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Phylogenetic Network Dissimilarity Measures that Take Branch Lengths into Account

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Comparative Genomics (RECOMB-CG 2022)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13234))

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Abstract

Dissimilarity measures for phylogenetic trees have long been used for analyzing inferred trees and understanding the performance of phylogenetic methods. Given their importance, a wide array of such measures have been developed, some of which are based on the tree topologies alone, and others that also take branch lengths into account. Similarly, a number of dissimilarity measures of phylogenetic networks have been developed in the last two decades. However, to the best of our knowledge, all these measures are based solely on the topologies of phylogenetic networks and ignore branch lengths. In this paper, we propose two phylogenetic network dissimilarity measures that take both topology and branch lengths into account. We demonstrate the behavior of these two measures on pairs of related networks. Furthermore, we show how these measures can be used to cluster a set of phylogenetic networks obtained by an inference method, illustrating this application on the posterior sample of phylogenetic networks. Both measures are implemented in the publicly available software package PhyloNet.

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References

  1. Allen, B.L., Steel, M.: Subtree transfer operations and their induced metrics on evolutionary trees. Ann. Comb. 5(1), 1–15 (2001). https://doi.org/10.1007/s00026-001-8006-8

    Article  MathSciNet  MATH  Google Scholar 

  2. Avino, M., Ng, G.T., He, Y., Renaud, M.S., Jones, B.R., Poon, A.F.Y.: Tree shape-based approaches for the comparative study of cophylogeny. Ecol. Evol. 9(12), 6756–6771 (2019). https://doi.org/10.1002/ece3.5185

    Article  Google Scholar 

  3. Billera, L.J., Holmes, S.P., Vogtmann, K.: Geometry of the space of phylogenetic trees. Adv. Appl. Math. 27(4), 733–767 (2001)

    Article  MathSciNet  Google Scholar 

  4. Bouckaert, R., et al.: BEAST 2.5: an advanced software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 15(4), 1–28 (2019). https://doi.org/10.1371/journal.pcbi.1006650

    Article  Google Scholar 

  5. Bouvel, M., Gambette, P., Mansouri, M.: Counting phylogenetic networks of level 1 and 2. J. Math. Biol. 81(6), 1357–1395 (2020). https://doi.org/10.1007/s00285-020-01543-5

    Article  MathSciNet  MATH  Google Scholar 

  6. Cardona, G., Llabrés, M., Rosselló, F., Valiente, G.: Metrics for phylogenetic networks I: generalizations of the Robinson-Foulds metric. IEEE/ACM Trans. Comput. Biol. Bioinform. 6(1), 46–61 (2008)

    Article  Google Scholar 

  7. Cardona, G., Llabrés, M., Rosselló, F., Valiente, G.: Metrics for phylogenetic networks II: nodal and triplets metrics. IEEE/ACM Trans. Comput. Biol. Bioinform. 6(3), 454–469 (2008)

    Article  Google Scholar 

  8. Dinh, V., Bilge, A., Zhang, C., Matsen IV, F.A.: Probabilistic path hamiltonian Monte Carlo. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 70, pp. 1009–1018. PMLR (2017)

    Google Scholar 

  9. Elworth, R.A.L., Ogilvie, H.A., Zhu, J., Nakhleh, L.: Advances in computational methods for phylogenetic networks in the presence of hybridization. In: Warnow, T. (ed.) Bioinformatics and Phylogenetics. CB, vol. 29, pp. 317–360. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-10837-3_13

    Chapter  Google Scholar 

  10. Felsenstein, J.: The number of evolutionary trees. Syst. Zool. 27(1), 27–33 (1978)

    Article  Google Scholar 

  11. Gavryushkin, A., Drummond, A.J.: The space of ultrametric phylogenetic trees. J. Theor. Biol. 403, 197–208 (2016). https://doi.org/10.1016/j.jtbi.2016.05.001

    Article  MathSciNet  MATH  Google Scholar 

  12. Heled, J., Drummond, A.J.: Bayesian inference of species trees from multilocus data. Mol. Biol. Evol. 27(3), 570–580 (2009). https://doi.org/10.1093/molbev/msp274

    Article  Google Scholar 

  13. Hoffer, B.L.: Language borrowing and the indices of adaptability and receptivity. Intercult. Commun. Stud. 14(2), 53–72 (2005)

    Google Scholar 

  14. Hudson, R.R.: Generating samples under a Wright-Fisher neutral model of genetic variation. Bioinformatics 18(2), 337–338 (2002). https://doi.org/10.1093/bioinformatics/18.2.337

    Article  MathSciNet  Google Scholar 

  15. Jain, C., Rodriguez-R, L.M., Phillippy, A.M., Konstantinidis, K.T., Aluru, S.: High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9(1), 5114 (2018). https://doi.org/10.1038/s41467-018-07641-9

    Article  Google Scholar 

  16. Kannan, L., Wheeler, W.: Maximum parsimony on phylogenetic networks. Algorithms Mol. Biol. 7, 9 (2012). https://doi.org/10.1186/1748-7188-7-9

    Article  Google Scholar 

  17. Koskela, J.: Zig-zag sampling for discrete structures and non-reversible phylogenetic MCMC. J. Comput. Graph. Stat. (2022). https://doi.org/10.1080/10618600.2022.2032722. Accepted for publication

  18. Kuhner, M.K., Felsenstein, J.: A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Mol. Biol. Evol. 11(3), 459–468 (1994)

    Google Scholar 

  19. Maddison, D.R.: The discovery and importance of multiple islands of most-parsimonious trees. Syst. Biol. 40(3), 315–328 (1991)

