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 | Â | 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) | Â | 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. |