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Learning Micro-C from Hi-C with diffusion models.

Tong Liu1, Hao Zhu1, Zheng Wang1

  • 1Department of Computer Science, University of Miami, Coral Gables, Florida, United States of America.

Plos Computational Biology
|May 17, 2024
PubMed
Summary
This summary is machine-generated.

HiC2MicroC uses denoising diffusion probabilistic models to predict Micro-C chromatin interactions from Hi-C data, enhancing loop detection and genomic feature analysis. This method improves upon existing regression techniques, offering a valuable tool for analyzing chromatin organization.

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Area of Science:

  • Genomics
  • Computational Biology
  • Epigenetics

Background:

  • Micro-C offers nucleosome resolution chromatin interaction data, surpassing traditional Hi-C in signal-to-noise and loop detection.
  • Limited Micro-C datasets hinder comprehensive analysis compared to abundant Hi-C data.

Purpose of the Study:

  • To develop a computational method (HiC2MicroC) for predicting Micro-C data from existing Hi-C datasets.
  • To leverage denoising diffusion probabilistic models (DDPM) for enhanced chromatin interaction prediction.

Main Methods:

  • Trained DDPM and regression models using human foreskin fibroblast (HFFc6) cell line data.
  • Evaluated prediction accuracy across six cell types at 5-kb and 1-kb resolutions.
  • Compared HiC2MicroC performance against regression models and validated against P পাঁচটিCMicro-C and ChIA-PET data.

Main Results:

  • HiC2MicroC successfully recovered Micro-C loops, including those missed by Hi-C.
  • Predicted loops frequently anchored CTCF binding sites convergently.
  • Recovered loops exhibited genomic and epigenetic properties consistent with Micro-C data, linking enhancers and promoters.

Conclusions:

  • HiC2MicroC effectively enhances Hi-C data towards Micro-C resolution using DDPM.
  • The method accurately predicts biologically relevant chromatin loops, validated by multiple experimental techniques.
  • HiC2MicroC provides a powerful computational tool for in-depth analysis of chromatin organization from Hi-C data.