MARC Record from Library of Congress

LEADER: 02055cam a2200409 i 4500
001 2014024218
003 DLC
005 20150701081604.0
008 140623t20152015flua b 001 0 eng
010 $a 2014024218
020 $a9781466595002 (hardcover : alk. paper)
020 $a1466595000 (hardcover : alk. paper)
035 $a(DNLM)101635331
040 $aDNLM/DLC$cDLC$erda$dDLC
042 $apcc
050 00 $aQP623$b.K67 2015
060 10 $aQU 58.7
082 00 $a572.8/8$223
100 1 $aKorpelainen, Eija,$eauthor.
245 10 $aRNA-seq data analysis :$ba practical approach /$cEija Korpelainen, Jarno Tuimala, Panu Somervuo, Mikael Huss, Garry Wong.
264 1 $aBoca Raton :$bCRC Press, Taylor & Francis Group,$c[2015]
264 4 $c©2015
300 $axxiv, 298 pages :$billustrations ;$c24 cm
336 $atext$2rdacontent
337 $aunmediated$2rdamedia
338 $avolume$2rdacarrier
490 0 $aChapman & Hall/CRC Mathematical and computational biology series
500 $a"A Chapman & Hall book."
504 $aIncludes bibliographical references and index.
520 $a"RNA-seq offers unprecedented information about transcriptome, but harnessing this information with bioinformatics tools is typically a bottleneck. This self-contained guide enables researchers to examine differential expression at gene, exon, and transcript level and to discover novel genes, transcripts, and whole transcriptomes. Each chapter starts with theoretical background, followed by descriptions of relevant analysis tools. The book also provides examples using command line tools and the R statistical environment. For non-programming scientists, the same examples are covered using open source software with a graphical user interface"--$cProvided by publisher.
650 12 $aSequence Analysis, RNA$xmethods.
650 12 $aTranscriptome.
650 22 $aStatistics as Topic.
700 1 $aTuimala, Jarno,$eauthor.
700 1 $aSomervuo, Panu,$eauthor.
700 1 $aHuss, Mikael,$eauthor.
700 1 $aWong, Garry,$eauthor.