Department of Computer Science, University of Texas at Austin, Austin, TX 78712, USA.
Laboratoire de Biométrie et Biologie Evolutive, CNRS, UMR5558, Université Lyon 1, 69622, Villeurbanne, France.
Science. 2014 Dec 12;346(6215):1250463. doi: 10.1126/science.1250463. Epub 2014 Dec 11.
Gene tree incongruence arising from incomplete lineage sorting (ILS) can reduce the accuracy of concatenation-based estimations of species trees. Although coalescent-based species tree estimation methods can have good accuracy in the presence of ILS, they are sensitive to gene tree estimation error. We propose a pipeline that uses bootstrapping to evaluate whether two genes are likely to have the same tree, then it groups genes into sets using a graph-theoretic optimization and estimates a tree on each subset using concatenation, and finally produces an estimated species tree from these trees using the preferred coalescent-based method. Statistical binning improves the accuracy of MP-EST, a popular coalescent-based method, and we use it to produce the first genome-scale coalescent-based avian tree of life.
由不完全谱系分选（ILS）引起的基因树不一致性会降低基于连锁的物种树估计的准确性。尽管基于合并的物种树估计方法在存在 ILS 的情况下可以具有很好的准确性，但它们对基因树估计误差很敏感。我们提出了一个使用自举法来评估两个基因是否可能具有相同的树的流水线，然后使用图论优化将基因分组到集合中，然后使用连锁在每个子集上估计一棵树，最后使用首选的基于合并的方法从这些树中生成估计的物种树。统计分箱提高了流行的基于合并的 MP-EST 方法的准确性，我们使用它来生成第一个基于基因组规模的鸟类生命树的基于合并的方法。
