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Next Generation Microarray Bioinformatics

Overview of attention for book
Cover of 'Next Generation Microarray Bioinformatics'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 A Primer on the Current State of Microarray Technologies
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    Chapter 2 The KEGG Databases and Tools Facilitating Omics Analysis: Latest Developments Involving Human Diseases and Pharmaceuticals.
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    Chapter 3 Next Generation Microarray Bioinformatics
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    Chapter 4 Analyzing Cancer Samples with SNP Arrays
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    Chapter 5 Classification Approaches for Microarray Gene Expression Data Analysis
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    Chapter 6 Biclustering of time series microarray data.
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    Chapter 7 Using the Bioconductor GeneAnswers Package to Interpret Gene Lists
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    Chapter 8 Analysis of Isoform Expression from Splicing Array Using Multiple Comparisons
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    Chapter 9 Functional Comparison of Microarray Data Across Multiple Platforms Using the Method of Percentage of Overlapping Functions
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    Chapter 10 Performance Comparison of Multiple Microarray Platforms for Gene Expression Profiling
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    Chapter 11 Integrative Approaches for Microarray Data Analysis
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    Chapter 12 Modeling Gene Regulation Networks Using Ordinary Differential Equations
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    Chapter 13 Nonhomogeneous Dynamic Bayesian Networks in Systems Biology
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    Chapter 14 Inference of Regulatory Networks from Microarray Data with R and the Bioconductor Package qpgraph
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    Chapter 15 Effective Non-linear Methods for Inferring Genetic Regulation from Time-Series Microarray Gene Expression Data
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    Chapter 16 An overview of the analysis of next generation sequencing data.
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    Chapter 17 How to Analyze Gene Expression Using RNA-Sequencing Data
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    Chapter 18 Analyzing ChIP-seq Data: Preprocessing, Normalization, Differential Identification, and Binding Pattern Characterization.
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    Chapter 19 Identifying Differential Histone Modification Sites from ChIP‐seq Data
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    Chapter 20 ChIP-Seq Data Analysis: Identification of Protein–DNA Binding Sites with SISSRs Peak-Finder
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    Chapter 21 Using ChIPMotifs for De Novo Motif Discovery of OCT4 and ZNF263 Based on ChIP-Based High-Throughput Experiments.
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    Chapter 22 Hidden Markov Models for Controlling False Discovery Rate in Genome-Wide Association Analysis
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    Chapter 23 Employing Gene Set Top Scoring Pairs to Identify Deregulated Pathway-Signatures in Dilated Cardiomyopathy from Integrated Microarray Gene Expression Data
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    Chapter 24 JAMIE: A Software Tool for Jointly Analyzing Multiple ChIP-chip Experiments
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    Chapter 25 Epigenetic Analysis: ChIP-chip and ChIP-seq
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    Chapter 26 BiNGS!SL-seq: A Bioinformatics Pipeline for the Analysis and Interpretation of Deep Sequencing Genome-Wide Synthetic Lethal Screen.
Attention for Chapter 17: How to Analyze Gene Expression Using RNA-Sequencing Data
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Chapter title
How to Analyze Gene Expression Using RNA-Sequencing Data
Chapter number 17
Book title
Next Generation Microarray Bioinformatics
Published in
Methods in molecular biology, January 2012
DOI 10.1007/978-1-61779-400-1_17
Pubmed ID
Book ISBNs
978-1-61779-399-8, 978-1-61779-400-1
Authors

Ramsköld D, Kavak E, Sandberg R, Daniel Ramsköld, Ersen Kavak, Rickard Sandberg, Ramsköld, Daniel, Kavak, Ersen, Sandberg, Rickard

Abstract

RNA-Seq is arising as a powerful method for transcriptome analyses that will eventually make microarrays obsolete for gene expression analyses. Improvements in high-throughput sequencing and efficient sample barcoding are now enabling tens of samples to be run in a cost-effective manner, competing with microarrays in price, excelling in performance. Still, most studies use microarrays, partly due to the ease of data analyses using programs and modules that quickly turn raw microarray data into spreadsheets of gene expression values and significant differentially expressed genes. Instead RNA-Seq data analyses are still in its infancy and the researchers are facing new challenges and have to combine different tools to carry out an analysis. In this chapter, we provide a tutorial on RNA-Seq data analysis to enable researchers to quantify gene expression, identify splice junctions, and find novel transcripts using publicly available software. We focus on the analyses performed in organisms where a reference genome is available and discuss issues with current methodology that have to be solved before RNA-Seq data can utilize its full potential.

X Demographics

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

Geographical breakdown

Country Count As %
Sweden 6 4%
United States 4 2%
Germany 2 1%
United Kingdom 2 1%
South Africa 1 <1%
Brazil 1 <1%
Colombia 1 <1%
India 1 <1%
Unknown 151 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 57 34%
Researcher 45 27%
Student > Master 15 9%
Student > Bachelor 13 8%
Professor > Associate Professor 9 5%
Other 19 11%
Unknown 11 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 78 46%
Biochemistry, Genetics and Molecular Biology 28 17%
Medicine and Dentistry 20 12%
Immunology and Microbiology 8 5%
Neuroscience 5 3%
Other 16 9%
Unknown 14 8%
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 25 July 2014.
All research outputs
#13,861,788
of 22,659,164 outputs
Outputs from Methods in molecular biology
#3,886
of 13,019 outputs
Outputs of similar age
#151,666
of 244,041 outputs
Outputs of similar age from Methods in molecular biology
#240
of 473 outputs
Altmetric has tracked 22,659,164 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 13,019 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 68% of its peers.
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We're also able to compare this research output to 473 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.