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MicroRNA Detection and Target Identification

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Cover of 'MicroRNA Detection and Target Identification'

Table of Contents

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    Book Overview
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    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 2: High-Throughput RT-qPCR for the Analysis of Circulating MicroRNAs
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Chapter title
High-Throughput RT-qPCR for the Analysis of Circulating MicroRNAs
Chapter number 2
Book title
MicroRNA Detection and Target Identification
Published in
Methods in molecular biology, April 2017
DOI 10.1007/978-1-4939-6866-4_2
Pubmed ID
Book ISBNs
978-1-4939-6864-0, 978-1-4939-6866-4
Authors

Geok Wee Tan, Lu Ping Tan

Editors

Tamas Dalmay

Abstract

Reverse transcription followed by real-time or quantitative polymerase chain reaction (RT-qPCR) is the gold standard for validation of results from transcriptomic profiling studies such as microarray and RNA sequencing. The current need for most studies, especially biomarker studies, is to evaluate the expression levels or fold changes of many transcripts in a large number of samples. With conventional low to medium throughput qPCR platforms, many qPCR plates would have to be run and a significant amount of RNA input per sample will be required to complete the experiments. This is particularly challenging when the size of study material (small biopsy, laser capture microdissected cells, biofluid, etc.), time, and resources are limited. A sensitive and high-throughput qPCR platform is therefore optimal for the evaluation of many transcripts in a large number of samples because the time needed to complete the entire experiment is shortened and the usage of lab consumables as well as RNA input per sample are low. Here, the methods of high-throughput RT-qPCR for the analysis of circulating microRNAs are described. Two distinctive qPCR chemistries (probe-based and intercalating dye-based) can be applied using the methods described here.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 33%
Researcher 3 25%
Student > Bachelor 2 17%
Unknown 3 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 33%
Agricultural and Biological Sciences 2 17%
Environmental Science 1 8%
Medicine and Dentistry 1 8%
Unknown 4 33%