PUZZLEVersion 4.0.1
February 1999
Copyright 1995-99 by Korbinian Strimmer and Arndt von Haeseler
Korbinian Strimmer, email: Korbinian.Strimmer@mchp.siemens.de, Siemens AG, Corporate Technology, Neural Computation (ZT IK 4), Otto-Hahn-Ring 6, D-81730 München, Germany (at the time of this writing; for an up-to-date address please check http://members.tripod.de/korbi/).
Arndt von Haeseler, email: haeseler@eva.mpg.de, Max Planck Institute for Evolutionary Anthropology, Inselstr. 22, D-04103 Leipzig, Germany.
PUZZLE is a computer program to reconstruct phylogenetic trees from molecular sequence data by maximum likelihood. It implements a fast tree search algorithm, quartet puzzling, that allows analysis of large data sets and automatically assigns estimations of support to each internal branch. PUZZLE also computes pairwise maximum likelihood distances as well as branch lengths for user specified trees. Branch lengths can also be calculated under the clock-assumption. In addition, PUZZLE offers a novel method, likelihood mapping, to investigate the support of a hypothesized internal branch without computing an overall tree and to visualize the phylogenetic content of a sequence alignment. PUZZLE also conducts a number of statistical tests on the data set (chi-square test for homogeneity of base composition, likelihood ratio to test the clock hypothesis, Kishino-Hasegawa test). The models of substitution provided by PUZZLE are TN, HKY, F84, SH for nucleotides, Dayhoff, JTT, mtREV24, BLOSUM 62 for amino acids, and F81 for two-state data. Rate heterogeneity is modelled by a discrete Gamma distribution and by allowing invariable sites. The corresponding parameters can be inferred from the data set.
PUZZLE is available free of charge from
We suggest that this documentation should be read before using PUZZLE the first time. If you do not have the time to read this manual completely please do read at least the sections Input/Output Conventions and Quick Start below. Then you should be able to use the PUZZLE program, especially if you have some experience with the PHYLIP programs. The other sections should be read at a later time, though.
To find out what's new in version 4.0.1 please read the Version History.
tar xvf puzzle-401.tarThe newly created directory "puzzle-4.0.1" contains four subdirectories called "doc", "data", "bin", and "src". The "doc" directory contains the documentation files in plain text and HTML format. The "data" directory contains example input files. The "src" folder contains the ANSI C sources of PUZZLE. Switch to this directory by typing
cd puzzle-4.0.1/srcTo compile we recommed the GNU gcc (or GNU egcs) compiler. If gcc is installed just type
make installand the executable "puzzle" is compiled and put into the "bin" directory. Then type
make cleanand everything will be nicely cleaned up. If your compiler is not the GNU gcc compiler then you have to modify the makefile. Usually it suffices to change the name of the compiler (CC = cc instead of CC = gcc in the first lines of the makefile). If you still have a problem to compile then your compiler or its runtime library is most probabably not ANSI compliant (e.g. old SUN compilers). In most cases you may succeed compiling if you change some parameters in the "makefile". Ask your local UNIX expert for help.
If the compilation of the PUZZLE program succeeded without any problem but running the "puzzle" executable does not run the PUZZLE maximum likelihood program rename the executable (e.g., to "puzzle401") as some UNIX systems come with a preinstalled game that is also called "puzzle".
The MacOS executables have been compiled for the PowerMac by Florian Burckhardt using Metrowerks CodeWarrior. PUZZLE runs successfully both on System 7 and Sytem 8. (Alternative: get MkLinux or PPC Linux for your Mac and install PUZZLE there. Older Macintoshes with 68k chip might try FreeBSD).
Get the Unix sources and unpack the package on your computer (ask your local VMS expert for help). Go to the subfolder "src" and compile PUZZLE using the command file "makefile.com".
(Gibbon:0.1393, ((Human:0.0414, Chimpanzee:0.0538)99:0.0175, Gorilla:0.0577)98:0.0531, Orangutan:0.1003);
With TreeView and TreeTool it is possible to view a tree with its branch lengths AND simultaneously with the support values for the internal branches (here 98% and 99%). Note, the PHYLIP programs DRAWTREE and DRAWGRAM may also be used with the CLUSTAL W treefile format. However, the current version (3.5) they simply ignores the internal labels and prints only the tree topology with branch lengths.
