Cophenetic metrics for phylogenetic trees, after Sokal and Rohlf

Cardona, Gabriel; Mir, Arnau; Rosselló, Francesc; Rotger, Lucía; Sanchez, David Jesse

doi:10.1186/1471-2105-14-3

Cited by 57 publications

(74 citation statements)

References 37 publications

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“…The technique is based on a recent algorithm [5] from the field of computational phylogenetics, where the objects to compare are labeled trees showing the inferred evolutionary relationships among various biological species. We adapt the algorithm to the BPM context, thus using process trees [4] as notation.…”

Section: The Final Publication Is Available At Springer Via Http://dxmentioning

confidence: 99%

“…Authors in [5] show that cophenetic values can also be applied to uniquely project labelled trees into a multidimensional vector space, allowing them to define a distance on labelled trees as Theorem 1 states.…”

Section: Definition 3 ([14]mentioning

confidence: 99%

“…-For processes with n > 2 activities, we will show that every strict subtree 5 of T is equal to another subtree of T . Let V T be a strict subtree of T .…”

Section: Cophenetic Distance Definitionmentioning

confidence: 99%

“…Although the matching Cophenetic distance is a quadratic-time algorithm [5], once we have chosen a particular matching ω, it is still computationally infeasible to compute this distance for each candidate ω in the minimum matching Cophenetic distance. In a practical scenario in which the size of the process trees made it impossible to test all possible matchings, one would be able to bound this ideal distance with an approximate node matching.…”

Section: Seq1 And2mentioning

confidence: 99%

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Process Model Comparison Based on Cophenetic Distance

Sanchez-Charles¹,

Muntés-Mulero²,

Carmona

et al. 2016

Lecture Notes in Business Information Processing

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Summary. The automated comparison of process models has received increasing attention in the last decade, due to the growing existence of process models and repositories, and the consequent need to assess similarities between the underlying processes. Current techniques for process model comparison are either structural (based on graph edit distances), or behavioural (through activity profiles or the analysis of the execution semantics). Accordingly, there is a gap between the quality of the information provided by these two families, i.e., structural techniques may be fast but inaccurate, whilst behavioural are accurate but complex. In this paper we present a novel technique, that is based on a well-known technique to compare labeled trees through the notion of Cophenetic distance. The technique lays between the two families of methods for comparing a process model: it has an structural nature, but can provide accurate information on the differences/similarities of two process models. The experimental evaluation on various benchmarks sets are reported, that position the proposed technique as a valuable tool for process model comparison.

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Section: The Final Publication Is Available At Springer Via Http://dxmentioning

confidence: 99%

Section: Definition 3 ([14]mentioning

confidence: 99%

“…-For processes with n > 2 activities, we will show that every strict subtree 5 of T is equal to another subtree of T . Let V T be a strict subtree of T .…”

Section: Cophenetic Distance Definitionmentioning

confidence: 99%

Section: Seq1 And2mentioning

confidence: 99%

See 2 more Smart Citations

Process Model Comparison Based on Cophenetic Distance

Sanchez-Charles¹,

Muntés-Mulero²,

Carmona

et al. 2016

Lecture Notes in Business Information Processing

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show abstract

“…This procedure results in two vectors for a tree T: In m(T) we have recorded each pendant length as 1, as each tip is 1 step from its immediate ancestor. The vector M, which is similar to the vector of cophenetic values (Sokal and Rohlf 1962;Cardona et al 2013) (see supplementary material, Supplementary Material online) depends on the tree's branch lengths, whereas m only depends on its structure or "topology." We combine m and M with a parameter k 2 ½0; 1, which determines how much the topology of the tree only (k ¼ 0), versus the tree with branch lengths (k ¼ 1), contributes.…”

Section: Introductionmentioning

confidence: 99%

Mapping phylogenetic trees to reveal distinct patterns of evolution

Kendall

Colijn

2015

Preprint

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Evolutionary relationships are frequently described by phylogenetic trees, but a central barrier in many fields is the difficulty of interpreting data containing conflicting phylogenetic signals. We present a metric-based method for comparing trees which extracts distinct alternative evolutionary relationships embedded in data. We demonstrate detection and resolution of phylogenetic uncertainty in a recent study of anole lizards, leading to alternate hypotheses about their evolutionary relationships. We use our approach to compare trees derived from different genes of Ebolavirus and find that the VP30 gene has a distinct phylogenetic signature composed of three alternatives that differ in the deep branching structure.

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Tree shape‐based approaches for the comparative study of cophylogeny

Avino

et al. 2019

Ecology and Evolution

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Cophylogeny is the congruence of phylogenetic relationships between two different groups of organisms due to their long‐term interaction. We investigated the use of tree shape distance measures to quantify the degree of cophylogeny. We implemented a reverse‐time simulation model of pathogen phylogenies within a fixed host tree, given cospeciation probability, host switching, and pathogen speciation rates. We used this model to evaluate 18 distance measures between host and pathogen trees including two kernel distances that we developed for labeled and unlabeled trees, which use branch lengths and accommodate different size trees. Finally, we used these measures to revisit published cophylogenetic studies, where authors described the observed associations as representing a high or low degree of cophylogeny. Our simulations demonstrated that some measures are more informative than others with respect to specific coevolution parameters especially when these did not assume extreme values. For real datasets, trees’ associations projection revealed clustering of high concordance studies suggesting that investigators are describing it in a consistent way. Our results support the hypothesis that measures can be useful for quantifying cophylogeny. This motivates their usage in the field of coevolution and supports the development of simulation‐based methods, i.e., approximate Bayesian computation, to estimate the underlying coevolutionary parameters.

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Cophenetic metrics for phylogenetic trees, after Sokal and Rohlf

Cited by 57 publications

References 37 publications

Process Model Comparison Based on Cophenetic Distance

Process Model Comparison Based on Cophenetic Distance

Mapping phylogenetic trees to reveal distinct patterns of evolution

Tree shape‐based approaches for the comparative study of cophylogeny

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