Computational Analysis of Hi-C Data.

Abstract

The chromatin organization in the 3D nuclear space is essential for genome functionality. This spatial organization encompasses different topologies at diverse scale lengths with chromosomes occupying distinct volumes and individual chromosomes folding into compartments, inside which the chromatin fiber is packed in large domains (as the topologically associating domains, TADs) and forms short-range interactions (as enhancer-promoter loops). The widespread adoption of high-throughput techniques derived from chromosome conformation capture (3C) has been instrumental in investigating the nuclear organization of chromatin. In particular, Hi-C has the potential to achieve the most comprehensive characterization of chromatin 3D structures, as in principle it can detect any pair of restriction fragments connected as a result of ligation by proximity. However, the analysis of the enormous amount of genomic data produced by Hi-C techniques requires the application of complex, multistep computational procedures that may constitute a difficult task also for expert computational biologists. In this chapter, we describe the computational analysis of Hi-C data obtained from the lymphoblastoid cell line GM12878, detailing the processing of raw data, the generation and normalization of the Hi-C contact map, the detection of TADs and chromatin interactions, and their visualization and annotation.