Non-bifurcating phylogenetic tree inference via the adaptive LASSO
This addresses a limitation in current maximum-likelihood methods for phylogenetics, enabling better analysis of rapidly evolving systems like viral-host interactions, though it is incremental as it applies an existing regularization technique to a new domain.
The paper tackled the problem of inferring densely sampled phylogenetic trees with zero-length branches, which reveal sampled ancestors and polytomies, by introducing adaptive-LASSO regularization estimators, showing it to be a practically useful approach.
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.