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The gene family-free median of three

Overview of attention for article published in Algorithms for Molecular Biology, May 2017
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Title
The gene family-free median of three
Published in
Algorithms for Molecular Biology, May 2017
DOI 10.1186/s13015-017-0106-z
Pubmed ID
Authors

Daniel Doerr, Metin Balaban, Pedro Feijão, Cedric Chauve

Abstract

The gene family-free framework for comparative genomics aims at providing methods for gene order analysis that do not require prior gene family assignment, but work directly on a sequence similarity graph. We study two problems related to the breakpoint median of three genomes, which asks for the construction of a fourth genome that minimizes the sum of breakpoint distances to the input genomes. We present a model for constructing a median of three genomes in this family-free setting, based on maximizing an objective function that generalizes the classical breakpoint distance by integrating sequence similarity in the score of a gene adjacency. We study its computational complexity and we describe an integer linear program (ILP) for its exact solution. We further discuss a related problem called family-free adjacencies for k genomes for the special case of [Formula: see text] and present an ILP for its solution. However, for this problem, the computation of exact solutions remains intractable for sufficiently large instances. We then proceed to describe a heuristic method, FFAdj-AM, which performs well in practice. The developed methods compute accurate positional orthologs for genomes comparable in size of bacterial genomes on simulated data and genomic data acquired from the OMA orthology database. In particular, FFAdj-AM performs equally or better when compared to the well-established gene family prediction tool MultiMSOAR. We study the computational complexity of a new family-free model and present algorithms for its solution. With FFAdj-AM, we propose an appealing alternative to established tools for identifying higher confidence positional orthologs.

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Mendeley readers

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Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 40%
Professor > Associate Professor 1 20%
Student > Ph. D. Student 1 20%
Unknown 1 20%
Readers by discipline Count As %
Computer Science 2 40%
Engineering 2 40%
Unknown 1 20%
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 31 May 2017.
All research outputs
#20,425,762
of 22,977,819 outputs
Outputs from Algorithms for Molecular Biology
#233
of 264 outputs
Outputs of similar age
#272,760
of 313,455 outputs
Outputs of similar age from Algorithms for Molecular Biology
#3
of 5 outputs
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