ArticleNature communications2024
A signal processing and deep learning framework for methylation detection using Oxford Nanopore sequencing.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
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.
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.
Who cites it
67 citing papers in PubMed.
- Epigenetic regulation of humoral immunity: molecular mechanisms, diseases and therapeutic targets.Signal transduction and targeted therapy · 2026Review
- The Power of Tiny Spaces.JACS Au · 2026Review
- DNA Methylation as a Programmable Information Layer: From Molecular Marks to Disease State Engineering.International journal of molecular sciences · 2026Review
- Leveraging ONT move table values for signal aware variant calling.Bioinformatics (Oxford, England) · 2026Article
- Beyond AI-Driven Curve Fitting: Mechanistic Insight as the Cornerstone of Reliable Sensing and Accurate Fitting.Small methods · 2026Article
- Comprehensive benchmarking of tools for nanopore-based detection of DNA methylation.Nature communications · 2026Article
- Long-read sequencing technologies and bioinformatics: a new perspective for decoding DNA methylation modifications.Journal of translational medicine · 2026Review
- A new integrated genetic and transcriptomic approach for investigating DUX4 and DUX4C.Journal of human genetics · 2026Article
- Functional genomic map of local adaptation in sorghum to guide allele mining.bioRxiv : the preprint server for biology · 2026Article
- From Spatial Epigenomes to Clinical Diagnostics: Integrative Methylomics Across Scales and Modalities.International journal of molecular sciences · 2026Review
- Article
- Draft genome sequence of the peptaibol producerMicrobiology resource announcements · 2026Article
- Article
- Setd1a Loss-of-function Disrupts Epigenetic Regulation of Ribosomal Genes via Altered DNA Methylation.Schizophrenia bulletin · 2026Article
- SquiDBase: a community resource of raw nanopore data from microbes.NAR genomics and bioinformatics · 2026Article
- Arms races between selfish genetic elements and their host defence in termites.Nature communications · 2026Article
- A New Complexity Layer: DNA Methylation and the Predictive Impact of Epigenetic Tests.International journal of molecular sciences · 2026Review
- Epigenetic regulation and antimicrobial resistance: functional roles of DNA methylation.FEMS microbiology reviews · 2026Review
- NanoSSL: attention mechanism-based self-supervised learning method for protein identification using nanopores.Bioinformatics (Oxford, England) · 2026Article
- Emergence of a methicillin-susceptibleFrontiers in cellular and infection microbiology · 2026Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Oxford Nanopore sequencing can detect DNA methylations from ionic current signal of single molecules, offering a unique advantage over conventional methods. Additionally, adaptive sampling, a software-controlled enrichment method for targeted sequencing, allows reduced representation methylation sequencing that can be applied to CpG islands or imprinted regions. Here we present DeepMod2, a comprehensive deep-learning framework for methylation detection using ionic current signal from Nanopore sequencing. DeepMod2 implements both a bidirectional long short-term memory (BiLSTM) model and a Transformer model and can analyze POD5 and FAST5 signal files generated on R9 and R10 flowcells. Additionally, DeepMod2 can run efficiently on central processing unit (CPU) through model pruning and can infer epihaplotypes or haplotype-specific methylation calls from phased reads. We use multiple publicly available and newly generated datasets to evaluate the performance of DeepMod2 under varying scenarios. DeepMod2 has comparable performance to Guppy and Dorado, which are the current state-of-the-art methods from Oxford Nanopore Technologies that remain closed-source. Moreover, we show a high correlation (r = 0.96) between reduced representation and whole-genome Nanopore sequencing. In summary, DeepMod2 is an open-source tool that enables fast and accurate DNA methylation detection from whole-genome or adaptive sequencing data on a diverse range of flowcell types.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.