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FastNet: Fast and Accurate Statistical Inference of Phylogenetic Networks Using Large-Scale Genomic Sequence Data

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Part of the Lecture Notes in Computer Science book series (LNCS, volume 11183)

Abstract

An emerging discovery in phylogenomics is that interspecific gene flow has played a major role in the evolution of many different organisms. To what extent is the Tree of Life not truly a tree reflecting strict “vertical” divergence, but rather a more general graph structure known as a phylogenetic network which also captures “horizontal” gene flow? The answer to this fundamental question not only depends upon densely sampled and divergent genomic sequence data, but also computational methods which are capable of accurately and efficiently inferring phylogenetic networks from large-scale genomic sequence datasets. Recent methodological advances have attempted to address this gap. However, in the 2016 performance study of Hejase and Liu, state-of-the-art methods fell well short of the scalability requirements of existing phylogenomic studies.

The methodological gap remains: how can phylogenetic networks be accurately and efficiently inferred using genomic sequence data involving many dozens or hundreds of taxa? In this study, we address this gap by proposing a new phylogenetic divide-and-conquer method which we call FastNet. We conduct a performance study involving a range of evolutionary scenarios, and we demonstrate that FastNet outperforms state-of-the-art methods in terms of computational efficiency and topological accuracy.

Keywords

Fastnet Phylogenetic Network Topological Accuracy Reticulation Edges Subproblem Decomposition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgments

We gratefully acknowledge the following support: NSF grants no. CCF-1565719 (to KJL), CCF-1714417 (to KJL), and DEB-1737898 (to GMB and KJL), BEACON grants (NSF STC Cooperative Agreement DBI-093954) to GMB and KJL, and computing resources provided by MSU HPCC. We would also like to acknowledge Daniel Neafsey for kindly sending us a processed version of the genomic sequence dataset from [36].

Supplementary material

473852_1_En_14_MOESM1_ESM.pdf (479 kb)
Supplementary material 1 (pdf 479 KB)

