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Stefan Grünewald,
Vincent Moulton and
Andreas Spillner. Consistency of the QNet algorithm for generating planar split networks from weighted quartets. In DAM, Vol. 157(10):2325-2334, 2009. Keywords: abstract network, consistency, from quartets, phylogenetic network, phylogeny, Program QNet, reconstruction, software. Note: http://dx.doi.org/10.1016/j.dam.2008.06.038.
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"Phylogenetic networks are a generalization of evolutionary or phylogenetic trees that allow the representation of conflicting signals or alternative evolutionary histories in a single diagram. Recently the Quartet-Net or "QNet" method was introduced, a method for computing a special kind of phylogenetic network called a split network from a collection of weighted quartet trees (i.e. phylogenetic trees with 4 leaves). This can be viewed as a quartet analogue of the distance-based Neighbor-Net (NNet) method for constructing outer-labeled planar split networks. In this paper, we prove that QNet is a consistent method, that is, we prove that if QNet is applied to a collection of weighted quartets arising from a circular split weight function, then it will return precisely this function. This key property of QNet not only ensures that it is guaranteed to produce a tree if the input corresponds to a tree, and an outer-labeled planar split network if the input corresponds to such a network, but also provides the main guiding principle for the design of the method. © 2008 Elsevier B.V. All rights reserved."
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David Bryant,
Vincent Moulton and
Andreas Spillner. Consistency of the Neighbor-Net Algorithm. In AMB, Vol. 2(8), 2007. Keywords: abstract network, consistency, from distances, NeighborNet. Note: http://dx.doi.org/10.1186/1748-7188-2-8.
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"Background: Neighbor-Net is a novel method for phylogenetic analysis that is currently being widely used in areas such as virology, bacteriology, and plant evolution. Given an input distance matrix, Neighbor-Net produces a phylogenetic network, a generalization of an evolutionary or phylogenetic tree which allows the graphical representation of conflicting phylogenetic signals. Results: In general, any network construction method should not depict more conflict than is found in the data, and, when the data is fitted well by a tree, the method should return a network that is close to this tree. In this paper we provide a formal proof that Neighbor-Net satisfies both of these requirements so that, in particular, Neighbor-Net is statistically consistent on circular distances. © 2007 Bryant et al; licensee BioMed Central Ltd."
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