ArticleNature communications2025
Accurate cross-species 5mC detection for Oxford Nanopore sequencing in plants with DeepPlant.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Comprehensive benchmarking of tools for nanopore-based detection of DNA methylation.Nature communications · 2026Article
- MethyNano: supervised contrastive pretraining enables robust and generalizable methylation detection from nanopore sequencing.Bioinformatics (Oxford, England) · 2026Article
- Z-Calling: a tool for A/Z (2,6-diaminopurine) base calling and dZ-DNA detection using PacBio HiFi reads.Communications biology · 2026Article
- A comprehensive ruminant microbial catalog (CRMC) reveals convergent selection for key vitamin-synthesizing pathways and genes across ruminants and human.GigaScience · 2026Article
- Bream: an open-source deep learning framework for simultaneous base calling and DNA methylation detection on novel nanopore sequencing platforms.Frontiers in genetics · 2026Article
- Non-CG DNA methylation in animal genomes.Nature genetics · 2025Review
- PlantDeepMeth: A Deep Learning Model for Predicting DNA Methylation States in Plants.Plants (Basel, Switzerland) · 2025Article
- DNA Methylation and Alternative Splicing Safeguard Genome and Transcriptome After a Retrotransposition Burst inInternational journal of molecular sciences · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nanopore sequencing enables comprehensive detection of 5-methylcytosine (5mC), particularly in repeat regions. However, CHH methylation detection in plants is limited by the scarcity of high-methylation positive samples, reducing generalization across species. Dorado, the only tool for plant 5mC detection on the R10.4 platform, lacks extensive species testing. Here, we develop DeepPlant, a deep learning model incorporating both Bi-LSTM and Transformer architectures, which significantly improves CHH detection accuracy and performs well for CpG and CHG motifs. We address the scarcity of methylation-positive CHH training samples through screening species with abundant high-methylation CHH sites using bisulfite-sequencing and generate datasets that cover diverse 9-mer motifs for training and testing DeepPlant. Evaluated across nine species, DeepPlant achieves high whole-genome methylation frequency correlations (0.705-0.838) with BS-seq data on CHH, improved by 23.4- 117.6% compared to Dorado. DeepPlant also demonstrates superior single-molecule accuracy and F1 score, offering strong generalization for plant epigenetics research.
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