Results

PhyML produces several results files:

<sequence file name>_phyml_lk.txt : site likelihood value(s)
<sequence file name>_phyml_tree.txt : inferred tree(s)
<sequence file name>_phyml_stat.txt : detailed execution statistics
<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 bootstrap analysis on the original data set. PhyML then returns the bootstrap tree with branch lengths and bootstrap values, using standard NEWICK format .
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 can be used to test alternative topologies, or to avoid being trapped into local optima. With the 'SPR' and 'SPR&NNI' options, it's also possible to add random starting trees to standard BioNJ starting tree.
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
-i (or --input) seq_file_name

seq_file_name is the sequence filename with its path from the current directory.

"; ?> 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 sites in the alignment.

The maximum number of characters in species name MUST not exceed 100. Blanks and the symbols “(),:” are not allowed within sequence names because the Newick tree format makes special use of these symbols. 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).

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

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
-d (or --datatype) data_type

data_type is 'nt' for nucleotide (default) and 'aa' for amino-acid sequences.
"; ?> This indicates if the sequence file contains DNA or amino-acids. The default choice is to analyse DNA sequences.
Sequence format
-q (or --sequential)

Changes interleaved format (default) to sequential format.
"; ?> The input sequences can be either in interleaved (default) or sequential format, see "Sequences" above.

Number of data sets
-n (or --multiple) nb_data_sets

nb_data_sets is an integer corresponding to the number of data sets to analyse.

"; ?> 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

Substitution model
-m (or --model) 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).

For amino-acid sequences, the default choice is LG (Le and Gascuel 2008) which tends to improve other models with standard globular proteins. The other models are WAG (Whelan and Goldman, 2001), (Dayhoff et al., 1978), mtREV (as implemented in Yang's PAML), JTT (Jones, Taylor and Thornton, 1992), 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).
Equilibrium frequencies
-f e, d, or \"fA fC fG fT\"

"; echo "e : the character frequencies are determined as follows :
- Nucleotide sequences: (Empirical) the equilibrium base frequencies are estimated by counting the occurence of the different bases in the alignment.
- Amino-acid sequences: (Empirical) the equilibrium amino-acid frequencies are estimated by counting the occurence of the different amino-acids in the alignment.
m : 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.
\"fA fC fG fT\" : only valid for nucleotide-based models. fA, fC, fG and fT are floating numbers that correspond to the frequencies of A, C, G and T respectively."; if ($_REQUEST['type'] == 'command') { echo "
Other options are discussed in the PhyML manual."; } } ?>
Transition / transversion ratio
-t (or --ts/tv) ts/tv_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
-v (or --pinv) prop_invar

"; ?> 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, but it should not be larger than the proportion of constant sites in the alignments. This parameter can also be estimated by maximising the likelihood of the phylogeny. The later makes the program slower.
Number of substitution rate categories
-c (or --nclasses) nb_subst_cat

"; ?> 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 shape parameter
-a (or --alpha) gamma

"; ?> 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)
-u (or --inputtree) user_tree_file

"; ?> Used as the starting tree(s) to be refined by the maximum likelihood algorithm. The default is to use a BIONJ distance-based tree. You can use additional random starting trees with the SPR and SPR & NNI options. 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). Trees can be either rooted or unrooted and multifurcations are allowed. Taxa names must, of course, match the corresponding sequence names. 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.
Type of tree improvement
-s (or --search) move

move : tree topology search operation. Can be either 'NNI' (default, fast) or 'SPR' (a bit slower than NNI) or 'BEST' (best of NNI and SPR search).

"; ?> 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.

--rand_start

Number of random tree(s)
"; if ($_REQUEST['type'] == 'command') echo "--n_rand_starts num

num is"; else echo "This option sets"; echo " the number of initial random trees to be used. It is only valid if SPR searches are to be performed.
Optimise starting tree(s) options
-o params

"; ?> 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). params = 'tlr'"; ?>
- You can keep the topology and optimise the branch lengths and model parameters (it is not possible to optimise the tree topology and keep the branch lengths). params = 'lr'"; ?>
- You can ask for no optimisation, PhyML just returns the likelihood of the starting tree(s). params = 'n'"; ?>
aLRT (approximate Likelihood-Ratio Test) for branches
-b (or --bootstrap) int

int = -1 : approximate likelihood ratio test returning aLRT statistics.
int = -2 : approximate likelihood ratio test returning Chi2-based parametric branch supports.
int = -3 : minimum of Chi2-based parametric and SH-like branch supports.
int = -4 : SH-like branch supports alone.

