ArticleNature communications2017
Multi-scale chromatin state annotation using a hierarchical hidden Markov model.
Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 65 citations in OpenAlex.
- Pan-cell type continuous chromatin state annotation of 1,698 epigenomes from the International Human Epigenome Consortium.Genome biology · 2026Article
- Uncovering topologically associating domains from three-dimensional genome maps with TADGATE.Nucleic acids research · 2025Article
- Understanding relationships between epigenetic marks and their application to robust assignment of chromatin states.Briefings in bioinformatics · 2024Article
- Systematic analysis identifies a connection between spatial and genomic variations of chromatin states.Cell systems · 2024Article
- From Nucleosomes to Compartments: Physicochemical Interactions Underlying Chromatin Organization.Annual review of biophysics · 2024Review
- EpiSegMix: a flexible distribution hidden Markov model with duration modeling for chromatin state discovery.Bioinformatics (Oxford, England) · 2024Article
- Broad H3K4me3 Domain Is Associated with Spatial Coherence during Mammalian Embryonic Development.bioRxiv : the preprint server for biology · 2023Article
- ChromGene: gene-based modeling of epigenomic data.Genome biology · 2023Article
- Toward a comprehensive catalog of regulatory elements.Human genetics · 2023Review
- Integrative chromatin domain annotation through graph embedding of Hi-C data.Bioinformatics (Oxford, England) · 2023Article
- Summarizing internal dynamics boosts differential analysis and functional interpretation of super enhancers.Nucleic acids research · 2022Article
- Model of Markov-Based Piano Note Recognition Algorithm and Piano Teaching Model Construction.Journal of environmental and public health · 2022Article
- Article
- Transcriptional enhancers and their communication with gene promoters.Cellular and molecular life sciences : CMLS · 2021Review
- Segmentation and genome annotation algorithms for identifying chromatin state and other genomic patterns.PLoS computational biology · 2021Review
- NucHMM: a method for quantitative modeling of nucleosome organization identifying functional nucleosome states distinctly associated with splicing potentiality.Genome biology · 2021Article
- SPIN reveals genome-wide landscape of nuclear compartmentalization.Genome biology · 2021Article
- Distinguishing cell phenotype using cell epigenotype.Science advances · 2020Article
- A comprehensive resource for retrieving, visualizing, and integrating functional genomics data.Life science alliance · 2020Article
- RT States: systematic annotation of the human genome using cell type-specific replication timing programs.Bioinformatics (Oxford, England) · 2019Article
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.