
                                  fdnaml 



Function

   Estimates nucleotide phylogeny by maximum likelihood

Description

   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.

Algorithm

   This program implements the maximum likelihood method for DNA
   sequences. The present version is faster than earlier versions of
   DNAML. Details of the algorithm are published in the paper by
   Felsenstein and Churchill (1996). The model of base substitution
   allows the expected frequencies of the four bases to be unequal,
   allows the expected frequencies of transitions and transversions to be
   unequal, and has several ways of allowing different rates of evolution
   at different sites.

   The assumptions of the present model are:
    1. Each site in the sequence evolves independently.
    2. Different lineages evolve independently.
    3. Each site undergoes substitution at an expected rate which is
       chosen from a series of rates (each with a probability of
       occurrence) which we specify.
    4. All relevant sites are included in the sequence, not just those
       that have changed or those that are "phylogenetically
       informative".
    5. A substitution consists of one of two sorts of events:
         1. The first kind of event consists of the replacement of the
            existing base by a base drawn from a pool of purines or a
            pool of pyrimidines (depending on whether the base being
            replaced was a purine or a pyrimidine). It can lead either to
            no change or to a transition.
         2. The second kind of event consists of the replacement of the
            existing base by a base drawn at random from a pool of bases
            at known frequencies, independently of the identity of the
            base which is being replaced. This could lead either to a no
            change, to a transition or to a transversion.
            The ratio of the two purines in the purine replacement pool
            is the same as their ratio in the overall pool, and similarly
            for the pyrimidines.
            The ratios of transitions to transversions can be set by the
            user. The substitution process can be diagrammed as follows:
            Suppose that you specified A, C, G, and T base frequencies of
            0.24, 0.28, 0.27, and 0.21.
               o First kind of event:
                 Determine whether the existing base is a purine or a
                 pyrimidine. Draw from the proper pool:

      Purine pool:                Pyrimidine pool:

     |               |            |               |
     |   0.4706 A    |            |   0.5714 C    |
     |   0.5294 G    |            |   0.4286 T    |
     | (ratio is     |            | (ratio is     |
     |  0.24 : 0.27) |            |  0.28 : 0.21) |
     |_______________|            |_______________|

               o Second kind of event:
                 Draw from the overall pool:

              |                  |
              |      0.24 A      |
              |      0.28 C      |
              |      0.27 G      |
              |      0.21 T      |
              |__________________|

   Note that if the existing base is, say, an A, the first kind of event
   has a 0.4706 probability of "replacing" it by another A. The second
   kind of event has a 0.24 chance of replacing it by another A. This
   rather disconcerting model is used because it has nice mathematical
   properties that make likelihood calculations far easier. A closely
   similar, but not precisely identical model having different rates of
   transitions and transversions has been used by Hasegawa et. al.
   (1985b). The transition probability formulas for the current model
   were given (with my permission) by Kishino and Hasegawa (1989).
   Another explanation is available in the paper by Felsenstein and
   Churchill (1996).

   Note the assumption that we are looking at all sites, including those
   that have not changed at all. It is important not to restrict
   attention to some sites based on whether or not they have changed;
   doing that would bias branch lengths by making them too long, and that
   in turn would cause the method to misinterpret the meaning of those
   sites that had changed.

   This program uses a Hidden Markov Model (HMM) method of inferring
   different rates of evolution at different sites. This was described in
   a paper by me and Gary Churchill (1996). It allows us to specify to
   the program that there will be a number of different possible
   evolutionary rates, what the prior probabilities of occurrence of each
   is, and what the average length of a patch of sites all having the
   same rate. The rates can also be chosen by the program to approximate
   a Gamma distribution of rates, or a Gamma distribution plus a class of
   invariant sites. The program computes the the likelihood by summing it
   over all possible assignments of rates to sites, weighting each by its
   prior probability of occurrence.

   For example, if we have used the C and A options (described below) to
   specify that there are three possible rates of evolution, 1.0, 2.4,
   and 0.0, that the prior probabilities of a site having these rates are
   0.4, 0.3, and 0.3, and that the average patch length (number of
   consecutive sites with the same rate) is 2.0, the program will sum the
   likelihood over all possibilities, but giving less weight to those
   that (say) assign all sites to rate 2.4, or that fail to have
   consecutive sites that have the same rate.

