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Related Experiment Video

Updated: Jul 3, 2026

Methyl-binding DNA capture Sequencing for Patient Tissues
08:40

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant

Wenran Li1, Shijia Yu1, Yingyu Cheng1

  • 1Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.

Plos Computational Biology
|July 1, 2026
PubMed
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DeepMethylation, a deep learning framework, accurately predicts genome-wide DNA methylation using sequence and epigenomic data. It aids in understanding epigenetic effects of genetic variants, advancing epigenetics research.

Area of Science:

  • Epigenetics and Genomics
  • Computational Biology and Bioinformatics

Background:

  • DNA methylation is a crucial epigenetic mark regulating gene expression, vital for development and disease.
  • Current genome-wide DNA methylation profiling methods are often slow and lack comprehensive coverage.

Purpose of the Study:

  • To develop a deep learning framework, DeepMethylation, for accurate prediction of CpG methylation status.
  • To integrate DNA sequence and tissue-specific epigenomic features for enhanced methylation prediction.
  • To introduce Delta DeepMethylation (DDM) for evaluating epigenetic effects of single nucleotide polymorphisms (SNPs) on DNA methylation.

Main Methods:

  • Developed DeepMethylation, a deep learning model integrating DNA sequence and epigenomic data.
  • Utilized feature importance analysis to understand contributions of epigenomic features.

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Last Updated: Jul 3, 2026

Methyl-binding DNA capture Sequencing for Patient Tissues
08:40

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution
13:47

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Published on: February 24, 2015

Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

  • Created Delta DeepMethylation (DDM) to predict SNP effects on methylation, validated against mQTLs and LD.

    • Main Results:

    • DeepMethylation achieved state-of-the-art performance (average AUROC 0.909) across various tissues.
    • The framework accurately imputes methylation beyond array-covered sites and extends coverage from 450k to EPIC arrays.
    • DDM predictions showed consistency with mQTLs and were not confounded by linkage disequilibrium (LD).

    Conclusions:

    • DeepMethylation offers a powerful tool for accurate genome-wide DNA methylation prediction.
    • The framework facilitates robust interpretation of regulatory variant effects on DNA methylation across tissues.
    • This approach advances epigenomic research by enabling efficient and comprehensive methylation analysis.