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In bioinformatics, neighbor joining is a bottom-up (agglomerative) clustering method for the creation of phylogenetic trees, created by Naruya Saitou and Masatoshi Nei in 1987. [1] Usually based on DNA or protein sequence data, the algorithm requires knowledge of the distance between each pair of taxa (e.g., species or sequences) to create the ...
Neighbor-joining methods apply general data clustering techniques to sequence analysis using genetic distance as a clustering metric. The simple neighbor-joining method produces unrooted trees, but it does not assume a constant rate of evolution (i.e., a molecular clock ) across lineages.
This method works by analyzing the sequences as a whole and using the UPGMA/neighbor-joining method to generate a distance matrix. A guide tree is calculated from the scores of the sequences in the matrix, then subsequently used to build the multiple sequence alignment by progressively aligning the sequences in order of similarity. [15]
Neighbor-joining methods apply general cluster analysis techniques to sequence analysis using genetic distance as a clustering metric. The simple neighbor-joining method produces unrooted trees, but it does not assume a constant rate of evolution (i.e., a molecular clock ) across lineages.
List of phylogenetics software. This list of phylogenetics software is a compilation of computational phylogenetics software used to produce phylogenetic trees. Such tools are commonly used in comparative genomics, cladistics, and bioinformatics. Methods for estimating phylogenies include neighbor-joining, maximum parsimony (also simply ...
Neighbor joining may be viewed as a greedy heuristic for the balanced minimum evolution (BME) criterion. Saito and Nei's 1987 NJ algorithm far predates the BME criterion of 2000. For two decades, researchers used NJ without a firm theoretical basis for why it works.
Neighbor-net. An example of a neighbor-net phylogenetic network generated by SplitsTree v4.6. NeighborNet[ 1] is an algorithm for constructing phylogenetic networks which is loosely based on the neighbor joining algorithm. Like neighbor joining, the method takes a distance matrix as input, and works by agglomerating clusters.
The simplest tree-rearrangement, known as nearest-neighbor interchange, exchanges the connectivity of four subtrees within the main tree. Because there are three possible ways of connecting four subtrees, [ 1 ] and one is the original connectivity, each interchange creates two new trees.