References

  1. 1.
    Abbott, R.J., Rieseberg, L.H.: Hybrid speciation. In: Seligman, E.R.A., Johnson, A. (eds.) Encyclopaedia of Life Sciences. Wiley, Hoboken (2012)Google Scholar
  2. 2.
    Akaike, H.: Information theory and an extension of the maximum likelihood principle. In: Parzen, E., Tanabe, K., Kitagawa, G. (eds.) Selected Papers of Hirotugu Akaike. Springer Series in Statistics (Perspectives in Statistics). Springer, New York (1998).  https://doi.org/10.1007/978-1-4612-1694-0_15CrossRefzbMATHGoogle Scholar
  3. 3.
    Akaike, H.: A new look at the statistical model identification. IEEE Trans. Autom. Control 19(6), 716–723 (1974)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Bandelt, H.-J., Dress, A.W.M.: A canonical decomposition theory for metrics on a finite set. Adv. Math. 92(1), 47–105 (1992)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Baroni, M., Semple, C., Steel, M.: Hybrids in real time. Syst. Biol. 55(1), 46–56 (2006)CrossRefGoogle Scholar
  6. 6.
    Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodological) 57(1), 289–300 (1995)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Bryant, D., Moulton, V.: Neighbor-Net: an agglomerative method for the construction of phylogenetic networks. Mol. Biol. Evol. 21(2), 255–265 (2004)CrossRefGoogle Scholar
  8. 8.
    Cardona, G., Rosselló, F., Valiente, G.: Tripartitions do not always discriminate phylogenetic networks. Math. Biosci. 211(2), 356–370 (2008)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Durand, E.Y., Patterson, N., Reich, D., Slatkin, M.: Testing for ancient admixture between closely related populations. Mol. Biol. Evol. 28(8), 2239–2252 (2011)CrossRefGoogle Scholar
  10. 10.
    Edwards, S.V.: Is a new and general theory of molecular systematics emerging? Evolution 63(1), 1–19 (2009)CrossRefGoogle Scholar
  11. 11.
    Felsenstein, J.: Cases in which parsimony or compatibility methods will be positively misleading. Syst. Biol. 27(4), 401–410 (1978)CrossRefGoogle Scholar
  12. 12.
    Felsenstein, J.: Inferring Phylogenies. Sinauer Associates, Sunderland, Massachusetts (2004)Google Scholar
  13. 13.
    Francis, A.R., Steel, M.: Which phylogenetic networks are merely trees with additional arcs? Syst. Biol. 64(5), 768–777 (2015)CrossRefGoogle Scholar
  14. 14.
    Gluck-Thaler, E., Slot, J.C.: Dimensions of horizontal gene transfer in eukaryotic microbial pathogens. PLoS Pathog. 11(10), e1005156 (2015)CrossRefGoogle Scholar
  15. 15.
    Green, R.E., et al.: A draft sequence of the Neandertal genome. Science 328(5979), 710–722 (2010)CrossRefGoogle Scholar
  16. 16.
    Hein, J., Schierup, M., Wiuf, C.: Gene Genealogies, Variation and Evolution: A Primer in Coalescent Theory. Oxford University Press, Oxford (2004)zbMATHGoogle Scholar
  17. 17.
    Hejase, H.A., Liu, K.J.: A scalability study of phylogenetic network inference methods using empirical datasets and simulations involving a single reticulation. BMC Bioinform. 17(1), 422 (2016)CrossRefGoogle Scholar
  18. 18.
    Hudson, R.R.: Generating samples under a wright-fisher neutral model of genetic variation. Bioinformatics 18(2), 337–338 (2002)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Huelsenbeck, J.P., Hillis, D.M.: Success of phylogenetic methods in the four-taxon case. Syst. Biol. 42(3), 247–264 (1993)CrossRefGoogle Scholar
  20. 20.
    Hurvich, C.M., Tsai, C.-L.: Regression and time series model selection in small samples. Biometrika 76(2), 297–307 (1989)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Huson, D.H., Rupp, R., Scornavacca, C.: Phylogenetic Networks: Concepts Algorithms and Applications. Cambridge University Press, Cambridge, United Kingdom (2010)CrossRefGoogle Scholar
  22. 22.
    Jukes, T.H., Cantor, C.R.: Evolution of Protein Molecules, p. 132. Academic Press, New York (1969)Google Scholar
  23. 23.
    Keeling, P.J., Palmer, J.D.: Horizontal gene transfer in eukaryotic evolution. Nat. Rev. Genet. 9(8), 605–618 (2008)CrossRefGoogle Scholar
  24. 24.
    Kingman, J.F.C.: The coalescent. Stoch. Process. Appl. 13(3), 235–248 (1982)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Leaché, A.D., Harris, R.B., Rannala, B., Yang, Z.: The influence of gene flow on species tree estimation: a simulation study. Syst. Biol. 63, 17–30 (2013)CrossRefGoogle Scholar
  26. 26.
    Liu, K., Raghavan, S., Nelesen, S., Linder, C.R., Warnow, T.: Rapid and accurate large-scale coestimation of sequence alignments and phylogenetic trees. Science 324(5934), 1561–1564 (2009)CrossRefGoogle Scholar
  27. 27.
