Data from: Generalized bootstrap supports for phylogenetic analyses of protein sequences incorporating alignment uncertainty

Phylogenetic reconstructions are essential in genomics data analyses and depend on accurate multiple sequence alignment (MSA) models. We show that all currently available large-scale progressive multiple alignment methods are numerically unstable when dealing with amino-acid sequences. They produce significantly different output when changing sequence input order. We used the HOMFAM protein sequences dataset to show that on datasets larger than 100 sequences, this instability affects on average 21.5% of the aligned residues. The resulting Maximum Likelihood trees estimated from these multiple sequence alignments are equally unstable with over 38% of the branches being sensitive to the sequence input order. We established that about two-thirds of this uncertainty stems from the unordered nature of children nodes within the guide trees used to estimate MSAs. To quantify this uncertainty we developed unistrap, a novel approach that estimates the combined effect of alignment uncertainty and site sampling on phylogenetic tree branch supports. Compared to the regular bootstrap procedure, unistrap provides branch support estimates that take into account a larger fraction of the parameters impacting tree instability when processing datasets containing a large number of sequences.系统发育重建（Phylogenetic reconstructions）是基因组数据分析的核心环节，其可靠性高度依赖于精准的多序列比对（multiple sequence alignment, MSA）模型。我们的研究证实，当前所有可获取的大规模渐进式多序列比对方法，在处理氨基酸序列时均存在数值不稳定性：当改变输入序列的顺序时，其输出结果会出现显著差异。我们借助HOMFAM蛋白质序列数据集开展验证，结果表明，当数据集包含的序列数超过100条时，此类不稳定性平均会影响21.5%的比对残基位点。基于此类多序列比对结果构建的最大似然（Maximum Likelihood）系统发育树，同样存在显著不稳定性：超过38%的分支对输入序列的顺序极为敏感。我们进一步明确，约三分之二的此类不确定性源于用于生成多序列比对的引导树（guide trees）内部子节点的无序属性。为量化这一不确定性，我们开发了名为unistrap的全新方法，该方法可同时评估比对不确定性与位点抽样对系统发育树分支支持度的综合影响。相较于常规自举（bootstrap）法，unistrap在处理大规模序列数据集时，能够纳入更多影响树结构不稳定性的参数，从而得到更为全面的分支支持度估计结果。