Universitat Politècnica de Catalunya. Departament de Ciències de la Computació
Universitat Politècnica de Catalunya. ALBCOM - Algorismia, Bioinformàtica, Complexitat i Mètodes Formals
2021-12-01
The Robinson-Foulds (RF) distance, one of the most widely used metrics for comparing phylogenetic trees, has the advantage of being intuitive, with a natural interpretation in terms of common splits, and it can be computed in linear time, but it has a very low resolution, and it may become trivial for phylogenetic trees with overlapping taxa, that is, phylogenetic trees that share some but not all of their leaf labels. In this article, we study the properties of the Generalized Robinson-Foulds (GRF) distance, a recently proposed metric for comparing any structures that can be described by multisets of multisets of labels, when applied to rooted phylogenetic trees with overlapping taxa, which are described by sets of clusters, that is, by sets of sets of labels. We show that the GRF distance has a very high resolution, it can also be computed in linear time, and it is not (uniformly) equivalent to the RF distance.
This research was partially supported by the Spanish Ministry of Science, Innovation and Universitiesand the European Regional Development Fund through project PGC2018-096956-B-C43 (FEDER/MICINN/AEI), and by the Agency for Management of University and Research Grants (AGAUR) throughgrant 2017-SGR-786 (ALBCOM).
Peer Reviewed
Postprint (published version)
Article
English
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica; Phylogeny; Metrics; Phylogenetic tree; Robinson-Foulds distance; Filogènia
Mary Ann Liebert, Inc. Publishers
https://www.liebertpub.com/doi/10.1089/cmb.2021.0342
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-096956-B-C43/ES/DESARROLLO DE ESTRATEGIAS -OMICAS PARA DESVELAR PANGENOMAS, COEVOLUCION VIRICA Y ADAPTACION A LOS EXTREMOS DE CONCENTRACION SALINA-SP4/
info:eu-repo/grantAgreement/AGAUR/2017 SGR 786
https://creativecommons.org/licenses/by-nc/4.0/
Open Access
Attribution-NonCommercial 4.0 International
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