
                                  ffitch 



Function

   Fitch-Margoliash and Least-Squares Distance Methods

Description

   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.

Algorithm

   The programs FITCH, KITSCH, and NEIGHBOR are for dealing with data
   which comes in the form of a matrix of pairwise distances between all
   pairs of taxa, such as distances based on molecular sequence data,
   gene frequency genetic distances, amounts of DNA hybridization, or
   immunological distances. In analyzing these data, distance matrix
   programs implicitly assume that:
     * Each distance is measured independently from the others: no item
       of data contributes to more than one distance.
     * The distance between each pair of taxa is drawn from a
       distribution with an expectation which is the sum of values (in
       effect amounts of evolution) along the tree from one tip to the
       other. The variance of the distribution is proportional to a power
       p of the expectation.

   These assumptions can be traced in the least squares methods of
   programs FITCH and KITSCH but it is not quite so easy to see them in
   operation in the Neighbor-Joining method of NEIGHBOR, where the
   independence assumptions is less obvious.

   THESE TWO ASSUMPTIONS ARE DUBIOUS IN MOST CASES: independence will not
   be expected to be true in most kinds of data, such as genetic
   distances from gene frequency data. For genetic distance data in which
   pure genetic drift without mutation can be assumed to be the mechanism
   of change CONTML may be more appropriate. However, FITCH, KITSCH, and
   NEIGHBOR will not give positively misleading results (they will not
   make a statistically inconsistent estimate) provided that additivity
   holds, which it will if the distance is computed from the original
   data by a method which corrects for reversals and parallelisms in
   evolution. If additivity is not expected to hold, problems are more
   severe. A short discussion of these matters will be found in a review
   article of mine (1984a). For detailed, if sometimes irrelevant,
   controversy see the papers by Farris (1981, 1985, 1986) and myself
   (1986, 1988b).

   For genetic distances from gene frequencies, FITCH, KITSCH, and
   NEIGHBOR may be appropriate if a neutral mutation model can be assumed
   and Nei's genetic distance is used, or if pure drift can be assumed
   and either Cavalli-Sforza's chord measure or Reynolds, Weir, and
   Cockerham's (1983) genetic distance is used. However, in the latter
   case (pure drift) CONTML should be better.

   Restriction site and restriction fragment data can be treated by
   distance matrix methods if a distance such as that of Nei and Li
   (1979) is used. Distances of this sort can be computed in PHYLIp by
   the program RESTDIST.

   For nucleic acid sequences, the distances computed in DNADIST allow
   correction for multiple hits (in different ways) and should allow one
   to analyse the data under the presumption of additivity. In all of
   these cases independence will not be expected to hold. DNA
   hybridization and immunological distances may be additive and
   independent if transformed properly and if (and only if) the standards
   against which each value is measured are independent. (This is rarely
   exactly true).

   FITCH and the Neighbor-Joining option of NEIGHBOR fit a tree which has
   the branch lengths unconstrained. KITSCH and the UPGMA option of
   NEIGHBOR, by contrast, assume that an "evolutionary clock" is valid,
   according to which the true branch lengths from the root of the tree
   to each tip are the same: the expected amount of evolution in any
   lineage is proportional to elapsed time.

Usage

   Here is a sample session with ffitch


% ffitch 
Fitch-Margoliash and Least-Squares Distance Methods
Phylip distance matrix file: fitch.dat
Phylip tree file (optional): 
Phylip fitch program output file [fitch.ffitch]: 

Adding species:
   1. Bovine
   2. Mouse
   3. Gibbon
   4. Orang
   5. Gorilla
   6. Chimp
   7. Human

Output written to file "fitch.ffitch"

Tree also written onto file "fitch.treefile"

Done.