   The Hidden Markov Model framework for rate variation among sites was
   independently developed by Yang (1993, 1994, 1995). We have
   implemented a general scheme for a Hidden Markov Model of rates; we
   allow the rates and their prior probabilities to be specified
   arbitrarily by the user, or by a discrete approximation to a Gamma
   distribution of rates (Yang, 1995), or by a mixture of a Gamma
   distribution and a class of invariant sites.

   This feature effectively removes the artificial assumption that all
   sites have the same rate, and also means that we need not know in
   advance the identities of the sites that have a particular rate of
   evolution.

   Another layer of rate variation also is available. The user can assign
   categories of rates to each site (for example, we might want first,
   second, and third codon positions in a protein coding sequence to be
   three different categories. This is done with the categories input
   file and the C option. We then specify (using the menu) the relative
   rates of evolution of sites in the different categories. For example,
   we might specify that first, second, and third positions evolve at
   relative rates of 1.0, 0.8, and 2.7.

   If both user-assigned rate categories and Hidden Markov Model rates
   are allowed, the program assumes that the actual rate at a site is the
   product of the user-assigned category rate and the Hidden Markov Model
   regional rate. (This may not always make perfect biological sense: it
   would be more natural to assume some upper bound to the rate, as we
   have discussed in the Felsenstein and Churchill paper). Nevertheless
   you may want to use both types of rate variation.

Usage

   Here is a sample session with fdnaml


% fdnaml -printdata -ncategories 2 -categories "1111112222222" -rate "1.0 2.0"
-gamma h -nhmmcategories 5 -hmmrates "0.264 1.413 3.596 7.086 12.641" -hmmproba
bilities "0.522 0.399 0.076 0.0036 0.000023" -lambda 1.5 -weight "0111111111110
" 
Estimates nucleotide phylogeny by maximum likelihood
Input (aligned) nucleotide sequence set(s): dnaml.dat
Phylip tree file (optional): 
Phylip dnaml program output file [dnaml.fdnaml]: 


 mulsets: false
 datasets : 1
 rctgry : true
 gama : false
 invar : false
 numwts : 1
 numseqs : 1

 ctgry: true
 categs : 2
 rcategs : 5
 auto_: false
 freqsfrom : true
 global : false
 hypstate : false
 improve : false
 invar : false
 jumble : false
 njumble : 1
 lngths : false
 lambda : 1.000000
 cv : 1.000000
 freqa : 0.000000
 freqc : 0.000000
 freqg : 0.000000
 freqt : 0.000000
 outgrno : 1
 outgropt: false
 trout : true
 ttratio : 2.000000
 ttr : false
 usertree : false
 weights: true
 printdata : true
 progress : true
 treeprint: true
 interleaved : false


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


Output written to file "dnaml.fdnaml"

Tree also written onto file "dnaml.treefile"

Done.


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

   Example 2


% fdnaml -printdata -njumble 3 -seed 3  
Estimates nucleotide phylogeny by maximum likelihood
Input (aligned) nucleotide sequence set(s): dnaml.dat
Phylip tree file (optional): 
Phylip dnaml program output file [dnaml.fdnaml]: 


 mulsets: false
 datasets : 1
 rctgry : false
 gama : false
 invar : false
 numwts : 0
 numseqs : 1

 ctgry: false
 categs : 1
 rcategs : 1
 auto_: false
 freqsfrom : true
 global : false
 hypstate : false
 improve : false
 invar : false
 jumble : true
 njumble : 3
 lngths : false
 lambda : 1.000000
 cv : 1.000000
 freqa : 0.000000
 freqc : 0.000000
 freqg : 0.000000
 freqt : 0.000000
 outgrno : 1
 outgropt: false
 trout : true
 ttratio : 2.000000
 ttr : false
 usertree : false
 weights: false
 printdata : true
 progress : true
 treeprint: true
 interleaved : false


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

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

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


Output written to file "dnaml.fdnaml"

Tree also written onto file "dnaml.treefile"

Done.


