A Linear Time Solution to the Labeled Robinson–Foulds Distance Problem

Briand, Samuel; Dessimoz, Christophe; El-Mabrouk, Nadia; Nevers, Yannis

doi:10.1093/sysbio/syac028

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…However, like the above distances, it does not allow for multiple occurrences of mutations during the tumor history (Jahn et al, 2021 ). Other generalizations of the RF distance have also been proposed for gene trees (Briand et al, 2022 ; Briand et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

The k-Robinson–Foulds Dissimilarity Measures for Comparison of Labeled Trees

Khayatian,

Valiente,

Zhang

2024

Journal of Computational Biology

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Understanding the mutational history of tumor cells is a critical endeavor in unraveling the mechanisms that drive the onset and progression of cancer. Modeling tumor cell evolution with labeled trees motivates researchers to develop different measures to compare labeled trees. Although the Robinson–Foulds (RF) distance is widely used for comparing species trees, its applicability to labeled trees reveals certain limitations. This study introduces the k -RF dissimilarity measures, tailored to address the challenges of labeled tree comparison. The RF distance is succinctly expressed as n -RF in the space of labeled trees with n nodes. Like the RF distance, the k -RF is a pseudometric for multiset-labeled trees and becomes a metric in the space of 1-labeled trees. By setting k to a small value, the k -RF dissimilarity can capture analogous local regions in two labeled trees with different size or different labels.

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Section: Introductionmentioning

confidence: 99%

The k-Robinson–Foulds Dissimilarity Measures for Comparison of Labeled Trees

Khayatian,

Valiente,

Zhang

2024

Journal of Computational Biology

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“…However, like the above distances, it does not allow for multiple occurrences of mutations during the tumor history [9]. Other generalization of the RF distance have also been proposed for gene trees [1,2].…”

Section: Introductionmentioning

confidence: 99%

The k-RF Measures for Labeled Trees

Khayatian¹,

Valiente²,

Zhang³

2023

Preprint

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Investigating the mutational history of tumor cells is important for understanding the underlying mechanisms of cancer and its evolution. Now that the evolution of tumor cells is modeled using labeled trees, researchers are motivated to propose different measures for the comparison of mutation trees and other labeled trees. While the Robinson-Foulds distance is widely used for the comparison of phylogenetic trees, it has weaknesses when it is applied to labeled trees. Here, k-Robinson-Foulds dissimilarity measures are introduced for labeled tree comparison.

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“…However, it is important to note that the sublinear algorithm is not exact and may introduce some degree of error in the computed distances. Recently, a linear time and space algorithm [21] was proposed that addresses the labelled Robinson-Foulds (RF) distance problem, considering labels assigned to both internal and leaf nodes of the trees. However, their distance is based on edit distance operations applied to nodes and thus do not produce the exact RF value.…”

Section: Introductionmentioning

confidence: 99%

Computing RF Tree Distance over Succinct Representations

Branco,

Vaz,

Francisco

2023

Algorithms

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There are several tools available to infer phylogenetic trees, which depict the evolutionary relationships among biological entities such as viral and bacterial strains in infectious outbreaks or cancerous cells in tumor progression trees. These tools rely on several inference methods available to produce phylogenetic trees, with resulting trees not being unique. Thus, methods for comparing phylogenies that are capable of revealing where two phylogenetic trees agree or differ are required. An approach is then proposed to compute a similarity or dissimilarity measure between trees, with the Robinson–Foulds distance being one of the most used, and which can be computed in linear time and space. Nevertheless, given the large and increasing volume of phylogenetic data, phylogenetic trees are becoming very large with hundreds of thousands of leaves. In this context, space requirements become an issue both while computing tree distances and while storing trees. We propose then an efficient implementation of the Robinson–Foulds distance over tree succinct representations. Our implementation also generalizes the Robinson–Foulds distances to labelled phylogenetic trees, i.e., trees containing labels on all nodes, instead of only on leaves. Experimental results show that we are able to still achieve linear time while requiring less space. Our implementation in C++ is available as an open-source tool.

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A Linear Time Solution to the Labeled Robinson–Foulds Distance Problem

Cited by 5 publications

References 32 publications

The k-Robinson–Foulds Dissimilarity Measures for Comparison of Labeled Trees

The k-Robinson–Foulds Dissimilarity Measures for Comparison of Labeled Trees

The k-RF Measures for Labeled Trees

Computing RF Tree Distance over Succinct Representations

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