ArticleNature communications2024
Rockfish: A transformer-based model for accurate 5-methylcytosine prediction from 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 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR.Journal of translational medicine · 2025Pooled it
- Comprehensive benchmarking of tools for nanopore-based detection of DNA methylation.Nature communications · 2026Article
- Article
- A universal deep learning framework for empowering nanopore identification by reinforcing temporal signals.Nature communications · 2026Article
- GnnDebugger: GNN based error correction in De Bruijn Graphs.BMC bioinformatics · 2026Article
- Long-read sequencing technologies and bioinformatics: a new perspective for decoding DNA methylation modifications.Journal of translational medicine · 2026Review
- Advances and challenges in non-canonical nucleic acids data storage.Nature communications · 2026Review
- Advancements in DNA methylation technologies and their application in cancer diagnosis.Epigenetics · 2025Review
- Computational analysis of DNA methylation from long-read sequencing.Nature reviews. Genetics · 2025Review
- Identifying DNA methylation types and methylated base positions from bacteria using nanopore sequencing with multi-scale neural network.Bioinformatics (Oxford, England) · 2025Article
- NANOME: A Nextflow pipeline for haplotype-aware allele-specific consensus DNA methylation detection by nanopore long-read sequencing.bioRxiv : the preprint server for biology · 2025Article
- Resolving sequencing-based HIV-1 epitranscriptomics.Epigenomics · 2025Review
- Accurate cross-species 5mC detection for Oxford Nanopore sequencing in plants with DeepPlant.Nature communications · 2025Article
- Uncalled4 improves nanopore DNA and RNA modification detection via fast and accurate signal alignment.Nature methods · 2025Article
- The effects of different dietary nutritional levels on meat quality, rumen microbiota, and muscle metabolomics in Tibetan Plateau yaks.Frontiers in microbiomes · 2025Article
- DNA sequence analysis landscape: a comprehensive review of DNA sequence analysis task types, databases, datasets, word embedding methods, and language models.Frontiers in medicine · 2025Review
- A review on the applications of Transformer-based language models for nucleotide sequence analysis.Computational and structural biotechnology journal · 2025Review
- Understanding the Impact of Individual Nucleotide on Oxford Nanopore Current Signals With Interpretable Prediction Models.Bioinformatics and biology insights · 2025Article
- Methods for Detection and Mapping of Methylated and Hydroxymethylated Cytosine in DNA.Biomolecules · 2024Review
- Rockfish: A transformer-based model for accurate 5-methylcytosine prediction from nanopore sequencing.Nature communications · 2024Article
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5 authors.
Funding
Abstract
DNA methylation plays an important role in various biological processes, including cell differentiation, ageing, and cancer development. The most important methylation in mammals is 5-methylcytosine mostly occurring in the context of CpG dinucleotides. Sequencing methods such as whole-genome bisulfite sequencing successfully detect 5-methylcytosine DNA modifications. However, they suffer from the serious drawbacks of short read lengths and might introduce an amplification bias. Here we present Rockfish, a deep learning algorithm that significantly improves read-level 5-methylcytosine detection by using Nanopore sequencing. Rockfish is compared with other methods based on Nanopore sequencing on R9.4.1 and R10.4.1 datasets. There is an increase in the single-base accuracy and the F1 measure of up to 5 percentage points on R.9.4.1 datasets, and up to 0.82 percentage points on R10.4.1 datasets. Moreover, Rockfish shows a high correlation with whole-genome bisulfite sequencing, requires lower read depth, and achieves higher confidence in biologically important regions such as CpG-rich promoters while being computationally efficient. Its superior performance in human and mouse samples highlights its versatility for studying 5-methylcytosine methylation across varied organisms and diseases. Finally, its adaptable architecture ensures compatibility with new versions of pores and chemistry as well as modification types.
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