ATGC: PhyML (original) (raw)
PhyML 3.0: new algorithms, methods and utilities
Guindon S., Dufayard J.F., Lefort V., Anisimova M., Hordijk W., Gascuel O.
Systematic Biology, 59(3):307-21, 2010.
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PhyML online execution
Input Data
| Sequence alignment (PHYLIP format) | Drop alignment file here or | file DNA | example (AA file) (DNA file) amino-Acids |
|---|
Substitution Model
| | User defined Substitution model Equilibrium frequencies Transition / transversion ratio fixed estimated Proportion of invariable sites fixed estimated Rate across sites model Number of substitution rate classes Gamma shape parameter fixed estimated | | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | | Automatic model selection by SMS Selection criterion AIC (Akaike Information Criterion) BIC (Bayesian Information Criterion) If you use SMS, please cite:"SMS: Smart Model Selection in PhyML." Vincent Lefort, Jean-Emmanuel Longueville, Olivier Gascuel. Molecular Biology and Evolution, 34(9):2422-2424, 2017. |
Optimization options
| Starting tree(s) | file | BioNJ | |
|---|---|---|---|
| Constraint tree | yes | no | |
| Optimize tree topology? | yes | no | |
| Optimize edge lengths? | yes | no | |
| Add random starting trees Using random starting trees makes the tree search more thorough, increasing the likelihood to find a better tree | [# of starting trees ] | yes | no |
Branch Supports
| Fast likelihood-based methods | yes | no | |
|---|---|---|---|
| Standard bootstrap analysis This is the "classical" (i.e., non-parametric) bootstrap analysis | [# of repeats ] | yes | no |
| Transfer bootstrap analysis This is the "new" bootstrap analysis as described in Lemoine et al. (Nature, 556 (7702), 452-456, 2018) | [# of repeats ] | yes | no |
Extra options
| Keep duplicate sequences during analysis By default, duplicate sequences are removed at the start of the tree building and then re-inserted at the end. Use 'yes' only if you encouter issues with the default settings | yes | no |
|---|---|---|
| Print site likelihood | yes | no |
| Infer ancestral sequences Generate an extra output file containing ancestral sequences | yes | no |
| Name of your analysis You may want to add a tag/name to your analysis. This is helpful in case you are analyzing the same data sets under different options (e.g., distinct models) |
About you...
| Academic Private company | |
|---|---|
| Name of your university/institute | |
| Your email | |
| Help PhyML | Make a donation ! Our servers are running many PhyML analyses every day. In fact, we are currently performing more than 300,000 CPU hours of computation with PhyML on our plateform every year! Please donate using this linkhttps://fondation-cnrs.org/en/donate/ select "Software support PhyML". Donations to PhyML via CNRS Fondation will be used to upgrade our computer servers and hire engineers that will help us maintain and optimize the code of PhyML. |