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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA23-05845S" target="_blank" >GA23-05845S: Určování infekčních hrozeb v reálném čase ze surových nanoporových signálů pomocí technik strojového učení</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2026

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Frontiers in Microbiology

ISSN

1664-302X

e-ISSN

—

Svazek periodika

17

Číslo periodika v rámci svazku

1748934

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

11

Strana od-do

—

Kód UT WoS článku

001705571300001

EID výsledku v databázi Scopus

2-s2.0-105031644209