ArticleComputational and structural biotechnology journal2024
BaseNet: A transformer-based toolkit for nanopore sequencing signal decoding.
Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Review
- Computational Genomics for Resistome Characterization: Current Advancements and Future Challenges Under a One Health Perspective.Antibiotics (Basel, Switzerland) · 2026Review
- Nanopore direct RNA sequencing and the epitranscriptome: Advances in mapping native RNA landscapes.iMeta · 2026Review
- Addressing the Challenges of Solid-State Nanopores: Strategies for Performance Enhancement.International journal of molecular sciences · 2026Review
- Bream: an open-source deep learning framework for simultaneous base calling and DNA methylation detection on novel nanopore sequencing platforms.Frontiers in genetics · 2026Article
- RMNet: An RNA m6A Cross-species Methylation Detection Method for Nanopore Sequencing.Current drug targets · 2025Article
- Understanding the Impact of Individual Nucleotide on Oxford Nanopore Current Signals With Interpretable Prediction Models.Bioinformatics and biology insights · 2025Article
- Next-generation sequencing-based tools or nanopore-based tools: which is more suitable for short tandem repeats genotyping of nanopore sequencing?Bioinformatics advances · 2025Article
- GCRTcall: a transformer based basecaller for nanopore RNA sequencing enhanced by gated convolution and relative position embedding via joint loss training.Frontiers in genetics · 2024Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Nanopore sequencing provides a rapid, convenient and high-throughput solution for nucleic acid sequencing. Accurate basecalling in nanopore sequencing is crucial for downstream analysis. Traditional approaches such as Hidden Markov Models (HMM), Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN) have improved basecalling accuracy but there is a continuous need for higher accuracy and reliability. In this study, we introduce BaseNet (https://github.com/liqingwen98/BaseNet), an open-source toolkit that utilizes transformer models for advanced signal decoding in nanopore sequencing. BaseNet incorporates both autoregressive and non-autoregressive transformer-based decoding mechanisms, offering state-of-the-art algorithms freely accessible for future improvement. Our research indicates that cross-attention weights effectively map the relationship between current signals and base sequences, joint loss training through adding a pair of forward and reverse decoder facilitate model converge, and large-scale pre-trained models achieve superior decoding accuracy. This study helps to advance the field of nanopore sequencing signal decoding, contributes to technological advancements, and provides novel concepts and tools for researchers and practitioners.
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Registered trials
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