   Go to the output files for this example

Command line arguments

   Standard (Mandatory) qualifiers:
  [-sequence]          seqsetall  File containing one or more sequence
                                  alignments
  [-intreefile]        tree       Phylip tree file (optional)
  [-outfile]           outfile    [*.fdnaml] Phylip dnaml program output file

   Additional (Optional) qualifiers (* if not always prompted):
   -ncategories        integer    [1] Number of substitution rate categories
                                  (Integer from 1 to 9)
*  -rate               array      Rate for each category
*  -categories         properties File of substitution rate categories
   -weights            properties Weights file
*  -lengths            boolean    [N] Use branch lengths from user trees
   -ttratio            float      [2.0] Transition/transversion ratio (Number
                                  0.001 or more)
   -[no]freqsfrom      toggle     [Y] Use empirical base frequencies from
                                  seqeunce input
*  -basefreq           array      [0.25 0.25 0.25 0.25] Base frequencies for A
                                  C G T/U (use blanks to separate)
   -gamma              menu       [Constant rate] Rate variation among sites
                                  (Values: g (Gamma distributed rates); i
                                  (Gamma+invariant sites); h (User defined HMM
                                  of rates); n (Constant rate))
*  -gammacoefficient   float      [1] Coefficient of variation of substitution
                                  rate among sites (Number 0.001 or more)
*  -ngammacat          integer    [1] Number of categories (1-9) (Integer from
                                  1 to 9)
*  -invarcoefficient   float      [1] Coefficient of variation of substitution
                                  rate among sites (Number 0.001 or more)
*  -ninvarcat          integer    [1] Number of categories (1-9) including one
                                  for invariant sites (Integer from 1 to 9)
*  -invarfrac          float      [0.0] Fraction of invariant sites (Number
                                  from 0.000 to 1.000)
*  -nhmmcategories     integer    [1] Number of HMM rate categories (Integer
                                  from 1 to 9)
*  -hmmrates           array      [1.0] HMM category rates
*  -hmmprobabilities   array      [1.0] Probability for each HMM category
*  -adjsite            boolean    [N] Rates at adjacent sites correlated
*  -lambda             float      [1.0] Mean block length of sites having the
                                  same rate (Number 1.000 or more)
*  -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)
*  -global             boolean    [N] Global rearrangements
   -outgrno            integer    [0] Species number to use as outgroup
                                  (Integer 0 or more)
   -[no]rough          boolean    [Y] Speedier but rougher analysis
   -[no]trout          toggle     [Y] Write out trees to tree file
*  -outtreefile        outfile    [*.fdnaml] 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
   -hypstate           boolean    [N] Reconstruct hypothetical sequence

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

   "-sequence" associated qualifiers
   -sbegin1            integer    Start of each sequence to be used
   -send1              integer    End of each sequence to be used
   -sreverse1          boolean    Reverse (if DNA)
   -sask1              boolean    Ask for begin/end/reverse
   -snucleotide1       boolean    Sequence is nucleotide
   -sprotein1          boolean    Sequence is protein
   -slower1            boolean    Make lower case
   -supper1            boolean    Make upper case
   -sformat1           string     Input sequence format
   -sdbname1           string     Database name
   -sid1               string     Entryname
   -ufo1               string     UFO features
   -fformat1           string     Features format
   -fopenfile1         string     Features file name

   "-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

   fdnaml reads any normal sequence USAs.

  Input files for usage example

  File: dnaml.dat

   5   13
Alpha     AACGTGGCCAAAT
Beta      AAGGTCGCCAAAC
Gamma     CATTTCGTCACAA
Delta     GGTATTTCGGCCT
Epsilon   GGGATCTCGGCCC

Output file format

   fdnaml output starts by giving the number of species, the number of
   sites, and the base frequencies for A, C, G, and T that have been
   specified. It then prints out the transition/transversion ratio that
   was specified or used by default. It also uses the base frequencies to
   compute the actual transition/transversion ratio implied by the
   parameter.

   If the R (HMM rates) option is used a table of the relative rates of
   expected substitution at each category of sites is printed, as well as
   the probabilities of each of those rates.

   There then follow the data sequences, if the user has selected the
   menu option to print them out, with the base sequences printed in
   groups of ten bases along the lines of the Genbank and EMBL formats.
   The trees found are printed as an unrooted tree topology (possibly
   rooted by outgroup if so requested). The internal nodes are numbered
   arbitrarily for the sake of identification. The number of trees
   evaluated so far and the log likelihood of the tree are also given.
   Note that the trees printed out have a trifurcation at the base. The
   branch lengths in the diagram are roughly proportional to the
   estimated branch lengths, except that very short branches are printed
   out at least three characters in length so that the connections can be
   seen.

