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+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +6427 +10.21105/joss.06427 + +PhyloX: A Python package for complete phylogenetic +network workflows + + + +https://orcid.org/0000-0002-5192-1470 + +Janssen +Remie + + + + + +National Institute for Public Health and the Environment, +Bioinformatics and Computing group, Bilthoven, The +Netherlands + + + + +25 +4 +2024 + +9 +103 +6427 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2024 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +Bioinformatics +Phylogenetics +Graph theory + + + + + + Summary +

PhyloX is a Python package with tools for generating, manipulating, + and analysing phylogenetic networks. It uses the NetworkX package + (Hagberg + et al., 2008) for basic graph operations. This has the added + benefit that the powerful graph tools from NetworkX can be used + directly on the phylogenetic networks as well. The aim of the package + is to be of general use to phylogenetic network researchers, with a + current focus on I/O, random generation of networks, cherry-picking + methods, rearrangement operations, and the identification of classes + and properties of networks.

+ + + Phylogenetic networks +

In the study of the evolutionary history of biological species and + languages, it is common to represent putative histories using graphs. + Traditionally, at least in biology, these graphs are most often trees, + such as the well-known tree drawn by Charles Darwin in one of his + notebooks. A tree like this is called a phylogenetic tree. In some + cases, the evolutionary history includes complex processes like + horizontal gene transfer and hybridisation. These processes cause a + reticulate (i.e., network-like) structure in the + evolutionary history, which requires phylogenetic networks to be used + for representing the evolutionary histories.

+

A directed phylogenetic network (e.g., + (Huson + et al., 2010)) is a directed acyclic graph with four types of + nodes: - a root: an in-degree 0, out-degree 1 node, - + a labelled set of leaves: in-degree 1, out-degree 0 + nodes, - a set of reticulation nodes: in-degree + + 1]]> + >1, + out-degree 1 nodes, - a set of tree nodes: in-degree + 1, out-degree + 1]]> + >1 + nodes.

+

A network is binary if each reticulation node has + in-degree 2, and each tree node has in-degree 2. An undirected + phylogenetic network is the underlying undirected graph of a + directed phylogenetic network, retaining the labelling of the leaf + nodes.

+ + Network properties +

When analysing or comparing phylogenetic networks or phylogenetic + network methods, it can be helpful to extract some (numerical) + parameters from the networks. Some of the most used properties are + the reticulation number (the number of reticulation + nodes in a binary network), the number of blobs + (biconnected components of the network), and the + level (the maximum reticulation number among all + blobs of the network). Of course, the list of studied properties and + parameters is much longer, including, for example, the recently + introduced + + B2-balance + index of the network + (François + et al., 2021).

+ + + Classes of networks +

In research on phylogenetic networks, it is common to restrict + attention to some well-known classes of phylogenetic networks. These + classes put additional restrictions on the definition of a network, + for the benefit of computational efficiency, to model certain + biological restraints, or for both.

+

Kong et al. + (2022) + gives a good overview of most well-known classes of directed + phylogenetic networks and their biological interpretation. For + example, tree-child networks are networks in which each ancestral + species has a descendant among the extant taxa (the leaves) through + only mutation in the network + (Cardona + et al., 2009). Mathematically, tree-child networks are + characterised as networks in which each non-leaf node has at least + one child that is not a reticulation node.

+ + + Cherry-picking +

A basic structure in any network or tree is the + cherry, a pair of leaves with a common parent. A + modified version often found in phylogenetic networks is the + reticulated cherry, an ordered pair of leaves + + + (x,y) + that are related through the three edges + + + (px,x), + + + (py,y), + and + + (py,px).

+

A common modification to a phylogenetic network is to + pick or (or reduce) a cherry or + reticulated cherry. To pick a cherry + + (x,y), + one removes the leaf + + x + from the network together with its incoming edge and then suppresses + the resulting degree 2 node if the shared parent of + + + x + and + + y + had out-degree 2. Suppressing a degree 2 node + consists in removing the node and its two incident edges, and + replacing them all with one new edge. To pick a reticulated cherry + + + (x,y), + one removes the edge + + (py,px) + and suppresses all resulting degree 2 nodes. The reverse action of + picking a cherry is called adding a cherry.

