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Gene Expression Analysis

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Cover of 'Gene Expression Analysis'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Overview of Gene Expression Analysis: Transcriptomics
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    Chapter 2 RNA-Seq and Expression Arrays: Selection Guidelines for Genome-Wide Expression Profiling
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    Chapter 3 A Guide for Designing and Analyzing RNA-Seq Data
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    Chapter 4 SureSelect XT RNA Direct: A Technique for Expression Analysis Through Sequencing of Target-Enriched FFPE Total RNA
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    Chapter 5 Simultaneous, Multiplexed Detection of RNA and Protein on the NanoString ® nCounter ® Platform
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    Chapter 6 Transcript Profiling Using Long-Read Sequencing Technologies
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    Chapter 7 Making and Sequencing Heavily Multiplexed, High-Throughput 16S Ribosomal RNA Gene Amplicon Libraries Using a Flexible, Two-Stage PCR Protocol
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    Chapter 8 MicroRNA Expression Analysis: Next-Generation Sequencing
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    Chapter 9 Identification of Transcriptional Regulators That Bind to Long Noncoding RNAs by RNA Pull-Down and RNA Immunoprecipitation
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    Chapter 10 Single-Cell mRNA-Seq Using the Fluidigm C1 System and Integrated Fluidics Circuits
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    Chapter 11 Current and Future Methods for mRNA Analysis: A Drive Toward Single Molecule Sequencing
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    Chapter 12 Expression Profiling of Differentially Regulated Genes in Fanconi Anemia
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    Chapter 13 A Review of Transcriptome Analysis in Pulmonary Vascular Diseases
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    Chapter 14 Differential Gene Expression Analysis of Plants
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    Chapter 15 High Throughput Sequencing-Based Approaches for Gene Expression Analysis
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    Chapter 16 Network Analysis of Gene Expression
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    Chapter 17 Analysis of ChIP-Seq and RNA-Seq Data with BioWardrobe
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    Chapter 18 Bayesian Network to Infer Drug-Induced Apoptosis Circuits from Connectivity Map Data
  20. Altmetric Badge
    Chapter 19 Erratum to: RNA-Seq and Expression Arrays: Selection Guidelines for Genome-Wide Expression Profiling
Attention for Chapter 11: Current and Future Methods for mRNA Analysis: A Drive Toward Single Molecule Sequencing
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Chapter title
Current and Future Methods for mRNA Analysis: A Drive Toward Single Molecule Sequencing
Chapter number 11
Book title
Gene Expression Analysis
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7834-2_11
Pubmed ID
Book ISBNs
978-1-4939-7833-5, 978-1-4939-7834-2
Authors

Anthony Bayega, Somayyeh Fahiminiya, Spyros Oikonomopoulos, Jiannis Ragoussis, Bayega, Anthony, Fahiminiya, Somayyeh, Oikonomopoulos, Spyros, Ragoussis, Jiannis

Abstract

The transcriptome encompasses a range of species including messenger RNA, and other noncoding RNA such as rRNA, tRNA, and short and long noncoding RNAs. Due to the huge role played by mRNA in development and disease, several methods have been developed to sequence and characterize mRNA, with RNA sequencing (RNA-Seq) emerging as the current method of choice particularly for large high-throughput studies. Short-read RNA-Seq which involves sequencing of short cDNA fragments and computationally assembling them to reconstruct the transcriptome, or aligning them to a reference is the most widely used approach. However, due to inherent limitations of this approach in de novo transcriptome assembly and isoform quantification, long-read RNA-Seq approaches, which also happen to be single molecule sequencing approaches, are increasingly becoming the standard for de novo transcriptome assembly and isoform quantification. In this chapter, we review the technical aspects of the current methods of RNA-Seq, both short and long-read approaches, and data analysis methods available. We discuss recent advances in single-cell RNA-Seq and direct RNA-Seq approaches, which perhaps will dominate the future of RNA-Seq.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 77 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 22%
Student > Master 10 13%
Researcher 9 12%
Other 7 9%
Student > Bachelor 5 6%
Other 6 8%
Unknown 23 30%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 24 31%
Agricultural and Biological Sciences 12 16%
Pharmacology, Toxicology and Pharmaceutical Science 4 5%
Chemistry 3 4%
Computer Science 2 3%
Other 8 10%
Unknown 24 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 17 May 2018.
All research outputs
#14,112,239
of 23,056,273 outputs
Outputs from Methods in molecular biology
#3,976
of 13,196 outputs
Outputs of similar age
#232,842
of 442,477 outputs
Outputs of similar age from Methods in molecular biology
#397
of 1,499 outputs
Altmetric has tracked 23,056,273 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,196 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 68% 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 442,477 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,499 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.