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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
  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
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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 1: Overview of Gene Expression Analysis: Transcriptomics
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  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

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Chapter title
Overview of Gene Expression Analysis: Transcriptomics
Chapter number 1
Book title
Gene Expression Analysis
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7834-2_1
Pubmed ID
Book ISBNs
978-1-4939-7833-5, 978-1-4939-7834-2
Authors

Nalini Raghavachari, Natàlia Garcia-Reyero

Abstract

Currently, the study of the transcriptome is widely used to interpret the functional elements of the genome and molecular constituents of cells and tissues in an effort to unravel biological pathways associated with development and disease. The advent of technologies is now enabling the study of such comprehensive transcriptional characterization of mRNA, miRNA, lncRNA, and small RNA in a robust and successful manner. Transcriptomic strategies are gaining momentum across diverse areas of biological, plant sciences, medical, clinical, and pharmaceutical research for biomarker discovery, and disease diagnosis and prognosis.

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X Demographics

The data shown below were collected from the profiles of 2 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 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 42 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 8 19%
Student > Master 8 19%
Student > Ph. D. Student 6 14%
Student > Doctoral Student 3 7%
Researcher 2 5%
Other 5 12%
Unknown 10 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 13 31%
Medicine and Dentistry 4 10%
Agricultural and Biological Sciences 4 10%
Chemistry 2 5%
Psychology 1 2%
Other 5 12%
Unknown 13 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 18 May 2018.
All research outputs
#14,988,291
of 23,056,273 outputs
Outputs from Methods in molecular biology
#4,746
of 13,196 outputs
Outputs of similar age
#255,896
of 442,476 outputs
Outputs of similar age from Methods in molecular biology
#509
of 1,499 outputs
Altmetric has tracked 23,056,273 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% 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 59% 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 442,476 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% 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 60% of its contemporaries.