+

These modifications are used in computational tools, for example + to reconstruct networks from ancestral profiles + (Bai + et al., 2021; + Cardona + et al., 2024; + Erdős + et al., 2019), to check whether one tree-child network is + contained in another + (Janssen + & Murakami, 2021), or to combine multiple trees into one + network + (Iersel, + Janssen, Jones, Murakami, & Zeh, 2022; + Linz + & Semple, 2019). Their versatile use has also led to the + introduction of the class of orchard networks + (Erdős + et al., 2019; + Janssen + & Murakami, 2021). This class contains all networks that + can be reduced to a single leaf using cherry-picking operations. + Networks from this class can be interpreted as trees with horizontal + gene transfer arcs + (Iersel, + Janssen, Jones, & Murakami, 2022).

+ + + Rearranging networks +

For phylogenetic inference problems, it is often necessary to use + heuristics that search through a space of networks. Such a space of + networks takes the shape of a graph, whose objects are all networks + with a common set of leaf labels (the sampled taxa) and sometimes + also a set number of reticulations. The edges of the graph + correspond to small changes made to a network: there is an edge + between two networks if one can make a modification to one of the + networks to arrive at the second network.

+

The modifications that are allowed are well-defined as types of + rearrangement operations. These operations can be + horizontal, keeping the reticulation number the + same, or vertical, changing the reticulation + number. Most horizontal operations are a variation on or restriction + of the rooted subtree prune and regraft + (rSPR) operation + (Bordewich + et al., 2017; + Gambette + et al., 2017).

+

The names for vertical moves have not been standardised, but they + generally do the same. A vertical move that removes a reticulation + removes an incoming edge of a reticulation node, and then suppresses + the resulting degree 2 nodes. A vertical move that adds a + reticulation does the reverse: it subdivides two + edges of the network, and adds a new edge between the two new degree + 2 nodes (e.g. + (Bordewich + et al., 2017)).

+

As mentioned, rearrangement moves can be used to traverse a space + of networks. This is used, for example, to sample posterior + distributions in Bayesian analyses Zhang et al. + (2018) + and to find networks that score high on a maximum likelihood + criterium + (Wen, + Yu, Hahn, et al., 2016; + Yu + et al., 2014).

+ + + Generating networks +

To test phylogenetic network methods, one either needs to source + or create a test set of networks. Creating them is often the simpler + option, so methods to randomly generate phylogenetic networks are + ready at hand. Moreover, these methods are often based on + evolutionary models that are defined on a high level, i.e., with + explicit events for processes such as speciation, extinction, and + hybridisation.

+

The paper + (Janssen + & Liu, 2021) contains a comparison of several + ‘generators’, including several previously existing ones (e.g., + (Pons + et al., 2019) and + (Zhang + et al., 2018)) and a new extension of a tree generator to + networks.

+ + + Representing networks +

Because phylogenetic networks are graphs, a common representation + is as a list of edges. Another commonly used representation is the + extended Newick format + (Cardona + et al., 2008). The extended Newick notation has a further + extension (Rich Newick format) that adds numerical parameters to the + edges of the network, such as the branch length and the inheritance + probability (for incoming edges of a reticulation node) + (Barnett, + 2012; + Wen + et al., 2018).

+ + + + PhyloX Functionality +

PhyloX is equipped to handle all the aspects of phylogenetic + networks mentioned in the previous section. It is written primarily + for explorative research into algorithmic aspects of phylogenetic + networks, although application-focused implementations can also be + realised with it. An example is the software + (Julien + et al., 2023) for the paper + (Bernardini + et al., 2023), which uses cherry-picking methods in combination + with machine learning to efficiently combine a large number of trees + into a phylogenetic network. This software shares some of its basic + code with the + cherrypicking + module and the + generators + module of PhyloX.

+ + I/O +

PhyloX handles all stages of a phylogenetic workflow involving + networks. This starts and ends with the input/output of networks. + The + DiNetwork + class, which is used to represent phylogenetic networks + in PhyloX, inherits from the + DiGraph + class of NetworkX + (Hagberg + et al., 2008). Hence, phylox.DiNetwork + objects can simply be created using the API of + networkx.DiGraph and adding labels to the + leaves:

+ from phylox import DiNetwork +from phylox.constants import LABEL_ATTR + +network = DiNetwork() +network.add_edges_from(((0,1),(1,2),(1,3))) +network.nodes[2][LABEL_ATTR] = "leaf1" +network.nodes[3][LABEL_ATTR] = "leaf2" +

The same can be achieved with a modified initialisation of + DiNetwork:

+ from phylox import DiNetwork + +network = DiNetwork( + edges=((0,1),(1,2),(1,3)), + labels=[(2,"leaf1"), (3,"leaf2")] +) +

Alternatively, the network can be initialised from a Newick + string with

+ from phylox import DiNetwork + +network = DiNetwork.from_newick("((leaf1,leaf2));") +

NetworkX also provides functionality to output networks in + several formats. For example, it is possible to output the list of + edges or to create a drawing of the network. Of course, output as + Newick string is also available with PhyloX (with + network.newick() for a network called + network as in the example code blocks above). + This outputs all edge information in rich Newick format by default, + but can also be forced to output an extended Newick string without + edge information.

