{"doi":"10.1038/ncomms15011","title":"Multi-scale chromatin state annotation using a hierarchical hidden Markov model","abstract":"Chromatin-state analysis is widely applied in the studies of development and diseases. However, existing methods operate at a single length scale, and therefore cannot distinguish large domains from isolated elements of the same type. To overcome this limitation, we present a hierarchical hidden Markov model, diHMM, to systematically annotate chromatin states at multiple length scales. We apply diHMM to analyse a public ChIP-seq data set. diHMM not only accurately captures nucleosome-level information, but identifies domain-level states that vary in nucleosome-level state composition, spatial distribution and functionality. The domain-level states recapitulate known patterns such as super-enhancers, bivalent promoters and Polycomb repressed regions, and identify additional patterns whose biological functions are not yet characterized. By integrating chromatin-state information with gene expression and Hi-C data, we identify context-dependent functions of nucleosome-level states. Thus, diHMM provides a powerful tool for investigating the role of higher-order chromatin structure in gene regulation.","journal":"Nature Communications","year":2017,"id":3459,"datarank":1.8950696929371968,"base_score":4.189654742026425,"endowment":4.189654742026425,"self_citation_contribution":0.6284482113039639,"citation_network_contribution":1.266621481633233,"self_endowment_contribution":0.6284482113039639,"citer_contribution":1.266621481633233,"corpus_percentile":null,"corpus_rank":null,"citation_count":65,"citer_count":53,"citers_with_citation_signal":40,"citers_with_endowment":40,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0604,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2017-04-07","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":36155,"name":"Wouter Meuleman","orcid":"0000-0002-1196-5401","position":1,"is_corresponding":false},{"id":36156,"name":"Jialiang Huang","orcid":"0000-0002-5218-1144","position":2,"is_corresponding":false},{"id":36157,"name":"Kimberly Glass","orcid":"0000-0003-4394-5779","position":3,"is_corresponding":false},{"id":2737,"name":"Luca Pinello","orcid":"0000-0003-1195-9607","position":4,"is_corresponding":false},{"id":36158,"name":"Jianrong Wang","orcid":"0000-0002-9290-4888","position":5,"is_corresponding":false},{"id":14693,"name":"Sharon L. R. Kardia","orcid":"0000-0002-9853-3379","position":6,"is_corresponding":false},{"id":31866,"name":"Guo‐Cheng Yuan","orcid":"0000-0002-2283-4714","position":7,"is_corresponding":false},{"id":36154,"name":"Eugenio Marco","orcid":"0000-0002-3675-263X","position":0,"is_corresponding":true}],"reference_count":43,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"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":[]}