression analysis. Annals of Statistics 14: 1261-1295.

Yang, Z. 1993. Maximum-likelihood estimation of phylogeny from DNA sequences when substitution rates differ over sites. Molecular Biology and Evolution 10: 1396-1401.

Yang, Z. 1994. Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: approximate methods. Journal of Molecular Evolution 39: 306-314.

Yang, Z. 1995. A space-time process model for the evolution of DNA sequences. Genetics 139: 993-1005.

Zharkikh, A. and W.-H. Li. 1995. Estimation of confidence in phylogeny: the complete-and-partial bootstrap technique. Molecular Biology and Evolution 4: 44-63.

## Credits

Over the years various granting agencies have contributed to the support of the PHYLIP project (at first without knowing it). They are:

Years | Agency | Grant or Contract Number
1999-2003 | NSF | BIR-9527687
1999-2002 | NIH NIGMS | R01 GM51929-04
1999-2001 | NIH NIMH | R01 HG01989-01
1995-1999 | NIH NIGMS | R01 GM51929-01
1992-1995 | National Science Foundation | DEB-9207558
1992-1994 | NIH NIGMS Shannon Award | 2 R55 GM41716-04
1989-1992 | NIH NIGMS | 1 R01-GM41716-01
1990-1992 | National Science Foundation | BSR-8918333
1987-1990 | National Science Foundation | BSR-8614807
1979-1987 | U.S. Department of Energy | DE-AM06-76RLO2225 TA DE-AT06-76EV71005

I am particularly grateful to program administrators William Moore, Irene Eckstrand, Peter Arzberger, and Conrad Istock, who have gone beyond the call of duty to make sure that PHYLIP continued.

Booby prizes for funding are awarded to:

- The people at the U.S. Department of Energy who, in 1987, decided they were "not interested in phylogenies",
- The members of the Systematics Panel of NSF who twice (in 1989 and 1992) positively recommended that my applications not be funded. I am very grateful to program director William Moore for courageously overruling their decision the first time. The 1992 NSF Systematics Panel could claim no credit for PHYLIP whatsoever.
- The members of the 1992 Genetics Study Section of NIH who rated my proposal in the 53rd percentile (I don't know if that's 53rd from the top or the bottom, but does it matter?), thus denying it funding. I am, however, grateful to the NIGMS administrators, especially Irene Eckstrand, who supported giving me a "Shannon award" partially funding my work for a period in spite of this rating.
- The reviewers at NSF Population Biology in early 2003 who gave my proposal too anemic a rating: ("The work has the potential to define the field for many years to come .... All agreed that the proposal is somewhat vague. There was also some concern that the proposed work is too ambitious.")
- The reviewers at the Genetics Study Section of the NIH in early 2004 who said such nice things about my proposal ("One is likely to look back in 20 years and marvel that these questions were not actively pursued by more theoreticians" and "The first inclination is to give this PI all the money he wants!"), but then proceeded to give my proposal a 30.2 percentile rating. Next time you can tone down the praise as long as you improve the ratings, OK?

The original Camin-Sokal parsimony program and the polymorphism parsimony program were written by me in 1977 and 1978. They were Pascal versions of earlier FORTRAN programs I wrote in 1966 and 1967 using the same algorithm to infer phylogenies under the Camin-Sokal and polymorphism parsimony criteria. Harvey Motulsky worked for me as a programmer in 1971 and wrote FORTRAN programs to carry out the Camin-Sokal, Dollo, and polymorphism methods (he is better-known these days as the author of the scientific data analysis package GraphPad). But most of the early work on PHYLIP other than my own was by Jerry Shurman and Mark Moehring. Jerry Shurman worked for me in the summers of 1979 and 1980, and Mark Moehring worked for me in the summers of 1980 and 1981. Both wrote original versions of many of the other programs, based on the original versions of my Camin-Sokal parsimony program and POLYM. These formed the basis of Version 1 of the Package, first distributed in October, 1980.

Version 2, released in the spring of 1982, involved a fairly complete rewrite by me of many of those programs. Hisashi Horino for version 3.3 reworked some parts of the programs CLIQUE and CONSENSE to make their output more comprehensible, and has added some code to the tree-drawing programs DRAWGRAM and DRAWTREE as well. He also worked on some of the Drawtree and Drawgram driver code.

