Identification of Topologically Associating Domains (TADs) and Long-distance Enhancer–gene/gene–gene Interactions with Hi-C and HiChIP

Background

The genetic diversity of organisms is the fundamental basis for forming complex and diverse global ecosystems. Adequate and comprehensive knowledge of genetic information will help us understand the nature of evolution more deeply and thus improve our living environment without affecting the survival of other species. The study of the interpretation of genetic information from multiple perspectives has been booming (Misteli, 2020; Purugganan and Jackson, 2021; Song et al., 2021; Zhang et al., 2021). Among them, the 3D genome has become a fascinating study field. It has been extensively used to comprehensively study how the hierarchy of 3D genome regulates biological processes such as evolution, development, and resistance to pathogens by combining 1D genomics, transcriptomics, and epigenomics (Fullwood et al., 2009; Lieberman-Aiden et al., 2009; Mumbach et al., 2017; Sun et al., 2018; Wang et al., 2018; Norrie et al., 2019; Zheng and Xie, 2019; Yang et al., 2022). So far, there is a growing body of literature on topologically associating domains (TADs) and loops, since they are finely architected to regulate changes in genetic information (Rao et al., 2014; Wang et al., 2018; Zheng et al., 2019; Espinola et al., 2021; Hoencamp, 2021). Hence, accurate identification of TAD and loop structures is essential for understanding how the hierarchy of 3D genome architecture regulates biological processes.

With the innovation in experimental methods of 3D genomic study and the advancement of sequencing technologies, a large number of software tools were developed to identify 3D genome structures (Ay et al., 2014; Wang et al., 2015; Forcato et al., 2017; Bhattacharyya et al., 2019; Yardımcı et al., 2019; Fernandez et al., 2020; Wolff et al., 2020; Mourad, 2022). They have contributed to a boom in the field of 3D genomic research. This also increases the difficulties for researchers to realize them (Forcato et al., 2017). Therefore, a complete order pipeline is very useful for scientific researchers. To achieve this, we summarized the processing of several software tools, including identifying TADs by TADLib and Juicer, inferring loops of Hi-C data by Fit-Hi-C and Juicer, and inferring loops of HiChIP data by FitHiChIP and hichipper. The integration of these software into a whole pipeline by combining HiC-Pro and Juicebox can provide final visualization results from raw reads (Figure 1). The pipeline provides a comprehensive 3D genome analysis process that is applicable not only to Hi-C datasets but also compatible with HiChIP datasets. Moreover, the pipeline eliminates the need for complex intermediate steps in multiple software, providing users a simple and efficient experience while preserving the software's customizable function. In summary, the pipeline provides user-friendly features that are especially beneficial for researchers who are new to 3D genome analysis.