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RNA-Seq Data Analysis : A practical approach

By: Contributor(s): Material type: TextTextSeries: Chapman and Hall/CRC mathematical & computational biology seriesPublication details: Boca Raton CRC Press, Taylor & Francis Group 2015Description: xxiv, 298 p. : ill ; 24 cmISBN:
  • 9781466595002
Subject(s): DDC classification:
  • 572.88 KOR-R
Contents:
Chapter 1. Introduction to RNA-seq -- chapter 2. Introduction to RNA-seq data analysis -- chapter 3. Quality control and preprocessing -- chapter 4. Aligning reads to reference -- chapter 5. Transcriptome assembly -- chapter 6. Quantitation and annotation-based quality control -- chapter 7. RNA-seq analysis framework in R and bioconductor -- chapter 8. Differential expression analysis -- chapter 9. Analysis of differential exon usage -- chapter 10. Annotating the results -- chapter 11. Visualization -- chapter 12. Small noncoding RNAs -- chapter 13. Computational analysis of small noncoding RNA sequencing data.
Summary: "RNA-seq offers unprecedented information about transcriptome, but harnessing this information with bioinformatics tools is typically a bottleneck. This self-contained guide enables researchers to examine differential expression at gene, exon, and transcript level and to discover novel genes, transcripts, and whole transcriptomes. Each chapter starts with theoretical background, followed by descriptions of relevant analysis tools. The book also provides examples using command line tools and the R statistical environment. For non-programming scientists, the same examples are covered using open source software with a graphical user interface"-
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Item type Current library Home library Call number Status Date due Barcode
Book Book Dept. of Computational Biology and Bioinformatics Processing Center Dept. of Computational Biology and Bioinformatics 572.88 KOR-R (Browse shelf(Opens below)) Available DCB3190

Chapter 1. Introduction to RNA-seq -- chapter 2. Introduction to RNA-seq data analysis -- chapter 3. Quality control and preprocessing -- chapter 4. Aligning reads to reference -- chapter 5. Transcriptome assembly -- chapter 6. Quantitation and annotation-based quality control -- chapter 7. RNA-seq analysis framework in R and bioconductor -- chapter 8. Differential expression analysis -- chapter 9. Analysis of differential exon usage -- chapter 10. Annotating the results -- chapter 11. Visualization -- chapter 12. Small noncoding RNAs -- chapter 13. Computational analysis of small noncoding RNA sequencing data.

"RNA-seq offers unprecedented information about transcriptome, but harnessing this information with bioinformatics tools is typically a bottleneck. This self-contained guide enables researchers to examine differential expression at gene, exon, and transcript level and to discover novel genes, transcripts, and whole transcriptomes. Each chapter starts with theoretical background, followed by descriptions of relevant analysis tools. The book also provides examples using command line tools and the R statistical environment. For non-programming scientists, the same examples are covered using open source software with a graphical user interface"-

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