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RNA Abundance Analysis

Overview of attention for book
Cover of 'RNA Abundance Analysis'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 SuperSAGE: Powerful Serial Analysis of Gene Expression
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    Chapter 2 Northern Blot Analysis for Expression Profiling of mRNAs and Small RNAs
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    Chapter 3 Construction of RNA-Seq Libraries from Large and Microscopic Tissues for the Illumina Sequencing Platform
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    Chapter 4 Strand-Specific RNA-seq Applied to Malaria Samples
  6. Altmetric Badge
    Chapter 5 RNA In Situ Hybridization in Arabidopsis
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    Chapter 6 Laser Microdissection of Cells and Isolation of High-Quality RNA After Cryosectioning
  8. Altmetric Badge
    Chapter 7 Detection and Quantification of Alternative Splicing Variants Using RNA-seq
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    Chapter 8 Separating and Analyzing Sulfur-Containing RNAs with Organomercury Gels
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    Chapter 9 RNAse Mapping and Quantitation of RNA Isoforms
  11. Altmetric Badge
    Chapter 10 Detection and Quantification of Viral and Satellite RNAs in Plant Hosts
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    Chapter 11 In Situ Detection of Mature miRNAs in Plants Using LNA-Modified DNA Probes
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    Chapter 12 Small RNA Isolation and Library Construction for Expression Profiling of Small RNAs from Neurospora and Fusarium Using Illumina High-Throughput Deep Sequencing
  14. Altmetric Badge
    Chapter 13 Isolation and Profiling of Protein-Associated Small RNAs
  15. Altmetric Badge
    Chapter 14 New Virus Discovery by Deep Sequencing of Small RNAs.
  16. Altmetric Badge
    Chapter 15 Global Assembly of Expressed Sequence Tags
  17. Altmetric Badge
    Chapter 16 RNA Abundance Analysis
  18. Altmetric Badge
    Chapter 17 RNA Abundance Analysis
Attention for Chapter 17: RNA Abundance Analysis
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Citations

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Chapter title
RNA Abundance Analysis
Chapter number 17
Book title
RNA Abundance Analysis
Published in
Methods in molecular biology, April 2012
DOI 10.1007/978-1-61779-839-9_17
Pubmed ID
22589137
Book ISBNs
978-1-61779-838-2, 978-1-61779-839-9
Authors

Weixiong Zhang, Xuefeng Zhou, Jing Xia, Xiang Zhou

Editors

Hailing Jin, Walter Gassmann

Abstract

Next-generation sequencing (NGS) is becoming a routine experimental technology. It has been a great success in recent years to profile small-RNA species using NGS. Indeed, a large quantity of small-RNA profiling data has been generated from NGS, and computational methods have been developed to process and analyze NGS data for the purpose of identification of novel and expressed small noncoding RNAs and analysis of their roles in nearly all biological processes and pathways in eukaryotes. We discuss here the computational procedures and major steps for identification of microRNAs and natural antisense transcript-originated small interfering RNAs from NGS small-RNA profiling data.

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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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 47%
Student > Ph. D. Student 2 13%
Professor > Associate Professor 2 13%
Student > Master 1 7%
Student > Doctoral Student 1 7%
Other 1 7%
Unknown 1 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 33%
Biochemistry, Genetics and Molecular Biology 2 13%
Mathematics 1 7%
Computer Science 1 7%
Social Sciences 1 7%
Other 2 13%
Unknown 3 20%
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 18 May 2012.
All research outputs
#17,657,116
of 22,665,794 outputs
Outputs from Methods in molecular biology
#7,132
of 13,025 outputs
Outputs of similar age
#120,535
of 161,505 outputs
Outputs of similar age from Methods in molecular biology
#24
of 45 outputs
Altmetric has tracked 22,665,794 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,025 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 40th percentile – i.e., 40% of its peers scored the same or lower than it.
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 161,505 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 45 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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