Median quartet tree search algorithms using optimal subtree prune and regraft

Gene trees can be different from the species tree due to biological processes and inference errors. One way to obtain a species tree is to find one that maximizes some measure of similarity to a set of gene trees. The number of shared quartets between a potential species tree and gene trees provides...

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Bibliographic Details
Published in:Algorithms for molecular biology Vol. 19; no. 1; pp. 12 - 18
Main Authors: Arasti, Shayesteh, Mirarab, Siavash
Format: Journal Article
Language:English
Published: London BioMed Central 13.03.2024
Springer Nature B.V
BMC
Subjects:
Accuracy
Algorithms
Bioinformatics
Biological activity
Biomedical and Life Sciences
Cellular and Medical Topics
Climbing
Computational Biology/Bioinformatics
Discordance
Dynamic programming
Gene tree discordance
Heuristic methods
Life Sciences
Mathematical models
Optimization
Phylogenetics
Phylogeny
Physiological
Problem solving
Quartet distance
Quartet score
Search algorithms
Selected papers from WABI 2023
Statistical analysis
Statistical models
Subtree prune and regraft
Tree search
Trees
Quartet distance
Phylogenetics
Gene tree discordance
Subtree prune and regraft
Quartet score
Tree search
ASTRAL
ISSN:1748-7188, 1748-7188
Online Access:Get full text
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Summary:Gene trees can be different from the species tree due to biological processes and inference errors. One way to obtain a species tree is to find one that maximizes some measure of similarity to a set of gene trees. The number of shared quartets between a potential species tree and gene trees provides a statistically justifiable score; if maximized properly, it could result in a statistically consistent estimator of the species tree under several statistical models of discordance. However, finding the median quartet score tree, one that maximizes this score, is NP-Hard, motivating several existing heuristic algorithms. These heuristics do not follow the hill-climbing paradigm used extensively in phylogenetics. In this paper, we make theoretical contributions that enable an efficient hill-climbing approach. Specifically, we show that a subtree of size m can be placed optimally on a tree of size n in quasi-linear time with respect to n and (almost) independently of m . This result enables us to perform subtree prune and regraft (SPR) rearrangements as part of a hill-climbing search. We show that this approach can slightly improve upon the results of widely-used methods such as ASTRAL in terms of the optimization score but not necessarily accuracy.
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ISSN:1748-7188
1748-7188
DOI:10.1186/s13015-024-00257-3