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

Command line arguments

   Standard (Mandatory) qualifiers:
  [-datafile]          distances  File containing one or more distance
                                  matrices
  [-intreefile]        tree       Phylip tree file (optional)
  [-outfile]           outfile    [*.ffitch] Phylip fitch program output file

   Additional (Optional) qualifiers (* if not always prompted):
   -matrixtype         menu       [s] Type of input data matrix (Values: s
                                  (Square); u (Upper triangular); l (Lower
                                  triangular))
   -minev              boolean    [N] Minimum evolution
*  -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)
   -outgrno            integer    [0] Species number to use as outgroup
                                  (Integer 0 or more)
   -power              float      [2.0] Power (Any numeric value)
*  -lengths            boolean    [N] Use branch lengths from user trees
*  -negallowed         boolean    [N] Negative branch lengths allowed
*  -global             boolean    [N] Global rearrangements
   -replicates         boolean    [N] Subreplicates
   -[no]trout          toggle     [Y] Write out trees to tree file
*  -outtreefile        outfile    [*.ffitch] 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

   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 dying program messages

Input file format

   ffitch reads any normal sequence USAs.

  Input files for usage example

  File: fitch.dat

    7
Bovine      0.0000  1.6866  1.7198  1.6606  1.5243  1.6043  1.5905
Mouse       1.6866  0.0000  1.5232  1.4841  1.4465  1.4389  1.4629
Gibbon      1.7198  1.5232  0.0000  0.7115  0.5958  0.6179  0.5583
Orang       1.6606  1.4841  0.7115  0.0000  0.4631  0.5061  0.4710
Gorilla     1.5243  1.4465  0.5958  0.4631  0.0000  0.3484  0.3083
Chimp       1.6043  1.4389  0.6179  0.5061  0.3484  0.0000  0.2692
Human       1.5905  1.4629  0.5583  0.4710  0.3083  0.2692  0.0000

Output file format

   ffitch output consists of an unrooted tree and the lengths of the
   interior segments. The sum of squares is printed out, and if P = 2.0
   Fitch and Margoliash's "average percent standard deviation" is also
   computed and printed out. This is the sum of squares, divided by N-2,
   and then square-rooted and then multiplied by 100 (n is the number of
   species on the tree):

     APSD = ( SSQ / (N-2) )1/2 x 100.

   where N is the total number of off-diagonal distance measurements that
   are in the (square) distance matrix. If the S (subreplication) option
   is in force it is instead the sum of the numbers of replicates in all
   the non-diagonal cells of the distance matrix. But if the L or R
   option is also in effect, so that the distance matrix read in is
   lower- or upper-triangular, then the sum of replicates is only over
   those cells actually read in. If S is not in force, the number of
   replicates in each cell is assumed to be 1, so that N is n(n-1), where
   n is the number of species. The APSD gives an indication of the
   average percentage error. The number of trees examined is also printed
   out.

  Output files for usage example

  File: fitch.ffitch


   7 Populations

Fitch-Margoliash method version 3.6b

                  __ __             2
                  \  \   (Obs - Exp)
Sum of squares =  /_ /_  ------------
                                2
                   i  j      Obs

Negative branch lengths not allowed


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


remember: this is an unrooted tree!

Sum of squares =     0.01375

Average percent standard deviation =     1.85418

Between        And            Length
-------        ---            ------
   1          Mouse             0.76985
   1             2              0.41983
   2             3              0.04986
   3             4              0.02121
   4             5              0.03695
   5          Human             0.11449
   5          Chimp             0.15471
   4          Gorilla           0.15680
   3          Orang             0.29209
   2          Gibbon            0.35537
   1          Bovine            0.91675


  File: fitch.treefile

(Mouse:0.76985,((((Human:0.11449,Chimp:0.15471):0.03695,
Gorilla:0.15680):0.02121,Orang:0.29209):0.04986,Gibbon:0.35537):0.41983,Bovine:
0.91675);

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
   efitch       Fitch-Margoliash and Least-Squares Distance Methods
   ekitsch      Fitch-Margoliash method with contemporary tips
   eneighbor    Phylogenies from distance matrix by N-J or UPGMA method
   fkitsch      Fitch-Margoliash method with contemporary tips
   fneighbor    Phylogenies from distance matrix by N-J or UPGMA method

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.
