Institut de Systématique, Evolution, Biodiversité (ISYEB UMR7205-CNRS, Muséum National d'Histoire Naturelle, SU, EPHE, UA), 75005 Paris, France.
Institut Pasteur, Université Paris Cité, G5 Evolutionary Genomics of RNA Viruses, 75015, Paris, France.
Syst Biol. 2023 Dec 30;72(6):1280-1295. doi: 10.1093/sysbio/syad052.
The bootstrap method is based on resampling sequence alignments and re-estimating trees. Felsenstein's bootstrap proportions (FBP) are the most common approach to assess the reliability and robustness of sequence-based phylogenies. However, when increasing taxon sampling (i.e., the number of sequences) to hundreds or thousands of taxa, FBP tend to return low support for deep branches. The transfer bootstrap expectation (TBE) has been recently suggested as an alternative to FBP. TBE is measured using a continuous transfer index in [0,1] for each bootstrap tree, instead of the binary {0,1} index used in FBP to measure the presence/absence of the branch of interest. TBE has been shown to yield higher and more informative supports while inducing a very low number of falsely supported branches. Nonetheless, it has been argued that TBE must be used with care due to sampling issues, especially in datasets with a high number of closely related taxa. In this study, we conduct multiple experiments by varying taxon sampling and comparing FBP and TBE support values on different phylogenetic depths, using empirical datasets. Our results show that the main critique of TBE stands in extreme cases with shallow branches and highly unbalanced sampling among clades, but that TBE is still robust in most cases, while FBP is inescapably negatively impacted by high taxon sampling. We suggest guidelines and good practices in TBE (and FBP) computing and interpretation.
自举法是基于重采样序列比对和重新估计树。费希尔氏自举比例（FBP）是评估基于序列系统发育的可靠性和稳健性的最常用方法。然而，当增加分类群采样（即序列数量）到数百或数千个分类群时，FBP 往往会对深支返回低支持。转移自举期望（TBE）最近被提议作为 FBP 的替代方法。TBE 是使用连续转移指数在 [0,1] 中测量的，而不是 FBP 中用于测量感兴趣分支的存在/不存在的二进制 {0,1} 索引。TBE 已被证明可以产生更高和更具信息量的支持，同时诱导非常少的错误支持分支。尽管如此，有人认为 TBE 必须谨慎使用，因为存在采样问题，特别是在具有大量密切相关分类群的数据集上。在这项研究中，我们通过改变分类群采样并在不同的系统发育深度上比较 FBP 和 TBE 支持值来进行多项实验，使用经验数据集。我们的结果表明，TBE 的主要批评是在浅支和分支之间高度不平衡采样的极端情况下，但 TBE 在大多数情况下仍然稳健，而 FBP 不可避免地受到高分类群采样的负面影响。我们建议在 TBE（和 FBP）计算和解释方面的指导方针和良好实践。
