ArticleBMC bioinformatics2013
Discovering and mapping chromatin states using a tree hidden Markov model.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Pan-cell type continuous chromatin state annotation of 1,698 epigenomes from the International Human Epigenome Consortium.Genome biology · 2026Article
- Investigation into the Anti-hyperglycemic Traits and Bioactive Constituents of Postbiotics Derived from Lacticaseibacillus paracasei NCUH012072.Probiotics and antimicrobial proteins · 2026Article
- Robust chromatin state annotation.Genome research · 2024Article
- Computational methods to explore chromatin state dynamics.Briefings in bioinformatics · 2022Review
- Continuous chromatin state feature annotation of the human epigenome.Bioinformatics (Oxford, England) · 2022Article
- Universal annotation of the human genome through integration of over a thousand epigenomic datasets.Genome biology · 2022Article
- Article
- 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
- Inferring time series chromatin states for promoter-enhancer pairs based on Hi-C data.BMC genomics · 2021Article
- Tree-Based Co-Clustering Identifies Chromatin Accessibility Patterns Associated With Hematopoietic Lineage Structure.Frontiers in genetics · 2021Article
- A unified encyclopedia of human functional DNA elements through fully automated annotation of 164 human cell types.Genome biology · 2019Article
- EpiAlign: an alignment-based bioinformatic tool for comparing chromatin state sequences.Nucleic acids research · 2019Article
- Reveal cell type-specific regulatory elements and their characterized histone code classes via a hidden Markov model.BMC genomics · 2018Article
- Applications of ENCODE data to Systematic Analyses via Data Integration.Current opinion in systems biology · 2018Article
- ChromTime: modeling spatio-temporal dynamics of chromatin marks.Genome biology · 2018Article
- FUN-LDA: A Latent Dirichlet Allocation Model for Predicting Tissue-Specific Functional Effects of Noncoding Variation: Methods and Applications.American journal of human genetics · 2018Article
- Integrating ChIP-seq with other functional genomics data.Briefings in functional genomics · 2018Review
- Chromatin-state discovery and genome annotation with ChromHMM.Nature protocols · 2017Review
- EpiCompare: an online tool to define and explore genomic regions with tissue or cell type-specific epigenomic features.Bioinformatics (Oxford, England) · 2017Article
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Authors and funding
3 authors.
Funding
Abstract
New biological techniques and technological advances in high-throughput sequencing are paving the way for systematic, comprehensive annotation of many genomes, allowing differences between cell types or between disease/normal tissues to be determined with unprecedented breadth. Epigenetic modifications have been shown to exhibit rich diversity between cell types, correlate tightly with cell-type specific gene expression, and changes in epigenetic modifications have been implicated in several diseases. Previous attempts to understand chromatin state have focused on identifying combinations of epigenetic modification, but in cases of multiple cell types, have not considered the lineage of the cells in question.We present a Bayesian network that uses epigenetic modifications to simultaneously model 1) chromatin mark combinations that give rise to different chromatin states and 2) propensities for transitions between chromatin states through differentiation or disease progression. We apply our model to a recent dataset of histone modifications, covering nine human cell types with nine epigenetic modifications measured for each. Since exact inference in this model is intractable for all the scale of the datasets, we develop several variational approximations and explore their accuracy. Our method exhibits several desirable features including improved accuracy of inferring chromatin states, improved handling of missing data, and linear scaling with dataset size. The source code for our model is available at http:// http://github.com/uci-cbcl/tree-hmm.
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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.