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Peer-reviewedCC BYNature Communications

Comprehensive benchmarking of tools for nanopore-based detection of DNA methylation

  • Onkar Kulkarni1,2,3
  • Reuben Jacob Mathew2,3
  • Rhea Jana2,3
  • Lamuk Zaveri2,3
  • Sreenivas Ara2,3
  • Tulasi Nagabandi2,3
  • Nitesh Kumar Singh2,3
  • Karthik Bharadwaj Tallapaka1,2,3
  • Divya Tej Sowpati4,5
5 affiliations
  1. 1Academy of Scientific and Innovative Research, Ghaziabad, 201002, India
  2. 2CSIR Centre for Cellular and Molecular Biology, Hyderabad, India
  3. 3CSIR Centre for Cellular and Molecular Biology
  4. 4Academy of Scientific and Innovative Research, Ghaziabad, 201002, India. [email protected]
  5. 5CSIR Centre for Cellular and Molecular Biology, Hyderabad, India. [email protected]
Open at sourcePDFDOICite

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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Cite

BibTeXRIS
BibTeX
@article{kulkarni2026comprehensive,
  title = {{Comprehensive benchmarking of tools for nanopore-based detection of DNA methylation}},
  author = {Onkar Kulkarni and Reuben Jacob Mathew and Rhea Jana and Lamuk Zaveri and Sreenivas Ara and Tulasi Nagabandi and Nitesh Kumar Singh and Karthik Bharadwaj Tallapaka and Divya Tej Sowpati},
  journal = {Nature Communications},
  year = {2026},
  month = jul,
  doi = {10.1038/s41467-026-75183-6},
  url = {https://doi.org/10.1038/s41467-026-75183-6},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Comprehensive benchmarking of tools for nanopore-based detection of DNA methylation
AU  - Onkar Kulkarni
AU  - Reuben Jacob Mathew
AU  - Rhea Jana
AU  - Lamuk Zaveri
AU  - Sreenivas Ara
AU  - Tulasi Nagabandi
AU  - Nitesh Kumar Singh
AU  - Karthik Bharadwaj Tallapaka
AU  - Divya Tej Sowpati
PY  - 2026
DA  - 2026/07/15
JO  - Nature Communications
DO  - 10.1038/s41467-026-75183-6
UR  - https://doi.org/10.1038/s41467-026-75183-6
AB  - 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.
SN  - 2041-1723
ER  -