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MicroRNA Detection and Target Identification

Overview of attention for book
Cover of 'MicroRNA Detection and Target Identification'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Improved Denaturation of Small RNA Duplexes and Its Application for Northern Blotting
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    Chapter 2 High-Throughput RT-qPCR for the Analysis of Circulating MicroRNAs
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    Chapter 3 Genome-Wide Comparison of Next-Generation Sequencing and qPCR Platforms for microRNA Profiling in Serum
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    Chapter 4 Small RNA Profiling by Next-Generation Sequencing Using High-Definition Adapters
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    Chapter 5 Surface Acoustic Wave Lysis and Ion-Exchange Membrane Quantification of Exosomal MicroRNA
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    Chapter 6 Droplet Microfluidic Device Fabrication and Use for Isothermal Amplification and Detection of MicroRNA
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    Chapter 7 Interrogation of Functional miRNA–Target Interactions by CRISPR/Cas9 Genome Engineering
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    Chapter 8 Cell-Free Urinary MicroRNAs Expression in Small-Scale Experiments
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    Chapter 9 Peptide-Based Isolation of Argonaute Protein Complexes Using Ago-APP
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    Chapter 10 Predicting Functional MicroRNA-mRNA Interactions
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    Chapter 11 Computational and Experimental Identification of Tissue-Specific MicroRNA Targets
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    Chapter 12 sRNAtoolboxVM: Small RNA Analysis in a Virtual Machine
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    Chapter 13 An Assessment of the Next Generation of Animal miRNA Target Prediction Algorithms
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    Chapter 14 The UEA Small RNA Workbench: A Suite of Computational Tools for Small RNA Analysis
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    Chapter 15 Prediction of miRNA–mRNA Interactions Using miRGate
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    Chapter 16 Detection of microRNAs Using Chip-Based QuantStudio 3D Digital PCR
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    Chapter 17 MiRNA Quantitation with Microelectrode Sensors Enabled by Enzymeless Electrochemical Signal Amplification
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    Chapter 18 A Robust Protocol to Quantify Circulating Cancer Biomarker MicroRNAs
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    Chapter 19 MicroRNAs, Regulatory Networks, and Comorbidities: Decoding Complex Systems
  21. Altmetric Badge
    Chapter 20 Label-Free Direct Detection of MiRNAs with Poly-Silicon Nanowire Biosensors
  22. Altmetric Badge
    Chapter 21 Erratum to: Cell-Free Urinary MicroRNAs Expression in Small-Scale Experiments
Attention for Chapter 12: sRNAtoolboxVM: Small RNA Analysis in a Virtual Machine
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

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Chapter title
sRNAtoolboxVM: Small RNA Analysis in a Virtual Machine
Chapter number 12
Book title
MicroRNA Detection and Target Identification
Published in
Methods in molecular biology, April 2017
DOI 10.1007/978-1-4939-6866-4_12
Pubmed ID
Book ISBNs
978-1-4939-6864-0, 978-1-4939-6866-4
Authors

Cristina Gómez-Martín, Ricardo Lebrón, Antonio Rueda, José L. Oliver, Michael Hackenberg

Editors

Tamas Dalmay

Abstract

High-throughput sequencing (HTS) data for small RNAs (noncoding RNA molecules that are 20-250 nucleotides in length) can now be routinely generated by minimally equipped wet laboratories; however, the bottleneck in HTS-based research has shifted now to the analysis of such huge amount of data. One of the reasons is that many analysis types require a Linux environment but computers, system administrators, and bioinformaticians suppose additional costs that often cannot be afforded by small to mid-sized groups or laboratories. Web servers are an alternative that can be used if the data is not subjected to privacy issues (what very often is an important issue with medical data). However, in any case they are less flexible than stand-alone programs limiting the number of workflows and analysis types that can be carried out.We show in this protocol how virtual machines can be used to overcome those problems and limitations. sRNAtoolboxVM is a virtual machine that can be executed on all common operating systems through virtualization programs like VirtualBox or VMware, providing the user with a high number of preinstalled programs like sRNAbench for small RNA analysis without the need to maintain additional servers and/or operating systems.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 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 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 14%
Unknown 6 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 43%
Student > Ph. D. Student 2 29%
Student > Bachelor 1 14%
Professor 1 14%
Readers by discipline Count As %
Computer Science 2 29%
Biochemistry, Genetics and Molecular Biology 1 14%
Medicine and Dentistry 1 14%
Unknown 3 43%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 11 April 2018.
All research outputs
#4,096,010
of 22,965,074 outputs
Outputs from Methods in molecular biology
#1,069
of 13,137 outputs
Outputs of similar age
#72,259
of 309,748 outputs
Outputs of similar age from Methods in molecular biology
#18
of 264 outputs
Altmetric has tracked 22,965,074 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,137 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done particularly well, scoring higher than 91% 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 309,748 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 264 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.