E-Book, Englisch, 322 Seiten
Reihe: Chapman & Hall/CRC Mathematical and Computational Biology
A Practical Approach
E-Book, Englisch, 322 Seiten
Reihe: Chapman & Hall/CRC Mathematical and Computational Biology
ISBN: 978-1-4665-9501-9
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
RNA sequencing (RNA-seq) data offers unprecedented information about the transcriptome, but harnessing this information with bioinformatics tools is typically a bottleneck. RNA-seq Data Analysis: A Practical Approach enables researchers to examine differential expression at gene, exon, and transcript levels and to discover novel genes, transcripts, and whole transcriptomes.
Balanced Coverage of Theory and Practice
Each chapter starts with theoretical background, followed by descriptions of relevant analysis tools and practical examples. Accessible to both bioinformaticians and nonprogramming wet lab scientists, the examples illustrate the use of command-line tools, R, and other open source tools, such as the graphical Chipster software.
The Tools and Methods to Get Started in Your Lab
Taking readers through the whole data analysis workflow, this self-contained guide provides a detailed overview of the main RNA-seq data analysis methods and explains how to use them in practice. It is suitable for researchers from a wide variety of backgrounds, including biology, medicine, genetics, and computer science. The book can also be used in a graduate or advanced undergraduate course.
Zielgruppe
Bioinformaticians and computational biologists; statisticians in biological data analysis; biologists using next-generation sequencing; and graduate students taking advanced courses on RNA-seq analysis or next-generation sequencing.
Autoren/Hrsg.
Weitere Infos & Material
Introduction
Introduction to RNA-seq data analysis
Quality control and preprocessing
Aligning reads to reference and visualizing them in genomic context
Transcriptome assembly
Annotation-based quality control and quantitation of gene expression
RNA-seq analysis framework in R and Bioconductor
Differential expression analysis
Analysis of differential exon usage
Annotating the results
Visualization
Small non-coding RNAs
Computational analysis of small noncoding RNA sequencing data