+ + + Generating networks +

Networks can also be generated randomly in PhyloX, which can be + utilised to create test sets for new methods. The implemented + generators are based on the code from + (Janssen + & Liu, 2021). These include generators based on + evolutionary models, such as the + LGT + generator and the + ZODS + generator based on + (Pons + et al., 2019) and + (Zhang + et al., 2018), but also a [Metropolis-Hastings sampler] + enabling uniform sampling from classes of networks.

+

The latter makes use of a large part of the functionality of + PhyloX, especially when sampling orchard networks: after generating + or choosing a starting network, the + phylox.generators.mcmc.sample_mcmc_networks + randomly traverses the space of phylogenetic networks using the + rearrangement + module, and rejects proposals if the resulting network is + not orchard using the + cherry-picking + module.

+ 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, + add_root_if_necessary=True, + correct_symmetries=False, + seed=1234, +) +# 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") +

For this sampler to work correctly, the space of networks that is + sampled from needs to be connected. That is, it has to be possible + to transform each network into each other network in the space using + the selected rearrangement moves. In the example above, this means + that the space of orchard networks with 10 leaves and 5 + reticulations needs to be connected under tail moves and head moves + (i.e., rSPR moves).

+

This is something the user needs to check or prove themselves, as + it is not viable to check this computationally. Fortunately, such + connectivity results have been studied in detail + (Erdős + et al., 2021; + Iersel, + Janssen, Jones, & Murakami, 2022; + Janssen, + 2021; + Klawitter, + 2020). For example, the result needed to prove that this + example is correct can be found in + (Iersel, + Janssen, Jones, & Murakami, 2022).

+ + + Comparing networks +

Based on all the properties above, PhyloX provides a toolkit to + compare networks. For example, it can be used to determine whether + two networks are + isomorphic + (i.e., the same); whether they have the same + properties: + level, number of blobs, reticulation number, and number of + (reticulated) cherries; whether one is + contained + in the other if both are tree-child; and whether they are similar + with respect to a + rearrangement + distance.

+ + + + Statement of Need +

Currently, no Python package enables a full workflow for analysing + properties and methods of phylogenetic networks. Isolated scripts for + this purpose do appear on GitHub or as pseudocode regularly, most + often as part of publications studying one method or one property + (Janssen + et al., 2020; + Janssen, + 2021; + Janssen + & Murakami, 2020; + Pons + et al., 2019; + Zhang + et al., 2018). Combining such scripts requires substantial + work, for example because the phylogenetic networks themselves are + represented by different Python classes with their own methods.

+

This package, PhyloX, aims to bring these scripts together: it + standardises implementations of several basic objects related to + phylogenetic networks, such as the networks themselves, the labelling + of the nodes, and rearrangement moves. It currently implements a + limited but important set of basic functions: I/O for networks (e.g., + lists of edges and extended Newick format), network generation for + test sets, comparing networks resulting from reconstruction methods, + and computing several well-used network properties such as the + reticulation number, the level, and the number of cherries.

+ + Related packages +

As mentioned above, there are currently no Python packages that + enable a complete workflow for phylogenetic networks. However, some + Python packages are available that enable part of this workflow or a + very similar one. In this section, we compare the functionality of + several of these packages to PhyloX, focussing only on usability for + phylogenetic networks.

+ + PhyloNetwork +

Like PhyloX, + PhyloNetwork + is a Python package based on NetworkX. It has a richer + implementation for phylogenetic trees than PhyloX. For example, it + includes more tree-specific rearrangement moves, the calculation + of node properties such as the latest common ancestor (LCA), and + some presets for drawing networks.

+

However, it has very few methods for phylogenetic networks, and + most of those methods are also included in PhyloX. Another + advantage of using PhyloX over PhyloNetwork is the inclusion of + explicit random seeds. This is an important factor for the + reproducibility of research.

