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Next Generation Sequencing

Overview of attention for book
Cover of 'Next Generation Sequencing'

Table of Contents

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    Book Overview
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    Chapter 1 An Integrated Polysome Profiling and Ribosome Profiling Method to Investigate In Vivo Translatome
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    Chapter 2 Measuring Nascent Transcripts by Nascent-seq
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    Chapter 3 Genome-Wide Copy Number Alteration Detection in Preimplantation Genetic Diagnosis
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    Chapter 4 Multiplexed Targeted Sequencing for Oxford Nanopore MinION: A Detailed Library Preparation Procedure
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    Chapter 5 Hi-Plex for Simple, Accurate, and Cost-Effective Amplicon-based Targeted DNA Sequencing
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    Chapter 6 ClickSeq: Replacing Fragmentation and Enzymatic Ligation with Click-Chemistry to Prevent Sequence Chimeras
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    Chapter 7 Genome-Wide Analysis of DNA Methylation in Single Cells Using a Post-bisulfite Adapter Tagging Approach
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    Chapter 8 Sequencing of Genomes from Environmental Single Cells
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    Chapter 9 SNP Discovery from Single and Multiplex Genome Assemblies of Non-model Organisms
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    Chapter 10 CleanTag Adapters Improve Small RNA Next-Generation Sequencing Library Preparation by Reducing Adapter Dimers
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    Chapter 11 Sampling, Extraction, and High-Throughput Sequencing Methods for Environmental Microbial and Viral Communities
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    Chapter 12 A Bloody Primer: Analysis of RNA-Seq from Tissue Admixtures
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    Chapter 13 Next-Generation Sequencing of Genome-Wide CRISPR Screens
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    Chapter 14 Gene Profiling and T Cell Receptor Sequencing from Antigen-Specific CD4 T Cells
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    Chapter 15 Investigate Global Chromosomal Interaction by Hi-C in Human Naive CD4 T Cells
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    Chapter 16 Primer Extension, Capture, and On-Bead cDNA Ligation: An Efficient RNAseq Library Prep Method for Determining Reverse Transcription Termination Sites
Attention for Chapter 5: Hi-Plex for Simple, Accurate, and Cost-Effective Amplicon-based Targeted DNA Sequencing
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Chapter title
Hi-Plex for Simple, Accurate, and Cost-Effective Amplicon-based Targeted DNA Sequencing
Chapter number 5
Book title
Next Generation Sequencing
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7514-3_5
Pubmed ID
Book ISBNs
978-1-4939-7512-9, 978-1-4939-7514-3
Authors

Bernard J. Pope, Fleur Hammet, Tu Nguyen-Dumont, Daniel J. Park, Pope, Bernard J., Hammet, Fleur, Nguyen-Dumont, Tu, Park, Daniel J.

Abstract

Hi-Plex is a suite of methods to enable simple, accurate, and cost-effective highly multiplex PCR-based targeted sequencing (Nguyen-Dumont et al., Biotechniques 58:33-36, 2015). At its core is the principle of using gene-specific primers (GSPs) to "seed" (or target) the reaction and universal primers to "drive" the majority of the reaction. In this manner, effects on amplification efficiencies across the target amplicons can, to a large extent, be restricted to early seeding cycles. Product sizes are defined within a relatively narrow range to enable high-specificity size selection, replication uniformity across target sites (including in the context of fragmented input DNA such as that derived from fixed tumor specimens (Nguyen-Dumont et al., Biotechniques 55:69-74, 2013; Nguyen-Dumont et al., Anal Biochem 470:48-51, 2015), and application of high-specificity genetic variant calling algorithms (Pope et al., Source Code Biol Med 9:3, 2014; Park et al., BMC Bioinformatics 17:165, 2016). Hi-Plex offers a streamlined workflow that is suitable for testing large numbers of specimens without the need for automation.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 20%
Student > Postgraduate 2 13%
Student > Ph. D. Student 1 7%
Lecturer > Senior Lecturer 1 7%
Professor > Associate Professor 1 7%
Other 1 7%
Unknown 6 40%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 33%
Agricultural and Biological Sciences 2 13%
Computer Science 1 7%
Neuroscience 1 7%
Engineering 1 7%
Other 0 0%
Unknown 5 33%