{"doi":"10.1093/bioinformatics/btad314","title":"CscoreTool-M infers 3D sub-compartment probabilities within cell population","abstract":"MOTIVATION: Computational inference of genome organization based on Hi-C sequencing has greatly aided the understanding of chromatin and nuclear organization in three dimensions (3D). However, existing computational methods fail to address the cell population heterogeneity. Here we describe a probabilistic-modeling-based method called CscoreTool-M that infers multiple 3D genome sub-compartments from Hi-C data. RESULTS: The compartment scores inferred using CscoreTool-M represents the probability of a genomic region locating in a specific sub-compartment. Compared to published methods, CscoreTool-M is more accurate in inferring sub-compartments corresponding to both active and repressed chromatin. The compartment scores calculated by CscoreTool-M also help to quantify the levels of heterogeneity in sub-compartment localization within cell populations. By comparing proliferating cells and terminally differentiated non-proliferating cells, we show that the proliferating cells have higher genome organization heterogeneity, which is likely caused by cells at different cell-cycle stages. By analyzing 10 sub-compartments, we found a sub-compartment containing chromatin potentially related to the early-G1 chromatin regions proximal to the nuclear lamina in HCT116 cells, suggesting the method can deconvolve cell cycle stage-specific genome organization among asynchronously dividing cells. Finally, we show that CscoreTool-M can identify sub-compartments that contain genes enriched in housekeeping or cell-type-specific functions. AVAILABILITY AND IMPLEMENTATION: https://github.com/scoutzxb/CscoreTool-M.","journal":"Bioinformatics","year":2023,"id":383253,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9562,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":472953,"name":"Joseph R. Tran","orcid":"0000-0002-7467-8995","position":1,"is_corresponding":false},{"id":816085,"name":"Yixian Zheng","orcid":"0000-0002-1992-4014","position":2,"is_corresponding":false},{"id":816084,"name":"Xiaobin Zheng","orcid":"0000-0002-9599-8525","position":0,"is_corresponding":true}],"reference_count":34,"raw_metadata":null,"created_at":"2026-07-19T01:17:25.158896Z","pmid":"37166448","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}