To obtain a high quality picture of the output tree most conveniently, you can for example use use the TreeView program by Roderic Page. It is available free of charge and runs on MacOS and MS-Windows. It can be retrieved from
On a SUN workstation you can use the TreeTool program to display and manipulate PUZZLE trees (ftp://rdp.life.uiuc.edu/rdp/programs/TreeTool).
| Q_{ij} pi_j for i != j
R_{ij} = |
| - Sum_m Q_{im} pi_m for i == j
The matrix Q_{ij} is symmetric with Q_{ii} == 0 (digonals are zero). For
nucleotides the most general model built into PUZZLE is the Tamura-Nei
(TN) model. The matrix Q_{ij}for this model equals
| 4*t*gamma/(gamma+1) for i -> j pyrimidine transition
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Q_{ij} = | 4*t/(gamma+1) for i -> j purine transition
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| 1 for i -> j transversion
The parameter gamma is called the "Y/R transition parameter" whereas t
is the "Transition/transversion parameter". If gamma is equal to 1, we
get the HKY model (1985). Note, the ratio of the transition and transversion
rates (without frequencies) is 2*t = kappa. There is a subtle but important
difference between the transition-transversion parameter, the expected
transition-transversion ratio, and the observed transition transversion
ratio. The transition-transversion parameter simply is a parameter in the
rate matrix. The expected transition-transversion ratio is the ratio of
actually occuring transitions to actually occuring transversions taking
into account nucleotide frequencies in the alignment. Due to saturation
and multiple hits not all substitutions are observable. Thus, the observed
transition-transversion ratio counts observable transitions and transversions
only. If the base frequencies in the HKY model are homogeneous (pi_i =
0.25) HKY further reduces to the Kimura model. In this case t is identical
to the expected transition/transversion ratio. If t is set to 0.5 the Jukes-Cantor
model is obtained. The F84 model as implemented in the various PHYLIP programs
is a special case of the Tamura-Nei model.
For amino acids the matrix Q_{ij}is fixed and does not contain any free parameters. Depending on the type of input data four different Q_{ij} are available in PUZZLE. The Dayhoff and JTT matrices are for use with proteins encoded on nuclear DNA, and the mtREV24 matrix is for use with proteins encoded on mtDNA. The BLOSUM 62 model is for more distantly related amino acid sequences (Henikoff and Henikoff 1992).
For doublets (pairs of dependent nucleotides) the SH model is implemented in PUZZLE. The corresponding matrix Q_{ij} reads
| 2*t for i -> j transition substitution
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Q_{ij} = | 1 for i -> j transversion substitution
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| 0 for i -> j two substitutions
The SH model basically is a F81 model for single substitutions in doublets.
For invariable sites the parameter theta ("Fraction of invariable sites") determines the probability of a given site to be invariable. If a site is invariable the probability for the constant site patterns is pi_i, the frequency of each nucleotide (amino acid).
The rates r for variable sites are determined by a discrete Gamma distribution that approximates the continous Gamma distribution
alpha alpha-1 alpha r g(r) = ------------------------ alpha r e Gamma(alpha)where the parameter alpha ranges from alpha = infinity (no rate heterogeneity) to alpha < 1.0 (strong heterogeneity).
In previous versions of PUZZLE instead of alpha the related parameter eta = 1/(1+alpha) was used.
The total rate heterogeneity rho (Gu et al. 1995) of the model of rate heterogeneity combining invariable sites and a Gamma distribution is rho = (1+ theta alpha)/(1+alpha).
GENERAL OPTIONS b Type of analysis? Tree reconstruction k Tree search procedure? Quartet puzzling v Approximate quartet likelihood? No u List unresolved quartets? No n Number of puzzling steps? 1000 j List puzzling step trees? No o Display as outgroup? Gibbon z Compute clocklike branch lengths? No e Parameter estimates? Approximate (faster) x Parameter estimation uses? Neighbor-joining tree SUBSTITUTION PROCESS d Type of sequence input data? Nucleotides m Model of substitution? HKY (Hasegawa et al. 1985) t Transition/transversion parameter? Estimate from data set f Nucleotide frequencies? Estimate from data set RATE HETEROGENEITY w Model of rate heterogeneity? Uniform rate Confirm [y] or change [menu] settings:By typing the letters shown in the menu you can either change settings or enter new parameters. Some options (for example "m" and "w") can be invoked several times to switch through a number of different settings. The parameters of the models of sequence evolution can be estimated from the data by a variety of procedures based on maximum likelihood. The analysis is started by typing "y" at the input prompt.
