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Long Branch Effects Distort Maximum Likelihood Phylogenies in Simulations Despite Selection of the Correct Model

机译:尽管选择了正确的模型,但长分支效应扭曲了模拟中的最大似然系统发育

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摘要

The aim of our study was to test the robustness and efficiency of maximum likelihood with respect to different long branch effects on multiple-taxon trees. We simulated data of different alignment lengths under two different 11-taxon trees and a broad range of different branch length conditions. The data were analyzed with the true model parameters as well as with estimated and incorrect assumptions about among-site rate variation. If length differences between connected branches strongly increase, tree inference with the correct likelihood model assumptions can fail. We found that incorporating invariant sites together with distributed site rates in the tree reconstruction (+I) increases the robustness of maximum likelihood in comparison with models using only . The results show that for some topologies and branch lengths the reconstruction success of maximum likelihood under the correct model is still low for alignments with a length of 100,000 base positions. Altogether, the high confidence that is put in maximum likelihood trees is not always justified under certain tree shapes even if alignment lengths reach 100,000 base positions.
机译:我们研究的目的是针对多分类单元树的不同长分支效应,测试最大似然性的鲁棒性和效率。我们模拟了两种不同的11类分类树和不同分支长度条件下的不同排列长度的数据。使用真实的模型参数以及站点间速率变化的估计和错误假设对数据进行了分析。如果相连分支之间的长度差异大大增加,则使用正确的似然模型假设的树推断可能会失败。我们发现,与仅使用的模型相比,在树重构(+ I)中将不变的站点与分布式站点的速率结合在一起可以提高最大可能性的鲁棒性。结果表明,对于某些拓扑和分支长度,对于长度为100,000个基本位置的比对,在正确模型下最大似然重建的成功率仍然很低。总体而言,即使对齐长度达到100,000个基本位置,在某些树形结构下也始终无法证明对最大似然树的高置信度是正确的。

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