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Inferring gene duplications, transfers and losses can be done in a discrete framework

Overview of attention for article published in Journal of Mathematical Biology, September 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#48 of 655)
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

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Title
Inferring gene duplications, transfers and losses can be done in a discrete framework
Published in
Journal of Mathematical Biology, September 2015
DOI 10.1007/s00285-015-0930-z
Pubmed ID
Authors

Vincent Ranwez, Celine Scornavacca, Jean-Philippe Doyon, Vincent Berry

Abstract

In the field of phylogenetics, the evolutionary history of a set of organisms is commonly depicted by a species tree-whose internal nodes represent speciation events-while the evolutionary history of a gene family is depicted by a gene tree-whose internal nodes can also represent macro-evolutionary events such as gene duplications and transfers. As speciation events are only part of the events shaping a gene history, the topology of a gene tree can show incongruences with that of the corresponding species tree. These incongruences can be used to infer the macro-evolutionary events undergone by the gene family. This is done by embedding the gene tree inside the species tree and hence providing a reconciliation of those trees. In the past decade, several parsimony-based methods have been developed to infer such reconciliations, accounting for gene duplications ([Formula: see text]), transfers ([Formula: see text]) and losses ([Formula: see text]). The main contribution of this paper is to formally prove an important assumption implicitly made by previous works on these reconciliations, namely that solving the (maximum) parsimony [Formula: see text] reconciliation problem in the discrete framework is equivalent to finding a most parsimonious [Formula: see text] scenario in the continuous framework. In the process, we also prove several intermediate results that are useful on their own and constitute a theoretical toolbox that will likely facilitate future theoretical contributions in the field.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 1 7%
Unknown 14 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 27%
Student > Doctoral Student 2 13%
Professor 2 13%
Student > Ph. D. Student 2 13%
Student > Bachelor 1 7%
Other 3 20%
Unknown 1 7%
Readers by discipline Count As %
Computer Science 5 33%
Agricultural and Biological Sciences 4 27%
Biochemistry, Genetics and Molecular Biology 1 7%
Mathematics 1 7%
Unknown 4 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 23 September 2016.
All research outputs
#3,200,109
of 22,826,360 outputs
Outputs from Journal of Mathematical Biology
#48
of 655 outputs
Outputs of similar age
#44,197
of 267,016 outputs
Outputs of similar age from Journal of Mathematical Biology
#1
of 7 outputs
Altmetric has tracked 22,826,360 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 655 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done particularly well, scoring higher than 92% of its peers.
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 267,016 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them