Transcriptome Analysis : Introduction and Examples from the Neurosciences / by Alessandro Cellerino, Michele Sanguanini.
2018
QH455
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Details
Title
Transcriptome Analysis : Introduction and Examples from the Neurosciences / by Alessandro Cellerino, Michele Sanguanini.
ISBN
9788876426421 (electronic book)
8876426426 (electronic book)
8876426426 (electronic book)
Published
Pisa : Scuola Normale Superiore : Imprint: Edizioni della Normale, 2018.
Language
English
Description
1 online resource (xiv, 188 pages)
Other Standard Identifiers
10.1007/978-88-7642-642-1 doi
Call Number
QH455
Dewey Decimal Classification
570.285
Summary
The 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.
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Access limited to authorized users.
Digital File Characteristics
text file PDF
Added Author
Sanguanini, Michele. author.
Series
Concordia. Reihe Monographien ; 17.
Available in Other Form
Print version: 9788876426414
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Table of Contents
Preface
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.
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.