Motivation: 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.
Results: 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 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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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11026141 | PMC |
| http://dx.doi.org/10.1093/bioinformatics/btae178 | DOI Listing |
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