+

Note that code from PhyloNetwork and PhyloX may be easy to + combine, as both use NetworkX to implement the phylogenetic + network class.

+ + + Biopython - Phylo +

This phylogenetics module, + Phylo + (Talevich + et al., 2012), of the Biopython package + (Cock + et al., 2009) is built for phylogenetic analyses in Python. + However, it is set up for phylogenetic trees only. The encoding of + trees as sets of + clades + does not easily allow extension to networks, which makes it + unsuitable to use for these phylogenetic network methods.

+ + + DendroPy +

Like Biopython’s phylogenetics package, the + DendroPy + package focuses on phylogenetic trees + (Sukumaran + & Holder, 2010). Unlike Biopython, the implementation + of the trees in DendroPy is graph-based, making it more suited for + analyses of phylogenetic networks. This could still require large + changes, as some properties of trees are built into the code on a + fairly fundamental level, such as each node having (at most one) + parent + node.

+ + + + + Availability +

The code of PhyloX is available as an open-source project on + GitHub + under the BSD 3-Clause licence. The package is also available via + PyPI, + so it can be installed via pip (or pip in conda), and updates to the + release branch are automatically converted into new versions of the + package. The releases are recorded in + Zenodo, + so persistent identifiers can be used to cite specific releases of the + software. When citing this software, please make sure to also cite the + original source of the code, which is mentioned in the + documentation + of each method or class.

+ + + Acknowledgements +

Most of the code has been written in the form of separate scripts + during the author’s PhD project, which was conducted under Leo van + Iersel’s Vidi grant: 639.072.6

+

Anyone willing to contribute is very welcome to do so via pull + requests and issues on GitHub!

