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Decoding regulatory structures and features from epigenomics profiles: A Roadmap-ENCODE Variational Auto-Encoder

Ruifeng Hu1, Guangsheng Pei1, Peilin Jia1

  • 1Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.

Methods (San Diego, Calif.)
|November 2, 2019
PubMed
Summary

We developed a Variational Auto-Encoder (VAE) model to analyze large epigenomics datasets. This model compresses and represents epigenomic data, revealing patterns in histone marks and identifying tissue-specific epigenetic regulation.

Keywords:
EpigenomicHistone markRoadmap EpigenomicsTissue specificityVariational Auto-Encoder

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Area of Science:

  • Computational Biology
  • Genomics
  • Epigenetics

Background:

  • Massively parallel DNA sequencing technologies like ChIP-seq generate large-scale epigenomics data.
  • Advanced computational approaches, including deep learning, are crucial for mining complex functional genomics data.
  • Understanding epigenomic profiles across histone marks and tissue types is essential for biological regulation studies.

Purpose of the Study:

  • To implement a Variational Auto-Encoder (VAE) neural network framework for exploring large-scale epigenomics data.
  • To represent and compress epigenomic data from the Roadmap Epigenomics and ENCODE projects.
  • To investigate histone mark clustering and tissue/cell specificity within epigenomic data.

Main Methods:

  • Applied a Variational Auto-Encoder (VAE) model to 935 reference samples covering 28 tissues and 12 histone marks.
  • Utilized enhancer and promoter regions as annotation features and ChIP-seq signal values as feature values.
  • Performed parameter sweeps to identify optimal hyperparameters for the VAE model.

Main Results:

  • The Roadmap-ENCODE VAE (RE-VAE) model achieved data compression and feature representation.
  • Histone marks generally clustered well in the latent space, while tissue/cell clustering was less pronounced.
  • Tissue specificity was observed for certain histone marks (H3K4me3, H3K27ac) in large sample sets (e.g., blood, brain).
  • RE-VAE model identified contributive regions and genes in blood, validated by independent analysis.
  • The model successfully detected cancer cell lines with similar epigenomic profiles.

Conclusions:

  • Introduced and implemented a VAE model (RE-VAE) for representing large-scale epigenomics data.
  • The RE-VAE model can explore classifications of histone modifications and tissue/cell specificity.
  • Demonstrated the model's capability to classify new data with unknown sources, including cancer cell lines.