{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["40(4)"],"submitter":["Schmitz JE"],"funding":["ELIXIR-DE"],"pubmed_abstract":["<h4>Motivation</h4>Automated chromatin segmentation based on ChIP-seq (chromatin immunoprecipitation followed by sequencing) data reveals insights into the epigenetic regulation of chromatin accessibility. Existing segmentation methods are constrained by simplifying modeling assumptions, which may have a negative impact on the segmentation quality.<h4>Results</h4>We introduce EpiSegMix, a novel segmentation method based on a hidden Markov model with flexible read count distribution types and state duration modeling, allowing for a more flexible modeling of both histone signals and segment lengths. In a comparison with existing tools, ChromHMM, Segway, and EpiCSeg, we show that EpiSegMix is more predictive of cell biology, such as gene expression. Its flexible framework enables it to fit an"],"journal":["Bioinformatics (Oxford, England)"],"pagination":["btae178"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11026141"],"repository":["biostudies-literature"],"pubmed_title":["EpiSegMix: a flexible distribution hidden Markov model with duration modeling for chromatin state discovery."],"pmcid":["PMC11026141"],"pubmed_authors":["Rahmann S","Laufer L","Aggarwal N","Walter J","Salhab A","Schmitz JE"],"additional_accession":[]},"is_claimable":false,"name":"EpiSegMix: a flexible distribution hidden Markov model with duration modeling for chromatin state discovery.","description":"<h4>Motivation</h4>Automated chromatin segmentation based on ChIP-seq (chromatin immunoprecipitation followed by sequencing) data reveals insights into the epigenetic regulation of chromatin accessibility. Existing segmentation methods are constrained by simplifying modeling assumptions, which may have a negative impact on the segmentation quality.<h4>Results</h4>We introduce EpiSegMix, a novel segmentation method based on a hidden Markov model with flexible read count distribution types and state duration modeling, allowing for a more flexible modeling of both histone signals and segment lengths. In a comparison with existing tools, ChromHMM, Segway, and EpiCSeg, we show that EpiSegMix is more predictive of cell biology, such as gene expression. Its flexible framework enables it to fit an","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Mar","modification":"2026-05-29T11:26:10.228Z","creation":"2026-04-08T04:28:15.523Z"},"accession":"S-EPMC11026141","cross_references":{"pubmed":["38565260"],"doi":["10.1093/bioinformatics/btae178"]}}