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Sorting signed circular permutations by super short operations

Overview of attention for article published in Algorithms for Molecular Biology, July 2018
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Title
Sorting signed circular permutations by super short operations
Published in
Algorithms for Molecular Biology, July 2018
DOI 10.1186/s13015-018-0131-6
Pubmed ID
Authors

Andre R. Oliveira, Guillaume Fertin, Ulisses Dias, Zanoni Dias

Abstract

One way to estimate the evolutionary distance between two given genomes is to determine the minimum number of large-scale mutations, or genome rearrangements, that are necessary to transform one into the other. In this context, genomes can be represented as ordered sequences of genes, each gene being represented by a signed integer. If no gene is repeated, genomes are thus modeled as signed permutations of the form π = ( π 1 π 2 … π n ) , and in that case we can consider without loss of generality that one of them is the identity permutation ι n = ( 12 … n ) , and that we just need to sort the other (i.e., transform it into ι n ). The most studied genome rearrangement events are reversals, where a segment of the genome is reversed and reincorporated at the same location; and transpositions, where two consecutive segments are exchanged. Many variants, e.g., combining different types of (possibly constrained) rearrangements, have been proposed in the literature. One of them considers that the number of genes involved, in a reversal or a transposition, is never greater than two, which is known as the problem of sorting by super short operations (or SSOs). All problems considering SSOs in permutations have been shown to be in P , except for one, namely sorting signed circular permutations by super short reversals and super short transpositions. Here we fill this gap by introducing a new graph structure called cyclic permutation graph and providing a series of intermediate results, which allows us to design a polynomial algorithm for sorting signed circular permutations by super short reversals and super short transpositions.

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Geographical breakdown

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Unknown 1 100%

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Readers by professional status Count As %
Professor > Associate Professor 1 100%
Readers by discipline Count As %
Computer Science 1 100%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 27 July 2018.
All research outputs
#14,421,028
of 23,098,660 outputs
Outputs from Algorithms for Molecular Biology
#111
of 264 outputs
Outputs of similar age
#186,312
of 330,319 outputs
Outputs of similar age from Algorithms for Molecular Biology
#3
of 3 outputs
Altmetric has tracked 23,098,660 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 264 research outputs from this source. They receive a mean Attention Score of 3.2. 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 330,319 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.