Scalable Pathogen Detection from Next Generation DNA Sequencing with Deep Learning

TL;DR

This paper introduces MG2Vec, a deep learning transformer-based method for scalable, robust pathogen detection from raw metagenome sequences, enabling effective analysis of complex, real-world clinical data with minimal supervision.

Contribution

The work presents a novel transformer-based framework for learning robust representations from metagenome sequences, improving pathogen detection and generalization across diseases and species.

Findings

Effective detection of pathogens from uncurated clinical samples.

Learned representations generalize to unrelated pathogens.

Minimal human supervision required for accurate diagnostics.

Abstract

Next-generation sequencing technologies have enhanced the scope of Internet-of-Things (IoT) to include genomics for personalized medicine through the increased availability of an abundance of genome data collected from heterogeneous sources at a reduced cost. Given the sheer magnitude of the collected data and the significant challenges offered by the presence of highly similar genomic structure across species, there is a need for robust, scalable analysis platforms to extract actionable knowledge such as the presence of potentially zoonotic pathogens. The emergence of zoonotic diseases from novel pathogens, such as the influenza virus in 1918 and SARS-CoV-2 in 2019 that can jump species barriers and lead to pandemic underscores the need for scalable metagenome analysis. In this work, we propose MG2Vec, a deep learning-based solution that uses the transformer network as its backbone, to learn robust features from raw metagenome sequences for downstream biomedical tasks such as targeted and generalized pathogen detection. Extensive experiments on four increasingly challenging, yet realistic diagnostic settings, show that the proposed approach can help detect pathogens from uncurated, real-world clinical samples with minimal human supervision in the form of labels. Further, we demonstrate that the learned representations can generalize to completely unrelated pathogens across diseases and species for large-scale metagenome analysis. We provide a comprehensive evaluation of a novel representation learning framework for metagenome-based disease diagnostics with deep learning and provide a way forward for extracting and using robust vector representations from low-cost next generation sequencing to develop generalizable diagnostic tools.