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23<div class="major-title"> PHYML User's guide (PHYLIP-like interface)</div>
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52<h4>Overview</h4>
53<div style="text-align: justify;">
54PHYML is a software implementing a new method for building phylogenies
55from DNA and protein sequences using maximum likelihood. Data sets can
56be analysed under several models of evolution (JC69, K80, F81, F84,
57HKY85, TN93 and GTR for nucleotides and Dayhoff, JTT, mtREV, WAG,
58DCMut, 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.
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88
89<h4>The PHYLIP-like interface</h4>
90Download 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'.
91<br>
92<br>PHYML produces several results files :
93<li>&lt;sequence file name&gt;_phyml_lk.txt : likelihood value(s)
94<li>&lt;sequence file name&gt;_phyml_tree.txt : inferred tree(s)
95<li>&lt;sequence file name&gt;_phyml_stat.txt : detailed execution stats
96<li>&lt;sequence file name&gt;_phyml_boot_trees.txt : bootstrap trees (special case)
97<li>&lt;sequence file name&gt;_phyml_boot_stats.txt : bootstrap statistics (special case)
98
99<br><br>
100Here are the possible uses of PHYML :
101
102<p><li>One data set, one starting tree
103<br>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).
104
105
106<p><li>Several data sets, one starting tree
107<br>Several standard analysis start from the same intial tree with different data sets, without the bootstrap option.
108<br>The results are given in the order of the data sets.
109<br>This can be used to process multiple genes in a supertree approach.
110
111
112<p><li>One data set, several starting trees
113<br>Several standard analysis of  the same data set using different starting tree situations, without the bootstrap option.
114<br>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.
115<br>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.
116
117<p><li>Several data sets, several starting trees
118<br>Several standard runs, where each data set is analysed with the corresponding starting tree, without the bootstrap option.
119<br>The results are given in the order of the data sets.
120<br>This can be used when comparing the likelihood of various trees regarding different data sets.
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150<H4>Options</H4>
151
152<p><li><strong>Sequences</strong>
153The input sequence file is a standard PHYLIP file of aligned DNA or amino-acids sequences.
154It should look like this in interleaved format :
155<pre>
1565 60
157Tax1        CCATCTCACGGTCGGTACGATACACCTGCTTTTGGCAG
158Tax2        CCATCTCACGGTCAGTAAGATACACCTGCTTTTGGCGG
159Tax3        CCATCTCCCGCTCAGTAAGATACCCCTGCTGTTGGCGG
160Tax4        TCATCTCATGGTCAATAAGATACTCCTGCTTTTGGCGG
161Tax5        CCATCTCACGGTCGGTAAGATACACCTGCTTTTGGCGG
162
163GAAATGGTCAATATTACAAGGT
164GAAATGGTCAACATTAAAAGAT
165GAAATCGTCAATATTAAAAGGT
166GAAATGGTCAATCTTAAAAGGT
167GAAATGGTCAATATTAAAAGGT
168</pre>
169
170The same data set in sequential format:
171<br>
172<pre>
1735 60
174Tax1        CCATCTCACGGTCGGTACGATACACCTGCTTTTGGCAGGAAATGGTCAATATTACAAGGT
175Tax2        CCATCTCACGGTCAGTAAGATACACCTGCTTTTGGCGGGAAATGGTCAACATTAAAAGAT
176Tax3        CCATCTCCCGCTCAGTAAGATACCCCTGCTGTTGGCGGGAAATCGTCAATATTAAAAGGT
177Tax4        TCATCTCATGGTCAATAAGATACTCCTGCTTTTGGCGGGAAATGGTCAATCTTAAAAGGT
178Tax5        CCATCTCACGGTCGGTAAGATACACCTGCTTTTGGCGGGAAATGGTCAATATTAAAAGGT
179</pre>
180
181<br>On the first line is the number of taxa, a space, then the number of characters for each taxon.
182<br><br>
183The 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.
184<br><br>
185In 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).
