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The link between orthology relations and gene trees: a correction perspective

Overview of attention for article published in Algorithms for Molecular Biology, April 2016
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Title
The link between orthology relations and gene trees: a correction perspective
Published in
Algorithms for Molecular Biology, April 2016
DOI 10.1186/s13015-016-0067-7
Pubmed ID
Authors

Manuel Lafond, Riccardo Dondi, Nadia El-Mabrouk

Abstract

While tree-oriented methods for inferring orthology and paralogy relations between genes are based on reconciling a gene tree with a species tree, many tree-free methods are also available (usually based on sequence similarity). Recently, the link between orthology relations and gene trees has been formally considered from the perspective of reconstructing phylogenies from orthology relations. In this paper, we consider this link from a correction point of view. Indeed, a gene tree induces a set of relations, but the converse is not always true: a set of relations is not necessarily in agreement with any gene tree. A natural question is thus how to minimally correct an infeasible set of relations. Another natural question, given a gene tree and a set of relations, is how to minimally correct a gene tree so that the resulting gene tree fits the set of relations. We consider four variants of relation and gene tree correction problems, and provide hardness results for all of them. More specifically, we show that it is NP-Hard to edit a minimum of set of relations to make them consistent with a given species tree. We also show that the problem of finding a maximum subset of genes that share consistent relations is hard to approximate. We then demonstrate that editing a gene tree to satisfy a given set of relations in a minimum way is NP-Hard, where "minimum" refers either to the number of modified relations depicted by the gene tree or the number of clades that are lost. We also discuss some of the algorithmic perspectives given these hardness results.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 5%
Unknown 20 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 29%
Researcher 4 19%
Lecturer 2 10%
Student > Bachelor 2 10%
Student > Master 2 10%
Other 2 10%
Unknown 3 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 29%
Agricultural and Biological Sciences 5 24%
Computer Science 4 19%
Mathematics 1 5%
Unknown 5 24%
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 17 April 2016.
All research outputs
#18,451,892
of 22,862,742 outputs
Outputs from Algorithms for Molecular Biology
#197
of 264 outputs
Outputs of similar age
#197,903
of 269,982 outputs
Outputs of similar age from Algorithms for Molecular Biology
#11
of 12 outputs
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