RNA-seq Data Analysis

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A01=Eija Korpelainen
A01=Garry Wong
A01=Jarno Tuimala
A01=Mikael Huss
A01=Panu Somervuo
advanced transcriptome data workflow
Age Group_Uncategorized
Age Group_Uncategorized
and transcript levels
Annotation Package
Author_Eija Korpelainen
Author_Garry Wong
Author_Jarno Tuimala
Author_Mikael Huss
Author_Panu Somervuo
automatic-update
bam
BAM File
Bed File
Bioconductor Packages
bioinformaticians and nonprogramming wet lab scientists
Category1=Non-Fiction
Category=PS
Computational analysis of small noncoding RNA sequencing data
computational genomics
COP=United States
Data Set
De Bruijn Graph
Delivery_Delivery within 10-20 working days
differential
Differential Expression Analysis
differential expression at gene
end
Entrez Gene Id
eq_bestseller
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
exon
expression
FASTA File
FASTQ File
file
gene expression profiling
Gene Id
genome
Genome Browser
Human Embryonic Stem Cells
Introduction to RNA-seq data analysis
Language_English
MA Plot
NA NA
NA NA NA
noncoding RNA analysis
open source bioinformatics
PA=Available
pair
Paired End Read
Price_€50 to €100
PS=Active
read
reads
reference
Reference Genome
RNA Seq Data
RNA Seq Data Analysis
RNA Seq Experiment
RNA Seq Read
RNA Seq Study
RNA-seq analysis framework in R and Bioconductor
RNA-seq data analysis methods
sequencing quality control
softlaunch
Splice Junctions
Transcriptome Analysis
transcriptomics
UCSC Genome Browser

Product details

  • ISBN 9781466595002
  • Weight: 600g
  • Dimensions: 156 x 234mm
  • Publication Date: 19 Sep 2014
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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The State of the Art in Transcriptome Analysis RNA sequencing (RNA-seq) data offers unprecedented information about the transcriptome, but harnessing this information with bioinformatics tools is typically a bottleneck. RNA-seq Data Analysis: A Practical Approach enables researchers to examine differential expression at gene, exon, and transcript levels and to discover novel genes, transcripts, and whole transcriptomes.

Balanced Coverage of Theory and Practice.Each chapter starts with theoretical background, followed by descriptions of relevant analysis tools and practical examples. Accessible to both bioinformaticians and nonprogramming wet lab scientists, the examples illustrate the use of command-line tools, R, and other open source tools, such as the graphical Chipster software.

The Tools and Methods to Get Started in Your Lab. Taking readers through the whole data analysis workflow, this self-contained guide provides a detailed overview of the main RNA-seq data analysis methods and explains how to use them in practice. It is suitable for researchers from a wide variety of backgrounds, including biology, medicine, genetics, and computer science. The book can also be used in a graduate or advanced undergraduate course.

Eija Korpelainen, Jarno Tuimala, Panu Somervuo, Mikael Huss, Garry Wong

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