ArticleeLife2024
Benchmarking reveals superiority of deep learning variant callers on bacterial nanopore sequence data.
Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
56 citing papers in PubMed.
- K-MARVEL: K-Mer-based antimicrobial resistance virtual exploration lab.Nature communications · 2026Article
- Handling biological replicates in long-read RNA sequencing data by joining or not joining.Nature communications · 2026Article
- Resistance-CONKAT-seq Guided Discovery of a ClpP Active Natural Product from a Soil Metagenome.ACS chemical biology · 2026Article
- Performance of mechanically sheared DNA in multiplexed Oxford Nanopore sequencing forMicrobial genomics · 2026Article
- A Complete Telomere-To-Telomere Assembly ofEvolutionary applications · 2026Article
- Article
- Biosecurity and Diagnosis of Viral Hemorrhagic Fevers: Strategic Considerations for Military Medicine.Diagnostics (Basel, Switzerland) · 2026Review
- Genomic outbreak investigation of biosafety-level-3 pathogens using nanopore sequencing.Microbial genomics · 2026Article
- RiSpy: a feature selection-based fingerprinting framework for accurate identification of genome-edited rice lines.Briefings in bioinformatics · 2026Article
- TIPP-SD: A new method for species detection in microbiomes.PLoS computational biology · 2026Article
- Escherichia coli capsules enjoy a genomic revival.Nature microbiology · 2026Article
- Decentralized nanopore genomics reveals diverseMicrobial genomics · 2026Article
- High-resolution metagenome assembly for modern long reads with myloasm.Nature biotechnology · 2026Article
- Nanopore sequencing enables highly accurate genotyping and identification of resistance determinants in key nosocomial pathogens.Microbial genomics · 2026Article
- Nanopore long-read-only genome assembly of clinical Enterobacterales isolates is complete and accurate.Microbial genomics · 2026Article
- Interpreting antimicrobial resistance from bacterial whole-genome sequencing: prediction tools, database fragmentation, analytical trade-offs, and harmonized reporting.Frontiers in microbiology · 2026Review
- Efficient downsampling of genome alignments with Rasusa.GigaByte (Hong Kong, China) · 2026Article
- Deep learning application for genomic data analysis.BMB reports · 2026Review
- Benchmarking of Oxford Nanopore whole genome sequencing for germline variant and CpG methylation detection across Canada's national platform for genome sequencing and analysis.Frontiers in genetics · 2026Article
- The transformative impact of ultra-rapid nanopore sequencing in precision medicine.Frontiers in immunology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Variant calling is fundamental in bacterial genomics, underpinning the identification of disease transmission clusters, the construction of phylogenetic trees, and antimicrobial resistance detection. This study presents a comprehensive benchmarking of variant calling accuracy in bacterial genomes using Oxford Nanopore Technologies (ONT) sequencing data. We evaluated three ONT basecalling models and both simplex (single-strand) and duplex (dual-strand) read types across 14 diverse bacterial species. Our findings reveal that deep learning-based variant callers, particularly Clair3 and DeepVariant, significantly outperform traditional methods and even exceed the accuracy of Illumina sequencing, especially when applied to ONT's super-high accuracy model. ONT's superior performance is attributed to its ability to overcome Illumina's errors, which often arise from difficulties in aligning reads in repetitive and variant-dense genomic regions. Moreover, the use of high-performing variant callers with ONT's super-high accuracy data mitigates ONT's traditional errors in homopolymers. We also investigated the impact of read depth on variant calling, demonstrating that 10× depth of ONT super-accuracy data can achieve precision and recall comparable to, or better than, full-depth Illumina sequencing. These results underscore the potential of ONT sequencing, combined with advanced variant calling algorithms, to replace traditional short-read sequencing methods in bacterial genomics, particularly in resource-limited settings.
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Registered trials
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.