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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
  4. Altmetric Badge
    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 3: A Guide for Designing and Analyzing RNA-Seq Data
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Chapter title
A Guide for Designing and Analyzing RNA-Seq Data
Chapter number 3
Book title
Gene Expression Analysis
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7834-2_3
Pubmed ID
Book ISBNs
978-1-4939-7833-5, 978-1-4939-7834-2
Authors

Aniruddha Chatterjee, Antonio Ahn, Euan J. Rodger, Peter A. Stockwell, Michael R. Eccles, Chatterjee, Aniruddha, Ahn, Antonio, Rodger, Euan J., Stockwell, Peter A., Eccles, Michael R.

Abstract

The identity of a cell or an organism is at least in part defined by its gene expression and therefore analyzing gene expression remains one of the most frequently performed experimental techniques in molecular biology. The development of the RNA-Sequencing (RNA-Seq) method allows an unprecedented opportunity to analyze expression of protein-coding, noncoding RNA and also de novo transcript assembly of a new species or organism. However, the planning and design of RNA-Seq experiments has important implications for addressing the desired biological question and maximizing the value of the data obtained. In addition, RNA-Seq generates a huge volume of data and accurate analysis of this data involves several different steps and choices of tools. This can be challenging and overwhelming, especially for bench scientists. In this chapter, we describe an entire workflow for performing RNA-Seq experiments. We describe critical aspects of wet lab experiments such as RNA isolation, library preparation and the initial design of an experiment. Further, we provide a step-by-step description of the bioinformatics workflow for different steps involved in RNA-Seq data analysis. This includes power calculations, setting up a computational environment, acquisition and processing of publicly available data if desired, quality control measures, preprocessing steps for the raw data, differential expression analysis, and data visualization. We particularly mention important considerations for each step to provide a guide for designing and analyzing RNA-Seq data.

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X Demographics

The data shown below were collected from the profiles of 3 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 225 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 225 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 48 21%
Researcher 31 14%
Student > Master 26 12%
Student > Bachelor 25 11%
Student > Doctoral Student 15 7%
Other 24 11%
Unknown 56 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 68 30%
Agricultural and Biological Sciences 36 16%
Medicine and Dentistry 17 8%
Immunology and Microbiology 12 5%
Neuroscience 5 2%
Other 23 10%
Unknown 64 28%
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 22 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.