| fdollop

## Function

Dollo and polymorphism parsimony algorithm

## Description

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.

## Algorithm

This program carries out the Dollo and polymorphism parsimony methods. The Dollo parsimony method was first suggested in print in verbal form by Le Quesne (1974) and was first well-specified by Farris (1977). The method is named after Louis Dollo since he was one of the first to assert that in evolution it is harder to gain a complex feature than to lose it. The algorithm explains the presence of the state 1 by allowing up to one forward change 0-->1 and as many reversions 1-->0 as are necessary to explain the pattern of states seen. The program attempts to minimize the number of 1-->0 reversions necessary. The assumptions of this method are in effect:

1. We know which state is the ancestral one (state 0).
2. The characters are evolving independently.
3. Different lineages evolve independently.
4. The probability of a forward change (0-->1) is small over the evolutionary times involved.
5. The probability of a reversion (1-->0) is also small, but still far larger than the probability of a forward change, so that many reversions are easier to envisage than even one extra forward change.
6. Retention of polymorphism for both states (0 and 1) is highly improbable.
7. The lengths of the segments of the true tree are not so unequal that two changes in a long segment are as probable as one in a short segment.

One problem can arise when using additive binary recoding to represent a multistate character as a series of two-state characters. Unlike the Camin-Sokal, Wagner, and Polymorphism methods, the Dollo method can reconstruct ancestral states which do not exist. An example is given in my 1979 paper. It will be necessary to check the output to make sure that this has not occurred.

The polymorphism parsimony method was first used by me, and the results published (without a clear specification of the method) by Inger (1967). The method was independently published by Farris (1978a) and by me (1979). The method assumes that we can explain the pattern of states by no more than one origination (0-->1) of state 1, followed by retention of polymorphism along as many segments of the tree as are necessary, followed by loss of state 0 or of state 1 where necessary. The program tries to minimize the total number of polymorphic characters, where each polymorphism is counted once for each segment of the tree in which it is retained.

The assumptions of the polymorphism parsimony method are in effect:

1. The ancestral state (state 0) is known in each character.
2. The characters are evolving independently of each other.
3. Different lineages are evolving independently.
4. Forward change (0-->1) is highly improbable over the length of time involved in the evolution of the group.
5. Retention of polymorphism is also improbable, but far more probable that forward change, so that we can more easily envisage much polymorhism than even one additional forward change.
6. Once state 1 is reached, reoccurrence of state 0 is very improbable, much less probable than multiple retentions of polymorphism.
7. The lengths of segments in the true tree are not so unequal that we can more easily envisage retention events occurring in both of two long segments than one retention in a short segment.

That these are the assumptions of parsimony methods has been documented in a series of papers of mine: (1973a, 1978b, 1979, 1981b, 1983b, 1988b). For an opposing view arguing that the parsimony methods make no substantive assumptions such as these, see the papers by Farris (1983) and Sober (1983a, 1983b), but also read the exchange between Felsenstein and Sober (1986).

## Usage

Here is a sample session with fdollop

    % fdollop
    Dollo and polymorphism parsimony algorithm
    Input file: dollop.dat
    Input tree file:
    Output file [dollop.fdollop]:

    Dollo and polymorphism parsimony algorithm, version 3.6b

    Adding species:
       1. Alpha
       2. Beta
       3. Gamma
       4. Delta
       5. Epsilon

    Doing global rearrangements
      !---------!
       .........

    Output written to file "dollop.fdollop"

    Trees also written onto file "dollop.treefile"

Go to the input files for this example
Go to the output files for this example

## Command line arguments

       Standard (Mandatory) qualifiers:
      [-infile]            discretestates File containing one or more data sets
      [-intreefile]        tree       (no help text) tree value
      [-outfile]           outfile    Output file name

       Additional (Optional) qualifiers (* if not always prompted):
       -weights            properties Weights file
       -ancfile            properties Ancestral states file
       -method             menu       Parsimony method
    *  -njumble            integer    Number of times to randomise
    *  -seed               integer    Random number seed between 1 and 32767 (must
                                      be odd)
       -threshold          float      Threshold value
       -[no]trout          toggle     Write out trees to tree file
    *  -outtreefile        outfile    Tree file name
       -printdata          boolean    Print data at start of run
       -[no]progress       boolean    Print indications of progress of run
       -[no]treeprint      boolean    Print out tree
       -ancseq             boolean    Print states at all nodes of tree
       -stepbox            boolean    Print out steps in each character

       Advanced (Unprompted) qualifiers: (none)
       Associated qualifiers:

       "-outfile" associated qualifiers
       -odirectory3        string     Output directory

       "-outtreefile" associated qualifiers
       -odirectory         string     Output directory