+ + + + + + + + + HagbergAric A. + SchultDaniel A. + SwartPieter J. + + Exploring network structure, dynamics, and function using NetworkX + Proceedings of the 7th python in science conference + + VaroquauxGaël + VaughtTravis + MillmanJarrod + + Pasadena, CA USA + 2008 + https://www.osti.gov/biblio/960616 + 10.25080/TCWV9851 + 11 + 15 + + + + + + CockPeter J. A. + AntaoTiago + ChangJeffrey T. + ChapmanBrad A. + CoxCymon J. + DalkeAndrew + FriedbergIddo + HamelryckThomas + KauffFrank + WilczynskiBartek + HoonMichiel J. L. de + + Biopython: freely available Python tools for computational molecular biology and bioinformatics + Bioinformatics + 200903 + 25 + 11 + 1367-4803 + https://doi.org/10.1093/bioinformatics/btp163 + 10.1093/bioinformatics/btp163 + 1422 + 1423 + + + + + + TalevichEric + InvergoBrandon M + CockPeter JA + ChapmanBrad A + + Bio. Phylo: A unified toolkit for processing, analyzing and visualizing phylogenetic trees in biopython + BMC bioinformatics + Springer + 2012 + 13 + https://doi.org/10.1186/1471-2105-13-209 + 10.1186/1471-2105-13-209 + 1 + 9 + + + + + + SukumaranJeet + HolderMark T + + DendroPy: A python library for phylogenetic computing + Bioinformatics + Oxford University Press + 2010 + 26 + 12 + https://doi.org/10.1093/bioinformatics/btq228 + 10.1093/bioinformatics/btq228 + 1569 + 1571 + + + + + + JanssenRemie + + Rearranging phylogenetic networks + Delft University of Technology + 2021 + 978-94-6332-758-9 + https://doi.org/10.4233/uuid:1b713961-4e6d-4bb5-a7d0-37279084ee57 + 10.4233/uuid:1b713961-4e6d-4bb5-a7d0-37279084ee57 + + + + + + JanssenRemie + MurakamiYukihiro + + Linear time algorithm for tree-child network containment + International conference on algorithms for computational biology + Springer + 2020 + https://doi.org/10.1007/978-3-030-42266-0_8 + 10.1007/978-3-030-42266-0_8 + 93 + 107 + + + + + + PonsJoan Carles + ScornavaccaCeline + CardonaGabriel + + Generation of level-k LGT networks + IEEE/ACM transactions on computational biology and bioinformatics + IEEE + 2019 + 17 + 1 + https://doi.org/10.1109/TCBB.2019.2895344 + 10.1109/TCBB.2019.2895344 + 158 + 164 + + + + + + ZhangChi + OgilvieHuw A + DrummondAlexei J + StadlerTanja + + Bayesian inference of species networks from multilocus sequence data + Molecular biology and evolution + Oxford University Press + 2018 + 35 + 2 + https://doi.org/10.1093/molbev/msx307 + 10.1093/molbev/msx307 + 504 + 517 + + + + + + JanssenRemie + JonesMark + MurakamiYukihiro + + Combining networks using cherry picking sequences + International conference on algorithms for computational biology + Springer + 2020 + https://doi.org/10.1007/978-3-030-42266-0_7 + 10.1007/978-3-030-42266-0_7 + 77 + 92 + + + + + + KongSungsik + PonsJoan Carles + KubatkoLaura + WickeKristina + + Classes of explicit phylogenetic networks and their biological and mathematical significance + Journal of Mathematical Biology + Springer + 2022 + 84 + 6 + https://doi.org/10.1007/s00285-022-01746-y + 10.1007/s00285-022-01746-y + 47 + + + + + + + CardonaGabriel + RosselloFrancesc + ValienteGabriel + + Comparison of tree-child phylogenetic networks + IEEE/ACM Transactions on Computational Biology and Bioinformatics + 2009 + 6 + 4 + https://doi.org/10.1109/TCBB.2007.70270 + 10.1109/TCBB.2007.70270 + 552 + 569 + + + + + + HusonDaniel H + RuppRegula + ScornavaccaCeline + + Phylogenetic networks: Concepts, algorithms and applications + Cambridge University Press + 2010 + 9780521755962 + https://doi.org/10.1017/CBO9780511974076 + 10.1017/CBO9780511974076 + + + + + + ErdősPéter L. + SempleCharles + SteelMike + + A class of phylogenetic networks reconstructable from ancestral profiles + Mathematical Biosciences + 2019 + 313 + 0025-5564 + https://doi.org/10.1016/j.mbs.2019.04.009 + 10.1016/j.mbs.2019.04.009 + 33 + 40 + + + + + + BaiAllan + ErdősPéter L + SempleCharles + SteelMike + + Defining phylogenetic networks using ancestral profiles + Mathematical Biosciences + Elsevier + 2021 + 332 + https://doi.org/10.1016/j.mbs.2021.108537 + 10.1016/j.mbs.2021.108537 + 108537 + + + + + + + CardonaGabriel + PonsJoan Carles + RibasGerard + CoronadoTomas Martınez + + Comparison of orchard networks using their extended \mu-representation + IEEE/ACM Transactions on Computational Biology and Bioinformatics + IEEE + 2024 + https://doi.org/10.1109/TCBB.2024.3361390 + 10.1109/TCBB.2024.3361390 + + + + + + JanssenRemie + MurakamiYukihiro + + On cherry-picking and network containment + Theoretical Computer Science + Elsevier + 2021 + 856 + https://doi.org/10.1016/j.tcs.2020.12.031 + 10.1016/j.tcs.2020.12.031 + 121 + 150 + + + + + + IerselLeo van + JanssenRemie + JonesMark + MurakamiYukihiro + ZehNorbert + + A practical fixed-parameter algorithm for constructing tree-child networks from multiple binary trees + Algorithmica + Springer + 2022 + 84 + 4 + https://doi.org/10.1007/s00453-021-00914-8 + 10.1007/s00453-021-00914-8 + 917 + 960 + + + + + + LinzSimone + SempleCharles + + Attaching leaves and picking cherries to characterise the hybridisation number for a set of phylogenies + Advances in Applied Mathematics + Elsevier + 2019 + 105 + https://doi.org/10.1016/j.aam.2019.01.004 + 10.1016/j.aam.2019.01.004 + 102 + 129 + + + + + + IerselLeo van + JanssenRemie + JonesMark + MurakamiYukihiro + + Orchard networks are trees with additional horizontal arcs + Bulletin of Mathematical Biology + Springer + 2022 + 84 + 8 + https://doi.org/10.1007/s11538-022-01037-z + 10.1007/s11538-022-01037-z + 76 + + + + + + + FrançoisBienvenu + CardonaGabriel + CelineScornavacca + + Revisiting shao and sokal’s b 2 index of phylogenetic balance + Journal of Mathematical Biology + Springer Nature BV + 2021 + 83 + 5 + https://doi.org/10.1007/s00285-021-01662-7 + 10.1007/s00285-021-01662-7 + + + + + + JanssenRemie + LiuPengyu + + Comparing the topology of phylogenetic network generators + Journal of bioinformatics and computational biology + World Scientific + 2021 + 19 + 06 + https://doi.org/10.1142/S0219720021400126 + 10.1142/S0219720021400126 + 2140012 + + + + + + + CardonaGabriel + RossellóFrancesc + ValienteGabriel + + Extended newick: It is time for a standard representation of phylogenetic networks + BMC bioinformatics + Springer + 2008 + 9 + https://doi.org/10.1186/1471-2105-9-532 + 10.1186/1471-2105-9-532 + 1 + 8 + + + + + + BarnettRobert + + Rich newick format + Rich Newick Format - Phylonet - Rice University Campus Wiki + 201202 + https://wiki.rice.edu/confluence/display/PHYLONET/Rich+Newick+Format + + + + + + WenDingqiao + YuYun + ZhuJiafan + NakhlehLuay + + Inferring phylogenetic networks using PhyloNet + Systematic biology + Oxford University Press + 2018 + 67 + 4 + https://doi.org/10.1093/sysbio/syy015 + 10.1093/sysbio/syy015 + 735 + 740 + + + + + + WenDingqiao + YuYun + HahnMatthew W + NakhlehLuay + + Reticulate evolutionary history and extensive introgression in mosquito species revealed by phylogenetic network analysis + Molecular ecology + Wiley Online Library + 2016 + 25 + 11 + https://doi.org/10.1111/mec.13544 + 10.1111/mec.13544 + 2361 + 2372 + + + + + + YuYun + DongJianrong + LiuKevin J + NakhlehLuay + + Maximum likelihood inference of reticulate evolutionary histories + Proceedings of the National Academy of Sciences + National Acad Sciences + 2014 + 111 + 46 + https://doi.org/10.1073/pnas.1407950111 + 10.1073/pnas.1407950111 + 16448 + 16453 + + + + + + WenDingqiao + YuYun + NakhlehLuay + + Bayesian inference of reticulate phylogenies under the multispecies network coalescent + PLoS genetics + Public Library of Science San Francisco, CA USA + 2016 + 12 + 5 + https://doi.org/10.1073/pnas.1407950111 + 10.1073/pnas.1407950111 + e1006006 + + + + + + + BordewichMagnus + LinzSimone + SempleCharles + + Lost in space? Generalising subtree prune and regraft to spaces of phylogenetic networks + Journal of theoretical biology + Elsevier + 2017 + 423 + https://doi.org/10.1016/j.jtbi.2017.03.032 + 10.1016/j.jtbi.2017.03.032 + 1 + 12 + + + + + + KlawitterJonathan + + Spaces of phylogenetic networks + University of Auckland + 2020 + http://hdl.handle.net/2292/50188 + + + + + + GambettePhilippe + IerselLeo van + JonesMark + LafondManuel + PardiFabio + ScornavaccaCeline + + Rearrangement moves on rooted phylogenetic networks + PLoS computational biology + Public Library of Science San Francisco, CA USA + 2017 + 13 + 8 + https://doi.org/10.1371/journal.pcbi.1005611 + 10.1371/journal.pcbi.1005611 + e1005611 + + + + + + + JulienEsther + BernardiniGiulia + StougieLeen + IerselLeo van + + Source code for the paper "constructing phylogenetic networks via cherry picking and machine learning" + 4TU.ResearchData + 2023 + https://doi.org/10.4121/c679cd3c-0815-4021-a727-bcb8b9174b27.v1 + 10.4121/c679cd3c-0815-4021-a727-bcb8b9174b27.v1 + + + + + + BernardiniGiulia + IerselLeo van + JulienEsther + StougieLeen + + Constructing phylogenetic networks via cherry picking and machine learning + Algorithms for Molecular Biology + Springer + 2023 + 18 + 1 + https://doi.org/10.1186/s13015-023-00233-3 + 10.1186/s13015-023-00233-3 + 13 + + + + + + + ErdősPéter L. + FrancisAndrew + MezeiTamás Róbert + + Rooted NNI moves and distance-1 tail moves on tree-based phylogenetic networks + Discrete Applied Mathematics + 2021 + 294 + 0166-218X + https://www.sciencedirect.com/science/article/pii/S0166218X21000597 + 10.1016/j.dam.2021.02.016 + 205 + 213 + + + + +