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RNA Nanostructures

Overview of attention for book
Cover of 'RNA Nanostructures'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 A New Method to Predict Ion Effects in RNA Folding
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    Chapter 2 Computational Generation of RNA Nanorings
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    Chapter 3 Protocols for Molecular Dynamics Simulations of RNA Nanostructures
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    Chapter 4 Rolling Circle Transcription for the Self-Assembly of Multimeric RNAi Structures and Its Applications in Nanomedicine
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    Chapter 5 Computational Prediction of the Immunomodulatory Potential of RNA Sequences
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    Chapter 6 Cotranscriptional Production of Chemically Modified RNA Nanoparticles
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    Chapter 7 Supported Fluid Lipid Bilayer as a Scaffold to Direct Assembly of RNA Nanostructures
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    Chapter 8 Evaluation of Thermal Stability of RNA Nanoparticles by Temperature Gradient Gel Electrophoresis (TGGE) in Native Condition
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    Chapter 9 Design and Crystallography of Self-Assembling RNA Nanostructures
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    Chapter 10 X-Aptamer Selection and Validation
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    Chapter 11 Design and Preparation of Aptamer–siRNA Chimeras (AsiCs) for Targeted Cancer Therapy
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    Chapter 12 Cellular Delivery of siRNAs Using Bolaamphiphiles
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    Chapter 13 Preparation and Optimization of Lipid-Like Nanoparticles for mRNA Delivery
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    Chapter 14 Chitosan Nanoparticles for miRNA Delivery
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    Chapter 15 Synthesis of PLGA–Lipid Hybrid Nanoparticles for siRNA Delivery Using the Emulsion Method PLGA-PEG–Lipid Nanoparticles for siRNA Delivery
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    Chapter 16 Oxime Ether Lipids as Transfection Agents: Assembly and Complexation with siRNA
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    Chapter 17 Polycationic Probe-Guided Nanopore Single-Molecule Counter for Selective miRNA Detection
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    Chapter 18 Intracellular Reassociation of RNA–DNA Hybrids that Activates RNAi in HIV-Infected Cells
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    Chapter 19 Construction and In Vivo Testing of Prokaryotic Riboregulators
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    Chapter 20 Preparation of a Conditional RNA Switch
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    Chapter 21 Rational Engineering of a Modular Group I Ribozyme to Control Its Activity by Self-Dimerization
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    Chapter 22 CRISPR-Cas RNA Scaffolds for Transcriptional Programming in Yeast
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    Chapter 23 Using Planar Phi29 pRNA Three-Way Junction to Control Size and Shape of RNA Nanoparticles for Biodistribution Profiling in Mice
Attention for Chapter 5: Computational Prediction of the Immunomodulatory Potential of RNA Sequences
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Chapter title
Computational Prediction of the Immunomodulatory Potential of RNA Sequences
Chapter number 5
Book title
Methods in Molecular Biology
Published in
Methods in molecular biology, July 2017
DOI 10.1007/978-1-4939-7138-1_5
Pubmed ID
Book ISBNs
978-1-4939-7137-4, 978-1-4939-7138-1
Authors

Nagpal, Gandharva, Chaudhary, Kumardeep, Dhanda, Sandeep Kumar, Raghava, Gajendra Pal Singh, Gandharva Nagpal, Kumardeep Chaudhary, Sandeep Kumar Dhanda, Gajendra Pal Singh Raghava

Abstract

Advances in the knowledge of various roles played by non-coding RNAs have stimulated the application of RNA molecules as therapeutics. Among these molecules, miRNA, siRNA, and CRISPR-Cas9 associated gRNA have been identified as the most potent RNA molecule classes with diverse therapeutic applications. One of the major limitations of RNA-based therapeutics is immunotoxicity of RNA molecules as it may induce the innate immune system. In contrast, RNA molecules that are potent immunostimulators are strong candidates for use in vaccine adjuvants. Thus, it is important to understand the immunotoxic or immunostimulatory potential of these RNA molecules. The experimental techniques for determining immunostimulatory potential of siRNAs are time- and resource-consuming. To overcome this limitation, recently our group has developed a web-based server "imRNA" for predicting the immunomodulatory potential of RNA sequences. This server integrates a number of modules that allow users to perform various tasks including (1) generation of RNA analogs with reduced immunotoxicity, (2) identification of highly immunostimulatory regions in RNA sequence, and (3) virtual screening. This server may also assist users in the identification of minimum mutations required in a given RNA sequence to minimize its immunomodulatory potential that is required for designing RNA-based therapeutics. Besides, the server can be used for designing RNA-based vaccine adjuvants as it may assist users in the identification of mutations required for increasing immunomodulatory potential of a given RNA sequence. In summary, this chapter describes major applications of the "imRNA" server in designing RNA-based therapeutics and vaccine adjuvants (http://www.imtech.res.in/raghava/imrna/).

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The data shown below were collected from the profiles of 3 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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 26%
Student > Ph. D. Student 5 22%
Student > Bachelor 2 9%
Student > Master 2 9%
Other 1 4%
Other 3 13%
Unknown 4 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 26%
Agricultural and Biological Sciences 5 22%
Medicine and Dentistry 4 17%
Immunology and Microbiology 1 4%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Other 0 0%
Unknown 6 26%
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 09 November 2023.
All research outputs
#14,529,779
of 24,776,799 outputs
Outputs from Methods in molecular biology
#3,803
of 13,896 outputs
Outputs of similar age
#160,579
of 319,287 outputs
Outputs of similar age from Methods in molecular biology
#51
of 270 outputs
Altmetric has tracked 24,776,799 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,896 research outputs from this source. They receive a mean Attention Score of 3.5. This one has gotten more attention than average, scoring higher than 71% 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 319,287 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 270 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.