Polynomial-Time Statistical Estimation of Species Trees Under Gene Duplication and Loss

Journal of computational biology

article

Brandon Legried¹  Erin K. Molloy²  Tandy Warnow³  Sébastien Roch¹ 

[1] Department of Mathematics, University of Wisconsin-Madison;Department of Computer Science, University of California;Department of Computer Science, University of Illinois at Urbana-Champaign

关键词: ASTRAL;    estimation;    gene duplication and loss;    identifiability;    species trees;    statistical consistency.;   

DOI  :  10.1089/cmb.2020.0424

来源: Mary Ann Liebert, Inc. Publishers

【 摘 要 】

Phylogenomics—the estimation of species trees from multilocus data sets—is a common step in many biological studies. However, this estimation is challenged by the fact that genes can evolve under processes, including incomplete lineage sorting (ILS) and gene duplication and loss (GDL), that make their trees different from the species tree. In this article, we address the challenge of estimating the species tree under GDL. We show that species trees are identifiable under a standard stochastic model for GDL, and that the polynomial-time algorithm ASTRAL-multi, a recent development in the ASTRAL suite of methods, is statistically consistent under this GDL model. We also provide a simulation study evaluating ASTRAL-multi for species tree estimation under GDL.

【 授权许可】

Unknown