PhyML Explanation

Substitution Models

On this interface, the default evolutionary model for nucleotide data is GTR + gamma (designed for sequences with significant between-site rate heterogeneity, such as HIV). The default evolutionary model for protein data is HIVb (designed for between-patient HIV-1 data). These models may not be suitable for your data! We recommend first testing your data with FindModel for DNA, or with ProtTest for protein. For additional information about evolutionary models, see references below.

Mailback

This web interface will send your results by e-mail if any of the following are true:

• The box for "always e-mail results" is checked.
• File size is > 1 M.
• Bootstrapping is selected.
• Any estimating options are selected.

File sizes for bootstrapping

Bootstrapping puts a high load on web servers. We recommend the following maximums:

# Bootstraps	Max. filesize
0	2M
100	100K
500	50K
1000	10K

From the PhyML manual:

(For the full PhyML manual, see PhyML 3.0 User Guide.)

• 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 maximizing 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 maximizing 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 maximizing 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 equals 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.
  
  
• Optimize starting tree(s) options
  You can optimize the starting tree(s) in three ways:
  • You can optimize the topology, the branch lengths and rate parameters (transition/transversion ratio, proportion of invariant sites, gamma distribution parameter),
  • You can keep the topology and optimize the branch lengths and rate parameters (it is not possible to optimize the tree topology and keep the branch lengths and rate parameters),
  • You can ask for no optimization, PHYML just computes the likelihood of the starting tree(s).
  
  
• Equilibrium frequencies
  • Empirical : the character frequencies are determined as follows :
    - Nucleotide sequences: (Empirical) the equilibrium base frequencies are estimated by counting the occurrence of the different bases in the alignment.
    - Amino-acid sequences: (Empirical) the equilibrium amino-acid frequencies are estimated by counting the occurrence of the different amino-acids in the alignment.
  • Estimated : the character frequencies are determined as follows :
    - Nucleotide sequences: (ML) the equilibrium base frequencies are estimated using maximum likelihood
    - Amino-acid sequences: (Model) the equilibrium amino-acid frequencies are estimated using the frequencies defined by the substitution model.
  
  
• Type of tree improvement
  PhyML is able to perform two kinds of tree topology improvement: NNI (Nearest Neighbor Interchange) and SPR (Subtree Pruning and Regrafting). You can ask PhyML to compute only NNI (faster) or only SPR (a bit slower) or the both SPR & NNI and keep the best.

References for Substitution Models:

Nucleotide Model	Reference
HKY85	Hasegawa M, Kishino H, Yano T (1985). "Dating of human-ape splitting by a molecular clock of mitochondrial DNA". Journal of Molecular Evolution 22: 160–174.
JC69	Jukes TH and Cantor CR (1969). Evolution of Protein Molecules. New York: Academic Press. pp. 21–132.
K80	Kimura M (1980). "A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences". Journal of Molecular Evolution 16: 111–120
F81	Felsenstein J (1981). "Evolutionary trees from DNA sequences: a maximum likelihood approach". Journal of Molecular Evolution 17: 368–376
F84	(Felsenstein, 1989)
TN93	Tamura K, Nei M (1993). "Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees". Molecular Biology and Evolution 10 (3): 512–526.
GTR	Tavaré S (1986). "Some Probabilistic and Statistical Problems in the Analysis of DNA Sequences". Lectures on Mathematics in the Life Sciences (American Mathematical Society) 17: 57–86.

Amino acid Model	Reference
Blosum62	"Amino acid substitution matrices from protein blocks." Henikoff S. & Henikoff J. PNAS 89, 10915–10919 (1992).
CpREV	"Plastid genome phylogeny and a model of amino acid substitution for proteins encoded by chloroplast DNA." Adachi J.P.W., Martin W. & Hasegawa M. Journal of Molecular Evolution 50, 348–358 (2000).
Dayhoff	"A model of evolutionary change in proteins." Dayhoff M., Schwartz R. & Orcutt B. In Dayhoff, M. (ed.) Atlas of Protein Sequence and Structure, vol. 5, 345–352 (National Biomedical Research Foundation, Washington, D. C., 1978).
DCMut	"Different versions of the Dayhoff rate matrix." Kosiol C. & Goldman N. Molecular Biology and Evolution 22, 193–19 (2004).
FLU
HIVb HIVw	Nickle, D.C., Heath, L., Jensen, M.A., Gilbert, P.B., Mullins, J.I., and Kosakovsky Pond, S.L. 2007. HIV-specific probabilistic models of protein evolution. PLoS ONE 2: e503
JTT	"The rapid generation of mutation data matrices from protein sequences." Jones D., Taylor W. & Thornton J. Computer Applications in the Biosciences (CABIOS) 8, 275–282 (1992).
LG	"An improved general amino-acid replacement matrix." Le S. & Gascuel O. Mol. Biol. Evol. 25(7):1307-20 (2008
MtArt	Abascal, F., Posada, D., and Zardoya, R. 2007. MtArt: a new model of amino acid replacement for Arthropoda. Mol Biol Evol 24: 1-5
MtMam	"Conflict among individual mitochondrial proteins in resolving the phylogeny of eutherian orders." Cao Y. et al. Journal of Molecular Evolution 47, 307–322 (1998)
MtREV
RtREV	"rtREV: an amino acid substitution matrix for inference of retrovirus and reverse transcriptase phylogeny." Dimmic M., Rest J., Mindell D. & Goldstein D. Journal of Molecular Evolution 55, 65–73 (2002).
VT	"Modeling amino acid replacement." Muller T. & Vingron M. Journal of Computational Biology 7, 761–776 (2000).
WAG	"A general empirical model of protein evolution derived from multiple protein families using a maximum-likelihood approach." Whelan S. & Goldman N. Mol. Biol. Evol. 18, 691–699 (2001)