The term “neighbors” refers to a node‐pair which is separated by another node. The NJ method (Saitou and Nei, 1987) is a particular case of the “star decomposition method”, where raw data are arranged in a distance matrix and nodes are created (see the example below) whereas, in the NJ method, the separation of nodes is adjusted by average divergence from all other nodes.
The principle of the neighbor‐joining (NJ) technique is minimum evolution, which selects the tree with minimum branch‐length. It is based on a very fast, greedy heuristic algorithm that generates sub‐trees, and the closest sub‐trees are joined to each other to yield the final tree, in a step‐wise manner. The total branch length is the shortest for the true tree.
- The NJ method can be applied for large datasets relating to the taxa with varying degrees of divergence (hence, the tree will show different lengths for different branches).
- Multiple substitutions can be corrected.
- Some of the sequence information is lost in the NJ method due to the nature of the algorithm.
- Minimum mutational events explain the evolution of the molecular sequences.
- The branch length of the tree with known topology represents the different rate of evolutionary changes.
To construct a phylogenetic tree, using ...