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A fast structural multiple alignment method for long RNA sequences

Overview of attention for article published in BMC Bioinformatics, January 2008
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (59th percentile)

Mentioned by

twitter
2 X users
wikipedia
1 Wikipedia page

Citations

dimensions_citation
99 Dimensions

Readers on

mendeley
77 Mendeley
citeulike
5 CiteULike
connotea
2 Connotea
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Title
A fast structural multiple alignment method for long RNA sequences
Published in
BMC Bioinformatics, January 2008
DOI 10.1186/1471-2105-9-33
Pubmed ID
Authors

Yasuo Tabei, Hisanori Kiryu, Taishin Kin, Kiyoshi Asai

Abstract

Aligning multiple RNA sequences is essential for analyzing non-coding RNAs. Although many alignment methods for non-coding RNAs, including Sankoff's algorithm for strict structural alignments, have been proposed, they are either inaccurate or computationally too expensive. Faster methods with reasonable accuracies are required for genome-scale analyses.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 3%
Brazil 2 3%
Spain 2 3%
United States 2 3%
Denmark 1 1%
Sweden 1 1%
Japan 1 1%
Canada 1 1%
Unknown 65 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 26%
Student > Ph. D. Student 16 21%
Student > Master 15 19%
Student > Bachelor 5 6%
Student > Doctoral Student 5 6%
Other 9 12%
Unknown 7 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 35 45%
Biochemistry, Genetics and Molecular Biology 13 17%
Computer Science 13 17%
Environmental Science 3 4%
Immunology and Microbiology 1 1%
Other 3 4%
Unknown 9 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 17 March 2012.
All research outputs
#6,378,772
of 22,663,969 outputs
Outputs from BMC Bioinformatics
#2,469
of 7,246 outputs
Outputs of similar age
#35,625
of 155,059 outputs
Outputs of similar age from BMC Bioinformatics
#14
of 37 outputs
Altmetric has tracked 22,663,969 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 7,246 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 64% 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 155,059 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 74% of its contemporaries.
We're also able to compare this research output to 37 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 59% of its contemporaries.