EXPERIMENT_TITLE: GSM4873988: SA1 Hi-C rep 2; Homo sapiens; Hi-C
EXPERIMENT_DESIGN_LIBRARY_DESCRIPTOR_LIBRARY_CONSTRUCTION_PROTOC: TruSeq libraries prepared from 3C template Hi-C was performed essentially as previously described (Rao et al., 2014). Briefly, for each template roughly 10 million cells were crosslinked using 2% formaldehyde. In nucleus restriction was performed using MboI. Restriction overhangs were filled in with biotinylated nucleotides followed by blunt-end ligation. A streptavidin pull-down was performed to enrich for ligated sequences. Pulled-down DNA was end-repaired and an A-overhang was added. Sequencing adapters were ligated to the DNA samples to create sequencing libraries.
STUDY_DESCRIPTOR_STUDY_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 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
SAMPLE_TITLE: SA1 Hi-C rep 2
SAMPLE_SAMPLE_ATTRIBUTES_SAMPLE_ATTRIBUTE: [{"TAG": "source_name", "VALUE": "Hap1 SA1"}, {"TAG": "cell line", "VALUE": "Hap1"}, {"TAG": "genotype", "VALUE": "SA1 knockout"}]
Pool_Member_@attributes_sample_title: SA1 Hi-C rep 2
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