Evidence map›Paper›PMID 22276164›Full record

ArticlePloS one2012

Impact of deep coalescence on the reliability of species tree inference from different types of DNA markers in mammals.

Alejandro Sánchez-Gracia, Jose Castresana

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
11.6field-weighted citation impact, top 2% of its field
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

13 citing papers in PubMed, 39 citations in OpenAlex.

  1. Evolutionary History of theAnimals : an open access journal from MDPI · 2026
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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 at 1 institution in 1 country.

Alejandro Sánchez-GraciaInstitute of Evolutionary Biology (CSIC-UPF), Barcelona, Spain.
Jose Castresana
Institut de Biologia Evolutiva · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An important challenge for phylogenetic studies of closely related species is the existence of deep coalescence and gene tree heterogeneity. However, their effects can vary between species and they are often neglected in phylogenetic analyses. In addition, a practical problem in the reconstruction of shallow phylogenies is to determine the most efficient set of DNA markers for a reliable estimation. To address these questions, we conducted a multilocus simulation study using empirical values of nucleotide diversity and substitution rates obtained from a wide range of mammals and evaluated the performance of both gene tree and species tree approaches to recover the known speciation times and topological relationships. We first show that deep coalescence can be a serious problem, more than usually assumed, for the estimation of speciation times in mammals using traditional gene trees. Furthermore, we tested the performance of different sets of DNA markers in the determination of species trees using a coalescent approach. Although the best estimates of speciation times were obtained, as expected, with the use of an increasing number of nuclear loci, our results show that similar estimations can be obtained with a much lower number of genes and the incorporation of a mitochondrial marker, with its high information content. Thus, the use of the combined information of both nuclear and mitochondrial markers in a species tree framework is the most efficient option to estimate recent speciation times and, consequently, the underlying species tree.

Indexed as

PhylogenyAnimalsComputer SimulationGenetic MarkersMammalsGenetic Markers

Identifiers

PMID22276164
PMCPMC3261886
OpenAlexW2000646917

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.