Trees were estimated using Bayesian inference (BI), Maximum likelihood (ML), and Maximum Parsimony (MP) optimization criteria.
Maximum parsimony character optimization analyses indicate two to three independent derivations of venom glands within the Siluriformes.
The evolution of venom glands within the order Siluriformes was examined by performing maximum parsimony character optimization analyses on several previously published siluriform phylogenies that were reconstructed from both morphological [ 26, 30] and molecular [ 28] data.
This age was estimated from an angiosperm-wide tree using non-parametric rate smoothing (NPRS) applied to branch lengths obtained using accelerated transformation optimization in maximum parsimony (ACCTRAN).
If GTP is as difficult an optimization problem as maximum parsimony, with as messy data, experience suggests that this heuristic is not likely to perform very well.
This is an optimization problem entirely analogous to maximum parsimony or likelihood, but in which the optimality criterion is the summed duplication score (or perhaps the summed duplication plus loss scores) across all gene trees for a given species tree.
The focus is also due in part to two other characteristics of distance-based methods: they are very efficient compared to optimization searches such as maximum parsimony and maximum likelihood; and they remain the most commonly used in routine phylogenetic reconstruction neighbor-joining [ 10] and minimum evolution [ 11] account foreconstruction neighbor-joiningto phylogenetic methods.
Typically, molecular phylogenetic tree construction starts from a set of sequences (DNA or proteins), computation of a multiple sequence alignment, and then, based on the multiple sequence alignment, construction of a tree using one or several optimization criteria, such as distance, maximum parsimony, minimum evolution, maximum likelihood, and Bayesian inference.
The aligned matrix was analyzed using maximum parsimony (MP) and maximum likelihood (ML) optimality criteria and Bayesian Inference (BI).
