2007
DOI: 10.1186/1748-7188-2-8
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Consistency of the Neighbor-Net Algorithm

Abstract: 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.

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Cited by 36 publications
(39 citation statements)
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References 18 publications
(18 reference statements)
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“…The split (NeighborNet) network [24][26] from the masked alignment obviously showed less conflict than the split network from the unmasked alignment, especially within subfamilies of formicoids. Nevertheless, conflicting signal is obvious, e.g.…”
Section: Resultsmentioning
confidence: 93%
“…The split (NeighborNet) network [24][26] from the masked alignment obviously showed less conflict than the split network from the unmasked alignment, especially within subfamilies of formicoids. Nevertheless, conflicting signal is obvious, e.g.…”
Section: Resultsmentioning
confidence: 93%
“…In the present study the effect of recombination cannot be properly assessed because of the limited sampling. However, we cannot rule out the possibility of recombination, which can lead to evolutionary histories that are non-tree-like with reticulate patterns (Bryant & al., 2007), such as those found in the NN analysis ( Figs. 3 & 4).…”
Section: Discussionmentioning
confidence: 99%
“…Then the agglomerated nodes are expanded to produce the planar splits graph that represents the desired phylogenetic network. The aggregation procedure of the Nnet algorithm implicitly defines a circular split system, which can be shown to be consistent in the sense that for any distance matrix that is a linear combination of split metrics deriving from a circular split system, Nnet recovers the original circular split system, see [19] for the mathematical details. It has been observed that phylogenetic distance data are often circular or at most mildly non-circular, see e.g.…”
Section: Methodsmentioning
confidence: 99%