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已发表的稀疏超级矩阵的分辨率和支持度存疑:全面树搜索的重要性。

Dubious resolution and support from published sparse supermatrices: the importance of thorough tree searches.

作者信息

Simmons Mark P, Goloboff Pablo A

机构信息

Department of Biology, Colorado State University, Fort Collins, CO 80523, USA.

Consejo Nacional de Investigaciones Científicas y Técnicas, Miguel Lillo 205, 4000 S.M. de Tucumán, Argentina; Instituto Miguel Lillo, Facultad de Ciencias Naturales, Miguel Lillo 205, 4000 S.M. de Tucumán, Argentina.

出版信息

Mol Phylogenet Evol. 2014 Sep;78:334-48. doi: 10.1016/j.ympev.2014.06.002. Epub 2014 Jun 11.

DOI:10.1016/j.ympev.2014.06.002
PMID:24929246
Abstract

We re-analyzed 10 sparse supermatrices wherein the original authors relied primarily or entirely upon maximum likelihood phylogenetic analyses implemented in RAxML and quantified branch support using the bootstrap. We compared the RAxML-based topologies and bootstrap values with both superficial- and relatively thorough-tree-search parsimony topologies and bootstrap values. We tested for clades that were resolved by RAxML but properly unsupported by checking if the SH-like aLRT equals zero and/or if the parsimony-optimized minimum branch length equals zero. Four of our conclusions are as follows. (1) Despite sampling nearly 50,000 characters, highly supported branches in a RAxML tree may be entirely unsupported because of missing data. (2) One should not rely entirely upon RAxML SH-like aLRT, RAxML bootstrap, or superficial parsimony bootstrap methods to rigorously quantify branch support for sparse supermatrices. (3) A fundamental factor that favors thorough parsimony analyses of sparse supermatrices is being able to distinguish between clades that are unequivocally supported by the data from those that are not; superficial likelihood analyses that quantify branch support using the bootstrap cannot be relied upon to always make this distinction. (4) The SH-like aLRT and parsimony-optimized-minimum-branch-length tests generally identify the same properly unsupported clades; the latter is a more severe test.

摘要

我们重新分析了10个稀疏超级矩阵,其中原作者主要或完全依赖于在RAxML中实施的最大似然系统发育分析,并使用自展法对分支支持度进行量化。我们将基于RAxML的拓扑结构和自展值与表面的和相对全面的树形搜索简约拓扑结构及自展值进行了比较。我们通过检查SH-like aLRT是否等于零和/或简约优化的最小分支长度是否等于零,来测试由RAxML解析但支持不足的分支。我们的四个结论如下。(1)尽管采样了近50,000个字符,但由于数据缺失,RAxML树中得到高度支持的分支可能完全没有支持。(2)不应完全依赖RAxML的SH-like aLRT、RAxML自展法或表面简约自展法来严格量化稀疏超级矩阵的分支支持度。(3)有利于对稀疏超级矩阵进行全面简约分析的一个基本因素是能够区分数据明确支持的分支和不支持的分支;使用自展法量化分支支持度的表面似然分析不能总是依赖于做出这种区分。(4)SH-like aLRT和简约优化的最小分支长度检验通常会识别出相同的支持不足的分支;后者是一个更严格的检验。

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