Phylogenetic network

A phylogenetic network is any graph used to visualize evolutionary relationships (either abstractly or explicitly)[1] between nucleotide sequences, genes, chromosomes, genomes, or species.[2] They are employed when reticulation events such as hybridization, horizontal gene transfer, recombination, or gene duplication and loss are believed to be involved. They differ from phylogenetic trees by the explicit modeling of richly linked networks, by means of the addition of hybrid nodes (nodes with two parents) instead of only tree nodes (a hierarchy of nodes, each with only one parent).[3] Phylogenetic trees are a subset of phylogenetic networks. Phylogenetic networks can be inferred and visualised with software such as SplitsTree,[4] the R-package, phangorn,[5][6] and, more recently, Dendroscope. A standard format for representing phylogenetic networks is a variant of Newick format which is extended to support networks as well as trees.[7]

Many kinds and subclasses of phylogenetic networks have been defined based on the biological phenomenon they represent or which data they are built from (hybridization networks, usually built from rooted trees, ancestral recombination graphs (ARGs) from binary sequences, median networks from a set of splits, optimal realizations and reticulograms from a distance matrix), or restrictions to get computationally tractable problems (galled trees, and their generalizations level-k phylogenetic networks, tree-child or tree-sibling phylogenetic networks).

Microevolution

Phylogenetic trees also have trouble depicting microevolutionary events, for example the geographical distribution of muskrat or fish populations of a given species among river networks, because there is no species boundary to prevent gene flow between populations. Therefore, a more general phylogenetic network better depicts these situations.[8]

Rooted vs unrooted
Unrooted phylogenetic network
Let X be a set of taxa. An unrooted phylogenetic network N on X is any undirected graph whose leaves are bijectively labeled by the taxa in X.

A number of different types of unrooted phylogenetic networks are in use like split networks and quasi-median networks. In most cases, such networks only depict relations between taxa, without giving information about the evolutionary history. Although some methods produce unrooted networks that can be interpreted as undirected versions of rooted networks, which do represent a phylogeny.

Rooted phylogenetic network
Let X be a set of taxa. A rooted phylogenetic network N on X is a rooted directed acyclic graph where the set of leaves is bijectively labeled by the taxa in X.

Rooted phylogenetic networks, like rooted phylogenetic trees, give explicit representations of evolutionary history. This means that they visualize the order in which the species diverged (speciated), converged (hybridized), and transferred genetic material (horizontal gene transfer).

Classes of networks

For computational purposes, studies often restrict their attention to classes of networks: subsets of all networks with certain properties. Although computational simplicity is the main goal, most of these classes have a biological justification as well. Some prominent classes currently used in the mathematical phylogenetics literature are tree-child networks,[9] tree-based networks,[10] and level-k networks[11][12]

Software to compute phylogenetic networks

References
  1. Huson DH, Scornavacca C (2011). "A survey of combinatorial methods for phylogenetic networks". Genome Biology and Evolution. 3: 23–35. doi:10.1093/gbe/evq077. PMC 3017387. PMID 21081312.
  2. Huson DH, Rupp R, Scornavacca C (2010). Phylogenetic Networks. Cambridge University Press. Archived from the original on 2014-07-14. Retrieved 2010-03-23.
  3. Arenas M, Valiente G, Posada D (December 2008). "Characterization of reticulate networks based on the coalescent with recombination". Molecular Biology and Evolution. 25 (12): 2517–20. doi:10.1093/molbev/msn219. PMC 2582979. PMID 18927089.
  4. Huson DH, Bryant D (February 2006). "Application of phylogenetic networks in evolutionary studies". Molecular Biology and Evolution. 23 (2): 254–67. doi:10.1093/molbev/msj030. PMID 16221896.
  5. Schliep K, Potts AJ, Morrison DA, Grimm GW (2017). "Intertwining phylogenetic trees and networks". Methods in Ecology and Evolution. 8 (10): 1212–1220. doi:10.1111/2041-210X.12760.
  6. Schliep KP (2018). "R package: Estimating phylogenetic trees with phangorn" (PDF).
  7. Cardona G, Rosselló F, Valiente G (December 2008). "Extended Newick: it is time for a standard representation of phylogenetic networks". BMC Bioinformatics. 9: 532. doi:10.1186/1471-2105-9-532. PMC 2621367. PMID 19077301.
  8. Legendre P, Makarenkov V (April 2002). "Reconstruction of biogeographic and evolutionary networks using reticulograms". Systematic Biology. 51 (2): 199–216. doi:10.1080/10635150252899725. PMID 12028728.
  9. Cardona G, Rosselló F, Valiente G (October 2009). "Comparison of tree-child phylogenetic networks". IEEE/ACM Transactions on Computational Biology and Bioinformatics. 6 (4): 552–69. arXiv:0708.3499. doi:10.1109/TCBB.2007.70270. hdl:2117/7146. PMID 19875855.
  10. Francis AR, Steel M (September 2015). "Which Phylogenetic Networks are Merely Trees with Additional Arcs?". Systematic Biology. 64 (5): 768–77. doi:10.1093/sysbio/syv037. PMC 4538883. PMID 26070685.
  11. Choy C, Jansson J, Sadakane K, Sung WK (2005-05-20). "Computing the maximum agreement of phylogenetic networks". Theoretical Computer Science. Pattern Discovery in the Post Genome. 335 (1): 93–107. doi:10.1016/j.tcs.2004.12.012. ISSN 0304-3975.
  12. "ISIPhyNC - Information System on Inclusions of Phylogenetic Network Classes". phylnet.univ-mlv.fr. Retrieved 2019-06-13.
  13. Arenas M, Patricio M, Posada D, Valiente G (May 2010). "Characterization of phylogenetic networks with NetTest". BMC Bioinformatics. 11: 268. doi:10.1186/1471-2105-11-268. PMC 2880032. PMID 20487540.
  14. Samson, Stéphane; Lord, Étienne; Makarenkov, Vladimir (26 May 2022). "SimPlot++: a Python application for representing sequence similarity and detecting recombination". Bioinformatics. 38 (11): 3118–3120. doi:10.1093/bioinformatics/btac287.

Further reading
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