[1] Distance-matrix methods are frequently used as the basis for progressive and iterative types of multiple sequence alignment.
The main disadvantage of distance-matrix methods is their inability to efficiently use information about local high-variation regions that appear across multiple subtrees.
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.
An additional improvement that corrects for correlations between distances that arise from many closely related sequences in the data set can also be applied at increased computational cost.
To counter potential complications noted above, and to find the best tree for the data, distance analysis can also incorporate a tree-search protocol that seeks to satisfy an explicit optimality criterion.
Two optimality criteria are commonly applied to distance data, minimum evolution (ME) and least squares inference.
In contrast, ME accepts the tree with the shortest sum of branch lengths, and thus minimizes the total amount of evolution assumed.
If the rate of evolution were equal in all sampled lineages (a molecular clock), and if the tree were completely balanced (equal numbers of taxa on both sides of any split, to counter the node density effect), UPGMA should not produce a biased result.
These expectations are not met by most datasets, and although UPGMA is somewhat robust to their violation, it is not commonly used for phylogeny estimation.
Under these models, the tree is estimated unrooted; rooting, and consequently determination of polarity, is performed after the analysis.
Despite these potential problems, distance methods are extremely fast, and they often produce a reasonable estimate of phylogeny.
They also permit analyses that account for the possibility that the rate at which particular nucleotides are incorporated into sequences may vary over the tree, using LogDet distances.
For some network-estimation methods (notably NeighborNet), the abstraction of information about individual characters in distance data are an advantage.
Distance methods are popular among molecular systematists, a substantial number of whom use NJ without an optimization stage almost exclusively.
However, the nearly instantaneous NJ implementations, the ability to incorporate an evolutionary model in a speedy analysis, LogDet distances, network estimation methods, and the occasional need to summarize relationships with a single number all mean that distance methods will probably stay in the mainstream for a long time to come.