Phylogenetic tree inference using deep DNA sequencing is reshaping our understanding of rapidly evolving systems, such as the within-host battle between viruses and the immune system. Densely sampled phylogenetic trees can contain special features, including "sampled ancestors" in which we sequence a genotype along with its direct descendants, and "polytomies" in which multiple descendants arise simultaneously. These features are apparent after identifying zero-length branches in the tree. However, current maximum-likelihood based approaches are not capable of revealing such zero-length branches. In this paper, we find these zero-length branches by introducing adaptive-LASSO-type regularization estimators to phylogenetics, deriving their properties, and showing regularization to be a practically useful approach for phylogenetics.

中文翻译：

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通过自适应 LASSO 进行非分叉系统发育树推断

使用深度 DNA 测序进行的系统发育树推断正在重塑我们对快速进化系统的理解，例如病毒与免疫系统之间的宿主内部战斗。密集采样的系统发育树可以包含特殊特征，包括我们对基因型及其直接后代进行测序的“采样祖先”，以及同时出现多个后代的“多分体”。在识别树中的零长度分支后，这些特征是显而易见的。然而，当前基于最大似然的方法无法揭示这种零长度分支。在本文中，我们通过将自适应 LASSO 类型的正则化估计量引入系统发育学，推导它们的特性，找到这些零长度分支，