Puget predicts gene expression across cell types using sequence and 3D chromatin organization data

Hang, S.; Wang, X.; Murtaza, G.; Jha, A.; Wen, B.; Fang, T.; Sanders, J.; Wang, S.; Noble, W. S.

doi:10.1101/2025.11.19.689320

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bioRxiv · 10.1101/2025.11.19.689320

Puget predicts gene expression across cell types using sequence and 3D chromatin organization data

Hang, S.·Wang, X.·Murtaza, G.·Jha, A.·Wen, B.·Fang, T.·Sanders, J.·Wang, S.·Noble, W. S.

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Abstract

Gene expression is governed by both linear DNA sequence and three-dimensional (3D) chromatin architecture. Most gene expression prediction models rely on sequence alone, thereby failing to capture structural context and to generalize to unseen cell types. We present Puget, a deep learning model that predicts cell type-specific gene expression from sequence and Hi-C data, which captures 3D chromatin organization. Puget pairs pretrained sequence and Hi-C encoders with a lightweight transformer decoder. Using paired Hi-C/RNA-seq from 36 human and 4 mouse biosamples, we evaluate Pugets ability to generalize to held-out genes, held-out biosamples, and from human to mouse. Relative to a sequence-only baseline, Puget improves cross-biosample Pearson correlation by up to 25% on highly variable genes in training biosamples and, unlike the sequence-only model, generalizes to held-out biosamples and across species. In addition, in silico perturbation experiments show that Puget can prioritize experimentally validated enhancer-gene pairs. Together, these results highlight a generalizable approach for modeling gene expression from sequence and 3D chromatin organization.

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Hang, S., Wang, X., Murtaza, G., Jha, A., Wen, B., Fang, T., Sanders, J., Wang, S., Noble, W. S.. 2025-11-20. Puget predicts gene expression across cell types using sequence and 3D chromatin organization data. https://doi.org/10.1101/2025.11.19.689320

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