A fast implementation of aLRT in PhyML.

Implemented by Jean-François Dufayard from original PhyML, based on Anisimova and Gascuel (2006) paper. Please cite:
"Approximate likelihood ratio test for branchs: A fast, accurate and powerful alternative."
Anisimova M., Gascuel O.
Systematic Biology, 55(4), 539-552, 2006.


Here you can find a beta-version of binaries that implement the algorithm described in our Syst. Biol. paper. Feedback and comments are much welcome.

This PhyML version contains two main improvements: All together this program performs aLRT for all branches and improves the tree likelihood, without increasing (in average) the computing time of original PhyML.


PhyML-aLRT uses the same arguments as the original PHYML. Five branch supports are available:
  1. Felsenstein’s bootstrap,
  2. aLRT statistics,
  3. aLRT parametric (Chi2-based, see our Syst. Biol. paper) branch support,
  4. aLRT non-parametric branch support based on a Shimodaira-Hasegawa-like procedure (not described in our Syst. Biol. paper),
  5. and a combination of these two latters supports, that is, the minimum value of both.
Default is aLRT SH-like branch support. Type "X" in PHYLYP-like interface to select your preferred option. In the command line version, you have to replace the number of bootstrap samples by -1, -2, -3 or -4 to obtain aLRT statistics, aLRT parametric branch support, combined parametric and non-parametric branch support, and non-parametric SH branch support, respectively, and 0 for no support.


Binaries for various platforms are available here.

From Felsenstein’s bootstrap to aLRT

Both approaches are clearly different, as detailed in our Syst. Biol. paper, and we encourage any user to read this paper for better understanding of the differences. Basically:

Test dataset

Here is a protein dataset (C8 alpha chain precursor) that can be used with aLRT-PhyML for test purpose. The resulting trees that are obtained with three support options are shown below. To get those trees in Newick format, click on figures. This example shows that bootstrap proportions and aLRT SH-like supports basically agree, while Chi2-based supports tend to be more liberal. We also display the tree computed by the original PhyML, which has a loglikelihood 4.1 points below that of the new version.


For any questions about the algorithm, the options of the program or the source code, you can contact: