Herefore, the most important scripts, commands, and options and their purpose are
illustrated in this chapter. After you have worked through this chapter you will
understand the impact of epigenetic sequencing approaches and you will be able to
perform the ChIP-Seq data analysis workflow—from receiving your raw data after
sequencing to motif discovery in your identified ChIP-Seq peaks/regions.
12.1 Introduction
Epigenetic sequencing approaches allow to study heritable or acquired changes in gene
activity caused by mechanisms other than DNA sequence changes. Epigenetic analysis
research can involve studying alterations in DNA methylation, DNA–protein interactions,
chromatin accessibility, histone modifications, and more, on a genome-wide scale. In this
textbook we focus on analyzing ChIP-Seq data based on DNA–protein interaction of
transcription factors or (modified) histones. However, the sequencing data analysis workflow
is, with minor differences, similar for all approaches. The main aim of ChIP-Seq approaches
is to identify genetic regulatory networks (GRNs) to determine transcriptionally active genes
in any cell type of interest. Genes are transcribed by RNA Polymerase II, but binding by
specific transcription factors is required to initialize this process. The following simplified
illustration depicts the phenomenon of gene regulation by a specific regulatory protein
(transcription factor, TF), without which transcription does not occur (Fig. 12.1).
Thus, ChIP-Seq data provide insights into regulation events by identification of transcription factor binding sites, so-called binding motifs, within a promoter sequence or other
regulatory sequences (enhancer/silencer). Moreover, ChIP-Seq data can be used to track
histone modifications across the genome, and narrow in on chromatin structure and
function. Next Generation Sequencing reads from ChIP-Seq experiments can be evaluated
by different software tools in different ways. This textbook describes an open source
software called HOMER [1] and Bioconductor packages in R to analyze ChIP-Seq data.
12.2 DNA Quality and ChIP-Seq Library Preparation
For successful ChIP-Seq approaches, one must generate high-quality ChIP-DNA templates
to obtain the best sequencing outcomes. ChIP-Seq experiments typically begin with the
formaldehyde cross-linking of protein–DNA complexes in cells or tissue. The chromatin is
then extracted and fragmented, either through enzymatic digestion or sonication, and
DNA–protein fragments are immunoprecipitated with target-specific antibodies (the target
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M. Kappelmann-Fenzl
illustrated in this chapter. After you have worked through this chapter you will
understand the impact of epigenetic sequencing approaches and you will be able to
perform the ChIP-Seq data analysis workflow—from receiving your raw data after
sequencing to motif discovery in your identified ChIP-Seq peaks/regions.
12.1 Introduction
Epigenetic sequencing approaches allow to study heritable or acquired changes in gene
activity caused by mechanisms other than DNA sequence changes. Epigenetic analysis
research can involve studying alterations in DNA methylation, DNA–protein interactions,
chromatin accessibility, histone modifications, and more, on a genome-wide scale. In this
textbook we focus on analyzing ChIP-Seq data based on DNA–protein interaction of
transcription factors or (modified) histones. However, the sequencing data analysis workflow
is, with minor differences, similar for all approaches. The main aim of ChIP-Seq approaches
is to identify genetic regulatory networks (GRNs) to determine transcriptionally active genes
in any cell type of interest. Genes are transcribed by RNA Polymerase II, but binding by
specific transcription factors is required to initialize this process. The following simplified
illustration depicts the phenomenon of gene regulation by a specific regulatory protein
(transcription factor, TF), without which transcription does not occur (Fig. 12.1).
Thus, ChIP-Seq data provide insights into regulation events by identification of transcription factor binding sites, so-called binding motifs, within a promoter sequence or other
regulatory sequences (enhancer/silencer). Moreover, ChIP-Seq data can be used to track
histone modifications across the genome, and narrow in on chromatin structure and
function. Next Generation Sequencing reads from ChIP-Seq experiments can be evaluated
by different software tools in different ways. This textbook describes an open source
software called HOMER [1] and Bioconductor packages in R to analyze ChIP-Seq data.
12.2 DNA Quality and ChIP-Seq Library Preparation
For successful ChIP-Seq approaches, one must generate high-quality ChIP-DNA templates
to obtain the best sequencing outcomes. ChIP-Seq experiments typically begin with the
formaldehyde cross-linking of protein–DNA complexes in cells or tissue. The chromatin is
then extracted and fragmented, either through enzymatic digestion or sonication, and
DNA–protein fragments are immunoprecipitated with target-specific antibodies (the target
178
M. Kappelmann-Fenzl