Elucidating spatiotemporal chromatin dynamics with multi-stage differential variations from Hi-C

Zhongshen Li, Jixiang Yu, Shen You, Leyi Wei, Qiuzhen Lin, Xiangtao Li, Ka-Chun Wong*

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

High-throughput sequencing such as Hi-C captures spatiotemporal chromatin interactions, revealing the intricate interplays within transcriptional regulation and chromatin dynamics during cellular reprogramming and developmental processes. However, the forecast on chromatin dynamics in successive developmental stages remains challenging due to the inherent complexity of spatial and temporal patterns in Hi-C data across developmental stages. Towards such a direction, we present StarMie, a deep learning framework that integrates the spatiotemporal-aware module and the multi-stage differential variation module to predict high-throughput chromatin interactions in next developmental stages. Our comprehensive evaluation demonstrates that StarMie outperforms existing methods and sufficiently captures discriminative spatial and temporal dependencies as well as inter-stage-level variations of chromatin interactions. Moreover, the dual importance of both spatial and temporal information in Hi-C data is observed in parameter analysis. Ablation studies also confirm the essential role of each component in StarMie. Furthermore, five cross-species case studies support StarMie's cross-species generalizability and its capability to extract universal chromatin interaction patterns in different developmental stages. In-depth analysis demonstrates that StarMie uncovers conserved genomic logic in cardiac development and disease. Overall, this work paves a new approach for exploring genome reprogramming and development through predictive modeling of Hi-C dynamics. © 2025 Elsevier B.V.
Original languageEnglish
Article number113516
JournalKnowledge-Based Systems
Volume317
Online published6 Apr 2025
DOIs
Publication statusPublished - 23 May 2025

Funding

This research was substantially sponsored by the research project (Grant No 32170654 and Grant No 32000464) supported by the National Natural Science Foundation of China and was substantially supported by the Shenzhen Research Institute, City University of Hong Kong. The work described in this paper was substantially supported by the grant from the Research Grants Council of the Hong Kong Special Administrative Region [CityU 11203723]. The work described in this paper was partially supported by the grants from City University of Hong Kong (CityU 7030022, C1056-24G, CityU 9667265) and Innovation and Technology Commission (ITB/FBL/9037/22/S).

Research Keywords

  • Spatiotemporal Hi-C data
  • Spatiotemporal predictive learning
  • Swin transformer
  • Vision state space model

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