   A table is printed showing the length of each tree segment (in units
   of expected nucleotide substitutions per site), as well as (very)
   rough confidence limits on their lengths. If a confidence limit is
   negative, this indicates that rearrangement of the tree in that region
   is not excluded, while if both limits are positive, rearrangement is
   still not necessarily excluded because the variance calculation on
   which the confidence limits are based results in an underestimate,
   which makes the confidence limits too narrow.

   In addition to the confidence limits, the program performs a crude
   Likelihood Ratio Test (LRT) for each branch of the tree. The program
   computes the ratio of likelihoods with and without this branch length
   forced to zero length. This done by comparing the likelihoods changing
   only that branch length. A truly correct LRT would force that branch
   length to zero and also allow the other branch lengths to adjust to
   that. The result would be a likelihood ratio closer to 1. Therefore
   the present LRT will err on the side of being too significant. YOU ARE
   WARNED AGAINST TAKING IT TOO SERIOUSLY. If you want to get a better
   likelihood curve for a branch length you can do multiple runs with
   different prespecified lengths for that branch, as discussed above in
   the discussion of the L option.

   One should also realize that if you are looking not at a
   previously-chosen branch but at all branches, that you are seeing the
   results of multiple tests. With 20 tests, one is expected to reach
   significance at the P = .05 level purely by chance. You should
   therefore use a much more conservative significance level, such as .05
   divided by the number of tests. The significance of these tests is
   shown by printing asterisks next to the confidence interval on each
   branch length. It is important to keep in mind that both the
   confidence limits and the tests are very rough and approximate, and
   probably indicate more significance than they should. Nevertheless,
   maximum likelihood is one of the few methods that can give you any
   indication of its own error; most other methods simply fail to warn
   the user that there is any error! (In fact, whole philosophical
   schools of taxonomists exist whose main point seems to be that there
   isn't any error, that the "most parsimonious" tree is the best tree by
   definition and that's that).

   The log likelihood printed out with the final tree can be used to
   perform various likelihood ratio tests. One can, for example, compare
   runs with different values of the expected transition/transversion
   ratio to determine which value is the maximum likelihood estimate, and
   what is the allowable range of values (using a likelihood ratio test,
   which you will find described in mathematical statistics books). One
   could also estimate the base frequencies in the same way. Both of
   these, particularly the latter, require multiple runs of the program
   to evaluate different possible values, and this might get expensive.

   If the U (User Tree) option is used and more than one tree is
   supplied, and the program is not told to assume autocorrelation
   between the rates at different sites, the program also performs a
   statistical test of each of these trees against the one with highest
   likelihood. If there are two user trees, the test done is one which is
   due to Kishino and Hasegawa (1989), a version of a test originally
   introduced by Templeton (1983). In this implementation it uses the
   mean and variance of log-likelihood differences between trees, taken
   across sites. If the two trees' means are more than 1.96 standard
   deviations different then the trees are declared significantly
   different. This use of the empirical variance of log-likelihood
   differences is more robust and nonparametric than the classical
   likelihood ratio test, and may to some extent compensate for the any
   lack of realism in the model underlying this program.

   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 sum of log likelihoods
   across sites 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 log-likelihood,
   log-likelihoods 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 highest log-likelihood 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
   log-likelihoods of each tree, the differences of each from the highest
   one, the variance of that quantity as determined by the log-likelihood
   differences at individual sites, and a conclusion as to whether that
   tree is or is not significantly worse than the best one. However the
   test is not available if we assume that there is autocorrelation of
   rates at neighboring sites (option A) and is not done in those cases.

   The branch lengths printed out are scaled in terms of expected numbers
   of substitutions, counting both transitions and transversions but not
   replacements of a base by itself, and scaled so that the average rate
   of change, averaged over all sites analyzed, is set to 1.0 if there
   are multiple categories of sites. This means that whether or not there
   are multiple categories of sites, the expected fraction of change for
   very small branches is equal to the branch length. Of course, when a
   branch is twice as long this does not mean that there will be twice as
   much net change expected along it, since some of the changes occur in
   the same site and overlie or even reverse each other. The branch
   length estimates here are in terms of the expected underlying numbers
   of changes. That means that a branch of length 0.26 is 26 times as
   long as one which would show a 1% difference between the nucleotide
   sequences at the beginning and end of the branch. But we would not
   expect the sequences at the beginning and end of the branch to be 26%
   different, as there would be some overlaying of changes.

