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Using deep learning for gene detection and classification in raw nanopore signals.

Marketa Nykrynova¹, Roman Jakubicek¹, Vojtech Barton¹

¹Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czechia.

Frontiers in Microbiology

|October 3, 2022

Summary

This study introduces a neural network to analyze raw nanopore sequencing signals, bypassing traditional basecalling for faster genomic region detection and classification.

Keywords:

MLST bacterial typing nanopore sequencing neural network squiggles

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Area of Science:

Genomics and Bioinformatics
Computational Biology
Molecular Biology

Background:

Nanopore sequencing offers advantages like speed, portability, and long reads compared to next-generation sequencing.
Current data postprocessing (basecalling, assembly) is a significant bottleneck, delaying clinical applications.
Identifying specific genomic regions from raw nanopore signals (squiggles) is challenging.

Purpose of the Study:

To develop a neural network method for direct analysis of raw nanopore signals (squiggles).
To detect and classify specific genomic regions without the need for basecalling.
To accelerate genomic data analysis for clinical practice.

Main Methods:

A novel neural network was designed to process raw nanopore sequencing signals.
The method directly analyzes 'squiggles' to identify and classify genomic regions.
The neural network can optionally direct basecalling of identified regions.

Main Results:

The neural network successfully detects and classifies specific genomic regions directly from raw nanopore signals.
This approach bypasses the computationally intensive basecalling step for certain analyses.
The method enables direct analysis of raw signals or targeted basecalling of relevant sequences.

Conclusions:

A neural network-based method can significantly streamline nanopore sequencing data analysis.
Direct analysis of raw signals reduces postprocessing time and computational demands.
This approach has the potential for real-time genomic analysis during sequencing runs.