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 conformation capture 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 a 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 ?SA1 cells gain intra-TAD interactions and increase compartmentalisation. ?SA2 cells have longer loops and a less compartmentalised genome. These results suggest that cohesinSA1 forms longer loops, while cohesinSA2 plays a role in forming and maintaining intra-TAD interactions. The differences in loop-forming activity affect whole chromosome organisation consistent with a model where loops and compartments counterbalance each other. We show that GENOVA is an easy to use R-package, that allows researchers to explore Hi-C data in great detail. Overall design: Hi-C; Hap1 SA1 and SA2 knock-out cells