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<p>Noncoding mutations play a critical role in regulating gene expression, yet predicting their effects across diverse tissues and cell types remains a challenge. Here we present EMO, a transformer-based model that integrates DNA sequence with chromatin accessibility data (assay for transposase-accessible chromatin with sequencing) to predict the regulatory impact of noncoding single nucleotide polymorphisms on gene expression. A key component of EMO is its ability to incorporate personalized functional genomic profiles, enabling individual-level and disease-contextual predictions and addressing critical limitations of current approaches. EMO generalizes across tissues and cell types by modeling both short- and long-range regulatory interactions and capturing dynamic gene expression changes associated with disease progression. In benchmark evaluations, the pretraining-based EMO framework outperformed existing models, with fine-tuning small-sample tissues enhancing the model’s ability to fit target tissues. In single-cell contexts, EMO accurately identified cell-type-specific regulatory patterns and successfully captured the effects of disease-associated single nucleotide polymorphisms in conditions, linking genetic variation to disease-relevant pathways.</p>

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Predicting the regulatory impacts of noncoding variants on gene expression through epigenomic integration across tissues and single-cell landscapes

  • 期刊论文

  • 发表日期：

    2025年9月26日

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  • Zhe Liu,
  • Yihang Bao,
  • An Gu,
  • Weichen Song,
  • Guan Ning Lin

摘要

Noncoding mutations play a critical role in regulating gene expression, yet predicting their effects across diverse tissues and cell types remains a challenge. Here we present EMO, a transformer-based model that integrates DNA sequence with chromatin accessibility data (assay for transposase-accessible chromatin with sequencing) to predict the regulatory impact of noncoding single nucleotide polymorphisms on gene expression. A key component of EMO is its ability to incorporate personalized functional genomic profiles, enabling individual-level and disease-contextual predictions and addressing critical limitations of current approaches. EMO generalizes across tissues and cell types by modeling both short- and long-range regulatory interactions and capturing dynamic gene expression changes associated with disease progression. In benchmark evaluations, the pretraining-based EMO framework outperformed existing models, with fine-tuning small-sample tissues enhancing the model’s ability to fit target tissues. In single-cell contexts, EMO accurately identified cell-type-specific regulatory patterns and successfully captured the effects of disease-associated single nucleotide polymorphisms in conditions, linking genetic variation to disease-relevant pathways.