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Molecular Biology and Evolution 17:1401-1409 (2000)
© 2000 Society for Molecular Biology and Evolution

Regular Article

NJML: A Hybrid Algorithm for the Neighbor-Joining and Maximum-Likelihood Methods

Satoshi Ota and Wen-Hsiung Li,

Department of Ecology and Evolution, University of Chicago

Abstract

In the reconstruction of a large phylogenetic tree, the most difficult part is usually the problem of how to explore the topology space to find the optimal topology. We have developed a "divide-and-conquer" heuristic algorithm in which an initial neighbor-joining (NJ) tree is divided into subtrees at internal branches having bootstrap values higher than a threshold. The topology search is then conducted by using the maximum-likelihood method to reevaluate all branches with a bootstrap value lower than the threshold while keeping the other branches intact. Extensive simulation showed that our simple method, the neighbor-joining maximum-likelihood (NJML) method, is highly efficient in improving NJ trees. Furthermore, the performance of the NJML method is nearly equal to or better than existing time-consuming heuristic maximum-likelihood methods. Our method is suitable for reconstructing relatively large molecular phylogenetic trees (number of taxa >= 16).


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