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Visualizing and Annotating Hi-C Data

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Hi-C Data Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2301))

Abstract

Epigenomics studies require the combined analysis and integration of multiple types of data and annotations to extract biologically relevant information. In this context, sophisticated data visualization techniques are fundamental to identify meaningful patterns in the data in relation to the genomic coordinates. Data visualization for Hi-C contact matrices is even more complex as each data point represents the interaction between two distant genomic loci and their three-dimensional positioning must be considered. In this chapter we illustrate how to obtain sophisticated plots showing Hi-C data along with annotations for other genomic features and epigenomics data. For the example code used in this chapter we rely on a Bioconductor package able to handle even high-resolution Hi-C datasets. The provided examples are explained in details and highly customizable, thus facilitating their extension and adoption by end users for other studies.

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Acknowledgments

We acknowledge support by AIRC Start-up grant 2015 n.16841 to F.F.; AIRC fellowship n.21012 to K.P.

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Authors and Affiliations

  1. IFOM—The FIRC Institute of Molecular Oncology, Milan, Italy

    Koustav Pal & Francesco Ferrari

  2. Institute of Molecular Genetics “Luigi Luca Cavalli-Sforza”, National Research Council, Pavia, Italy

    Francesco Ferrari

Authors
  1. Koustav Pal
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  2. Francesco Ferrari
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Corresponding authors

Correspondence to Koustav Pal or Francesco Ferrari .

Editor information

Editors and Affiliations

  1. Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy

    Silvio Bicciato

  2. IFOM, The FIRC Institute of Molecular Oncology, Milan, Italy

    Francesco Ferrari

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Pal, K., Ferrari, F. (2022). Visualizing and Annotating Hi-C Data. In: Bicciato, S., Ferrari, F. (eds) Hi-C Data Analysis. Methods in Molecular Biology, vol 2301. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1390-0_5

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  • DOI: https://doi.org/10.1007/978-1-0716-1390-0_5

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1389-4

  • Online ISBN: 978-1-0716-1390-0

  • eBook Packages: Springer Protocols

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