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EnHiC: learning fine-resolution Hi-C contact maps using a generative adversarial framework.

Yangyang Hu1, Wenxiu Ma2

  • 1Department of Computer Science and Engineering.

Bioinformatics (Oxford, England)
|July 12, 2021
PubMed
Summary
This summary is machine-generated.

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EnHiC enhances low-resolution chromosome conformation capture (Hi-C) data to high resolution using a novel generative adversarial network. This method improves the detection of chromatin interactions and topologically associated domains, making high-resolution Hi-C more accessible.

Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • High-throughput chromosome conformation capture (Hi-C) enables genome-wide chromatin interaction mapping.
  • Generating high-resolution Hi-C data requires expensive deep sequencing, limiting its application.
  • Machine learning, particularly neural networks, offers a potential solution for enhancing Hi-C data resolution.

Purpose of the Study:

  • To introduce EnHiC, a novel method for predicting high-resolution Hi-C matrices from low-resolution input data.
  • To leverage generative adversarial networks (GANs) and non-negative matrix factorization principles for Hi-C data enhancement.
  • To improve the accessibility and utility of high-resolution Hi-C data for biological research.

Main Methods:

  • Developed EnHiC, a GAN-based framework for Hi-C matrix resolution enhancement.

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  • Incorporated non-negative matrix factorization-inspired techniques to extract rank-1 features from multi-scale low-resolution matrices.
  • Validated EnHiC using three human Hi-C datasets.

    • Main Results:

    • EnHiC accurately and reliably enhanced the resolution of Hi-C matrices.
    • EnHiC outperformed existing GAN-based models in Hi-C data enhancement.
    • Predicted high-resolution matrices from EnHiC facilitated accurate detection of topologically associated domains and fine-scale chromatin interactions.

    Conclusions:

    • EnHiC provides an effective computational approach to generate high-resolution Hi-C data from low-resolution inputs.
    • The method significantly improves the analysis of chromatin architecture and interactions.
    • EnHiC is publicly available, promoting wider adoption and further research in the field.