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Introduction to Data Types in Epigenomics

  • Chapter
Computational and Statistical Epigenomics

Part of the book series: Translational Bioinformatics ((TRBIO,volume 7))

Abstract

The epigenome is the collection of all epigenetic modifications occurring on a genome. To properly generate, analyze, and understand epigenomic data has become increasingly important in basic and applied research, because epigenomic modifications have been broadly associated with differentiation, development, and disease processes, thereby also constituting attractive drug targets. In this chapter, we introduce the reader to the different aspects of epigenomics (e.g., DNA methylation and histone marks, among others), by briefly reviewing the most relevant underlying biological concepts and by describing the different experimental protocols and the analysis of the associated data types. Furthermore, for each type of epigenetic modification we describe the most relevant analysis pipelines, data repositories, and other resources. We conclude that any epigenomic investigation needs to carefully align the selection of the experimental protocols with the subsequent bioinformatics analysis and vice versa, as the effect sizes can be small and thereby escape detection if an integrative design is not well considered.

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Authors and Affiliations

  1. Department of Medicine, Unit of Computational Medicine, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, Solna, Stockholm, Sweden

    Francesco Marabita, Jesper Tegnér & David Gomez-Cabrero

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  1. Francesco Marabita
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  2. Jesper Tegnér
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  3. David Gomez-Cabrero
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Correspondence to Francesco Marabita .

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  1. CAS Key Laboratory of Computational Biology, Chinese Academy of Sciences and Max-Planck Gesellschaft Partner, Institute for Computational Biology, Shanghai, China

    Andrew E. Teschendorff

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Marabita, F., Tegnér, J., Gomez-Cabrero, D. (2015). Introduction to Data Types in Epigenomics. In: Teschendorff, A. (eds) Computational and Statistical Epigenomics. Translational Bioinformatics, vol 7. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9927-0_1

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