Indirect identification of horizontal gene transfer.

Binary relation Fitch graph Gene families Horizontal gene transfer Indirect phylogenetic methods Later-divergence-time Polynomial-time recognition algorithm Xenology

Journal

Journal of mathematical biology
ISSN: 1432-1416
Titre abrégé: J Math Biol
Pays: Germany
ID NLM: 7502105

Informations de publication

Date de publication:
03 07 2021
Historique:
received: 16 12 2020
accepted: 13 06 2021
revised: 06 04 2021
entrez: 4 7 2021
pubmed: 5 7 2021
medline: 3 9 2021
Statut: epublish

Résumé

Several implicit methods to infer horizontal gene transfer (HGT) focus on pairs of genes that have diverged only after the divergence of the two species in which the genes reside. This situation defines the edge set of a graph, the later-divergence-time (LDT) graph, whose vertices correspond to genes colored by their species. We investigate these graphs in the setting of relaxed scenarios, i.e., evolutionary scenarios that encompass all commonly used variants of duplication-transfer-loss scenarios in the literature. We characterize LDT graphs as a subclass of properly vertex-colored cographs, and provide a polynomial-time recognition algorithm as well as an algorithm to construct a relaxed scenario that explains a given LDT. An edge in an LDT graph implies that the two corresponding genes are separated by at least one HGT event. The converse is not true, however. We show that the complete xenology relation is described by an rs-Fitch graph, i.e., a complete multipartite graph satisfying constraints on the vertex coloring. This class of vertex-colored graphs is also recognizable in polynomial time. We finally address the question "how much information about all HGT events is contained in LDT graphs" with the help of simulations of evolutionary scenarios with a wide range of duplication, loss, and HGT events. In particular, we show that a simple greedy graph editing scheme can be used to efficiently detect HGT events that are implicitly contained in LDT graphs.

Identifiants

pubmed: 34218334
doi: 10.1007/s00285-021-01631-0
pii: 10.1007/s00285-021-01631-0
pmc: PMC8254804
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

10

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Auteurs

David Schaller (D)

Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, 04109, Leipzig, Germany.
Bioinformatics Group, Department of Computer Science, Leipzig University, Härtelstraße 16-18, 04107, Leipzig, Germany.
Interdisciplinary Center of Bioinformatics, Leipzig University, Härtelstraße 16-18, 04107, Leipzig, Germany.

Manuel Lafond (M)

Department of Computer Science, Université de Sherbrooke, 2500 boul. de l'Université, Sherbrooke, QC, J1K 2R1, Canada.

Peter F Stadler (PF)

Bioinformatics Group, Department of Computer Science, Leipzig University, Härtelstraße 16-18, 04107, Leipzig, Germany.
Interdisciplinary Center of Bioinformatics, Leipzig University, Härtelstraße 16-18, 04107, Leipzig, Germany.
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig University, Härtelstraße 16-18, 04107, Leipzig, Germany.
Competence Center for Scalable Data Services and Solutions, Leipzig University, Härtelstraße 16-18, 04107, Leipzig, Germany.
Leipzig Research Center for Civilization Diseases, Leipzig University, Härtelstraße 16-18, 04107, Leipzig, Germany.
Max-Planck-Institute for Mathematics in the Sciences, Inselstraße 22, 04103, Leipzig, Germany.
Inst. f. Theoretical Chemistry, University of Vienna, Währingerstraße 17, 1090, Wien, Austria.
Facultad de Ciencias, Universidad National de Colombia, Sede Bogotá, Colombia.
Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM, 87501, USA.

Nicolas Wieseke (N)

Swarm Intelligence and Complex Systems Group, Department of Computer Science, Leipzig University, Augustusplatz 10, 04109, Leipzig, Germany.

Marc Hellmuth (M)

Department of Mathematics, Faculty of Science, Stockholm University, 106 91, Stockholm, Sweden. marc.hellmuth@math.su.se.

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