DNA methylation plays a critical role in cancer progression. Long-read sequencing is now enabling us to unlock cancer epigenetics through 5mC and m6A detection, revealing new insights into biomarkers and tumor heterogeneity.

Unlocking cancer DNA methylation insights with Long-Read Sequencing

DNA methylation is a fundamental epigenetic modification that plays a crucial role in regulating gene expression, maintaining genome stability and mediating cell differentiation. Unlike genetic mutations, which permanently alter DNA sequences, methylation is reversible, making it a promising biomarker for disease detection and treatment.

In cancer, DNA methylation changes play a critical role in disease progression by silencing tumor suppressor genes and activating oncogenes. This dynamic regulation of gene expression not only drives tumor evolution but also shapes key disease drivers like metastasis, immune evasion and resistance to therapy[1]. As a result, studying methylation landscapes is becoming essential for understanding tumor heterogeneity, identifying new biomarkers and uncovering potential therapeutic targets[2].

While next-generation sequencing (NGS) has transformed our ability to study DNA methylation, traditional short-read approaches still face significant limitations—especially when analyzing structurally complex and repetitive genomic regions[3]. Whole-genome bisulfite sequencing (WGBS), the gold standard for methylation analysis, relies on chemical conversion of unmethylated cytosines to uracil—a process that damages DNA, biases GC-rich regions and reduces reliability in low-input or degraded samples, such as cell-free DNA (cfDNA) samples commonly used in cancer research[4].

Recent breakthroughs in long-read sequencing (LRS) have ushered in the next step change in methylation analysis. Unlike short-read methods, LRS unlocks the ability to directly detect methylation from native DNA without bisulfite conversion, providing a higher-resolution view of the epigenome[5, 6]. With platforms like PacBio 5-base HiFi sequencing and Oxford Nanopore Technologies (ONT) nanopore sequencing, we can now analyze methylation profiles with greater accuracy, repeatability and scalability.

In this blog, we explore the role of DNA methylation in cancer research, the limitations of traditional sequencing, and how long-read technologies enable direct, high-accuracy methylation detection.

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The role of DNA methylation in cancer

Cancer is not only influenced by genetic mutations, but also by widespread epigenetic alterations, particularly changes in DNA methylation patterns. 5-methylcytosine (5mC) is the most abundant and well-studied DNA methylation mark. Sometimes referred to as the “fifth base”, 5mC is formed by the addition of a methyl group at the 5th carbon position of cytosine, a reaction catalyzed by DNA methyltransferase (DNMT) enzymes found in CpG dinucleotides[7].

Figure 1: A) 5-methylcytosine (5mC) methylation occurs when DNA methyltransferase (DNMT) enzymes catalyze the addition of a methyl group to the fifth carbon position of cytosines, most often occurring within CpG dinucleotides. B) Differences in DNA methylation patterning between normal (top) and cancer (bottom) cells across the genome.

In healthy cells, 5mC plays a vital role in regulating gene expression, maintaining chromatin structure, and preserving genomic stability. It is involved in key biological processes such as X-chromosome inactivation, genomic imprinting, and suppression of repetitive elements. However, in cancer, 5mC patterns are widely disrupted, driving tumor progression[2]. The two most well-characterized changes are global DNA hypomethylation and focal hypermethylation of tumor suppressor genes.

Global DNA hypomethylation

One of the most striking features of the cancer methylome is a genome-wide reduction in 5mC, particularly in repetitive elements, centromeric regions and intergenic sequences​. This hypomethylation contributes to genomic instability, activating oncogenes and transposable elements, which can drive tumor progression. Loss of 5mC in CpG-poor regions is associated with increased aneuploidy, chromosomal rearrangements, and loss of heterozygosity—hallmarks of aggressive cancers[8]​.

