Assessing DNA methylation detection for primary human tissue using Nanopore sequencing

Rylee Genner; Stuart Akeson; Melissa Meredith; Pilar Alvarez Jerez; Laksh Malik; Breeana Baker; Abigail Miano-Burkhardt; CARD-long-read Team; Benedict Paten; Kimberley J. Billingsley; Cornelis Blauwendraat; Miten Jain

doi:10.1101/gr.279159.124

Assessing DNA methylation detection for primary human tissue using Nanopore sequencing

¹Center for Alzheimer's and Related Dementias, National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland 20892, USA;
²Department of Biology, Johns Hopkins University, Baltimore, Maryland 21218, USA;
³Department of Bioengineering, Northeastern University, Boston, Massachusetts 02115, USA;
⁴Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California 95064, USA;
⁵Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, United Kingdom;
⁶Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland 20892, USA;
⁷Department of Physics, Northeastern University, Boston, Massachusetts 02115, USA;
⁸Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts 02115, USA

↵9 These authors contributed equally to this work.
↵10 These authors contributed equally to this work.

Corresponding authors: mi.jain{at}northeastern.edu,kimberley.billingsley{at}nih.gov, cornelis.blauwendraat{at}nih.gov

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 [PacBio]) 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 data sets: 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 data sets. Subtle differences in methylation data sets 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.

Footnotes

[Supplemental material is available for this article.]
Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.279159.124.
Freely available online through the Genome Research Open Access option.

Received February 19, 2024.
Accepted February 11, 2025.

This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.