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The State of the Art in Transcriptome Analysis

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.

TOC:

Table of Contents

 

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