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Targeted DNA Methylation Analysis by Next-generation Sequencing
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High-fidelity bidirectional translation between single-cell transcriptomes and DNA methylomes with scBOND.

Kehan Lang1, Chenyang Jia1, Siyu Li1

  • 1School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China.

Genome Research
|March 26, 2026
PubMed
Summary
This summary is machine-generated.

We developed scBOND, a novel computational framework for bidirectional cross-modal translation between single-cell RNA sequencing (scRNA-seq) and single-cell DNA methylation (scDNAm) data. scBOND accurately infers epigenetic landscapes from gene expression and vice versa, enhancing biological discovery.

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Area of Science:

  • Computational Biology
  • Genomics
  • Epigenetics
  • Single-cell Multiomics

Background:

  • Single-cell multiomic sequencing provides deep insights into cellular heterogeneity by profiling gene expression and epigenetic landscapes simultaneously.
  • Technical challenges and high costs limit the widespread application of these advanced sequencing technologies.
  • Existing computational methods for cross-modality translation between scRNA-seq and scDNAm data have limitations, including unidirectionality and inadequate modeling of regulatory associations.

Purpose of the Study:

  • To introduce scBOND, a bidirectional cross-modal translation framework for scRNA-seq and scDNAm data.
  • To address limitations of existing methods by improving context-specific association modeling and biological relevance in evaluation.
  • To enhance model generalization in data-scarce scenarios using a biologically informed augmentation strategy (scBOND-Aug).

Main Methods:

  • Developed scBOND, a bidirectional framework utilizing a mixture-of-experts block for context-dependent patterns and self-attention for signal fidelity.
  • Implemented scBOND-Aug, a variant incorporating biologically informed data augmentation to improve performance with limited paired data.
  • Evaluated scBOND and scBOND-Aug against baseline methods using extensive experimental datasets.

Main Results:

  • scBOND consistently outperformed baseline methods in both translation directions, achieving high accuracy while preserving cellular structure.
  • The framework successfully identified subtle, functionally significant differences between closely related cell types in mouse embryonic data.
  • scBOND reconstructed scDNAm profiles from RNA-only data, revealing cell-type and stage-specific regulatory mechanisms in human neurons and oligodendrocyte lineage.

Conclusions:

  • scBOND offers a robust and accurate solution for bidirectional cross-modal translation between scRNA-seq and scDNAm data.
  • The framework effectively recovers biological signals and enhances the discovery of regulatory mechanisms, even with limited paired data.
  • scBOND and scBOND-Aug significantly advance the computational analysis of single-cell multiomic data, broadening accessibility and insight generation.