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# phyml

- 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/PHYML/usersguide_phyliplike.html
       PHYML User's guide (PHYLIP-like interface)

### Overview

PHYML is a software implementing a new method for building phylogenies from DNA and protein sequences using maximum likelihood. Data sets can be analysed under several models of evolution (JC69, K80, F81, F84, HKY85, TN93 and GTR for nucleotides and Dayhoff, JTT, mtREV, WAG, DCMut, RtREV, CpREV, VT, Blosum62 and MtMam for amino acids). A discrete-gamma model (Yang, 1994) is implemented to accommodate rate variation among sites. Invariable sites can also be taken into account. PHYML has been compared to several other softwares using extensive simulations. The results indicate that its topological accuracy is at least as high as that of fastDNAml, while being much faster.

### The PHYLIP-like interface

Download the binary files ; you can execute PHYML by double-clicking on the "phyml" file or by opening a shell window and typing "phyml" without parameters. The interactive command-line interface is PHYLIP-like. You can change the default value of an option by typing its corresponding character and validate your settings by typing 'Y'. PHYML produces several results files :
<sequence file name>_phyml_lk.txt : likelihood value(s)
<sequence file name>_phyml_tree.txt : inferred tree(s)
<sequence file name>_phyml_stat.txt : detailed execution stats

      <sequence file name>_phyml_boot_trees.txt : bootstrap trees (special
    case)

      <sequence file name>_phyml_boot_stats.txt : bootstrap statistics
    (special case)
    Here are the possible uses of PHYML :

      One data set, one starting tree
    Standard analysis under a given substitution model, PHYML then returns
    the inferred tree. Moreover, a special option allows to perform
    non-parametric bootstrapp analysis on the original data set. PHYML then
    returns the bootstrap tree with branch lengths and bootstrap values,
    using standard NEWICK format (an option gives the pseudo trees in a
    *_boot_trees.txt file).

      Several data sets, one starting tree
    Several standard analysis start from the same intial tree with
    different data sets, without the bootstrap option.
    The results are given in the order of the data sets.
    This can be used to process multiple genes in a supertree approach.

      One data set, several starting trees
    Several standard analysis of the same data set using different starting
    tree situations, without the bootstrap option.
    All results are given in the order of the trees. Moreover, the most
    likely tree is provided in the *_best_stat.txt and *_best_tree.txt
    files.
    This should be used to avoid being trapped into local optima and then
    obtain better trees. Fast parsimony methods can be used to obtain a set
    of starting trees.

      Several data sets, several starting trees
    Several standard runs, where each data set is analysed with the
    corresponding starting tree, without the bootstrap option.
    The results are given in the order of the data sets.
    This can be used when comparing the likelihood of various trees
    regarding different data sets.

### Options

         Sequences The input sequence file is a standard PHYLIP file of
       aligned DNA or amino-acids sequences. It should look like this in
       interleaved format :
    5 60
    Tax1        CCATCTCACGGTCGGTACGATACACCTGCTTTTGGCAG
    Tax2        CCATCTCACGGTCAGTAAGATACACCTGCTTTTGGCGG
    Tax3        CCATCTCCCGCTCAGTAAGATACCCCTGCTGTTGGCGG
    Tax4        TCATCTCATGGTCAATAAGATACTCCTGCTTTTGGCGG
    Tax5        CCATCTCACGGTCGGTAAGATACACCTGCTTTTGGCGG

### GAAATGGTCAATATTACAAGGT GAAATGGTCAACATTAAAAGAT GAAATCGTCAATATTAAAAGGT GAAATGGTCAATCTTAAAAGGT GAAATGGTCAATATTAAAAGGT

       The same data set in sequential format:
    5 60
    Tax1        CCATCTCACGGTCGGTACGATACACCTGCTTTTGGCAGGAAATGGTCAATATTACAAGGT
    Tax2        CCATCTCACGGTCAGTAAGATACACCTGCTTTTGGCGGGAAATGGTCAACATTAAAAGAT
    Tax3        CCATCTCCCGCTCAGTAAGATACCCCTGCTGTTGGCGGGAAATCGTCAATATTAAAAGGT
    Tax4        TCATCTCATGGTCAATAAGATACTCCTGCTTTTGGCGGGAAATGGTCAATCTTAAAAGGT
    Tax5        CCATCTCACGGTCGGTAAGATACACCTGCTTTTGGCGGGAAATGGTCAATATTAAAAGGT

