NJML: a hybrid algorithm for the neighbor-joining and maximum-likelihood methods |
| |
Authors: | Ota S Li W H |
| |
Institution: | Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA. |
| |
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). |
| |
Keywords: | |
本文献已被 PubMed Oxford 等数据库收录! |
|