Inferring phylogenetic relationships of polyploid species and their diploid ancestors (leading to reticulate phylogenies in the case of an allopolyploid origin) based on multi-locus sequence data is complicated by the unknown assignment of alleles found in polyploids to diploid subgenomes. A parsimony-based approach to this problem has been proposed by Oberprieler et al. (Methods Ecol Evol 8:835–849, 2017), however, its implementation is of limited practical value. In addition to previously identified shortcomings, it has been found that in some cases, the obtained results barely satisfy the applied criterion. To be of better use to other researchers, a reimplementation with methodological refinement appears to be indispensable. We present the AllCoPol package, which provides a heuristic method for assigning alleles from polyploids to diploid subgenomes based on the Minimizing Deep Coalescences (MDC) criterion in multi-locus sequence datasets. An additional consensus approach further allows to assess the confidence of phylogenetic reconstructions. Simulations of tetra- and hexaploids show that under simplifying assumptions such as completely disomic inheritance, the topological errors of reconstructed phylogenies are similar to those of MDC species trees based on the true allele partition. AllCoPol is a Python package for phylogenetic reconstructions of polyploids offering enhanced functionality as well as improved usability. The included methods are supplied as command line tools without the need for prior programming knowledge.

中文翻译：

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AllCoPol：推断多倍体中的等位基因共同祖先

基于多基因座序列数据推断多倍体物种及其二倍体祖先的系统发育关系（导致同质多倍体起源的网状系统发育），由于多倍体中发现的等位基因未知分配给二倍体亚基因组而变得复杂。Oberprieler等人提出了一种基于简约的方法来解决该问题。（Methods Ecol Evol 8：835–849，2017），但是，其实现的实用价值有限。除了先前发现的缺点外，还发现在某些情况下，所获得的结果几乎不能满足所应用的标准。为了使其他研究人员更好地使用，在方法上进行改进以实现重新实现似乎是必不可少的。我们展示了AllCoPol软件包，它提供了一种启发式方法，可根据多基因座序列数据集中的最小化深度合并（MDC）准则将多倍体的等位基因分配给二倍体亚基因组。另外的共识方法还允许评估系统发育重建的可信度。四倍体和六倍体的模拟显示，在简化假设（例如完全二体遗传）下，重建的系统发育的拓扑错误与基于真实等位基因分区的MDC物种树的拓扑错误相似。AllCoPol是一个Python软件包，用于系统重建多倍体，提供增强的功能以及更高的可用性。所包含的方法作为命令行工具提供，而无需事先的编程知识。