186<br>
187<br>
188
189<center>
190
191<table>
192<tr valign="top">
193<td>
194
195<table border cols=2>
196<caption> Table 1 - Nucleotide character coding </caption>
197<tr> <td align="center"> Character </td> <td align="center"> Nucleotide </td> </tr>
198
199<tr> <td align="center"> A </td> <td align="center"> Adenosine </td> </tr>
200<tr> <td align="center"> G </td> <td align="center"> Guanine </td> </tr>
201<tr> <td align="center"> C </td> <td align="center"> Cytosine </td> </tr>
202<tr> <td align="center"> T </td> <td align="center"> Thymine </td> </tr>
203<tr> <td align="center"> U </td> <td align="center"> Uracil </td> </tr>
204
205<tr> <td align="center"> M </td> <td align="center"> A or C </td> </tr>
206<tr> <td align="center"> R </td> <td align="center"> A or G </td> </tr>
207<tr> <td align="center"> W </td> <td align="center"> A or T </td> </tr>
208
209<tr> <td align="center"> S </td> <td align="center"> C or G </td> </tr>
210<tr> <td align="center"> Y </td> <td align="center"> C or T </td> </tr>
211<tr> <td align="center"> K </td> <td align="center"> G or T </td> </tr>
212
213<tr> <td align="center"> B </td> <td align="center"> C or G or T </td> </tr>
214<tr> <td align="center"> D </td> <td align="center"> A or G or T </td> </tr>
215<tr> <td align="center"> H </td> <td align="center"> A or C or T </td> </tr>
216
217<tr> <td align="center"> V </td> <td align="center"> A or C or G </td> </tr>
218<tr> <td align="center"> N or X or ? </td> <td align="center"> unknown </td> </tr>
219</table>
220
221</td>
222<td>
223
224<table border cols=2>
225<caption> Table 2 - Amino-acid character coding </caption>
226<tr> <td align="center"> Character </td> <td align="center"> Amino-acid </td> </tr>
227
228<tr> <td align="center"> A </td> <td align="center"> Alanine </td> </tr>
229<tr> <td align="center"> R </td> <td align="center"> Arginine </td> </tr>
230<tr> <td align="center"> N or B </td> <td align="center"> Asparagine </td> </tr>
231
232<tr> <td align="center"> D </td> <td align="center"> Aspartic acid </td> </tr>
233<tr> <td align="center"> C </td> <td align="center"> Cysteine </td> </tr>
234<tr> <td align="center"> Q or Z </td> <td align="center"> Glutamine </td> </tr>
235
236<tr> <td align="center"> E </td> <td align="center"> Glutamic acid </td> </tr>
237<tr> <td align="center"> G </td> <td align="center"> Glycine </td> </tr>
238<tr> <td align="center"> H </td> <td align="center"> Histidine </td> </tr>
239
240<tr> <td align="center"> I </td> <td align="center"> Isoleucine </td> </tr>
241<tr> <td align="center"> L </td> <td align="center"> Leucine </td> </tr>
242<tr> <td align="center"> K </td> <td align="center"> Lysine </td> </tr>
243
244<tr> <td align="center"> M </td> <td align="center"> Methionine </td> </tr>
245<tr> <td align="center"> F </td> <td align="center"> Phenylalanine </td> </tr>
246<tr> <td align="center"> P </td> <td align="center"> Proline </td> </tr>
247
248<tr> <td align="center"> S </td> <td align="center"> Serine </td> </tr>
249<tr> <td align="center"> T </td> <td align="center"> Threonine </td> </tr>
250<tr> <td align="center"> W </td> <td align="center"> Tryptophan </td> </tr>
251
252<tr> <td align="center"> Y </td> <td align="center"> Tyrosine </td> </tr>
253<tr> <td align="center"> V </td> <td align="center"> Valine </td> </tr>
254<tr> <td align="center"> X or ? </td> <td align="center"> unknown </td> </tr>
255
256</table>
257
258</td>
259</tr>
260</table>
261
262</center>
263
264
265
266<br><br><li><strong>Data type</strong><br>
267This indicates if the sequence file contains DNA or amino-acids. The default choice is to analyse DNA sequences.
268
269
270
271<br><br><li><strong>Sequence format</strong><br>
272The input sequences can be either in interleaved (default) or sequential format, see "Sequences" above.
