1. Benchmarking reveals superiority of deep learning variant callers on bacterial nanopore sequence data.
- Author
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Hall MB, Wick RR, Judd LM, Nguyen AN, Steinig EJ, Xie O, Davies M, Seemann T, Stinear TP, and Coin L
- Subjects
- Nanopores, High-Throughput Nucleotide Sequencing methods, Genomics methods, Genetic Variation, Deep Learning, Benchmarking, Genome, Bacterial, Nanopore Sequencing methods, Bacteria genetics, Bacteria classification
- 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., Competing Interests: MH, RW, LJ, AN, ES, OX, MD, TS, TS No competing interests declared, LC Has received support from ONT to present his findings at scientific conferences; ONT played no role in study design, execution, analysis, or publication. Received research funding from ONT unrelated to this project, (© 2024, Hall et al.)
- Published
- 2024
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