Assessing DNA methylation detection for primary human tissue using nanopore sequencing
- Rylee Genner1,
- Stuart Akeson2,
- Melissa Meredith3,
- Pilar Alvarez Jerez4,
- Laksh Malik4,
- Breeana Baker4,
- Abigail Miano-Burkhardt5,
- CARD-long-read Team6,
- Benedict Paten3,
- Kimberley J Billingsley4,
- Cornelis Blauwendraat4 and
- Miten Jain7,8
- 1 National Institute on Aging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Johns Hopkins University;
- 2 Northeastern University;
- 3 University of California Santa Cruz;
- 4 National Institute on Aging, National Institute of Neurological Disorders and Stroke, National Institutes of Health;
- 5 National Institute on Aging;
- 6 -;
- 7 National Institute on Aging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Northeastern University
Abstract
DNA methylation most commonly occurs as 5-methylcytosine (5mC) in the human genome and has been associated with human diseases. Recent developments in single-molecule sequencing technologies (Oxford Nanopore Technologies (ONT) and Pacific Biosciences) have enabled readouts of long, native DNA molecules, including cytosine methylation. ONT recently upgraded their Nanopore sequencing chemistry and kits from the R9 to the R10 version, which yielded increased accuracy and sequencing throughput. However the effects on methylation detection have not yet been documented. Here, we performed a series of computational analyses to characterize differences in Nanopore-based 5mC detection between the ONT R9 and R10 chemistries. We compared 5mC calls in R9 and R10 for three human genome datasets: a cell line, a frontal cortex brain sample, and a blood sample. We performed an in-depth analysis on CpG islands and homopolymer regions, and documented high concordance for methylation detection among sequencing technologies. The strongest correlation was observed between Nanopore R10 and Illumina bisulfite technologies for cell line-derived datasets. Subtle differences in methylation datasets between technologies can impact analysis tools such as differential methylation calling software. Our findings show that comparisons can be drawn between methylation data from different Nanopore chemistries using guided hypotheses. This work will facilitate comparison among Nanopore data cohorts derived using different chemistries from large scale sequencing efforts, such as the NIH CARD Long Read Initiative.
- Received February 19, 2024.
- Accepted February 11, 2025.
- Published by Cold Spring Harbor Laboratory Press
This manuscript is Open Access.
This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International license), as described at http://creativecommons.org/licenses/by-nc/4.0/.
