ArticleBioinformatics (Oxford, England)2024
EpiSegMix: a flexible distribution hidden Markov model with duration modeling for chromatin state discovery.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Pan-cell type continuous chromatin state annotation of 1,698 epigenomes from the International Human Epigenome Consortium.Genome biology · 2026Article
- Integrated flexible DNA methylation-chromatin segmentation modeling enhances epigenomic state annotation.Nucleic acids research · 2026Article
- Article
- Microplastic predictive modelling with the integration of Artificial Neural Networks and Hidden Markov Models (ANN-HMM).Journal of environmental health science & engineering · 2024Article
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6 authors.
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Abstract
motivationAutomated 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.
resultsWe 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 accurate probabilistic model, which has the potential to increase the biological interpretability of chromatin states. AVAILABILITY AND IMPLEMENTATION: Source code: https://gitlab.com/rahmannlab/episegmix.
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