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# neighbor.doc

- DISCLAIMER
- DOCUMENTATION

## DISCLAIMER

### This file has been automatically converted from the original documentation for easy use inside the ARB help system. Differences compared with the original documentation are unintentionally caused by the conversion process. In doubt please refer to the original documentation!

## DOCUMENTATION

    # generated from ../../GDE/PHYLIP/doc/neighbor.html
                                                                   version 3.6

NEIGHBOR -- Neighbor-Joining and UPGMA methods
(C) Copyright 1991-2000 by the University of Washington. Written by Joseph Felsenstein. Permission is granted to copy this document provided that no fee is charged for it and that this copyright notice is not removed.
This program implements the Neighbor-Joining method of Nei and Saitou (1987) and the UPGMA method of clustering. The program was written by Mary Kuhner and Jon Yamato, using some code from program FITCH. An important part of the code was translated from FORTRAN code from the neighbor-joining program written by Naruya Saitou and by Li Jin, and is used with the kind permission of Drs. Saitou and Jin.
NEIGHBOR constructs a tree by successive clustering of lineages, setting branch lengths as the lineages join. The tree is not rearranged thereafter. The tree does not assume an evolutionary clock, so that it is in effect an unrooted tree. It should be somewhat similar to the tree obtained by FITCH. The program cannot evaluate a User tree, nor can it prevent branch lengths from becoming negative. However the algorithm is far faster than FITCH or KITSCH. This will make it particularly effective in their place for large studies or for bootstrap or jackknife resampling studies which require runs on multiple data sets.
The UPGMA option constructs a tree by successive (agglomerative) clustering using an average-linkage method of clustering. It has some relationship to KITSCH, in that when the tree topology turns out the same, the branch lengths with UPGMA will turn out to be the same as with the P = 0 option of KITSCH.
The options for NEIGHBOR are selected through the menu, which looks like this:

### Neighbor-Joining/UPGMA method version 3.6a3

    Settings for this run:
      N       Neighbor-joining or UPGMA tree?  Neighbor-joining
      O                        Outgroup root?  No, use as outgroup species  1
      L         Lower-triangular data matrix?  No
      R         Upper-triangular data matrix?  No
      S                        Subreplicates?  No
      J     Randomize input order of species?  No. Use input order
      M           Analyze multiple data sets?  No
      0   Terminal type (IBM PC, ANSI, none)?  (none)
      1    Print out the data at start of run  No
      2  Print indications of progress of run  Yes
      3                        Print out tree  Yes
      4       Write out trees onto tree file?  Yes

Y to accept these or type the letter for one to change
Most of the input options (L, R, S, J, and M) are as given in the Distance Matrix Programs documentation file, that file, and their input format is the same as given there. The O (Outgroup) option is described in the main documentation file of this package. It is not available when the UPGMA option is selected. The Jumble option (J) does not allow multiple jumbles (as most of the other programs that have it do), as there is no objective way of choosing which of the multiple results is best, there being no explicit criterion for optimality of the tree.
Option N chooses between the Neighbor-Joining and UPGMA methods. Option S is the usual Subreplication option. Here, however, it is present only to allow NEIGHBOR to read the input data: the number of replicates is actually ignored, even though it is read in. Note that this means that one cannot use it to have missing data in the input file, if NEIGHBOR is to be used.
The output consists of an tree (rooted if UPGMA, unrooted if Neighbor-Joining) and the lengths of the interior segments. The Average Percent Standard Deviation is not computed or printed out. If the tree found by Neighbor is fed into FITCH as a User Tree, it will compute this quantity if one also selects the N option of FITCH to ensure that none of the branch lengths is re-estimated.
As NEIGHBOR runs it prints out an account of the successive clustering levels, if you allow it to. This is mostly for reassurance and can be suppressed using menu option 2. In this printout of cluster levels the word "OTU" refers to a tip species, and the word "NODE" to an interior node of the resulting tree.
The constants available for modification at the beginning of the program are "namelength" which gives the length of a species name, and the usual boolean constants that initiliaze the terminal type. There is no feature saving multiply trees tied for best, partly because we do not expect exact ties except in cases where the branch lengths make the nature of the tie obvious, as when a branch is of zero length.

    The major advantage of NEIGHBOR is its speed: it requires a time only
    proportional to the square of the number of species. It is
    significantly faster than version 3.5 of this program. By contrast
    FITCH and KITSCH require a time that rises as the fourth power of the
    number of species. Thus NEIGHBOR is well-suited to bootstrapping
    studies and to analysis of very large trees. Our simulation studies
    (Kuhner and Felsenstein, 1994) show that, contrary to statements in the
    literature by others, NEIGHBOR does not get as accurate an estimate of
    the phylogeny as does FITCH. However it does nearly as well, and in
    view of its speed this will make it a quite useful program.
      __________________________________________________________________

TEST DATA SET

        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 FROM TEST DATA SET (with all numerical options on)
7 Populations

### Neighbor-Joining/UPGMA method version 3.6a3

Neighbor-joining method
Negative branch lengths allowed

    Name                       Distances
    ----                       ---------

    Bovine        0.00000   1.68660   1.71980   1.66060   1.52430   1.60430
                  1.59050
    Mouse         1.68660   0.00000   1.52320   1.48410   1.44650   1.43890
                  1.46290
    Gibbon        1.71980   1.52320   0.00000   0.71150   0.59580   0.61790
                  0.55830
    Orang         1.66060   1.48410   0.71150   0.00000   0.46310   0.50610
                  0.47100
    Gorilla       1.52430   1.44650   0.59580   0.46310   0.00000   0.34840
                  0.30830
    Chimp         1.60430   1.43890   0.61790   0.50610   0.34840   0.00000
                  0.26920
    Human         1.59050   1.46290   0.55830   0.47100   0.30830   0.26920
                  0.00000

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

### remember: this is an unrooted tree!

    Between        And            Length
    -------        ---            ------
       1          Mouse           0.76891
       1             2            0.42027
       2          Gibbon          0.35793
       2             5            0.04648
       5          Orang           0.28469
       5             4            0.02696
       4          Gorilla         0.15393
       4             3            0.03982
       3          Chimp           0.15167
       3          Human           0.11753
       1          Bovine          0.91769