Nonbifurcating Phylogenetic Tree Inference via the Adaptive LASSO

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 article, we find these zero-length branches by introducing adaptive-LASSO-type regularization estimators for the branch lengths of phylogenetic trees, deriving their properties, and showing regularization to be a practically useful approach for phylogenetics. Supplementary materials for this article are available online.

Automated summary

This research introduces adaptive-LASSO-type regularization estimators to identify zero-length branches in phylogenetic trees, proving regularization to be a practical method in phylogenetics. This approach helps uncover special features in densely sampled phylogenetic trees, such as sampled ancestors and polytomies.