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

Autor:	van der Weide RH; Division of Gene Regulation, Oncode Institute and The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands., van den Brand T; Division of Gene Regulation, Oncode Institute and The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands., Haarhuis JHI; Division of Cell Biology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, The Netherlands., Teunissen H; Division of Gene Regulation, Oncode Institute and The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands., Rowland BD; Division of Cell Biology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, The Netherlands., de Wit E; Division of Gene Regulation, Oncode Institute and The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands.
Jazyk:	angličtina
Zdroj:	NAR genomics and bioinformatics [NAR Genom Bioinform] 2021 May 22; Vol. 3 (2), pp. lqab040. Date of Electronic Publication: 2021 May 22 (Print Publication: 2021).
DOI:	10.1093/nargab/lqab040
Abstrakt:	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 ΔSA1 cells gain intra-TAD interactions and increase compartmentalisation. ΔSA2 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. (© The Author(s) 2021. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)
Databáze:	MEDLINE
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