Focal hypermethylation of tumor suppressor genes

At the same time, tumor cells exhibit hypermethylation at promoter CpG islands of key tumor suppressor genes, leading to gene silencing and loss of function. This process is particularly common in genes controlling cell cycle regulation, apoptosis and DNA repair[7]​. For example:

• GSTP1, an important detoxification enzyme, is hypermethylated in ~90% of prostate cancers, leading to its inactivation​.
• p16INK4a, a key regulator of the cell cycle, is hypermethylated in ~20% of lung cancers, preventing proper tumor suppression​.
• BRCA1, a crucial DNA repair gene, is hypermethylated in ~12% of breast and ovarian cancers, increasing genomic instability​.
• MGMT, a DNA repair enzyme, is frequently hypermethylated in gliomas and colorectal cancer, reducing the cell’s ability to repair DNA damage and increasing susceptibility to mutations in key oncogenes such as p53 and KRAS.

The ability to reliably detect 5mC alterations is invaluable for cancer researchers, as the marker provides valuable insights into tumor evolution, biomarker discovery and potential therapeutic targets.

N6-methyladenine (m6A): An emerging epigenetic player

While 5mC has long been recognized as a major regulator of gene expression, recent discoveries highlight N6-methyladenine (m6A) as a crucial regulatory mark in RNA. Unlike 5mC, which primarily affects gene silencing and chromatin structure, m6A influences RNA metabolism, including splicing, stability and translation efficiency. This dynamic modification is reversible, mediated by a balance of “writers” (methyltransferases), “erasers” (demethylases) and “readers” (proteins like YTHDF1-3)[9].

In cancer, m6A dysregulation plays a dual role, either promoting or suppressing tumorigenesis depending on the context. Increased m6A deposition can enhance oncogene translation, as seen in acute myeloid leukemia and lung cancer, while m6A loss stabilizes tumor suppressor transcripts in glioblastoma, affecting disease progression​[10]. Additionally, m6A modifications are implicated in therapy resistance and immune evasion, making them a potential biomarker for cancer progression and treatment response[11].

Detecting m6A modifications has been technically challenging due to its dynamic nature and the limitations of traditional sequencing methods, which cannot accurately distinguish m6A modifications.

Limitations of bisulfite sequencing in methylation detection

Although NGS has been instrumental in advancing epigenetics, traditional short-read approaches like WGBS face significant challenges in cancer methylation studies. These limitations not only affect accuracy in complex genomic regions but also reduce scalability and throughput due to time-consuming sample preparation workflows.

A key limitation of short-reads is their inability to resolve methylation patterns in repetitive and structurally complex regions of the genome. Since WGBS relies on bisulfite conversion to distinguish methylated from unmethylated cytosines, it creates high sequence redundancy, making it difficult to map reads accurately. This is especially problematic in transposable elements, CpG-dense regulatory regions and structural variants, where short-read sequencing struggles to assign methylation status with confidence​[12]. This can result in missing crucial methylation signals linked to cancer, as many epigenetic modifications occur in these difficult-to-map regions.

Additionally, short-read approaches often fail to associate DNA methylation with large structural variants (SVs), which are often drivers of tumor evolution​. Unlike long-read sequencing, which can phase methylation patterns across large haplotype blocks, WGBS struggles to capture allele-specific methylation differences, further limiting its resolution in cancer research[3]​.

Poor performance with low-quality or degraded samples

A significant challenge for WGBS in cancer research is its poor compatibility with low-input or degraded DNA samples, such as cell-free DNA (cfDNA) from liquid biopsies​. Traditionally, the bisulfite treatment process has led to a great deal of sample loss, making it difficult for applications where staring material is scarce. Despite advancements to reduce the impact of bisulfite treatment, it still gives rise to several potential issues:

• Severe DNA degradation, particularly in already compromised samples like cfDNA.
• Low sequencing coverage, increasing false positives and negatives in methylation calls.
• Shorter DNA fragments post-bisulfite treatment, making it difficult to detect long-range methylation interactions​.

