ArticleFrontiers in genetics2019
Causalcall: Nanopore Basecalling Using a Temporal Convolutional Network.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Review
- Nanopore direct RNA sequencing for RNA modification analysis: workflow assessment and computational tool benchmarking.Advanced biotechnology · 2026Article
- Rawsamble: overlapping raw nanopore signals using a hash-based seeding mechanism.Bioinformatics (Oxford, England) · 2026Article
- A dual context-aware basecaller for nanopore direct RNA sequencing.Nature communications · 2026Article
- NanoSSL: attention mechanism-based self-supervised learning method for protein identification using nanopores.Bioinformatics (Oxford, England) · 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
- MetaGeno: a chromosome-wise multi-task genomic framework for ischaemic stroke risk prediction.Briefings in bioinformatics · 2025Article
- Direct profiling of non-adenosines in poly(A) tails of endogenous and therapeutic mRNAs with Ninetails.Nature communications · 2025Article
- Article
- Nanopore sequencing of protozoa: Decoding biological information on a string of biochemical molecules into human-readable signals.Computational and structural biotechnology journal · 2025Article
- SqueezeCall: nanopore basecalling using a Squeezeformer network.GigaByte (Hong Kong, China) · 2025Article
- BaseNet: A transformer-based toolkit for nanopore sequencing signal decoding.Computational and structural biotechnology journal · 2024Article
- Moving Beyond Oxford Nanopore Standard Procedures: New Insights from Water and Multiple Fish Microbiomes.International journal of molecular sciences · 2024Article
- Step-by-Step Metagenomics for Food Microbiome Analysis: A Detailed Review.Foods (Basel, Switzerland) · 2024Review
- RUBICON: a framework for designing efficient deep learning-based genomic basecallers.Genome biology · 2024Article
- 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
- Article
- Lokatt: a hybrid DNA nanopore basecaller with an explicit duration hidden Markov model and a residual LSTM network.BMC bioinformatics · 2023Article
- Nanopore-based direct sequencing of RNA transcripts with 10 different modified nucleotides reveals gaps in existing technology.G3 (Bethesda, Md.) · 2023Article
- Estimated Nucleotide Reconstruction Quality Symbols of Basecalling Tools for Oxford Nanopore Sequencing.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nanopore sequencing is promising because of its long read length and high speed. During sequencing, a strand of DNA/RNA passes through a biological nanopore, which causes the current in the pore to fluctuate. During basecalling, context-dependent current measurements are translated into the base sequence of the DNA/RNA strand. Accurate and fast basecalling is vital for downstream analyses such as genome assembly and detecting single-nucleotide polymorphisms and genomic structural variants. However, owing to the various changes in DNA/RNA molecules, noise during sequencing, and limitations of basecalling methods, accurate basecalling remains a challenge. In this paper, we propose Causalcall, which uses an end-to-end temporal convolution-based deep learning model for accurate and fast nanopore basecalling. Developed on a temporal convolutional network (TCN) and a connectionist temporal classification decoder, Causalcall directly identifies base sequences of varying lengths from current measurements in long time series. In contrast to the basecalling models using recurrent neural networks (RNNs), the convolution-based model of Causalcall can speed up basecalling by matrix computation. Experiments on multiple species have demonstrated the great potential of the TCN-based model to improve basecalling accuracy and speed when compared to an RNN-based model. Besides, experiments on genome assembly indicate the utility of Causalcall in reference-based genome assembly.
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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.