RNA-seq data analysis : a practical approach

By:

Korpelainen, Eija

Contributor(s):

Material type: Text

9781466595002

Subject(s):

DDC classification:

572.88 23 KOR-R

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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Holdings
Item type	Current library	Collection	Call number	Status	Notes	Date due	Barcode	Item holds
Books	IIITD Reference	Biology	CB 572.88 KOR-R (Browse shelf(Opens below))	Available	DBT Project Grant		008792

Total holds: 0

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Previous	CB 572.86 PIE-G Genetics :	CB 572.8633 BRO-G Gene cloning and DNA analysis :	CB 572.8633 DUR-B Biological sequence analysis :	CB 572.88 KOR-R RNA-seq data analysis : a practical approach	CB 573.88 NEL-F From Photon to Neuron :	CB 574.88 SEG-B Biological kinetics	CB 574.88 SEG-M Mathematical models in molecular and cellular Biology	Next

"A Chapman & Hall book."

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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