Bisulfite conversion introduces sequencing bias by causing extensive DNA degradation and reducing sequence complexity, particularly in GC-rich regions. Since cancer genomics studies must often work with low-input and degraded samples, WGBS fails to provide reliable methylation data for key epigenetic biomarkers in many clinical and translational studies.

Newer enzymatic methylation sequencing approaches, such as BioModal’s duplex sequencing and NEB’s Enzymatic Methyl-seq (EM-seq), aim to circumvent WGBS-induced DNA degradation[13]. While these methods offer advantages over traditional bisulfite sequencing, they still rely on short-reads, which inherently limits methylation analysis in dark regions of the genome, fails to capture long-range methylation patterns and struggles with repeat elements and SVs.

As cancer research shifts toward large-scale, clinically relevant epigenetic profiling, traditional short-read methods are becoming less viable. By improving accuracy, scalability and efficiency, LRS is paving the way for a more comprehensive and clinically actionable approach to methylation analysis.

How long-read sequencing is transforming cancer epigenetics

LRS eliminates many of the constraints of bisulfite sequencing approaches by detecting DNA methylation directly from native DNA, without the need for bisulfite conversion or PCR amplification. Platforms like PacBio HiFi and ONT nanopore enable high-resolution methylation analysis, even in low-quality samples, making them substantially more effective for gaining reliable sequencing data from cfDNA samples[14].

In cancer research applications, LRS unlocks the ability to resolve complex methylation patterns in large SVs and repeat expansions that contribute to tumor heterogeneity and therapy resistance. As we have highlighted, many cancer-related methylation changes occur in CpG islands within tumor suppressor genes, transposable elements and large structural variants, which WGBS struggles to map accurately​. PacBio HiFi sequencing has been shown to detect ~3% more CpG sites than WGBS, particularly in regulatory regions that play critical roles in tumor progression[3].

 Detecting 5mC and m6A with LRS

LRS has transformed our ability to study DNA methylation, and with ONT nanopore sequencing, RNA methylation can be resolved too; offering a comprehensive, high-resolution view of epigenetic regulation in cancer. PacBio’s 5-base HiFi sequencing is well-established for its ability to detect 5mC directly in native DNA, providing highly accurate CpG methylation profiling across complex and repetitive genomic regions without requiring bisulfite conversion[15]​.

With the introduction of PacBio’s new SPRQ chemistry, HiFi sequencing has seen further advancements in methylation detection. The update improves 5mC calling accuracy by 10% and, for the first time, enables direct on-instrument detection of 6mA, significantly enhancing its multiomics capabilities​. The new 6mA calling feature means PacBio Revio systems can now run the Fiber-seq assay, a method that enables simultaneous detection of DNA sequence, 5mC and chromatin accessibility, providing a wealth of valuable multiomic information in a single assay.

Similarly, ONT nanopore sequencing platforms have been demonstrated to identify 5mC and m6A, but these rely on refined basecalling models​[16, 17]​

These advances highlight LRS as an increasingly powerful tool for methylation analysis, enabling researchers to integrate DNA, 5mC and chromatin data within the same sequencing framework. As chemistry and bioinformatics innovations continue, we are entering a new era of epigenetic research where LRS can reveal deeper insights into how epigenetic modifications contribute to oncogenesis.

Bioinformatics solutions for LRS methylation analysis

LRS generates significantly larger datasets than WGBS, increasing computational complexity and bioinformatics demands. While WGBS infers methylation status through cytosine conversion, LRS detects modifications directly from native DNA, requiring advanced signal-processing algorithms. Additionally, many cancer-associated methylation changes occur within large SVs, making it essential to integrate variant calling with methylation analysis for accurate interpretation.

