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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Deep learning-based real-time detection of novel pathogens during sequencing.

Jakub M Bartoszewicz¹, Ulrich Genske¹, Bernhard Y Renard¹

¹Digital Engineering Faculty, Hasso Plattner Institute, University of Postdam, Prof.-Dr.-Helmert-Straße 2-3, 14482 Brandenburg, Germany.

Briefings in Bioinformatics

|July 23, 2021

Summary

Rapid pathogen detection is crucial for pandemic prevention. New machine learning models predict pathogenic potential from very short DNA sequences in real-time, significantly improving early outbreak detection.

Keywords:

biosecurity deep learning next-generation sequencing novel pathogens pathogen detection

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Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Published on: June 3, 2019

Area of Science:

Genomics and Bioinformatics
Pathogen Detection
Machine Learning in Biology

Background:

Emerging infectious diseases pose significant global health risks.
Next-generation sequencing (NGS) is vital for early pathogen identification.
Current real-time analysis tools struggle with novel or unknown pathogens.

Purpose of the Study:

To develop a real-time pathogenic potential prediction workflow.
To identify the minimum sequence length required for accurate pathogen inference.
To improve early detection capabilities for novel and unknown pathogens.

Main Methods:

Trained deep neural networks to classify short DNA reads from Illumina and Nanopore sequencing.
Integrated classification models with HiLive2, a real-time Illumina mapper.
Evaluated performance on simulated and real-world data, including SARS-CoV-2.

Main Results:

Achieved an 80-fold sensitivity increase after only 50 Illumina cycles compared to real-time mapping.
Demonstrated that the first 250 bp of Nanopore reads (0.5s) yield more accurate predictions than finished long reads.
Outperformed existing machine learning and sequence alignment methods on diverse datasets.

Conclusions:

Short subsequences are sufficient for accurate real-time pathogenic potential prediction.
The developed workflow significantly enhances early detection of unknown pathogens.
This approach has potential applications in biosecurity screening of synthetic sequences.