Impact of Nanopore Flow Cell Types on DNA Methylation Detection

Third-generation sequencing technologies have revolutionized the study of epigenetic characteristics in human diseases, with Oxford Nanopore Technologies (ONT) at the forefront of long-read sequencing. ONT has made rapid improvements in flow cell designs, which greatly increased its sequencing accuracy but, at the same time, led to some projects utilizing different flow cell types, mainly R9 vs. R10, across samples. Whether and how the flow cell types affect genome-wide DNA methylation detection remains incompletely understood. Here, we used both flow cell types to analyze 6 human renal cell carcinoma (RCC) samples and compared the results. While there was a highly significant correlation between 5-methylcytosine (5mC) detected by R9 and R10 flow cells, we also observed substantial differences. R9 flow cells over-estimated 5mC levels at hypomethylated chromatin regions, mostly at promoters, while under-estimated 5mC at hypermethylated chromatin regions, enriched at intronic and intergenic regions. Such deviations in detection were likely caused by substantially lower sequencing accuracy of R9 flow cells, due to its mechanics, especially having problems sequencing homopolymeric DNA elements, such as CpG islands, leading to both higher false-positive and false-negative detections. Interestingly, such systematic errors were largely mitigated by batch-correction software, improving data comparability. In summary, our study reports superior performance of R10 flow cells, leading to much higher accuracy in base sequencing and DNA methylation detection.

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Publication Details

Journal
Serican Journal of Medicine
Published
2025-03-26
DOI
https://doi.org/10.17161/sjm.v2i2.23664
Primary Topic
Nanopore and Nanochannel Transport Studies
Type
article
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article

Impact of Nanopore Flow Cell Types on DNA Methylation Detection

Xiaodong Lü, Rebecca S. Arnold, Jonathan C. Zhao, Viraj Master et al.
Serican Journal of Medicine
Nanopore and Nanochannel Transport Studies
article

Impact of Nanopore Flow Cell Types on DNA Methylation Detection

Xiaodong Lü, Rebecca S. Arnold, Jonathan C. Zhao, Viraj Master, Xianglin Shi, Xinyue Chen, Shaojun Yu
article en

Abstract

Third-generation sequencing technologies have revolutionized the study of epigenetic characteristics in human diseases, with Oxford Nanopore Technologies (ONT) at the forefront of long-read sequencing. ONT has made rapid improvements in flow cell designs, which greatly increased its sequencing accuracy but, at the same time, led to some projects utilizing different flow cell types, mainly R9 vs. R10, across samples. Whether and how the flow cell types affect genome-wide DNA methylation detection remains incompletely understood. Here, we used both flow cell types to analyze 6 human renal cell carcinoma (RCC) samples and compared the results. While there was a highly significant correlation between 5-methylcytosine (5mC) detected by R9 and R10 flow cells, we also observed substantial differences. R9 flow cells over-estimated 5mC levels at hypomethylated chromatin regions, mostly at promoters, while under-estimated 5mC at hypermethylated chromatin regions, enriched at intronic and intergenic regions. Such deviations in detection were likely caused by substantially lower sequencing accuracy of R9 flow cells, due to its mechanics, especially having problems sequencing homopolymeric DNA elements, such as CpG islands, leading to both higher false-positive and false-negative detections. Interestingly, such systematic errors were largely mitigated by batch-correction software, improving data comparability. In summary, our study reports superior performance of R10 flow cells, leading to much higher accuracy in base sequencing and DNA methylation detection.

Serican Journal of MedicineVol. 2(2)
Emory University (US)
Openalex Percentile: Top 94%
Nanopore and Nanochannel Transport Studies
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Impact of Nanopore Flow Cell Types on DNA Methylation Detection — Xiaodong Lü, Rebecca S. Arnold, et al. · Serican Journal of Medicine (2025) | TGRS Research Map | TGRS