The following table lists in alphabetical order all PUZZLE options. Be aware, however, not all of them are accessible at the same time:
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Gamma rate heterogeneity parameter alpha. It is the so-called shape parameter of the Gamma distribution. |
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Type of analysis. Allows to switch between tree reconstruction by maximum likelihood and likelihood mapping. |
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Number of rate categories (4-8) for the discrete Gamma distribution (rate heterogeneity). |
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Data type. Specifies whether nucleotide or amino acid sequences serve as input. Is automatically suggested by inspection of the input data. |
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Approximation option. Determines whether an approximate or the exact likelihood function is used to estimate parameters of the models of sequence evolution. The approximate likelihood function is in most cases sufficient and is faster. |
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Base frequencies. The maximum likelihood calculation needs the frequency of each nucleotide (amino acid, doublet) as input. PUZZLE estimates these values from the sequence input data. This option allows specification of other values. |
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Group sequences in clusters. Allows to define clusters of sequences as needed for the likelihood mapping analysis. Only available when likelihood mapping is selected ("b"). |
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Codon positions or definition of doublets. For nucleotide data only. If the TN or HKY model of substitution is used and the number of sites in the alignment is a multiple of three the analysis can be restricted to each of the three codon positions and to the 1st and 2nd positions. If the SH model is used this options allows to specify that the 1st and 2nd codon positions in the alignment define a doublet. |
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Fraction of invariable sites. Probability of a site to be invariable. This parameter can be estimated from the data by PUZZLE (only if the approximation option for the likelihood function is turned off). |
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List puzzling steps trees. Writes all intermediate trees (puzzling step trees) of a quartet puzzling tree into a file. |
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Tree search. Determines how the overall tree is obtained. The topology is either computed with the quartet puzzling algorithm or is defined by the user. Maximum likelihood branch lengths will be computed for this tree. Alternatively, a maximum likelihood distance matrix only can also be computed (no overall tree). |
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Location of root. Only for computation of clock-like maximum likelihood branch lengths. Allows to specify the branch where the root should be placed in an unrooted tree topology. For example, in the tree (a,b,(c,d)) l = 1 places the root at the branch leading to sequence a whereas l=5 places the root at the internal branch. |
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Model of substitution. The following models are implemented for nucleotides: the Tamura-Nei (TN) model, the Hasegawa et al. (HKY) model, and the Schöniger & von Haeseler (SH) model. The SH model describes the evolution of pairs of dependent nucleotides (pairs are the first and the second nucleotide, the third and the fourth nucleotide and so on). It allows for specification of the transition-transversion ratio. The originally proposed model (Schöniger & von Haeseler 1994) is obtained by setting the transition-transversion parameter to 0.5. The Jukes-Cantor (1969), the Felsenstein (1981), and the Kimura (1980) model are all special cases of the HKY model. For amino acid sequence data the Dayhoff et al. (Dayhoff) model, the Jones et al. (JTT) model, the Adachi and Hasegawa (mtREV24) model, and the Henikoff and Henikoff (BLOSUM 62) substitution model are implemented in PUZZLE. The mtREV24 model describes the evolution of amino acids encoded on mtDNA, and BLOSUM 62 is for distantly related amino acid sequences. For more information please read the section in this manual about models of sequence evolution. See also option "w" (model of rate heterogeneity). |
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Number of puzzling steps. Parameter of the quartet puzzling tree search (meaning comparable to the number of bootstrap replicates). Generally, the more sequences are used the more puzzling steps are advised. The dfault value varies depending on he number of sequences. |
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Outgroup. For displaying purposes of the unrooted quartet puzzling tree only. The default outgroup is the first sequence of the data set. |
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Constrain the TN model to the F84 model. This option is only available for the Tamura-Nei model. With this option the expected (!) transition-transversion ratio for the F84 model can be entered, and PUZZLE computes the corresponding parameters of the TN model (this depends on base frequencies of the data). This allows to compare the results of PUZZLE and the PHYLIP maximum likelihood programs which use the F84 model. |
