INPUT FORMAT AND OPTIONS
Subject to these assumptions, the program is a correct maximum likelihood method. The input is fairly standard, with one addition. As usual the first line of the file gives the number of species and the number of sites. There follows the character W if the Weights options is being used.
Next come the species data. Each sequence starts on a new line, has a ten-character species name that must be blank-filled to be of that length, followed immediately by the species data in the one-letter code. The sequences must either be in the "interleaved" or "sequential" formats described in the Molecular Sequence Programs documentation. The I option selects between them. The sequences can have internal blanks in the sequence but there must be no extra blanks at the end of the terminated line. Note that a blank is not a valid symbol for a deletion.
After that are the lines (if any) containing the information for the C, and W options, as described below.
The options are selected using an interactive menu. The menu looks like this:
Nucleic acid sequence Maximum Likelihood method, version 3.5c Settings for this run: U Search for best tree? Yes T Transition/transversion ratio: 2.0000 F Use empirical base frequencies? Yes C One category of substitution rates? Yes G Global rearrangements? No J Randomize input order of sequences? No. Use input order O Outgroup root? No, use as outgroup species 1 M Analyze multiple data sets? No I Input sequences interleaved? Yes 0 Terminal type (IBM PC, VT52, ANSI)? ANSI 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 Are these settings correct? (type Y or the letter for one to change)The user either types "Y" (followed, of course, by a carriage-return) if the settings shown are to be accepted, or the letter or digit corresponding to an option that is to be changed.
The options U, J, O, M, and 0 are the usual ones. They are described in the main documentation file of this package. Option I is the same as in other molecular sequence programs and is described in the molecular sequence programs documentation.
The T option in this program does not stand for Threshold, but instead is the Transition/transversion option. The user is prompted for a real number greater than 0.0, as the expected ratio of transitions to transversions. Note that this is not the ratio of the first to the second kinds of events, but the
resulting expected ratio of transitions to transversions. The exact relationship between these two quantities depends on the frequencies in the base pools. The default value of the T parameter if you do not use the T option is 2.0.
The F (Frequencies) option is one which may save users much time. If you want to use the empirical frequencies of the bases, observed in the input sequences, as the base frequencies, you simply use the default setting of the F option. These empirical frequencies are not really the maximum likelihood estimates of the base frequencies, but they will often be close to those values (what they are is maximum likelihood estimates under a "star" or "explosion" phylogeny). If you change the setting of the F option you will be prompted for the frequencies of the four bases. These must add to 1 and are to be typed on one line separated by blanks, not commas.
The C (categories) option allows the user to specify how many categories of substitution rates there will be, and what are the rates and probabilities for each. The user first is asked how many categories there will be (for the moment there is an upper limit of 9, which should not be restrictive). Then the program asks for the rates for each category. These rates are only meaningful relative to each other, so that rates 1.0, 2.0, and 2.4 have the exact same effect as rates 2.0, 4.0, and 4.8. Note that a category can have rate of change 0, so that this allows us to take into account that there may be a category of sites that are invariant. Note that the run time of the program will be proportional to the number of rate categories: twice as many categories means twice as long a run. Finally the program will ask for the probabilities of a random site falling into each of these categories. These probabilities must be nonnegative and sum to 1. Default for the program is one category, with rate 1.0 and probability 1.0 (actually the rate does not matter in that case).
If more than one category is specified, then another option, R, becomes visible in the menu. This allows us to specify that we want to assume that sites that have the same rate category are expected to be clustered. The program asks for the value of the average patch length. This is an expected length of patches that have the same rate. If it is 1, the rates of successive sites will be independent. If it is, say, 10.25, then the chance of change to a new rate will be 1/10.25 after every site. However the "new rate" is randomly drawn from the mix of rates, and hence could even be the same. So the actual observed length of patches with the same rate will be somewhat larger than 10.25. Note below that if you choose multiple patches, there will be an estimate in the output file as to which combination of rate categories contributed most to the likelihood.
With the current options C and R the program has gained greatly in its ability to infer different rates at different sites and estimate phylogenies under a more realistic model. Note that Likelihood Ratio Tests can be used to test whether one combination of rates is significantly better than another.
The G (global search) option causes, after the last species is added to the tree, each possible group to be removed and re-added. This improves the result, since the position of every species is reconsidered. It approximately triples the run-time of the program.
If the U (user tree) option is chosen another option appears in the menu, the L option. If it is selected, it signals the program that it should take any branch lengths that are in the user tree and simply evaluate the likelihood of that tree, without further altering those branch lengths. This means that if some branches have lengths and others do not, the program will estimate the lengths of those that do not have lengths given in the user tree. Note that the program RETREE can be used to add and remove lengths from a tree. This means that we can test hypothesis such as that a certain branch has zero length, and by doing a series of runs with different specified lengths for a branch we can plot a likelihood curve for its branch length while allowing all other branches to adjust their lengths to it. If all branches have lengths specified, none of them will be iterated. This is useful to allow a tree produced by another method to have its likelihood evaluated. The L option has no effect and does not appear in the menu if the U option is not used.
