Evidence map›Paper›PMID 31860090›Full record

ArticleMolecular biology and evolution2020

Probabilities of Unranked and Ranked Anomaly Zones under Birth-Death Models.

Anastasiia Kim, Noah A Rosenberg, James H Degnan

Abstract read
In one paragraph

Article in Molecular biology and evolution, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Anastasiia KimDepartment of Mathematics and Statistics, University of New Mexico, Albuquerque, NM.
Noah A RosenbergDepartment of Biology, Stanford University, Stanford, CA.
James H DegnanDepartment of Mathematics and Statistics, University of New Mexico, Albuquerque, NM.

Funding

Mathematical and computational analysis for species tree inferenceR01GM117590 · NIGMS · UNIVERSITY OF ALASKA FAIRBANKS · PI RHODES, JOHN · 2015 to 2018
$1.5M
Scalable Coalescent Inference for Large Data SetsR01GM131404 · NIGMS · STANFORD UNIVERSITY · PI PALACIOS, JULIA · 2018 to 2021
$1.2M
NIGMS NIH HHS R01 GM117590NIGMS NIH HHS R01 GM131404
6 · The paper itself

Abstract

A labeled gene tree topology that is more probable than the labeled gene tree topology matching a species tree is called "anomalous." Species trees that can generate such anomalous gene trees are said to be in the "anomaly zone." Here, probabilities of "unranked" and "ranked" gene tree topologies under the multispecies coalescent are considered. A ranked tree depicts not only the topological relationship among gene lineages, as an unranked tree does, but also the sequence in which the lineages coalesce. In this article, we study how the parameters of a species tree simulated under a constant-rate birth-death process can affect the probability that the species tree lies in the anomaly zone. We find that with more than five taxa, it is possible for species trees to have both anomalous unranked and ranked gene trees. The probability of being in either type of anomaly zone increases with more taxa. The probability of anomalous gene trees also increases with higher speciation rates. We observe that the probabilities of unranked anomaly zones are higher and grow much faster than those of ranked anomaly zones as the speciation rate increases. Our simulation shows that the most probable ranked gene tree is likely to have the same unranked topology as the species tree. We design the software PRANC, which computes probabilities of ranked gene tree topologies given a species tree under the coalescent model.

Indexed as

Models, GeneticPhylogenySoftwareComputer SimulationProof of Concept Studyanomalous gene treecoalescentgene treephylogenyspecies tree

Identifiers

PMID31860090
PMCPMC7182218

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.