On the first line is the number of taxa, a space, then the number of characters for each taxon. The maximum number of characters in species name MUST not exceed 50. Blanks within the species name are NOT allowed. However, blanks (one or more) MUST appear at the end of each species name. In a sequence, three special characters '.', '-', and '?' may be used: a dot '.' means the same character as in the first sequence, a dash '-' means an alignment gap and a question mark '?' means an undetermined nucleotide. Sites at which one or more sequences involve '-' are NOT excluded from the analysis. Therefore, gaps are treated as unknown character (like '?') on the grounds that ''we don't know what would be there if something were there'' (J. Felsenstein, PHYLIP documentation). Finally, standard ambiguity characters for nucleotides are accepted (Table 1).
CAPTION: Table 1 - Nucleotide character coding

     Character  Nucleotide
         A       Adenosine
         G        Guanine
         C       Cytosine
         T        Thymine
         U        Uracil
         M        A or C
         R        A or G
         W        A or T
         S        C or G
         Y        C or T
         K        G or T
         B      C or G or T
         D      A or G or T
         H      A or C or T
         V      A or C or G
    N or X or ?   unknown

CAPTION: Table 2 - Amino-acid character coding

    Character  Amino-acid
        A        Alanine
        R       Arginine
     N or B    Asparagine
        D     Aspartic acid
        C       Cysteine
     Q or Z     Glutamine
        E     Glutamic acid
        G        Glycine
        H       Histidine
        I      Isoleucine
        L        Leucine
        K        Lysine
        M      Methionine
        F     Phenylalanine
        P        Proline
        S        Serine
        T       Threonine
        W      Tryptophan
        Y       Tyrosine
        V        Valine
     X or ?      unknown

      Data type
    This indicates if the sequence file contains DNA or amino-acids. The
    default choice is to analyse DNA sequences.

      Sequence format
    The input sequences can be either in interleaved (default) or
    sequential format, see "Sequences" above.

         Number of data sets
       Multiple data sets are allowed, e.g. to perform bootstrap analysis
       using SEQBOOT (from the PHYLIP package). In this case, the data sets
       are given one after the other, in the formats above explained. For
       example (with three data sets):
    5 60
    Tax1        CCATCTCACGGTCGGTACGATACACCTGCTTTTGGCAGGAAATGGTCAATATTACAAGGT
    Tax2        CCATCTCACGGTCAGTAAGATACACCTGCTTTTGGCGGGAAATGGTCAACATTAAAAGAT
    Tax3        CCATCTCCCGCTCAGTAAGATACCCCTGCTGTTGGCGGGAAATCGTCAATATTAAAAGGT
    Tax4        TCATCTCATGGTCAATAAGATACTCCTGCTTTTGGCGGGAAATGGTCAATCTTAAAAGGT
    Tax5        CCATCTCACGGTCGGTAAGATACACCTGCTTTTGGCGGGAAATGGTCAATATTAAAAGGT

    5 60
    Tax1        CCATCTCACGGTCGGTACGATACACCTGCTTTTGGCAGGAAATGGTCAATATTACAAGGT
    Tax2        CCATCTCACGGTCAGTAAGATACACCTGCTTTTGGCGGGAAATGGTCAACATTAAAAGAT
    Tax3        CCATCTCCCGCTCAGTAAGATACCCCTGCTGTTGGCGGGAAATCGTCAATATTAAAAGGT
    Tax4        TCATCTCATGGTCAATAAGATACTCCTGCTTTTGGCGGGAAATGGTCAATCTTAAAAGGT
    Tax5        CCATCTCACGGTCGGTAAGATACACCTGCTTTTGGCGGGAAATGGTCAATATTAAAAGGT

    5 60
    Tax1        CCATCTCACGGTCGGTACGATACACCTGCTTTTGGCAGGAAATGGTCAATATTACAAGGT
    Tax2        CCATCTCACGGTCAGTAAGATACACCTGCTTTTGGCGGGAAATGGTCAACATTAAAAGAT
    Tax3        CCATCTCCCGCTCAGTAAGATACCCCTGCTGTTGGCGGGAAATCGTCAATATTAAAAGGT
    Tax4        TCATCTCATGGTCAATAAGATACTCCTGCTTTTGGCGGGAAATGGTCAATCTTAAAAGGT
    Tax5        CCATCTCACGGTCGGTAAGATACACCTGCTTTTGGCGGGAAATGGTCAATATTAAAAGGT

      Perform bootstrap and Number of pseudo data sets
    When there is only one data set you can ask PHYML to generate
    bootstrapped pseudo data sets from this original data set. PHYML then
    returns the bootstrap tree with branch lengths and bootstrap values,
    using standard NEWICK format. The "Print pseudo trees" option gives the
    pseudo trees in a *_boot_trees.txt file.

