%A Zhijun Han, Gang Wei %T Computational tools for Hi-C data analysis %0 Journal Article %D 2017 %J Quant. Biol. %J Quantitative Biology %@ 2095-4689 %R 10.1007/s40484-017-0113-6 %P 215-225 %V 5 %N 3 %U {https://journal.hep.com.cn/qb/EN/10.1007/s40484-017-0113-6 %8 2017-08-24 %X

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 long-range 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.