       General qualifiers:
       -auto               boolean    Turn off prompts
       -stdout             boolean    Write standard output
       -filter             boolean    Read standard input, write standard output
       -options            boolean    Prompt for standard and additional values
       -debug              boolean    Write debug output to program.dbg
       -verbose            boolean    Report some/full command line options
       -help               boolean    Report command line options. More
                                      information on associated and general
                                      qualifiers can be found with -help -verbose
       -warning            boolean    Report warnings
       -error              boolean    Report errors
       -fatal              boolean    Report fatal errors
       -die                boolean    Report deaths

Standard (Mandatory) qualifiers | Allowed values | Default
--- | --- | ---
[-infile] (Parameter 1) | File containing one or more data sets | Discrete states file |
[-intreefile] (Parameter 2) | (no help text) tree value | Phylogenetic tree |
[-outfile] (Parameter 3) | Output file name | Output file | <sequence>.fdollop
Additional (Optional) qualifiers | Allowed values | Default
-weights | Weights file | Property value(s) |
-ancfile | Ancestral states file | Property value(s) |
-method | Parsimony method |
d | (Dollo)
p | (Polymorphism)
| d
-njumble | Number of times to randomise | Integer 0 or more | 0
-seed | Random number seed between 1 and 32767 (must be odd) | Integer from 1 to 32767 | 1
-threshold | Threshold value | Number 0.000 or more | $(infile.discretesize)
-[no]trout | Write out trees to tree file | Toggle value Yes/No | Yes
-outtreefile | Tree file name | Output file |
-printdata | Print data at start of run | Boolean value Yes/No | No
-[no]progress | Print indications of progress of run | Boolean value Yes/No | Yes
-[no]treeprint | Print out tree | Boolean value Yes/No | Yes
-ancseq | Print states at all nodes of tree | Boolean value Yes/No | No
-stepbox | Print out steps in each character | Boolean value Yes/No | No
Advanced (Unprompted) qualifiers | Allowed values | Default
(none)

## Input file format

fdollop reads discrete character data with "?", "P", "B" states allowed. .

### (0,1) Discrete character data

These programs are intended for the use of morphological systematists who are dealing with discrete characters, or by molecular evolutionists dealing with presence-absence data on restriction sites. One of the programs (PARS) allows multistate characters, with up to 8 states, plus the unknown state symbol "?". For the others, the characters are assumed to be coded into a series of (0,1) two-state characters. For most of the programs there are two other states possible, "P", which stands for the state of Polymorphism for both states (0 and 1), and "?", which stands for the state of ignorance: it is the state "unknown", or "does not apply". The state "P" can also be denoted by "B", for "both".

There is a method invented by Sokal and Sneath (1963) for linear sequences of character states, and fully developed for branching sequences of character states by Kluge and Farris (1969) for recoding a multistate character into a series of two-state (0,1) characters. Suppose we had a character with four states whose character-state tree had the rooted form:

                   1 ---> 0 ---> 2
                          |
                          |
                          V
                          3

so that 1 is the ancestral state and 0, 2 and 3 derived states. We can represent this as three two-state characters:

                    Old State           New States
                    --- -----           --- ------
                        0                  001
                        1                  000
                        2                  011
                        3                  101

The three new states correspond to the three arrows in the above character state tree. Possession of one of the new states corresponds to whether or not the old state had that arrow in its ancestry. Thus the first new state corresponds to the bottommost arrow, which only state 3 has in its ancestry, the second state to the rightmost of the top arrows, and the third state to the leftmost top arrow. This coding will guarantee that the number of times that states arise on the tree (in programs MIX, MOVE, PENNY and BOOT) or the number of polymorphic states in a tree segment (in the Polymorphism option of DOLLOP, DOLMOVE, DOLPENNY and DOLBOOT) will correctly correspond to what would have been the case had our programs been able to take multistate characters into account. Although I have shown the above character state tree as rooted, the recoding method works equally well on unrooted multistate characters as long as the connections between the states are known and contain no loops.

However, in the default option of programs DOLLOP, DOLMOVE, DOLPENNY and DOLBOOT the multistate recoding does not necessarily work properly, as it may lead the program to reconstruct nonexistent state combinations such as 010. An example of this problem is given in my paper on alternative phylogenetic methods (1979).

If you have multistate character data where the states are connected in a branching "character state tree" you may want to do the binary recoding yourself. Thanks to Christopher Meacham, the package contains a program, FACTOR, which will do the recoding itself. For details see the documentation file for FACTOR.

We now also have the program PARS, which can do parsimony for unordered character states.

### Input files for usage example

### File: dollop.dat

         5    6
    Alpha     110110
    Beta      110000
    Gamma     100110
    Delta     001001
    Epsilon   001110

## Output file format

fdollop output is standard: a list of equally parsimonious trees, and, if the user selects menu option 4, a table of the numbers of reversions or retentions of polymorphism necessary in each character. If any of the ancestral states has been specified to be unknown, a table of reconstructed ancestral states is also provided. When reconstructing the placement of forward changes and reversions under the Dollo method, keep in mind that each polymorphic state in the input data will require one "last minute" reversion. This is included in the tabulated counts. Thus if we have both states 0 and 1 at a tip of the tree the program will assume that the lineage had state 1 up to the last minute, and then state 0 arose in that population by reversion, without loss of state 1.

