NanoSimFormer: an end-to-end Transformer-based nanopore signal simulator with basecaller guidance

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ID: 317588
2026
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Abstract
Abstract Motivation High-fidelity simulation of nanopore sequencing signals is critical for rigorous benchmarking and validation of the nanopore signal processing pipeline. However, existing signal simulators often fail to capture the non-linear dynamics of nanopore current signals, relying on static pore models or lacking optimization objectives tied to basecalling, resulting in synthetic signals with low basecalling accuracy and fidelity. Results We introduce NanoSimFormer, an end-to-end Transformer-based signal simulator that integrates basecaller guidance during training to generate high-fidelity nanopore signals. NanoSimFormer achieves a median basecalling accuracy exceeding 99% and Q-scores above 22.8 for Oxford Nanopore Technologies’ latest DNA R10.4.1 and direct RNA sequencing, closely mirroring real experimental baselines. It faithfully recapitulates experimental variant calling performance across the five human samples, achieving F1-scores of 0.9953–0.9973 and 0.7862–0.8612 for single-nucleotide polymorphisms and small indels detections, respectively. Compared with previous simulators, NanoSimFormer also substantially reduces false positives in homopolymer and short tandem repeat regions. NanoSimFormer-derived reads enable high-quality de novo bacterial assembly with consensus error rates below one mismatch per 100 kbp and maintain high correlations with experimental abundance in metagenomic and transcriptomic datasets. Availability and implementation NanoSimFormer is freely available on GitHub at: https://github.com/BioinfoSZU/NanoSimFormer. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7164890260 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Shaohui Xie, Lulu Ding, L Liu, Yew Soon Ong, Jianqiang Li, Zexuan Zhu
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag402
URL
https://doi.org/10.1093/bioinformatics/btag402
Keywords Keywords not found

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