273
274
275
276<br><br><li><strong>Number of data sets</strong><br>
277Multiple 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):
278<pre>
2795 60
280Tax1        CCATCTCACGGTCGGTACGATACACCTGCTTTTGGCAGGAAATGGTCAATATTACAAGGT
281Tax2        CCATCTCACGGTCAGTAAGATACACCTGCTTTTGGCGGGAAATGGTCAACATTAAAAGAT
282Tax3        CCATCTCCCGCTCAGTAAGATACCCCTGCTGTTGGCGGGAAATCGTCAATATTAAAAGGT
283Tax4        TCATCTCATGGTCAATAAGATACTCCTGCTTTTGGCGGGAAATGGTCAATCTTAAAAGGT
284Tax5        CCATCTCACGGTCGGTAAGATACACCTGCTTTTGGCGGGAAATGGTCAATATTAAAAGGT
285
2865 60
287Tax1        CCATCTCACGGTCGGTACGATACACCTGCTTTTGGCAGGAAATGGTCAATATTACAAGGT
288Tax2        CCATCTCACGGTCAGTAAGATACACCTGCTTTTGGCGGGAAATGGTCAACATTAAAAGAT
289Tax3        CCATCTCCCGCTCAGTAAGATACCCCTGCTGTTGGCGGGAAATCGTCAATATTAAAAGGT
290Tax4        TCATCTCATGGTCAATAAGATACTCCTGCTTTTGGCGGGAAATGGTCAATCTTAAAAGGT
291Tax5        CCATCTCACGGTCGGTAAGATACACCTGCTTTTGGCGGGAAATGGTCAATATTAAAAGGT
292
2935 60
294Tax1        CCATCTCACGGTCGGTACGATACACCTGCTTTTGGCAGGAAATGGTCAATATTACAAGGT
295Tax2        CCATCTCACGGTCAGTAAGATACACCTGCTTTTGGCGGGAAATGGTCAACATTAAAAGAT
296Tax3        CCATCTCCCGCTCAGTAAGATACCCCTGCTGTTGGCGGGAAATCGTCAATATTAAAAGGT
297Tax4        TCATCTCATGGTCAATAAGATACTCCTGCTTTTGGCGGGAAATGGTCAATCTTAAAAGGT
298Tax5        CCATCTCACGGTCGGTAAGATACACCTGCTTTTGGCGGGAAATGGTCAATATTAAAAGGT
299</pre>
300
301
302
303<br><br><li><strong>Perform bootstrap and Number of pseudo data sets</strong><br>
304When 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.
305
306
307
308
309<br><br><a name="models"></a><li><strong>Substitution model</strong><br>
310A nucleotide or amino-acid substitution model.
311For 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, Tavar&eacute; 1986, Rodriguez et al. 1990). The rate matrices of these models are given in Swofford et al. (1996).
312<br>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 :
313<center><table border=1>
314<tr><td>digit 1<td>digit 2<td>digit 3<td>digit 4<td>digit 5<td>digit 6</tr>
315<tr><td>A&lt;-&gt;C<td>A&lt;-&gt;G<td>A&lt;-&gt;T<td>C&lt;-&gt;G<td>C&lt;-&gt;T<td>G&lt;-&gt;T</tr>
316</table></center>
317<br>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.
318<br>010010 corresponds to a model where the A&lt;-&gt;G and C&lt;-&gt;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.
319For amino-acid sequences, the default choice is JTT (Jones, Taylor and
320Thornton, 1992). The other models are Dayhoff (Dayhoff et al., 1978),
321mtREV (as implemented in Yang's PAML), WAG (Whelan and Goldman, 2001)
322and DCMut (Kosiol and Goldman, 2005),  RtREV (Dimmic et al.), CpREV (Adachi et al., 2000)
323VT (Muller and Vingron, 2000), Blosum62 (Henikoff anf Henikoff, 1992) and
324MtMam (Cao, 1998).
