A practical guide for essential analyses of Hi-C data

The Hi-C method has been widely applied to study the spatial organization of genomes. Different from other omics data sets, Hi-C data contain complicated genomic information; thus, even though many bioinformatics tools have been developed, it is still challenging to process, analyze, and interpret Hi-C results accurately. In this chapter, we aim to provide a practical guide for how we can approach essential analyses of Hi-C data to generate high-quality and publishable results. We also share our experience interpreting Hi-C results in the published work to demonstrate how we learn from these results.