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New in silico approach to assessing RNA secondary structures with non-canonical base pairs

Overview of attention for article published in BMC Bioinformatics, September 2015
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  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

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1 Wikipedia page

Citations

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39 Mendeley
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Title
New in silico approach to assessing RNA secondary structures with non-canonical base pairs
Published in
BMC Bioinformatics, September 2015
DOI 10.1186/s12859-015-0718-6
Pubmed ID
Authors

Agnieszka Rybarczyk, Natalia Szostak, Maciej Antczak, Tomasz Zok, Mariusz Popenda, Ryszard Adamiak, Jacek Blazewicz, Marta Szachniuk

Abstract

The function of RNA is strongly dependent on its structure, so an appropriate recognition of this structure, on every level of organization, is of great importance. One particular concern is the assessment of base-base interactions, described as the secondary structure, the knowledge of which greatly facilitates an interpretation of RNA function and allows for structure analysis on the tertiary level. The RNA secondary structure can be predicted from a sequence using in silico methods often adjusted with experimental data, or assessed from 3D structure atom coordinates. Computational approaches typically consider only canonical, Watson-Crick and wobble base pairs. Handling of non-canonical interactions, important for a full description of RNA structure, is still very difficult. We introduce our novel approach to assessing an extended RNA secondary structure, which characterizes both canonical and non-canonical base pairs, along with their type classification. It is based on predicting the RNA 3D structure from a user-provided sequence or a secondary structure that only describes canonical base pairs, and then deriving the extended secondary structure from atom coordinates. In our example implementation, this was achieved by integrating the functionality of two fully automated, high fidelity methods in a computational pipeline: RNAComposer for the 3D RNA structure prediction and RNApdbee for base-pair annotation. The presented methodology ties together existing applications for RNA 3D structure prediction and base-pair annotation. The example performance, applying RNAComposer and RNApdbee, reveals better accuracy in non-canonical base pair assessment than the compared methods that directly predict RNA secondary structure.

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X Demographics

The data shown below were collected from the profiles of 5 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 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 3%
Poland 1 3%
Unknown 37 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 23%
Researcher 8 21%
Student > Master 7 18%
Student > Bachelor 5 13%
Professor > Associate Professor 3 8%
Other 3 8%
Unknown 4 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 23%
Computer Science 9 23%
Biochemistry, Genetics and Molecular Biology 7 18%
Engineering 4 10%
Chemistry 2 5%
Other 4 10%
Unknown 4 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 13 June 2023.
All research outputs
#6,079,573
of 24,071,812 outputs
Outputs from BMC Bioinformatics
#2,157
of 7,498 outputs
Outputs of similar age
#67,886
of 271,267 outputs
Outputs of similar age from BMC Bioinformatics
#39
of 124 outputs
Altmetric has tracked 24,071,812 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 7,498 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 70% 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 271,267 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 124 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 69% of its contemporaries.