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评价亚分支方法在系统发育推断中的相对性能。

Evaluation of the Relative Performance of the Subflattenings Method for Phylogenetic Inference.

机构信息

School of Natural Sciences (Discipline of Mathematics), University of Tasmania, Hobart, Australia.

出版信息

Bull Math Biol. 2023 Jan 30;85(3):19. doi: 10.1007/s11538-023-01120-z.

Abstract

The algebraic properties of flattenings and subflattenings provide direct methods for identifying edges in the true phylogeny-and by extension the complete tree-using pattern counts from a sequence alignment. The relatively small number of possible internal edges among a set of taxa (compared to the number of binary trees) makes these methods attractive; however, more could be done to evaluate their effectiveness for inferring phylogenetic trees. This is the case particularly for subflattenings, and the work we present here makes progress in this area. We introduce software for constructing and evaluating subflattenings for splits, utilising a number of methods to make computing subflattenings more tractable. We then present the results of simulations we have performed in order to compare the effectiveness of subflattenings to that of flattenings in terms of split score distributions, and susceptibility to possible biases. We find that subflattenings perform similarly to flattenings in terms of the distribution of split scores on the trees we examined, but may be less affected by bias arising from both split size/balance and long branch attraction. These insights are useful for developing effective algorithms to utilise these tools for the purpose of inferring phylogenetic trees.

摘要

展平与次展平的代数性质为使用序列比对中的模式计数来识别真实系统发育中的边缘(并由此扩展到完整的树)提供了直接的方法。与二叉树的数量相比,一组分类单元中可能的内部边缘数量相对较少(compared to the number of binary trees),这使得这些方法具有吸引力;然而,我们还可以做更多的工作来评估它们推断系统发育树的有效性。对于次展平尤其如此,我们在这里介绍的工作在这方面取得了进展。我们引入了用于构建和评估分裂的次展平的软件,利用多种方法使计算次展平更加可行。然后,我们呈现了我们进行的模拟的结果,以便根据分裂分数分布和对可能偏差的敏感性来比较次展平与展平的有效性。我们发现,在我们检查的树中,次展平在分裂分数分布方面与展平表现相似,但可能受分裂大小/平衡和长枝吸引引起的偏差的影响较小。这些见解对于开发有效的算法来利用这些工具推断系统发育树是有用的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc80/9886601/62f81648f9e9/11538_2023_1120_Fig1_HTML.jpg

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