bioSBM: a random graph model to integrate epigenomic data in chromatin structure prediction
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| Format: | Preprint |
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2024
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| _version_ | 1866908532301889536 |
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| author | Zhang, Alex Chen Yi Rosa, Angelo Sanguinetti, Guido |
| author_facet | Zhang, Alex Chen Yi Rosa, Angelo Sanguinetti, Guido |
| contents | The spatial organization of chromatin within the nucleus plays a crucial role in gene expression and genome function. However, the quantitative relationship between this organization and nuclear biochemical processes remains under debate. In this study, we present a graph-based generative model, bioSBM, designed to capture long-range chromatin interaction patterns from Hi-C data and, importantly, simultaneously link these patterns to biochemical features. Applying bioSBM to Hi-C maps of the GM12878 lymphoblastoid cell line, we identified a latent structure of chromatin interactions, revealing 7 distinct communities that strongly align with known biological annotations. Additionally, we infer a linear transformation that maps biochemical observables, such as histone marks, to the parameters of the generative graph model, enabling accurate genome-wide predictions of chromatin contact maps on out-of-sample data, both within the same cell line, and on the completely unseen HCT116 cell line under RAD21 depletion. These findings highlight bioSBM's potential as a powerful tool for elucidating the relationship between biochemistry and chromatin architecture and predicting long-range genome organization from independent biochemical data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_14425 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | bioSBM: a random graph model to integrate epigenomic data in chromatin structure prediction Zhang, Alex Chen Yi Rosa, Angelo Sanguinetti, Guido Quantitative Methods Biological Physics The spatial organization of chromatin within the nucleus plays a crucial role in gene expression and genome function. However, the quantitative relationship between this organization and nuclear biochemical processes remains under debate. In this study, we present a graph-based generative model, bioSBM, designed to capture long-range chromatin interaction patterns from Hi-C data and, importantly, simultaneously link these patterns to biochemical features. Applying bioSBM to Hi-C maps of the GM12878 lymphoblastoid cell line, we identified a latent structure of chromatin interactions, revealing 7 distinct communities that strongly align with known biological annotations. Additionally, we infer a linear transformation that maps biochemical observables, such as histone marks, to the parameters of the generative graph model, enabling accurate genome-wide predictions of chromatin contact maps on out-of-sample data, both within the same cell line, and on the completely unseen HCT116 cell line under RAD21 depletion. These findings highlight bioSBM's potential as a powerful tool for elucidating the relationship between biochemistry and chromatin architecture and predicting long-range genome organization from independent biochemical data. |
| title | bioSBM: a random graph model to integrate epigenomic data in chromatin structure prediction |
| topic | Quantitative Methods Biological Physics |
| url | https://arxiv.org/abs/2409.14425 |