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Utility of characters evolving at diverse rates of evolution to resolve quartet trees with unequal branch lengths: analytical predictions of long-branch effects

Overview of attention for article published in BMC Ecology and Evolution, May 2015
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Title
Utility of characters evolving at diverse rates of evolution to resolve quartet trees with unequal branch lengths: analytical predictions of long-branch effects
Published in
BMC Ecology and Evolution, May 2015
DOI 10.1186/s12862-015-0364-7
Pubmed ID
Authors

Zhuo Su, Jeffrey P Townsend

Abstract

The detection and avoidance of "long-branch effects" in phylogenetic inference represents a longstanding challenge for molecular phylogenetic investigations. A consequence of parallelism and convergence, long-branch effects arise in phylogenetic inference when there is unequal molecular divergence among lineages, and they can positively mislead inference based on parsimony especially, but also inference based on maximum likelihood and Bayesian approaches. Long-branch effects have been exhaustively examined by simulation studies that have compared the performance of different inference methods in specific model trees and branch length spaces. In this paper, by generalizing the phylogenetic signal and noise analysis to quartets with uneven subtending branches, we quantify the utility of molecular characters for resolution of quartet phylogenies via parsimony. Our quantification incorporates contributions toward the correct tree from either signal or homoplasy (i.e. "the right result for either the right reason or the wrong reason"). We also characterize a highly conservative lower bound of utility that incorporates contributions to the correct tree only when they correspond to true, unobscured parsimony-informative sites (i.e. "the right result for the right reason"). We apply the generalized signal and noise analysis to classic quartet phylogenies in which long-branch effects can arise due to unequal rates of evolution or an asymmetrical topology. Application of the analysis leads to identification of branch length conditions in which inference will be inconsistent and reveals insights regarding how to improve sampling of molecular loci and taxa in order to correctly resolve phylogenies in which long-branch effects are hypothesized to exist. The generalized signal and noise analysis provides analytical prediction of utility of characters evolving at diverse rates of evolution to resolve quartet phylogenies with unequal branch lengths. The analysis can be applied to identifying characters evolving at appropriate rates to resolve phylogenies in which long-branch effects are hypothesized to occur.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 3%
Unknown 32 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 18%
Student > Bachelor 5 15%
Researcher 5 15%
Professor > Associate Professor 4 12%
Professor 3 9%
Other 9 27%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 58%
Biochemistry, Genetics and Molecular Biology 11 33%
Immunology and Microbiology 1 3%
Earth and Planetary Sciences 1 3%
Chemistry 1 3%
Other 0 0%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 August 2015.
All research outputs
#14,915,133
of 25,374,647 outputs
Outputs from BMC Ecology and Evolution
#2,489
of 3,714 outputs
Outputs of similar age
#134,086
of 278,956 outputs
Outputs of similar age from BMC Ecology and Evolution
#42
of 66 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,714 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.5. This one is in the 31st percentile – i.e., 31% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 278,956 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 66 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.