↓ Skip to main content

Reconstructing a SuperGeneTree minimizing reconciliation

Overview of attention for article published in BMC Bioinformatics, October 2015
Altmetric Badge

Mentioned by

twitter
1 X user
facebook
1 Facebook page

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
8 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Reconstructing a SuperGeneTree minimizing reconciliation
Published in
BMC Bioinformatics, October 2015
DOI 10.1186/1471-2105-16-s14-s4
Pubmed ID
Authors

Manuel Lafond, Aïda Ouangraoua, Nadia El-Mabrouk

Abstract

Combining a set of trees on partial datasets into a single tree is a classical method for inferring large phylogenetic trees. Ideally, the combined tree should display each input partial tree, which is only possible if input trees do not contain contradictory phylogenetic information. The simplest version of the supertree problem is thus to state whether a set of trees is compatible, and if so, construct a tree displaying them all. Classically, supertree methods have been applied to the reconstruction of species trees. Here we rather consider reconstructing a super gene tree in light of a known species tree S. We define the supergenetree problem as finding, among all supertrees displaying a set of input gene trees, one supertree minimizing a reconciliation distance with S. We first show how classical exact methods to the supertree problem can be extended to the supergenetree problem. As all these methods are highly exponential, we also exhibit a natural greedy heuristic for the duplication cost, based on minimizing the set of duplications preceding the first speciation event. We then show that both the supergenetree problem and its restriction to minimizing duplications preceding the first speciation are NP-hard to approximate within a n1-ϵ factor, for any 0 < ϵ < 1. Finally, we show that a restriction of this problem to uniquely labeled speciation gene trees, which is relevant to many biological applications, is also NP-hard. Therefore, we introduce new avenues in the field of supertrees, and set the theoretical basis for the exploration of various algorithmic aspects of the problems.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Estonia 1 13%
Brazil 1 13%
Unknown 6 75%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 50%
Professor 1 13%
Student > Master 1 13%
Researcher 1 13%
Professor > Associate Professor 1 13%
Other 0 0%
Readers by discipline Count As %
Computer Science 3 38%
Agricultural and Biological Sciences 2 25%
Biochemistry, Genetics and Molecular Biology 1 13%
Mathematics 1 13%
Unknown 1 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 05 October 2015.
All research outputs
#17,774,664
of 22,829,683 outputs
Outputs from BMC Bioinformatics
#5,936
of 7,287 outputs
Outputs of similar age
#185,409
of 275,403 outputs
Outputs of similar age from BMC Bioinformatics
#112
of 143 outputs
Altmetric has tracked 22,829,683 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,287 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 13th percentile – i.e., 13% 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 275,403 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 143 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.