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Comparative genomics meets topology: a novel view on genome median and halving problems

Overview of attention for article published in BMC Bioinformatics, November 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

Mentioned by

twitter
5 tweeters

Citations

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5 Dimensions

Readers on

mendeley
3 Mendeley
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Title
Comparative genomics meets topology: a novel view on genome median and halving problems
Published in
BMC Bioinformatics, November 2016
DOI 10.1186/s12859-016-1263-7
Pubmed ID
Authors

Nikita Alexeev, Pavel Avdeyev, Max A. Alekseyev

Abstract

Genome median and genome halving are combinatorial optimization problems that aim at reconstruction of ancestral genomes by minimizing the number of evolutionary events between them and genomes of the extant species. While these problems have been widely studied in past decades, their solutions are often either not efficient or not biologically adequate. These shortcomings have been recently addressed by restricting the problems solution space. We show that the restricted variants of genome median and halving problems are, in fact, closely related. We demonstrate that these problems have a neat topological interpretation in terms of embedded graphs and polygon gluings. We illustrate how such interpretation can lead to solutions to these problems in particular cases. This study provides an unexpected link between comparative genomics and topology, and demonstrates advantages of solving genome median and halving problems within the topological framework.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 67%
Professor > Associate Professor 1 33%
Readers by discipline Count As %
Computer Science 2 67%
Unknown 1 33%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 30 July 2017.
All research outputs
#8,186,782
of 15,918,909 outputs
Outputs from BMC Bioinformatics
#2,722
of 5,768 outputs
Outputs of similar age
#116,837
of 290,812 outputs
Outputs of similar age from BMC Bioinformatics
#190
of 438 outputs
Altmetric has tracked 15,918,909 research outputs across all sources so far. This one is in the 48th percentile – i.e., 48% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,768 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 52% 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 290,812 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 59% of its contemporaries.
We're also able to compare this research output to 438 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.