ArticleBMC bioinformatics2020
GPU accelerated adaptive banded event alignment for rapid comparative nanopore signal analysis.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers.
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77 citing papers in PubMed.
- Characterization of METTL3/14-mediated m6A modification in human transcriptome using Nanopore direct RNA sequencing.PLoS genetics · 2026Article
- In vitro reconstitution of chromatin replication recapitulates symmetric histone recycling.Nature communications · 2026Article
- Comprehensive benchmarking of tools for nanopore-based detection of DNA methylation.Nature communications · 2026Article
- Long-read sequencing technologies and bioinformatics: a new perspective for decoding DNA methylation modifications.Journal of translational medicine · 2026Review
- Assessment of nanopore RNA modification calling in human cell lines and synthetic systems.Genome biology · 2026Article
- CurrentView: a tool for visualization and comparison of nanopore ionic current signals.Bioinformatics (Oxford, England) · 2026Article
- Centromeric retrotransposons shape chromosomal evolution beyond pericentromeric regions.Mobile DNA · 2026Article
- Raw signal segmentation for estimating RNA modification from Nanopore direct RNA sequencing data.eLife · 2026Article
- Skin Colour in Salamanders Is Modulated by Both Epitranscriptomic Methylation and Gene Expression.Molecular ecology · 2026Article
- Rawsamble: overlapping raw nanopore signals using a hash-based seeding mechanism.Bioinformatics (Oxford, England) · 2026Article
- Unlocking the full potential of nanopore sequencing: tips, tricks, and advanced data analysis techniques.Nucleic acids research · 2026Review
- Article
- Comprehensive mapping of RNA modification dynamics and crosstalk via deep learning and nanopore direct RNA-sequencing.Nature communications · 2026Article
- Ab initio detection of multiple epitranscriptomic modifications from Oxford nanopore technology direct RNA sequencing data.Briefings in bioinformatics · 2026Article
- Comprehensive discovery of mNature communications · 2025Article
- Structural insights into cauliflower mitoribosome in translation state and in association with a late assembly factor.Nature communications · 2025Article
- Article
- Disruption of the mRNA m6A writer complex triggers autoimmunity in Arabidopsis.PLoS genetics · 2025Article
- Long-read genome sequencing and multi-omics in aging and neurodegeneration.medRxiv : the preprint server for health sciences · 2025Article
- Enhanced detection of RNA modifications in Escherichia coli utilizing direct RNA sequencing.Cell reports methods · 2025Article
17 more citing papers are in PubMed but not listed here.
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundNanopore sequencing enables portable, real-time sequencing applications, including point-of-care diagnostics and in-the-field genotyping. Achieving these outcomes requires efficient bioinformatic algorithms for the analysis of raw nanopore signal data. However, comparing raw nanopore signals to a biological reference sequence is a computationally complex task. The dynamic programming algorithm called Adaptive Banded Event Alignment (ABEA) is a crucial step in polishing sequencing data and identifying non-standard nucleotides, such as measuring DNA methylation. Here, we parallelise and optimise an implementation of the ABEA algorithm (termed f5c) to efficiently run on heterogeneous CPU-GPU architectures.
resultsBy optimising memory, computations and load balancing between CPU and GPU, we demonstrate how f5c can perform ∼3-5 × faster than an optimised version of the original CPU-only implementation of ABEA in the Nanopolish software package. We also show that f5c enables DNA methylation detection on-the-fly using an embedded System on Chip (SoC) equipped with GPUs.
conclusionsOur work not only demonstrates that complex genomics analyses can be performed on lightweight computing systems, but also benefits High-Performance Computing (HPC). The associated source code for f5c along with GPU optimised ABEA is available at https://github.com/hasindu2008/f5c .
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