What You Will Learn in This Chapter
In this chapter, we introduce the concept of RNA-Seq analyses. First, we start to
provide an overview of a typical RNA-Seq experiment that includes extraction of
sample RNA, enrichment, and cDNA library preparation. Next, we review tools for
quality control and data pre-processing followed by a standard workflow to perform
RNA-Seq analyses. For this purpose, we discuss two common RNA-Seq strategies,
that is a reference-based alignment and a de novo assembly approach. We learn how
to do basic downstream analyses of RNA-Seq data, including quantification of
expressed genes, differential gene expression (DE) between different groups as
well as functional gene analysis. Eventually, we provide a best-practice example
for a reference-based RNA-Seq analysis from beginning to end, including all necessary tools and steps on GitHub: https://github.com/grimmlab/BookChapter-RNASeq-Analyses.
11.1 Introduction
The central dogma of molecular biology integrates the flow of information encoded in
DNA via transcription into RNA molecules that eventually translates into proteins inside a
cell (Fig. 11.1, also see Chap. 1, Section 0). Any alteration, e.g. due to genetic, lifestyle, or
environmental factors, might change the phenotype of an organism [1]. These alterations, e.
g. copy number variations or mutational modifications of RNA molecules, affect the
regulation of biological activities within individual cells [2, 3]. The entirety of all coding
and non-coding RNAs derived from a cell at a certain point in time is referred to as the
transcriptome [4]. Apparently, any change in the transcriptome culminates into functional
alterations at both cellular and organismic level. Therefore, quantifying transcriptome
variations and/or gene expression profiling remains crucial for understanding phenotypic
alterations associated with disease and development [5, 6].
In the past, quantitative polymerase chain reaction (qPCR) was used as the tool of
choice for quantifying transcripts and for performing gene expression analyses. Although
qPCR remains a cheap and accurate technique for analyzing small sets of genes or groups
of genes, it fails to scale to genome-wide level [7]. The introduction of DNA helped to scale
transcriptomic studies to a genome-wide level due to their ability to accurately analyze
thousands of transcripts at low cost [8, 9]. However, requirements of a priori knowledge of
genome sequence, cross-hybridization errors, presence of artifacts, and the inability to
analyze alternate splicing and non-coding RNAs limit the usage of microarrays [10, 11].
Currently, next-generation sequencing (NGS) has revolutionized the transcriptomic analysis landscape due to higher coverage, detection of low abundance and novel transcripts,
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R. Bharti and D. G. Grimm
In this chapter, we introduce the concept of RNA-Seq analyses. First, we start to
provide an overview of a typical RNA-Seq experiment that includes extraction of
sample RNA, enrichment, and cDNA library preparation. Next, we review tools for
quality control and data pre-processing followed by a standard workflow to perform
RNA-Seq analyses. For this purpose, we discuss two common RNA-Seq strategies,
that is a reference-based alignment and a de novo assembly approach. We learn how
to do basic downstream analyses of RNA-Seq data, including quantification of
expressed genes, differential gene expression (DE) between different groups as
well as functional gene analysis. Eventually, we provide a best-practice example
for a reference-based RNA-Seq analysis from beginning to end, including all necessary tools and steps on GitHub: https://github.com/grimmlab/BookChapter-RNASeq-Analyses.
11.1 Introduction
The central dogma of molecular biology integrates the flow of information encoded in
DNA via transcription into RNA molecules that eventually translates into proteins inside a
cell (Fig. 11.1, also see Chap. 1, Section 0). Any alteration, e.g. due to genetic, lifestyle, or
environmental factors, might change the phenotype of an organism [1]. These alterations, e.
g. copy number variations or mutational modifications of RNA molecules, affect the
regulation of biological activities within individual cells [2, 3]. The entirety of all coding
and non-coding RNAs derived from a cell at a certain point in time is referred to as the
transcriptome [4]. Apparently, any change in the transcriptome culminates into functional
alterations at both cellular and organismic level. Therefore, quantifying transcriptome
variations and/or gene expression profiling remains crucial for understanding phenotypic
alterations associated with disease and development [5, 6].
In the past, quantitative polymerase chain reaction (qPCR) was used as the tool of
choice for quantifying transcripts and for performing gene expression analyses. Although
qPCR remains a cheap and accurate technique for analyzing small sets of genes or groups
of genes, it fails to scale to genome-wide level [7]. The introduction of DNA helped to scale
transcriptomic studies to a genome-wide level due to their ability to accurately analyze
thousands of transcripts at low cost [8, 9]. However, requirements of a priori knowledge of
genome sequence, cross-hybridization errors, presence of artifacts, and the inability to
analyze alternate splicing and non-coding RNAs limit the usage of microarrays [10, 11].
Currently, next-generation sequencing (NGS) has revolutionized the transcriptomic analysis landscape due to higher coverage, detection of low abundance and novel transcripts,
144
R. Bharti and D. G. Grimm