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A reconstruction problem for a class of phylogenetic networks with lateral gene transfers

Overview of attention for article published in Algorithms for Molecular Biology, December 2015
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About this Attention Score

  • Among the highest-scoring outputs from this source (#46 of 194)
  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

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2 tweeters
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2 Google+ users

Citations

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13 Mendeley
Title
A reconstruction problem for a class of phylogenetic networks with lateral gene transfers
Published in
Algorithms for Molecular Biology, December 2015
DOI 10.1186/s13015-015-0059-z
Pubmed ID
Authors

Gabriel Cardona, Joan Carles Pons, Francesc Rosselló

Abstract

Lateral, or Horizontal, Gene Transfers are a type of asymmetric evolutionary events where genetic material is transferred from one species to another. In this paper we consider LGT networks, a general model of phylogenetic networks with lateral gene transfers which consist, roughly, of a principal rooted tree with its leaves labelled on a set of taxa, and a set of extra secondary arcs between nodes in this tree representing lateral gene transfers. An LGT network gives rise in a natural way to a principal phylogenetic subtree and a set of secondary phylogenetic subtrees, which, roughly, represent, respectively, the main line of evolution of most genes and the secondary lines of evolution through lateral gene transfers. We introduce a set of simple conditions on an LGT network that guarantee that its principal and secondary phylogenetic subtrees are pairwise different and that these subtrees determine, up to isomorphism, the LGT network. We then give an algorithm that, given a set of pairwise different phylogenetic trees [Formula: see text] on the same set of taxa, outputs, when it exists, the LGT network that satisfies these conditions and such that its principal phylogenetic tree is [Formula: see text] and its secondary phylogenetic trees are [Formula: see text].

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 38%
Researcher 2 15%
Student > Master 2 15%
Professor 1 8%
Student > Bachelor 1 8%
Other 1 8%
Unknown 1 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 38%
Computer Science 3 23%
Biochemistry, Genetics and Molecular Biology 2 15%
Mathematics 1 8%
Environmental Science 1 8%
Other 0 0%
Unknown 1 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 16 April 2018.
All research outputs
#3,509,783
of 12,808,036 outputs
Outputs from Algorithms for Molecular Biology
#46
of 194 outputs
Outputs of similar age
#94,241
of 350,679 outputs
Outputs of similar age from Algorithms for Molecular Biology
#2
of 21 outputs
Altmetric has tracked 12,808,036 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 194 research outputs from this source. They receive a mean Attention Score of 2.9. This one has done well, scoring higher than 75% 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 350,679 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 72% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.