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RNA-seq data analysis : a practical approach / Eija Korpelainen, Jarno Tuimala, Panu Somervuo, Mikael Huss, Garry Wong.

By: Contributor(s): Material type: TextTextSeries: Chapman & Hall/CRC Mathematical and computational biology seriesPublication details: Boca Raton : CRC Press, Taylor & Francis Group, [2015]Edition: 1st edDescription: xxiv, 298 pages : illustrations ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781466595002 (hardcover : alk. paper)
  • 1466595000 (hardcover : alk. paper)
Subject(s): DDC classification:
  • 572.88 23
LOC classification:
  • QP623 .K67 2015
NLM classification:
  • QU 58.7
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 Call number Status Date due Barcode
Books Books Centeral Library Second Floor - Biotechnology 572.88 K.E.R 2015 (Browse shelf(Opens below)) Available 23734

Includes bibliographical references and index.

"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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