ArticleNature communications2025
Training data diversity enhances the basecalling of novel RNA modification-induced nanopore sequencing readouts.
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 14 papers.
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Who cites it
14 citing papers in PubMed.
- Advanced deep learning strategies in nanopore RNA sequencing.RNA biology · 2026Review
- Nanopore direct RNA sequencing and the epitranscriptome: Advances in mapping native RNA landscapes.iMeta · 2026Review
- Assessment of nanopore RNA modification calling in human cell lines and synthetic systems.Genome biology · 2026Article
- Advances and challenges in non-canonical nucleic acids data storage.Nature communications · 2026Review
- Article
- The new era of single-molecule RNA modification detection through nanopore base-calling models.Nature reviews. Molecular cell biology · 2026Review
- Structure-informed models for ionic current prediction in nanopore sequencing of expanded dna alphabets.Nucleic acids research · 2025Article
- Motif caller for sequence reconstruction in motif-based DNA storage.Scientific reports · 2025Article
- A reference-guided iterative approach to polish the nanopore sequencing basecalling for therapeutic RNA quality control.Communications biology · 2025Article
- The mMolecular biomedicine · 2025Review
- Clinical and Molecular Presentation of a Patient with Paternal Uniparental Isodisomy of Chromosome 16.International journal of molecular sciences · 2025Article
- MoDorado: enhanced detection of tRNA modifications in nanopore sequencing by off-label use of modification callers.Nucleic acids research · 2025Article
- Direct detection of 8-oxo-dG using nanopore sequencing.Nature communications · 2025Article
- Training data diversity enhances the basecalling of novel RNA modification-induced nanopore sequencing readouts.Nature communications · 2025Article
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8 authors.
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Abstract
Accurately basecalling sequence backbones in the presence of nucleotide modifications remains a substantial challenge in nanopore sequencing bioinformatics. It has been extensively demonstrated that state-of-the-art basecallers are less compatible with modification-induced sequencing signals. A precise basecalling, on the other hand, serves as the prerequisite for virtually all the downstream analyses. Here, we report that basecallers exposed to diverse training modifications gain the generalizability to analyze novel modifications. With synthesized oligos as the model system, we precisely basecall various out-of-sample RNA modifications. From the representation learning perspective, we attribute this generalizability to basecaller representation space expanded by diverse training modifications. Taken together, we conclude increasing the training data diversity as a paradigm for building modification-tolerant nanopore sequencing basecallers.
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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.