2018/05/28 by Cheng Zhang, Zhang, Cheng, Vu Dinh +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Earth and Planetary Sciences · #Evolution and Paleontology Studies #FOS: Biological sciences #FOS: Computer and information sciences #Genetic diversity and population structure #Genomics and Phylogenetic Studies #Machine Learning (stat.ML) #Populations and Evolution (q-bio.PE)
paper · pdf · doi:10.48550/arxiv.1805.11073
openalex publication_date 2018/05/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 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.