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PATTERNA: transcriptome-wide search for functional RNA elements via structural data signatures

Overview of attention for article published in Genome Biology, March 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

Mentioned by

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1 blog
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34 X users

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88 Mendeley
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Title
PATTERNA: transcriptome-wide search for functional RNA elements via structural data signatures
Published in
Genome Biology, March 2018
DOI 10.1186/s13059-018-1399-z
Pubmed ID
Authors

Mirko Ledda, Sharon Aviran

Abstract

Establishing a link between RNA structure and function remains a great challenge in RNA biology. The emergence of high-throughput structure profiling experiments is revolutionizing our ability to decipher structure, yet principled approaches for extracting information on structural elements directly from these data sets are lacking. We present PATTERNA, an unsupervised pattern recognition algorithm that rapidly mines RNA structure motifs from profiling data. We demonstrate that PATTERNA detects motifs with an accuracy comparable to commonly used thermodynamic models and highlight its utility in automating data-directed structure modeling from large data sets. PATTERNA is versatile and compatible with diverse profiling techniques and experimental conditions.

X Demographics

X Demographics

The data shown below were collected from the profiles of 34 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 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 88 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 28%
Student > Ph. D. Student 23 26%
Student > Bachelor 6 7%
Other 5 6%
Student > Master 5 6%
Other 9 10%
Unknown 15 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 34 39%
Agricultural and Biological Sciences 18 20%
Chemistry 9 10%
Computer Science 8 9%
Engineering 2 2%
Other 3 3%
Unknown 14 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 28. 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 August 2018.
All research outputs
#1,398,387
of 25,394,764 outputs
Outputs from Genome Biology
#1,108
of 4,470 outputs
Outputs of similar age
#30,681
of 344,933 outputs
Outputs of similar age from Genome Biology
#17
of 49 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,470 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has done well, scoring higher than 75% 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 344,933 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 49 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 65% of its contemporaries.