Hybrids in Real Time

Baroni, Mihaela; Semple, Charles; Steel, Mike

doi:10.1080/10635150500431197

Cited by 90 publications

(115 citation statements)

References 13 publications

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“…However, as noted in Baroni et al (2006), one can always make such a network temporal by allowing for additional taxa that do not introduce new reticulation vertices. Such taxa may correspond to unsampled or extinct taxa.…”

Section: Modeling Reticulate Evolutionmentioning

confidence: 99%

“…It is well-known and easily proved that, as C N is acyclic, it has a vertex v with indegree zero. Let (v, v Baroni et al (2006) have provided the algorithm TempRep which determines whether a given hybridization network N has a temporal representation or not. To describe the idea of TempRep, let V be the vertex set of N .…”

Section: Proposition 33 Let N Be An Hgt Network Then N Has a Tempormentioning

confidence: 99%

“…This algorithm takes two rooted binary phylogenetic X -trees T and T , and an ordering O of an acyclic-agreement forest F of T and T as input, and outputs either (i) a temporal hybridization network on X that displays T and T , and preserves O, or (ii) a statement indicating that there is no such network. Baroni et al (2006), have previously presented a similar algorithm. Called HybridPhylogeny, this algorithm has the same input as TemporalHybrid but without an ordering O of F. The task for HybridPhylogeny is simply to construct one of potentially many hybridization networks that display T and T .…”

Section: The Algorithm Temporalhybridmentioning

confidence: 99%

“…By allowing for additional taxa that for instance correspond to unsampled or extinct taxa one can always transform a non-temporal reticulation network into a temporal one without introducing any new reticulation events (Baroni et al 2006;Moret et al 2004). For example, consider the reticulation network shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…For the purposes of the introduction, simply think of the forest as a smallest collection of (disjoint) subtrees common to T and T . From this forest, the algorithm HybridPhylogeny (Baroni et al 2006) can be applied to reconstruct a hybridization network that explains T and T , and in which the number of hybridization events equates to the size of the forest. However, despite its practical application, HybridPhylogeny does not guarantee that the resulting network is temporal.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing and reconstructing reticulation networks under timing constraints

2009

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Reticulation networks are now frequently used to model the history of life for various groups of species whose evolutionary past is likely to include reticulation events such as horizontal gene transfer or hybridization. However, the reconstructed networks are rarely guaranteed to be temporal. If a reticulation network is temporal, then it satisfies the two biologically motivated timing constraints of instantaneously occurring reticulation events and successively occurring speciation events. On the other hand, if a reticulation network is not temporal, it is always possible to make it temporal by adding a number of additional unsampled or extinct taxa. In the first half of the paper, we show that deciding whether a given number of additional taxa is sufficient to transform a non-temporal reticulation network into a temporal one is an NP-complete problem. As one is often given a set of gene trees instead of a network in the context of hybridization, this motivates the second half of the paper which provides an algorithm, called TemporalHybrid, for reconstructing a temporal hybridization network that simultaneously explains the ancestral history of two trees or indicates that no such network exists. We further derive two methods to decide whether or not a temporal hybridization network exists for two given trees and illustrate one of the methods on a grass data set.All authors contributed equally to this work.

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Section: Modeling Reticulate Evolutionmentioning

confidence: 99%

Section: Proposition 33 Let N Be An Hgt Network Then N Has a Tempormentioning

confidence: 99%

Section: The Algorithm Temporalhybridmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analyzing and reconstructing reticulation networks under timing constraints

2009

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Phylogenetics: Parsimony, Networks, and Distance Methods

Penny

Hendy

Holland

2007

Handbook of Statistical Genetics

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The accurate recovery of evolutionary trees is a major problem in mathematical inference. The relevant theory comes from mathematics (graph theory, combinatorics, and Markov chains), statistics (likelihood, Bayesian methods, resampling, and exploratory data analysis), operations research (optimisation, and search heuristics), and computer science (complexity theory). Different ways of presenting molecular data are described and a consistent terminology is presented. The problem of the combinatorial explosion in the numbers of potential trees leads to a description of the complexity of algorithms. The use of simple Markov models for studying the evolution of DNA sequences is described. Methods for inferring trees are analysed under the three components of, an optimality criterion, searching tree space, and assumptions or knowledge about the mechanism of evolution (which can allow compensation for multiple (unobserved) changes at a site). The analysis of methods in this chapter concentrates on parsimony and distance methods for inferring trees, but also discusses the limited range of cases when maximum parsimony and maximum likelihood are equivalent. A section describes the use of networks to represent the complexities in the data or to summarise the variability of the output trees. An overview of tree-building methods includes several strategies for searching tree space, from complete searches, branch-and-bound searches, to different forms of heuristic approaches. INTRODUCTIONThe mathematics behind phylogenetic programs is interesting in that it comes from several areas of pure and applied mathematics, both discrete and continuous. These include graph theory, combinatorics, and Markov chains from mathematics, together with statistics (likelihood, Bayesian analysis, resampling), operations research (optimisation, search heuristics), and computer science (complexity theory). Fortunately, the underlying concepts are relatively simple (even if the mathematics is complex) and in this chapter we only consider the concepts. Our description of the mathematics is informal, more formal 489 Handbook of Statistical Genetics, Third Edition . E dited by D . J. Balding, M . Bishop and C. Cannings.

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Paraphyletic groups as natural units of biological classification

Hörandl

Stuessy

2010

TAXON

142

110

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Despite the broad acceptance of phylogenetic principles in biological classification, a fundamental question still exists on how to classify paraphyletic groups. Much of the controversy appears due to (1) historical shifts in terminology and definitions, (2) neglect of focusing on evolutionary processes for understanding origins of natural taxa, (3) a narrow perspective on dimensions involved with reconstructing phylogeny, and (4) acceptance of lower levels of information content and practicability as a trade‐off for ease of arriving at formal classifications. Monophyly in evolutionary biology originally had a broader definition, that of describing a group with common ancestry. This definition thus includes both paraphyletic and monophyletic groups in the sense of Hennig. We advocate returning to a broader definition, supporting use of Ashlock's term holophyly as replacement for monophyly s.str. By reviewing processes involved in the production of phylogenetic patterns (budding, merging, and splitting), we demonstrate that paraphyly is a natural transitional stage in the evolution of taxa, and that it occurs regularly along with holophyly. When a new holophyletic group arises, it usually coexists for some time with its paraphyletic stem group. Paraphyly and holophyly, therefore, represent relational and temporal evolutionary stages. Paraphyletic groups exist at all levels of diversification in all kingdoms of eukaryotes, and they have traditionally been recognized because of their descent‐based similarity. We review different methodological approaches for recognition of monophyletic groups s.l. (i.e., both holophyletic and paraphyletic), which are essential for discriminating from polyphyly that is unacceptable in classification. For arriving at taxonomic decisions, natural processes, information content, and practicability are essential criteria. We stress using shared descent as a primary grouping principle, but also emphasize the importance of degrees of divergence plus similarity (cohesiveness of evolutionary features) as additional criteria for classification.

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Hybrids in Real Time

Cited by 90 publications

References 13 publications

Analyzing and reconstructing reticulation networks under timing constraints

Analyzing and reconstructing reticulation networks under timing constraints

Phylogenetics: Parsimony, Networks, and Distance Methods

Paraphyletic groups as natural units of biological classification

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