Hi-C analysis: from data generation to integration

Abstract

In the epigenetics field, large-scale functional genomics datasets of ever-increasing size and complexity have been produced using experimental techniques based on high-throughput sequencing. In particular, the study of the 3D organization of chromatin has raised increasing interest, thanks to the development of advanced experimental techniques. In this context, Hi-C has been widely adopted as a high-throughput method to measure pairwise contacts between virtually any pair of genomic loci, thus yielding unprecedented challenges for analyzing and handling the resulting complex datasets. In this review, we focus on the increasing complexity of available Hi-C datasets, which parallels the adoption of novel protocol variants. We also review the complexity of the multiple data analysis steps required to preprocess Hi-C sequencing reads and extract biologically meaningful information. Finally, we discuss solutions for handling and visualizing such large genomics datasets.

2019—A year in Biophysical Reviews

“…Edited by Assoc. Prof. Joshua Ho and Dr. Eleni Giannoulatou (Ho and Giannoulatou 2019) the Big Data Issue contained 16 contributions dealing with subjects as diverse as Bayesian statistical analysis (Yau and Campbell 2019), the dangers of using statistical tests rooted in the assumption of a normal distribution (Mar 2019), Hi-C analysis of interactions between genomic loci (Pal et al 2019), bioinformatics-based discovery of cancer causing mutations (Nussinov et al 2019), and machine learning in predicting cancer patient treatment outcomes (Mehreen and Aittokallio 2019).…”

FAN-C: A Feature-rich Framework for the Analysis and Visualisation of C data

“…The large amounts of Hi-C data and increasingly specialised research questions have led to the development of diverse Hi-C analysis tools. Typically, these fall into one, rarely multiple of the following categories: Hi-C matrix generation, feature analysis, and visualisation (Pal et al 2019;Ay and Noble 2015;Ing-Simmons and Vaquerizas 2019). Hi-C matrix generation tools convert FASTQ data from a Hi-C experiment into a normalised matrix of interaction strengths between pairs of genomic regions, accounting for false-positive interactions in the process.…”

Broadening our understanding of the genetics of Juvenile Idiopathic Arthritis (JIA): Interrogation of three dimensional chromatin structures and genetic regulatory elements within JIA-associated risk loci

“…TAD boundaries are determined by the presence of anchor structures consisting of the transcription regulator, CTCF, and cohesin [ 13 ], and serve to facilitate the interactions between specific genomic regions while constraining others [ 14 ]. TADs can be identified using non-directed chromatin capture techniques such as HiC [ 15 ], which allows for the detection of pairwise contacts between any two genomic locations based on their physical proximity in the 3D genome [ 16 ]. These physical interactions can visualized using software programs like Juicebox [ 17 ] and/or by querying publically available chromatin data via the 3D Genome Browser [ 18 ].…”