      Substitution model
    A nucleotide or amino-acid substitution model. For DNA sequences, the
    default choice is HKY85 (Hasegawa et al., 1985). This model is
    analogous to K80 (Kimura, 1980), but allows for different base
    frequencies. The other models are JC69 (Jukes and Cantor, 1969), K80
    (Kimura, 1980), F81 (Felsenstein, 1981), F84 (Felsenstein, 1989), TN93
    (Tamura and Nei, 1993) and GTR (e.g., Lanave et al. 1984, Tavaré 1986,
    Rodriguez et al. 1990). The rate matrices of these models are given in
    Swofford et al. (1996).
    It is also possible to specify a custom substitution model, considering
    that six substitution rate parameters and four equilibrium frequencies
    define time-reversible DNA substitution models. The substitution rates
    are defined by a string of six digits :

    digit 1 digit 2 digit 3 digit 4 digit 5 digit 6
    A<->C   A<->G   A<->T   C<->G   C<->T   G<->T

000000 defines a model where the six relative rate parameters are equal : this corresponds to the JC69 model if the equilibrium frequencies are equal (0.25), or the F81 model if they are different. 010010 corresponds to a model where the A<->G and C<->T rates are optimised independently of the other parameters : this is the K80 model if base frequencies are equal (0.25), or the HKY85 model if they are different. 010020 is the TN93 model. 012345 is the GTR model. This notation is very concise and allows to define a wide range of models in a comprehensive framework. For amino-acid sequences, the default choice is JTT (Jones, Taylor and Thornton, 1992). The other models are Dayhoff (Dayhoff et al., 1978), mtREV (as implemented in Yang's PAML), WAG (Whelan and Goldman, 2001) and DCMut (Kosiol and Goldman, 2005), RtREV (Dimmic et al.), CpREV (Adachi et al., 2000) VT (Muller and Vingron, 2000), Blosum62 (Henikoff anf Henikoff, 1992) and MtMam (Cao, 1998).

      Base frequency estimates
    Under most of the nucleotide based models (except JC69 and K2P), base
    frequencies can be estimated from the data (empirical) or adjusted so
    as to maximise the likelihood (ML). The later makes the program slower.
    Comparing the results obtained under the two options might be useful
    when analysing sequences that correspond to concatenations of several
    genes with different nucleotide compositions.

      Transition / transversion ratio
    With DNA sequences, it is possible to set the transition/transversion
    ratio, except for the JC69 and F81 models, or to estimate its value by
    maximising the likelihood of the phylogeny. The later makes the program
    slower. The default value is 4.0. The definition of the
    transition/transversion ratio is the same as in PAML (Yang, 1994). In
    PHYLIP, the ''transition/transversion rate ratio'' is used instead. 4.0
    in PHYML roughly corresponds to 2.0 in PHYLIP.

      Proportion of invariable sites
    The default is to consider that the data set does not contain
    invariable sites (0.0). However, this proportion can be set to any
    value in the 0.0-1.0 range. This parameter can also be estimated by
    maximising the likelihood of the phylogeny. The later makes the program
    slower.

      Number of substitution rate categories
    The default is having all the sites evolving at the same rate, hence
    having one substitution rate category. A discrete-gamma distribution
    can be used to account for variable substitution rates among sites, in
    which case the number of categories that defines this distribution is
    supplied by the user. The higher this number, the better is the
    goodness-of-fit regarding the continuous distribution. The default is
    to use four categories, in this case the likelihood of the phylogeny at
    one site is averaged over four conditional likelihoods corresponding to
    four rates and the computation of the likelihood is four times slower
    than with a unique rate. Number of categories less than four or higher
    than eight are not recommended. In the first case, the discrete
    distribution is a poor approximation of the continuous one. In the
    second case, the computational burden becomes high and an higher number
    of categories is not likely to enhance the accuracy of phylogeny
    estimation.

