↓ Skip to main content

Gene Expression Analysis

Overview of attention for book
Gene Expression Analysis
Springer New York

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Overview of Gene Expression Analysis: Transcriptomics
  3. Altmetric Badge
    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
  5. Altmetric Badge
    Chapter 4 SureSelect XT RNA Direct: A Technique for Expression Analysis Through Sequencing of Target-Enriched FFPE Total RNA
  6. Altmetric Badge
    Chapter 5 Simultaneous, Multiplexed Detection of RNA and Protein on the NanoString ® nCounter ® Platform
  7. Altmetric Badge
    Chapter 6 Transcript Profiling Using Long-Read Sequencing Technologies
  8. Altmetric Badge
    Chapter 7 Making and Sequencing Heavily Multiplexed, High-Throughput 16S Ribosomal RNA Gene Amplicon Libraries Using a Flexible, Two-Stage PCR Protocol
  9. Altmetric Badge
    Chapter 8 MicroRNA Expression Analysis: Next-Generation Sequencing
  10. Altmetric Badge
    Chapter 9 Identification of Transcriptional Regulators That Bind to Long Noncoding RNAs by RNA Pull-Down and RNA Immunoprecipitation
  11. Altmetric Badge
    Chapter 10 Single-Cell mRNA-Seq Using the Fluidigm C1 System and Integrated Fluidics Circuits
  12. Altmetric Badge
    Chapter 11 Current and Future Methods for mRNA Analysis: A Drive Toward Single Molecule Sequencing
  13. Altmetric Badge
    Chapter 12 Expression Profiling of Differentially Regulated Genes in Fanconi Anemia
  14. Altmetric Badge
    Chapter 13 A Review of Transcriptome Analysis in Pulmonary Vascular Diseases
  15. Altmetric Badge
    Chapter 14 Differential Gene Expression Analysis of Plants
  16. Altmetric Badge
    Chapter 15 High Throughput Sequencing-Based Approaches for Gene Expression Analysis
  17. Altmetric Badge
    Chapter 16 Network Analysis of Gene Expression
  18. Altmetric Badge
    Chapter 17 Analysis of ChIP-Seq and RNA-Seq Data with BioWardrobe
  19. Altmetric Badge
    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
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (70th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

twitter
3 X users
patent
1 patent

Readers on

mendeley
245 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Article details
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.

Login to access the Attention Digest and the Sentiment Analysis related to this output.

Timeline Attention over time Attention Score history
Login to access the full charts related to this output.
Activity
Login to access the full charts related to this output.
X Demographics

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 demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 245 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Login to view Mendeley reader trends over time.

Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 245 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 47 19%
Researcher 30 12%
Student > Bachelor 27 11%
Student > Master 26 11%
Student > Doctoral Student 17 7%
Other 24 10%
Unknown 74 30%
Readers by discipline
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 69 28%
Agricultural and Biological Sciences 37 15%
Medicine and Dentistry 17 7%
Immunology and Microbiology 11 4%
Neuroscience 5 2%
Other 24 10%
Unknown 82 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 14 January 2020.
All research outputs
#8,695,406
of 30,202,275 outputs
Outputs from Methods in molecular biology
#2,515
of 15,354 outputs
Outputs of similar age
#135,280
of 464,975 outputs
Outputs of similar age from Methods in molecular biology
#178
of 1,482 outputs
Altmetric has tracked 30,202,275 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 15,354 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done well, scoring higher than 83% 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 464,975 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 70% of its contemporaries.
We're also able to compare this research output to 1,482 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.