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Integrative Epigenomics

  • Chapter
Book cover Computational and Statistical Epigenomics

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

Abstract

In the post-genomic era, various types of functional omics data are emerging. As a result, big omics data are accumulating at an explosive rate. Epigenomics, including genome-wide DNA methylation and histone modifications, are important components of functional genomics, and play an essential role in elucidating many fundamental biological processes. Integration of epigenomic data with genomic, transcriptomic and proteomic data is increasingly valued to uncover full pictures of biological systems. Simple intersection of epigenetic features may provide interesting clues of novel patterns. Various machine learning methods are utilized to help understand chromosome segmentation and epigenetic regulation of transcription. Additionally, cluster analyses are frequently applied in cancer classifications. In this chapter, we briefly review commonly used integration methods and algorithms.

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Authors and Affiliations

  1. Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China

    Ming Su, Xiaoyang Dou, Hao Cheng & Jing-Dong J. Han

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  1. Ming Su
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  2. Xiaoyang Dou
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  3. Hao Cheng
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  4. Jing-Dong J. Han
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Correspondence to Jing-Dong J. Han .

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Editors and Affiliations

  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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Su, M., Dou, X., Cheng, H., Han, JD.J. (2015). Integrative 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_6

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