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Bacterial Regulatory RNA

Overview of attention for book
Cover of 'Bacterial Regulatory RNA'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Workflow for a Computational Analysis of an sRNA Candidate in Bacteria
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    Chapter 2 Guidelines for Inferring and Characterizing a Family of Bacterial trans-Acting Small Noncoding RNAs
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    Chapter 3 Bioinformatic Approach for Prediction of CsrA/RsmA-Regulating Small RNAs in Bacteria
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    Chapter 4 Host-Pathogen Transcriptomics by Dual RNA-Seq
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    Chapter 5 Identification of New Bacterial Small RNA Targets Using MS2 Affinity Purification Coupled to RNA Sequencing
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    Chapter 6 Assessment of External Guide Sequences’ (EGS) Efficiency as Inducers of RNase P-Mediated Cleavage of mRNA Target Molecules
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    Chapter 7 Evaluating the Effect of Small RNAs and Associated Chaperones on Rho-Dependent Termination of Transcription In Vitro
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    Chapter 8 Mapping Changes in Cell Surface Protein Expression Through Selective Labeling of Live Cells
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    Chapter 9 Fluorescence-Based Methods for Characterizing RNA Interactions In Vivo
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    Chapter 10 Mutational Analysis of sRNA–mRNA Base Pairing by Electrophoretic Mobility Shift Assay
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    Chapter 11 An Integrated Cell-Free Assay to Study Translation Regulation by Small Bacterial Noncoding RNAs
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    Chapter 12 Quantitative Super-Resolution Imaging of Small RNAs in Bacterial Cells
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    Chapter 13 Extraction and Analysis of RNA Isolated from Pure Bacteria-Derived Outer Membrane Vesicles
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    Chapter 14 Absolute Regulatory Small Noncoding RNA Concentration and Decay Rates Measurements in Escherichia coli
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    Chapter 15 High-Resolution, High-Throughput Analysis of Hfq-Binding Sites Using UV Crosslinking and Analysis of cDNA (CRAC)
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    Chapter 16 Producing Hfq/Sm Proteins and sRNAs for Structural and Biophysical Studies of Ribonucleoprotein Assembly
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    Chapter 17 Single-Molecule FRET Assay to Observe the Activity of Proteins Involved in RNA/RNA Annealing
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    Chapter 18 Techniques to Analyze sRNA Protein Cofactor Self-Assembly In Vitro
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    Chapter 19 Sequence-Specific Affinity Chromatography of Bacterial Small Regulatory RNA-Binding Proteins from Bacterial Cells
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    Chapter 20 Identification of Small RNA–Protein Partners in Plant Symbiotic Bacteria
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    Chapter 21 A Modular Genetic System for High-Throughput Profiling and Engineering of Multi-Target Small RNAs
Attention for Chapter 15: High-Resolution, High-Throughput Analysis of Hfq-Binding Sites Using UV Crosslinking and Analysis of cDNA (CRAC)
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Chapter title
High-Resolution, High-Throughput Analysis of Hfq-Binding Sites Using UV Crosslinking and Analysis of cDNA (CRAC)
Chapter number 15
Book title
Bacterial Regulatory RNA
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7634-8_15
Pubmed ID
Book ISBNs
978-1-4939-7633-1, 978-1-4939-7634-8
Authors

Brandon Sy, Julia Wong, Sander Granneman, David Tollervey, David Gally, Jai J. Tree, Sy, Brandon, Wong, Julia, Granneman, Sander, Tollervey, David, Gally, David, Tree, Jai J.

Abstract

Small regulatory nonprotein-coding RNAs (sRNAs) have emerged as ubiquitous and abundant regulators of gene expression in a diverse cross section of bacteria. They play key roles in most aspects of bacterial physiology, including central metabolism, nutrient acquisition, virulence, biofilm formation, and outer membrane composition. RNA sequencing technologies have accelerated the identification of bacterial regulatory RNAs and are now being employed to understand their functions. Many regulatory RNAs require protein partners for activity, or modulate the activity of interacting proteins. Understanding how and where proteins interact with the transcriptome is essential to elucidate the functions of the many sRNAs. Here, we describe the implementation in bacteria of a UV-crosslinking technique termed CRAC that allows stringent, transcriptome-wide recovery of bacterial RNA-protein interaction sites in vivo and at base-pair resolution. We have used CRAC to map protein-RNA interaction sites for the RNA chaperone Hfq and ribonuclease RNase E in pathogenic E. coli, and toxins from toxin-antitoxin systems in Mycobacterium smegmatis, demonstrating the broad applicability of this technique.

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X Demographics

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 %
Student > Ph. D. Student 6 26%
Researcher 4 17%
Lecturer > Senior Lecturer 2 9%
Professor 2 9%
Student > Doctoral Student 2 9%
Other 2 9%
Unknown 5 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 35%
Immunology and Microbiology 4 17%
Agricultural and Biological Sciences 3 13%
Computer Science 1 4%
Nursing and Health Professions 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 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 28 February 2018.
All research outputs
#14,094,152
of 23,025,074 outputs
Outputs from Methods in molecular biology
#3,966
of 13,170 outputs
Outputs of similar age
#232,705
of 442,363 outputs
Outputs of similar age from Methods in molecular biology
#395
of 1,499 outputs
Altmetric has tracked 23,025,074 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,170 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 68% 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 442,363 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,499 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 71% of its contemporaries.