The detection of differentially interacting chromatin regions requires at least two Hi-C datasets.Īnalysis of two or more Hi-C datasets poses a challenge related to a potential bias between datasets. These regions may be associated with loss or gain of TAD boundaries, change in TAD sizes, breaking or establishing promoter-enhancer interactions, thus pointing toward regulatory consequences. Analogous to the differential gene expression analysis, the comparative analysis of Hi-C datasets is intended to reveal pairs of regions which are differentially interacting between conditions. One of the most important tasks in functional genomics studies is the detection of differences between two or more conditions ( Dixon et al., 2015 Bonev et al., 2017 Rao et al., 2017), e.g., tumor-normal states ( Taberlay et al., 2016 Rickman et al., 2012 Barutcu et al., 2015). The bulk of this tutorial uses the R programming environment and can be performed on most operating systems and a single computer.Įarly analysis of individual Hi-C datasets illuminated basic properties of the 3D structure of the genome (A/B compartments, Topologically Associated Domains (TADs), and chromatin loops ( Lieberman-Aiden et al., 2009 Imakaev et al., 2012a Yaffe and Tanay, 2011 Dixon et al., 2012 Rao et al., 2014). Finally, visualization of the results and downstream interpretation of the differentially interacting regions are discussed. We present the three protocols describing the usage of the multiHiCcompare, diffHic, and FIND R packages for performing a comparative analysis of Hi-C experiments. We then describe the data normalization and comparative analysis process. We describe the process of obtaining Hi-C data from public repositories and give suggestions for pre-processing pipelines if the user intends to analyze their own raw data. The workflow presented here describes how to analyze and interpret a comparative Hi-C experiment. all interactions and can provide new insights into genomic regulation. Chromatin conformation capture (Hi-C) technologies capture the structure of the chromatin on a global scale by measuring all vs. The three-dimensional (3D) interactions of chromatin regulate cell type-specific gene expression, recombination, X chromosome inactivation, and many other genomic processes.
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