My more recent part-time programmers Akiko Fuseki, Sean Lamont, Andrew Keeffe, Daniel Yek, Dan Fineman, Patrick Colacurcio, Mike Palczewski, and Doug Buxton gave me substantial help with the current release, and their excellent work is greatly appreciated. Akiko, in over 10 years of excellent work, did much of the hard work of adding new features and changing old ones in the 3.4 and 3.5 releases, centralized many of the C routines in support files, and is responsible for the new versions of DNAPARS and PARS. Andrew prepared the Macintosh version, wrote RETREE, added the ray-tracing and PICT code to the DRAW programs and has since done much other work. Sean was central to the conversion to C, and tested it extensively. My (then) postdoctoral fellow Mary Kuhner and her associate Jon Yamato created NEIGHBOR, the neighbor-joining and UPGMA program, for the current release, for which I am also grateful (Naruya Saitou and Li Jin kindly encouraged us to use some of the code from their own implementation of this method).

I am very grateful to over 300 users for algorithmic suggestions, complaints about features (or lack of features), and information about the behavior of their operating systems and compilers. A list of some of their names will be found at the credits page on the PHYLIP web site which is at `http://evolution.gs.washington.edu/phylip/credits.html`

A major contribution to this package has been made by others writing programs or parts of programs. Chris Meacham contributed the important program FACTOR, long demanded by users, and the even more important ones PLOTREE and PLOTGRAM. Important parts of the code in DRAWGRAM and DRAWTREE were taken over from those two programs. Kent Fiala wrote function "reroot" to do outgroup-rooting, which was an essential part of many programs in earlier versions. Someone at the Western Australia Institute of Technology suggested the name PHYLIP (by writing it the label on the outside of a magnetic tape). Perhaps it was the late Julian Ford (I've lost the relevant letter).

The distribution of the package also owes much to Buz Wilson and Willem Ellis, who put a lot of effort into the early distributions of the PCDOS and Macintosh versions respectively. Christopher Meacham and Tom Duncan for three versions distributed a printed version of these documentation files (they are no longer able to do so), and I am very grateful to them for those efforts. William H.E. Day and F. James Rohlf have been very helpful in setting up the listserver news bulletin service which succeeded the PHYLIP newsletter for a time.

I also wish to thank the people who have made computer resources available to me, mostly in the loan of use of microcomputers. These include Jeremy Field, Clem Furlong, Rick Garber, Dan Jacobson, Rochelle Kochin, Monty Slatkin, Jim Archie, Jim Thomas, and George Gilchrist.

I should also note the computers used to develop this package: These include a CDC 6400, two DECSystem 1090s, my trusty old SOL-20, my old Osborne-1, a VAX 11/780, a VAX 8600, a MicroVAX I, a DECstation 3100, my old Toshiba 1100+, my DECstation 5000/200, a DECstation 5000/125, a Compudyne 486DX/33, a Trinity Genesis 386SX, a Zenith Z386, a Mac Classic, a DEC Alphastation 400 4/233, a Pentium 120, a Pentium 200, a PowerMac 6100, and a Macintosh G3. (One of the reasons we have been successful in achieving compatibility between different computer systems is that I have had to run them myself under so many different operating systems and compilers).

## Other Phylogeny Programs Available Elsewhere

A comprehensive list of phylogeny programs is maintained at the PHYLIP web site on the Phylogeny Programs pages:

`http://evolution.gs.washington.edu/phylip/software.html`

Here we will simply mention some of the major general-purpose programs. For many more and much more, see those web pages.

PAUP* A comprehensive program with parsimony, likelihood, and distance matrix methods. It competes with PHYLIP to be responsible for the most trees published. Written by David Swofford and distributed by Sinauer Associates of Sunderland, Massachusetts. It is described in a web page. at `http://www.sinauer.com/detail.php?id=8060`. Current prices are $100 for the Macintosh version, $85 for the Windows version, and $150 for Unix versions for many kinds of workstations.

MacClade An interactive Macintosh program to rearrange trees and watch the changes in the fit of the trees to data as judged by parsimony. MacClade has a great many features including a spreadsheet data editor and many different descriptive statistics for different kinds of data. It is particularly designed to export and import data to and from PAUP*. MacClade is available for $100 from Sinauer Associates, of Sunderland, Massachusetts. It is described in a web page at `http://www.sinauer.com/detail.php?id=4707`. MacClade is also described on its Web page, at `http://phylogeny.arizona.edu/macclade/macclade.html`.

