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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
  8. Altmetric Badge
    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
  17. Altmetric Badge
    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
  20. Altmetric Badge
    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 15: Prediction of miRNA–mRNA Interactions Using miRGate
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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 (72nd percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

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Chapter title
Prediction of miRNA–mRNA Interactions Using miRGate
Chapter number 15
Book title
MicroRNA Detection and Target Identification
Published in
Methods in molecular biology, April 2017
DOI 10.1007/978-1-4939-6866-4_15
Pubmed ID
28439836
Book ISBNs
978-1-4939-6864-0, 978-1-4939-6866-4
Authors

Eduardo Andrés-León, Gonzalo Gómez-López, David G. Pisano, Andrés-León, Eduardo, Gómez-López, Gonzalo, Pisano, David G.

Editors

Tamas Dalmay

Abstract

miRGate ( http://mirgate.bioinfo.cnio.es /) is a freely available database that contains predicted and experimentally validated microRNA-messenger RNA (miRNA-mRNA) target pairs. This resource includes novel predictions from five well-established algorithms, but recalculated from a common and comprehensive sequence dataset. It includes all 3'-UTR sequences of all known genes of the three more widely employed genomes (human, mouse, and rat), and all annotated miRNA sequences from those genomes. Besides, it also contains predictions for all genes in human targeted by miRNA viruses such as Epstein-Barr and Kaposi sarcoma-associated herpes virus.The approach intends to circumvent one of the main drawbacks in this area, as diverse sequences and gene database versions cause poor overlap among different target prediction methods even with experimentally confirmed targets. As a result, miRGate predictions have been successfully validated using functional assays in several laboratories.This chapter describes how a user can access target information via miRGate's web interface. It also shows how automatically access the database through the programmatic interface based on representational state transfer services (REST), using the application programming interface (API) available at http://mirgate.bioinfo.cnio.es/API .

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X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 19%
Researcher 3 19%
Student > Postgraduate 2 13%
Student > Master 2 13%
Student > Doctoral Student 1 6%
Other 3 19%
Unknown 2 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 19%
Computer Science 2 13%
Neuroscience 2 13%
Medicine and Dentistry 2 13%
Immunology and Microbiology 1 6%
Other 2 13%
Unknown 4 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 18 January 2024.
All research outputs
#5,411,714
of 25,323,244 outputs
Outputs from Methods in molecular biology
#1,550
of 14,183 outputs
Outputs of similar age
#85,855
of 316,116 outputs
Outputs of similar age from Methods in molecular biology
#24
of 251 outputs
Altmetric has tracked 25,323,244 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 14,183 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done well, scoring higher than 88% 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 316,116 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 251 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 90% of its contemporaries.

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