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Open AccessJournal ArticleDOI

Neighbor-Joining Revealed

Olivier Gascuel, +1 more
- 01 Nov 2006 - 
- Vol. 23, Iss: 11, pp 1997-2000
TLDR
A rigorous answer to this question has recently been provided by further mathematical investigation, and the purpose of this note is to highlight these results and their significance for interpreting NJ.
Abstract
It is nearly 20 years since the landmark paper (Saitou and Nei, 1987) in MBE introducing Neighbor-Joining (NJ). The method has become the most widely-used method for building phylogenetic trees from distances, and the original paper has been cited about 13,000 times (Science Citation Index ). Yet the question 'what does the NJ method seek to do?' has until recently proved somewhat elusive, leading to some imprecise claims and misunderstanding. However a rigorous answer to this question has recently been provided by further mathematical investigation, and the purpose of this note is to highlight these results and their significance for interpreting NJ. The origins of this story lie in a paper by Pauplin (2000) though its continuation has unfolded in more mathematically-inclined literature. Our aim here is to make these findings more widely accessible.

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Citations
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Journal ArticleDOI

FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix

TL;DR: FastTree is a method for constructing large phylogenies and for estimating their reliability, instead of storing a distance matrix, that uses sequence profiles of internal nodes in the tree to implement Neighbor-Joining and uses heuristics to quickly identify candidate joins.
Journal Article

Fast Tree: Computing Large Minimum-Evolution Trees with Profiles instead of a Distance Matrix

TL;DR: FastTree as mentioned in this paper uses sequence profiles of internal nodes in the tree to implement neighbor-joining and uses heuristics to quickly identify candidate joins, then uses nearest-neighbor interchanges to reduce the length of the tree.
Journal ArticleDOI

TYGS is an automated high-throughput platform for state-of-the-art genome-based taxonomy

TL;DR: TYGS, the Type (Strain) Genome Server, a user-friendly high-throughput web server for genome-based prokaryote taxonomy and analysis connected to a large, continuously growing database of genomic, taxonomic and nomenclatural information.
Journal ArticleDOI

FastME 2.0: A Comprehensive, Accurate, and Fast Distance-Based Phylogeny Inference Program

TL;DR: FastME as discussed by the authors is based on balanced minimum evolution, which is the very principle of Neighbor Joining (NJ). FastME improves over NJ by performing topological moves using fast, sophisticated algorithms.

Brief Communication FastME 2.0: A Comprehensive, Accurate, and Fast Distance-Based Phylogeny Inference Program

TL;DR: The new 2.0 version of FastME includes Subtree Pruning and Regrafting, while remaining as fast as NJ and providing a number of facilities: Distance estimation for DNA and proteins with various models and options, bootstrapping, and parallel computations.
References
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Journal ArticleDOI

The neighbor-joining method: a new method for reconstructing phylogenetic trees.

TL;DR: The neighbor-joining method and Sattath and Tversky's method are shown to be generally better than the other methods for reconstructing phylogenetic trees from evolutionary distance data.
Journal ArticleDOI

A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences.

TL;DR: Some examples were worked out using reported globin sequences to show that synonymous substitutions occur at much higher rates than amino acid-altering substitutions in evolution.
Book

Molecular Evolution and Phylogenetics

TL;DR: This chapter discusses the molecular basis of evolution, the evolution of organisms based on the fossil record, and the implications of these events for phylogenetic inference.
Journal ArticleDOI

Construction of Phylogenetic Trees

Walter M. Fitch, +1 more
- 20 Jan 1967 - 
Journal ArticleDOI

A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates.

TL;DR: Parsimony and compatibility had particular difficulty with inaccuracy and bias when substitution rates varied among different branches, and maximum likelihood was the most successful method overall, although for short sequences Fitch-Margoliash and neighbor joining were sometimes better.
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