MrBayes The leading program for Bayesian inference of phylogenies. It uses Markov Chain Monte Carlo inference to assess support for clades and to infer posterior distrubutions of parameters. Produced by John Huelsenbeck and Fredrik Ronquist, it is available at its web site at `http://morphbank.ebc.uu.se/mrbayes/` as a Macintosh or Windows executable, or in source code in C.

MEGA A Windows and DOS program by Sudhir Kumar of Arizona State University (written together with Koichiro Tamura and Masatoshi Nei while he was a student in Nei's lab at Pennsylvania State University). It can carry out parsimony and distance matrix methods for DNA sequence data. Version 2.1 for Windows can be downloaded from the MEGA web site at `http://www.megasoftware.net`.

PAML Ziheng Yang of the Department of Genetics and Biometry at University College, London has written this package of programs to carry out likelihood analysis of DNA and protein sequence data. It is one of the only packages able to use the codon model for protein sequence data which takes the genetic code reasonably fully into account. PAML is particularly strong in the options for coping with variability of rates of evolution from site to site, though it is less able than some other packages to search effectively for the best tree. It is available as C source code and as Macintosh and Windows executables from its web site at `http://abacus.gene.ucl.ac.uk/software/paml.html`.

TREE-PUZZLE This package by Korbinian Strimmer and Arndt von Haeseler was begun when they were at the Uviversität Munchen in Germany. TREE-PUZZLE can carry out likelihood methods for DNA and protein data, searching by the strategy of "quartet puzzling" which they invented. It can also compute distances. It superimposes trees estimated from many quartets of species. TREE-PUZZLE is available for Unix, Macintoshes, or Windows from their web site at `http://www.tree-puzzle.de/`.

DAMBE A package written by Xuhua Xia of the Department of Biology of the University of Ottawa (formerly of the University of Hong Kong). Its initials stand for Data Analysis in Molecular Biology and Evolution. DAMBE is a general-purpose package for DNA and protein sequence phylogenies. It can read and convert a number of file formats, and has many features for descriptive statistics, and can compute a number of commonly-used distance matrix measures and infer phylogenies by parsimony, distance, or likelihood methods, including bootstrapping and jackknifing. There are a number of kinds of statistical tests of trees available and it can also display phylogenies. DAMBE includes a copy of ClustalW as well; DAMBE consists of Windows executables. It is available from its web site at `http://aix1.uottawa.ca/~xxia/software/software.htm`.

Hennig86 A fast parsimony program by J. S. Farris of the Naturhistoriska Riksmuseet in Stockholm, Sweden for discrete characters data (it can handle DNA if its states are recoded to be digits). Reputed to be faster than PAUP*. The program is distributed as an executable and costs $50, plus $5 mailing costs ($10 outside of of the U.S.). The user's name should be stated, as copies are personalized as a copy-protection measure. It is distributed by Arnold Kluge, Amphibians and Reptiles, Museum of Zoology, University of Michigan, Ann Arbor, Michigan 48109-1079, U.S.A. (`akluge @ umich.edu`) and by Diana Lipscomb at George Washington University (`BIODL @ gwuvm.gwu.edu`).

RnA J. S. Farris's very fast program which uses parsimony to carry out jackknifing resampling of DNA sequence data. This would be nearly equivalent in properties to bootstrapping if the jackknifing were sampling random halves of the data, but Farris prefers to have each jackknife sample delete a fraction 1/e of the data, which will give most groups too much support (he would disagree with this statement). RnA is available from Arnold Kluge, Amphibians and Reptiles, Museum of Zoology, University of Michigan, Ann Arbor, Michigan 48109-1079, U.S.A. (`akluge @ umich.edu`) and Diana Lipscomb at George Washington University (`BIODL @ gwuvm.gwu.edu`) who may be contacted for details. The cost is about $30 US.

NONA Pablo Goloboff, of the Instituto Miguel Lillo in Tucumán, Argentina has written these very fast parsimony programs, capable of some relevant forms of weighted parsimony, which can handle either DNA sequence data or discrete characters. It is available as shareware from `http://www.cladistics.com/aboutNona.htm` There is a 30 day free trial, after which NONA must be purchased separately by sending a check for $40.00 to either directly to the the author, or to: James M. Carpenter, Attn: NONA, Division of Invertebrate Zoology, American Museum of Natural History, Central Park West at 79th Street, New York, NY 10024.

