scholarly journals Likelihood-Based Inference of Phylogenetic Networks from Sequence Data by PhyloDAG

2015 ◽  
pp. 126-140 ◽  
Author(s):  
Quan Nguyen ◽  
Teemu Roos
Keyword(s):  
Sequence Data ◽  
Phylogenetic Networks

scholarly journals Fast and accurate statistical inference of phylogenetic networks using large-scale genomic sequence data

2017 ◽  
Author(s):  
Hussein A. Hejase ◽  
Natalie VandePol ◽  
Gregory M. Bonito ◽  
Kevin J. Liu
Keyword(s):  
Gene Flow ◽  
Large Scale ◽  
Genomic Sequence ◽  
State Of The Art ◽  
Sequence Data ◽  
Phylogenetic Network ◽  
Phylogenetic Networks ◽  
Divide And Conquer ◽  
Performance Study ◽  
Art Methods

AbstractAn 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 compu-tational methods which are capable of accurately and efficiently inferring phylogenetic networks from large-scale genomic sequence datasets. Re-cent methodological advances have attempted to address this gap. How-ever, in the 2016 performance study of Hejase and Liu, state-of-the-art methods fell well short of the scalability requirements of existing phy-logenomic studies.The methodological gap remains: how can phylogenetic networks be ac-curately 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 evolu-tionary scenarios, and we demonstrate that FastNet outperforms state-of-the-art methods in terms of computational efficiency and topological accuracy.

scholarly journals Inference of Species Phylogenies from Bi-allelic Markers Using Pseudo-likelihood

2018 ◽  
Author(s):  
Jiafan Zhu ◽  
Luay Nakhleh
Keyword(s):  
Network Inference ◽  
Sequence Data ◽  
Phylogenetic Network ◽  
Simulated Data ◽  
Biological Data ◽  
Phylogenetic Networks ◽  
Gene Trees ◽  
Multispecies Coalescent ◽  
Pseudo Likelihood ◽  
Computational Bottleneck

AbstractMotivationPhylogenetic networks represent reticulate evolutionary histories. Statistical methods for their inference under the multispecies coalescent have recently been developed. A particularly powerful approach uses data that consist of bi-allelic markers (e.g., single nucleotide polymorphism data) and allows for exact likelihood computations of phylogenetic networks while numerically integrating over all possible gene trees per marker. While the approach has good accuracy in terms of estimating the network and its parameters, likelihood computations remain a major computational bottleneck and limit the method’s applicability.ResultsIn this paper, we first demonstrate why likelihood computations of networks take orders of magnitude more time when compared to trees. We then propose an approach for inference of phylo-genetic networks based on pseudo-likelihood using bi-allelic markers. We demonstrate the scalability and accuracy of phylogenetic network inference via pseudo-likelihood computations on simulated data. Furthermore, we demonstrate aspects of robustness of the method to violations in the underlying assumptions of the employed statistical model. Finally, we demonstrate the application of the method to biological data. The proposed method allows for analyzing larger data sets in terms of the numbers of taxa and reticulation events. While pseudo-likelihood had been proposed before for data consisting of gene trees, the work here uses sequence data directly, offering several advantages as we discuss.AvailabilityThe methods have been implemented in PhyloNet (http://bioinfocs.rice.edu/phylonet)[email protected], [email protected]

Diversification of the North American Doellingeria-Eucephalus Clade (Astereae: Asteraceae) Inferred from Molecular and Morphological Evidence

2019 ◽  
Vol 44 (4) ◽  
pp. 930-942
Author(s):  
Geraldine A. Allen ◽  
Luc Brouillet ◽  
John C. Semple ◽  
Heidi J. Guest ◽  
Robert Underhill
Keyword(s):  
North American ◽  
Sequence Data ◽  
Phylogenetic Analyses ◽  
Sister Group ◽  
Morphological Evidence ◽  
South American ◽  
New Combinations ◽  
The North ◽  
Hybridization Event ◽  
Ancient Hybridization

Abstract—Doellingeria and Eucephalus form the earliest-diverging clade of the North American Astereae lineage. Phylogenetic analyses of both nuclear and plastid sequence data show that the Doellingeria-Eucephalus clade consists of two main subclades that differ from current circumscriptions of the two genera. Doellingeria is the sister group to E. elegans, and the Doellingeria + E. elegans subclade in turn is sister to the subclade containing all remaining species of Eucephalus. In the plastid phylogeny, the two subclades are deeply divergent, a pattern that is consistent with an ancient hybridization event involving ancestral species of the Doellingeria-Eucephalus clade and an ancestral taxon of a related North American or South American group. Divergence of the two Doellingeria-Eucephalus subclades may have occurred in association with northward migration from South American ancestors. We combine these two genera under the older of the two names, Doellingeria, and propose 12 new combinations (10 species and two varieties) for all species of Eucephalus.

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