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Number of quartets in a likelihood mapping analysis. Equal to the number of dots in the likelihood mapping diagram. By default 10000 dots/quartets are assumed. To use all possible quartets in clustered likelihood mapping you have to specify a value of q=0. |
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Y/R transition parameter. This option is only available for the TN model. This parameter is the ratio of the rates for pyrimidine transitions and purine transitions. You don't need to specify this parameter as PUZZLE estimates it from the data. For precise definition please read the section in this manual about models of sequence evolution. |
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Symmetrize doublet frequencies. This option is only available for the SH model. With this option the doublet frequencies are symmetrized. For example, the frequencies of "AT" and "TA" are set to the average of both frequencies. |
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Transition/transversion parameter. For nucleotide data only. You don't need to specify this parameter as PUZZLE estimates it from the data. The precise definition of this parameter is given in the section on models of sequence evolution in this manual. |
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Show unresolved quartets. During the quartet puzzling tree search PUZZLE counts the number of unresolved quartet trees. An unresolved quartet is a quartet where the maximum likelihood values for each of the three possible quartet topologies are so similar that it is not possible to prefer one of them (Strimmer, Goldman, and von Haeseler 1996). If this option is selected you'll get a detailed list of all starlike quartets. Note, for some data sets there may be a lot of unresolved quartets. In this case a list of all unresolved quartets is probably not very useful and also needs a lot of disk space. |
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Approximate quartet likelihood. For the quartet puzzling tree search only. Only for very small data sets it is necessary to compute an exact maximum likelihood. For larger data sets this option should always be turned on. This option was hidden in some previous versions. |
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Model of rate heterogeneity. PUZZLE provides several different models of rate heterogeneity: uniform rate over all sites (rate homogeneity), Gamma distributed rates, two rates (1 invariable + 1 variable), and a mixed model (1 invariable rate + Gamma distributed rates). All necessary parameters can be estimated by PUZZLE. Note that whenever invariable sites are taken into account the parameter estimation will invoke the "e" option to use an exact likelihood function. For more detailed information please read the section in this manual about models of sequence evolution. See also option "m" (model of substitution). |
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Selects the methods used in the estimation of the model parameters. Neighbor-joining tree means that a NJ tree is used to estimate the parameters. Quartet sampling means that a number of random sets of four sequences are selected to estimate parameters. |
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Starts analysis. |
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Computation of clock-like maximum likelihood branch lengths. This option also invokes the likelihood ratio clock test. |
PUZZLE also tests with a 5% level chi-square-test whether the base composition of each sequence is identical to the average base composition of the whole alignment. All sequences with deviating composition are listed in the output file. It is desired that no sequence (possibly except for the outgroup) has a deviating base composition. Otherwise a basic assumption implicit in the maximum likelihood calculation is violated.
A hidden feature of PUZZLE (since version 2.5) is the employment of a weighting scheme of quartets (Strimmer, Goldman, and von Haeseler 1997) in the quartet puzzling tree search.
PUZZLE also computes the average distance between all pairs of sequences (maximum likelihood distances). The average distances can be viewed as a rough measure for the overall sequence divergence.
If more than one input tree is provided PUZZLE uses the Kishino-Hasegawa test (1989) to check which trees are significantly worse than the best tree.
If clock-like maximum-likelihood branch lengths are computed PUZZLE checks with the help of a likelihood-ratio test (Felsenstein 1988) whether the data set is clock-like.
PUZZLE also detects sequences that occur more than once in the data and that therefore can be removed from the data set.
If rate heterogeneity is taken into account in the analysis PUZZLE also computes the most probable assignment of rate categories to sequence positions, according Felsenstein and Churchill (1996).
| Parameter (Symbol) | Minimal Value | Maximal Value |
| Transition/transversion parameter (t) | 0.20 | 30.00 |
| Y/R transition parameter (gamma) | 0.10 | 6.00 |
| Fraction of invariable sites (theta) | 0.00 | 0.99 |
| Gamma rate heterogeneity parameter (alpha) | 0.01 | 0.99 |
puzzle << ! t 10 y !All other parameters can also be accessed the same way. In future versions of PUZZLE, however, we plan to support command switches as well.
