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Computational tools for Hi-C data analysis

Quantitative Biology volume 5, pages 215–225 (2017)Cite this article

Abstract

Background

In eukaryotic genome, chromatin is not randomly distributed in cell nuclei, but instead is organized into higher-order structures. Emerging evidence indicates that these higher-order chromatin structures play important roles in regulating genome functions such as transcription and DNA replication. With the advancement in 3C (chromosome conformation capture) based technologies, Hi-C has been widely used to investigate genome-wide longrange chromatin interactions during cellular differentiation and oncogenesis. Since the first publication of Hi-C assay in 2009, lots of bioinformatic tools have been implemented for processing Hi-C data from mapping raw reads to normalizing contact matrix and high interpretation, either providing a whole workflow pipeline or focusing on a particular process.

Results

This article reviews the general Hi-C data processing workflow and the currently popular Hi-C data processing tools. We highlight on how these tools are used for a full interpretation of Hi-C results.

Conclusions

Hi-C assay is a powerful tool to investigate the higher-order chromatin structure. Continued development of novel methods for Hi-C data analysis will be necessary for better understanding the regulatory function of genome organization.

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Acknowledgments

This work is supported by the National Basic Research Program of China (Nos. 2016YFA0100703 and 2015CB964800) and the National Natural Science Foundation of China (No. 31271354).

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Authors and Affiliations

  1. CAS Key Laboratory of Computational Biology, Collaborative Innovation Center for Genetics and Developmental Biology, CASMPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China

    Zhijun Han & Gang Wei

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Zhijun Han

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  1. Zhijun Han
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  2. Gang Wei
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Correspondence to Gang Wei.

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Han, Z., Wei, G. Computational tools for Hi-C data analysis. Quant Biol 5, 215–225 (2017). https://doi.org/10.1007/s40484-017-0113-6

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  • DOI: https://doi.org/10.1007/s40484-017-0113-6

Keywords

  • 3D genome structure
  • Hi-C data processing tool
  • chromatin interactions