To meet these challenges, specialized bioinformatics tools have been developed to optimize methylation analysis in LRS, including:

• pb-CpG-tools – A PacBio-specific toolkit for high-confidence 5mC calling from HiFi sequencing data, improving accuracy in complex regions​[3].
• MethBat – A methylation analysis toolkit designed for PacBio HiFi reads, enabling accurate detection, visualization and comparative analysis of 5mC and other DNA modifications across large-scale epigenetic studies.
• Fibertools – A machine-learning-based bioinformatics pipeline that enhances m6A detection accuracy (>90% precision and recall), enabling researchers to study RNA modifications at high resolution​[18].
• Nanopolish & DeepSignal – Advanced computational models designed for ONT nanopore sequencing, improving base modification identification and methylation phasing[3]​.

 If you’re looking to transition from bisulfite sequencing to a long-read platform, it’s important to consider the increased computing and bioinformatics infrastructure required to process and analyze vast datasets efficiently.

The impact of PacBio’s SPRQ chemistry upgrade

PacBio’s new SPRQ chemistry represents a significant advancement for LRS methylation detection​. This upgrade enhances the Revio™ system, making high-accuracy sequencing more accessible, efficient and scalable. One of the most transformative aspects of SPRQ chemistry is its ability to reduce DNA input requirements by four-fold, needing only 500 ng of DNA per sample​. This improvement is particularly beneficial for low-yield or degraded samples, like cfDNA and saliva.

Figure 2: The introduction of SPRQ chemistry for PacBio HiFi sequencing reduces the DNA input requirement from 2000 ng to 500 ng, enabling more efficient sequencing of low-yield or degraded samples, such as and cfDNA. Source: PacBio.

SPRQ chemistry also delivers a 33% increase in data output per SMRT Cell, significantly improving sequencing throughput and capacity. It also brings critical upgrades in epigenetic analysis, including:

• 10% improvement in 5mC CpG detection accuracy, making HiFi sequencing a stronger alternative to traditional methylation arrays.
• New on-instrument 6mA calling, enabling direct integration with Fiber-seq, a method that combines chromatin architecture with DNA methylation mapping​
• Enhanced multiomics capabilities enable simultaneous genome, CpG methylome, chromatin epigenome and transcriptome analysis from a single Revio run, advancing multiomics research in oncology​

By reducing input requirements, increasing throughput, and enhancing methylation detection, SPRQ chemistry accelerates long-read sequencing adoption in cancer research. Researchers can now study tumor heterogeneity, detect structural variations and phase epigenetic modifications with unprecedented accuracy, opening new doors in biomarker discovery and precision oncology.

The future of DNA methylation analysis

As the field of cancer epigenetics evolves, DNA methylation analysis is poised to play an increasingly critical role in understanding tumor biology, gene regulation and disease progression. The ability to map epigenetic modifications with high accuracy and resolution is transforming our understanding of how methylation changes contribute to oncogenesis, from tumor suppressor gene silencing to the activation of transposable elements.

Thanks to advancements in LRS, we now have access to tools that overcome the limitations of traditional short-read methods. Technologies like PacBio Revio with SPRQ upgrades and ONT nanopore provide direct detection of methylation markers, offering unprecedented insights into epigenetic regulation, allele-specific methylation and structural variant-associated modifications.

At Eremid^®, we provide cutting-edge long-read sequencing solutions powered by the latest PacBio Revio and ONT nanopore platforms. As a PacBio Certified Service Provider, our Genomics Services Lab features Revio systems with the latest SPRQ upgrades, enabling high-throughput, high-accuracy 5mC and m6A detection with lower DNA input requirements.

Beyond sequencing, we offer expert guidance and high-performance computing infrastructure, enabling efficient processing of large-scale LRS datasets with precision and reliability. Our in-house bioinformatics team develops custom pipelines for methylation analysis, variant phasing and structural variant detection, ensuring comprehensive, high-quality insights—even from degraded cfDNA or saliva samples.

Ready to take your DNA methylation research to the next level?