      Gamma distribution parameter
    The shape of a gamma distribution is defined by this numerical
    parameter. The higher its value, the lower the variation of
    substitution rates among sites (this option is used when having more
    than 1 substitution rate category). The default value is 1.0. It
    corresponds to a moderate variation. Values less than say 0.7
    correspond to high variations. Values between 0.7 and 1.5 corresponds
    to moderate variations. Higher values correspond to low variations.
    This value can be fixed by the user. It can also be estimated by
    maximising the likelihood of the phylogeny.

      Starting tree(s)
    Used as the starting tree(s) to be refined by the maximum likelihood
    algorithm. The default is to use a BIONJ distance-based tree. It is
    also possible to supply one or several trees in NEWICK format, one per
    line in the file, which must be written in the standard parenthesis
    representation (NEWICK format) ; the branch lengths must be given, and
    the tree(s) must be unrooted. Labels on branches (such as bootstrap
    proportions) are supported. Therefore, a tree with four taxa named A,
    B, C, and D with a bootstrap value equal to 90 on its internal branch,
    should look like this:
    (A:0.02,B:0.004,(C:0.1,D:0.04)90:0.05);
    If you give several trees and analyse several data sets the two numbers
    must match.

      Optimise starting tree(s) options
    You can optimise the starting tree(s) in three ways : - You can
    optimise the topology, the branch lengths and rate parameters
    (transition/transversion ratio, proportion of invariant sites, gamma
    distribution parameter), - You can keep the topology and optimise the
    branch lengths and rate parameters (it is not possible to optimise the
    tree topology and keep the branch lengths), - You can ask for no
    optimisation, PHYML just returns the likelihood of the starting
    tree(s).

### References

Z. Yang (1994) J. Mol. Evol. 39, 306-14.
S. Ota & W.-H. Li (2001) Mol. Biol. Evol. 18, 1983-1992.
N. Saitou & M. Nei (1987) Mol. Biol. Evol. 4(4), 406-425.

      W. Bruno, N. D. Socci, & A. L. Halpern (2000) Mol. Biol.
    Evol. 17, 189-197.

J. Felsenstein (1989) Cladistics 5, 164-166.

      G. J. Olsen, H. Matsuda, R. Hagstrom, & R. Overbeek (1994)
    CABIOS 10, 41-48.

N. Goldman (1993) J. Mol. Evol. 36, 182-198.
M. Kimura (1980) J. Mol. Evol. 16, 111-120.

      T. H. Jukes & C. R. Cantor (1969) in Mammalian Protein Metabolism,
    ed. H. N. Munro. (Academic Press, New York) Vol. III, pp. 21-132.

M. Hasegawa, H. Kishino, & T. Yano (1985) J. Mol. Evol. 22, 160-174.
J. Felsenstein (1981) J. Mol. Evol. 17, 368-376.

      David L. Swofford, Gary J. Olsen, Peter J. Waddel, & David M. Hillis
    (1996) in Molecular Systematics, eds. David M. Hillis, Craig Moritz, &
    Barbara K. Mable. (Sinauer Associates, Inc., Sunderland, Massachusetts,
    USA).

K. Tamura & M. Nei (1993) Mol. Biol. Evol. 10, 512-526.

      Lanave C, Preparata G., Saccone C. and Serio G.. (1984) A new method
    for calculating evolutionary substitution rates. J. Mol. Evol.
    20, 86-93.

      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.

      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.

      S. Whelan and N. Goldman. (2001). A general empirical model of
    protein evolution derived from multiple protein families using a
    maximum-likelihood approach Mol. Biol. Evol. 18, 691-699

      Dimmic M.W., J.S. Rest, D.P. Mindell, and D. Goldstein. 2002.
    RArtREV: An amino acid substitution matrix for inference of retrovirus
    and reverse transcriptase phylogeny. Journal of Molecular Evolution 55:
    65-73.

      Adachi, J., P. Waddell, W. Martin, and M. Hasegawa. 2000. Plastid
    genome phylogeny and a model of amino acid substitution for proteins
    encoded by chloroplast DNA. Journal of Molecular Evolution 50:348-358.

      Muller, T., and M. Vingron. 2000. Modeling amino acid replacement.
    Journal of Computational Biology 7:761-776.

      Henikoff, S., and J. G. Henikoff. 1992. Amino acid substitution
    matrices from protein blocks. Proc. Natl. Acad. Sci., U.S.A.
    89:10915-10919.

      Cao, Y. et al. 1998 Conflict amongst individual mitochondrial
    proteins in resolving the phylogeny of eutherian orders. Journal of
    Molecular Evolution 15:1600-1611.