ArticleNature communications2026
Comprehensive benchmarking of tools for nanopore-based detection of DNA methylation.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Comprehensive benchmarking of tools for nanopore-based detection of DNA methylation.Nature communications · 2026Article
- LEMONmethyl-seq: Targeted long-read DNA methylation profiling reveals dynamics of CRISPR epigenome editing and endogenous DNA methylation patterns.bioRxiv : the preprint server for biology · 2026Article
- Connecting Epigenetic and Genetic Diversity of LTR Retrotransposons in Sunflower (Plants (Basel, Switzerland) · 2026Article
- Decoding bacterial methylomes in four public health-relevant microbial species: nanopore sequencing enables reproducible analysis of DNA modifications.BMC genomics · 2025Article
- Overcoming challenges in metagenomic AMR surveillance with nanopore sequencing: a case study on fluoroquinolone resistance.Frontiers in microbiology · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Oxford Nanopore (ONT) sequencing offers direct detection of DNA base modifications. Numerous tools have been developed to leverage this advantage. However, their performance remains unclear. Here, using diverse bacterial, plant, and mammalian datasets, we systematically evaluate the current landscape of nanopore methylation tools. We demonstrate that although most recent tools perform well, older models remain the reliable choice for studying CpG methylation. Conversely, newer models show substantial improvement in identifying 5-methylcytosine in non-CpG contexts, 6-methyladenine, and 4-methylcytosine. Further, we highlight the sensitivity of tools to confounding methylation nearby, assess their computational performance, and evaluate the effects of sequencing depth, methylation abundance, read quality, and basecalling mode. We provide reusable pipelines and open access datasets to empower future benchmarking efforts. Our work thus details the strengths and limitations of the state-of-the-art methylation models and outlines practical guidelines for researchers using nanopore sequencing to study DNA methylation.
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Registered trials
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