![]() ![]() The reduction of computing time is dramatic in comparison with other maximum-likelihood packages, while the likelihood maximization ability tends to be higher. We used extensive and realistic computer simulations to show that the topological accuracy of this new method is at least as high as that of the existing maximum-likelihood programs and much higher than the performance of distance-based and parsimony approaches. Due to this simultaneous adjustment of the topology and branch lengths, only a few iterations are sufficient to reach an optimum. This algorithm starts from an initial tree built by a fast distance-based method and modifies this tree to improve its likelihood at each iteration. The core of this method is a simple hill-climbing algorithm that adjusts tree topology and branch lengths simultaneously. We describe a new approach, based on the maximum- likelihood principle, which clearly satisfies these requirements. Abstract: The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods.
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