Skip to main content

Table 1 Short description of the most commonly used methods to infer phylogenies.

From: A reference guide for tree analysis and visualization

Method Input Description
Neigbor-Joining (NJ) Distance Matrix Iterative clustering method based on the minimum-evolution criterion; the topology with the least total branch length is preferred at each step.
UPGMA   Agglomerative hierarchical clustering based on the average linkage method.
Maximum Parsimony (MP) Phylogenetic Feature Matrix Alternative evolutionary trees are generated; the one that satisfies the parsimony optimal criterion is considered as the best estimation: under maximum parsimony, the preferred phylogenetic tree is the tree that requires the smallest number of evolutionary changes.
Maximum Likelihood (ML)   Alternative evolutionary trees are generated; the probability of an evolutionary event at any given point on a tree is stochastically modelled: under maximum likelihood, the preferred phylogenetic tree is the one with the highest likelihood.
Markov Chain Monte Carlo (MCMC) Both Bayesian inference method; alternative evolutionary trees are generated combining a posterior distribution for a feature and a model of evolution, based on the prior for that feature and the likelihood of the data, generated by a multiple alignment: unlike MP and ML a set of equally optimal trees may be produced. MCMC simulation is used to sample trees towards a credible subset.
  1. The distance matrix methods are faster, computationally less expensive and can thus be applied in larger data sets. Nevertheless, the other methods are considered to produce better results.