Species Tree Inference from Genomic Sequences Using the Log-Det Distance
Author(s) -
Elizabeth S. Allman,
Colby Long,
John A. Rhodes
Publication year - 2019
Publication title -
siam journal on applied algebra and geometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.052
H-Index - 15
ISSN - 2470-6566
DOI - 10.1137/18m1194134
Subject(s) - inference , ultrametric space , tree (set theory) , mathematics , coalescent theory , sorting , tree structure , genetic distance , biology , computer science , algorithm , combinatorics , artificial intelligence , gene , phylogenetics , discrete mathematics , genetics , genetic variation , binary tree , metric space
The log-det distance between two aligned DNA sequences was introduced as a tool for statistically consistent inference of a gene tree under simple non-mixture models of sequence evolution. Here we prove that the log-det distance, coupled with a distance-based tree construction method, also permits consistent inference of species trees under mixture models appropriate to aligned genomic-scale sequences data. Data may include sites from many genetic loci, which evolved on different gene trees due to incomplete lineage sorting on an ultrametric species tree, with different time-reversible substitution processes. The simplicity and speed of distance-based inference suggests log-det based methods should serve as benchmarks for judging more elaborate and computationally-intensive species trees inference methods.
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