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MicroRNA Detection and Target Identification

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

Table of Contents

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    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
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    Chapter 20 Label-Free Direct Detection of MiRNAs with Poly-Silicon Nanowire Biosensors
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    Chapter 21 Erratum to: Cell-Free Urinary MicroRNAs Expression in Small-Scale Experiments
Attention for Chapter 11: Computational and Experimental Identification of Tissue-Specific MicroRNA Targets
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Chapter title
Computational and Experimental Identification of Tissue-Specific MicroRNA Targets
Chapter number 11
Book title
MicroRNA Detection and Target Identification
Published in
Methods in molecular biology, April 2017
DOI 10.1007/978-1-4939-6866-4_11
Pubmed ID
28439832
Book ISBNs
978-1-4939-6864-0, 978-1-4939-6866-4
Authors

Raheleh Amirkhah, Hojjat Naderi Meshkin, Ali Farazmand, John E. J. Rasko, Ulf Schmitz

Editors

Tamas Dalmay

Abstract

In this chapter we discuss computational methods for the prediction of microRNA (miRNA) targets. More specifically, we consider machine learning-based approaches and explain why these methods have been relatively unsuccessful in reducing the number of false positive predictions. Further we suggest approaches designed to improve their performance by considering tissue-specific target regulation. We argue that the miRNA targetome differs depending on the tissue type and introduce a novel algorithm that predicts miRNA targets specifically for colorectal cancer. We discuss features of miRNAs and target sites that affect target recognition, and how next-generation sequencing data can support the identification of novel miRNAs, differentially expressed miRNAs and their tissue-specific mRNA targets. In addition, we introduce some experimental approaches for the validation of miRNA targets as well as web-based resources sharing predicted and validated miRNA target interactions.

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X Demographics

The data shown below were collected from the profiles of 2 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 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 21%
Other 2 8%
Student > Bachelor 2 8%
Student > Master 2 8%
Researcher 2 8%
Other 4 17%
Unknown 7 29%
Readers by discipline Count As %
Medicine and Dentistry 5 21%
Biochemistry, Genetics and Molecular Biology 3 13%
Agricultural and Biological Sciences 2 8%
Nursing and Health Professions 1 4%
Computer Science 1 4%
Other 1 4%
Unknown 11 46%
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 29 April 2017.
All research outputs
#18,171,423
of 23,344,526 outputs
Outputs from Methods in molecular biology
#7,419
of 13,338 outputs
Outputs of similar age
#221,761
of 310,648 outputs
Outputs of similar age from Methods in molecular biology
#149
of 263 outputs
Altmetric has tracked 23,344,526 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 13,338 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
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 310,648 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 263 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.

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