    Article  Google Scholar 

  20. Michael, T.P., VanBuren, R.: Building near-complete plant genomes. Curr. Opin. Plant Biol. 54, 26–33 (2020). https://doi.org/10.1016/j.pbi.2019.12.009. Genome studies and molecular genetics

    Article  Google Scholar 

  21. Nakhleh, L.: Evolutionary phylogenetic networks: models and issues. In: Heath, L., Ramakrishnan, N. (eds.) Problem Solving Handbook in Computational Biology and Bioinformatics, pp. 125–158. Springer, Boston (2010). https://doi.org/10.1007/978-0-387-09760-2_7

    Chapter  Google Scholar 

  22. Nakhleh, L.: A metric on the space of reduced phylogenetic networks. IEEE/ACM Trans. Comput. Biol. Bioinform. 7(2), 218–222 (2010). https://doi.org/10.1109/TCBB.2009.2

    Article  Google Scholar 

  23. Pardi, F., Scornavacca, C.: Reconstructible phylogenetic networks: do not distinguish the indistinguishable. PLoS Comput. Biol. 11(4), 1–23 (2015). https://doi.org/10.1371/journal.pcbi.1004135

    Article  Google Scholar 

  24. Rambaut, A., Grass, N.C.: Seq-Gen: an application for the Monte Carlo simulation of DNA sequence evolution along phylogenetic trees. Bioinformatics 13(3), 235–238 (1997). https://doi.org/10.1093/bioinformatics/13.3.235

    Article  Google Scholar 

  25. Rhie, A., et al.: Towards complete and error-free genome assemblies of all vertebrate species. Nature 592(7856), 737–746 (2021). https://doi.org/10.1038/s41586-021-03451-0

    Article  Google Scholar 

  26. Robinson, D., Foulds, L.: Comparison of phylogenetic trees. Math. Biosci. 53(1), 131–147 (1981). https://doi.org/10.1016/0025-5564(81)90043-2

    Article  MathSciNet  MATH  Google Scholar 

  27. Rokas, A., Williams, B.L., King, N., Carroll, S.B.: Genome-scale approaches to resolving incongruence in molecular phylogenies. Nature 425(6960), 798–804 (2003)

    Article  Google Scholar 

  28. Steel, M.A., Penny, D.: Distributions of tree comparison metrics-some new results. Syst. Biol. 42(2), 126–141 (1993)

    Google Scholar 

  29. Stockham, C., Wang, L.S., Warnow, T.: Statistically based postprocessing of phylogenetic analysis by clustering. Bioinformatics 18(suppl_1), S285–S293 (2002)

    Google Scholar 

  30. Swofford, D., Olson, G., Waddell, P., Hillis, D.: Phylogenetic inference. In: Hillis, D., Moritz, C., Mable, B. (eds.) Molecular Systematics, pp. 407–514. Sinauer Associates, Sunderland (2004). Chap. 11

    Google Scholar 

  31. Than, C., Ruths, D., Nakhleh, L.: PhyloNet: a software package for analyzing and reconstructing reticulate evolutionary relationships. BMC Bioinform. 9(1), 1–16 (2008). https://doi.org/10.1186/1471-2105-9-322

    Article  Google Scholar 

  32. Wen, D., Nakhleh, L.: Coestimating reticulate phylogenies and gene trees from multilocus sequence data. Syst. Biol. 67(3), 439–457 (2017). https://doi.org/10.1093/sysbio/syx085

    Article  Google Scholar 

  33. Wen, D., Yu, Y., Zhu, J., Nakhleh, L.: Inferring phylogenetic networks using PhyloNet. Syst. Biol. 67(4), 735–740 (2018)

    Article  Google Scholar 

  34. Whidden, C., Matsen, F.A., IV.: Quantifying MCMC exploration of phylogenetic tree space. Syst. Biol. 64(3), 472–491 (2015). https://doi.org/10.1093/sysbio/syv006

    Article  Google Scholar 

  35. Zhang, C., Ogilvie, H.A., Drummond, A.J., Stadler, T.: Bayesian inference of species networks from multilocus sequence data. Mol. Biol. Evol. 35(2), 504–517 (2017). https://doi.org/10.1093/molbev/msx307

    Article  Google Scholar 

  36. Zhu, J., Yu, Y., Nakhleh, L.: In the light of deep coalescence: revisiting trees within networks. BMC Bioinform. 17(14), 415 (2016). https://doi.org/10.1186/s12859-016-1269-1

    Article  Google Scholar 

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Acknowledgements

We thank Zhen Cao for contributing the MCMC posterior sample files for the simulated data set. This work was supported in part by NSF grants CCF-1514177, CCF-1800723 and DBI-2030604 to L.N.

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Authors and Affiliations

  1. Rice University, Houston, TX, 77005, USA

    Berk A. Yakici, Huw A. Ogilvie & Luay Nakhleh

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  1. Berk A. Yakici
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  2. Huw A. Ogilvie
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  3. Luay Nakhleh
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Correspondence to Luay Nakhleh .

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Editors and Affiliations

  1. University of Saskatchewan, Saskatoon, SK, Canada

    Lingling Jin

  2. Carnegie Mellon University, Pittsburgh, PA, USA

    Dannie Durand

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Yakici, B.A., Ogilvie, H.A., Nakhleh, L. (2022). Phylogenetic Network Dissimilarity Measures that Take Branch Lengths into Account. In: Jin, L., Durand, D. (eds) Comparative Genomics. RECOMB-CG 2022. Lecture Notes in Computer Science(), vol 13234. Springer, Cham. https://doi.org/10.1007/978-3-031-06220-9_6

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  • DOI: https://doi.org/10.1007/978-3-031-06220-9_6

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