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RNA Structure Determination

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Attention for Chapter 14: RNA Structure Determination
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  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

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Chapter title
RNA Structure Determination
Chapter number 14
Book title
RNA Structure Determination
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-6433-8_14
Pubmed ID
Book ISBNs
978-1-4939-6431-4, 978-1-4939-6433-8
Authors

Piatkowski, Pawel, Kasprzak, Joanna M, Kumar, Deepak, Magnus, Marcin, Chojnowski, Grzegorz, Bujnicki, Janusz M, Pawel Piatkowski, Joanna M. Kasprzak, Deepak Kumar, Marcin Magnus, Grzegorz Chojnowski, Janusz M. Bujnicki

Abstract

RNA encompasses an essential part of all known forms of life. The functions of many RNA molecules are dependent on their ability to form complex three-dimensional (3D) structures. However, experimental determination of RNA 3D structures is laborious and challenging, and therefore, the majority of known RNAs remain structurally uncharacterized. To address this problem, computational structure prediction methods were developed that either utilize information derived from known structures of other RNA molecules (by way of template-based modeling) or attempt to simulate the physical process of RNA structure formation (by way of template-free modeling). All computational methods suffer from various limitations that make theoretical models less reliable than high-resolution experimentally determined structures. This chapter provides a protocol for computational modeling of RNA 3D structure that overcomes major limitations by combining two complementary approaches: template-based modeling that is capable of predicting global architectures based on similarity to other molecules but often fails to predict local unique features, and template-free modeling that can predict the local folding, but is limited to modeling the structure of relatively small molecules. Here, we combine the use of a template-based method ModeRNA with a template-free method SimRNA. ModeRNA requires a sequence alignment of the target RNA sequence to be modeled with a template of the known structure; it generates a model that predicts the structure of a conserved core and provides a starting point for modeling of variable regions. SimRNA can be used to fold small RNAs (<80 nt) without any additional structural information, and to refold parts of models for larger RNAs that have a correctly modeled core. ModeRNA can be either downloaded, compiled and run locally or run through a web interface at http://genesilico.pl/modernaserver/ . SimRNA is currently available to download for local use as a precompiled software package at http://genesilico.pl/software/stand-alone/simrna and as a web server at http://genesilico.pl/SimRNAweb . For model optimization we use QRNAS, available at http://genesilico.pl/qrnas .

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 29%
Student > Bachelor 3 21%
Student > Ph. D. Student 3 21%
Professor 1 7%
Student > Master 1 7%
Other 1 7%
Unknown 1 7%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 43%
Agricultural and Biological Sciences 5 36%
Chemistry 1 7%
Unknown 2 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 26 September 2016.
All research outputs
#14,861,841
of 22,889,074 outputs
Outputs from Methods in molecular biology
#4,703
of 13,133 outputs
Outputs of similar age
#219,042
of 393,722 outputs
Outputs of similar age from Methods in molecular biology
#469
of 1,471 outputs
Altmetric has tracked 22,889,074 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,133 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 59% 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 393,722 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,471 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 63% of its contemporaries.