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  • Review Article
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Computational analysis of DNA methylation from long-read sequencing

Nature Reviews Genetics volume 26, pages 620–634 (2025)Cite this article

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Abstract

DNA methylation is a critical epigenetic mechanism in numerous biological processes, including gene regulation, development, ageing and the onset of various diseases such as cancer. Studies of methylation are increasingly using single-molecule long-read sequencing technologies to simultaneously measure epigenetic states such as DNA methylation with genomic variation. These long-read data sets have spurred the continuous development of advanced computational methods to gain insights into the roles of methylation in regulating chromatin structure and gene regulation. In this Review, we discuss the computational methods for calling methylation signals, contrasting methylation between samples, analysing cell-type diversity and gaining additional genomic insights, and then further discuss the challenges and future perspectives of tool development for DNA methylation research.

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Fig. 1: Computational workflow for long-read methylation analysis.
Fig. 2: Applications of long-read methylation analyses.

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Acknowledgements

The authors thank L. Paulin, K. Hansen, A. Wengler, C. Saunders and P. Rescheneder for discussions, and E. Dolzhenko for help with Fig. 2. Y.F. and F.J.S. are supported by National Institutes of Health (NIH) (1UG3NS132105-01, UM1DA058229 and 1U01HG011758-01). W.T. is supported by HG009190 (National Human Genome Research Institute (NHGRI)).

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Author notes

  1. These authors contributed equally: Winston Timp, Fritz J. Sedlazeck.

Authors and Affiliations

  1. Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA

    Yilei Fu  (付译磊) & Fritz J. Sedlazeck

  2. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Winston Timp

  3. Department of Computer Science, Rice University, Houston, TX, USA

    Fritz J. Sedlazeck

  4. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA

    Fritz J. Sedlazeck

Authors
  1. Yilei Fu  (付译磊)
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  2. Winston Timp
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  3. Fritz J. Sedlazeck
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Contributions

All authors researched the literature and contributed substantially to discussion of the content; Y.F. wrote the article and all authors reviewed and/or edited the manuscript before submission.

Corresponding author

Correspondence to Fritz J. Sedlazeck.

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Competing interests

F.J.S. receives research support from Illumina, Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT). W.T. has two patents (8,748,091 and 8,394,584) licensed to ONT. Y.F. declares no competing interests.

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Nature Reviews Genetics thanks Duncan Sproul, Quentin Gouil and Kai Wang for their contribution to the peer review of this work.

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Related links

6mASCOPE: https://github.com/fanglab/6mASCOPE

bsseq: https://rdrr.io/github/hansenlab/bsseq/man/read.modkit.html

ccsmeth: https://github.com/PengNi/ccsmeth

ccsmethphase: https://github.com/PengNi/ccsmethphase

cfDNA: https://github.com/billytcl/nanopore_cfDNA

cfNano: https://github.com/methylgrammarlab/cfdna-ont

CNS tumour tissue-type atlas: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE109381

DeepMod2: https://github.com/WGLab/DeepMod2

DeepSignal2: https://github.com/bioinfomaticsCSU/deepsignal

Dorado: https://github.com/nanoporetech/dorado

DSS R Package: https://bioconductor.org/packages/release/bioc/vignettes/DSS/inst/doc/DSS.html

fibertools: https://github.com/fiberseq/fibertools-rs

GTEx: https://gtexportal.org/home/

Guppy: https://nanoporetech.com

Integrative Genomics Viewer (IGV): https://igv.org/

Jasmine: https://github.com/PacificBiosciences/jasmine

LongReadDNAmCTClassifier: https://github.com/ejh243/LongReadDNAmCTClassifier

MethBat: https://github.com/PacificBiosciences/MethBat

MethPhaser: https://github.com/treangenlab/methphaser

methplotlib: https://github.com/wdecoster/methplotlib

methylartist: https://github.com/adamewing/methylartist

modbamtools: https://github.com/rrazaghi/modbamtools

ModKit: https://nanoporetech.github.io/modkit/

NanoMethPhase: https://github.com/vahidAK/NanoMethPhase

Nanomix: https://github.com/Jonbroad15/nanomix

Nanopolish/f5c: https://github.com/jts/nanopolish

Normal human cell types atlas: https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE186458

Pan-tissue DNA methylation atlas: https://github.com/aet21/EpiSCORE

pb-CpG-tools: https://github.com/PacificBiosciences/pb-CpG-tools

PoreMeth2: https://github.com/Lab-CoMBINE/PoreMeth2

Rockfish: https://github.com/lbcb-sci/rockfish

scMethBank: https://ngdc.cncb.ac.cn/methbank/scm/documentation

Sturgeon: https://github.com/marcpaga/sturgeon

TCGA Pan-Cancer atlas: https://portal.gdc.cancer.gov/

TLDR: https://github.com/adamewing/tldr

TRGT: https://github.com/PacificBiosciences/trgt

UNCALLED4: https://github.com/skovaka/uncalled4

Glossary

Attention model

A machine learning method that determines the relative importance of each component in a sequence.

Beta distribution

A continuous probability distribution defined from 0 to 1.

Bivariate shifting level model

A statistical model used to analyse paired data with shifting levels.

Genomic imprinting

A process of gene silencing through DNA methylation — the repressed allele is methylated whereas the active allele is unmethylated.

Graph genomes

A representation of genomic variation using a graph structure.

Haplotype phasing

The process of determining which variants are located on the same chromosome copy (that is, haplotype).

Hypermethylation

The increase in DNA methylation levels at specific genomic regions, often associated with gene silencing and implicated in various diseases, including cancer.

Hypomethylation

The decrease in DNA methylation levels, which can lead to genomic instability, overexpression of oncogenes or reactivation of transposable elements.

k-mer

A substring of length k from a DNA, RNA or protein sequence.

Long short-term memory model

A specialized type of recurrent neural network (RNN) capable of learning long-term dependencies by incorporating a memory cell structure, mitigating the vanishing gradient problem.

Non-negative matrix factorization

A dimensionality reduction technique that decomposes a matrix into non-negative components.

Phase block

A contiguous genomic region where haplotypes are resolved.

Recurrent neural networks

(RNNs). A class of neural network designed to process sequential data by maintaining a memory of past inputs, commonly used in time-series analysis and text prediction.

Transformer architectures

Deep learning models that leverage self-attention mechanisms to process sequences in parallel, achieving state-of-the-art performance in tasks such as language modelling and sequence alignment.

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Fu, Y., Timp, W. & Sedlazeck, F.J. Computational analysis of DNA methylation from long-read sequencing. Nat Rev Genet 26, 620–634 (2025). https://doi.org/10.1038/s41576-025-00822-5

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