If the user selects menu option 5, a table is printed out after each tree, showing for each branch whether there are known to be changes in the branch, and what the states are inferred to have been at the top end of the branch. If the inferred state is a "?" there may be multiple equally-parsimonious assignments of states; the user must work these out for themselves by hand.

If the A option is used, then the program will infer, for any character whose ancestral state is unknown ("?") whether the ancestral state 0 or 1 will give the best tree. If these are tied, then it may not be possible for the program to infer the state in the internal nodes, and these will all be printed as ".". If this has happened and you want to know more about the states at the internal nodes, you will find helpful to use DOLMOVE to display the tree and examine its interior states, as the algorithm in DOLMOVE shows all that can be known in this case about the interior states, including where there is and is not amibiguity. The algorithm in DOLLOP gives up more easily on displaying these states.

If the U (User Tree) option is used and more than one tree is supplied, the program also performs a statistical test of each of these trees against the best tree. This test, which is a version of the test proposed by Alan Templeton (1983) and evaluated in a test case by me (1985a). It is closely parallel to a test using log likelihood differences invented by Kishino and Hasegawa (1989), and uses the mean and variance of step differences between trees, taken across characters. If the mean is more than 1.96 standard deviations different then the trees are declared significantly different. The program prints out a table of the steps for each tree, the differences of each from the highest one, the variance of that quantity as determined by the step differences at individual characters, and a conclusion as to whether that tree is or is not significantly worse than the best one. It is important to understand that the test assumes that all the binary characters are evolving independently, which is unlikely to be true for many suites of morphological characters.

If there are more than two trees, the test done is an extension of the KHT test, due to Shimodaira and Hasegawa (1999). They pointed out that a correction for the number of trees was necessary, and they introduced a resampling method to make this correction. In the version used here the variances and covariances of the sums of steps across characters are computed for all pairs of trees. To test whether the difference between each tree and the best one is larger than could have been expected if they all had the same expected number of steps, numbers of steps for all trees are sampled with these covariances and equal means (Shimodaira and Hasegawa's "least favorable hypothesis"), and a P value is computed from the fraction of times the difference between the tree's value and the lowest number of steps exceeds that actually observed. Note that this sampling needs random numbers, and so the program will prompt the user for a random number seed if one has not already been supplied. With the two-tree KHT test no random numbers are used.

In either the KHT or the SH test the program prints out a table of the number of steps for each tree, the differences of each from the lowest one, the variance of that quantity as determined by the differences of the numbers of steps at individual characters, and a conclusion as to whether that tree is or is not significantly worse than the best one.

If option 6 is left in its default state the trees found will be written to a tree file, so that they are available to be used in other programs. If the program finds multiple trees tied for best, all of these are written out onto the output tree file. Each is followed by a numerical weight in square brackets (such as [0.25000]). This is needed when we use the trees to make a consensus tree of the results of bootstrapping or jackknifing, to avoid overrepresenting replicates that find many tied trees.

### Output files for usage example

### File: dollop.fdollop

    Dollo and polymorphism parsimony algorithm, version 3.6b

    Dollo parsimony method

    One most parsimonious tree found:

      +-----------Delta
    --3
      !  +--------Epsilon
      +--4
         !  +-----Gamma
         +--2
            !  +--Beta
            +--1
               +--Alpha

    requires a total of      3.000

### File: dollop.treefile

    (Delta,(Epsilon,(Gamma,(Beta,Alpha))));

## Data files

None

## Notes

None.

## References

None.

## Warnings

None.

## Diagnostic Error Messages

None.

## Exit status

It always exits with status 0.

## Known bugs

None.

## See also

Program name | Description
--- | ---
eclique | Largest clique program
edollop | Dollo and polymorphism parsimony algorithm
edolpenny | Penny algorithm Dollo or polymorphism
efactor | Multistate to binary recoding program
emix | Mixed parsimony algorithm
epenny | Penny algorithm, branch-and-bound to find all most parsimonious trees
fclique | Largest clique program
fdolpenny | Penny algorithm Dollo or polymorphism
ffactor | Multistate to binary recoding program
fmix | Mixed parsimony algorithm
fmove | Interactive mixed method parsimony
fpars | Discrete character parsimony
fpenny | Penny algorithm, branch-and-bound to find all most parsimonious trees

## Author(s)

This program is an EMBOSS conversion of a program written by Joe Felsenstein as part of his PHYLIP package.

Although we take every care to ensure that the results of the EMBOSS version are identical to those from the original package, we recommend that you check your inputs give the same results in both versions before publication.

Please report all bugs in the EMBOSS version to the EMBOSS bug team, not to the original author.

## History

Written (2004) - Joe Felsenstein, University of Washington.

Converted (August 2004) to an EMBASSY program by the EMBOSS team.

## Target users

This program is intended to be used by everyone and everything, from naive users to embedded scripts.