{"database":"bioimages","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":[null],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-BIAD1602"],"repository":["bioimages"],"figure_sub":["Specimen","Funding","Study Component","organisation","Biosample","Associations","Image acquisition"],"pubmed_authors":["Gianluca Pegoraro","Tom Misteli","Adib Keikhosravi","Krishnendu Guin"],"additional_accession":[]},"is_claimable":false,"name":"Simulation and quantitative analysis of spatial centromere distribution patterns","description":"A prominent feature of eukaryotic chromosomes are centromeres, which are specialized regions\nof repetitive DNA required for faithful chromosome segregation during cell division. In interphase\ncells centromeres are non-randomly positioned in the three-dimensional space of the nucleus in a\ncell-type specific manner. The functional relevance and the cellular mechanisms underlying this\nobservation are unknown, and quantitative methods to measure distribution patterns of\ncentromeres in 3D space are needed. Here we have developed an analytical framework that\ncombines robust clustering metrics and advanced modeling techniques for the quantitative analysis\nof centromere distributions at the single cell level. To identify a robust quantitative measure for\ncentromere clustering, we benchmarked six m","dates":{"release":"2025-01-30T00:00:00Z","modification":"2025-01-30T20:23:37.325Z","creation":"2025-01-30T20:23:37.325Z"},"accession":"S-BIAD1602","cross_references":{}}