FastNet: Fast and Accurate Statistical Inference of Phylogenetic Networks Using Large-Scale Genomic Sequence Data
- 1 Downloads
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.
KeywordsFastnet Phylogenetic Network Topological Accuracy Reticulation Edges Subproblem Decomposition
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 .
- 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.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
- 12.Felsenstein, J.: Inferring Phylogenies. Sinauer Associates, Sunderland, Massachusetts (2004)Google Scholar
- 22.Jukes, T.H., Cantor, C.R.: Evolution of Protein Molecules, p. 132. Academic Press, New York (1969)Google Scholar
- 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
- 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
- 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