   Confidence limits on the branch lengths are also given. Of course a
   negative value of the branch length is meaningless, and a confidence
   limit overlapping zero simply means that the branch length is not
   necessarily significantly different from zero. Because of limitations
   of the numerical algorithm, branch length estimates of zero will often
   print out as small numbers such as 0.00001. If you see a branch length
   that small, it is really estimated to be of zero length. Note that
   versions 2.7 and earlier of this program printed out the branch
   lengths in terms of expected probability of change, so that they were
   scaled differently.

   Another possible source of confusion is the existence of negative
   values for the log likelihood. This is not really a problem; the log
   likelihood is not a probability but the logarithm of a probability.
   When it is negative it simply means that the corresponding probability
   is less than one (since we are seeing its logarithm). The log
   likelihood is maximized by being made more positive: -30.23 is worse
   than -29.14.

   At the end of the output, if the R option is in effect with multiple
   HMM rates, the program will print a list of what site categories
   contributed the most to the final likelihood. This combination of HMM
   rate categories need not have contributed a majority of the
   likelihood, just a plurality. Still, it will be helpful as a view of
   where the program infers that the higher and lower rates are. Note
   that the use in this calculations of the prior probabilities of
   different rates, and the average patch length, gives this inference a
   "smoothed" appearance: some other combination of rates might make a
   greater contribution to the likelihood, but be discounted because it
   conflicts with this prior information. See the example output below to
   see what this printout of rate categories looks like. A second list
   will also be printed out, showing for each site which rate accounted
   for the highest fraction of the likelihood. If the fraction of the
   likelihood accounted for is less than 95%, a dot is printed instead.

   Option 3 in the menu controls whether the tree is printed out into the
   output file. This is on by default, and usually you will want to leave
   it this way. However for runs with multiple data sets such as
   bootstrapping runs, you will primarily be interested in the trees
   which are written onto the output tree file, rather than the trees
   printed on the output file. To keep the output file from becoming too
   large, it may be wisest to use option 3 to prevent trees being printed
   onto the output file.

   Option 4 in the menu controls whether the tree estimated by the
   program is written onto a tree file. The default name of this output
   tree file is "outtree". If the U option is in effect, all the
   user-defined trees are written to the output tree file.

   Option 5 in the menu controls whether ancestral states are estimated
   at each node in the tree. If it is in effect, a table of ancestral
   sequences is printed out (including the sequences in the tip species
   which are the input sequences). In that table, if a site has a base
   which accounts for more than 95% of the likelihood, it is printed in
   capital letters (A rather than a). If the best nucleotide accounts for
   less than 50% of the likelihood, the program prints out an ambiguity
   code (such as M for "A or C") for the set of nucleotides which, taken
   together, account for more half of the likelihood. The ambiguity codes
   are listed in the sequence programs documentation file. One limitation
   of the current version of the program is that when there are multiple
   HMM rates (option R) the reconstructed nucleotides are based on only
   the single assignment of rates to sites which accounts for the largest
   amount of the likelihood. Thus the assessment of 95% of the
   likelihood, in tabulating the ancestral states, refers to 95% of the
   likelihood that is accounted for by that particular combination of
   rates.

  Output files for usage example

  File: dnaml.fdnaml


Nucleic acid sequence Maximum Likelihood method, version 3.6b

 5 species,  13  sites

    Site categories are:

             1111112222 222


    Sites are weighted as follows:

             01111 11111 110


Name            Sequences
----            ---------

Alpha        AACGTGGCCA AAT
Beta         AAGGTCGCCA AAC
Gamma        CATTTCGTCA CAA
Delta        GGTATTTCGG CCT
Epsilon      GGGATCTCGG CCC



Empirical Base Frequencies:

   A       0.23636
   C       0.29091
   G       0.25455
  T(U)     0.21818

Transition/transversion ratio =   2.000000


State in HMM    Rate of change    Probability

        1           0.264            0.522
        2           1.413            0.399
        3           3.596            0.076
        4           7.086            0.0036
        5          12.641            0.000023



Site category   Rate of change

        1           1.000
        2           2.000



                                                             +Epsilon
  +----------------------------------------------------------3
  |                                                          +Delta
  |
  |  +------Gamma
  2--1
  |  +Beta
  |
  +-Alpha


remember: this is an unrooted tree!