"; ?> aLRT is a statistical test to compute branch supports. It applies to every (internal) branch and is computed along PhyML run on the original data set. Thus, aLRT is much faster than standard bootstrap which requires running PhyML 100-1,000 times with resampled data sets. As with any test, the aLRT branch support is significant when it is larger than 0.90-0.99. With good quality data (enough signal and sites), the sets of branches with bootstrap proportion >0.75 and aLRT>0.9 (SH-like option) tend to be similar.
Perform bootstrap and number of resampled data sets
-b (or --bootstrap) int

int > 0 : int is the number of bootstrap replicates.
int = 0 : neither approximate likelihood ratio test nor bootstrap values are computed.

"; ?> When there is only one data set you can ask PhyML to generate resampled bootstrap data sets from this original data set. PhyML then returns the bootstrap tree with branch lengths and bootstrap values, using standard NEWICK format.

--r_seed

num

Print likelihood
--print_site_lnl

Print the likelihood for each site in file *_phyml_lk.txt.
Print phylogenies
--print_trace

Print each phylogeny explored during the tree search process in file *_phyml_trace.txt.

References

"A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood."

Guindon S. & Gascuel O.

Systematic Biology 52, 696–704 (2003).
"RAxML-VI-HPC: Maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models."

Stamatakis A.

Bioinformatics 22, 2688–2690 (2006).
"Genetic algorithm approaches for the phylogenetic analysis of large bio-logical sequence datasets under the maximum likelihood criterion."

Zwickl D.

Ph.D. thesis, The University of Texas at Austin (2006).
"Evolution of protein molecules."

Jukes T. & Cantor C.

In Munro, H. (ed.) Mammalian Protein Metabolism, vol. III, chap. 24, 21–132 (Academic Press, New York, 1969).
"A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences."

Kimura M.

Journal of Molecular Evolution 16, 111–120 (1980).
"Evolutionary trees from DNA sequences: a maximum likelihood approach."

Felsenstein J.

Journal of Molecular Evolution 17, 368–376 (1981).
"PHYLIP (PHYLogeny Inference Package) version 3.6a2."

Felsenstein J.

(Distributed by the author, Department of Genetics, University of Washington, Seattle, 1993).
"Dating of the Human-Ape splitting by a molecular clock of mitochondrial-DNA."

Hasegawa M., Kishino H. & Yano, T.

Journal of Molecular Evolution 22, 160–174 (1985).
"Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees."

Tamura K. & Nei M.

Molecular Biology and Evolution 10, 512–526 (1993).
"A new method for calculating evolutionary substitution rates."

Lanave C., Preparata G., Saccone C. & Serio G.

Journal of Molecular Evolution 20, 86–93 (1984).
"Some probabilistic and statistical problems on the analysis of DNA sequences."

Tavaré S.

Lectures on Mathematics in the Life Sciences 17, 57–86 (1986).
"An improved general amino-acid replacement matrix."

Le S. & Gascuel O.

Mol. Biol. Evol. 25(7):1307-20 (2008).
"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).
"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).
"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).
"Amino acid substitution matrices from protein blocks."

Henikoff S. & Henikoff J.

PNAS 89, 10915–10919 (1992).
"MOLPHY version 2.3. programs for molecular phylogenetics based on maximum likelihood."

Adachi J. & Hasegawa M.

In Ishiguro, M. et al. (eds.) Computer Science Monographs, 28 (The Institute of Statistical Mathematics, Tokyo, 1996).
"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).
"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).
"Different versions of the Dayhoff rate matrix."

Kosiol C. & Goldman N.

Molecular Biology and Evolution 22, 193–19 (2004).
"Modeling amino acid replacement."

Muller T. & Vingron M.

Journal of Computational Biology 7, 761–776 (2000).
"Conflict among individual mitochondrial proteins in resolving the phylogeny of eutherian orders."

Cao Y. et al.

Journal of Molecular Evolution 47, 307–322 (1998).
"BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data."

Gascuel O.

Molecular Biology and Evolution 14, 685–695 (1997).
"Modeltest: testing the model of DNA substitution."

Posada D. & Crandall K.

Bioinformatics 14, 817–918 (1998).
"Prottest: selection of best-fit models of protein evolution."

Abascal F., Zardoya R. & Posada D.

Bioinformatics 21, 2104–2105 (2005).
"Markov-modulated Markov chains and the covarion process of molecular evolution."

Galtier N. & Jean-Marie A.

Journal of Computational Biology 11, 727–733 (2004).