Consistency of the Neighbor-Net Algorithm
David Bryant1, Vincent Moulton2 and Andreas Spillner2
1Department of Mathematics, University of Auckland, Private Bag 92019, Auckland, NZ
2School of Computing Sciences, University of East Anglia, Norwich, NR4 7TJ, UK
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.
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 requirewments so that, in particular, Neighbor-Net is statistically consistent on circular distances.
Algorithms for Molecular Biology 2007, 2:8. This is an Open Access article distributed under the terms of the Creative Commons Attribution License.