Ln Likelihood =   -57.89164

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

     2          Alpha             0.26902     (     zero,     0.80593) *
     2             3              7.83947     (     zero,    22.44277) **
     3          Epsilon           0.13647     (     zero,     0.59801)
     3          Delta             0.13781     (     zero,     0.59867)
     2             1              0.00006     (     zero,     0.54477)
     1          Gamma             0.95525     (     zero,     2.45593) **
     1          Beta              0.04509     (     zero,     0.48078)

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

Combination of categories that contributes the most to the likelihood:

             1132121111 211

Most probable category at each site if > 0.95 probability ("." otherwise)

             .......... ...

  File: dnaml.treefile

((Epsilon:0.13647,Delta:0.13781):7.83947,(Gamma:0.95525,
Beta:0.04509):0.00006,Alpha:0.26902);

  Output files for usage example 2

  File: dnaml.fdnaml


Nucleic acid sequence Maximum Likelihood method, version 3.6b

 5 species,  13  sites

Name            Sequences
----            ---------

Alpha        AACGTGGCCA AAT
Beta         AAGGTCGCCA AAC
Gamma        CATTTCGTCA CAA
Delta        GGTATTTCGG CCT
Epsilon      GGGATCTCGG CCC



Empirical Base Frequencies:

   A       0.24615
   C       0.29231
   G       0.24615
  T(U)     0.21538

Transition/transversion ratio =   2.000000


     +Beta
  +--2
  |  |                                            +Epsilon
  |  +--------------------------------------------1
  |                                               +--------Delta
  |
  3------------------------------Gamma
  |
  +-----Alpha


remember: this is an unrooted tree!

Ln Likelihood =   -72.25088

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

     3          Alpha             0.20753     (     zero,     0.56587)
     3             2              0.09399     (     zero,     0.40897)
     2          Beta              0.00006     (     zero,     0.32900)
     2             1              1.51297     (     zero,     3.31131) **
     1          Epsilon           0.00006     (     zero,     0.34299)
     1          Delta             0.28136     (     zero,     0.62653) **
     3          Gamma             1.01654     (     zero,     2.33183) **

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


  File: dnaml.treefile

((Beta:0.00006,(Epsilon:0.00006,Delta:0.28136):1.51297):0.09399,
Gamma:1.01654,Alpha:0.20753);

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
   distmat      Creates a distance matrix from multiple alignments
   ednacomp     DNA compatibility algorithm
   ednadist     Nucleic acid sequence Distance Matrix program
   ednainvar    Nucleic acid sequence Invariants method
   ednaml       Phylogenies from nucleic acid Maximum Likelihood
   ednamlk      Phylogenies from nucleic acid Maximum Likelihood with clock
   ednapars     DNA parsimony algorithm
   ednapenny    Penny algorithm for DNA
   eprotdist    Protein distance algorithm
   eprotpars    Protein parsimony algorithm
   erestml      Restriction site Maximum Likelihood method
   eseqboot     Bootstrapped sequences algorithm
   fdiscboot    Bootstrapped discrete sites algorithm
   fdnacomp     DNA compatibility algorithm
   fdnadist     Nucleic acid sequence Distance Matrix program
   fdnainvar    Nucleic acid sequence Invariants method
   fdnamlk      Estimates nucleotide phylogeny by maximum likelihood
   fdnamove     Interactive DNA parsimony
   fdnapars     DNA parsimony algorithm
   fdnapenny    Penny algorithm for DNA
   fdolmove     Interactive Dollo or Polymorphism Parsimony
   ffreqboot    Bootstrapped genetic frequencies algorithm
   fproml       Protein phylogeny by maximum likelihood
   fpromlk      Protein phylogeny by maximum likelihood
   fprotdist    Protein distance algorithm
   fprotpars    Protein parsimony algorithm
   frestboot    Bootstrapped restriction sites algorithm
   frestdist    Distance matrix from restriction sites or fragments
   frestml      Restriction site maximum Likelihood method
   fseqboot     Bootstrapped sequences algorithm
   fseqbootall  Bootstrapped sequences algorithm

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.
