ArticleNature communications2025
Methylation reference datasets from quartet DNA materials for benchmarking epigenome sequencing.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Integrative Epigenomics: Bioinformatics Strategies for Multi-Omics Data Analysis in Health and Disease.Epigenomes · 2026Review
- DNA Methylation as a Programmable Information Layer: From Molecular Marks to Disease State Engineering.International journal of molecular sciences · 2026Review
- Prompt Engineering Accelerates the Data-Driven Discovery of Photocatalysts via an LLM-Based Model Ensemble Strategy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Article
- Methylation reference datasets from quartet DNA materials for benchmarking epigenome sequencing.Nature communications · 2025Article
Corrections and comments
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Authors and funding
25 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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