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  • Open Access

bioSBM: A Random Graph Model to Integrate Epigenomic Data in Chromatin Structure Prediction

Alex Chen Yi Zhang1,2,*, Angelo Rosa2,†, and Guido Sanguinetti2,‡

  • *Contact author: alexchenyi.zhang@ist.ac.at
  • †Contact author: anrosa@sissa.it
  • ‡Contact author: gsanguin@sissa.it
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PRX Life 3, 043006 – Published 21 October, 2025

DOI: https://doi.org/10.1103/gy1p-4256

Abstract

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 seven 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.

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PRX Life 3, 043006– Published 21 October, 2025
  • Received 26 September 2024
  • Accepted 9 September 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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