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In the first set, we assumed that the transition/transversion rate ratio () were 300 for high sequence divergence and 1,000 for low sequence divergence.For each model tree, we generated 100 random sets of sequence data and constructed MP and ME trees for each data set.This modification was done by replacing each branch length by a random variable that followed a gamma distribution with the mean equal to the original branch length and the gamma shape parameter (] for a similar method).The six model trees used here were generated independently.Similarly, in minimum-evolution (ME) methods (Edwards and Cavalli-Sforza 1963 ).
This algorithm is a rough search of MP trees, and it often fails to find the true optimal (MP) tree.
This indicates that at least in the present case, selecting of a substitution model by using the likelihood ratio test or the AIC index is not appropriate.
When and the extent of sequence divergence is high, the NJ method with p distance often shows a better performance than ML methods with the JC model.
In the case of ME methods, a simple algorithm called neighbor joining (NJ; Saitou and Nei 1987 ) was shown to be as efficient as the standard ME method in almost all cases examined.
However, the simple algorithms used for MP and ML methods were ad hoc and did not work well under certain conditions.
Here we show by extensive computer simulation that when the number of nucleotide sequences () is relatively small, the simple MP or ML tree search algorithms such as the stepwise addition (SA) plus nearest neighbor interchange (NNI) search and the SA plus subtree pruning regrafting (SPR) search are as efficient as the extensive search algorithms such as the SA plus tree bisection-reconnection (TBR) search in inferring the true tree.