325
326
327<br><br><li><strong>Base frequency estimates</strong><br>
328Under most of the nucleotide based models (except JC69 and K2P), base frequencies can be
329estimated from the data (empirical) or adjusted so as to maximise
330the likelihood (ML). The later makes the program slower. Comparing the
331results obtained under the two options might be useful when analysing sequences that correspond
332to concatenations of several genes with different nucleotide compositions.
333
334<br><br><li><strong>Transition / transversion ratio</strong><br>
335With 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.
336
337
338
339<br><br><li><strong>Proportion of invariable sites</strong><br>
340The 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.
341
342
343
344<br><br><li><strong>Number of substitution rate categories</strong><br>
345The 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.
346
347
348
349<br><br><li><strong>Gamma distribution parameter</strong><br>
350The 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.
351
352
353
354
355
356
357
358<br><br><li><strong>Starting tree(s)</strong><br>
359Used 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:
360<br>
361<tt>
362(A:0.02,B:0.004,(C:0.1,D:0.04)90:0.05);
363</tt>
364<br>If you give several trees and analyse several data sets the two numbers must match.
365
366
367
368<br><br><li><strong>Optimise starting tree(s) options</strong><br>
369    You can optimise the starting tree(s) in three ways :
370-   You can optimise the topology, the branch lengths and rate parameters (transition/transversion ratio, proportion of invariant sites, gamma distribution parameter),
371-   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),
372-   You can ask for no optimisation, PHYML just returns the likelihood of the starting tree(s).
373
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404
405<h4>References</h4>
406
407<font size="-1"> 
408
409
410<li> Z.&nbsp;Yang (1994)
411<em>J. Mol. Evol.</em>&nbsp;<b>39</b>,&nbsp;306-14. </li> 
412<li> S.&nbsp;Ota &amp; W.-H. Li (2001)
413
414<em>Mol. Biol. Evol.</em>&nbsp;<b>  18</b>,&nbsp;1983-1992. </li>
415<li> N.&nbsp;Saitou &amp; M.&nbsp;Nei (1987)
416<em>Mol. Biol. Evol.</em>&nbsp;<b>  4</b>(4),&nbsp;406-425. </li>
417
418<li> W.&nbsp;Bruno, N.&nbsp;D. Socci, &amp; A.&nbsp;L. Halpern (2000) <em>Mol. Biol.
419  Evol.</em>&nbsp;<b>17</b>,&nbsp;189-197. </li>
420<li> J.&nbsp;Felsenstein (1989)
421<em>Cladistics</em>&nbsp;<b>5</b>,&nbsp;164-166. </li>
422
423<li> G.&nbsp;J. Olsen, H.&nbsp;Matsuda, R.&nbsp;Hagstrom, &amp;
424R.&nbsp;Overbeek (1994) <em>  CABIOS</em>&nbsp;<b>10</b>,&nbsp;41-48. </li>
425<li> N.&nbsp;Goldman (1993)
426
427<em>J. Mol. Evol.</em>&nbsp;<b>36</b>,&nbsp;182-198. </li>
428<li> M.&nbsp;Kimura (1980)
429<em>J. Mol. Evol.</em>&nbsp;<b>16</b>,&nbsp;111-120. </li>
430<li> T.&nbsp;H. Jukes &amp; C.&nbsp;R. Cantor (1969) in <em>Mammalian Protein
431  Metabolism</em>,  ed. H.&nbsp;N. Munro.  (Academic Press, New York) Vol. III,  pp.
432  21-132. </li>
433
434<li> M.&nbsp;Hasegawa, H.&nbsp;Kishino, &amp; T.&nbsp;Yano (1985)
435<em>J. Mol. Evol.</em>&nbsp;<b>  22</b>,&nbsp;160-174. </li>
436<li> J.&nbsp;Felsenstein (1981)
437<em>J. Mol. Evol.</em>&nbsp;<b>17</b>,&nbsp;368-376. </li>
438
439<li> David&nbsp;L. Swofford, Gary&nbsp;J. Olsen, Peter&nbsp;J. Waddel, &amp; David&nbsp;M. Hillis
440  (1996) in <em>Molecular Systematics</em>,  eds. David&nbsp;M. Hillis, Craig Moritz,
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