RT Journal
A1 Genner, Rylee
A1 Akeson, Stuart
A1 Meredith, Melissa
A1 Jerez, Pilar Alvarez
A1 Malik, Laksh
A1 Baker, Breeana
A1 Miano-Burkhardt, Abigail
A1 CARD-long-read Team
A1 Paten, Benedict
A1 Billingsley, Kimberley J.
A1 Blauwendraat, Cornelis
A1 Jain, Miten
T1 Assessing DNA methylation detection for primary human tissue using Nanopore sequencing
JF Genome Research 
JO Genome Research 
YR 2025 
FD April 01 
VO 35 
IS 4 
SP 632 
OP 643 
DO 10.1101/gr.279159.124 
UL http://genome.cshlp.org/content/35/4/632.abstract 
AB 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.