Exelixis Laboratory, Scientific Computing Group, Heidelberg Institute for Theoretical Studies (HITS gGmbH), Schloss-Wolfsbrunnenweg 35, D-69118 Heidelberg, Germany.
Syst Biol. 2013 Jan 1;62(1):162-6. doi: 10.1093/sysbio/sys078. Epub 2012 Sep 6.
The presence of rogue taxa (rogues) in a set of trees can frequently have a negative impact on the results of a bootstrap analysis (e.g., the overall support in consensus trees). We introduce an efficient graph-based algorithm for rogue taxon identification as well as an interactive webservice implementing this algorithm. Compared with our previous method, the new algorithm is up to 4 orders of magnitude faster, while returning qualitatively identical results. Because of this significant improvement in scalability, the new algorithm can now identify substantially more complex and compute-intensive rogue taxon constellations. On a large and diverse collection of real-world data sets, we show that our method yields better supported reduced/pruned consensus trees than any competing rogue taxon identification method. Using the parallel version of our open-source code, we successfully identified rogue taxa in a set of 100 trees with 116 334 taxa each. For simulated data sets, we show that when removing/pruning rogue taxa with our method from a tree set, we consistently obtain bootstrap consensus trees as well as maximum-likelihood trees that are topologically closer to the respective true trees.
在一组树中存在离群分类单元（rogue taxa，rogues）通常会对自举分析的结果产生负面影响（例如，共识树中的整体支持度）。我们引入了一种有效的基于图的算法来识别离群分类单元，以及一个实现该算法的交互式网络服务。与我们之前的方法相比，新算法的速度快了 4 个数量级，同时返回的结果质量相同。由于可扩展性的显著提高，新算法现在可以识别更复杂和计算密集型的离群分类单元组合。在大量多样化的真实数据集上，我们表明，与任何竞争的离群分类单元识别方法相比，我们的方法生成了具有更好支持度的简化/修剪共识树。使用我们的开源代码的并行版本，我们成功地在 100 棵树的集合中识别了离群分类单元，每棵树包含 116334 个分类单元。对于模拟数据集，我们表明，当使用我们的方法从树集合中删除/修剪离群分类单元时，我们始终获得拓扑上更接近相应真实树的自举共识树和最大似然树。
