Evidence map›Paper›PMID 42302398›Full record

ArticleBioinformatics (Oxford, England)2026

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

Shaohui Xie, Lulu Ding, Ling Liu, Yew Soon Ong, Jianqiang Li, Zexuan Zhu

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Shaohui XieCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
Lulu DingNational Engineering Laboratory for Big Data System Computing, Shenzhen University, Shenzhen, China.
Ling LiuGuangzhou Institute of Technology, Xidian University, Guangzhou, China.
Yew Soon OngCollege of Computing and Data Science, Nanyang Technological University, Singapore.
Jianqiang LiNational Engineering Laboratory for Big Data System Computing, Shenzhen University, Shenzhen, China.
Zexuan ZhuNational Engineering Laboratory for Big Data System Computing, Shenzhen University, Shenzhen, China.ORCID 0000-0001-8479-6904

Funding

National Key Research and Development Program of China 2022YFF1202104National Natural Science Foundation of China 32401256National Natural Science Foundation of China 62471310National Natural Science Foundation of China 62571401
6 · The paper itself

Abstract

motivationHigh-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.

resultsWe 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.

Indexed as

NanoporesNanopore SequencingSoftwareHumansPolymorphism, Single NucleotideSequence Analysis, DNA

Identifiers

PMID42302398
PMCPMC13312124

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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.