Evidence map›Paper›PMID 38468278›Full record

ArticleGenome biology2024

A comparison of methods for detecting DNA methylation from long-read sequencing of human genomes.

Brynja D Sigurpalsdottir, Olafur A Stefansson, Guillaume Holley, Doruk Beyter, Florian Zink, Marteinn Þ Hardarson, Sverrir Þ Sverrisson, Nina Kristinsdottir, Droplaug N Magnusdottir, Olafur Þ Magnusson and 3 more

Open access · goldAbstract read
In one paragraph

Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
76citing papers in PubMed, 1 pooled it
18.2field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

76 citing papers in PubMed, 1 synthesis or guideline pooled it, 79 citations in OpenAlex.

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  9. Population-scale detection of methylation outliers from long-read genome sequencing.medRxiv : the preprint server for health sciences · 2026
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16 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 2 institutions in 1 country.

Brynja D SigurpalsdottirdeCODE Genetics/Amgen Inc., Sturlugata 8, Reykjavík, Iceland. Brynja.Sigurpalsdottir@decode.is.ORCID 0000-0001-5227-2372
Olafur A StefanssondeCODE Genetics/Amgen Inc., Sturlugata 8, Reykjavík, Iceland.
Guillaume HolleydeCODE Genetics/Amgen Inc., Sturlugata 8, Reykjavík, Iceland.
Doruk BeyterdeCODE Genetics/Amgen Inc., Sturlugata 8, Reykjavík, Iceland.
Florian ZinkdeCODE Genetics/Amgen Inc., Sturlugata 8, Reykjavík, Iceland.
Marteinn Þ HardarsondeCODE Genetics/Amgen Inc., Sturlugata 8, Reykjavík, Iceland.
Sverrir Þ SverrissondeCODE Genetics/Amgen Inc., Sturlugata 8, Reykjavík, Iceland.
Nina KristinsdottirdeCODE Genetics/Amgen Inc., Sturlugata 8, Reykjavík, Iceland.
Droplaug N MagnusdottirdeCODE Genetics/Amgen Inc., Sturlugata 8, Reykjavík, Iceland.
Olafur Þ MagnussondeCODE Genetics/Amgen Inc., Sturlugata 8, Reykjavík, Iceland.
Daniel F GudbjartssondeCODE Genetics/Amgen Inc., Sturlugata 8, Reykjavík, Iceland.
Bjarni V HalldorssondeCODE Genetics/Amgen Inc., Sturlugata 8, Reykjavík, Iceland. Bjarni.Halldorsson@decode.is.
Kari StefanssondeCODE Genetics/Amgen Inc., Sturlugata 8, Reykjavík, Iceland.
deCODE Genetics (Iceland) · ISReykjavík University · IS

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLong-read sequencing can enable the detection of base modifications, such as CpG methylation, in single molecules of DNA. The most commonly used methods for long-read sequencing are nanopore developed by Oxford Nanopore Technologies (ONT) and single molecule real-time (SMRT) sequencing developed by Pacific Bioscience (PacBio). In this study, we systematically compare the performance of CpG methylation detection from long-read sequencing.

resultsWe demonstrate that CpG methylation detection from 7179 nanopore-sequenced DNA samples is highly accurate and consistent with 132 oxidative bisulfite-sequenced (oxBS) samples, isolated from the same blood draws. We introduce quality filters for CpGs that further enhance the accuracy of CpG methylation detection from nanopore-sequenced DNA, while removing at most 30% of CpGs. We evaluate the per-site performance of CpG methylation detection across different genomic features and CpG methylation rates and demonstrate how the latest R10.4 flowcell chemistry and base-calling algorithms improve methylation detection from nanopore sequencing. Additionally, we show how the methylation detection of 50 SMRT-sequenced genomes compares to nanopore sequencing and oxBS.

conclusionsThis study provides the first systematic comparison of CpG methylation detection tools for long-read sequencing methods. We compare two commonly used computational methods for the detection of CpG methylation in a large number of nanopore genomes, including samples sequenced using the latest R10.4 nanopore flowcell chemistry and 50 SMRT sequenced samples. We provide insights into the strengths and limitations of each sequencing method as well as recommendations for standardization and evaluation of tools designed for genome-scale modified base detection using long-read sequencing.

Indexed as

DNA MethylationGenome, HumanDNAHigh-Throughput Nucleotide SequencingHumansSequence Analysis, DNADNA

Identifiers

PMID38468278
PMCPMC10929077
OpenAlexW4392651484

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.