Direct Gene Detection in Raw Nanopore Signals Using Transformer Neural Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0201495" target="_blank" >RIV/00216305:26220/26:0201495 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.13164/eeict.2025.13" target="_blank" >http://dx.doi.org/10.13164/eeict.2025.13</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.13164/eeict.2025.13" target="_blank" >10.13164/eeict.2025.13</a>

Result language

angličtina

Original language name

Direct Gene Detection in Raw Nanopore Signals Using Transformer Neural Networks

Original language description

Nanopore sequencing has transformed genomics by enabling real-time analysis of DNA and RNA in a compact, cost-effective device. However, conventional workflows require a separate basecalling step to convert raw electrical signals into nucleotide sequences, which can introduce errors and delay downstream analyses such as gene detection. Here, we present a novel approach that bypasses basecalling by directly analyzing raw nanopore signals using a transformer-based neural network. By adapting a model originally designed for ECG classification, we developed a system capable of detecting specific antibiotic resistance genes in Klebsiella pneumoniae samples. Raw signals were preprocessed through downsampling, z-normalization, and segmentation into 5,000-sample windows, yielding a dataset of 13,080 labeled segments. Experimental results demonstrate that our model effectively distinguishes gene-containing segments from non-target signals, achieving up to 80% accuracy in the “no target gene” category. In contrast, accuracy for other gene categories was lower, indicating that further optimization of the model is required. This direct-signal approach not only reduces the computational burden associated with basecalling but also streamlines the workflow, promising faster diagnostic turnaround times. These findings provide a significant step toward integrating advanced deep learning methods with nanopore sequencing for rapid, on-site genomic analysis and have potential applications in clinical diagnostics and epidemiological surveillance.

Czech name

—

Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20200 - Electrical engineering, Electronic engineering, Information engineering

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

2025

Confidentiality

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

Article name in the collection

Proceedings II of the Conference Student Eeict

ISBN

9788021463202

ISSN

—

e-ISSN

2788-1334

Number of pages

4

Pages from-to

13-16

Publisher name

Brno University of Technology

Place of publication

—

Event location

Brno

Event date

Apr 29, 2025

Type of event by nationality

CST - Celostátní akce

UT code for WoS article

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