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Nonbifurcating Phylogenetic Tree Inference via the Adaptive LASSO.

ABSTRACT: 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 for the branch lengths of phylogenetic trees, deriving their properties, and showing regularization to be a practically useful approach for phylogenetics.

SUBMITTER: Zhang C

PROVIDER: S-EPMC8300927 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Nonbifurcating Phylogenetic Tree Inference via the Adaptive LASSO.

Zhang Cheng C Dinh V U VU Matsen Frederick A FA

Journal of the American Statistical Association 20200720 534

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 <i>sampled ancestors</i> in which we sequence a genotype along with its direct descendants, and <i>polytomies</i> in which multiple descendants arise simultaneously. These features are apparent after identifying zero-length branches in the t ...[more]

PMID: 34305211

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