Hi-C analyses with GENOVA: a case study with cohesin variants

Conformation capture-approaches like Hi-C can elucidate chromosome structure at a genome-wide scale. Hi-C datasets are large and require specialised software. Here, we present GENOVA: a user-friendly software package to analyse and visualise chromosome conformation capture (3C) data. GENOVA is an R-package that includes the most common Hi-C analyses, such as compartment and insulation score analysis. It can create annotated heatmaps to visualise the contact frequency at a specific locus and aggregate Hi-C signal over user-specified genomic regions such as ChIP-seq data. Finally, our package supports output from the major mapping-pipelines. We demonstrate the capabilities of GENOVA by analysing Hi-C data from HAP1 cell lines in which the cohesin-subunits SA1 and SA2 were knocked out. We find that Delta SA1 cells gain intra-TAD interactions and increase compartmentalisation. Delta SA2 cells have longer loops and a less compartmentalised genome. These results suggest that cohesin(SA1) forms longer loops, while cohesin(SA2) plays a role in forming and maintaining intra-TAD interactions. Our data supports the model that the genome is provided structure in 3D by the counter-balancing of loop formation on one hand, and compartmentalization on the other hand. By differentially controlling loops, cohesin(SA1) and cohesin(SA2) therefore also affect nuclear compartmentalization. We show that GENOVA is an easy to use R-package, that allows researchers to explore Hi-C data in great detail.

Automated summary

GENOVA is a user-friendly software package for analyzing and visualizing chromosome conformation capture data, including common Hi-C analyses. By analyzing Hi-C data from HAP1 cell lines with knocked out cohesin subunits SA1 and SA2, different effects on genome structure and interactions were observed.