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000843588 020__ $$a9788876426421$$q(electronic book)
000843588 020__ $$a8876426426$$q(electronic book)
000843588 0247_ $$a10.1007/978-88-7642-642-1$$2doi
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000843588 035__ $$aSP(OCoLC)1040652153
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000843588 049__ $$aISEA
000843588 050_4 $$aQH455
000843588 08204 $$a570.285$$223
000843588 1001_ $$aCellerino, Alessandro.$$eauthor.
000843588 24510 $$aTranscriptome Analysis :$$bIntroduction and Examples from the Neurosciences /$$cby Alessandro Cellerino, Michele Sanguanini.
000843588 264_1 $$aPisa :$$bScuola Normale Superiore :$$bImprint: Edizioni della Normale,$$c2018.
000843588 300__ $$a1 online resource (xiv, 188 pages)
000843588 336__ $$atext$$btxt$$2rdacontent
000843588 337__ $$acomputer$$bc$$2rdamedia
000843588 338__ $$aonline resource$$bcr$$2rdacarrier
000843588 347__ $$atext file$$bPDF$$2rda
000843588 4901_ $$aCRM Series ;$$v17
000843588 5050_ $$aPreface -- Introduction: why study transcriptomics? -- 1. Data distribution and visualisation -- 2. Next-generation RNA sequencing -- 3. RNA-seq raw data processing -- 4. Differentially expressed gene detection & analysis -- 5. Unbiased clustering methods -- 6. Knowledge-based clustering methods -- 7. Network analysis -- 8. Mesoscale transcriptome analysis -- 9. Microscale transcriptome analysis -- Bibliography -- Index.
000843588 506__ $$aAccess limited to authorized users.
000843588 520__ $$aThe goal of this book is to be an accessible guide for undergraduate and graduate students to the new field of data-driven biology. Next-generation sequencing technologies have put genome-scale analysis of gene expression into the standard toolbox of experimental biologists. Yet, biological interpretation of high-dimensional data is made difficult by the lack of a common language between experimental and data scientists. By combining theory with practical examples of how specific tools were used to obtain novel insights in biology, particularly in the neurosciences, the book intends to teach students how to design, analyse, and extract biological knowledge from transcriptome sequencing experiments. Undergraduate and graduate students in biomedical and quantitative sciences will benefit from this text as well as academics untrained in the subject.
000843588 650_0 $$aBioinformatics.
000843588 650_0 $$aSystems biology.
000843588 650_0 $$aBiomathematics.
000843588 650_0 $$aGenetic transcription.
000843588 7001_ $$aSanguanini, Michele.$$eauthor.
000843588 77608 $$iPrint version: $$z9788876426414
000843588 830_0 $$aConcordia.$$pReihe Monographien ;$$v17.
000843588 852__ $$bebk
000843588 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-88-7642-642-1$$zOnline Access$$91397441.1
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000843588 980__ $$aBIB
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000843588 983__ $$aOnline
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