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Gene Expression Analysis

Overview of attention for book
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
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    Chapter 19 Erratum to: RNA-Seq and Expression Arrays: Selection Guidelines for Genome-Wide Expression Profiling
Attention for Chapter 2: RNA-Seq and Expression Arrays: Selection Guidelines for Genome-Wide Expression Profiling
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Chapter title
RNA-Seq and Expression Arrays: Selection Guidelines for Genome-Wide Expression Profiling
Chapter number 2
Book title
Gene Expression Analysis
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7834-2_2
Pubmed ID
29767356
Book ISBNs
978-1-4939-7833-5, 978-1-4939-7834-2
Authors

Jessica Minnier, Nathan D. Pennock, Quichen Guo, Pepper Schedin, Christina A. Harrington, Qiuchen Guo, Minnier, Jessica, Pennock, Nathan D., Guo, Qiuchen, Schedin, Pepper, Harrington, Christina A.

Abstract

The development of genome-wide gene expression profiling technologies over the past two decades has produced great opportunity for researchers to explore the transcriptome and to better understand biological systems and their perturbation. In this chapter we provide an overview of microarray and massively parallel sequencing technologies and their application to gene expression analysis. We discuss factors that impact expression data generation and analysis that which should be considered in the application of these technology platforms. We further present the results of a simple illustration study to highlight performance similarities and differences in expression profiling of protein-coding mRNAs with each platform. Based on technical and analytical differences between the two platforms, reports in the literature comparing arrays and RNA-Seq for gene expression, and our own example study and experience, we provide recommendations for platform selection for gene expression studies.

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X Demographics

The data shown below were collected from the profile of 1 X user 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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Professor 3 19%
Other 2 13%
Student > Doctoral Student 2 13%
Student > Bachelor 2 13%
Student > Master 2 13%
Other 2 13%
Unknown 3 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 25%
Biochemistry, Genetics and Molecular Biology 3 19%
Medicine and Dentistry 2 13%
Mathematics 1 6%
Sports and Recreations 1 6%
Other 1 6%
Unknown 4 25%
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 17 May 2018.
All research outputs
#15,514,052
of 23,056,273 outputs
Outputs from Methods in molecular biology
#5,402
of 13,196 outputs
Outputs of similar age
#269,948
of 442,476 outputs
Outputs of similar age from Methods in molecular biology
#597
of 1,499 outputs
Altmetric has tracked 23,056,273 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% 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 is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
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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 is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.

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