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DCJ-RNA - double cut and join for RNA secondary structures

Overview of attention for article published in BMC Bioinformatics, October 2017
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Title
DCJ-RNA - double cut and join for RNA secondary structures
Published in
BMC Bioinformatics, October 2017
DOI 10.1186/s12859-017-1830-6
Pubmed ID
Authors

Ghada H. Badr, Haifa A. Al-aqel

Abstract

Genome rearrangements are essential processes for evolution and are responsible for existing varieties of genome architectures. Many studies have been conducted to obtain an algorithm that identifies the minimum number of inversions that are necessary to transform one genome into another; this allows for genome sequence representation in polynomial time. Studies have not been conducted on the topic of rearranging a genome when it is represented as a secondary structure. Unlike sequences, the secondary structure preserves the functionality of the genome. Sequences can be different, but they all share the same structure and, therefore, the same functionality. This paper proposes a double cut and join for RNA secondary structures (DCJ-RNA) algorithm. This algorithm allows for the description of evolutionary scenarios that are based on secondary structures rather than sequences. The main aim of this paper is to suggest an efficient algorithm that can help researchers compare two ribonucleic acid (RNA) secondary structures based on rearrangement operations. The results, which are based on real datasets, show that the algorithm is able to count the minimum number of rearrangement operations, as well as to report an optimum scenario that can increase the similarity between the two structures. The algorithm calculates the distance between structures and reports a scenario based on the minimum rearrangement operations required to make the given structure similar to the other. DCJ-RNA can also be used to measure the distance between the two structures. This can help identify the common functionalities between different species.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Postgraduate 1 25%
Student > Ph. D. Student 1 25%
Researcher 1 25%
Professor > Associate Professor 1 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 50%
Physics and Astronomy 1 25%
Neuroscience 1 25%

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 10 February 2018.
All research outputs
#9,994,872
of 12,487,420 outputs
Outputs from BMC Bioinformatics
#3,846
of 4,653 outputs
Outputs of similar age
#228,024
of 311,375 outputs
Outputs of similar age from BMC Bioinformatics
#362
of 458 outputs
Altmetric has tracked 12,487,420 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,653 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 8th percentile – i.e., 8% of its peers scored the same or lower than it.
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We're also able to compare this research output to 458 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.