xviii ◾ Preface
using the graphical Chipster soware in addition to command line tools.
All soware used in examples are open source and freely available.
OUTLINE OF THE CONTENTS
e “Introduction” section in Chapters 1 and 2 discusses the dierent
applications of RNA-seq, ranging from discovery of genes and transcripts
to dierential expression analysis and discovery of mutations and fusion
genes. It gives an overview of RNA-seq data analysis and discusses impor-
tant aspects of experimental planning.
e rst part is devoted to mapping reads to references and de novo
assembly. As the quality of reads strongly inuences both, a chapter on
quality control and preprocessing is also included. Chapter 3 discusses sev-
eral quality issues characteristic to high-throughput sequencing data and
tools for detecting and solving them. Chapter 4 describes the challenges
in mapping RNA-seq reads to a reference and introduces some commonly
used aligners with practical examples. Tools for manipulating alignment
les and genome browsers for visualizing reads in genomic context are
introduced. Chapter 5 describes the elements of transcriptome assembly.
e relevance of data-processing steps such as ltering, trimming, and
error correction in RNA-seq assembly is discussed. Fundamental con-
cepts such as splicing graph, de Bruijn graph, and path traversal in an
assembly graph are explained. Dierences between genome and transcrip-
tome assembly are discussed. Two approaches to reconstruct full-length
transcripts are explained: mapping-based and de novo assembly. Both
approaches are also demonstrated with practical examples.
e second part is devoted to statistical analyses predominantly car-
ried out with the R soware that is supplemented with the tools produced
by the Bioconductor project. Chapter 6 discusses the dierent quantita-
tion approaches and tools, as well as annotation-based quality metrics.
Chapter 7 describes the R- and Bioconductor-based framework for the
analysis of RNA-seq data and how to import the data in R. e main
dierences between the statistical and bioinformatic tools in R are also
discussed. Chapters 8 and 9 discuss dierent options for analyzing dif-
ferential expression of genes, transcripts, and exons and show how to
perform the analysis using R/Bioconductor tools and some standalone
tools. Chapter 10 oers solutions for annotating results, and Chapter 11
describes dierent ways of producing informative visualizations to display
the central results.
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