Data from: Why concatenation fails near the anomaly zone

Genome-scale sequencing has been of great benefit in recovering species trees, but has not provided final answers. Despite the rapid accumulation of molecular sequences, resolving short and deep branches of the tree of life has remained a challenge, and has prompted the development of new strategies that can make the best use of available data. One such strategy – the concatenation of gene alignments – can be successful when coupled with many tree estimation methods, but has also been shown to fail when there are high levels of incomplete lineage sorting. Here, we focus on the failure of likelihood-based methods in retrieving a rooted, asymmetric four-taxon species tree from concatenated data when the species tree is in or near the anomaly zone – a region of parameter space where the most common gene tree does not match the species tree because of incomplete lineage sorting. First, we use coalescent theory to prove that most informative sites will support the species tree in the anomaly zone, and that as a consequence maximum-parsimony succeeds in recovering the species tree from concatenated data. We further show that maximum-likelihood tree estimation from concatenated data fails both inside and outside the anomaly zone, and that this failure cannot be easily predicted from the topology of the most common gene tree. We show that likelihood-based methods often fail in a region partially overlapping the anomaly zone, likely because of the lower relative cost of substitutions on discordant gene tree branches that are absent from the species tree. Our results confirm and extend previous reports on the performance of these methods applied to concatenated data from a rooted, asymmetric four-taxon species tree, and highlight avenues for future work improving the performance of methods aimed at recovering species tree.全基因组测序（Genome-scale sequencing）在物种树（species tree）恢复领域已展现出巨大价值，但却无法给出最终定论。尽管分子序列数据的积累速度极快，解析生命之树的短深分支始终是一项核心挑战，这推动了可充分利用现有数据的新型分析策略的发展。其中一类经典策略——基因比对序列的串联（concatenation of gene alignments）——在搭配多种树结构推断方法时可取得成功，但也被证实会在不完全谱系分选（incomplete lineage sorting）水平较高的场景下失效。 本文聚焦于一类失效场景：当物种树处于或接近异常区（anomaly zone）时，基于似然的方法无法从串联数据中准确检索出有根不对称四分类群物种树。异常区是一类参数空间区域，在此区域内由于不完全谱系分选，最常见的基因树拓扑结构与物种树并不匹配。首先，我们借助溯祖理论（coalescent theory）证明：异常区内绝大多数信息位点将支持物种树的拓扑结构，因此最大简约法（maximum-parsimony）可通过串联数据成功恢复物种树。 我们进一步证实，从串联数据中进行最大似然树推断（maximum-likelihood tree estimation），无论在异常区内部还是外部均会出现失效情况，且该失效无法通过最常见基因树的拓扑结构进行简单预测。研究发现，基于似然的方法常在与异常区部分重叠的区域内失效，这一现象大概率源于：相较于物种树中未包含的、与物种树分支不一致的基因树分支上的替换事件，其相对替换成本更低。本研究证实并拓展了此前针对有根不对称四分类群物种树的串联数据所应用方法的性能相关研究报道，并为未来优化物种树恢复方法的性能指明了可行方向。