If problems are encountered PUZZLE terminates program execution and returns a plain text error message. Depending on the severity errors are classified into three groups:
| "HALT " errors: | Very severe. You should never ever see one of these messages. If so, please contact the developers! |
| "Unable to proceed" errors: | Harmless but annoying. Mostly memory errors (not enough RAM) or problems with the format of the input files. |
| Other errors: | Completely uncritical. Occur mostly when options of PUZZLE are being set. |
However, this is not true. According to Hillis findings (Hillis, 1996), quartets can be hard, but extra information helps. That is, if all you have are data on species (A, B, C, D) then it might be relatively difficult to find the correct tree for them. But if you have additional data (species E, F, G, ...) and try to find a tree for all the species, then that part of the tree relating (A, B, C, D) will more likely be correct than if you had just the data for (A, B, C, D). In Hillis's big 'model' tree, there are many examples of subsets of 4 species which in themselves might be hard to resolve correctly, but which are correctly resolved thanks to the (...large amount of...) additional data. PUZZLE (quartet puzzling) also gains advantage from extra data in the same way. It's 'understanding' or resolution of the quartet (A, B, C, D) might be incorrect, but the information on the relationships of (A, B, C, D) implicit in its treatment of (A, B, C, E), (A, B, E, D), (A, E, C, D), (E, B, C, D), (A, B, C, F), (A, B, F, D), (A, F, C, D), (F, B, C, D), (A, B, C, G), etc. etc. should overcome this problem.
The facts about how well PUZZLE actually works have been investigated in the Strimmer and von Haeseler (1996) and Strimmer, Goldman, and von Haeseler (1997) papers. Their results cannot be altered by Hillis's findings. Considered as a heuristic search for maximum likelihood trees, quartet puzzling works very well.
(This section follows N. Goldman, personal communication).
Adachi, J., and M. Hasegawa. 1996. Model of amino acid substitution in proteins encoded by mitochondrial DNA. J. Mol. Evol. 42: 459-468.
Dayhoff, M. O., R. M. Schwartz, and B. C. Orcutt. 1978. A model of evolutionary change in proteins. In: Dayhoff, M. O. (ed.) Atlas of Protein Sequence Structur, Vol. 5, Suppl. 3. National Biomedical Research Foundation, Washington DC, pp. 345-352.
Felsenstein, J. 1981. Evolutionary trees from DNA sequences: A maximum likelihood approach. J. Mol. Evol. 17: 368-76.
Felsenstein, J. 1988. Phylogenies from molecular sequences: Inference and reliability. Annu. Rev. Genet. 22: 521-565.
Felsenstein, J. 1993. PHYLIP (Phylogeny Inference Package) version 3.5c. Distributed by the author. Department of Genetics, University of Washington, Seattle.
Felsenstein, J., and G.A. Churchill. 1996. A hidden Markov model approach to variation among sites in rate of evolution. Mol. Biol. Evol. 13: 93-104.
Gu, X., Y.-X. Fu, and W.-H. Li. 1995. Maximum likelihood estimation of the heterogeneity of substitution rate among nucleotide sites. Mol. Biol. Evol.12: 546-557.
Hasegawa, M., H. Kishino, and K. Yano. 1985. Dating of the human-ape splitting by a molecular clock of mitochondrial DNA. J. Mol. Evol. 22: 160-174.
Henikoff, S., J. G. Henikoff. 1992. Amino acid substitution matrices from protein blocks. PNAS (USA) 89:10915-10919.
Hillis, D. M. 1996. Inferring complex phylogenies. Nature 383:130-131.
Jukes, T. H., and C. R. Cantor. 1969. Evolution of protein molecules. In: Munro, H. N. (ed.) Mammalian Protein Metabolism, New York: Academic Press, pp. 21-132.
Jones, D. T., W. R. Taylor, and J. M. Thornton. 1992. The rapid generation of mutation data matrices from protein sequences. CABIOS 8: 275-282.
Kimura, M. 1980. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16: 111-120.
Kishino, H., and M. Hasegawa. 1989. Evaluation of the maximum likelihood estimate of the evolutionary tree topologies from DNA sequence data, and the branching order in Hominoidea. J. Mol. Evol. 29: 170-179.
Tamura, K., and M. Nei. 1993. Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Mol. Biol. Evol. 10: 512-526.
Tamura K. 1994. Model selection in the estimation of the number of nucleotide substitutions. Mol. Biol. Evol. 11: 154-157.
Thompson, J. D., D. G. Higgins, and T. J. Gibson. 1994. CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, positions-specific gap penalties and weight matrix choice. Nucl. Acids Res. 22: 4673-4680.
