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Disease Gene Identification

Overview of attention for book
Cover of 'Disease Gene Identification'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Identification of Disease Susceptibility Alleles in the Next Generation Sequencing Era
  3. Altmetric Badge
    Chapter 2 Induced Pluripotent Stem Cells in Disease Modeling and Gene Identification
  4. Altmetric Badge
    Chapter 3 Development of Targeted Therapies Based on Gene Modification
  5. Altmetric Badge
    Chapter 4 What Can We Learn About Human Disease from the Nematode C. elegans?
  6. Altmetric Badge
    Chapter 5 Microbiome Sequencing Methods for Studying Human Diseases
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    Chapter 6 The Emerging Role of Long Noncoding RNAs in Human Disease
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    Chapter 7 Identification of Disease-Related Genes Using a Genome-Wide Association Study Approach
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    Chapter 8 Whole Genome Library Construction for Next Generation Sequencing
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    Chapter 9 Whole Exome Library Construction for Next Generation Sequencing
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    Chapter 10 Optimized Methodology for the Generation of RNA-Sequencing Libraries from Low-Input Starting Material: Enabling Analysis of Specialized Cell Types and Clinical Samples
  12. Altmetric Badge
    Chapter 11 Using Fluidigm C1 to Generate Single-Cell Full-Length cDNA Libraries for mRNA Sequencing
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    Chapter 12 MiSeq: A Next Generation Sequencing Platform for Genomic Analysis
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    Chapter 13 Methods for CpG Methylation Array Profiling Via Bisulfite Conversion
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    Chapter 14 miRNA Quantification Method Using Quantitative Polymerase Chain Reaction in Conjunction with C q Method
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    Chapter 15 Primary Airway Epithelial Cell Gene Editing Using CRISPR-Cas9
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    Chapter 16 RNA Interference to Knock Down Gene Expression
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    Chapter 17 Using Luciferase Reporter Assays to Identify Functional Variants at Disease-Associated Loci
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    Chapter 18 Physiologic Interpretation of GWAS Signals for Type 2 Diabetes
  20. Altmetric Badge
    Chapter 19 Identification of Genes for Hereditary Hemochromatosis
  21. Altmetric Badge
    Chapter 20 Identification of Driver Mutations in Rare Cancers: The Role of SMARCA4 in Small Cell Carcinoma of the Ovary, Hypercalcemic Type (SCCOHT)
  22. Altmetric Badge
    Chapter 21 The Rise and Fall and Rise of Linkage Analysis as a Technique for Finding and Characterizing Inherited Influences on Disease Expression
Attention for Chapter 12: MiSeq: A Next Generation Sequencing Platform for Genomic Analysis
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

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Chapter title
MiSeq: A Next Generation Sequencing Platform for Genomic Analysis
Chapter number 12
Book title
Disease Gene Identification
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7471-9_12
Pubmed ID
Book ISBNs
978-1-4939-7470-2, 978-1-4939-7471-9
Authors

Rupesh Kanchi Ravi, Kendra Walton, Mahdieh Khosroheidari

Abstract

MiSeq, Illumina's integrated next generation sequencing instrument, uses reversible-terminator sequencing-by-synthesis technology to provide end-to-end sequencing solutions. The MiSeq instrument is one of the smallest benchtop sequencers that can perform onboard cluster generation, amplification, genomic DNA sequencing, and data analysis, including base calling, alignment and variant calling, in a single run. It performs both single- and paired-end runs with adjustable read lengths from 1 × 36 base pairs to 2 × 300 base pairs. A single run can produce output data of up to 15 Gb in as little as 4 h of runtime and can output up to 25 M single reads and 50 M paired-end reads. Thus, MiSeq provides an ideal platform for rapid turnaround time. MiSeq is also a cost-effective tool for various analyses focused on targeted gene sequencing (amplicon sequencing and target enrichment), metagenomics, and gene expression studies. For these reasons, MiSeq has become one of the most widely used next generation sequencing platforms. Here, we provide a protocol to prepare libraries for sequencing using the MiSeq instrument and basic guidelines for analysis of output data from the MiSeq sequencing run.

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X Demographics

The data shown below were collected from the profiles of 4 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 306 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 306 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 49 16%
Student > Bachelor 48 16%
Student > Ph. D. Student 35 11%
Researcher 27 9%
Student > Doctoral Student 16 5%
Other 25 8%
Unknown 106 35%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 84 27%
Agricultural and Biological Sciences 37 12%
Medicine and Dentistry 20 7%
Immunology and Microbiology 11 4%
Environmental Science 11 4%
Other 29 9%
Unknown 114 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 10 February 2018.
All research outputs
#13,339,171
of 23,498,099 outputs
Outputs from Methods in molecular biology
#3,425
of 13,368 outputs
Outputs of similar age
#210,156
of 444,852 outputs
Outputs of similar age from Methods in molecular biology
#288
of 1,485 outputs
Altmetric has tracked 23,498,099 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,368 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 73% 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 444,852 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 51% of its contemporaries.
We're also able to compare this research output to 1,485 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.