000			02995cam a2200493 i 4500
999			_c9391 _d9391
001			18198153
003			OSt
005			20190724150727.0
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	0	0	_aQP623 _b.K67 2015
060	1	0	_aQU 58.7
082	0	0	_a572.88 _223 _bR
100	1		_aKorpelainen, Eija, _eauthor.
245	1	0	_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."
500			_apharmacy bookfair2015
504			_aIncludes bibliographical references and index.
505	0		_a 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.
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	1	2	_aSequence Analysis, RNA _xmethods.
650	1	2	_aTranscriptome.
650	2	2	_aStatistics as Topic.
700	1		_aTuimala, Jarno _eauthor. _933453
700	1		_aSomervuo, Panu, _eauthor.
700	1		_aHuss, Mikael, _eauthor.
700	1		_aWong, Garry, _eauthor.
856			_3Abstract _uhttp://repository.fue.edu.eg/xmlui/handle/123456789/2859
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cBK