title: NJML: a hybrid algorithm for the neighbor-joining and maximum-likelihood methods.
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).
authors.affiliation: Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA.
authors.forename: S
authors.initials: S
authors.lastname: Ota
authors.forename: W H
authors.initials: WH
authors.lastname: Li
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D005057:Eukaryotic Cells / Q000378:metabolism; D019143:Evolution, Molecular; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D010802:Phylogeny; D012335:RNA, Ribosomal / Q000235:genetics
chemical_list: D012335:RNA, Ribosomal