ArticleBioinformatics (Oxford, England)2025
Leveraging basecaller's move table to generate a lightweight k-mer model for nanopore sequencing analysis.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Systematic benchmarking of dorado basecalling models for RNA modification detection with highly multiplexed nanopore sequencing.Nucleic acids research · 2026Article
- CurrentView: a tool for visualization and comparison of nanopore ionic current signals.Bioinformatics (Oxford, England) · 2026Article
- Structure-informed models for ionic current prediction in nanopore sequencing of expanded dna alphabets.Nucleic acids research · 2025Article
- Article
- Pore-Based RNA Evaluation for Control of Integrity, Sequence, and Errors - Quality Control (PRECISE-QC).bioRxiv : the preprint server for biology · 2025Article
- ModiDeC: a multi-RNA modification classifier for direct nanopore sequencing.Nucleic acids research · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
motivationNanopore sequencing by Oxford Nanopore Technologies (ONT) enables direct analysis of DNA and RNA by capturing raw electrical signals. Different nanopore chemistries have varied k-mer lengths, current levels, and standard deviations, which are stored in "k-mer models." In cases where official models are lacking or unsuitable for specific sequencing conditions, tailored k-mer models are crucial to ensure precise signal-to-sequence alignment, analysis and interpretation. The process of transforming raw signal data into nucleotide sequences, known as basecalling, is a fundamental step in nanopore sequencing.
resultsIn this study, we leverage the move table produced by ONT's basecalling software to create a lightweight de novo k-mer model for RNA004 chemistry. We demonstrate the validity of our custom k-mer model by using it to guide signal-to-sequence alignment analysis, achieving high alignment rates (97.48%) compared to larger default models. Additionally, our 5-mer model exhibits similar performance as the default 9-mer models another analysis, such as detection of m6A RNA modifications. We provide our method, termed Poregen, as a generalizable approach for creation of custom, de novo k-mer models for nanopore signal data analysis. AVAILABILITY AND IMPLEMENTATION: Poregen is an open source package under an MIT license: https://github.com/hiruna72/poregen.
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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 Socratic graph.