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<p>The lack of quantitative methylation reference datasets (ground truth) and cross-laboratory reproducibility assessment hinders clinical translation of epigenome-wide sequencing technologies. Using certified Quartet DNA reference materials, here we generate 108 epigenome-sequencing datasets across three mainstream protocols (whole-genome bisulfite sequencing, enzymatic methyl-seq, and TET-assisted pyridine borane sequencing) with triplicates per sample across laboratories. We observe strand-specific methylation biases across all protocols and libraries. Cross-laboratory reproducibility analyses reveal high quantitative methylation levels agreement (mean Pearson correlation coefficient (PCC) = 0.96) but low detection concordance (mean Jaccard index = 0.36). Using consensus voting, we construct genome-wide quantitative methylation reference datasets serving as ground truth for proficiency testing. Key technical parameters–including mean CpG depth, coverage, and strand consistency–correlate strongly with reference-dependent quality metrics (recall, PCC, and RMSE). Collectively, these resources establish foundational standards for benchmarking emerging epigenomic technologies and analytical pipelines, enabling robust, standardized quality control in research and clinical applications.</p>

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Methylation reference datasets from quartet DNA materials for benchmarking epigenome sequencing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月16日

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  • Xiaorou Guo,
  • Qingwang Chen,
  • Yuanfeng Zhang,
  • Yujing Zhang,
  • Yaqing Liu,
  • Shumeng Duan,
  • Yu Ma,
  • Peng Ni,
  • Jianxin Wang,
  • Bo He,
  • Luyao Ren,
  • Ruiwen Ma,
  • Wanwan Hou,
  • Ying Yu,
  • Bingsi Li,
  • Fujun Qiu,
  • Yuan Sun,
  • Zhihong Zhang,
  • Weihong Xu,
  • Xiang Fang,
  • Jinming Li,
  • Leming Shi,
  • Rui Zhang,
  • Yuanting Zheng,
  • Lianhua Dong

摘要

The lack of quantitative methylation reference datasets (ground truth) and cross-laboratory reproducibility assessment hinders clinical translation of epigenome-wide sequencing technologies. Using certified Quartet DNA reference materials, here we generate 108 epigenome-sequencing datasets across three mainstream protocols (whole-genome bisulfite sequencing, enzymatic methyl-seq, and TET-assisted pyridine borane sequencing) with triplicates per sample across laboratories. We observe strand-specific methylation biases across all protocols and libraries. Cross-laboratory reproducibility analyses reveal high quantitative methylation levels agreement (mean Pearson correlation coefficient (PCC) = 0.96) but low detection concordance (mean Jaccard index = 0.36). Using consensus voting, we construct genome-wide quantitative methylation reference datasets serving as ground truth for proficiency testing. Key technical parameters–including mean CpG depth, coverage, and strand consistency–correlate strongly with reference-dependent quality metrics (recall, PCC, and RMSE). Collectively, these resources establish foundational standards for benchmarking emerging epigenomic technologies and analytical pipelines, enabling robust, standardized quality control in research and clinical applications.