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Circular sequence comparison: algorithms and applications

Overview of attention for article published in Algorithms for Molecular Biology, May 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#9 of 177)
  • High Attention Score compared to outputs of the same age (84th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

news
1 news outlet
twitter
2 tweeters

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
23 Mendeley
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Title
Circular sequence comparison: algorithms and applications
Published in
Algorithms for Molecular Biology, May 2016
DOI 10.1186/s13015-016-0076-6
Pubmed ID
Authors

Roberto Grossi, Costas S. Iliopoulos, Robert Mercas, Nadia Pisanti, Solon P. Pissis, Ahmad Retha, Fatima Vayani

Abstract

Sequence comparison is a fundamental step in many important tasks in bioinformatics; from phylogenetic reconstruction to the reconstruction of genomes. Traditional algorithms for measuring approximation in sequence comparison are based on the notions of distance or similarity, and are generally computed through sequence alignment techniques. As circular molecular structure is a common phenomenon in nature, a caveat of the adaptation of alignment techniques for circular sequence comparison is that they are computationally expensive, requiring from super-quadratic to cubic time in the length of the sequences. In this paper, we introduce a new distance measure based on q-grams, and show how it can be applied effectively and computed efficiently for circular sequence comparison. Experimental results, using real DNA, RNA, and protein sequences as well as synthetic data, demonstrate orders-of-magnitude superiority of our approach in terms of efficiency, while maintaining an accuracy very competitive to the state of the art.

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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 22%
Other 4 17%
Student > Ph. D. Student 4 17%
Researcher 3 13%
Professor 2 9%
Other 5 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 30%
Biochemistry, Genetics and Molecular Biology 7 30%
Computer Science 6 26%
Unspecified 3 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 May 2017.
All research outputs
#1,158,066
of 11,293,566 outputs
Outputs from Algorithms for Molecular Biology
#9
of 177 outputs
Outputs of similar age
#43,045
of 277,694 outputs
Outputs of similar age from Algorithms for Molecular Biology
#2
of 12 outputs
Altmetric has tracked 11,293,566 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 177 research outputs from this source. They receive a mean Attention Score of 2.8. This one has done particularly well, scoring higher than 94% 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 277,694 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.