Weighted neighbor joining: A likelihood-based approach to distance-based phylogeny reconstruction
Weighted neighbor joining: A likelihood-based approach to distance-based phylogeny reconstruction
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DOI：
10.1093/oxfordjournals.molbev.a026231
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发表时间：
2000-01-01
影响因子：
10.7
通讯作者：
Halpern, AL
中科院分区：
文献类型：
--
作者：
Bruno, WJ;Socci, ND;Halpern, AL
我们介绍了一种基于距离的同源性重建方法，称为“加权邻居加入”，或简称为“Weighbor”。与相邻连接一样，在每次迭代中连接两个分类群;然而，用于选择一对分类群进行连接的Weighbor标准考虑到距离估计中的误差对于较长距离呈指数级增大。该标准体现了一个似然函数的距离，这是建模为相关的高斯随机变量与不同的手段和方差，序列进化的概率模型下计算。Weighbor准则由两个项组成，一个是加性项，另一个是正性项，它们量化了加入对的含义。第一项评估与隐含外部分支的可加性的偏差，而第二项评估隐含内部分支具有正分支长度的置信度。与最大似然概率重建法相比，Weighbor方法速度更快，同时构建的树在定性和定量上都相似。Weighbor似乎相对免疫的“长分支吸引”和“长分支分散”的缺点观察到的邻居加入，BIONJ，和吝啬。
We introduce a distance-based phylogeny reconstruction method called "weighted neighbor joining," or "Weighbor" for short. As in neighbor joining, two taxa are joined in each iteration; however, the Weighbor criterion for choosing a pair of taxa to join takes into account that errors in distance estimates are exponentially larger for longer distances. The criterion embodies a likelihood function on the distances, which are modeled as correlated Gaussian random variables with different means and variances, computed under a probabilistic model for sequence evolution. The Weighbor criterion consists of two terms, an additivity term and a positivity term, that quantify the implications of joining the pair. The first term evaluates deviations from additivity of the implied external branches, while the second term evaluates confidence that the implied internal branch has a positive branch length. Compared with maximum-likelihood phylogeny reconstruction, Weighbor is much faster, while building trees that are qualitatively and quantitatively similar. Weighbor appears to be relatively immune to the "long branches attract" and "long branch distracts" drawbacks observed with neighbor joining, BIONJ, and parsimony.