Evidence map›Paper›PMID 27780899›Full record

ArticleSystematic biology2017

Displayed Trees Do Not Determine Distinguishability Under the Network Multispecies Coalescent.

Sha Zhu, James H Degnan

Abstract read
In one paragraph

Article in Systematic biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Reading tree leaves: inferring speciation anfd extinction processes using phylogenies.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025
    Review
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  8. The rigid hybrid number for two phylogenetic trees.Journal of mathematical biology · 2021
    Article
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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

2 authors.

Sha ZhuWellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK.
James H DegnanDepartment of Mathematics and Statistics, University of New Mexico, Albuquerque, NM 87110, USA.

Funding

Mathematical and computational analysis for species tree inferenceR01GM117590 · NIGMS · UNIVERSITY OF ALASKA FAIRBANKS · PI RHODES, JOHN · 2015 to 2018
$1.5M
NIGMS NIH HHS R01 GM117590
6 · The paper itself

Abstract

Recent work in estimating species relationships from gene trees has included inferring networks assuming that past hybridization has occurred between species. Probabilistic models using the multispecies coalescent can be used in this framework for likelihood-based inference of both network topologies and parameters, including branch lengths and hybridization parameters. A difficulty for such methods is that it is not always clear whether, or to what extent, networks are identifiable-that is whether there could be two distinct networks that lead to the same distribution of gene trees. For cases in which incomplete lineage sorting occurs in addition to hybridization, we demonstrate a new representation of the species network likelihood that expresses the probability distribution of the gene tree topologies as a linear combination of gene tree distributions given a set of species trees. This representation makes it clear that in some cases in which two distinct networks give the same distribution of gene trees when sampling one allele per species, the two networks can be distinguished theoretically when multiple individuals are sampled per species. This result means that network identifiability is not only a function of the trees displayed by the networks but also depends on allele sampling within species. We additionally give an example in which two networks that display exactly the same trees can be distinguished from their gene trees even when there is only one lineage sampled per species. [gene tree, hybridization, identifiability, maximum likelihood, species tree, phylogeny.].

Indexed as

Models, GeneticPhylogenyClassificationHybridization, GeneticLikelihood FunctionsSpecies Specificity

Identifiers

PMID27780899
PMCPMC5837799

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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.