Efficiently Inferring Pairwise Subtree Prune-and-Regraft Adjacencies between Phylogenetic Trees

Whidden, Chris; Matsen, F. A.

doi:10.1137/1.9781611975062.8

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…The good hash table stores the high likelihood topologies. Tree topologies are stored using an SDLNewick string representation (Whidden and Matsen IV, 2018), a left/right sorted variant of the Newick string format that ensures the same string representation for identical tree topologies.…”

Section: Methodsmentioning

confidence: 99%

Systematic Exploration of the High Likelihood Set of Phylogenetic Tree Topologies

et al. 2019

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Bayesian Markov chain Monte Carlo explores tree space slowly, in part because it frequently returns to the same tree topology. An alternative strategy would be to explore tree space systematically, and never return to the same topology. In this paper, we present an efficient parallelized method to map out the high likelihood set of phylogenetic tree topologies via systematic search, which we show to be a good approximation of the high posterior set of tree topologies. Here "likelihood" of a topology refers to the tree likelihood for the corresponding tree with optimized branch lengths. We call this method "phylogenetic topographer" (PT). The PT strategy is very simple: starting in a number of local topology maxima (obtained by hill-climbing from random starting points), explore out using local topology rearrangements, only continuing through topologies that are better than than some likelihood threshold below the best observed topology. We show that the normalized topology likelihoods are a useful proxy for the Bayesian posterior probability of those topologies. By using a non-blocking hash table keyed on unique representations of tree topologies, we avoid visiting topologies more than once across all concurrent threads exploring tree space. We demonstrate that PT can be used directly to approximate a Bayesian consensus tree topology. When combined with an accurate means of evaluating per-topology marginal likelihoods, PT gives an alternative procedure for obtaining Bayesian posterior distributions on phylogenetic tree topologies.

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Section: Methodsmentioning

confidence: 99%

Systematic Exploration of the High Likelihood Set of Phylogenetic Tree Topologies

et al. 2019

Self Cite

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“…This facilitates the discovery of islands during tree search without any need for computing tree-to-tree distances and clustering. Otherwise, branch rearrangement operation metric calculations are NP-hard problems (e.g., DasGupta et al 2000 ; Allen and Steel 2001 ; Bordewich and Semple 2005 ) and, despite attempts to develop efficient algorithms for calculating or approximating branch rearrangement metrics (e.g., Brown and Day 1984 ; DasGupta et al 2000 ; Goloboff 2008 ; Whidden and Matsen IV 2018 ), a posteriori identification of islands defined by branch rearrangement operation metrics from sets of trees remains computationally expensive. Given that NNI rearrangements are special cases of SPR rearrangements, which are special cases of TBR rearrangements ( Bryant 2004 ; Chernomor et al 2015 ), the number of TBR islands will be less than or equal to the number of SPR islands, which in turn will be less than or equal to the number of NNI islands ( Maddison 1991 ).…”

Section: Defining Islands Of Treesmentioning

confidence: 99%

On Defining and Finding Islands of Trees and Mitigating Large Island Bias

Silva

Wilkinson

2021

Systematic Biology

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How best can we summarise sets of phylogenetic trees? Systematists have relied heavily on consensus methods, but if tree distributions can be partitioned into distinct subsets it may be helpful to provide separate summaries of these rather than relying entirely upon a single consensus tree. How sets of trees can most helpfully be partitioned and represented leads to many open questions, but one natural partitioning is provided by the islands of trees found during tree searches. Islands that are of dissimilar size have been shown to yield majority-rule consensus trees dominated by the largest sets. We illustrate this large island bias and approaches that mitigate its impact by revisiting a recent analysis of phylogenetic relationships of living and fossil amphibians. We introduce a revised definition of tree islands based on any tree-to-tree pairwise distance metric that usefully extends the notion to any set or multiset of trees, as might be produced by, for example, Bayesian or bootstrap methods, and that facilitates finding tree islands a posteriori. We extract islands from a tree distribution obtained in a Bayesian analysis of the amphibian data to investigate their impact in that context, and we compare the partitioning produced by tree islands with those resulting from some alternative approaches. Distinct subsets of trees, such as tree islands, should be of interest because of what they may reveal about evolution and/or our attempts to understand it, and are an important, sometimes overlooked, consideration when building and interpreting consensus trees.

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“…Usually, they are used by heuristic algorithms like FastME by Lefort et al [49], FastTree and FastTree2 by Price et al [62,63], BNNI and FASTNNI [16], which search for an optimal tree structure. These techniques are Nearest Neighbor Interchanges (NNI) [43], Subtree Pruning and Regrafting (SPR) [89], and Tree Bisection and Reconnection (TBR) [2]. All of these techniques are implemented seeking to optimize some criterion such as balanced minimum evolution (BME) [49], the OLS version of criterion (OLSME) [49] or WLS version of criterion (WLSME) [17].…”

Section: Neighbor-joining and Variantsmentioning

confidence: 99%

Distance-based phylogenetic inference from typing data: a unifying view

Vaz,

Nascimento,

Carriço

et al. 2020

Preprint

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Typing methods are widely used in the surveillance of infectious diseases, outbreaks investigation and studies of the natural history of an infection. And their use is becoming standard, in particular with the introduction of High Throughput Sequencing (HTS). On the other hand, the data being generated is massive and many algorithms have been proposed for phylogenetic analysis of typing data, addressing both correctness and scalability issues. Most of the distance-based algorithms for inferring phylogenetic trees follow the closestpair joining scheme. This is one of the approaches used in hierarchical clustering. And although phylogenetic inference algorithms may seem rather different, the main difference among them resides on how one defines cluster proximity and on which optimization criterion is used. Both cluster proximity and optimization criteria rely often on a model of evolution. In this work we review, and we provide an unified view of these algorithms. This is an important step not only to better understand such algorithms, but also to identify possible computational bottlenecks and improvements, important to deal with large data sets.

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Efficiently Inferring Pairwise Subtree Prune-and-Regraft Adjacencies between Phylogenetic Trees

Cited by 5 publications

References 15 publications

Systematic Exploration of the High Likelihood Set of Phylogenetic Tree Topologies

Systematic Exploration of the High Likelihood Set of Phylogenetic Tree Topologies

On Defining and Finding Islands of Trees and Mitigating Large Island Bias

Distance-based phylogenetic inference from typing data: a unifying view

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