Evidence map›Paper›PMID 31691135›Full record

ArticleJournal of mathematical biology2020

Reciprocal best match graphs.

Manuela Geiß, Peter F Stadler, Marc Hellmuth

Abstract read
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Article in Journal of mathematical biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. The Theory of Gene Family Histories.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  3. Relative timing information and orthology in evolutionary scenarios.Algorithms for molecular biology : AMB · 2023
    Article
  4. Indirect identification of horizontal gene transfer.Journal of mathematical biology · 2021
    Article
  5. Article
  6. Article
  7. Reconstruction of time-consistent species trees.Algorithms for molecular biology : AMB · 2020
    Article
  8. From pairs of most similar sequences to phylogenetic best matches.Algorithms for molecular biology : AMB · 2020
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Manuela GeißBioinformatics Group, Department of Computer Science, Leipzig University, Härtelstraße 16-18, 04107, Leipzig, Germany.
Peter F StadlerBioinformatics Group, Department of Computer Science, Leipzig University, Härtelstraße 16-18, 04107, Leipzig, Germany.
Marc HellmuthInstitute of Mathematics and Computer Science, University of Greifswald, Walther-Rathenau-Straße 47, 17487, Greifswald, Germany. mhellmuth@mailbox.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Reciprocal best matches play an important role in numerous applications in computational biology, in particular as the basis of many widely used tools for orthology assessment. Nevertheless, very little is known about their mathematical structure. Here, we investigate the structure of reciprocal best match graphs (RBMGs). In order to abstract from the details of measuring distances, we define reciprocal best matches here as pairwise most closely related leaves in a gene tree, arguing that conceptually this is the notion that is pragmatically approximated by distance- or similarity-based heuristics. We start by showing that a graph G is an RBMG if and only if its quotient graph w.r.t. a certain thinness relation is an RBMG. Furthermore, it is necessary and sufficient that all connected components of G are RBMGs. The main result of this contribution is a complete characterization of RBMGs with 3 colors/species that can be checked in polynomial time. For 3 colors, there are three distinct classes of trees that are related to the structure of the phylogenetic trees explaining them. We derive an approach to recognize RBMGs with an arbitrary number of colors; it remains open however, whether a polynomial-time for RBMG recognition exists. In addition, we show that RBMGs that at the same time are cographs (co-RBMGs) can be recognized in polynomial time. Co-RBMGs are characterized in terms of hierarchically colored cographs, a particular class of vertex colored cographs that is introduced here. The (least resolved) trees that explain co-RBMGs can be constructed in polynomial time.

Indexed as

PhylogenyComputational BiologyMolecular Sequence AnnotationSequence Analysis, DNAHierarchically colored cographPairwise best hitPhylogenetic treeReciprocal best match heuristicsVertex colored graph

Identifiers

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Registered trials

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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.