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RNA-seq data analysis : = a practica...

Korpelainen, Eija.

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RNA-seq data analysis : = a practical approach /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	RNA-seq data analysis :/ Eija Korpelainen, Jarno Tuimala, Panu Somervuo, Mikael Huss, Garry Wong.
其他題名:	a practical approach /
其他作者:	Korpelainen, Eija.
出版者:	Boca Raton :CRC Press, Taylor & Francis Group, : 2015.,
面頁冊數:	xxiv, 298 p. :ill. ;24 cm.
附註:	"A Chapman & Hall book.".
標題:	Sequence Analysis, RNA - methods. -
ISBN:	9781466595002

RNA-seq data analysis : = a practical approach /
RNA-seq data analysis :a practical approach /Eija Korpelainen, Jarno Tuimala, Panu Somervuo, Mikael Huss, Garry Wong. - Boca Raton :CRC Press, Taylor & Francis Group,2015. - xxiv, 298 p. :ill. ;24 cm. - Chapman & Hall/CRC Mathematical and computational biology series..

"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.

ISBN: 9781466595002GBP44.99

LCCN: 2014024218Subjects--Topical Terms:

2121451
Sequence Analysis, RNA
--methods.

LC Class. No.: QP623 / .K67 2015

Dewey Class. No.: 572.8/8

National Library of Medicine Call No.: QU 58.7

RNA-seq data analysis : = a practical approach /
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300 $a xxiv, 298 p. : $b ill. ; $c 24 cm.
490 1 0 $a Chapman & Hall/CRC Mathematical and computational biology series.
500 $a "A Chapman & Hall book.".
504 $a Includes bibliographical references and index.
520 # $a 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.
650 1 2 $a Sequence Analysis, RNA $x methods. $3 2121451
650 1 2 $a Transcriptome. $3 2121452
650 2 2 $a Statistics as Topic. $3 2017472
700 1 # $a Korpelainen, Eija. $3 2121450