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

Abstract

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 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 ΔSA1 cells gain intra-TAD interactions and increase compartmentalisation. ΔSA2 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. 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, cohesinSA1 and cohesinSA2 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.

The 3D enhancer network of the developing T cell genome is shaped by SATB1

“…b Zoomed-in region of chromosome 9 (indicated by a black arrow in a ) depicts a topologically associating domain and its disruption in CTCF-depleted mESCs 6 and RAD21-depleted DP T cells 56 but not in SATB1-depleted murine thymocytes (this study). c Genome-wide log2 fold change of relative contact probabilities between WT and SATB1-, CTCF- and RAD21-depleted cells, calculated using GENOVA 57 . d Saddle plot analysis was computed using GENOVA 57 to illustrate the abundance of inter- vs intra-compartment interactions.…”

“…c Genome-wide log2 fold change of relative contact probabilities between WT and SATB1-, CTCF- and RAD21-depleted cells, calculated using GENOVA 57 . d Saddle plot analysis was computed using GENOVA 57 to illustrate the abundance of inter- vs intra-compartment interactions. The visualization represents the difference between a factor-depleted versus WT observed/expected output of the saddle analysis.…”

Dynamic chromosomal interactions and control of heterochromatin positioning by Ki-67

“…Hi-C reads were processed with HiC-Pro (Servant et al , 2015), which performs mapping, generation of contact matrices (100 kb, 1 Mb) and ICE normalization. Matrices were loaded in R and further processed with GENOVA 1.0.0.9 (van der Weide et al , 2021).…”

Cohesin erases genomic-proximity biases to drive stochastic Protocadherin expression for proper neural wiring

“…For in situ Hi-C experiments, raw FASTQ files were processed using HiC-Pro (see Supplementary Table 2) 38 . Hi-C pyramid plots and difference maps were generated using GENOVA 39 .…”