Welcome to PhyloX’s documentation!

phylox

PhyloX is a python package for parsing, manipulating, and analysing phylogenetic networks. By building upon the widely used NetworkX package, PhyloX provides a simple and intuitive interface for working with phylogenetic networks. Unlike other packages like Biopython and DendroPy, PhyloX is designed to work with phylogenetic networks, rather than phylogenetic trees. Moreover, PhyloX is designed to work with networks that have internally labelled nodes, multiple roots, and non-binary nodes (although not all methods are implemented for all these types of networks).

Some of the features of PhyloX include:

Reading and writing phylogenetic networks in extended Newick format with internal node labels, edge lengths, support values, and inheritance probabilities.- Generating random phylogenetic networks with several different models.
Modifying and analysing phylogenetic networks using rearrangement moves - Calculating the rearrangement distance between two network (exact and heuristically). - Generating the set of all networks that can be reached from a given network using a single rearrangement move. - Moves are robust objects, that give meaningful error messages when they are applied to networks that they are not applicable to.
Cherry-picking methods for phylogenetic networks. - Checking whether a network is orchard - Network containment checking for tree-child networks using cherry-picking sequences. - Combining networks using cherry-picking sequences.
Isomorpism functions for phylogenetic networks. - Checking whether two networks are isomorphic. - Counting the number of automorphisms of a network.

A use-case that neatly combines many of these features is the following. Suppose we want a test set of orchard networks with 10 leaves and 5 reticulations. These networks can be generated randomly with a Metropolis-Hasting sampling.

We start with an arbitrary orchard network with 10 leaves and 5 reticulations, which can be generated with the function phylox.generators.randomTC:generate_network_random_tree_child_sequence(). Then repeatedly apply some (large number of) random rearrangement moves, only actually applying moves if the resulting network is orchard. This can be done with the built-in MCMC sampler, phylox.generators.mcmc:sample_mcmc_networks(). To ensure all sampled networks are orchard, we use the restriction_map argument to specify that only orchard networks should be sampled, and to ensure the right number of leaves and reticulations are sampled, we set the move_type_probabilities for vertical moves to 0. We can use the sample_size argument to specify how many networks we want to sample. Finally, we write the sampled networks to a file in newick format.

The final code looks like this:

from phylox.generators.randomTC import generate_network_random_tree_child_sequence
from phylox.generators.mcmc import sample_mcmc_networks
from phylox.classes import is_orchard
from phylox.rearrangement.move import MoveType

# Generate an arbitrary orchard network with 10 leaves and 5 reticulations
start_network = generate_network_random_tree_child_sequence(10, 5, seed=4321)
# Generate 100 orchard networks with 10 leaves and 5 reticulations
sampled_networks = sample_mcmc_networks(
   start_network,
   {MoveType.TAIL: 0.5, MoveType.HEAD: 0.5},
   number_of_samples=100,
   burn_in=5,
   restriction_map=is_orchard,
   seed=1234,
   add_root_if_necessary=True,
   correct_symmetries=False,
)
# Write the sampled networks to a file
with open("sampled_networks.nwk", "w") as f:
   for network in sampled_networks:
      f.write(network.newick() + "\n")

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