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References

1. Hanahan D (2022) Hallmarks of Cancer: New Dimensions. Cancer Discov 12:31–46. https://doi.org/10.1158/2159-8290.CD-21-1059
2. Lakshminarasimhan R, Liang G The Role of DNA Methylation in Cancer. https://doi.org/10.1007/978-3-319-43624-1_7
3. Sigurpalsdottir BD, Stefansson OA, Holley G, et al (2024) A comparison of methods for detecting DNA methylation from long-read sequencing of human genomes. Genome Biol 25:1–21. https://doi.org/10.1186/S13059-024-03207-9/FIGURES/3
4. Olova N, Krueger F, Andrews S, et al (2018) Comparison of whole-genome bisulfite sequencing library preparation strategies identifies sources of biases affecting DNA methylation data. Genome Biol 19:. https://doi.org/10.1186/S13059-018-1408-2
5. Liu Y, Rosikiewicz W, Pan Z, et al (2021) DNA methylation-calling tools for Oxford Nanopore sequencing: a survey and human epigenome-wide evaluation. Genome Biol 22:. https://doi.org/10.1186/s13059-021-02510-z
6. Ni P, Nie F, Zhong Z, et al (2023) DNA 5-methylcytosine detection and methylation phasing using PacBio circular consensus sequencing. Nature Communications 2023 14:1 14:1–13. https://doi.org/10.1038/s41467-023-39784-9
7. Skvortsova K, Stirzaker C, Taberlay P (2019) The DNA methylation landscape in cancer. Essays Biochem 63:797–811. https://doi.org/10.1042/EBC20190037
8. Ehrlich M (2019) DNA hypermethylation in disease: mechanisms and clinical relevance. Epigenetics 14:1141–1163. https://doi.org/10.1080/15592294.2019.1638701
9. Jiang X, Liu B, Nie Z, et al (2021) The role of m6A modification in the biological functions and diseases. Signal Transduction and Targeted Therapy 2021 6:1 6:1–16. https://doi.org/10.1038/s41392-020-00450-x
10. Jha A, Bohaczuk SC, Mao Y, et al (2023) DNA-m6A calling and integrated long-read epigenetic and genetic analysis with fibertools. bioRxiv. https://doi.org/10.1101/2023.04.20.537673
11. Chen X, Yuan Y, Zhou F, et al (2025) m6A RNA methylation: a pivotal regulator of tumor immunity and a promising target for cancer immunotherapy. Journal of Translational Medicine 2025 23:1 23:1–19. https://doi.org/10.1186/S12967-025-06221-Y
12. Zhuo X, Tomlinson C, Belter EA, et al (2024) Characterizing cytosine methylation of polymorphic human transposable element insertions using human pangenome resources
13. Olova NN, Andrews S (2025) Whole Genome Methylation Sequencing via Enzymatic Conversion (EM-seq): Protocol, Data Processing, and Analysis. Methods Mol Biol 2866:73–98. https://doi.org/10.1007/978-1-0716-4192-7_5
14. Chera A, Stancu-Cretu M, Zabet NR, Bucur O (2024) Shedding light on DNA methylation and its clinical implications: the impact of long-read-based nanopore technology. Epigenetics & Chromatin 2024 17:1 17:1–15. https://doi.org/10.1186/S13072-024-00558-2
15. Ni P, Nie F, Zhong Z, et al (2023) DNA 5-methylcytosine detection and methylation phasing using PacBio circular consensus sequencing. Nature Communications 2023 14:1 14:1–13. https://doi.org/10.1038/s41467-023-39784-9
16. Hendra C, Pratanwanich PN, Wan YK, et al (2022) Detection of m6A from direct RNA sequencing using a multiple instance learning framework. Nat Methods 19:1590–1598. https://doi.org/10.1038/s41592-022-01666-1
17. Teng H, Stoiber M, Bar-Joseph Z, Kingsford C (2024) Detecting m6A RNA modification from nanopore sequencing using a semi-supervised learning framework. bioRxiv. https://doi.org/10.1101/2024.01.06.574484
18. Wang Z, Zhou J, Zhang H, et al (2022) RNA m6A methylation in cancer. Mol Oncol 17:195. https://doi.org/10.1002/1878-0261.13326