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An Algorithm for Template-Based Prediction of Secondary Structures of Individual RNA Sequences

Overview of attention for article published in Frontiers in Genetics, October 2017
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Title
An Algorithm for Template-Based Prediction of Secondary Structures of Individual RNA Sequences
Published in
Frontiers in Genetics, October 2017
DOI 10.3389/fgene.2017.00147
Pubmed ID
Authors

Josef Pánek, Martin Modrák, Marek Schwarz

Abstract

While understanding the structure of RNA molecules is vital for deciphering their functions, determining RNA structures experimentally is exceptionally hard. At the same time, extant approaches to computational RNA structure prediction have limited applicability and reliability. In this paper we provide a method to solve a simpler yet still biologically relevant problem: prediction of secondary RNA structure using structure of different molecules as a template. Our method identifies conserved and unconserved subsequences within an RNA molecule. For conserved subsequences, the template structure is directly transferred into the generated structure and combined with de-novo predicted structure for the unconserved subsequences with low evolutionary conservation. The method also determines, when the generated structure is unreliable. The method is validated using experimentally identified structures. The accuracy of the method exceeds that of classical prediction algorithms and constrained prediction methods. This is demonstrated by comparison using large number of heterogeneous RNAs. The presented method is fast and robust, and useful for various applications requiring knowledge of secondary structures of individual RNA sequences.

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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 %
Student > Ph. D. Student 4 29%
Unspecified 1 7%
Student > Bachelor 1 7%
Researcher 1 7%
Student > Master 1 7%
Other 0 0%
Unknown 6 43%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 21%
Agricultural and Biological Sciences 3 21%
Computer Science 1 7%
Unspecified 1 7%
Unknown 6 43%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 24 January 2018.
All research outputs
#17,917,778
of 23,005,189 outputs
Outputs from Frontiers in Genetics
#6,156
of 12,065 outputs
Outputs of similar age
#232,082
of 324,392 outputs
Outputs of similar age from Frontiers in Genetics
#60
of 76 outputs
Altmetric has tracked 23,005,189 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 12,065 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 40th percentile – i.e., 40% of its peers scored the same or lower than it.
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We're also able to compare this research output to 76 others from the same source and published within six weeks on either side of this one. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.