    Liu, K., et al.: SATé-II: Very fast and accurate simultaneous estimation of multiple sequence alignments and phylogenetic trees. Syst. Biol. 61(1), 90–106 (2012)CrossRefGoogle Scholar
  28. 28.
    Liu, K.J., Steinberg, E., Yozzo, A., Song, Y., Kohn, M.H., Nakhleh, L.: Interspecific introgressive origin of genomic diversity in the house mouse. Proc. Nat. Acad. Sci. 112(1), 196–201 (2015)CrossRefGoogle Scholar
  29. 29.
    McInerney, J.O., Cotton, J.A., Pisani, D.: The prokaryotic tree of life: past, present... and future? Trends Ecol. Evol. 23(5), 276–281 (2008)CrossRefGoogle Scholar
  30. 30.
    Metzker, M.L.: Sequencing technologies - the next generation. Nat. Rev. Genet. 11(1), 31–46 (2010)CrossRefGoogle Scholar
  31. 31.
    Mirarab, S., Warnow, T.: ASTRAL-II: coalescent-based species tree estimation with many hundreds of taxa and thousands of genes. Bioinformatics 31(12), i44–i52 (2015)CrossRefGoogle Scholar
  32. 32.
    Mirarab, S., Reaz, R., Bayzid, M.S., Zimmermann, T., Swenson, M.S., Warnow, T.: ASTRAL: genome-scale coalescent-based species tree estimation. Bioinformatics 30(17), i541–i548 (2014)CrossRefGoogle Scholar
  33. 33.
    Mirarab, S., Nguyen, N., Guo, S., Wang, L.-S., Kim, J., Warnow, T.: PASTA: ultra-large multiple sequence alignment for nucleotide and amino-acid sequences. J. Comput. Biol. 22(5), 377–386 (2015)CrossRefGoogle Scholar
  34. 34.
    Nakhleh, L.: Computational approaches to species phylogeny inference and gene tree reconciliation. Trends Ecol. Evol. 28(12), 719–728 (2013)CrossRefGoogle Scholar
  35. 35.
    Nakhleh, L., Sun, J., Warnow, T., Linder, C.R., Moret, B.M., Tholse, A.: Towards the development of computational tools for evaluating phylogenetic network reconstruction methods. In: Pacific Symposium on Biocomputing, vol. 8, pp. 315–326. World Scientific (2003)Google Scholar
  36. 36.
    Neafsey, D.E.: Highly evolvable malaria vectors: the genomes of 16 Anopheles mosquitoes. Science 347(6217), 1258522 (2015)CrossRefGoogle Scholar
  37. 37.
    Price, M., Dehal, P., Arkin, A.: FastTree 2 - approximately maximum-likelihood trees for large alignments. PLoS ONE 5(3), e9490 (2010)CrossRefGoogle Scholar
  38. 38.
    Rambaut, A., Grassly, N.C.: Seq-Gen: an application for the Monte Carlo simulation of DNA sequence evolution along phylogenetic trees. Comput. Appl. Biosci. 13, 235–238 (1997)Google Scholar
  39. 39.
    Reich, D., et al.: Genetic history of an archaic hominin group from Denisova Cave in Siberia. Nature 468(7327), 1053–1060 (2010)CrossRefGoogle Scholar
  40. 40.
    Sanderson, M.J.: r8s: inferring absolute rates of molecular evolution and divergence times in the absence of a molecular clock. Bioinformatics 19(2), 301–302 (2003)CrossRefGoogle Scholar
  41. 41.
    Schwarz, G.: Estimating the dimension of a model. Annal. Stat. 6(2), 461–464 (1978)MathSciNetCrossRefGoogle Scholar
  42. 42.
    Solís-Lemus, C., Ané, C.: Inferring phylogenetic networks with maximum pseudo-likelihood under incomplete lineage sorting. PLoS Genet. 12(3), 1–21 (2016)CrossRefGoogle Scholar
  43. 43.
    Than, C., Ruths, D., Nakhleh, L.: PhyloNet: a software package for analyzing and reconstructing reticulate evolutionary relationships. BMC Bioinform. 9(1), 322 (2008)CrossRefGoogle Scholar
  44. 44.
    The Heliconious Genome Consortium: Butterfly genome reveals promiscuous exchange of mimicry adaptations among species. Nature 487(7405), 94–98 (2012)CrossRefGoogle Scholar
  45. 45.
    Yun, Y., Nakhleh, L.: A maximum pseudo-likelihood approach for phylogenetic networks. BMC Genomics 16(Suppl 10), S10 (2015)CrossRefGoogle Scholar
  46. 46.
    Yu, Y., Cuong, T., Degnan, J.H., Nakhleh, L.: Coalescent histories on phylogenetic networks and detection of hybridization despite incomplete lineage sorting. Syst. Biol. 60(2), 138–149 (2011)CrossRefGoogle Scholar
  47. 47.
    Yu, Y., Degnan, J.H., Nakhleh, L.: The probability of a gene tree topology within a phylogenetic network with applications to hybridization detection. PLoS Genet. 8(4), pp. e1002660 (2012)Google Scholar
  48. 48.
    Yu, Y., Dong, J., Liu, K.J., Nakhleh, L.: Maximum likelihood inference of reticulate evolutionary histories. Proc. Nat. Acad. Sci. 111(46), 16448–16453 (2014)CrossRefGoogle Scholar
  49. 49.
    Zhang, L.: On tree-based phylogenetic networks. J. Comput. Biol. 23(7), 553–565 (2016)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Simons Center for Quantitative Biology, Cold Spring Harbor LaboratoryCold Spring HarborUSA
  2. 2.Department of Plant, Soil and Microbial SciencesMichigan State UniversityEast LansingUSA
  3. 3.Department of Computer Science and EngineeringMichigan State UniversityEast LansingUSA

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