The W (Weights) option is invoked in the usual way, with only weights 0 and 1 allowed. It selects a set of sites to be analyzed, ignoring the others. The sites selected are those with weight 1. If the W option is not invoked, all sites are analyzed.
The 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 C (Categories) 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, 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. As with CONTML, 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. This test, due to
Kishino and
Hasegawa (1989), uses the mean and variance of log-likelihood differences
between trees, taken across sites. If the mean is 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.
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 Kishino-Hasegawa-Templeton test is not available if we
assume that there is autocorrelation of rates at neighboring sites (option R)
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 lengths 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 C option is in effect with multiple rate
categories, the program will print a list of what site categories contributed
the most to the final likelihood. This combination of 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.
The constants defined at the beginning of the program include "maxtrees",
the maximum number of user trees that can be processed. It is small (10) at
present to same some further memory but the cost of increasing it is not very
great. Other constants include "maxcategories", the maximum number of site
categories, "namelength", the length of species names in characters, and three
others, "smoothings", "iterations", and "epsilon", that help "tune" the
algorithm and define the compromise between execution speed and the quality of
the branch lengths found by iteratively maximizing the likelihood. Reducing
iterations and smoothings, and increasing epsilon, will result in faster
execution but a worse result. These values will not usually have to be
changed.
The program spends most of its time doing real arithmetic. Any software
or hardware changes that speed up that arithmetic will speed it up by a nearly
proportional amount. For example, microcomputers having a numeric co-processor
will run the program much faster, if the compiled program uses it. The
algorithm, with separate and independent computations occurring for each
pattern, lends itself readily to parallel processing.
This program, which in version 2.6 replaced the old version of DNAML, is
not derived directly from it but instead was developed by modifying CONTML,
with which it shares many of its data structures and much of its strategy. It
was speeded up by two major developments, the use of aliasing of nucleotide
sites (version 3.1) and pretabulation of some exponentials (added by Akiko
Fuseki in version 3.4). In version 3.5 the Hidden Markov Chain code was added
and the method of iterating branch lengths was changed from an EM algorithm to
direct search. The Hidden Markov Chain code slows things down, especially if
there is autocorrelation between sites, so this version is slower than version
3.4. Nevertheless we hope that the sacrifice is worth it.
Two changes that are needed in the future are to put in some way of
allowing for base composition of nucleotide sequences in different parts of the
phylogeny, and to re-introduce the code that allowed us to assign relative
rates of change to individual sites. By allowing prespecified rates and also
allowing the Hidden Markov Chain method of inferring rates at different sites,
we could then deal with cases in which (say) first, second, and third positions
in a coding sequence had rates of (say) 1.0 : 0.8 : 2.7 but the Hidden Markov
Chain also inferred rates that varied along the length of the coding sequence.
This would be much more realistic.
OUTPUT FORMAT
PROGRAM CONSTANTS
PAST AND FUTURE OF THE PROGRAM
TEST DATA SET
5 13
Alpha AACGTGGCCAAAT
Beta AAGGTCGCCAAAC
Gamma CATTTCGTCACAA
Delta GGTATTTCGGCCT
Epsilon GGGATCTCGGCCC
(it was run with two categories with rates 1.0 and 3.2, and with
probabilities 0.4 and 0.6 for these rates, and with patch length
parameter = 1.5)
CONTENTS OF OUTPUT FILE (with all numerical options on)
Nucleic acid sequence Maximum Likelihood method, version 3.5c
Site category Rate of change Probability
1 1.000 0.400
2 3.200 0.600
Expected length of a patch of sites having the same rate = 1.500
Name Sequences
---- ---------
Alpha AACGTGGCCA AAT
Beta ..G..C.... ..C
Gamma C.TT.C.T.. C.A
Delta GGTA.TT.GG CC.
Epsilon GGGA.CT.GG CCC
Empirical Base Frequencies:
A 0.24615
C 0.29231
G 0.24615
T(U) 0.21538
Transition/transversion ratio = 2.000000
(Transition/transversion parameter = 1.523077)
+Epsilon
+-------------------------------------------------3
+--2 +----Delta
! !
! +Beta
!
--1---------------------Gamma
!
+---Alpha
remember: this is an unrooted tree!
Ln Likelihood = -72.22208
Examined 15 trees
Between And Length Approx. Confidence Limits
------- --- ------ ------- ---------- ------
1 2 0.06639 ( zero, 0.46657) **
2 3 3.32192 ( zero, infinity)
3 Epsilon 0.00006 ( zero, 0.30554) **
3 Delta 0.31809 ( zero, 0.78094) **
2 Beta 0.00003 ( zero, 0.41027) **
1 Gamma 1.47499 ( zero, 3.68232)
1 Alpha 0.27845 ( zero, 0.72666) **
* = significantly positive, P < 0.05
** = significantly positive, P < 0.01
Combination of categories that contributes the most to the likelihood:
2222111111 222
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Maintained 15 Jul 1996 -- by Martin Hilbers(e-mail:M.P.Hilbers@dl.ac.uk)