@article{
 title = {Learning the formation mechanism of domain-level chromatin states with epigenomics data},
 type = {article},
 year = {2019},
 pages = {2047-2056},
 volume = {116},
 websites = {https://doi.org/10.1016/j.bpj.2019.04.006},
 publisher = {Biophysical Society},
 id = {b4698dcc-97ec-3f5c-b185-bdb4da6eae0a},
 created = {2019-11-07T19:04:06.446Z},
 file_attached = {true},
 profile_id = {7caa5d1e-e498-3efc-950d-2841e4174da7},
 last_modified = {2021-03-30T07:37:01.214Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Xie2019},
 folder_uuids = {5a581cdf-c2f7-40cd-b429-bb4991430339},
 private_publication = {false},
 abstract = {Epigenetic modifications can extend over long genomic regions to form domain-level chromatin states that play critical roles in gene regulation. The molecular mechanism for the establishment and maintenance of these states is not fully understood and remains challenging to study with existing experimental techniques. Here, we took a data-driven approach and parameterized an information-theoretic model to infer the formation mechanism of domain-level chromatin states from genome-wide epigenetic modification profiles. This model reproduces statistical correlations among histone modifications and identifies well-known states. Importantly, it predicts drastically different mechanisms and kinetic pathways for the formation of euchromatin and heterochromatin. In particular, long, strong enhancer and promoter states grow gradually from short but stable regulatory elements via a multistep process. On the other hand, the formation of heterochromatin states is highly cooperative, and no intermediate states are found along the transition path. This cooperativity can arise from a chromatin looping-mediated spreading of histone methylation mark and supports collapsed, globular three-dimensional conformations rather than regular fibril structures for heterochromatin. We further validated these predictions using changes of epigenetic profiles along cell differentiation. Our study demonstrates that information-theoretic models can go beyond statistical analysis to derive insightful kinetic information that is otherwise difficult to access.},
 bibtype = {article},
 author = {Xie, Wen Jun and Zhang, Bin},
 doi = {10.1016/j.bpj.2019.04.006},
 journal = {Biophysical Journal}
}

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