                                   fdollop



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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
Phylip character discrete states file: dollop.dat
Phylip tree file (optional):
Phylip dollop program output file [dollop.fdollop]:


Dollo and polymorphism parsimony algorithm, version 3.69

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

Dollo and polymorphism parsimony algorithm
Version: EMBOSS:6.2.0

   Standard (Mandatory) qualifiers:
  [-infile]            discretestates File containing one or more data sets
  [-intreefile]        tree       Phylip tree file (optional)
  [-outfile]           outfile    [*.fdollop] Phylip dollop program output
                                  file

   Additional (Optional) qualifiers (* if not always prompted):
   -weights            properties Phylip weights file (optional)
   -ancfile            properties Ancestral states file
   -method             menu       [d] Parsimony method (Values: d (Dollo); p
                                  (Polymorphism))
*  -njumble            integer    [0] Number of times to randomise (Integer 0
                                  or more)
*  -seed               integer    [1] Random number seed between 1 and 32767
                                  (must be odd) (Integer from 1 to 32767)
   -threshold          float      [$(infile.discretesize)] Threshold value
                                  (Number 0.000 or more)
   -[no]trout          toggle     [Y] Write out trees to tree file
*  -outtreefile        outfile    [*.fdollop] Phylip tree output file
                                  (optional)
   -printdata          boolean    [N] Print data at start of run
   -[no]progress       boolean    [Y] Print indications of progress of run
   -[no]treeprint      boolean    [Y] Print out tree
   -ancseq             boolean    [N] Print states at all nodes of tree
   -stepbox            boolean    [N] 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 first file to standard output
   -filter             boolean    Read first file from standard input, write
                                  first file to 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 and exit. 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 dying program messages
   -version            boolean    Report version number and exit


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 3.69

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
                    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

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.
