Evidence map›Paper›PMID 34218334›Full record

ArticleJournal of mathematical biology2021

Indirect identification of horizontal gene transfer.

David Schaller, Manuel Lafond, Peter F Stadler, Nicolas Wieseke, Marc Hellmuth

Abstract read
In one paragraph

Article in Journal of mathematical biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Predicting horizontal gene transfers with perfect transfer networks.Algorithms for molecular biology : AMB · 2024
    Article
  3. The Theory of Gene Family Histories.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  4. Relative timing information and orthology in evolutionary scenarios.Algorithms for molecular biology : AMB · 2023
    Article
  5. Article
  6. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

David SchallerMax Planck Institute for Mathematics in the Sciences, Inselstraße 22, 04109, Leipzig, Germany.
Manuel LafondDepartment of Computer Science, Université de Sherbrooke, 2500 boul. de l'Université, Sherbrooke, QC, J1K 2R1, Canada.
Peter F StadlerBioinformatics Group, Department of Computer Science, Leipzig University, Härtelstraße 16-18, 04107, Leipzig, Germany.
Nicolas WiesekeSwarm Intelligence and Complex Systems Group, Department of Computer Science, Leipzig University, Augustusplatz 10, 04109, Leipzig, Germany.
Marc HellmuthDepartment of Mathematics, Faculty of Science, Stockholm University, 106 91, Stockholm, Sweden. marc.hellmuth@math.su.se.ORCID 0000-0002-1620-5508

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

AlgorithmsGene Transfer, HorizontalPhylogenyBinary relationFitch graphGene familiesHorizontal gene transferIndirect phylogenetic methodsLater-divergence-timePolynomial-time recognition algorithmXenology

Identifiers

PMID34218334
PMCPMC8254804

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.