Basecalling-free resistance gene identification using a hybrid transformer in raw nanopore signals

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0201457" target="_blank" >RIV/00216305:26220/26:0201457 - isvavai.cz</a>
Result on the web
<a href="https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1748934/full" target="_blank" >https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1748934/full</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3389/fmicb.2026.1748934" target="_blank" >10.3389/fmicb.2026.1748934</a>

Result language
angličtina
Original language name
Basecalling-free resistance gene identification using a hybrid transformer in raw nanopore signals
Original language description
Nanopore sequencing enables real-time access to raw signal data, which brings new possibilities for rapid genomic diagnostics. However, current workflows still primarily rely on basecalling, a computationally intensive step that slows subsequent analysis and limits real-time use. In addition, most current approaches that work with raw signals focus on simple read-level classification tasks and are not designed to detect and localize specific genes, particularly complex genomic features such as antibiotic resistance genes (ARGs). Here, we show that the hybrid convolutional-transformer model, NanoResFormer, can detect clinically relevant ARGs directly from raw nanopore signals without basecalling. The model captures both local and long-range signal patterns and employs a floating-window strategy to process inputs of varying lengths efficiently. In proof-of-concept experiments, NanoResFormer achieved a sensitivity of 92.6% and a precision of over 93%, with short latency, enabling real-time resistome profiling already during sequencing. The proposed approach, therefore, provides rapid access to crucial information, accelerating decision-making in clinical diagnostics and pathogen surveillance.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/GA23-05845S" target="_blank" >GA23-05845S: Real-time determination of infection threats from raw nanopore signals using machine learning techniques</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2026
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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