{"doi":"10.1016/j.celrep.2015.02.001","title":"Single-Cell DNA Methylome Sequencing and Bioinformatic Inference of Epigenomic Cell-State Dynamics","abstract":null,"journal":"Cell Reports","year":2015,"id":624166,"datarank":0.9123328365564671,"base_score":6.082218910376446,"endowment":6.082218910376446,"self_citation_contribution":0.9123328365564671,"citation_network_contribution":0.0,"self_endowment_contribution":0.9123328365564671,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":437,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1613377,"name":"Nathan C. 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Our assay is optimized for profiling many samples at low coverage, and we describe a bioinformatic method that analyzes collections of single-cell methylomes to infer cell-state dynamics. Using these technological advances, we studied epigenomic cell-state dynamics in three in vitro models of cellular differentiation and pluripotency, where we observed characteristic patterns of epigenome remodeling and cell-to-cell heterogeneity. The described method enables single-cell analysis of DNA methylation in a broad range of biological systems, including embryonic development, stem cell differentiation, and cancer. 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