TNT This program, by Pablo Goloboff, J. S. Farris, and Kevin Nixon, is for searching large data sets for most parsimonious trees. The authors are respectively at the Instituto Miguel Lillo in Tucuman, Argentina, the Naturhistoriska Riksmuseet in Stockholm, Sweden, and the Hortorium, Cornell University, Ithaca, New York. TNT is described as faster than other methods, though not faster than NONA for small to medium data sets. It is distributed from its web site at `http://www.cladistics.com/aboutTNT.html`, and the web site `http://www.cladistics.com/webtnt.html` makes a beta version available for download. The trial versions are free, but expire after a certain number of runs. After that, one can send a check for $80 to: James M. Carpenter, Attn: TNT, Division of Invertebrate Zoology, American Museum of Natural History, Central Park West at 79th Street, New York, NY 10024 (see the web page for further registration details).

These are only a few of the almost 250 different phylogeny packages that are now available (as of July, 2004 - the number keeps increasing). The others are described (and web links and ftp addresses provided) at my Phylogeny Programs web pages at the address given above.

## How You Can Help Me

Simply let me know of any problems you have had adapting the programs to your computer. I can often make "transparent" changes that, by making the code avoid the wilder, woolier, and less standard parts of C, not only help others who have your machine but even improve the chance of the programs functioning on new machines. I would like fairly detailed information on what gave trouble, on what operating system, machine, and (if relevant) compiler, and what had to be done to make the programs work. I am sometimes able to do some over-the-telephone trouble-shooting, particularly if I don't have to pay for the call, but electronic mail is a the best way for me to be asked about problems, as you can include your input and output files so I can see what is going on (please do not send them as Attachments, but as part of the body of a message). I'd really like these programs to be able to run with only routine changes on absolutely everything, down to and possibly including the Amana Touchmatic Radarange Microwave Oven which was an Intel 8080 system (in fact, early versions of this package did run successfully on Intel 8080 systems running the CP/M operating system). A PalmPilot version is contemplated too.

I would also like to know timings of programs from the package, when run on the three test input files provided above, for various computer and compiler combinations, so that I can provide this information in the section on speeds of this document.

For the phylogeny plotting programs DRAWGRAM and DRAWTREE, I am particularly interested in knowing what has to be done to adapt them for other graphic file formats.

You can also be helpful to PHYLIP users in your part of the world by helping them get the latest version of PHYLIP from our web site and by helping them with any problems they may have in getting PHYLIP working on their data.

Your help is appreciated. I am always happy to hear suggestions for features and programs that ought to be incorporated in the package, but please do not be upset if I turn out to have already considered the particular possibility you suggest and decided against it.

## In Case of Trouble

Read The (documentation) Files Meticulously ("RTFM"). If that doesn't solve the problem, please check the Frequently Asked Questions web page at the PHYLIP web site:

`http://evolution.gs.washington.edu/phylip/faq.html`

and the PHYLIP Bugs web page at that site:

`http://evolution.gs.washington.edu/phylip/bugs.html`

If none of these answers your question, get in touch with me. My electronic mail address is given below. If you do ask about a problem, please specify the program name, version of the package, computer operating system, and send me your data file so I can test the problem. Do not send your data file as an e-mail Attachment but instead as the body of a message. I read the e-mail on a Unix system, which makes it impossible to read some formats of attachments without running around to other machines and moving the files there. This is one of my least favorite activities, so please do not use attachments. Also it will help if you have the relevant output and documentation files so that you can refer to them in any correspondence. I can also be reached by telephone by calling me in my office: +1-(206)-543-0150, or at home: +1-(206)-526-9057 (how's that for user support!). If I cannot be reached at either place, a message can be left at the office of the Department of Genome Sciences, +1-(206)-221-7377 but I prefer strongly that I not call you, as in any phone consultation the least you can do is pay the phone bill. Better yet, use electronic mail.

Particularly if you are in a part of the world distant from me, you may also want to try to get in touch with other users of PHYLIP nearby. I can also, if requested, provide a list of nearby users.

Joe Felsenstein Department of Genome Sciences University of Washington Box 357730 Seattle, Washington 98195-7730, U.S.A.

Electronic mail addresses: `joe @ gs.washington.edu`