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According to the ML criterion, the tree that makes our protein alignment the most probable evolutionary outcome given a specific model of protein evolution is considered the best estimate of the true phylogeny of our proteins [66].
The most probable evolutionary scenario is as follows.
In fact, it has been argued that the usual optimal alignment corresponds to the most probable evolutionary path.
The most probable evolutionary scenario to explain the phylogenetic origin and distribution pattern of individual 5' flanking regions is shown in Figure 1.
Then, we use an original simulation approach in order to infer the most probable evolutionary scenario for the settlement of the area, taking into account the effects of both demography and natural selection at some loci.
To reconstruct the most probable evolutionary history of the BFo genomes after the split from Pantoea/Erwinia, core genomes for the Pantoea and Erwinia genera were reconstructed and compared with the genome of BFo2.
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Among the traditional tree-like techniques developed to solve the gene tree incongruence problem there are two widely used approaches of gene concatenation and consensus tree reconstruction, both of which result in the inference of a single phylogenetic tree as the most probable representation of the evolutionary history of species.
The likelihood and Bayesian ancestral reconstruction methodology that we use produces the most probable ancestral sequence given certain evolutionary model assumptions, along with a posterior probability for alternative amino acids at each position.
We report the probability of the most probable path for each of the equally probable gene segment sets.
Consistent with the original evolutionary definition of orthology, it would probably be best to restrict this further to the most probable ancestral copy number, whose function was probably conserved.
To test for the presence of discrete evolutionary lineages we estimated the most probable number of genetic clusters (K) among populations using a spatially explicit Bayesian clustering mixture model in BAPS.
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