Saitou, N., and M. Nei. 1987. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4: 1406-425.
Schöniger, M., and A. von Haeseler. 1994. A stochastic model for the evolution of autocorrelated DNA sequences. Mol. Phyl. Evol. 3: 240-247.
Strimmer, K., and A. von Haeseler. 1996. Quartet puzzling: a quartet maximum likelihood method for reconstructing tree topologies. Mol. Biol. Evol. 13: 964-969.
Strimmer, K., N. Goldman, and A. von Haeseler. 1997. Bayesian probabilities and quartet puzzling. Mol. Biol. Evol. 14: 210-211.
Strimmer, K., and A. von Haeseler. 1997. Likelihood-mapping: a simple method to visualize phylogenetic content of a sequence alignment. PNAS (USA). 94:6815-6819.
Yang, Z. 1994. Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: approximate methods. J. Mol. Evol. 39:306-314.
| 4.0.1 | Maintainance release. Correction of mtREV matrix. Fix of the "intree bug". Removal of stringent runtime-compatibility check to allow out-of-the-box compile on alpha. More accurate gamma distribution allowing 16 instead of 8 categories and ensuring a better alpha > 1.0. Update of documentation (mainly address changes). More Unix-like file layout, and change of licence to GPL. |
| 4.0 | Executables for Windows 95/NT and OS/2 instead of MS-DOS. Computation of clock-like branch lengths (also for amino acids and for non-binary trees). Automatic likelihood ratio clock test. Model for two-state sequences data (0,1) included. Display of most probable assignment of rates to sites. Identification of groups of identical sequences. Possibility to read multiple input trees. Kishino-Hasegawa test to check whether trees are significantly different. BLOSUM 62 model of amino acid substitution (Henikoff-Henikoff 1992). Use of parameter alpha instead of eta (for rate heterogeneity). Improvements to user interface. SH model can be applied to 1st and 2nd codon positions. Automatic check for compatible compiler settings. Workaround for severe runtime problem when the gcc compiler was used. |
| 3.1 | Much improved user interface to rate heterogeneity (less confusing menu, rearranged outfile, additional out-of-range check). Possibility to read rooted input trees (automatic removal of basal bifurcation). Computation of average distance between all pairs of sequences. Fix of a bug that caused PUZZLE 3.0 to crash on some systems (DEC Alpha). Cosmetic changes in program and documentation. |
| 3.0 | Rate heterogeneity included in all models of substitution (Gamma distribution plus invariable sites). Likelihood mapping analysis with Postscript output added. Much more sophisticated maximum likelihood parameter estimation for all model parameters including those of rate heterogeneity. Codon positions selectable. Update to mtREV24. New icon. Less verbose runtime messages. HTML documentation. Better internal error classification. More information in outfile (number of constant postions etc.). |
| 2.5.1 | Fix of a bug (present only in version 2.5) related to computation of the variance of the maximum likelihood branch lengths that caused occasional crashs of PUZZLE on some systems when applied to data sets containing many very similar sequences. Drop of support for non-FPU Macintosh version. Corrections in manual. |
| 2.5 | Improved QP algorithm (Strimmer, Goldman, and von Haeseler 1997). Bug fixes in ML engine, computation of ML distances and ML branch lengths, optional input of a user tree, F84 model added, estimation of all TN model parameters and corresponding standard errors, CLUSTAL W treefile convention adopted to allowe to show branch lengths and QP support values simultaneously, display of unresolved quartets, update of mtREV matrix, source code more compatible with some almost-ANSI compilers, more safety checks in the code. |
| 2.4 | Automatic data type recognition, chi-square-test on base composition, automatic selection of best amino acid model, estimation of transition-transversion parameter, ASCII plot of quartet puzzling tree into the outfile. |
| 2.3 | More models, many usability improvements, built-in consensus tree routines, more supported systems, bug fixes, no more dependencies of input order. First EBI distributed version. |
| 2.2 | Optimized internal data structure requiring much less computer memory. Bug fixes. |
| 2.1 | Bug fixes concerning algorithm and transition/transversion parameter. |
| 2.0 | Complete revision merging the maximum likelihood and the quartet puzzling routines into one user friendly program. First electronic distribution. |
| 1.0 | First public release, presented at the 1995 phylogenetic workshop (15-17 June 1995) at the University of Bielefeld, Germany. |