NJML: A hybrid algorithm for the neighbor-joining and maximum-likelihood methods
NJML: A hybrid algorithm for the neighbor-joining and maximum-likelihood methods
复制标题
DOI：
10.1093/oxfordjournals.molbev.a026423
复制
发表时间：
2000-09-01
影响因子：
10.7
通讯作者：
Li, WH
中科院分区：
文献类型：
--
作者：
Ota, S;Li, WH
在大型系统发育树的重建中，最困难的部分通常是如何探索拓扑空间以找到最优拓扑的问题。我们开发了一种分而治之的启发式算法，在该算法中，初始邻居加入(NJ)树在引导值高于阈值的内部分支处被划分为子树。然后使用最大似然法进行拓扑搜索，在保持其他分枝不变的情况下，重新评估Bootstrap值低于阈值的所有分枝。大量的模拟实验表明，我们的简单方法--邻居加入最大似然(NJML)方法在改进NJ树方面是非常有效的。此外，NJML方法的性能几乎相当于或优于现有的耗时的启发式最大似然方法。该方法适用于构建较大的分子系统发育树(类群数目大于或等于16个)。
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 blanches 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 greater than or equal to 16).