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<p>DNA methylation is a critical epigenetic mark across numerous species, and identifying differentially methylated regions (DMRs) is essential for understanding genome regulation. Most existing DMR detection methods require predefined sample conditions, limiting the discovery of new epigenetic patterns, especially when group identities are unknown or uncertain, as is common in clinical settings. Additionally, only a very few approaches enable comparisons across multiple conditions. To address this significant gap, we present metilene<sup>3</sup>, a method for rapid, multi-condition DMR detection that operates in both supervised and unsupervised modes, using user-provided labels or autonomously clustering unlabeled samples. By segmenting the genome based on multiple pairwise methylation difference signals, metilene<sup>3</sup> enables sample classification and DMR-anchored inference of epigenetic relationships. Using simulated and diverse human datasets, we show that metilene<sup>3</sup> accurately detects DMRs, robustly clusters samples, and holds the potential to reveal new regulatory elements and sample stratifications. Specifically, in a pancreatic tissue dataset, metilene<sup>3</sup> identifies DMRs enriched for key transcription factors involved in pancreatic cancer development, hinting towards an altered NFKB-NFAT regulatory program. Together, metilene<sup>3</sup> provides a fast, interpretable framework for exploring heterogeneous methylomes and discovering epigenetic patterns across complex biological and clinical datasets.</p>

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metilene3: identifying DMRs across multiple conditions with auto-classification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月4日

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  • Zhihan Zhu,
  • Stephan H. Bernhart,
  • Frank Jühling,
  • Helene Kretzmer,
  • Steve Hoffmann

摘要

DNA methylation is a critical epigenetic mark across numerous species, and identifying differentially methylated regions (DMRs) is essential for understanding genome regulation. Most existing DMR detection methods require predefined sample conditions, limiting the discovery of new epigenetic patterns, especially when group identities are unknown or uncertain, as is common in clinical settings. Additionally, only a very few approaches enable comparisons across multiple conditions. To address this significant gap, we present metilene3, a method for rapid, multi-condition DMR detection that operates in both supervised and unsupervised modes, using user-provided labels or autonomously clustering unlabeled samples. By segmenting the genome based on multiple pairwise methylation difference signals, metilene3 enables sample classification and DMR-anchored inference of epigenetic relationships. Using simulated and diverse human datasets, we show that metilene3 accurately detects DMRs, robustly clusters samples, and holds the potential to reveal new regulatory elements and sample stratifications. Specifically, in a pancreatic tissue dataset, metilene3 identifies DMRs enriched for key transcription factors involved in pancreatic cancer development, hinting towards an altered NFKB-NFAT regulatory program. Together, metilene3 provides a fast, interpretable framework for exploring heterogeneous methylomes and discovering epigenetic patterns across complex biological and clinical datasets.