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MASTR: multiple alignment and structure prediction of non-coding RNAs using simulated annealing

Overview of attention for article published in Bioinformatics, November 2007
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2 Wikipedia pages

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68 Mendeley
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6 CiteULike
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2 Connotea
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Article details
Title
MASTR: multiple alignment and structure prediction of non-coding RNAs using simulated annealing
Published in
Bioinformatics, November 2007
DOI 10.1093/bioinformatics/btm525
Pubmed ID
Authors
Abstract

As more non-coding RNAs are discovered, the importance of methods for RNA analysis increases. Since the structure of ncRNA is intimately tied to the function of the molecule, programs for RNA structure prediction are necessary tools in this growing field of research. Furthermore, it is known that RNA structure is often evolutionarily more conserved than sequence. However, few existing methods are capable of simultaneously considering multiple sequence alignment and structure prediction.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 2 3%
France 2 3%
New Zealand 1 1%
United Kingdom 1 1%
Denmark 1 1%
Unknown 61 90%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 19 28%
Student > Ph. D. Student 14 21%
Professor > Associate Professor 8 12%
Student > Master 7 10%
Student > Doctoral Student 4 6%
Other 10 15%
Unknown 6 9%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 39 57%
Computer Science 11 16%
Biochemistry, Genetics and Molecular Biology 7 10%
Immunology and Microbiology 2 3%
Neuroscience 1 1%
Other 1 1%
Unknown 7 10%
Attention Score in Context

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 21 February 2008.
All research outputs
#8,535,472
of 25,374,917 outputs
Outputs from Bioinformatics
#6,957
of 12,809 outputs
Outputs of similar age
#28,907
of 85,194 outputs
Outputs of similar age from Bioinformatics
#18
of 41 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 12,809 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 34th percentile – i.e., 34% of its peers scored the same or lower than it.
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 85,194 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.