Methylation reference datasets from quartet DNA materials for benchmarking epigenome sequencing

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 () and Lianhua Dong ()
Additional contact information
Xiaorou Guo: Fudan University
Qingwang Chen: Fudan University
Yuanfeng Zhang: Beijing Hospital/National Center of Gerontology
Yujing Zhang: National Institute of Metrology
Yaqing Liu: Fudan University
Shumeng Duan: Fudan University
Yu Ma: Beijing Hospital/National Center of Gerontology
Peng Ni: Central South University
Jianxin Wang: Central South University
Bo He: Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies
Luyao Ren: Fudan University
Ruiwen Ma: Fudan University
Wanwan Hou: Fudan University
Ying Yu: Fudan University
Bingsi Li: Burning Rock Biotech
Fujun Qiu: Burning Rock Biotech
Yuan Sun: Burning Rock Biotech
Zhihong Zhang: Burning Rock Biotech
Weihong Xu: Fudan University
Xiang Fang: National Institute of Metrology
Jinming Li: Beijing Hospital/National Center of Gerontology
Leming Shi: Fudan University
Rui Zhang: Beijing Hospital/National Center of Gerontology
Yuanting Zheng: Fudan University
Lianhua Dong: National Institute of Metrology

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-64250-z

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